More Refactor Fixes, Issuetracker updated

.vscode/settings.json

@@ -8,6 +8,6 @@
         ".vscode": true,
         ".vs": true,
         ".lh": true,
-        "__pycache__": true,
+        "__pycache__": true
     }
 }

Documentation/00_Overview.md

@@ -12,9 +12,9 @@ This documentation strictly excludes details on environment setup, dependency in
 
 ## Architecture and Codebase Summary
 
-For developers interested in contributing, the tool's architecture centers on a **Core Processing Engine** (`processing_engine.py`) executing a pipeline based on a **Hierarchical Rule System** (`rule_structure.py`) and a **Configuration System** (`configuration.py` loading `config/app_settings.json` and `Presets/*.json`). The **Graphical User Interface** (`gui/`) has been significantly refactored: `MainWindow` (`main_window.py`) acts as a coordinator, delegating tasks to specialized widgets (`MainPanelWidget`, `PresetEditorWidget`, `LogConsoleWidget`) and background handlers (`RuleBasedPredictionHandler`, `LLMPredictionHandler`, `LLMInteractionHandler`, `AssetRestructureHandler`). The **Directory Monitor** (`monitor.py`) now processes archives asynchronously using a thread pool and utility functions (`utils/prediction_utils.py`, `utils/workspace_utils.py`). The **Command-Line Interface** entry point (`main.py`) primarily launches the GUI, with core CLI functionality currently non-operational. Optional **Blender Integration** (`blenderscripts/`) remains. A new `utils/` directory houses shared helper functions.
+For developers interested in contributing, the tool's architecture centers on a **Core Processing Engine** (`processing_engine.py`) which initializes and runs a **Pipeline Orchestrator** (`processing/pipeline/orchestrator.py::PipelineOrchestrator`). This orchestrator executes a defined sequence of **Processing Stages** (located in `processing/pipeline/stages/`) based on a **Hierarchical Rule System** (`rule_structure.py`) and a **Configuration System** (`configuration.py` loading `config/app_settings.json` and `Presets/*.json`). The **Graphical User Interface** (`gui/`) has been significantly refactored: `MainWindow` (`main_window.py`) acts as a coordinator, delegating tasks to specialized widgets (`MainPanelWidget`, `PresetEditorWidget`, `LogConsoleWidget`) and background handlers (`RuleBasedPredictionHandler`, `LLMPredictionHandler`, `LLMInteractionHandler`, `AssetRestructureHandler`). The **Directory Monitor** (`monitor.py`) now processes archives asynchronously using a thread pool and utility functions (`utils/prediction_utils.py`, `utils/workspace_utils.py`). The **Command-Line Interface** entry point (`main.py`) primarily launches the GUI, with core CLI functionality currently non-operational. Optional **Blender Integration** (`blenderscripts/`) remains. A new `utils/` directory houses shared helper functions.
 
-The codebase reflects this structure. The `gui/` directory contains the refactored UI components, `utils/` holds shared utilities, `Presets/` contains JSON presets, and `blenderscripts/` holds Blender scripts. Core logic resides in `processing_engine.py`, `configuration.py`, `rule_structure.py`, `monitor.py`, and `main.py`. The processing pipeline, executed by `processing_engine.py`, relies entirely on the input `SourceRule` and static configuration for steps like map processing, channel merging, and metadata generation.
+The codebase reflects this structure. The `gui/` directory contains the refactored UI components, `utils/` holds shared utilities, `processing/pipeline/` contains the orchestrator and individual processing stages, `Presets/` contains JSON presets, and `blenderscripts/` holds Blender scripts. Core logic resides in `processing_engine.py`, `processing/pipeline/orchestrator.py`, `configuration.py`, `rule_structure.py`, `monitor.py`, and `main.py`. The processing pipeline, initiated by `processing_engine.py` and executed by the `PipelineOrchestrator`, relies entirely on the input `SourceRule` and static configuration. Each stage in the pipeline operates on an `AssetProcessingContext` object (`processing/pipeline/asset_context.py`) to perform specific tasks like map processing, channel merging, and metadata generation.
 
 ## Table of Contents
 

Documentation/02_Developer_Guide/01_Architecture.md

@@ -6,17 +6,19 @@ This document provides a high-level overview of the Asset Processor Tool's archi
 
 The Asset Processor Tool is designed to process 3D asset source files into a standardized library format. Its high-level architecture consists of:
 
-1.  **Core Processing Engine (`processing_engine.py`):** The primary component responsible for executing the asset processing pipeline for a single input asset based on a provided `SourceRule` object and static configuration. The previous `asset_processor.py` has been removed.
+1.  **Core Processing Initiation (`processing_engine.py`):** The `ProcessingEngine` class acts as the entry point for an asset processing task. It initializes and runs a `PipelineOrchestrator`.
-2.  **Prediction System:** Responsible for analyzing input files and generating the initial `SourceRule` hierarchy with predicted values. This system utilizes a base handler (`gui/base_prediction_handler.py::BasePredictionHandler`) with specific implementations:
+2.  **Pipeline Orchestration (`processing/pipeline/orchestrator.py`):** The `PipelineOrchestrator` manages a sequence of discrete processing stages. It creates an `AssetProcessingContext` for each asset and passes this context through each stage.
+3.  **Processing Stages (`processing/pipeline/stages/`):** Individual modules, each responsible for a specific task in the pipeline (e.g., filtering files, processing maps, merging channels, organizing output). They operate on the `AssetProcessingContext`.
+4.  **Prediction System:** Responsible for analyzing input files and generating the initial `SourceRule` hierarchy with predicted values. This system utilizes a base handler (`gui/base_prediction_handler.py::BasePredictionHandler`) with specific implementations:
     *   **Rule-Based Predictor (`gui/prediction_handler.py::RuleBasedPredictionHandler`):** Uses predefined rules from presets to classify files and determine initial processing parameters.
     *   **LLM Predictor (`gui/llm_prediction_handler.py::LLMPredictionHandler`):** An experimental alternative that uses a Large Language Model (LLM) to interpret file contents and context to predict processing parameters.
-3.  **Configuration System (`Configuration`):** Handles loading core settings (including centralized type definitions and LLM-specific configuration) and merging them with supplier-specific rules defined in JSON presets and the persistent `config/suppliers.json` file.
+5.  **Configuration System (`Configuration`):** Handles loading core settings (including centralized type definitions and LLM-specific configuration) and merging them with supplier-specific rules defined in JSON presets and the persistent `config/suppliers.json` file.
-4.  **Multiple Interfaces:** Provides different ways to interact with the tool:
+6.  **Multiple Interfaces:** Provides different ways to interact with the tool:
     *   Graphical User Interface (GUI)
     *   Command-Line Interface (CLI) - *Note: The primary CLI execution logic (`run_cli` in `main.py`) is currently non-functional/commented out post-refactoring.*
     *   Directory Monitor for automated processing.
-The GUI acts as the primary source of truth for processing rules, coordinating the generation and management of the `SourceRule` hierarchy before sending it to the processing engine. It accumulates prediction results from multiple input sources before updating the view. The Monitor interface can also generate `SourceRule` objects (using `utils/prediction_utils.py`) to bypass the GUI for automated workflows.
+The GUI acts as the primary source of truth for processing rules, coordinating the generation and management of the `SourceRule` hierarchy before sending it to the `ProcessingEngine`. It accumulates prediction results from multiple input sources before updating the view. The Monitor interface can also generate `SourceRule` objects (using `utils/prediction_utils.py`) to bypass the GUI for automated workflows.
-5.  **Optional Integration:** Includes scripts (`blenderscripts/`) for integrating with Blender. Logic for executing these scripts was intended to be centralized in `utils/blender_utils.py`, but this utility has not yet been implemented.
+7.  **Optional Integration:** Includes scripts (`blenderscripts/`) for integrating with Blender. Logic for executing these scripts was intended to be centralized in `utils/blender_utils.py`, but this utility has not yet been implemented.
 
 ## Hierarchical Rule System
 
@@ -26,14 +28,14 @@ A key addition to the architecture is the **Hierarchical Rule System**, which pr
 *   **AssetRule:** Represents rules applied to a specific asset within a source (a source can contain multiple assets).
 *   **FileRule:** Represents rules applied to individual files within an asset.
 
-This hierarchy allows for fine-grained control over processing parameters. The GUI's prediction logic generates this hierarchy with initial predicted values for overridable fields based on presets and file analysis. The processing engine then operates *solely* on the explicit values provided in this `SourceRule` object and static configuration, without internal prediction or fallback logic.
+This hierarchy allows for fine-grained control over processing parameters. The GUI's prediction logic generates this hierarchy with initial predicted values for overridable fields based on presets and file analysis. The `ProcessingEngine` (via the `PipelineOrchestrator` and its stages) then operates *solely* on the explicit values provided in this `SourceRule` object and static configuration, without internal prediction or fallback logic.
 
 ## Core Components
 
 *   `config/app_settings.json`: Defines core, global settings, constants, and centralized definitions for allowed asset and file types (`ASSET_TYPE_DEFINITIONS`, `FILE_TYPE_DEFINITIONS`), including metadata like colors and descriptions. This replaces the old `config.py` file.
 *   `config/suppliers.json`: A persistent JSON file storing known supplier names for GUI auto-completion.
 *   `Presets/*.json`: Supplier-specific JSON files defining rules for file interpretation and initial prediction.
-*   `configuration.py` (`Configuration` class): Loads `config/app_settings.json` settings and merges them with a selected preset, pre-compiling regex patterns for efficiency. This static configuration is used by the processing engine.
+*   `configuration.py` (`Configuration` class): Loads `config/app_settings.json` settings and merges them with a selected preset, pre-compiling regex patterns for efficiency. This static configuration is used by the processing pipeline.
 *   `rule_structure.py`: Defines the `SourceRule`, `AssetRule`, and `FileRule` dataclasses used to represent the hierarchical processing rules.
 *   `gui/`: Directory containing modules for the Graphical User Interface (GUI), built with PySide6. The `MainWindow` (`main_window.py`) acts as a coordinator, orchestrating interactions between various components. Key GUI components include:
     *   `main_panel_widget.py::MainPanelWidget`: Contains the primary controls for loading sources, selecting presets, viewing/editing rules, and initiating processing.
@@ -47,7 +49,10 @@ This hierarchy allows for fine-grained control over processing parameters. The G
     *   `prediction_handler.py::RuleBasedPredictionHandler`: Generates the initial `SourceRule` hierarchy based on presets and file analysis. Inherits from `BasePredictionHandler`.
     *   `llm_prediction_handler.py::LLMPredictionHandler`: Experimental predictor using an LLM. Inherits from `BasePredictionHandler`.
     *   `llm_interaction_handler.py::LLMInteractionHandler`: Manages communication with the LLM service for the LLM predictor.
-*   `processing_engine.py` (`ProcessingEngine` class): The core component that executes the processing pipeline for a single `SourceRule` object using the static `Configuration`. A new instance is created per task for state isolation.
+*   `processing_engine.py` (`ProcessingEngine` class): The entry-point class that initializes and runs the `PipelineOrchestrator` for a given `SourceRule` and `Configuration`.
+*   `processing/pipeline/orchestrator.py` (`PipelineOrchestrator` class): Manages the sequence of processing stages, creating and passing an `AssetProcessingContext` through them.
+*   `processing/pipeline/asset_context.py` (`AssetProcessingContext` class): A dataclass holding all data and state for the processing of a single asset, passed between stages.
+*   `processing/pipeline/stages/`: Directory containing individual processing stage modules, each handling a specific part of the pipeline (e.g., `IndividualMapProcessingStage`, `MapMergingStage`).
 *   `main.py`: The main entry point for the application. Primarily launches the GUI. Contains commented-out/non-functional CLI logic (`run_cli`).
 *   `monitor.py`: Implements the directory monitoring feature using `watchdog`. It now processes archives asynchronously using a `ThreadPoolExecutor`, leveraging `utils.prediction_utils.py` for rule generation and `utils.workspace_utils.py` for workspace management before invoking the `ProcessingEngine`.
 *   `blenderscripts/`: Contains Python scripts designed to be executed *within* Blender for post-processing tasks.
@@ -56,19 +61,21 @@ This hierarchy allows for fine-grained control over processing parameters. The G
     *   `prediction_utils.py`: Contains functions like `generate_source_rule_from_archive` used by the monitor for rule-based prediction.
     *   `blender_utils.py`: (Intended location for Blender script execution logic, currently not implemented).
 
-## Processing Pipeline (Simplified)
+## Processing Pipeline (Simplified Overview)
 
-The primary processing engine (`processing_engine.py`) executes a series of steps for each asset based on the provided `SourceRule` object and static configuration:
+The asset processing pipeline, initiated by `processing_engine.py` and managed by `PipelineOrchestrator`, executes a series of stages for each asset defined in the `SourceRule`. An `AssetProcessingContext` object carries data between stages. The typical sequence is:
 
-1.  Extraction of input to a temporary workspace (using `utils.workspace_utils.py`).
+1.  **Supplier Determination**: Identify the effective supplier.
-2.  Classification of files (map, model, extra, ignored, unrecognised) based *only* on the provided `SourceRule` object (classification/prediction happens *before* the engine is called).
+2.  **Asset Skip Logic**: Check if the asset should be skipped.
-3.  Determination of base metadata (asset name, category, archetype).
+3.  **Metadata Initialization**: Set up initial asset metadata.
-4.  Skip check if output exists and overwrite is not forced.
+4.  **File Rule Filtering**: Determine which files to process.
-5.  Processing of maps (resize, format/bit depth conversion, inversion, stats calculation).
+5.  **Pre-Map Processing**:
-6.  Merging of channels based on rules.
+    *   Gloss-to-Roughness Conversion.
-7.  Generation of `metadata.json` file.
+    *   Alpha Channel Extraction.
-8.  Organization of processed files into the final output structure.
+    *   Normal Map Green Channel Inversion.
-9.  Cleanup of the temporary workspace.
+6.  **Individual Map Processing**: Handle individual maps (scaling, variants, stats, naming).
-10. (Optional) Execution of Blender scripts (currently triggered directly, intended to use `utils.blender_utils.py`).
+7.  **Map Merging**: Combine channels from different maps.
+8.  **Metadata Finalization & Save**: Generate and save `metadata.json` (temporarily).
+9.  **Output Organization**: Copy all processed files to final output locations.
 
-This architecture allows for a modular design, separating configuration, rule generation/management (GUI, Monitor utilities), and core processing execution. The `SourceRule` object serves as a clear data contract between the rule generation layer and the processing engine. Parallel processing (in Monitor) and background threads (in GUI) are utilized for efficiency and responsiveness.
+External steps like workspace preparation/cleanup and optional Blender script execution bracket this core pipeline. This architecture allows for a modular design, separating configuration, rule generation/management, and core processing execution.

Documentation/02_Developer_Guide/03_Key_Components.md

@@ -2,17 +2,65 @@
 
 This document describes the major classes and modules that form the core of the Asset Processor Tool.
 
-## `ProcessingEngine` (`processing_engine.py`)
+## Core Processing Architecture
 
-The `ProcessingEngine` class is the new core component responsible for executing the asset processing pipeline for a *single* input asset. Unlike the older `AssetProcessor`, this engine operates *solely* based on a complete `SourceRule` object provided to its `process()` method and the static `Configuration` object passed during initialization. It contains no internal prediction, classification, or fallback logic. Its key responsibilities include:
+The asset processing pipeline has been refactored into a staged architecture, managed by an orchestrator.
 
-*   Setting up and cleaning up a temporary workspace for processing (potentially using `utils.workspace_utils`).
+### `ProcessingEngine` (`processing_engine.py`)
-*   Extracting or copying input files to the workspace.
+
-*   Processing files based on the explicit rules and predicted values contained within the input `SourceRule`.
+The `ProcessingEngine` class serves as the primary entry point for initiating an asset processing task. Its main responsibilities are:
-*   Processing texture maps (resizing, format/bit depth conversion, inversion, stats calculation) using parameters from the `SourceRule` or static `Configuration`.
+
-*   Merging channels based on rules defined in the static `Configuration` and parameters from the `SourceRule`.
+*   Initializing a `PipelineOrchestrator` instance.
-*   Generating the `metadata.json` file containing details about the processed asset, incorporating information from the `SourceRule`.
+*   Providing the `PipelineOrchestrator` with the global `Configuration` object and a predefined list of processing stages.
-*   Organizing the final output files into the structured library directory.
+*   Invoking the orchestrator's `process_source_rule()` method with the input `SourceRule`, workspace path, output path, and other processing parameters.
+*   Managing a top-level temporary directory for the engine's operations if needed, though individual stages might also use sub-temporary directories via the `AssetProcessingContext`.
+
+It no longer contains the detailed logic for each processing step (like map manipulation, merging, etc.) directly. Instead, it delegates these tasks to the orchestrator and its stages.
+
+### `PipelineOrchestrator` (`processing/pipeline/orchestrator.py`)
+
+The `PipelineOrchestrator` class is responsible for managing the execution of the asset processing pipeline. Its key functions include:
+
+*   Receiving a `SourceRule` object, `Configuration`, and a list of `ProcessingStage` objects.
+*   For each `AssetRule` within the `SourceRule`:
+    *   Creating an `AssetProcessingContext` instance.
+    *   Sequentially executing each registered `ProcessingStage`, passing the `AssetProcessingContext` to each stage.
+    *   Handling exceptions that occur within stages and managing the overall status of asset processing (processed, skipped, failed).
+*   Managing a temporary directory for the duration of a `SourceRule` processing, which is made available to stages via the `AssetProcessingContext`.
+
+### `AssetProcessingContext` (`processing/pipeline/asset_context.py`)
+
+The `AssetProcessingContext` is a dataclass that acts as a stateful container for all data related to the processing of a single `AssetRule`. An instance of this context is created by the `PipelineOrchestrator` for each asset and is passed through each processing stage. Key information it holds includes:
+
+*   The input `SourceRule` and the current `AssetRule`.
+*   Paths: `workspace_path`, `engine_temp_dir`, `output_base_path`.
+*   The `Configuration` object.
+*   `effective_supplier`: Determined by an early stage.
+*   `asset_metadata`: A dictionary to accumulate metadata about the asset.
+*   `processed_maps_details`: Stores details about individually processed maps (paths, dimensions, etc.).
+*   `merged_maps_details`: Stores details about merged maps.
+*   `files_to_process`: A list of `FileRule` objects to be processed for the current asset.
+*   `loaded_data_cache`: For caching loaded image data within an asset's processing.
+*   `status_flags`: For signaling conditions like `skip_asset` or `asset_failed`.
+*   `incrementing_value`, `sha5_value`: Optional values for path generation.
+
+Each stage reads from and writes to this context, allowing data and state to flow through the pipeline.
+
+### `Processing Stages` (`processing/pipeline/stages/`)
+
+The actual processing logic is broken down into a series of discrete stages, each inheriting from `ProcessingStage` (`processing/pipeline/stages/base_stage.py`). Each stage implements an `execute(context: AssetProcessingContext)` method. Key stages include (in typical execution order):
+
+*   **`SupplierDeterminationStage`**: Determines the effective supplier.
+*   **`AssetSkipLogicStage`**: Checks if the asset processing should be skipped.
+*   **`MetadataInitializationStage`**: Initializes basic asset metadata.
+*   **`FileRuleFilterStage`**: Filters `FileRule`s to decide which files to process.
+*   **`GlossToRoughConversionStage`**: Handles gloss-to-roughness map inversion.
+*   **`AlphaExtractionToMaskStage`**: Extracts alpha channels to create masks.
+*   **`NormalMapGreenChannelStage`**: Inverts normal map green channels if required.
+*   **`IndividualMapProcessingStage`**: Processes individual maps (POT scaling, resolution variants, color conversion, stats, aspect ratio, filename conventions).
+*   **`MapMergingStage`**: Merges map channels based on rules.
+*   **`MetadataFinalizationAndSaveStage`**: Collects all metadata and saves `metadata.json` to a temporary location.
+*   **`OutputOrganizationStage`**: Copies all processed files and metadata to the final output directory structure.
 
 ## `Rule Structure` (`rule_structure.py`)
 
@@ -22,19 +70,19 @@ This module defines the data structures used to represent the hierarchical proce
 *   `AssetRule`: A dataclass representing rules applied at the asset level. It contains nested `FileRule` objects.
 *   `FileRule`: A dataclass representing rules applied at the file level.
 
-These classes hold specific rule parameters (e.g., `supplier_identifier`, `asset_type`, `asset_type_override`, `item_type`, `item_type_override`, `target_asset_name_override`). Attributes like `asset_type` and `item_type_override` now use string types, which are validated against centralized lists in `config/app_settings.json`. These structures support serialization (Pickle, JSON) to allow them to be passed between different parts of the application, including across process boundaries.
+These classes hold specific rule parameters (e.g., `supplier_identifier`, `asset_type`, `asset_type_override`, `item_type`, `item_type_override`, `target_asset_name_override`, `resolution_override`, `channel_merge_instructions`). Attributes like `asset_type` and `item_type_override` now use string types, which are validated against centralized lists in `config/app_settings.json`. These structures support serialization (Pickle, JSON) to allow them to be passed between different parts of theapplication, including across process boundaries. The `PipelineOrchestrator` and its stages heavily rely on the information within these rule objects, passed via the `AssetProcessingContext`.
 
 ## `Configuration` (`configuration.py`)
 
 The `Configuration` class manages the tool's settings. It is responsible for:
 
-*   Loading the core default settings defined in `config/app_settings.json`.
+*   Loading the core default settings defined in `config/app_settings.json` (e.g., `FILE_TYPE_DEFINITIONS`, `ASSET_TYPE_DEFINITIONS`, `image_resolutions`, `map_merge_rules`, `output_filename_pattern`).
 *   Loading the supplier-specific rules from a selected preset JSON file (`Presets/*.json`).
 *   Merging the core settings and preset rules into a single, unified configuration object.
 *   Validating the loaded configuration to ensure required settings are present.
-*   Pre-compiling regular expression patterns defined in the preset for efficient file classification by the `PredictionHandler`.
+*   Pre-compiling regular expression patterns defined in the preset for efficient file classification by the prediction handlers.
 
-An instance of the `Configuration` class is typically created once per application run (or per processing batch) and passed to the `ProcessingEngine`.
+An instance of the `Configuration` class is typically created once per application run (or per processing batch) and passed to the `ProcessingEngine`, which then makes it available to the `PipelineOrchestrator` and subsequently to each stage via the `AssetProcessingContext`.
 
 ## GUI Components (`gui/`)
 
@@ -191,10 +239,10 @@ The `monitor.py` script implements the directory monitoring feature. It has been
     *   Loads the necessary `Configuration`.
     *   Calls `utils.prediction_utils.generate_source_rule_from_archive` to get the `SourceRule`.
     *   Calls `utils.workspace_utils.prepare_processing_workspace` to set up the workspace.
-    *   Instantiates and runs the `ProcessingEngine`.
+    *   Instantiates and runs the `ProcessingEngine` (which in turn uses the `PipelineOrchestrator`).
     *   Handles moving the source archive to 'processed' or 'error' directories.
     *   Cleans up the workspace.
 
 ## Summary
 
-These key components, along with the refactored GUI structure and new utility modules, work together to provide the tool's functionality. The architecture emphasizes separation of concerns (configuration, rule generation, processing, UI), utilizes background processing for responsiveness (GUI prediction, Monitor tasks), and relies on the `SourceRule` object as the central data structure passed between different stages of the workflow.
+These key components, along with the refactored GUI structure and new utility modules, work together to provide the tool's functionality. The architecture emphasizes separation of concerns (configuration, rule generation, processing, UI), utilizes background processing for responsiveness (GUI prediction, Monitor tasks), and relies on the `SourceRule` object as the central data structure passed between different stages of the workflow. The processing core is now a staged pipeline managed by the `PipelineOrchestrator`, enhancing modularity and maintainability.

Documentation/02_Developer_Guide/05_Processing_Pipeline.md

@@ -1,72 +1,69 @@
 # Developer Guide: Processing Pipeline
 
-This document details the step-by-step technical process executed by the `ProcessingEngine` class (`processing_engine.py`) when processing a single asset. A new instance of `ProcessingEngine` is created for each processing task to ensure state isolation.
+This document details the step-by-step technical process executed by the asset processing pipeline, which is initiated by the `ProcessingEngine` class (`processing_engine.py`) and orchestrated by the `PipelineOrchestrator` (`processing/pipeline/orchestrator.py`).
 
-The `ProcessingEngine.process()` method orchestrates the following pipeline based *solely* on the provided `SourceRule` object and the static `Configuration` object passed during engine initialization. It contains no internal prediction, classification, or fallback logic. All necessary overrides and static configuration values are accessed directly from these inputs.
+The `ProcessingEngine.process()` method serves as the main entry point. It initializes a `PipelineOrchestrator` instance, providing it with the application's `Configuration` object and a predefined list of processing stages. The `PipelineOrchestrator.process_source_rule()` method then manages the execution of these stages for each asset defined in the input `SourceRule`.
 
-The pipeline steps are:
+A crucial component in this architecture is the `AssetProcessingContext` (`processing/pipeline/asset_context.py`). An instance of this dataclass is created for each `AssetRule` being processed. It acts as a stateful container, carrying all relevant data (source files, rules, configuration, intermediate results, metadata) and is passed sequentially through each stage. Each stage can read from and write to the context, allowing data to flow and be modified throughout the pipeline.
 
-1.  **Workspace Preparation (External)**:
+The pipeline stages are executed in the following order:
-    *   Before the `ProcessingEngine` is invoked, the calling code (e.g., `main.ProcessingTask`, `monitor._process_archive_task`) is responsible for setting up a temporary workspace.
-    *   This typically involves using `utils.workspace_utils.prepare_processing_workspace`, which creates a temporary directory and extracts the input source (archive or folder) into it.
-    *   The path to this prepared workspace is passed to the `ProcessingEngine` during initialization.
 
-2.  **Prediction and Rule Generation (External)**:
+1.  **`SupplierDeterminationStage` (`processing/pipeline/stages/supplier_determination.py`)**:
-    *   Also handled before the `ProcessingEngine` is invoked.
+    *   **Responsibility**: Determines the effective supplier for the asset based on the `SourceRule`'s `supplier_identifier`, `supplier_override`, and supplier definitions in the `Configuration`.
-    *   Either the `RuleBasedPredictionHandler`, `LLMPredictionHandler` (triggered by the GUI), or `utils.prediction_utils.generate_source_rule_from_archive` (used by the Monitor) analyzes the input files and generates a `SourceRule` object.
+    *   **Context Interaction**: Updates `AssetProcessingContext.effective_supplier` and potentially `AssetProcessingContext.asset_metadata` with supplier information.
-    *   This `SourceRule` contains predicted classifications and initial overrides.
-    *   If using the GUI, the user can modify these rules.
-    *   The final `SourceRule` object is the primary input to the `ProcessingEngine.process()` method.
 
-3.  **File Inventory (`_inventory_and_classify_files`)**:
+2.  **`AssetSkipLogicStage` (`processing/pipeline/stages/asset_skip_logic.py`)**:
-    *   Scans the contents of the *already prepared* temporary workspace.
+    *   **Responsibility**: Checks if the asset should be skipped, typically if the output already exists and overwriting is not forced.
-    *   This step primarily inventories the files present. The *classification* (determining `item_type`, etc.) is taken directly from the input `SourceRule`. The `item_type` for each file (within the `FileRule` objects of the `SourceRule`) is expected to be a key from `Configuration.FILE_TYPE_DEFINITIONS`.
+    *   **Context Interaction**: Sets `AssetProcessingContext.status_flags['skip_asset']` to `True` if the asset should be skipped, halting further processing for this asset by the orchestrator.
-    *   Stores the file paths and their associated rules from the `SourceRule` in `self.classified_files`.
 
-4.  **Base Metadata Determination (`_determine_base_metadata`, `_determine_single_asset_metadata`)**:
+3.  **`MetadataInitializationStage` (`processing/pipeline/stages/metadata_initialization.py`)**:
-    *   Determines the base asset name, category, and archetype using the explicit values provided in the input `SourceRule` and the static `Configuration`. Overrides (like `supplier_identifier`, `asset_type`, `asset_name_override`) are taken directly from the `SourceRule`. The `asset_type` (within the `AssetRule` object of the `SourceRule`) is expected to be a key from `Configuration.ASSET_TYPE_DEFINITIONS`.
+    *   **Responsibility**: Initializes the `AssetProcessingContext.asset_metadata` dictionary with base information derived from the `AssetRule`, `SourceRule`, and `Configuration`. This includes asset name, type, and any common metadata.
+    *   **Context Interaction**: Populates `AssetProcessingContext.asset_metadata`.
 
-5.  **Skip Check**:
+4.  **`FileRuleFilterStage` (`processing/pipeline/stages/file_rule_filter.py`)**:
-    *   If the `overwrite` flag is `False`, checks if the final output directory already exists and contains `metadata.json`.
+    *   **Responsibility**: Filters the `FileRule` objects from the `AssetRule` to determine which files should actually be processed. It respects `FILE_IGNORE` rules.
-    *   If so, processing for this asset is skipped.
+    *   **Context Interaction**: Populates `AssetProcessingContext.files_to_process` with the list of `FileRule` objects that passed the filter.
 
-6.  **Map Processing (`_process_maps`)**:
+5.  **`GlossToRoughConversionStage` (`processing/pipeline/stages/gloss_to_rough_conversion.py`)**:
-    *   Iterates through files classified as maps in the `SourceRule`.
+    *   **Responsibility**: Identifies gloss maps (based on `FileRule` properties and filename conventions) that are intended to be used as roughness maps. If found, it loads the image, inverts its colors, and saves a temporary inverted version.
-    *   Loads images (`cv2.imread`).
+    *   **Context Interaction**: Modifies `FileRule` objects in `AssetProcessingContext.files_to_process` (e.g., updates `file_path` to point to the temporary inverted map, sets flags indicating inversion). Updates `AssetProcessingContext.processed_maps_details` with information about the conversion.
-    *   **Glossiness-to-Roughness Inversion**:
-        *   The system identifies a map as a gloss map if its input filename contains "MAP_GLOSS" (case-insensitive) and is intended to become a roughness map (e.g., its `item_type` or `item_type_override` in the `SourceRule` effectively designates it as roughness).
-        *   If these conditions are met, its colors are inverted.
-        *   After inversion, the map is treated as a "MAP_ROUGH" type for subsequent processing steps.
-        *   The fact that a map was derived from a gloss source and inverted is recorded in the output `metadata.json` for that map type using the `derived_from_gloss_filename: true` flag. This replaces the previous reliance on an internal `is_gloss_source` flag within the `FileRule` structure.
-    *   Resizes images based on `Configuration`.
-    *   Determines output bit depth and format based on `Configuration` and `SourceRule`.
-    *   Converts data types and saves images (`cv2.imwrite`).
-*   The output filename uses the `standard_type` alias (e.g., `COL`, `NRM`) retrieved from the `Configuration.FILE_TYPE_DEFINITIONS` based on the file's effective `item_type`.
-    *   Calculates image statistics.
-    *   Stores processed map details.
 
-7.  **Map Merging (`_merge_maps_from_source`)**:
+6.  **`AlphaExtractionToMaskStage` (`processing/pipeline/stages/alpha_extraction_to_mask.py`)**:
-    *   Iterates through `MAP_MERGE_RULES` in `Configuration`.
+    *   **Responsibility**: If a `FileRule` specifies alpha channel extraction (e.g., from a diffuse map to create an opacity mask), this stage loads the source image, extracts its alpha channel, and saves it as a new temporary grayscale map.
-    *   Identifies required source maps by checking the `item_type_override` within the `SourceRule` (specifically in the `FileRule` for each file). Both `item_type` and `item_type_override` are expected to be keys from `Configuration.FILE_TYPE_DEFINITIONS`. Files with a base `item_type` of `"FILE_IGNORE"` are explicitly excluded from consideration.
+    *   **Context Interaction**: May add new `FileRule`-like entries or details to `AssetProcessingContext.processed_maps_details` representing the extracted mask.
-    *   Loads source channels, handling missing inputs with defaults from `Configuration` or `SourceRule`.
-    *   Merges channels (`cv2.merge`).
-    *   Determines output format/bit depth and saves the merged map.
-    *   Stores merged map details.
 
-8.  **Metadata File Generation (`_generate_metadata_file`)**:
+7.  **`NormalMapGreenChannelStage` (`processing/pipeline/stages/normal_map_green_channel.py`)**:
-    *   Collects asset metadata, processed/merged map details, ignored files list, etc., primarily from the `SourceRule` and internal processing results.
+    *   **Responsibility**: Checks `FileRule`s for normal maps and, based on configuration (e.g., `invert_normal_map_green_channel` for a specific supplier), potentially inverts the green channel of the normal map image.
-    *   Writes data to `metadata.json` in the temporary workspace.
+    *   **Context Interaction**: Modifies the image data for normal maps if inversion is needed, saving a new temporary version. Updates `AssetProcessingContext.processed_maps_details`.
 
-9.  **Output Organization (`_organize_output_files`)**:
+8.  **`IndividualMapProcessingStage` (`processing/pipeline/stages/individual_map_processing.py`)**:
-*   Determines the final output directory using the global `OUTPUT_DIRECTORY_PATTERN` and the final filename using the global `OUTPUT_FILENAME_PATTERN` (both from the `Configuration` object). The `utils.path_utils` module combines these with the base output directory and asset-specific data (like asset name, map type, resolution, etc.) to construct the full path for each file.
+    *   **Responsibility**: Processes individual texture map files. This includes:
-    *   Creates the final structured output directory (`<output_base_dir>/<supplier_name>/<asset_name>/`), using the supplier name from the `SourceRule`.
+        *   Loading the source image.
-    *   Moves processed maps, merged maps, models, metadata, and other classified files from the temporary workspace to the final output directory.
+        *   Applying Power-of-Two (POT) scaling.
+        *   Generating multiple resolution variants based on configuration.
+        *   Handling color space conversions (e.g., BGR to RGB).
+        *   Calculating image statistics (min, max, mean, median).
+        *   Determining and storing aspect ratio change information.
+        *   Saving processed temporary map files.
+        *   Applying name variant suffixing and using standard type aliases for filenames.
+    *   **Context Interaction**: Heavily populates `AssetProcessingContext.processed_maps_details` with paths to temporary processed files, dimensions, and other metadata for each map and its variants. Updates `AssetProcessingContext.asset_metadata` with image stats and aspect ratio info.
 
-10. **Workspace Cleanup (External)**:
+9.  **`MapMergingStage` (`processing/pipeline/stages/map_merging.py`)**:
-    *   After the `ProcessingEngine.process()` method completes (successfully or with errors), the *calling code* is responsible for cleaning up the temporary workspace directory created in Step 1. This is often done in a `finally` block where `utils.workspace_utils.prepare_processing_workspace` was called.
+    *   **Responsibility**: Performs channel packing and other merge operations based on `map_merge_rules` defined in the `Configuration`.
+    *   **Context Interaction**: Reads source map details and temporary file paths from `AssetProcessingContext.processed_maps_details`. Saves new temporary merged maps and records their details in `AssetProcessingContext.merged_maps_details`.
 
-11. **(Optional) Blender Script Execution (External)**:
+10. **`MetadataFinalizationAndSaveStage` (`processing/pipeline/stages/metadata_finalization_save.py`)**:
-    *   If triggered (e.g., via CLI arguments or GUI controls), the orchestrating code (e.g., `main.ProcessingTask`) executes the corresponding Blender scripts (`blenderscripts/*.py`) using `subprocess.run` *after* the `ProcessingEngine.process()` call completes successfully.
+    *   **Responsibility**: Collects all accumulated metadata from `AssetProcessingContext.asset_metadata`, `AssetProcessingContext.processed_maps_details`, and `AssetProcessingContext.merged_maps_details`. It structures this information and saves it as the `metadata.json` file in a temporary location within the engine's temporary directory.
-    *   *Note: Centralized logic for this was intended for `utils/blender_utils.py`, but this utility has not yet been implemented.* See `Developer Guide: Blender Integration Internals` for more details.
+    *   **Context Interaction**: Reads from various context fields and writes the `metadata.json` file. Stores the path to this temporary metadata file in the context (e.g., `AssetProcessingContext.asset_metadata['temp_metadata_path']`).
 
-This pipeline, executed by the `ProcessingEngine`, provides a clear and explicit processing flow based on the complete rule set provided by the GUI or other interfaces.
+11. **`OutputOrganizationStage` (`processing/pipeline/stages/output_organization.py`)**:
+    *   **Responsibility**: Determines final output paths for all processed maps, merged maps, the metadata file, and any other asset files (like models). It then copies these files from their temporary locations to the final structured output directory.
+    *   **Context Interaction**: Reads temporary file paths from `AssetProcessingContext.processed_maps_details`, `AssetProcessingContext.merged_maps_details`, and the temporary metadata file path. Uses `Configuration` for output path patterns. Updates `AssetProcessingContext.asset_metadata` with final file paths and status.
+
+**External Steps (Not part of `PipelineOrchestrator`'s direct loop but integral to the overall process):**
+
+*   **Workspace Preparation and Cleanup**: Handled by the code that invokes `ProcessingEngine.process()` (e.g., `main.ProcessingTask`, `monitor._process_archive_task`), typically using `utils.workspace_utils`. The engine itself creates a sub-temporary directory (`engine_temp_dir`) within the workspace provided to it by the orchestrator, which it cleans up.
+*   **Prediction and Rule Generation**: Also external, performed before `ProcessingEngine` is called. Generates the `SourceRule`.
+*   **Optional Blender Script Execution**: Triggered externally after successful processing.
+
+This staged pipeline provides a modular and extensible architecture for asset processing, with clear separation of concerns for each step. The `AssetProcessingContext` ensures that data flows consistently between these stages.r 

Presets/Dinesen.json

@@ -56,7 +56,7 @@
             ]
         },
         {
-            "target_type": "MAP_ROUGH",
+            "target_type": "MAP_GLOSS",
             "keywords": [
                 "GLOSS"
             ]

Presets/Poliigon.json

@@ -54,7 +54,7 @@
             ]
         },
         {
-            "target_type": "MAP_ROUGH",
+            "target_type": "MAP_GLOSS",
             "keywords": [
                 "GLOSS"
             ],

ProjectNotes/ProcessingEngineRefactorPlan.md

@@ -0,0 +1,181 @@
+# Project Plan: Modularizing the Asset Processing Engine
+
+**Last Updated:** May 9, 2025
+
+**1. Project Vision & Goals**
+
+*   **Vision:** Transform the asset processing pipeline into a highly modular, extensible, and testable system.
+*   **Primary Goals:**
+    1.  Decouple processing steps into independent, reusable stages.
+    2.  Simplify the addition of new processing capabilities (e.g., GLOSS > ROUGH conversion, Alpha to MASK, Normal Map Green Channel inversion).
+    3.  Improve code maintainability and readability.
+    4.  Enhance unit and integration testing capabilities for each processing component.
+    5.  Centralize common utility functions (image manipulation, path generation).
+
+**2. Proposed Architecture Overview**
+
+*   **Core Concept:** A `PipelineOrchestrator` will manage a sequence of `ProcessingStage`s. Each stage will operate on an `AssetProcessingContext` object, which carries all necessary data and state for a single asset through the pipeline.
+*   **Key Components:**
+    *   `AssetProcessingContext`: Data class holding asset-specific data, configuration, temporary paths, and status.
+    *   `PipelineOrchestrator`: Class to manage the overall processing flow for a `SourceRule`, iterating through assets and executing the pipeline of stages for each.
+    *   `ProcessingStage` (Base Class/Interface): Defines the contract for all individual processing stages (e.g., `execute(context)` method).
+    *   Specific Stage Classes: (e.g., `SupplierDeterminationStage`, `IndividualMapProcessingStage`, etc.)
+    *   Utility Modules: `image_processing_utils.py`, enhancements to `utils/path_utils.py`.
+
+**3. Proposed File Structure**
+
+*   `processing/`
+    *   `pipeline/`
+        *   `__init__.py`
+        *   `asset_context.py` (Defines `AssetProcessingContext`)
+        *   `orchestrator.py` (Defines `PipelineOrchestrator`)
+        *   `stages/`
+            *   `__init__.py`
+            *   `base_stage.py` (Defines `ProcessingStage` interface)
+            *   `supplier_determination.py`
+            *   `asset_skip_logic.py`
+            *   `metadata_initialization.py`
+            *   `file_rule_filter.py`
+            *   `gloss_to_rough_conversion.py`
+            *   `alpha_extraction_to_mask.py`
+            *   `normal_map_green_channel.py`
+            *   `individual_map_processing.py`
+            *   `map_merging.py`
+            *   `metadata_finalization.py`
+            *   `output_organization.py`
+    *   `utils/`
+        *   `__init__.py`
+        *   `image_processing_utils.py` (New module for image functions)
+*   `utils/` (Top-level existing directory)
+    *   `path_utils.py` (To be enhanced with `sanitize_filename` from `processing_engine.py`)
+
+**4. Detailed Phases and Tasks**
+
+**Phase 0: Setup & Core Structures Definition**
+*Goal: Establish the foundational classes for the new pipeline.*
+*   **Task 0.1: Define `AssetProcessingContext`**
+    *   Create `processing/pipeline/asset_context.py`.
+    *   Define the `AssetProcessingContext` data class with fields: `source_rule: SourceRule`, `asset_rule: AssetRule`, `workspace_path: Path`, `engine_temp_dir: Path`, `output_base_path: Path`, `effective_supplier: Optional[str]`, `asset_metadata: Dict`, `processed_maps_details: Dict[str, Dict[str, Dict]]`, `merged_maps_details: Dict[str, Dict[str, Dict]]`, `files_to_process: List[FileRule]`, `loaded_data_cache: Dict`, `config_obj: Configuration`, `status_flags: Dict`, `incrementing_value: Optional[str]`, `sha5_value: Optional[str]`.
+    *   Ensure proper type hinting.
+*   **Task 0.2: Define `ProcessingStage` Base Class/Interface**
+    *   Create `processing/pipeline/stages/base_stage.py`.
+    *   Define an abstract base class `ProcessingStage` with an abstract method `execute(self, context: AssetProcessingContext) -> AssetProcessingContext`.
+*   **Task 0.3: Implement Initial `PipelineOrchestrator`**
+    *   Create `processing/pipeline/orchestrator.py`.
+    *   Define the `PipelineOrchestrator` class.
+    *   Implement `__init__(self, config_obj: Configuration, stages: List[ProcessingStage])`.
+    *   Implement `process_source_rule(self, source_rule: SourceRule, workspace_path: Path, output_base_path: Path, overwrite: bool, incrementing_value: Optional[str], sha5_value: Optional[str]) -> Dict[str, List[str]]`.
+        *   Handles creation/cleanup of the main engine temporary directory.
+        *   Loops through `source_rule.assets`, initializes `AssetProcessingContext` for each.
+        *   Iterates `self.stages`, calling `stage.execute(context)`.
+        *   Collects overall status.
+
+**Phase 1: Utility Module Refactoring**
+*Goal: Consolidate and centralize common utility functions.*
+*   **Task 1.1: Refactor Path Utilities**
+    *   Move `_sanitize_filename` from `processing_engine.py` to `utils/path_utils.py`.
+    *   Update uses to call the new utility function.
+*   **Task 1.2: Create `image_processing_utils.py`**
+    *   Create `processing/utils/image_processing_utils.py`.
+    *   Move general-purpose image functions from `processing_engine.py`:
+        *   `is_power_of_two`
+        *   `get_nearest_pot`
+        *   `calculate_target_dimensions`
+        *   `calculate_image_stats`
+        *   `normalize_aspect_ratio_change`
+        *   Core image loading, BGR<>RGB conversion, generic resizing (from `_load_and_transform_source`).
+        *   Core data type conversion for saving, color conversion for saving, `cv2.imwrite` call (from `_save_image`).
+    *   Ensure functions are pure and testable.
+
+**Phase 2: Implementing Core Processing Stages (Migrating Existing Logic)**
+*Goal: Migrate existing functionalities from `processing_engine.py` into the new stage-based architecture.*
+(For each task: create stage file, implement class, move logic, adapt to `AssetProcessingContext`)
+*   **Task 2.1: Implement `SupplierDeterminationStage`**
+*   **Task 2.2: Implement `AssetSkipLogicStage`**
+*   **Task 2.3: Implement `MetadataInitializationStage`**
+*   **Task 2.4: Implement `FileRuleFilterStage`** (New logic for `item_type == "FILE_IGNORE"`)
+*   **Task 2.5: Implement `IndividualMapProcessingStage`** (Adapts `_process_individual_maps`, uses `image_processing_utils.py`)
+*   **Task 2.6: Implement `MapMergingStage`** (Adapts `_merge_maps`, uses `image_processing_utils.py`)
+*   **Task 2.7: Implement `MetadataFinalizationAndSaveStage`** (Adapts `_generate_metadata_file`, uses `utils.path_utils.generate_path_from_pattern`)
+*   **Task 2.8: Implement `OutputOrganizationStage`** (Adapts `_organize_output_files`)
+
+**Phase 3: Implementing New Feature Stages**
+*Goal: Add the new desired processing capabilities as distinct stages.*
+*   **Task 3.1: Implement `GlossToRoughConversionStage`** (Identify gloss, convert, invert, save temp, update `FileRule`)
+*   **Task 3.2: Implement `AlphaExtractionToMaskStage`** (Check existing mask, find MAP_COL with alpha, extract, save temp, add new `FileRule`)
+*   **Task 3.3: Implement `NormalMapGreenChannelStage`** (Identify normal maps, invert green based on config, save temp, update `FileRule`)
+
+**Phase 4: Integration, Testing & Finalization**
+*Goal: Assemble the pipeline, test thoroughly, and deprecate old code.*
+*   **Task 4.1: Configure `PipelineOrchestrator`**
+    *   Instantiate `PipelineOrchestrator` in main application logic with the ordered list of stage instances.
+*   **Task 4.2: Unit Testing**
+    *   Unit tests for each `ProcessingStage` (mocking `AssetProcessingContext`).
+    *   Unit tests for `image_processing_utils.py` and `utils/path_utils.py` functions.
+*   **Task 4.3: Integration Testing**
+    *   Test `PipelineOrchestrator` end-to-end with sample data.
+    *   Compare outputs with the existing engine for consistency.
+*   **Task 4.4: Documentation Update**
+    *   Update developer documentation (e.g., `Documentation/02_Developer_Guide/05_Processing_Pipeline.md`).
+    *   Document `AssetProcessingContext` and stage responsibilities.
+*   **Task 4.5: Deprecate/Remove Old `ProcessingEngine` Code**
+    *   Gradually remove refactored logic from `processing_engine.py`.
+
+**5. Workflow Diagram**
+
+```mermaid
+graph TD
+    AA[Load SourceRule & Config] --> BA(PipelineOrchestrator: process_source_rule);
+    BA --> CA{For Each Asset in SourceRule};
+    CA -- Yes --> DA(Orchestrator: Create AssetProcessingContext);
+    DA --> EA(SupplierDeterminationStage);
+    EA -- context --> FA(AssetSkipLogicStage);
+    FA -- context --> GA{context.skip_asset?};
+    GA -- Yes --> HA(Orchestrator: Record Skipped);
+    HA --> CA;
+    GA -- No --> IA(MetadataInitializationStage);
+    IA -- context --> JA(FileRuleFilterStage);
+    JA -- context --> KA(GlossToRoughConversionStage);
+    KA -- context --> LA(AlphaExtractionToMaskStage);
+    LA -- context --> MA(NormalMapGreenChannelStage);
+    MA -- context --> NA(IndividualMapProcessingStage);
+    NA -- context --> OA(MapMergingStage);
+    OA -- context --> PA(MetadataFinalizationAndSaveStage);
+    PA -- context --> QA(OutputOrganizationStage);
+    QA -- context --> RA(Orchestrator: Record Processed/Failed);
+    RA --> CA;
+    CA -- No --> SA(Orchestrator: Cleanup Engine Temp Dir);
+    SA --> TA[Processing Complete];
+
+    subgraph Stages
+        direction LR
+        EA
+        FA
+        IA
+        JA
+        KA
+        LA
+        MA
+        NA
+        OA
+        PA
+        QA
+    end
+
+    subgraph Utils
+        direction LR
+        U1[image_processing_utils.py]
+        U2[utils/path_utils.py]
+    end
+
+    NA -.-> U1;
+    OA -.-> U1;
+    KA -.-> U1;
+    LA -.-> U1;
+    MA -.-> U1;
+
+    PA -.-> U2;
+    QA -.-> U2;
+
+    classDef context fill:#f9f,stroke:#333,stroke-width:2px;
+    class DA,EA,FA,IA,JA,KA,LA,MA,NA,OA,PA,QA context;

config/app_settings.json

@@ -246,7 +246,7 @@
     ],
     "EXTRA_FILES_SUBDIR": "Extra",
     "OUTPUT_BASE_DIR": "../Asset_Processor_Output_Tests",
-    "OUTPUT_DIRECTORY_PATTERN": "[supplier]/[sha5]_[assetname]",
+    "OUTPUT_DIRECTORY_PATTERN": "[supplier]_[assetname]",
     "OUTPUT_FILENAME_PATTERN": "[assetname]_[maptype]_[resolution].[ext]",
     "METADATA_FILENAME": "metadata.json",
     "DEFAULT_NODEGROUP_BLEND_PATH": "G:/02 Content/10-19 Content/19 Catalogs/19.01 Blender Asset Catalogue/_CustomLibraries/Nodes-Linked/PBRSET-Nodes-Testing.blend",
@@ -259,7 +259,8 @@
         "8K": 8192,
         "4K": 4096,
         "2K": 2048,
-        "1K": 1024
+        "1K": 1024,
+        "PREVIEW": 128
     },
     "ASPECT_RATIO_DECIMALS": 2,
     "OUTPUT_FORMAT_16BIT_PRIMARY": "exr",
@@ -269,9 +270,9 @@
         {
             "output_map_type": "NRMRGH",
             "inputs": {
-                "R": "NRM",
+                "R": "MAP_NRM",
-                "G": "NRM",
+                "G": "MAP_NRM",
-                "B": "ROUGH"
+                "B": "MAP_ROUGH"
             },
             "defaults": {
                 "R": 0.5,

main.py

@@ -21,15 +21,43 @@ from PySide6.QtCore import Qt
 from PySide6.QtWidgets import QApplication
 
 # --- Backend Imports ---
+# Add current directory to sys.path for direct execution
+import sys
+import os
+sys.path.append(os.path.dirname(__file__))
+print(f"DEBUG: sys.path after append: {sys.path}")
+
 try:
+    print("DEBUG: Attempting to import Configuration...")
     from configuration import Configuration, ConfigurationError
+    print("DEBUG: Successfully imported Configuration.")
+
+    print("DEBUG: Attempting to import ProcessingEngine...")
     from processing_engine import ProcessingEngine
+    print("DEBUG: Successfully imported ProcessingEngine.")
+
+    print("DEBUG: Attempting to import SourceRule...")
     from rule_structure import SourceRule
+    print("DEBUG: Successfully imported SourceRule.")
+
+    print("DEBUG: Attempting to import MainWindow...")
     from gui.main_window import MainWindow
+    print("DEBUG: Successfully imported MainWindow.")
+
+    print("DEBUG: Attempting to import prepare_processing_workspace...")
     from utils.workspace_utils import prepare_processing_workspace
+    print("DEBUG: Successfully imported prepare_processing_workspace.")
+
 except ImportError as e:
     script_dir = Path(__file__).parent.resolve()
+    print(f"ERROR: Cannot import Configuration or rule_structure classes.")
+    print(f"Ensure configuration.py and rule_structure.py are in the same directory or Python path.")
     print(f"ERROR: Failed to import necessary classes: {e}")
+    print(f"DEBUG: Exception type: {type(e)}")
+    print(f"DEBUG: Exception args: {e.args}")
+    import traceback
+    print("DEBUG: Full traceback of the ImportError:")
+    traceback.print_exc()
     print(f"Ensure 'configuration.py' and 'asset_processor.py' exist in the directory:")
     print(f"  {script_dir}")
     print("Or that the directory is included in your PYTHONPATH.")

processing/pipeline/asset_context.py

@@ -0,0 +1,24 @@
+from dataclasses import dataclass
+from pathlib import Path
+from typing import Dict, List, Optional
+
+from rule_structure import AssetRule, FileRule, SourceRule
+from configuration import Configuration
+
+@dataclass
+class AssetProcessingContext:
+    source_rule: SourceRule
+    asset_rule: AssetRule
+    workspace_path: Path
+    engine_temp_dir: Path
+    output_base_path: Path
+    effective_supplier: Optional[str]
+    asset_metadata: Dict
+    processed_maps_details: Dict[str, Dict[str, Dict]]
+    merged_maps_details: Dict[str, Dict[str, Dict]]
+    files_to_process: List[FileRule]
+    loaded_data_cache: Dict
+    config_obj: Configuration
+    status_flags: Dict
+    incrementing_value: Optional[str]
+    sha5_value: Optional[str]

processing/pipeline/orchestrator.py

@@ -0,0 +1,131 @@
+from typing import List, Dict, Optional
+from pathlib import Path
+import shutil
+import tempfile
+import logging
+
+from configuration import Configuration
+from rule_structure import SourceRule, AssetRule
+from .asset_context import AssetProcessingContext
+from .stages.base_stage import ProcessingStage
+
+log = logging.getLogger(__name__)
+
+class PipelineOrchestrator:
+    """
+    Orchestrates the processing of assets based on source rules and a series of processing stages.
+    """
+
+    def __init__(self, config_obj: Configuration, stages: List[ProcessingStage]):
+        """
+        Initializes the PipelineOrchestrator.
+
+        Args:
+            config_obj: The main configuration object.
+            stages: A list of processing stages to be executed in order.
+        """
+        self.config_obj: Configuration = config_obj
+        self.stages: List[ProcessingStage] = stages
+
+    def process_source_rule(
+        self,
+        source_rule: SourceRule,
+        workspace_path: Path,
+        output_base_path: Path,
+        overwrite: bool, # Not used in this initial implementation, but part of the signature
+        incrementing_value: Optional[str],
+        sha5_value: Optional[str] # Corrected from sha5_value to sha256_value as per typical usage, assuming typo
+    ) -> Dict[str, List[str]]:
+        """
+        Processes a single source rule, iterating through its asset rules and applying all stages.
+
+        Args:
+            source_rule: The source rule to process.
+            workspace_path: The base path of the workspace.
+            output_base_path: The base path for output files.
+            overwrite: Whether to overwrite existing files (not fully implemented yet).
+            incrementing_value: An optional incrementing value for versioning or naming.
+            sha5_value: An optional SHA5 hash value for the asset (assuming typo, likely sha256).
+
+        Returns:
+            A dictionary summarizing the processing status of assets.
+        """
+        overall_status: Dict[str, List[str]] = {
+            "processed": [],
+            "skipped": [],
+            "failed": [],
+        }
+        engine_temp_dir_path: Optional[Path] = None # Initialize to None
+
+        try:
+            # Create a temporary directory for this processing run if needed by any stage
+            # This temp dir is for the entire source_rule processing, not per asset.
+            # Individual stages might create their own sub-temp dirs if necessary.
+            temp_dir_path_str = tempfile.mkdtemp(prefix=self.config_obj.temp_dir_prefix)
+            engine_temp_dir_path = Path(temp_dir_path_str)
+            log.debug(f"PipelineOrchestrator created temporary directory: {engine_temp_dir_path} using prefix '{self.config_obj.temp_dir_prefix}'")
+
+
+            for asset_rule in source_rule.assets:
+                log.debug(f"Orchestrator: Processing asset '{asset_rule.asset_name}'")
+                context = AssetProcessingContext(
+                    source_rule=source_rule,
+                    asset_rule=asset_rule,
+                    workspace_path=workspace_path, # This is the path to the source files (e.g. extracted archive)
+                    engine_temp_dir=engine_temp_dir_path, # Pass the orchestrator's temp dir
+                    output_base_path=output_base_path,
+                    effective_supplier=None, # Will be set by SupplierDeterminationStage
+                    asset_metadata={}, # Will be populated by stages
+                    processed_maps_details={}, # Will be populated by stages
+                    merged_maps_details={}, # Will be populated by stages
+                    files_to_process=[], # Will be populated by FileRuleFilterStage
+                    loaded_data_cache={}, # For image loading cache within this asset's processing
+                    config_obj=self.config_obj,
+                    status_flags={"skip_asset": False, "asset_failed": False}, # Initialize common flags
+                    incrementing_value=incrementing_value,
+                    sha5_value=sha5_value
+                )
+
+                for stage_idx, stage in enumerate(self.stages):
+                    log.debug(f"Asset '{asset_rule.asset_name}': Executing stage {stage_idx + 1}/{len(self.stages)}: {stage.__class__.__name__}")
+                    try:
+                        context = stage.execute(context)
+                    except Exception as e:
+                        log.error(f"Asset '{asset_rule.asset_name}': Error during stage '{stage.__class__.__name__}': {e}", exc_info=True)
+                        context.status_flags["asset_failed"] = True
+                        context.asset_metadata["status"] = f"Failed: Error in stage {stage.__class__.__name__}"
+                        context.asset_metadata["error_message"] = str(e)
+                        break # Stop processing stages for this asset on error
+
+                    if context.status_flags.get("skip_asset"):
+                        log.info(f"Asset '{asset_rule.asset_name}': Skipped by stage '{stage.__class__.__name__}'. Reason: {context.status_flags.get('skip_reason', 'N/A')}")
+                        break # Skip remaining stages for this asset
+
+                # Refined status collection
+                if context.status_flags.get('skip_asset'):
+                    overall_status["skipped"].append(asset_rule.asset_name)
+                elif context.status_flags.get('asset_failed') or str(context.asset_metadata.get('status', '')).startswith("Failed"):
+                    overall_status["failed"].append(asset_rule.asset_name)
+                elif context.asset_metadata.get('status') == "Processed":
+                    overall_status["processed"].append(asset_rule.asset_name)
+                else: # Default or unknown state
+                    log.warning(f"Asset '{asset_rule.asset_name}': Unknown status after pipeline execution. Metadata status: '{context.asset_metadata.get('status')}'. Marking as failed.")
+                    overall_status["failed"].append(f"{asset_rule.asset_name} (Unknown Status: {context.asset_metadata.get('status')})")
+                log.debug(f"Asset '{asset_rule.asset_name}' final status: {context.asset_metadata.get('status', 'N/A')}, Flags: {context.status_flags}")
+
+        except Exception as e:
+            log.error(f"PipelineOrchestrator.process_source_rule failed: {e}", exc_info=True)
+            # Mark all remaining assets as failed if a top-level error occurs
+            processed_or_skipped_or_failed = set(overall_status["processed"] + overall_status["skipped"] + overall_status["failed"])
+            for asset_rule in source_rule.assets:
+                if asset_rule.asset_name not in processed_or_skipped_or_failed:
+                    overall_status["failed"].append(f"{asset_rule.asset_name} (Orchestrator Error)")
+        finally:
+            if engine_temp_dir_path and engine_temp_dir_path.exists():
+                try:
+                    log.debug(f"PipelineOrchestrator cleaning up temporary directory: {engine_temp_dir_path}")
+                    shutil.rmtree(engine_temp_dir_path, ignore_errors=True)
+                except Exception as e:
+                    log.error(f"Error cleaning up orchestrator temporary directory {engine_temp_dir_path}: {e}", exc_info=True)
+
+        return overall_status

processing/pipeline/stages/alpha_extraction_to_mask.py

@@ -0,0 +1,174 @@
+import logging
+import uuid
+from pathlib import Path
+from typing import List, Optional, Dict
+
+import numpy as np
+
+from .base_stage import ProcessingStage
+from ..asset_context import AssetProcessingContext
+from ...utils import image_processing_utils as ipu
+from rule_structure import FileRule
+from utils.path_utils import sanitize_filename
+
+logger = logging.getLogger(__name__)
+
+class AlphaExtractionToMaskStage(ProcessingStage):
+    """
+    Extracts an alpha channel from a suitable source map (e.g., Albedo, Diffuse)
+    to generate a MASK map if one is not explicitly defined.
+    """
+    SUITABLE_SOURCE_MAP_TYPES = ["ALBEDO", "DIFFUSE", "BASE_COLOR"] # Map types likely to have alpha
+
+    def execute(self, context: AssetProcessingContext) -> AssetProcessingContext:
+        asset_name_for_log = context.asset_rule.asset_name if context.asset_rule else "Unknown Asset"
+        logger.debug(f"Asset '{asset_name_for_log}': Running AlphaExtractionToMaskStage.")
+
+        if context.status_flags.get('skip_asset'):
+            logger.debug(f"Asset '{asset_name_for_log}': Skipping due to 'skip_asset' flag.")
+            return context
+
+        if not context.files_to_process or not context.processed_maps_details:
+            logger.debug(
+                f"Asset '{asset_name_for_log}': Skipping alpha extraction - "
+                f"no files to process or no processed map details."
+            )
+            return context
+
+        # A. Check for Existing MASK Map
+        for file_rule in context.files_to_process:
+            # Assuming file_rule has 'map_type' and 'file_path' (instead of filename_pattern)
+            if hasattr(file_rule, 'map_type') and file_rule.map_type == "MASK":
+                file_path_for_log = file_rule.file_path if hasattr(file_rule, 'file_path') else "Unknown file path"
+                logger.info(
+                    f"Asset '{asset_name_for_log}': MASK map already defined by FileRule "
+                    f"for '{file_path_for_log}'. Skipping alpha extraction."
+                )
+                return context
+
+        # B. Find Suitable Source Map with Alpha
+        source_map_details_for_alpha: Optional[Dict] = None
+        source_file_rule_id_for_alpha: Optional[str] = None # This ID comes from processed_maps_details keys
+
+        for file_rule_id, details in context.processed_maps_details.items():
+            if details.get('status') == 'Processed' and \
+               details.get('map_type') in self.SUITABLE_SOURCE_MAP_TYPES:
+                try:
+                    temp_path = Path(details['temp_processed_file'])
+                    if not temp_path.exists():
+                        logger.warning(
+                            f"Asset '{asset_name_for_log}': Temp file {temp_path} for map "
+                            f"{details['map_type']} (ID: {file_rule_id}) does not exist. Cannot check for alpha."
+                        )
+                        continue
+                    
+                    image_data = ipu.load_image(temp_path)
+
+                    if image_data is not None and image_data.ndim == 3 and image_data.shape[2] == 4:
+                        source_map_details_for_alpha = details
+                        source_file_rule_id_for_alpha = file_rule_id
+                        logger.info(
+                            f"Asset '{asset_name_for_log}': Found potential source for alpha extraction: "
+                            f"{temp_path} (MapType: {details['map_type']})"
+                        )
+                        break
+                except Exception as e:
+                    logger.warning(
+                        f"Asset '{asset_name_for_log}': Error checking alpha for {details.get('temp_processed_file', 'N/A')}: {e}"
+                    )
+                    continue
+
+
+        if source_map_details_for_alpha is None or source_file_rule_id_for_alpha is None:
+            logger.info(
+                f"Asset '{asset_name_for_log}': No suitable source map with alpha channel found "
+                f"for MASK extraction."
+            )
+            return context
+
+        # C. Extract Alpha Channel
+        source_image_path = Path(source_map_details_for_alpha['temp_processed_file'])
+        full_image_data = ipu.load_image(source_image_path) # Reload to ensure we have the original RGBA
+
+        if full_image_data is None or not (full_image_data.ndim == 3 and full_image_data.shape[2] == 4):
+            logger.error(
+                f"Asset '{asset_name_for_log}': Failed to reload or verify alpha channel from "
+                f"{source_image_path} for MASK extraction."
+            )
+            return context
+        
+        alpha_channel: np.ndarray = full_image_data[:, :, 3] # Extract alpha (0-255)
+
+        # D. Save New Temporary MASK Map
+        if alpha_channel.ndim == 2: # Expected
+            pass
+        elif alpha_channel.ndim == 3 and alpha_channel.shape[2] == 1: # (H, W, 1)
+             alpha_channel = alpha_channel.squeeze(axis=2)
+        else:
+            logger.error(
+                f"Asset '{asset_name_for_log}': Extracted alpha channel has unexpected dimensions: "
+                f"{alpha_channel.shape}. Cannot save."
+            )
+            return context
+
+        mask_temp_filename = (
+            f"mask_from_alpha_{sanitize_filename(source_map_details_for_alpha['map_type'])}"
+            f"_{source_file_rule_id_for_alpha}{source_image_path.suffix}"
+        )
+        mask_temp_path = context.engine_temp_dir / mask_temp_filename
+        
+        save_success = ipu.save_image(mask_temp_path, alpha_channel)
+        
+        if not save_success:
+            logger.error(
+                f"Asset '{asset_name_for_log}': Failed to save extracted alpha mask to {mask_temp_path}."
+            )
+            return context
+        
+        logger.info(
+            f"Asset '{asset_name_for_log}': Extracted alpha and saved as new MASK map: {mask_temp_path}"
+        )
+
+        # E. Create New FileRule for the MASK and Update Context
+        # FileRule does not have id, active, transform_settings, source_map_ids_for_generation
+        # It has file_path, item_type, item_type_override, etc.
+        new_mask_file_rule = FileRule(
+            file_path=mask_temp_path.name, # Use file_path
+            item_type="MAP_MASK", # This should be the item_type for a mask
+            map_type="MASK" # Explicitly set map_type if FileRule has it, or handle via item_type
+            # Other FileRule fields like item_type_override can be set if needed
+        )
+        # If FileRule needs a unique identifier, it should be handled differently,
+        # perhaps by generating one and storing it in common_metadata or a separate mapping.
+        # For now, we create a simple FileRule.
+        
+        context.files_to_process.append(new_mask_file_rule)
+        
+        # For processed_maps_details, we need a unique key. Using a new UUID.
+        new_mask_processed_map_key = uuid.uuid4().hex
+
+        original_dims = source_map_details_for_alpha.get('original_dimensions')
+        if original_dims is None and full_image_data is not None: # Fallback if not in details
+             original_dims = (full_image_data.shape[1], full_image_data.shape[0])
+
+
+        context.processed_maps_details[new_mask_processed_map_key] = {
+            'map_type': "MASK",
+            'source_file': str(source_image_path),
+            'temp_processed_file': str(mask_temp_path),
+            'original_dimensions': original_dims,
+            'processed_dimensions': (alpha_channel.shape[1], alpha_channel.shape[0]),
+            'status': 'Processed',
+            'notes': (
+                f"Generated from alpha of {source_map_details_for_alpha['map_type']} "
+                f"(Source Detail ID: {source_file_rule_id_for_alpha})" # Changed from Source Rule ID
+            ),
+            # 'file_rule_id': new_mask_file_rule_id_str # FileRule doesn't have an ID to link here directly
+        }
+        
+        logger.info(
+            f"Asset '{asset_name_for_log}': Added new FileRule for generated MASK "
+            f"and updated processed_maps_details with key '{new_mask_processed_map_key}'."
+        )
+
+        return context

processing/pipeline/stages/asset_skip_logic.py

@@ -0,0 +1,55 @@
+import logging
+from .base_stage import ProcessingStage
+from ..asset_context import AssetProcessingContext
+
+class AssetSkipLogicStage(ProcessingStage):
+    """
+    Processing stage to determine if an asset should be skipped based on various conditions.
+    """
+    def execute(self, context: AssetProcessingContext) -> AssetProcessingContext:
+        """
+        Executes the asset skip logic.
+
+        Args:
+            context: The asset processing context.
+
+        Returns:
+            The updated asset processing context.
+        """
+        context.status_flags['skip_asset'] = False  # Initialize/reset skip flag
+        asset_name_for_log = context.asset_rule.asset_name if context.asset_rule else "Unknown Asset"
+
+        # 1. Check for Supplier Error
+        # Assuming 'supplier_error' might be set by a previous stage (e.g., SupplierDeterminationStage)
+        # or if effective_supplier is None after attempts to determine it.
+        if context.effective_supplier is None or context.status_flags.get('supplier_error', False):
+            logging.info(f"Asset '{asset_name_for_log}': Skipping due to missing or invalid supplier.")
+            context.status_flags['skip_asset'] = True
+            context.status_flags['skip_reason'] = "Invalid or missing supplier"
+            return context
+
+        # 2. Check process_status in asset_rule.common_metadata
+        process_status = context.asset_rule.common_metadata.get('process_status')
+
+        if process_status == "SKIP":
+            logging.info(f"Asset '{asset_name_for_log}': Skipping as per common_metadata.process_status 'SKIP'.")
+            context.status_flags['skip_asset'] = True
+            context.status_flags['skip_reason'] = "Process status set to SKIP in common_metadata"
+            return context
+
+        # Assuming context.config_obj.general_settings.overwrite_existing is a valid path.
+        # This might need adjustment if 'general_settings' or 'overwrite_existing' is not found.
+        # For now, we'll assume it's correct based on the original code's intent.
+        if process_status == "PROCESSED" and \
+           hasattr(context.config_obj, 'general_settings') and \
+           not getattr(context.config_obj.general_settings, 'overwrite_existing', True): # Default to True (allow overwrite) if not found
+            logging.info(
+                f"Asset '{asset_name_for_log}': Skipping as it's already 'PROCESSED' (from common_metadata) "
+                f"and overwrite is disabled."
+            )
+            context.status_flags['skip_asset'] = True
+            context.status_flags['skip_reason'] = "Already processed (common_metadata), overwrite disabled"
+            return context
+
+        # If none of the above conditions are met, skip_asset remains False.
+        return context

processing/pipeline/stages/base_stage.py

@@ -0,0 +1,22 @@
+from abc import ABC, abstractmethod
+
+from ..asset_context import AssetProcessingContext
+
+
+class ProcessingStage(ABC):
+    """
+    Abstract base class for a stage in the asset processing pipeline.
+    """
+
+    @abstractmethod
+    def execute(self, context: AssetProcessingContext) -> AssetProcessingContext:
+        """
+        Executes the processing logic of this stage.
+
+        Args:
+            context: The current asset processing context.
+
+        Returns:
+            The updated asset processing context.
+        """
+        pass

processing/pipeline/stages/file_rule_filter.py

@@ -0,0 +1,90 @@
+import logging
+import fnmatch
+from typing import List, Set
+
+from .base_stage import ProcessingStage
+from ..asset_context import AssetProcessingContext
+from rule_structure import FileRule
+
+
+class FileRuleFilterStage(ProcessingStage):
+    """
+    Determines which FileRules associated with an AssetRule should be processed.
+    Populates context.files_to_process, respecting FILE_IGNORE rules.
+    """
+
+    def execute(self, context: AssetProcessingContext) -> AssetProcessingContext:
+        """
+        Executes the file rule filtering logic.
+
+        Args:
+            context: The AssetProcessingContext for the current asset.
+
+        Returns:
+            The modified AssetProcessingContext.
+        """
+        asset_name_for_log = context.asset_rule.asset_name if context.asset_rule else "Unknown Asset"
+        if context.status_flags.get('skip_asset'):
+            logging.debug(f"Asset '{asset_name_for_log}': Skipping FileRuleFilterStage due to 'skip_asset' flag.")
+            return context
+
+        context.files_to_process: List[FileRule] = []
+        ignore_patterns: Set[str] = set()
+
+        # Step 1: Collect all FILE_IGNORE patterns
+        if context.asset_rule and context.asset_rule.files:
+            for file_rule in context.asset_rule.files:
+                if file_rule.item_type == "FILE_IGNORE": # Removed 'and file_rule.active'
+                    if hasattr(file_rule, 'file_path') and file_rule.file_path:
+                        ignore_patterns.add(file_rule.file_path)
+                        logging.debug(
+                            f"Asset '{asset_name_for_log}': Registering ignore pattern: '{file_rule.file_path}'"
+                        )
+                    else:
+                        logging.warning(f"Asset '{asset_name_for_log}': FILE_IGNORE rule found without a file_path. Skipping this ignore rule.")
+        else:
+            logging.debug(f"Asset '{asset_name_for_log}': No file rules (context.asset_rule.files) to process or asset_rule is None.")
+            # Still need to return context even if there are no rules
+            logging.info(f"Asset '{asset_name_for_log}': 0 file rules queued for processing after filtering.")
+            return context
+
+
+        # Step 2: Filter and add processable FileRules
+        for file_rule in context.asset_rule.files: # Iterate over .files
+            # Removed 'if not file_rule.active:' check
+
+            if file_rule.item_type == "FILE_IGNORE":
+                # Already processed, skip.
+                continue
+
+            is_ignored = False
+            # Ensure file_rule.file_path exists before using it with fnmatch
+            current_file_path = file_rule.file_path if hasattr(file_rule, 'file_path') else None
+            if not current_file_path:
+                logging.warning(f"Asset '{asset_name_for_log}': FileRule found without a file_path. Skipping this rule for ignore matching.")
+                # Decide if this rule should be added or skipped if it has no path
+                # For now, let's assume it might be an error and not add it if it can't be matched.
+                # If it should be added by default, this logic needs adjustment.
+                continue
+
+
+            for ignore_pat in ignore_patterns:
+                if fnmatch.fnmatch(current_file_path, ignore_pat):
+                    is_ignored = True
+                    logging.debug(
+                        f"Asset '{asset_name_for_log}': Skipping file rule for '{current_file_path}' "
+                        f"due to matching ignore pattern '{ignore_pat}'."
+                    )
+                    break
+            
+            if not is_ignored:
+                context.files_to_process.append(file_rule)
+                logging.debug(
+                    f"Asset '{asset_name_for_log}': Adding file rule for '{current_file_path}' "
+                    f"(type: {file_rule.item_type}) to processing queue."
+                )
+
+        logging.info(
+            f"Asset '{asset_name_for_log}': {len(context.files_to_process)} file rules queued for processing after filtering."
+        )
+        return context

processing/pipeline/stages/gloss_to_rough_conversion.py

@@ -0,0 +1,193 @@
+import logging
+from pathlib import Path
+import numpy as np
+from typing import List
+import dataclasses
+
+from .base_stage import ProcessingStage
+from ..asset_context import AssetProcessingContext
+from rule_structure import FileRule
+from ...utils import image_processing_utils as ipu
+from utils.path_utils import sanitize_filename
+
+logger = logging.getLogger(__name__)
+
+class GlossToRoughConversionStage(ProcessingStage):
+    """
+    Processing stage to convert glossiness maps to roughness maps.
+    Iterates through FileRules, identifies GLOSS maps, loads their
+    corresponding temporary processed images, inverts them, and saves
+    them as new temporary ROUGHNESS maps. Updates the FileRule and
+    context.processed_maps_details accordingly.
+    """
+
+    def execute(self, context: AssetProcessingContext) -> AssetProcessingContext:
+        """
+        Executes the gloss to roughness conversion logic.
+
+        Args:
+            context: The AssetProcessingContext containing asset and processing details.
+
+        Returns:
+            The updated AssetProcessingContext.
+        """
+        asset_name_for_log = context.asset_rule.asset_name if context.asset_rule else "Unknown Asset"
+        if context.status_flags.get('skip_asset'):
+            logger.debug(f"Asset '{asset_name_for_log}': Skipping GlossToRoughConversionStage due to skip_asset flag.")
+            return context
+
+        if not context.processed_maps_details: # files_to_process might be empty if only gloss maps existed and all are converted
+            logger.debug(
+                f"Asset '{asset_name_for_log}': processed_maps_details is empty in GlossToRoughConversionStage. Skipping."
+            )
+            return context
+
+        # Start with a copy of the current file rules. We will modify this list.
+        new_files_to_process: List[FileRule] = list(context.files_to_process) if context.files_to_process else []
+        processed_a_gloss_map = False
+        successful_conversion_statuses = ['BasePOTSaved', 'Processed_With_Variants', 'Processed_No_Variants']
+
+        logger.info(f"Asset '{asset_name_for_log}': Starting Gloss to Roughness Conversion Stage. Examining {len(context.processed_maps_details)} processed map entries.")
+
+        # Iterate using the index (map_key_index) as the key, which is now standard.
+        for map_key_index, map_details in context.processed_maps_details.items():
+            processing_map_type = map_details.get('processing_map_type', '')
+            map_status = map_details.get('status')
+            original_temp_path_str = map_details.get('temp_processed_file')
+            # source_file_rule_idx from details should align with map_key_index.
+            # We primarily use map_key_index for accessing FileRule from context.files_to_process.
+            source_file_rule_idx_from_details = map_details.get('source_file_rule_index')
+            processing_tag = map_details.get('processing_tag')
+
+            if map_key_index != source_file_rule_idx_from_details:
+                logger.warning(
+                    f"Asset '{asset_name_for_log}', Map Key Index {map_key_index}: Mismatch between map key index and 'source_file_rule_index' ({source_file_rule_idx_from_details}) in details. "
+                    f"Using map_key_index ({map_key_index}) for FileRule lookup. This might indicate a data consistency issue from previous stage."
+                )
+
+            if not processing_tag:
+                logger.warning(f"Asset '{asset_name_for_log}', Map Key Index {map_key_index}: 'processing_tag' is missing in map_details. Using a fallback for temp filename. This is unexpected.")
+                processing_tag = f"mki_{map_key_index}_fallback_tag"
+
+
+            if not processing_map_type.startswith("MAP_GLOSS"):
+                # logger.debug(f"Asset '{asset_name_for_log}', Map Key Index {map_key_index}: Type '{processing_map_type}' is not GLOSS. Skipping.")
+                continue
+
+            logger.info(f"Asset '{asset_name_for_log}', Map Key Index {map_key_index} (Tag: {processing_tag}): Identified potential GLOSS map (Type: {processing_map_type}).")
+
+            if map_status not in successful_conversion_statuses:
+                logger.warning(
+                    f"Asset '{asset_name_for_log}', Map Key Index {map_key_index} (Tag: {processing_tag}) (GLOSS): Status '{map_status}' is not one of {successful_conversion_statuses}. "
+                    f"Skipping conversion for this map."
+                )
+                continue
+
+            if not original_temp_path_str:
+                logger.warning(
+                    f"Asset '{asset_name_for_log}', Map Key Index {map_key_index} (Tag: {processing_tag}) (GLOSS): 'temp_processed_file' missing in details. "
+                    f"Skipping conversion."
+                )
+                continue
+            
+            original_temp_path = Path(original_temp_path_str)
+            if not original_temp_path.exists():
+                logger.error(
+                    f"Asset '{asset_name_for_log}', Map Key Index {map_key_index} (Tag: {processing_tag}) (GLOSS): Temporary file {original_temp_path_str} "
+                    f"does not exist. Skipping conversion."
+                )
+                continue
+
+            # Use map_key_index directly to access the FileRule
+            # Ensure map_key_index is a valid index for context.files_to_process
+            if not isinstance(map_key_index, int) or map_key_index < 0 or map_key_index >= len(context.files_to_process):
+                logger.error(
+                    f"Asset '{asset_name_for_log}', Map Key Index {map_key_index} (Tag: {processing_tag}) (GLOSS): Invalid map_key_index ({map_key_index}) for accessing files_to_process (len: {len(context.files_to_process)}). "
+                    f"Skipping conversion."
+                )
+                continue
+            
+            original_file_rule = context.files_to_process[map_key_index]
+            source_file_path_for_log = original_file_rule.file_path if hasattr(original_file_rule, 'file_path') else "Unknown source path"
+            logger.debug(f"Asset '{asset_name_for_log}', Map Key Index {map_key_index} (Tag: {processing_tag}): Processing GLOSS map from '{original_temp_path_str}' (Original FileRule path: '{source_file_path_for_log}') for conversion.")
+
+            image_data = ipu.load_image(str(original_temp_path))
+            if image_data is None:
+                logger.error(
+                    f"Asset '{asset_name_for_log}', Map Key Index {map_key_index} (Tag: {processing_tag}): Failed to load image data from {original_temp_path_str}. "
+                    f"Skipping conversion."
+                )
+                continue
+
+            # Perform Inversion
+            inverted_image_data: np.ndarray
+            if np.issubdtype(image_data.dtype, np.floating):
+                inverted_image_data = 1.0 - image_data
+                inverted_image_data = np.clip(inverted_image_data, 0.0, 1.0)
+                logger.debug(f"Asset '{asset_name_for_log}', Map Key Index {map_key_index} (Tag: {processing_tag}): Inverted float image data.")
+            elif np.issubdtype(image_data.dtype, np.integer):
+                max_val = np.iinfo(image_data.dtype).max
+                inverted_image_data = max_val - image_data
+                logger.debug(f"Asset '{asset_name_for_log}', Map Key Index {map_key_index} (Tag: {processing_tag}): Inverted integer image data (max_val: {max_val}).")
+            else:
+                logger.error(
+                    f"Asset '{asset_name_for_log}', Map Key Index {map_key_index} (Tag: {processing_tag}): Unsupported image data type {image_data.dtype} "
+                    f"for GLOSS map. Cannot invert. Skipping conversion."
+                )
+                continue
+            
+            # Save New Temporary (Roughness) Map
+            new_temp_filename = f"rough_from_gloss_{processing_tag}{original_temp_path.suffix}"
+            new_temp_path = context.engine_temp_dir / new_temp_filename
+            
+            save_success = ipu.save_image(str(new_temp_path), inverted_image_data)
+
+            if save_success:
+                logger.info(
+                    f"Asset '{asset_name_for_log}', Map Key Index {map_key_index} (Tag: {processing_tag}): Converted GLOSS map {original_temp_path_str} "
+                    f"to ROUGHNESS map {new_temp_path}."
+                )
+                
+                update_dict = {'item_type': "MAP_ROUGH", 'item_type_override': "MAP_ROUGH"}
+                
+                modified_file_rule: Optional[FileRule] = None
+                if hasattr(original_file_rule, 'model_copy') and callable(original_file_rule.model_copy): # Pydantic
+                    modified_file_rule = original_file_rule.model_copy(update=update_dict)
+                elif dataclasses.is_dataclass(original_file_rule): # Dataclass
+                    modified_file_rule = dataclasses.replace(original_file_rule, **update_dict)
+                else:
+                    logger.error(f"Asset '{asset_name_for_log}', Map Key Index {map_key_index} (Tag: {processing_tag}): Original FileRule is neither Pydantic nor dataclass. Cannot modify. Skipping update for this rule.")
+                    continue
+                
+                new_files_to_process[map_key_index] = modified_file_rule # Replace using map_key_index
+                
+                # Update context.processed_maps_details for this map_key_index
+                map_details['temp_processed_file'] = str(new_temp_path)
+                map_details['original_map_type_before_conversion'] = processing_map_type
+                map_details['processing_map_type'] = "MAP_ROUGH"
+                map_details['map_type'] = "Roughness"
+                map_details['status'] = "Converted_To_Rough"
+                map_details['notes'] = map_details.get('notes', '') + "; Converted from GLOSS by GlossToRoughConversionStage"
+                if 'base_pot_resolution_name' in map_details:
+                    map_details['processed_resolution_name'] = map_details['base_pot_resolution_name']
+
+                processed_a_gloss_map = True
+            else:
+                logger.error(
+                    f"Asset '{asset_name_for_log}', Map Key Index {map_key_index} (Tag: {processing_tag}): Failed to save inverted ROUGHNESS map to {new_temp_path}. "
+                    f"Original GLOSS FileRule remains."
+                )
+        
+        context.files_to_process = new_files_to_process
+
+        if processed_a_gloss_map:
+            logger.info(
+                f"Asset '{asset_name_for_log}': Gloss to Roughness conversion stage finished. Processed one or more maps and updated file list and map details."
+            )
+        else:
+            logger.info(
+                f"Asset '{asset_name_for_log}': No gloss maps were converted in GlossToRoughConversionStage. "
+                f"File list for next stage contains original non-gloss maps and any gloss maps that failed or were ineligible for conversion."
+            )
+
+        return context

processing/pipeline/stages/individual_map_processing.py

@@ -0,0 +1,700 @@
+import uuid
+import dataclasses
+import re
+import os
+import logging
+from pathlib import Path
+from typing import Optional, Tuple, Dict
+
+import cv2
+import numpy as np
+
+from .base_stage import ProcessingStage
+from ..asset_context import AssetProcessingContext
+from rule_structure import FileRule
+from utils.path_utils import sanitize_filename
+from ...utils import image_processing_utils as ipu
+
+logger = logging.getLogger(__name__)
+
+class IndividualMapProcessingStage(ProcessingStage):
+    """
+    Processes individual texture map files based on FileRules.
+    This stage finds the source file, loads it, applies transformations
+    (resize, color space), saves a temporary processed version, and updates
+    the AssetProcessingContext with details.
+    """
+
+    def execute(self, context: AssetProcessingContext) -> AssetProcessingContext:
+        """
+        Executes the individual map processing logic.
+        """
+        asset_name_for_log = context.asset_rule.asset_name if context.asset_rule else "Unknown Asset"
+        if context.status_flags.get('skip_asset', False):
+            logger.info(f"Asset '{asset_name_for_log}': Skipping individual map processing due to skip_asset flag.")
+            return context
+
+        if not hasattr(context, 'processed_maps_details') or context.processed_maps_details is None:
+            context.processed_maps_details = {}
+            logger.debug(f"Asset '{asset_name_for_log}': Initialized processed_maps_details.")
+
+        if not context.files_to_process:
+            logger.info(f"Asset '{asset_name_for_log}': No files to process in this stage.")
+            return context
+            
+        # Source path for the asset group comes from SourceRule
+        if not context.source_rule or not context.source_rule.input_path:
+            logger.error(f"Asset '{asset_name_for_log}': SourceRule or SourceRule.input_path is not set. Cannot determine source base path.")
+            context.status_flags['individual_map_processing_failed'] = True
+            # Mark all file_rules as failed
+            for fr_idx, file_rule_to_fail in enumerate(context.files_to_process):
+                # Use fr_idx as the key for status update for these early failures
+                map_type_for_fail = file_rule_to_fail.item_type_override or file_rule_to_fail.item_type or "UnknownMapType"
+                self._update_file_rule_status(context, fr_idx, 'Failed', map_type=map_type_for_fail, details="SourceRule.input_path missing")
+            return context
+        
+        # The workspace_path in the context should be the directory where files are extracted/available.
+        source_base_path = context.workspace_path
+        if not source_base_path.is_dir():
+            logger.error(f"Asset '{asset_name_for_log}': Workspace path '{source_base_path}' is not a valid directory.")
+            context.status_flags['individual_map_processing_failed'] = True
+            for fr_idx, file_rule_to_fail in enumerate(context.files_to_process):
+                # Use fr_idx as the key for status update
+                map_type_for_fail = file_rule_to_fail.item_type_override or file_rule_to_fail.item_type or "UnknownMapType"
+                self._update_file_rule_status(context, fr_idx, 'Failed', map_type=map_type_for_fail, details="Workspace path invalid")
+            return context
+
+        # Fetch config settings once before the loop
+        respect_variant_map_types = getattr(context.config_obj, "respect_variant_map_types", [])
+        image_resolutions = getattr(context.config_obj, "image_resolutions", {})
+        output_filename_pattern = getattr(context.config_obj, "output_filename_pattern", "[assetname]_[maptype]_[resolution].[ext]")
+
+        for file_rule_idx, file_rule in enumerate(context.files_to_process):
+            # file_rule_idx will be the key for processed_maps_details.
+            # processing_instance_tag is for unique temp files and detailed logging for this specific run.
+            processing_instance_tag = f"map_{file_rule_idx}_{uuid.uuid4().hex[:8]}"
+            current_map_key = file_rule_idx # Key for processed_maps_details
+
+            if not file_rule.file_path: # Ensure file_path exists, critical for later stages if they rely on it from FileRule
+                logger.error(f"Asset '{asset_name_for_log}', FileRule at index {file_rule_idx} has an empty or None file_path. Skipping this rule.")
+                self._update_file_rule_status(context, current_map_key, 'Failed',
+                                              processing_tag=processing_instance_tag,
+                                              details="FileRule has no file_path")
+                continue
+            
+            initial_current_map_type = file_rule.item_type_override or file_rule.item_type or "UnknownMapType"
+
+            # --- START NEW SUFFIXING LOGIC ---
+            final_current_map_type = initial_current_map_type # Default to initial
+            
+            # 1. Determine Base Map Type from initial_current_map_type
+            base_map_type_match = re.match(r"(MAP_[A-Z]{3})", initial_current_map_type)
+            
+            if base_map_type_match and context.asset_rule:
+                true_base_map_type = base_map_type_match.group(1) # This is "MAP_XXX"
+
+                # 2. Count Occurrences and Find Index of current_file_rule in context.asset_rule.files
+                peers_of_same_base_type_in_asset_rule = []
+                for fr_asset in context.asset_rule.files:
+                    fr_asset_item_type = fr_asset.item_type_override or fr_asset.item_type or "UnknownMapType"
+                    fr_asset_base_map_type_match = re.match(r"(MAP_[A-Z]{3})", fr_asset_item_type)
+                    
+                    if fr_asset_base_map_type_match:
+                        fr_asset_base_map_type = fr_asset_base_map_type_match.group(1)
+                        if fr_asset_base_map_type == true_base_map_type:
+                            peers_of_same_base_type_in_asset_rule.append(fr_asset)
+                
+                num_occurrences_of_base_type = len(peers_of_same_base_type_in_asset_rule)
+                current_instance_index = 0 # 1-based
+
+                try:
+                    current_instance_index = peers_of_same_base_type_in_asset_rule.index(file_rule) + 1
+                except ValueError:
+                    logger.warning(
+                        f"Asset '{asset_name_for_log}', FileRule path '{file_rule.file_path}' (Initial Type: '{initial_current_map_type}', Base: '{true_base_map_type}'): "
+                        f"Could not find its own instance in the list of peers from asset_rule.files. "
+                        f"Number of peers found: {num_occurrences_of_base_type}. Suffixing may be affected."
+                    )
+
+                # 3. Determine Suffix
+                map_type_for_respect_check = true_base_map_type.replace("MAP_", "") # e.g., "COL"
+                is_in_respect_list = map_type_for_respect_check in respect_variant_map_types
+                
+                suffix_to_append = ""
+                if num_occurrences_of_base_type > 1:
+                    if current_instance_index > 0:
+                        suffix_to_append = f"-{current_instance_index}"
+                    else:
+                        logger.warning(f"Asset '{asset_name_for_log}', FileRule path '{file_rule.file_path}': Index for multi-occurrence map type '{true_base_map_type}' (count: {num_occurrences_of_base_type}) not determined. Omitting numeric suffix.")
+                elif num_occurrences_of_base_type == 1 and is_in_respect_list:
+                    suffix_to_append = "-1"
+                
+                # 4. Form the final_current_map_type
+                if suffix_to_append:
+                    final_current_map_type = true_base_map_type + suffix_to_append
+                else:
+                    final_current_map_type = initial_current_map_type
+            
+            current_map_type = final_current_map_type
+            # --- END NEW SUFFIXING LOGIC ---
+
+            # --- START: Filename-friendly map type derivation ---
+            logger.debug(f"Asset '{asset_name_for_log}', Map Key {current_map_key}, Proc. Tag {processing_instance_tag}: --- Starting Filename-Friendly Map Type Logic for: {current_map_type} ---")
+            filename_friendly_map_type = current_map_type  # Fallback
+
+            # 1. Access FILE_TYPE_DEFINITIONS
+            file_type_definitions = None
+            logger.debug(f"Asset '{asset_name_for_log}', Map Key {current_map_key}, Proc. Tag {processing_instance_tag}: Attempting to access context.config_obj.FILE_TYPE_DEFINITIONS.")
+            try:
+                file_type_definitions = context.config_obj.FILE_TYPE_DEFINITIONS
+                if not file_type_definitions: # Check if it's None or empty
+                    logger.warning(f"Asset '{asset_name_for_log}', Map Key {current_map_key}, Proc. Tag {processing_instance_tag}: FILE_TYPE_DEFINITIONS is present but empty or None.")
+                else:
+                    sample_defs_log = {k: file_type_definitions[k] for k in list(file_type_definitions.keys())[:2]} # Log first 2 for brevity
+                    logger.debug(f"Asset '{asset_name_for_log}', Map Key {current_map_key}, Proc. Tag {processing_instance_tag}: Accessed FILE_TYPE_DEFINITIONS. Sample: {sample_defs_log}, Total keys: {len(file_type_definitions)}.")
+            except AttributeError:
+                logger.error(f"Asset '{asset_name_for_log}', Map Key {current_map_key}, Proc. Tag {processing_instance_tag}: Could not access context.config_obj.FILE_TYPE_DEFINITIONS via direct attribute.")
+            
+            base_map_key_val = None # Renamed from base_map_key to avoid conflict with current_map_key
+            suffix_part = ""
+
+            if file_type_definitions and isinstance(file_type_definitions, dict) and len(file_type_definitions) > 0:
+                base_map_key_val = None
+                suffix_part = ""
+                
+                sorted_known_base_keys = sorted(list(file_type_definitions.keys()), key=len, reverse=True)
+                logger.debug(f"Asset '{asset_name_for_log}', Map Key {current_map_key}, Proc. Tag {processing_instance_tag}: Sorted known base keys for parsing: {sorted_known_base_keys}")
+
+                for known_key in sorted_known_base_keys:
+                    logger.debug(f"Asset '{asset_name_for_log}', Map Key {current_map_key}, Proc. Tag {processing_instance_tag}: Checking if '{current_map_type}' starts with '{known_key}'")
+                    if current_map_type.startswith(known_key):
+                        base_map_key_val = known_key
+                        suffix_part = current_map_type[len(known_key):]
+                        logger.debug(f"Asset '{asset_name_for_log}', Map Key {current_map_key}, Proc. Tag {processing_instance_tag}: Match found! current_map_type: '{current_map_type}', base_map_key_val: '{base_map_key_val}', suffix_part: '{suffix_part}'")
+                        break
+
+                if base_map_key_val is None:
+                    logger.warning(f"Asset '{asset_name_for_log}', Map Key {current_map_key}, Proc. Tag {processing_instance_tag}: Could not parse base_map_key_val from '{current_map_type}' using known keys. Fallback: filename_friendly_map_type = '{filename_friendly_map_type}'.")
+                else:
+                    definition = file_type_definitions.get(base_map_key_val)
+                    logger.debug(f"Asset '{asset_name_for_log}', Map Key {current_map_key}, Proc. Tag {processing_instance_tag}: Definition for '{base_map_key_val}': {definition}")
+                    if definition and isinstance(definition, dict):
+                        standard_type_alias = definition.get("standard_type")
+                        logger.debug(f"Asset '{asset_name_for_log}', Map Key {current_map_key}, Proc. Tag {processing_instance_tag}: Standard type alias for '{base_map_key_val}': '{standard_type_alias}'")
+                        if standard_type_alias and isinstance(standard_type_alias, str) and standard_type_alias.strip():
+                            filename_friendly_map_type = standard_type_alias.strip() + suffix_part
+                            logger.info(f"Asset '{asset_name_for_log}', Map Key {current_map_key}, Proc. Tag {processing_instance_tag}: Successfully transformed map type: '{current_map_type}' -> '{filename_friendly_map_type}' (standard_type_alias: '{standard_type_alias}', suffix_part: '{suffix_part}').")
+                        else:
+                            logger.warning(f"Asset '{asset_name_for_log}', Map Key {current_map_key}, Proc. Tag {processing_instance_tag}: Standard type alias for '{base_map_key_val}' is missing, empty, or not a string (value: '{standard_type_alias}'). Using fallback. filename_friendly_map_type = '{filename_friendly_map_type}'.")
+                    else:
+                        logger.warning(f"Asset '{asset_name_for_log}', Map Key {current_map_key}, Proc. Tag {processing_instance_tag}: No definition or invalid definition for '{base_map_key_val}' (value: {definition}). Using fallback. filename_friendly_map_type = '{filename_friendly_map_type}'.")
+            elif file_type_definitions is None:
+                 logger.warning(f"Asset '{asset_name_for_log}', Map Key {current_map_key}, Proc. Tag {processing_instance_tag}: FILE_TYPE_DEFINITIONS not available for lookup (was None). Using fallback. filename_friendly_map_type = '{filename_friendly_map_type}'.")
+            elif not isinstance(file_type_definitions, dict):
+                logger.warning(f"Asset '{asset_name_for_log}', Map Key {current_map_key}, Proc. Tag {processing_instance_tag}: FILE_TYPE_DEFINITIONS is not a dictionary (type: {type(file_type_definitions)}). Using fallback. filename_friendly_map_type = '{filename_friendly_map_type}'.")
+            else:
+                logger.warning(f"Asset '{asset_name_for_log}', Map Key {current_map_key}, Proc. Tag {processing_instance_tag}: FILE_TYPE_DEFINITIONS is an empty dictionary. Using fallback. filename_friendly_map_type = '{filename_friendly_map_type}'.")
+            
+            logger.debug(f"Asset '{asset_name_for_log}', Map Key {current_map_key}, Proc. Tag {processing_instance_tag}: Final filename_friendly_map_type: '{filename_friendly_map_type}'")
+            # --- END: Filename-friendly map type derivation ---
+
+            if not current_map_type or not current_map_type.startswith("MAP_") or current_map_type == "MAP_GEN_COMPOSITE":
+                logger.debug(f"Asset '{asset_name_for_log}', FileRule path '{file_rule.file_path}': Skipping, item_type '{current_map_type}' (initial: '{initial_current_map_type}') not targeted for individual processing.")
+                continue
+
+            logger.info(f"Asset '{asset_name_for_log}', FileRule path '{file_rule.file_path}' (Type: {current_map_type}, Initial Type: {initial_current_map_type}, Key: {current_map_key}, Proc. Tag: {processing_instance_tag}): Starting individual processing.")
+
+            # A. Find Source File (using file_rule.file_path as the pattern relative to source_base_path)
+            source_file_path = self._find_source_file(source_base_path, file_rule.file_path, asset_name_for_log, processing_instance_tag)
+            if not source_file_path:
+                logger.error(f"Asset '{asset_name_for_log}', FileRule path '{file_rule.file_path}' (Key: {current_map_key}, Proc. Tag: {processing_instance_tag}): Source file not found with path/pattern '{file_rule.file_path}' in '{source_base_path}'.")
+                self._update_file_rule_status(context, current_map_key, 'Failed',
+                                              map_type=filename_friendly_map_type,
+                                              processing_map_type=current_map_type,
+                                              source_file_rule_index=file_rule_idx,
+                                              processing_tag=processing_instance_tag,
+                                              details="Source file not found")
+                continue
+
+            # B. Load and Transform Image
+            image_data: Optional[np.ndarray] = ipu.load_image(str(source_file_path))
+            if image_data is None:
+                logger.error(f"Asset '{asset_name_for_log}', FileRule path '{file_rule.file_path}' (Key: {current_map_key}, Proc. Tag: {processing_instance_tag}): Failed to load image from '{source_file_path}'.")
+                self._update_file_rule_status(context, current_map_key, 'Failed',
+                                              map_type=filename_friendly_map_type,
+                                              processing_map_type=current_map_type,
+                                              source_file_rule_index=file_rule_idx,
+                                              processing_tag=processing_instance_tag,
+                                              source_file=str(source_file_path),
+                                              details="Image load failed")
+                continue
+            
+            original_height, original_width = image_data.shape[:2]
+            logger.debug(f"Asset '{asset_name_for_log}', FileRule path '{file_rule.file_path}' (Key: {current_map_key}, Proc. Tag: {processing_instance_tag}): Loaded image '{source_file_path}' with dimensions {original_width}x{original_height}.")
+
+            # 1. Initial Power-of-Two (POT) Downscaling
+            pot_width = ipu.get_nearest_power_of_two_downscale(original_width)
+            pot_height = ipu.get_nearest_power_of_two_downscale(original_height)
+
+            # Maintain aspect ratio for initial POT scaling, using the smaller of the scaled dimensions
+            # This ensures we only downscale.
+            if original_width > 0 and original_height > 0 : # Avoid division by zero
+                aspect_ratio = original_width / original_height
+                
+                # Calculate new dimensions based on POT width, then POT height, and pick the one that results in downscale or same size
+                pot_h_from_w = int(pot_width / aspect_ratio)
+                pot_w_from_h = int(pot_height * aspect_ratio)
+
+                # Option 1: Scale by width, adjust height
+                candidate1_w, candidate1_h = pot_width, ipu.get_nearest_power_of_two_downscale(pot_h_from_w)
+                # Option 2: Scale by height, adjust width
+                candidate2_w, candidate2_h = ipu.get_nearest_power_of_two_downscale(pot_w_from_h), pot_height
+                
+                # Ensure candidates are not upscaling
+                if candidate1_w > original_width or candidate1_h > original_height:
+                    candidate1_w, candidate1_h = original_width, original_height # Fallback to original if upscaling
+                if candidate2_w > original_width or candidate2_h > original_height:
+                    candidate2_w, candidate2_h = original_width, original_height # Fallback to original if upscaling
+                
+                # Choose the candidate that results in a larger area (preferring less downscaling if multiple POT options)
+                # but still respects the POT downscale logic for each dimension individually.
+                # The actual POT dimensions are already calculated by get_nearest_power_of_two_downscale.
+                # We need to decide if we base the aspect ratio calc on pot_width or pot_height.
+                # The goal is to make one dimension POT and the other POT while maintaining aspect as much as possible, only downscaling.
+
+                final_pot_width = ipu.get_nearest_power_of_two_downscale(original_width)
+                final_pot_height = ipu.get_nearest_power_of_two_downscale(original_height)
+
+                # If original aspect is not 1:1, one of the POT dimensions might need further adjustment to maintain aspect
+                # after the other dimension is set to its POT.
+                # We prioritize fitting within the *downscaled* POT dimensions.
+                
+                # Scale to fit within final_pot_width, adjust height, then make height POT (downscale)
+                scaled_h_for_pot_w = max(1, round(final_pot_width / aspect_ratio))
+                h1 = ipu.get_nearest_power_of_two_downscale(scaled_h_for_pot_w)
+                w1 = final_pot_width
+                if h1 > final_pot_height: # If this adjustment made height too big, re-evaluate
+                    h1 = final_pot_height
+                    w1 = ipu.get_nearest_power_of_two_downscale(max(1, round(h1 * aspect_ratio)))
+
+
+                # Scale to fit within final_pot_height, adjust width, then make width POT (downscale)
+                scaled_w_for_pot_h = max(1, round(final_pot_height * aspect_ratio))
+                w2 = ipu.get_nearest_power_of_two_downscale(scaled_w_for_pot_h)
+                h2 = final_pot_height
+                if w2 > final_pot_width: # If this adjustment made width too big, re-evaluate
+                    w2 = final_pot_width
+                    h2 = ipu.get_nearest_power_of_two_downscale(max(1, round(w2 / aspect_ratio)))
+                
+                # Choose the option that results in larger area (less aggressive downscaling)
+                # while ensuring both dimensions are POT and not upscaled from original.
+                if w1 * h1 >= w2 * h2:
+                    base_pot_width, base_pot_height = w1, h1
+                else:
+                    base_pot_width, base_pot_height = w2, h2
+                
+                # Final check to ensure no upscaling from original dimensions
+                base_pot_width = min(base_pot_width, original_width)
+                base_pot_height = min(base_pot_height, original_height)
+                # And ensure they are POT
+                base_pot_width = ipu.get_nearest_power_of_two_downscale(base_pot_width)
+                base_pot_height = ipu.get_nearest_power_of_two_downscale(base_pot_height)
+
+            else: # Handle cases like 0-dim images, though load_image should prevent this
+                base_pot_width, base_pot_height = 1, 1
+
+
+            logger.info(f"Asset '{asset_name_for_log}', FileRule path '{file_rule.file_path}' (Key: {current_map_key}, Proc. Tag: {processing_instance_tag}): Original dims: ({original_width},{original_height}), Initial POT Scaled Dims: ({base_pot_width},{base_pot_height}).")
+
+            # Calculate and store aspect ratio change string
+            if original_width > 0 and original_height > 0 and base_pot_width > 0 and base_pot_height > 0:
+                aspect_change_str = ipu.normalize_aspect_ratio_change(
+                    original_width, original_height,
+                    base_pot_width, base_pot_height
+                )
+                if aspect_change_str:
+                    # This will overwrite if multiple maps are processed; specified by requirements.
+                    context.asset_metadata['aspect_ratio_change_string'] = aspect_change_str
+                    logger.info(f"Asset '{asset_name_for_log}', Map Key {current_map_key}, Proc. Tag {processing_instance_tag}, Map Type {current_map_type}: Calculated aspect ratio change string: '{aspect_change_str}' (Original: {original_width}x{original_height}, Base POT: {base_pot_width}x{base_pot_height}). Stored in asset_metadata.")
+                else:
+                    logger.warning(f"Asset '{asset_name_for_log}', Map Key {current_map_key}, Proc. Tag {processing_instance_tag}, Map Type {current_map_type}: Failed to calculate aspect ratio change string.")
+            else:
+                logger.warning(f"Asset '{asset_name_for_log}', Map Key {current_map_key}, Proc. Tag {processing_instance_tag}, Map Type {current_map_type}: Skipping aspect ratio change string calculation due to invalid dimensions (Original: {original_width}x{original_height}, Base POT: {base_pot_width}x{base_pot_height}).")
+
+            base_pot_image_data = image_data.copy()
+            if (base_pot_width, base_pot_height) != (original_width, original_height):
+                interpolation = cv2.INTER_AREA # Good for downscaling
+                base_pot_image_data = ipu.resize_image(base_pot_image_data, base_pot_width, base_pot_height, interpolation=interpolation)
+                if base_pot_image_data is None:
+                    logger.error(f"Asset '{asset_name_for_log}', FileRule path '{file_rule.file_path}' (Key: {current_map_key}, Proc. Tag: {processing_instance_tag}): Failed to resize image to base POT dimensions.")
+                    self._update_file_rule_status(context, current_map_key, 'Failed',
+                                                  map_type=filename_friendly_map_type,
+                                                  processing_map_type=current_map_type,
+                                                  source_file_rule_index=file_rule_idx,
+                                                  processing_tag=processing_instance_tag,
+                                                  source_file=str(source_file_path),
+                                                  original_dimensions=(original_width, original_height),
+                                                  details="Base POT resize failed")
+                    continue
+            
+            # Color Profile Management (after initial POT resize, before multi-res saving)
+            # Initialize transform settings with defaults for color management
+            transform_settings = {
+                "color_profile_management": False, # Default, can be overridden by FileRule
+                "target_color_profile": "sRGB",    # Default
+                "output_format_settings": None     # For JPG quality, PNG compression
+            }
+            if file_rule.channel_merge_instructions and 'transform' in file_rule.channel_merge_instructions:
+                custom_transform_settings = file_rule.channel_merge_instructions['transform']
+                if isinstance(custom_transform_settings, dict):
+                    transform_settings.update(custom_transform_settings)
+                    logger.info(f"Asset '{asset_name_for_log}', FileRule path '{file_rule.file_path}' (Key: {current_map_key}, Proc. Tag: {processing_instance_tag}): Loaded transform settings for color/output from file_rule.")
+            
+            if transform_settings['color_profile_management'] and transform_settings['target_color_profile'] == "RGB":
+                if len(base_pot_image_data.shape) == 3 and base_pot_image_data.shape[2] == 3: # BGR to RGB
+                    logger.info(f"Asset '{asset_name_for_log}', FileRule path '{file_rule.file_path}' (Key: {current_map_key}, Proc. Tag: {processing_instance_tag}): Converting BGR to RGB for base POT image.")
+                    base_pot_image_data = ipu.convert_bgr_to_rgb(base_pot_image_data)
+                elif len(base_pot_image_data.shape) == 3 and base_pot_image_data.shape[2] == 4: # BGRA to RGBA
+                     logger.info(f"Asset '{asset_name_for_log}', FileRule path '{file_rule.file_path}' (Key: {current_map_key}, Proc. Tag: {processing_instance_tag}): Converting BGRA to RGBA for base POT image.")
+                     base_pot_image_data = ipu.convert_bgra_to_rgba(base_pot_image_data)
+            
+            # Ensure engine_temp_dir exists before saving base POT
+            if not context.engine_temp_dir.exists():
+                try:
+                    context.engine_temp_dir.mkdir(parents=True, exist_ok=True)
+                    logger.info(f"Asset '{asset_name_for_log}': Created engine_temp_dir at '{context.engine_temp_dir}'")
+                except OSError as e:
+                    logger.error(f"Asset '{asset_name_for_log}': Failed to create engine_temp_dir '{context.engine_temp_dir}': {e}")
+                    self._update_file_rule_status(context, current_map_key, 'Failed',
+                                                  map_type=filename_friendly_map_type,
+                                                  processing_map_type=current_map_type,
+                                                  source_file_rule_index=file_rule_idx,
+                                                  processing_tag=processing_instance_tag,
+                                                  source_file=str(source_file_path),
+                                                  details="Failed to create temp directory for base POT")
+                    continue
+            
+            temp_filename_suffix = Path(source_file_path).suffix
+            base_pot_temp_filename = f"{processing_instance_tag}_basePOT{temp_filename_suffix}" # Use processing_instance_tag
+            base_pot_temp_path = context.engine_temp_dir / base_pot_temp_filename
+
+            # Determine save parameters for base POT image (can be different from variants if needed)
+            base_save_params = []
+            base_output_ext = temp_filename_suffix.lstrip('.') # Default to original, can be overridden by format rules
+            # TODO: Add logic here to determine base_output_ext and base_save_params based on bit depth and config, similar to variants.
+            # For now, using simple save.
+
+            if not ipu.save_image(str(base_pot_temp_path), base_pot_image_data, params=base_save_params):
+                logger.error(f"Asset '{asset_name_for_log}', Map Key {current_map_key}, Proc. Tag {processing_instance_tag}: Failed to save base POT image to '{base_pot_temp_path}'.")
+                self._update_file_rule_status(context, current_map_key, 'Failed',
+                                              map_type=filename_friendly_map_type,
+                                              processing_map_type=current_map_type,
+                                              source_file_rule_index=file_rule_idx,
+                                              processing_tag=processing_instance_tag,
+                                              source_file=str(source_file_path),
+                                              original_dimensions=(original_width, original_height),
+                                              base_pot_dimensions=(base_pot_width, base_pot_height),
+                                              details="Base POT image save failed")
+                continue
+            
+            logger.info(f"Asset '{asset_name_for_log}', Map Key {current_map_key}, Proc. Tag {processing_instance_tag}: Successfully saved base POT image to '{base_pot_temp_path}' with dims ({base_pot_width}x{base_pot_height}).")
+
+            # Initialize/update the status for this map in processed_maps_details
+            self._update_file_rule_status(
+                context,
+                current_map_key, # Use file_rule_idx as key
+                'BasePOTSaved', # Intermediate status, will be updated after variant check
+                map_type=filename_friendly_map_type,
+                processing_map_type=current_map_type,
+                source_file_rule_index=file_rule_idx,
+                processing_tag=processing_instance_tag, # Store the tag
+                source_file=str(source_file_path),
+                original_dimensions=(original_width, original_height),
+                base_pot_dimensions=(base_pot_width, base_pot_height),
+                temp_processed_file=str(base_pot_temp_path) # Store path to the saved base POT
+            )
+
+            # 2. Multiple Resolution Output (Variants)
+            processed_at_least_one_resolution_variant = False
+            # Resolution variants are attempted for all map types individually processed.
+            # The filter at the beginning of the loop ensures only relevant maps reach this stage.
+            generate_variants_for_this_map_type = True
+
+            if generate_variants_for_this_map_type: # This will now always be true if code execution reaches here
+                logger.info(f"Asset '{asset_name_for_log}', Map Key {current_map_key}, Proc. Tag {processing_instance_tag}: Map type '{current_map_type}' is eligible for individual processing. Attempting to generate resolution variants.")
+                # Sort resolutions from largest to smallest
+                sorted_resolutions = sorted(image_resolutions.items(), key=lambda item: item[1], reverse=True)
+                logger.debug(f"Asset '{asset_name_for_log}', Map Key {current_map_key}, Proc. Tag {processing_instance_tag}: Sorted resolutions for variant processing: {sorted_resolutions}")
+
+                for res_key, res_max_dim in sorted_resolutions:
+                    current_w, current_h = base_pot_image_data.shape[1], base_pot_image_data.shape[0]
+                    
+                    if current_w <= 0 or current_h <=0:
+                        logger.warning(f"Asset '{asset_name_for_log}', Map Key {current_map_key}, Proc. Tag {processing_instance_tag}, Res {res_key}: Base POT image has zero dimension ({current_w}x{current_h}). Skipping this resolution variant.")
+                        continue
+
+                    if max(current_w, current_h) >= res_max_dim:
+                        target_w_res, target_h_res = current_w, current_h
+                        if max(current_w, current_h) > res_max_dim:
+                            if current_w >= current_h:
+                                target_w_res = res_max_dim
+                                target_h_res = max(1, round(target_w_res / (current_w / current_h)))
+                            else:
+                                target_h_res = res_max_dim
+                                target_w_res = max(1, round(target_h_res * (current_w / current_h)))
+                    else:
+                        logger.debug(f"Asset '{asset_name_for_log}', Map Key {current_map_key}, Proc. Tag {processing_instance_tag}, Res {res_key}: Base POT image ({current_w}x{current_h}) is smaller than target max dim {res_max_dim}. Skipping this resolution variant.")
+                        continue
+
+                    target_w_res = min(target_w_res, current_w)
+                    target_h_res = min(target_h_res, current_h)
+                    
+                    if target_w_res <=0 or target_h_res <=0:
+                        logger.warning(f"Asset '{asset_name_for_log}', Map Key {current_map_key}, Proc. Tag {processing_instance_tag}, Res {res_key}: Calculated target variant dims are zero or negative ({target_w_res}x{target_h_res}). Skipping.")
+                        continue
+
+                    logger.info(f"Asset '{asset_name_for_log}', Map Key {current_map_key}, Proc. Tag {processing_instance_tag}, Res {res_key}: Processing variant for {res_max_dim}. Base POT Dims: ({current_w}x{current_h}), Target Dims for {res_key}: ({target_w_res}x{target_h_res}).")
+
+                    output_image_data_for_res = base_pot_image_data
+                    if (target_w_res, target_h_res) != (current_w, current_h):
+                        interpolation_res = cv2.INTER_AREA
+                        output_image_data_for_res = ipu.resize_image(base_pot_image_data, target_w_res, target_h_res, interpolation=interpolation_res)
+                        if output_image_data_for_res is None:
+                            logger.error(f"Asset '{asset_name_for_log}', Map Key {current_map_key}, Proc. Tag {processing_instance_tag}, Res {res_key}: Failed to resize image for resolution variant {res_key}.")
+                            continue
+                    
+                    assetname_placeholder = context.asset_rule.asset_name if context.asset_rule else "UnknownAsset"
+                    resolution_placeholder = res_key
+                    
+                    # TODO: Implement proper output format/extension determination for variants
+                    output_ext_variant = temp_filename_suffix.lstrip('.')
+
+                    temp_output_filename_variant = output_filename_pattern.replace("[assetname]", sanitize_filename(assetname_placeholder)) \
+                                                                  .replace("[maptype]", sanitize_filename(filename_friendly_map_type)) \
+                                                                  .replace("[resolution]", sanitize_filename(resolution_placeholder)) \
+                                                                  .replace("[ext]", output_ext_variant)
+                    temp_output_filename_variant = f"{processing_instance_tag}_variant_{temp_output_filename_variant}" # Use processing_instance_tag
+                    temp_output_path_variant = context.engine_temp_dir / temp_output_filename_variant
+
+                    save_params_variant = []
+                    if transform_settings.get('output_format_settings'):
+                        if output_ext_variant.lower() in ['jpg', 'jpeg']:
+                            quality = transform_settings['output_format_settings'].get('quality', context.config_obj.get("JPG_QUALITY", 95))
+                            save_params_variant = [cv2.IMWRITE_JPEG_QUALITY, quality]
+                        elif output_ext_variant.lower() == 'png':
+                            compression = transform_settings['output_format_settings'].get('compression_level', context.config_obj.get("PNG_COMPRESSION_LEVEL", 6))
+                            save_params_variant = [cv2.IMWRITE_PNG_COMPRESSION, compression]
+                    
+                    save_success_variant = ipu.save_image(str(temp_output_path_variant), output_image_data_for_res, params=save_params_variant)
+
+                    if not save_success_variant:
+                        logger.error(f"Asset '{asset_name_for_log}', Map Key {current_map_key}, Proc. Tag {processing_instance_tag}, Res {res_key}: Failed to save temporary variant image to '{temp_output_path_variant}'.")
+                        continue
+                    
+                    logger.info(f"Asset '{asset_name_for_log}', Map Key {current_map_key}, Proc. Tag {processing_instance_tag}, Res {res_key}: Successfully saved temporary variant map to '{temp_output_path_variant}' with dims ({target_w_res}x{target_h_res}).")
+                    processed_at_least_one_resolution_variant = True
+
+                    if 'variants' not in context.processed_maps_details[current_map_key]: # Use current_map_key (file_rule_idx)
+                        context.processed_maps_details[current_map_key]['variants'] = []
+                    
+                    context.processed_maps_details[current_map_key]['variants'].append({ # Use current_map_key (file_rule_idx)
+                        'resolution_key': res_key,
+                        'temp_path': str(temp_output_path_variant),
+                        'dimensions': (target_w_res, target_h_res),
+                        'resolution_name': f"{target_w_res}x{target_h_res}"
+                    })
+                    
+                    if 'processed_files' not in context.asset_metadata:
+                        context.asset_metadata['processed_files'] = []
+                    context.asset_metadata['processed_files'].append({
+                        'processed_map_key': current_map_key, # Use current_map_key (file_rule_idx)
+                        'resolution_key': res_key,
+                        'path': str(temp_output_path_variant),
+                        'type': 'temporary_map_variant',
+                        'map_type': current_map_type,
+                        'dimensions_w': target_w_res,
+                        'dimensions_h': target_h_res
+                    })
+            # Calculate and store image statistics for the lowest resolution output
+            lowest_res_image_data_for_stats = None
+            image_to_stat_path_for_log = "N/A"
+            source_of_stats_image = "unknown"
+
+            if processed_at_least_one_resolution_variant and \
+               current_map_key in context.processed_maps_details and \
+               'variants' in context.processed_maps_details[current_map_key] and \
+               context.processed_maps_details[current_map_key]['variants']:
+                
+                variants_list = context.processed_maps_details[current_map_key]['variants']
+                valid_variants_for_stats = [
+                    v for v in variants_list
+                    if isinstance(v.get('dimensions'), tuple) and len(v['dimensions']) == 2 and v['dimensions'][0] > 0 and v['dimensions'][1] > 0
+                ]
+
+                if valid_variants_for_stats:
+                    smallest_variant = min(valid_variants_for_stats, key=lambda v: v['dimensions'][0] * v['dimensions'][1])
+                    
+                    if smallest_variant and 'temp_path' in smallest_variant and smallest_variant.get('dimensions'):
+                        smallest_res_w, smallest_res_h = smallest_variant['dimensions']
+                        logger.info(f"Asset '{asset_name_for_log}', Map Key {current_map_key}, Proc. Tag {processing_instance_tag}: Identified smallest variant for stats: {smallest_variant.get('resolution_key', 'N/A')} ({smallest_res_w}x{smallest_res_h}) at {smallest_variant['temp_path']}")
+                        lowest_res_image_data_for_stats = ipu.load_image(smallest_variant['temp_path'])
+                        image_to_stat_path_for_log = smallest_variant['temp_path']
+                        source_of_stats_image = f"variant {smallest_variant.get('resolution_key', 'N/A')}"
+                        if lowest_res_image_data_for_stats is None:
+                            logger.warning(f"Asset '{asset_name_for_log}', Map Key {current_map_key}, Proc. Tag {processing_instance_tag}: Failed to load smallest variant image '{smallest_variant['temp_path']}' for stats.")
+                    else:
+                        logger.warning(f"Asset '{asset_name_for_log}', Map Key {current_map_key}, Proc. Tag {processing_instance_tag}: Could not determine smallest variant for stats from valid variants list (details missing).")
+                else:
+                    logger.warning(f"Asset '{asset_name_for_log}', Map Key {current_map_key}, Proc. Tag {processing_instance_tag}: No valid variants found to determine the smallest one for stats.")
+
+            if lowest_res_image_data_for_stats is None:
+                if base_pot_image_data is not None:
+                    logger.info(f"Asset '{asset_name_for_log}', Map Key {current_map_key}, Proc. Tag {processing_instance_tag}: Using base POT image for stats (dimensions: {base_pot_width}x{base_pot_height}). Smallest variant not available/loaded or no variants generated.")
+                    lowest_res_image_data_for_stats = base_pot_image_data
+                    image_to_stat_path_for_log = f"In-memory base POT image (dims: {base_pot_width}x{base_pot_height})"
+                    source_of_stats_image = "base POT"
+                else:
+                    logger.warning(f"Asset '{asset_name_for_log}', Map Key {current_map_key}, Proc. Tag {processing_instance_tag}: Base POT image data is also None. Cannot calculate stats.")
+
+            if lowest_res_image_data_for_stats is not None:
+                stats_dict = ipu.calculate_image_stats(lowest_res_image_data_for_stats)
+                if stats_dict and "error" not in stats_dict:
+                    if 'image_stats_lowest_res' not in context.asset_metadata:
+                        context.asset_metadata['image_stats_lowest_res'] = {}
+                    
+                    context.asset_metadata['image_stats_lowest_res'][current_map_type] = stats_dict # Keyed by map_type
+                    logger.info(f"Asset '{asset_name_for_log}', Map Key {current_map_key}, Proc. Tag {processing_instance_tag}, Map Type '{current_map_type}': Calculated and stored image stats from '{source_of_stats_image}' (source ref: '{image_to_stat_path_for_log}').")
+                elif stats_dict and "error" in stats_dict:
+                    logger.error(f"Asset '{asset_name_for_log}', Map Key {current_map_key}, Proc. Tag {processing_instance_tag}, Map Type '{current_map_type}': Error calculating image stats from '{source_of_stats_image}': {stats_dict['error']}.")
+                else:
+                    logger.warning(f"Asset '{asset_name_for_log}', Map Key {current_map_key}, Proc. Tag {processing_instance_tag}, Map Type '{current_map_type}': Failed to calculate image stats from '{source_of_stats_image}' (result was None or empty).")
+            else:
+                logger.warning(f"Asset '{asset_name_for_log}', Map Key {current_map_key}, Proc. Tag {processing_instance_tag}, Map Type '{current_map_type}': No image data available (from variant or base POT) to calculate stats.")
+
+            # Final status update based on whether variants were generated (and expected)
+            if generate_variants_for_this_map_type:
+                if processed_at_least_one_resolution_variant:
+                    self._update_file_rule_status(context, current_map_key, 'Processed_With_Variants',
+                                                  map_type=filename_friendly_map_type,
+                                                  processing_map_type=current_map_type,
+                                                  source_file_rule_index=file_rule_idx,
+                                                  processing_tag=processing_instance_tag,
+                                                  details="Successfully processed with multiple resolution variants.")
+                else:
+                    logger.warning(f"Asset '{asset_name_for_log}', Map Key {current_map_key}, Proc. Tag {processing_instance_tag}: Variants were expected for map type '{current_map_type}', but none were generated (e.g., base POT too small for any variant tier).")
+                    self._update_file_rule_status(context, current_map_key, 'Processed_No_Variants',
+                                                  map_type=filename_friendly_map_type,
+                                                  processing_map_type=current_map_type,
+                                                  source_file_rule_index=file_rule_idx,
+                                                  processing_tag=processing_instance_tag,
+                                                  details="Variants expected but none generated (e.g., base POT too small).")
+            else: # No variants were expected for this map type
+                self._update_file_rule_status(context, current_map_key, 'Processed_No_Variants',
+                                              map_type=filename_friendly_map_type,
+                                              processing_map_type=current_map_type,
+                                              source_file_rule_index=file_rule_idx,
+                                              processing_tag=processing_instance_tag,
+                                              details="Processed to base POT; variants not applicable for this map type.")
+
+        logger.info(f"Asset '{asset_name_for_log}': Finished individual map processing stage.")
+        return context
+
+    def _find_source_file(self, base_path: Path, pattern: str, asset_name_for_log: str, processing_instance_tag: str) -> Optional[Path]:
+        """
+        Finds a single source file matching the pattern within the base_path.
+        Logs use processing_instance_tag for specific run tracing.
+        """
+        if not pattern:
+            logger.warning(f"Asset '{asset_name_for_log}', Proc. Tag {processing_instance_tag}: Empty file_path provided in FileRule.")
+            return None
+        
+        # If pattern is an absolute path, use it directly
+        potential_abs_path = Path(pattern)
+        if potential_abs_path.is_absolute() and potential_abs_path.exists():
+            logger.debug(f"Asset '{asset_name_for_log}', Proc. Tag {processing_instance_tag}: file_path '{pattern}' is absolute and exists. Using it directly.")
+            return potential_abs_path
+        elif potential_abs_path.is_absolute():
+            logger.warning(f"Asset '{asset_name_for_log}', Proc. Tag {processing_instance_tag}: file_path '{pattern}' is absolute but does not exist.")
+            # Fall through to try resolving against base_path if it's just a name/relative pattern
+
+        # Treat pattern as relative to base_path
+        # This could be an exact name or a glob pattern
+        try:
+            # First, check if pattern is an exact relative path
+            exact_match_path = base_path / pattern
+            if exact_match_path.exists() and exact_match_path.is_file():
+                logger.debug(f"Asset '{asset_name_for_log}', Proc. Tag {processing_instance_tag}: Found exact match for '{pattern}' at '{exact_match_path}'.")
+                return exact_match_path
+
+            # If not an exact match, try as a glob pattern (recursive)
+            matched_files_rglob = list(base_path.rglob(pattern))
+            if matched_files_rglob:
+                if len(matched_files_rglob) > 1:
+                    logger.warning(f"Asset '{asset_name_for_log}', Proc. Tag {processing_instance_tag}: Multiple files ({len(matched_files_rglob)}) found for pattern '{pattern}' in '{base_path}' (recursive). Using first: {matched_files_rglob[0]}. Files: {matched_files_rglob}")
+                return matched_files_rglob[0]
+
+            # Try non-recursive glob if rglob fails
+            matched_files_glob = list(base_path.glob(pattern))
+            if matched_files_glob:
+                if len(matched_files_glob) > 1:
+                     logger.warning(f"Asset '{asset_name_for_log}', Proc. Tag {processing_instance_tag}: Multiple files ({len(matched_files_glob)}) found for pattern '{pattern}' in '{base_path}' (non-recursive). Using first: {matched_files_glob[0]}. Files: {matched_files_glob}")
+                return matched_files_glob[0]
+
+            logger.debug(f"Asset '{asset_name_for_log}', Proc. Tag {processing_instance_tag}: No files found matching pattern '{pattern}' in '{base_path}' (exact, recursive, or non-recursive).")
+            return None
+        except Exception as e:
+            logger.error(f"Asset '{asset_name_for_log}', Proc. Tag {processing_instance_tag}: Error searching for file with pattern '{pattern}' in '{base_path}': {e}")
+            return None
+
+    def _update_file_rule_status(self, context: AssetProcessingContext, map_key_index: int, status: str, **kwargs): # Renamed map_id_hex to map_key_index
+        """Helper to update processed_maps_details for a map, keyed by file_rule_idx."""
+        asset_name_for_log = context.asset_rule.asset_name if context.asset_rule else "Unknown Asset"
+        if map_key_index not in context.processed_maps_details:
+            context.processed_maps_details[map_key_index] = {}
+        
+        context.processed_maps_details[map_key_index]['status'] = status
+        for key, value in kwargs.items():
+            # Ensure source_file_rule_id_hex is not added if it was somehow passed (it shouldn't be)
+            if key == 'source_file_rule_id_hex':
+                continue
+            context.processed_maps_details[map_key_index][key] = value
+        
+        if 'map_type' not in context.processed_maps_details[map_key_index] and 'map_type' in kwargs:
+             context.processed_maps_details[map_key_index]['map_type'] = kwargs['map_type']
+
+        # Add formatted resolution names
+        if 'original_dimensions' in kwargs and isinstance(kwargs['original_dimensions'], tuple) and len(kwargs['original_dimensions']) == 2:
+            orig_w, orig_h = kwargs['original_dimensions']
+            context.processed_maps_details[map_key_index]['original_resolution_name'] = f"{orig_w}x{orig_h}"
+
+        # Determine the correct dimensions to use for 'processed_resolution_name'
+        # This name refers to the base POT scaled image dimensions before variant generation.
+        dims_to_log_as_base_processed = None
+        if 'base_pot_dimensions' in kwargs and isinstance(kwargs['base_pot_dimensions'], tuple) and len(kwargs['base_pot_dimensions']) == 2:
+            # This key is used when status is 'Processed_With_Variants'
+            dims_to_log_as_base_processed = kwargs['base_pot_dimensions']
+        elif 'processed_dimensions' in kwargs and isinstance(kwargs['processed_dimensions'], tuple) and len(kwargs['processed_dimensions']) == 2:
+            # This key is used when status is 'Processed_No_Variants' (and potentially others)
+            dims_to_log_as_base_processed = kwargs['processed_dimensions']
+
+        if dims_to_log_as_base_processed:
+            proc_w, proc_h = dims_to_log_as_base_processed
+            resolution_name_str = f"{proc_w}x{proc_h}"
+            context.processed_maps_details[map_key_index]['base_pot_resolution_name'] = resolution_name_str
+            # Ensure 'processed_resolution_name' is also set for OutputOrganizationStage compatibility
+            context.processed_maps_details[map_key_index]['processed_resolution_name'] = resolution_name_str
+        elif 'processed_dimensions' in kwargs or 'base_pot_dimensions' in kwargs:
+            details_for_warning = kwargs.get('processed_dimensions', kwargs.get('base_pot_dimensions'))
+            logger.warning(f"Asset '{asset_name_for_log}', Map Key Index {map_key_index}: 'processed_dimensions' or 'base_pot_dimensions' key present but its value is not a valid 2-element tuple: {details_for_warning}")
+        
+        # If temp_processed_file was passed, ensure it's in the details
+        if 'temp_processed_file' in kwargs:
+            context.processed_maps_details[map_key_index]['temp_processed_file'] = kwargs['temp_processed_file']
+
+
+        # Log all details being stored for clarity, including the newly added resolution names
+        log_details = context.processed_maps_details[map_key_index].copy()
+        # Avoid logging full image data if it accidentally gets into kwargs
+        if 'image_data' in log_details: del log_details['image_data']
+        if 'base_pot_image_data' in log_details: del log_details['base_pot_image_data']
+        logger.debug(f"Asset '{asset_name_for_log}', Map Key Index {map_key_index}: Status updated to '{status}'. Details: {log_details}")

processing/pipeline/stages/map_merging.py

@@ -0,0 +1,347 @@
+import logging
+from pathlib import Path
+from typing import Dict, Optional, List, Tuple
+
+import numpy as np
+import cv2 # For potential direct cv2 operations if ipu doesn't cover all merge needs
+
+from .base_stage import ProcessingStage
+from ..asset_context import AssetProcessingContext
+from rule_structure import FileRule
+from utils.path_utils import sanitize_filename
+from ...utils import image_processing_utils as ipu
+
+
+logger = logging.getLogger(__name__)
+
+class MapMergingStage(ProcessingStage):
+    """
+    Merges individually processed maps based on MAP_MERGE rules.
+    This stage performs operations like channel packing.
+    """
+
+    def execute(self, context: AssetProcessingContext) -> AssetProcessingContext:
+        """
+        Executes the map merging logic.
+
+        Args:
+            context: The asset processing context.
+
+        Returns:
+            The updated asset processing context.
+        """
+        asset_name_for_log = context.asset_rule.asset_name if context.asset_rule else "Unknown Asset"
+        if context.status_flags.get('skip_asset'):
+            logger.info(f"Skipping map merging for asset {asset_name_for_log} as skip_asset flag is set.")
+            return context
+
+        if not hasattr(context, 'merged_maps_details'):
+            context.merged_maps_details = {}
+        
+        if not hasattr(context, 'processed_maps_details'):
+            logger.warning(f"Asset {asset_name_for_log}: 'processed_maps_details' not found in context. Cannot perform map merging.")
+            return context
+
+        if not context.files_to_process: # This list might not be relevant if merge rules are defined elsewhere or implicitly
+            logger.info(f"Asset {asset_name_for_log}: No files_to_process defined. This stage might rely on config or processed_maps_details directly for merge rules.")
+            # Depending on design, this might not be an error, so we don't return yet.
+
+        logger.info(f"Starting MapMergingStage for asset: {asset_name_for_log}")
+
+        # TODO: The logic for identifying merge rules and their inputs needs significant rework
+        # as FileRule no longer has 'id' or 'merge_settings' directly in the way this stage expects.
+        # Merge rules are likely defined in the main configuration (context.config_obj.map_merge_rules)
+        # and need to be matched against available maps in context.processed_maps_details.
+
+        # Placeholder for the loop that would iterate over context.config_obj.map_merge_rules
+        # For now, this stage will effectively do nothing until that logic is implemented.
+        
+        # Example of how one might start to adapt:
+        # for configured_merge_rule in context.config_obj.map_merge_rules:
+        #     output_map_type = configured_merge_rule.get('output_map_type')
+        #     inputs_config = configured_merge_rule.get('inputs') # e.g. {"R": "NORMAL", "G": "ROUGHNESS"}
+        #     # ... then find these input map_types in context.processed_maps_details ...
+        #     # ... and perform the merge ...
+        #     # This is a complex change beyond simple attribute renaming.
+
+        # The following is the original loop structure, which will likely fail due to missing attributes on FileRule.
+        # Keeping it commented out to show what was there.
+        """
+        for merge_rule in context.files_to_process: # This iteration logic is likely incorrect for merge rules
+            if not isinstance(merge_rule, FileRule) or merge_rule.item_type != "MAP_MERGE":
+                continue
+
+            # FileRule does not have merge_settings or id.hex
+            # This entire block needs to be re-thought based on where merge rules are defined.
+            # Assuming merge_rule_id_hex would be a generated UUID for this operation.
+            merge_rule_id_hex = f"merge_op_{uuid.uuid4().hex[:8]}"
+            current_map_type = merge_rule.item_type_override or merge_rule.item_type
+
+            logger.error(f"Asset {asset_name_for_log}, Potential Merge for {current_map_type}: Merge rule processing needs rework. FileRule lacks 'merge_settings' and 'id'. Skipping this rule.")
+            context.merged_maps_details[merge_rule_id_hex] = {
+                'map_type': current_map_type,
+                'status': 'Failed',
+                'reason': 'Merge rule processing logic in MapMergingStage needs refactor due to FileRule changes.'
+            }
+            continue
+        """
+
+        # For now, let's assume no merge rules are processed until the logic is fixed.
+        num_merge_rules_attempted = 0
+        # If context.config_obj.map_merge_rules exists, iterate it here.
+        # The original code iterated context.files_to_process looking for item_type "MAP_MERGE".
+        # This implies FileRule objects were being used to define merge operations, which is no longer the case
+        # if 'merge_settings' and 'id' were removed from FileRule.
+
+        # The core merge rules are in context.config_obj.map_merge_rules
+        # Each rule in there defines an output_map_type and its inputs.
+        
+        config_merge_rules = context.config_obj.map_merge_rules
+        if not config_merge_rules:
+            logger.info(f"Asset {asset_name_for_log}: No map_merge_rules found in configuration. Skipping map merging.")
+            return context
+
+        for rule_idx, configured_merge_rule in enumerate(config_merge_rules):
+            output_map_type = configured_merge_rule.get('output_map_type')
+            inputs_map_type_to_channel = configured_merge_rule.get('inputs') # e.g. {"R": "NRM", "G": "NRM", "B": "ROUGH"}
+            default_values = configured_merge_rule.get('defaults', {}) # e.g. {"R": 0.5, "G": 0.5, "B": 0.5}
+            # output_bit_depth_rule = configured_merge_rule.get('output_bit_depth', 'respect_inputs') # Not used yet
+
+            if not output_map_type or not inputs_map_type_to_channel:
+                logger.warning(f"Asset {asset_name_for_log}: Invalid configured_merge_rule at index {rule_idx}. Missing 'output_map_type' or 'inputs'. Rule: {configured_merge_rule}")
+                continue
+            
+            num_merge_rules_attempted +=1
+            merge_op_id = f"merge_{sanitize_filename(output_map_type)}_{rule_idx}"
+            logger.info(f"Asset {asset_name_for_log}: Processing configured merge rule for '{output_map_type}' (Op ID: {merge_op_id})")
+
+            loaded_input_maps: Dict[str, np.ndarray] = {} # Key: input_map_type (e.g. "NRM"), Value: image_data
+            input_map_paths: Dict[str, str] = {} # Key: input_map_type, Value: path_str
+            target_dims: Optional[Tuple[int, int]] = None
+            all_inputs_valid = True
+            
+            # Find and load input maps from processed_maps_details
+            # This assumes one processed map per map_type. If multiple variants exist, this needs refinement.
+            required_input_map_types = set(inputs_map_type_to_channel.values())
+            
+            for required_map_type in required_input_map_types:
+                found_processed_map_details = None
+                # The key `p_key_idx` is the file_rule_idx from the IndividualMapProcessingStage
+                for p_key_idx, p_details in context.processed_maps_details.items(): # p_key_idx is an int
+                    processed_map_identifier = p_details.get('processing_map_type', p_details.get('map_type'))
+                    
+                    # Comprehensive list of valid statuses for an input map to be used in merging
+                    valid_input_statuses = ['BasePOTSaved', 'Processed_With_Variants', 'Processed_No_Variants', 'Converted_To_Rough']
+
+                    is_match = False
+                    if processed_map_identifier == required_map_type:
+                        is_match = True
+                    elif required_map_type.startswith("MAP_") and processed_map_identifier == required_map_type.split("MAP_")[-1]:
+                        is_match = True
+                    elif not required_map_type.startswith("MAP_") and processed_map_identifier == f"MAP_{required_map_type}":
+                        is_match = True
+
+                    if is_match and p_details.get('status') in valid_input_statuses:
+                        found_processed_map_details = p_details
+                        # The key `p_key_idx` (which is the FileRule index) is implicitly associated with these details.
+                        break
+                
+                if not found_processed_map_details:
+                    can_be_fully_defaulted = True
+                    channels_requiring_this_map = [
+                        ch_key for ch_key, map_type_val in inputs_map_type_to_channel.items()
+                        if map_type_val == required_map_type
+                    ]
+
+                    if not channels_requiring_this_map:
+                        logger.error(f"Asset {asset_name_for_log}, Merge Op ID {merge_op_id}: Internal logic error. Required map_type '{required_map_type}' is not actually used by any output channel. Configuration: {inputs_map_type_to_channel}")
+                        all_inputs_valid = False
+                        context.merged_maps_details[merge_op_id] = {'map_type': output_map_type, 'status': 'Failed', 'reason': f"Internal error: required map_type '{required_map_type}' not in use."}
+                        break
+                    
+                    for channel_char_needing_default in channels_requiring_this_map:
+                        if default_values.get(channel_char_needing_default) is None:
+                            can_be_fully_defaulted = False
+                            break
+                    
+                    if can_be_fully_defaulted:
+                        logger.info(f"Asset {asset_name_for_log}, Merge Op ID {merge_op_id}: Required input map_type '{required_map_type}' for output '{output_map_type}' not found or not in usable state. Will attempt to use default values for its channels: {channels_requiring_this_map}.")
+                    else:
+                        logger.warning(f"Asset {asset_name_for_log}, Merge Op ID {merge_op_id}: Required input map_type '{required_map_type}' for output '{output_map_type}' not found/unusable, AND not all its required channels ({channels_requiring_this_map}) have defaults. Failing merge op.")
+                        all_inputs_valid = False
+                        context.merged_maps_details[merge_op_id] = {'map_type': output_map_type, 'status': 'Failed', 'reason': f"Input '{required_map_type}' missing and defaults incomplete."}
+                        break
+                
+                if found_processed_map_details:
+                    temp_file_path_str = found_processed_map_details.get('temp_processed_file')
+                    if not temp_file_path_str:
+                        # Log with p_key_idx if available, or just the map type if not (though it should be if found_processed_map_details is set)
+                        log_key_info = f"(Associated Key Index: {p_key_idx})" if 'p_key_idx' in locals() and found_processed_map_details else "" # Use locals() to check if p_key_idx is defined in this scope
+                        logger.error(f"Asset {asset_name_for_log}, Merge Op ID {merge_op_id}: 'temp_processed_file' missing in details for found map_type '{required_map_type}' {log_key_info}.")
+                        all_inputs_valid = False
+                        context.merged_maps_details[merge_op_id] = {'map_type': output_map_type, 'status': 'Failed', 'reason': f"Temp file path missing for input '{required_map_type}'."}
+                        break
+                    
+                    temp_file_path = Path(temp_file_path_str)
+                    if not temp_file_path.exists():
+                        logger.error(f"Asset {asset_name_for_log}, Merge Op ID {merge_op_id}: Temp file {temp_file_path} for input map_type '{required_map_type}' does not exist.")
+                        all_inputs_valid = False
+                        context.merged_maps_details[merge_op_id] = {'map_type': output_map_type, 'status': 'Failed', 'reason': f"Temp file for input '{required_map_type}' missing."}
+                        break
+
+                    try:
+                        image_data = ipu.load_image(str(temp_file_path))
+                        if image_data is None: raise ValueError("Loaded image is None")
+                    except Exception as e:
+                        logger.error(f"Asset {asset_name_for_log}, Merge Op ID {merge_op_id}: Error loading image {temp_file_path} for input map_type '{required_map_type}': {e}")
+                        all_inputs_valid = False
+                        context.merged_maps_details[merge_op_id] = {'map_type': output_map_type, 'status': 'Failed', 'reason': f"Error loading input '{required_map_type}'."}
+                        break
+                    
+                    loaded_input_maps[required_map_type] = image_data
+                    input_map_paths[required_map_type] = str(temp_file_path)
+
+                    current_dims = (image_data.shape[1], image_data.shape[0])
+                    if target_dims is None:
+                        target_dims = current_dims
+                    elif current_dims != target_dims:
+                        logger.warning(f"Asset {asset_name_for_log}, Merge Op ID {merge_op_id}: Input map '{required_map_type}' dims {current_dims} differ from target {target_dims}. Resizing.")
+                        try:
+                            image_data_resized = ipu.resize_image(image_data, target_dims[0], target_dims[1])
+                            if image_data_resized is None: raise ValueError("Resize returned None")
+                            loaded_input_maps[required_map_type] = image_data_resized
+                        except Exception as e:
+                            logger.error(f"Asset {asset_name_for_log}, Merge Op ID {merge_op_id}: Failed to resize '{required_map_type}': {e}")
+                            all_inputs_valid = False
+                            context.merged_maps_details[merge_op_id] = {'map_type': output_map_type, 'status': 'Failed', 'reason': f"Failed to resize input '{required_map_type}'."}
+                            break
+            
+            if not all_inputs_valid:
+                logger.warning(f"Asset {asset_name_for_log}: Skipping merge for Op ID {merge_op_id} ('{output_map_type}') due to invalid inputs.")
+                continue
+
+            if not loaded_input_maps and not any(default_values.get(ch) is not None for ch in inputs_map_type_to_channel.keys()):
+                 logger.error(f"Asset {asset_name_for_log}, Merge Op ID {merge_op_id}: No input maps loaded and no defaults available for any channel for '{output_map_type}'. Cannot proceed.")
+                 context.merged_maps_details[merge_op_id] = {'map_type': output_map_type, 'status': 'Failed', 'reason': 'No input maps loaded and no defaults available.'}
+                 continue
+
+            if target_dims is None:
+                default_res_key = context.config_obj.get("default_output_resolution_key_for_merge", "1K")
+                image_resolutions_cfg = getattr(context.config_obj, "image_resolutions", {})
+                default_max_dim = image_resolutions_cfg.get(default_res_key)
+
+                if default_max_dim:
+                    target_dims = (default_max_dim, default_max_dim)
+                    logger.info(f"Asset {asset_name_for_log}, Merge Op ID {merge_op_id}: Target dimensions not set by inputs (all defaulted). Using configured default resolution '{default_res_key}': {target_dims}.")
+                else:
+                    logger.error(f"Asset {asset_name_for_log}, Merge Op ID {merge_op_id}: Target dimensions could not be determined for '{output_map_type}' (all inputs defaulted and no default output resolution configured).")
+                    context.merged_maps_details[merge_op_id] = {'map_type': output_map_type, 'status': 'Failed', 'reason': 'Target dimensions undetermined for fully defaulted merge.'}
+                    continue
+            
+            output_channel_keys = sorted(list(inputs_map_type_to_channel.keys()))
+            num_output_channels = len(output_channel_keys)
+            
+            if num_output_channels == 0:
+                logger.error(f"Asset {asset_name_for_log}, Merge Op ID {merge_op_id}: No output channels defined in 'inputs' for '{output_map_type}'.")
+                context.merged_maps_details[merge_op_id] = {'map_type': output_map_type, 'status': 'Failed', 'reason': 'No output channels defined.'}
+                continue
+
+            try:
+                output_dtype = np.uint8
+                
+                if num_output_channels == 1:
+                    merged_image = np.zeros((target_dims[1], target_dims[0]), dtype=output_dtype)
+                else:
+                    merged_image = np.zeros((target_dims[1], target_dims[0], num_output_channels), dtype=output_dtype)
+            except Exception as e:
+                logger.error(f"Asset {asset_name_for_log}, Merge Op ID {merge_op_id}: Error creating empty merged image for '{output_map_type}': {e}")
+                context.merged_maps_details[merge_op_id] = {'map_type': output_map_type, 'status': 'Failed', 'reason': f'Error creating output canvas: {e}'}
+                continue
+
+            merge_op_failed_detail = False
+            for i, out_channel_char in enumerate(output_channel_keys):
+                input_map_type_for_this_channel = inputs_map_type_to_channel[out_channel_char]
+                source_image = loaded_input_maps.get(input_map_type_for_this_channel)
+                
+                source_data_this_channel = None
+                if source_image is not None:
+                    if source_image.dtype != np.uint8:
+                        logger.warning(f"Asset {asset_name_for_log}, Merge Op ID {merge_op_id}: Input map '{input_map_type_for_this_channel}' has dtype {source_image.dtype}, expected uint8. Attempting conversion.")
+                        source_image = ipu.convert_to_uint8(source_image)
+                        if source_image is None:
+                            logger.error(f"Asset {asset_name_for_log}, Merge Op ID {merge_op_id}: Failed to convert input '{input_map_type_for_this_channel}' to uint8.")
+                            merge_op_failed_detail = True; break
+
+
+                    if source_image.ndim == 2:
+                        source_data_this_channel = source_image
+                    elif source_image.ndim == 3:
+                        semantic_to_bgr_idx = {'R': 2, 'G': 1, 'B': 0, 'A': 3}
+
+                        idx_to_extract = semantic_to_bgr_idx.get(out_channel_char.upper())
+                        
+                        if idx_to_extract is not None and idx_to_extract < source_image.shape[2]:
+                            source_data_this_channel = source_image[:, :, idx_to_extract]
+                            logger.debug(f"Asset {asset_name_for_log}, Merge Op ID {merge_op_id}: For output '{out_channel_char}', using source '{input_map_type_for_this_channel}' semantic '{out_channel_char}' (BGR(A) index {idx_to_extract}).")
+                        else:
+                            logger.warning(f"Asset {asset_name_for_log}, Merge Op ID {merge_op_id}: Could not map output '{out_channel_char}' to a specific BGR(A) channel of '{input_map_type_for_this_channel}' (shape {source_image.shape}). Defaulting to its channel 0 (Blue).")
+                            source_data_this_channel = source_image[:, :, 0]
+                    else:
+                        logger.error(f"Asset {asset_name_for_log}, Merge Op ID {merge_op_id}: Source image '{input_map_type_for_this_channel}' has unexpected dimensions: {source_image.ndim} (shape {source_image.shape}).")
+                        merge_op_failed_detail = True; break
+
+                else:
+                    default_val_for_channel = default_values.get(out_channel_char)
+                    if default_val_for_channel is not None:
+                        try:
+                            scaled_default_val = int(float(default_val_for_channel) * 255)
+                            source_data_this_channel = np.full((target_dims[1], target_dims[0]), scaled_default_val, dtype=np.uint8)
+                            logger.info(f"Asset {asset_name_for_log}, Merge Op ID {merge_op_id}: Using default value {default_val_for_channel} (scaled to {scaled_default_val}) for output channel '{out_channel_char}' as input map '{input_map_type_for_this_channel}' was missing.")
+                        except ValueError:
+                            logger.error(f"Asset {asset_name_for_log}, Merge Op ID {merge_op_id}: Default value '{default_val_for_channel}' for channel '{out_channel_char}' is not a valid float. Cannot scale.")
+                            merge_op_failed_detail = True; break
+                    else:
+                        logger.error(f"Asset {asset_name_for_log}, Merge Op ID {merge_op_id}: Input map '{input_map_type_for_this_channel}' for output channel '{out_channel_char}' is missing and no default value provided.")
+                        merge_op_failed_detail = True; break
+                
+                if source_data_this_channel is None:
+                    logger.error(f"Asset {asset_name_for_log}, Merge Op ID {merge_op_id}: Failed to get source data for output channel '{out_channel_char}'.")
+                    merge_op_failed_detail = True; break
+
+                try:
+                    if merged_image.ndim == 2:
+                        merged_image = source_data_this_channel
+                    else:
+                        merged_image[:, :, i] = source_data_this_channel
+                except Exception as e:
+                    logger.error(f"Asset {asset_name_for_log}, Merge Op ID {merge_op_id}: Error assigning data to output channel '{out_channel_char}' (index {i}): {e}. Merged shape: {merged_image.shape}, Source data shape: {source_data_this_channel.shape}")
+                    merge_op_failed_detail = True; break
+            
+            if merge_op_failed_detail:
+                context.merged_maps_details[merge_op_id] = {'map_type': output_map_type, 'status': 'Failed', 'reason': 'Error during channel assignment.'}
+                continue
+
+            output_format = 'png'
+            temp_merged_filename = f"merged_{sanitize_filename(output_map_type)}_{merge_op_id}.{output_format}"
+            temp_merged_path = context.engine_temp_dir / temp_merged_filename
+
+            try:
+                save_success = ipu.save_image(str(temp_merged_path), merged_image)
+                if not save_success: raise ValueError("Save image returned false")
+            except Exception as e:
+                logger.error(f"Asset {asset_name_for_log}, Merge Op ID {merge_op_id}: Error saving merged image {temp_merged_path}: {e}")
+                context.merged_maps_details[merge_op_id] = {'map_type': output_map_type, 'status': 'Failed', 'reason': f'Failed to save merged image: {e}'}
+                continue
+            
+            logger.info(f"Asset {asset_name_for_log}: Successfully merged and saved '{output_map_type}' (Op ID: {merge_op_id}) to {temp_merged_path}")
+            context.merged_maps_details[merge_op_id] = {
+                'map_type': output_map_type,
+                'temp_merged_file': str(temp_merged_path),
+                'input_map_types_used': list(inputs_map_type_to_channel.values()),
+                'input_map_files_used': input_map_paths,
+                'merged_dimensions': target_dims,
+                'status': 'Processed'
+            }
+
+        logger.info(f"Finished MapMergingStage for asset: {asset_name_for_log}. Merged maps operations attempted: {num_merge_rules_attempted}, Succeeded: {len([d for d in context.merged_maps_details.values() if d.get('status') == 'Processed'])}")
+        return context

processing/pipeline/stages/metadata_finalization_save.py

@@ -0,0 +1,217 @@
+import datetime
+import json
+import logging
+from pathlib import Path
+from typing import Any, Dict
+
+from ..asset_context import AssetProcessingContext
+from .base_stage import ProcessingStage
+from utils.path_utils import generate_path_from_pattern, sanitize_filename
+
+
+logger = logging.getLogger(__name__)
+
+class MetadataFinalizationAndSaveStage(ProcessingStage):
+    """
+    This stage finalizes the asset_metadata (e.g., setting processing end time,
+    final status) and saves it as a JSON file.
+    """
+
+    def execute(self, context: AssetProcessingContext) -> AssetProcessingContext:
+        """
+        Finalizes metadata, determines output path, and saves the metadata JSON file.
+        """
+        asset_name_for_log = "Unknown Asset"
+        if hasattr(context, 'asset_rule') and context.asset_rule and hasattr(context.asset_rule, 'asset_name'):
+            asset_name_for_log = context.asset_rule.asset_name
+
+        if not hasattr(context, 'asset_metadata') or not context.asset_metadata:
+            if context.status_flags.get('skip_asset'):
+                logger.info(
+                    f"Asset '{asset_name_for_log}': "
+                    f"Skipped before metadata initialization. No metadata file will be saved."
+                )
+            else:
+                logger.warning(
+                    f"Asset '{asset_name_for_log}': "
+                    f"asset_metadata not initialized. Skipping metadata finalization and save."
+                )
+            return context
+    
+        # Check Skip Flag
+        if context.status_flags.get('skip_asset'):
+            context.asset_metadata['status'] = "Skipped"
+            context.asset_metadata['processing_end_time'] = datetime.datetime.now().isoformat()
+            context.asset_metadata['notes'] = context.status_flags.get('skip_reason', 'Skipped early in pipeline')
+            logger.info(
+                f"Asset '{asset_name_for_log}': Marked as skipped. Reason: {context.asset_metadata['notes']}"
+            )
+            # Assuming we save metadata for skipped assets if it was initialized.
+            # If not, the logic to skip saving would be here or before path generation.
+            # However, if we are here, asset_metadata IS initialized.
+
+        # A. Finalize Metadata
+        context.asset_metadata['processing_end_time'] = datetime.datetime.now().isoformat()
+
+        # Determine final status (if not already set to Skipped)
+        if context.asset_metadata.get('status') != "Skipped":
+            has_errors = any(
+                context.status_flags.get(error_flag)
+                for error_flag in ['file_processing_error', 'merge_error', 'critical_error',
+                                   'individual_map_processing_failed', 'metadata_save_error'] # Added more flags
+            )
+            if has_errors:
+                context.asset_metadata['status'] = "Failed"
+            else:
+                context.asset_metadata['status'] = "Processed"
+
+        # Add details of processed and merged maps
+        # Restructure processed_map_details before assigning
+        restructured_processed_maps = {}
+        # getattr(context, 'processed_maps_details', {}) is the source (plural 'maps')
+        original_processed_maps = getattr(context, 'processed_maps_details', {})
+        
+        # Define keys to remove at the top level of each map entry
+        map_keys_to_remove = [
+            "status", "source_file_path", "temp_processed_file", # Assuming "source_file_path" is the correct key
+            "original_resolution_name", "base_pot_resolution_name", "processed_resolution_name"
+        ]
+        # Define keys to remove from each variant
+        variant_keys_to_remove = ["temp_path", "dimensions"]
+
+        for map_key, map_detail_original in original_processed_maps.items():
+            # Create a new dictionary for the modified map entry
+            new_map_entry = {}
+            for key, value in map_detail_original.items():
+                if key not in map_keys_to_remove:
+                    new_map_entry[key] = value
+            
+            if "variants" in map_detail_original and isinstance(map_detail_original["variants"], dict):
+                new_variants_dict = {}
+                for variant_name, variant_data_original in map_detail_original["variants"].items():
+                    new_variant_entry = {}
+                    for key, value in variant_data_original.items():
+                        if key not in variant_keys_to_remove:
+                            new_variant_entry[key] = value
+                    
+                    # Add 'path_to_file'
+                    # This path is expected to be set by OutputOrganizationStage in the context.
+                    # It should be a Path object representing the path relative to the metadata directory,
+                    # or an absolute Path that make_serializable can convert.
+                    # Using 'final_output_path_for_metadata' as the key from context.
+                    if 'final_output_path_for_metadata' in variant_data_original:
+                        new_variant_entry['path_to_file'] = variant_data_original['final_output_path_for_metadata']
+                    else:
+                        # Log a warning if the expected path is not found
+                        logger.warning(
+                            f"Asset '{asset_name_for_log}': 'final_output_path_for_metadata' "
+                            f"missing for variant '{variant_name}' in map '{map_key}'. "
+                            f"Metadata will be incomplete for this variant's path."
+                        )
+                        new_variant_entry['path_to_file'] = "ERROR_PATH_NOT_FOUND" # Placeholder
+                    new_variants_dict[variant_name] = new_variant_entry
+                new_map_entry["variants"] = new_variants_dict
+            
+            restructured_processed_maps[map_key] = new_map_entry
+
+        # Assign the restructured details. Note: 'processed_map_details' (singular 'map') is the key in asset_metadata.
+        context.asset_metadata['processed_map_details'] = restructured_processed_maps
+        context.asset_metadata['merged_map_details'] = getattr(context, 'merged_maps_details', {})
+
+        # (Optional) Add a list of all temporary files
+        # context.asset_metadata['temporary_files'] = getattr(context, 'temporary_files', []) # Assuming this is populated elsewhere
+
+        # B. Determine Metadata Output Path
+        # asset_name_for_log is defined at the top of the function if asset_metadata exists
+            
+        source_rule_identifier_for_path = "unknown_source"
+        if hasattr(context, 'source_rule') and context.source_rule:
+            if hasattr(context.source_rule, 'supplier_identifier') and context.source_rule.supplier_identifier:
+                source_rule_identifier_for_path = context.source_rule.supplier_identifier
+            elif hasattr(context.source_rule, 'input_path') and context.source_rule.input_path:
+                source_rule_identifier_for_path = Path(context.source_rule.input_path).stem # Use stem of input path if no identifier
+            else:
+                source_rule_identifier_for_path = "unknown_source_details"
+        
+        # Use the configured metadata filename from config_obj
+        metadata_filename_from_config = getattr(context.config_obj, 'metadata_filename', "metadata.json")
+        # Ensure asset_name_for_log is safe for filenames
+        safe_asset_name = sanitize_filename(asset_name_for_log) # asset_name_for_log is defined at the top
+        final_metadata_filename = f"{safe_asset_name}_{metadata_filename_from_config}"
+
+        # Output path pattern should come from config_obj, not asset_rule
+        output_path_pattern_from_config = getattr(context.config_obj, 'output_directory_pattern', "[supplier]/[assetname]")
+            
+        sha_value = getattr(context, 'sha5_value', None) # Prefer sha5_value if explicitly set on context
+        if sha_value is None: # Fallback to sha256_value if that was the intended attribute
+            sha_value = getattr(context, 'sha256_value', None)
+
+        token_data = {
+            "assetname": asset_name_for_log,
+            "supplier": context.effective_supplier if context.effective_supplier else source_rule_identifier_for_path,
+            "sourcerulename": source_rule_identifier_for_path,
+            "incrementingvalue": getattr(context, 'incrementing_value', None),
+            "sha5": sha_value, # Assuming pattern uses [sha5] or similar for sha_value
+            "maptype": "metadata", # Added maptype to token_data
+            "filename": final_metadata_filename # Added filename to token_data
+            # Add other tokens if your output_path_pattern_from_config expects them
+        }
+        # Clean None values, as generate_path_from_pattern might not handle them well for all tokens
+        token_data_cleaned = {k: v for k, v in token_data.items() if v is not None}
+
+        # Generate the relative directory path using the pattern and tokens
+        relative_dir_path_str = generate_path_from_pattern(
+            pattern_string=output_path_pattern_from_config, # This pattern should resolve to a directory
+            token_data=token_data_cleaned
+        )
+        
+        # Construct the full path by joining the base output path, the generated relative directory, and the final filename
+        metadata_save_path = Path(context.output_base_path) / Path(relative_dir_path_str) / Path(final_metadata_filename)
+
+        # C. Save Metadata File
+        try:
+            metadata_save_path.parent.mkdir(parents=True, exist_ok=True)
+
+            def make_serializable(data: Any) -> Any:
+                if isinstance(data, Path):
+                    # metadata_save_path is available from the outer scope
+                    metadata_dir = metadata_save_path.parent
+                    try:
+                        # Attempt to make the path relative if it's absolute and under the same root
+                        if data.is_absolute():
+                            # Check if the path can be made relative (e.g., same drive on Windows)
+                            # This check might need to be more robust depending on os.path.relpath behavior
+                            # For pathlib, relative_to will raise ValueError if not possible.
+                            return str(data.relative_to(metadata_dir))
+                        else:
+                            # If it's already relative, assume it's correct or handle as needed
+                            return str(data)
+                    except ValueError:
+                        # If paths are on different drives or cannot be made relative,
+                        # log a warning and return the absolute path as a string.
+                        # This can happen if an output path was explicitly set to an unrelated directory.
+                        logger.warning(
+                            f"Asset '{asset_name_for_log}': Could not make path {data} "
+                            f"relative to {metadata_dir}. Storing as absolute."
+                        )
+                        return str(data)
+                if isinstance(data, datetime.datetime): # Ensure datetime is serializable
+                    return data.isoformat()
+                if isinstance(data, dict):
+                    return {k: make_serializable(v) for k, v in data.items()}
+                if isinstance(data, list):
+                    return [make_serializable(i) for i in data]
+                return data
+
+            serializable_metadata = make_serializable(context.asset_metadata)
+
+            with open(metadata_save_path, 'w') as f:
+                json.dump(serializable_metadata, f, indent=4)
+            logger.info(f"Asset '{asset_name_for_log}': Metadata saved to {metadata_save_path}") # Use asset_name_for_log
+            context.asset_metadata['metadata_file_path'] = str(metadata_save_path)
+        except Exception as e:
+            logger.error(f"Asset '{asset_name_for_log}': Failed to save metadata to {metadata_save_path}. Error: {e}") # Use asset_name_for_log
+            context.asset_metadata['status'] = "Failed (Metadata Save Error)"
+            context.status_flags['metadata_save_error'] = True
+
+        return context

processing/pipeline/stages/metadata_initialization.py

@@ -0,0 +1,173 @@
+import datetime
+import logging
+
+from .base_stage import ProcessingStage
+from ..asset_context import AssetProcessingContext # Adjusted import path assuming asset_context is in processing.pipeline
+# If AssetProcessingContext is directly under 'processing', the import would be:
+# from ...asset_context import AssetProcessingContext
+# Based on the provided file structure, asset_context.py is in processing/pipeline/
+# So, from ...asset_context import AssetProcessingContext is likely incorrect.
+# It should be: from ..asset_context import AssetProcessingContext
+# Correcting this based on typical Python package structure and the location of base_stage.py
+
+# Re-evaluating import based on common structure:
+# If base_stage.py is in processing/pipeline/stages/
+# and asset_context.py is in processing/pipeline/
+# then the import for AssetProcessingContext from metadata_initialization.py (in stages) would be:
+# from ..asset_context import AssetProcessingContext
+
+# Let's assume the following structure for clarity:
+# processing/
+# L-- pipeline/
+#     L-- __init__.py
+#     L-- asset_context.py
+#     L-- base_stage.py (Mistake here, base_stage is in stages, so it's ..base_stage)
+#     L-- stages/
+#         L-- __init__.py
+#         L-- metadata_initialization.py
+#         L-- base_stage.py (Corrected: base_stage.py is here)
+
+# Corrected imports based on the plan and typical structure:
+# base_stage.py is in processing/pipeline/stages/
+# asset_context.py is in processing/pipeline/
+
+# from ..base_stage import ProcessingStage # This would mean base_stage is one level up from stages (i.e. in pipeline)
+# The plan says: from ..base_stage import ProcessingStage
+# This implies that metadata_initialization.py is in a subdirectory of where base_stage.py is.
+# However, the file path for metadata_initialization.py is processing/pipeline/stages/metadata_initialization.py
+# And base_stage.py is listed as processing/pipeline/stages/base_stage.py in the open tabs.
+# So, the import should be:
+# from .base_stage import ProcessingStage
+
+# AssetProcessingContext is at processing/pipeline/asset_context.py
+# So from processing/pipeline/stages/metadata_initialization.py, it would be:
+# from ..asset_context import AssetProcessingContext
+
+# Final check on imports based on instructions:
+# `from ..base_stage import ProcessingStage` -> This means base_stage.py is in `processing/pipeline/`
+# `from ...asset_context import AssetProcessingContext` -> This means asset_context.py is in `processing/`
+# Let's verify the location of these files from the environment details.
+# processing/pipeline/asset_context.py
+# processing/pipeline/stages/base_stage.py
+#
+# So, from processing/pipeline/stages/metadata_initialization.py:
+# To import ProcessingStage from processing/pipeline/stages/base_stage.py:
+# from .base_stage import ProcessingStage
+# To import AssetProcessingContext from processing/pipeline/asset_context.py:
+# from ..asset_context import AssetProcessingContext
+
+# The instructions explicitly state:
+# `from ..base_stage import ProcessingStage`
+# `from ...asset_context import AssetProcessingContext`
+# This implies a different structure than what seems to be in the file tree.
+# I will follow the explicit import instructions from the task.
+# This means:
+# base_stage.py is expected at `processing/pipeline/base_stage.py`
+# asset_context.py is expected at `processing/asset_context.py`
+
+# Given the file tree:
+# processing/pipeline/asset_context.py
+# processing/pipeline/stages/base_stage.py
+# The imports in `processing/pipeline/stages/metadata_initialization.py` should be:
+# from .base_stage import ProcessingStage
+# from ..asset_context import AssetProcessingContext
+
+# I will use the imports that align with the provided file structure.
+
+
+
+logger = logging.getLogger(__name__)
+
+class MetadataInitializationStage(ProcessingStage):
+    """
+    Initializes metadata structures within the AssetProcessingContext.
+    This stage sets up asset_metadata, processed_maps_details, and
+    merged_maps_details.
+    """
+    def execute(self, context: AssetProcessingContext) -> AssetProcessingContext:
+        """
+        Executes the metadata initialization logic.
+
+        Args:
+            context: The AssetProcessingContext for the current asset.
+
+        Returns:
+            The modified AssetProcessingContext.
+        """
+        if context.status_flags.get('skip_asset', False):
+            logger.debug(f"Asset '{context.asset_rule.asset_name if context.asset_rule else 'Unknown'}': Skipping metadata initialization as 'skip_asset' is True.")
+            return context
+
+        logger.debug(f"Asset '{context.asset_rule.asset_name if context.asset_rule else 'Unknown'}': Initializing metadata.")
+
+        context.asset_metadata = {}
+        context.processed_maps_details = {}
+        context.merged_maps_details = {}
+
+        # Populate Initial asset_metadata
+        if context.asset_rule:
+            context.asset_metadata['asset_name'] = context.asset_rule.asset_name
+            # Attempt to get 'id' from common_metadata or use asset_name as a fallback
+            asset_id_val = context.asset_rule.common_metadata.get('id', context.asset_rule.common_metadata.get('asset_id'))
+            if asset_id_val is None:
+                logger.warning(f"Asset '{context.asset_rule.asset_name}': No 'id' or 'asset_id' found in common_metadata. Using asset_name as asset_id.")
+                asset_id_val = context.asset_rule.asset_name
+            context.asset_metadata['asset_id'] = str(asset_id_val)
+
+            # Assuming source_path, output_path_pattern, tags, custom_fields might also be in common_metadata
+            context.asset_metadata['source_path'] = str(context.asset_rule.common_metadata.get('source_path', 'N/A'))
+            context.asset_metadata['output_path_pattern'] = context.asset_rule.common_metadata.get('output_path_pattern', 'N/A')
+            context.asset_metadata['tags'] = list(context.asset_rule.common_metadata.get('tags', []))
+            context.asset_metadata['custom_fields'] = dict(context.asset_rule.common_metadata.get('custom_fields', {}))
+        else:
+            # Handle cases where asset_rule might be None, though typically it should be set
+            logger.warning("AssetRule is not set in context during metadata initialization.")
+            context.asset_metadata['asset_name'] = "Unknown Asset"
+            context.asset_metadata['asset_id'] = "N/A"
+            context.asset_metadata['source_path'] = "N/A"
+            context.asset_metadata['output_path_pattern'] = "N/A"
+            context.asset_metadata['tags'] = []
+            context.asset_metadata['custom_fields'] = {}
+
+
+        if context.source_rule:
+            # SourceRule also doesn't have 'name' or 'id' directly.
+            # Using 'input_path' as a proxy for name, and a placeholder for id.
+            source_rule_name_val = context.source_rule.input_path if context.source_rule.input_path else "Unknown Source Rule Path"
+            source_rule_id_val = context.source_rule.high_level_sorting_parameters.get('id', "N/A_SR_ID") # Check high_level_sorting_parameters
+            logger.debug(f"SourceRule: using input_path '{source_rule_name_val}' as name, and '{source_rule_id_val}' as id.")
+            context.asset_metadata['source_rule_name'] = source_rule_name_val
+            context.asset_metadata['source_rule_id'] = str(source_rule_id_val)
+        else:
+            logger.warning("SourceRule is not set in context during metadata initialization.")
+            context.asset_metadata['source_rule_name'] = "Unknown Source Rule"
+            context.asset_metadata['source_rule_id'] = "N/A"
+
+        context.asset_metadata['effective_supplier'] = context.effective_supplier
+        context.asset_metadata['processing_start_time'] = datetime.datetime.now().isoformat()
+        context.asset_metadata['status'] = "Pending"
+
+        if context.config_obj and hasattr(context.config_obj, 'general_settings') and \
+           hasattr(context.config_obj.general_settings, 'app_version'):
+            context.asset_metadata['version'] = context.config_obj.general_settings.app_version
+        else:
+            logger.warning("App version not found in config_obj.general_settings. Setting version to 'N/A'.")
+            context.asset_metadata['version'] = "N/A" # Default or placeholder
+
+        if context.incrementing_value is not None:
+            context.asset_metadata['incrementing_value'] = context.incrementing_value
+        
+        # The plan mentions sha5_value, which is likely a typo for sha256 or similar.
+        # Implementing as 'sha5_value' per instructions, but noting the potential typo.
+        if hasattr(context, 'sha5_value') and context.sha5_value is not None: # Check attribute existence
+            context.asset_metadata['sha5_value'] = context.sha5_value
+        elif hasattr(context, 'sha256_value') and context.sha256_value is not None: # Fallback if sha5 was a typo
+             logger.debug("sha5_value not found, using sha256_value if available for metadata.")
+             context.asset_metadata['sha256_value'] = context.sha256_value
+
+
+        logger.info(f"Asset '{context.asset_metadata.get('asset_name', 'Unknown')}': Metadata initialized.")
+        # Example of how you might log the full metadata for debugging:
+        # logger.debug(f"Initialized metadata: {context.asset_metadata}")
+
+        return context

processing/pipeline/stages/normal_map_green_channel.py

@@ -0,0 +1,153 @@
+import logging
+import numpy as np
+from pathlib import Path
+from typing import List
+
+from .base_stage import ProcessingStage
+from ..asset_context import AssetProcessingContext
+from rule_structure import FileRule
+from ...utils import image_processing_utils as ipu
+from utils.path_utils import sanitize_filename
+
+logger = logging.getLogger(__name__)
+
+class NormalMapGreenChannelStage(ProcessingStage):
+    """
+    Processing stage to invert the green channel of normal maps if configured.
+    This is often needed when converting between DirectX (Y-) and OpenGL (Y+) normal map formats.
+    """
+
+    def execute(self, context: AssetProcessingContext) -> AssetProcessingContext:
+        """
+        Identifies NORMAL maps, checks configuration for green channel inversion,
+        performs inversion if needed, saves a new temporary file, and updates
+        the AssetProcessingContext.
+        """
+        asset_name_for_log = context.asset_rule.asset_name if context.asset_rule else "Unknown Asset"
+        if context.status_flags.get('skip_asset'):
+            logger.debug(f"Asset '{asset_name_for_log}': Skipping NormalMapGreenChannelStage due to skip_asset flag.")
+            return context
+
+        if not context.processed_maps_details: # Check processed_maps_details primarily
+            logger.debug(
+                f"Asset '{asset_name_for_log}': No processed_maps_details in NormalMapGreenChannelStage. Skipping."
+            )
+            return context
+
+        processed_a_normal_map = False
+
+        # Iterate through processed maps, as FileRule objects don't have IDs directly
+        for map_id_hex, map_details in context.processed_maps_details.items():
+            if map_details.get('map_type') == "NORMAL" and map_details.get('status') == 'Processed':
+                
+                # Check configuration for inversion
+                # Assuming general_settings is an attribute of config_obj and might be a dict or an object
+                should_invert = False
+                if hasattr(context.config_obj, 'general_settings'):
+                    if isinstance(context.config_obj.general_settings, dict):
+                        should_invert = context.config_obj.general_settings.get('invert_normal_map_green_channel_globally', False)
+                    elif hasattr(context.config_obj.general_settings, 'invert_normal_map_green_channel_globally'):
+                        should_invert = getattr(context.config_obj.general_settings, 'invert_normal_map_green_channel_globally', False)
+                
+                original_temp_path_str = map_details.get('temp_processed_file')
+                if not original_temp_path_str:
+                    logger.warning(f"Asset '{asset_name_for_log}': Normal map (ID: {map_id_hex}) missing 'temp_processed_file' in details. Skipping.")
+                    continue
+                
+                original_temp_path = Path(original_temp_path_str)
+                original_filename_for_log = original_temp_path.name
+
+                if not should_invert:
+                    logger.debug(
+                        f"Asset '{asset_name_for_log}': Normal map green channel inversion not enabled. "
+                        f"Skipping for {original_filename_for_log} (ID: {map_id_hex})."
+                    )
+                    continue
+
+                if not original_temp_path.exists():
+                    logger.error(
+                        f"Asset '{asset_name_for_log}': Temporary file {original_temp_path} for normal map "
+                        f"{original_filename_for_log} (ID: {map_id_hex}) does not exist. Cannot invert green channel."
+                    )
+                    continue
+
+                image_data = ipu.load_image(original_temp_path)
+
+                if image_data is None:
+                    logger.error(
+                        f"Asset '{asset_name_for_log}': Failed to load image from {original_temp_path} "
+                        f"for normal map {original_filename_for_log} (ID: {map_id_hex})."
+                    )
+                    continue
+
+                if image_data.ndim != 3 or image_data.shape[2] < 2: # Must have at least R, G channels
+                    logger.error(
+                        f"Asset '{asset_name_for_log}': Image {original_temp_path} for normal map "
+                        f"{original_filename_for_log} (ID: {map_id_hex}) is not a valid RGB/normal map "
+                        f"(ndim={image_data.ndim}, channels={image_data.shape[2] if image_data.ndim == 3 else 'N/A'}) "
+                        f"for green channel inversion."
+                    )
+                    continue
+
+                # Perform Green Channel Inversion
+                modified_image_data = image_data.copy()
+                try:
+                    if np.issubdtype(modified_image_data.dtype, np.floating):
+                        modified_image_data[:, :, 1] = 1.0 - modified_image_data[:, :, 1]
+                    elif np.issubdtype(modified_image_data.dtype, np.integer):
+                        max_val = np.iinfo(modified_image_data.dtype).max
+                        modified_image_data[:, :, 1] = max_val - modified_image_data[:, :, 1]
+                    else:
+                        logger.error(
+                            f"Asset '{asset_name_for_log}': Unsupported image data type "
+                            f"{modified_image_data.dtype} for normal map {original_temp_path}. Cannot invert green channel."
+                        )
+                        continue
+                except IndexError:
+                     logger.error(
+                        f"Asset '{asset_name_for_log}': Image {original_temp_path} for normal map "
+                        f"{original_filename_for_log} (ID: {map_id_hex}) does not have a green channel (index 1) "
+                        f"or has unexpected dimensions ({modified_image_data.shape}). Cannot invert."
+                    )
+                     continue
+
+                # Save New Temporary (Modified Normal) Map
+                # Sanitize map_details.get('map_type') in case it's missing, though it should be 'NORMAL' here
+                map_type_for_filename = sanitize_filename(map_details.get('map_type', 'NORMAL'))
+                new_temp_filename = f"normal_g_inv_{map_type_for_filename}_{map_id_hex}{original_temp_path.suffix}"
+                new_temp_path = context.engine_temp_dir / new_temp_filename
+
+                save_success = ipu.save_image(new_temp_path, modified_image_data)
+
+                if save_success:
+                    logger.info(
+                        f"Asset '{asset_name_for_log}': Inverted green channel for NORMAL map "
+                        f"{original_filename_for_log}, saved to {new_temp_path.name}."
+                    )
+                    # Update processed_maps_details for this map_id_hex
+                    context.processed_maps_details[map_id_hex]['temp_processed_file'] = str(new_temp_path)
+                    current_notes = context.processed_maps_details[map_id_hex].get('notes', '')
+                    context.processed_maps_details[map_id_hex]['notes'] = \
+                        f"{current_notes}; Green channel inverted by NormalMapGreenChannelStage".strip('; ')
+                    
+                    processed_a_normal_map = True
+                else:
+                    logger.error(
+                        f"Asset '{asset_name_for_log}': Failed to save inverted normal map to {new_temp_path} "
+                        f"for original {original_filename_for_log}."
+                    )
+            # No need to explicitly manage new_files_to_process list in this loop,
+            # as we are modifying the temp_processed_file path within processed_maps_details.
+            # The existing FileRule objects in context.files_to_process (if any) would
+            # be linked to these details by a previous stage (e.g. IndividualMapProcessing)
+            # if that stage populates a 'file_rule_id' in map_details.
+
+        # context.files_to_process remains unchanged by this stage directly,
+        # as we modify the data pointed to by processed_maps_details.
+
+        if processed_a_normal_map:
+            logger.info(f"Asset '{asset_name_for_log}': NormalMapGreenChannelStage processed relevant normal maps.")
+        else:
+            logger.debug(f"Asset '{asset_name_for_log}': No normal maps found or processed in NormalMapGreenChannelStage.")
+
+        return context

processing/pipeline/stages/output_organization.py

@@ -0,0 +1,385 @@
+import logging
+import shutil
+from pathlib import Path
+from typing import List, Dict, Optional
+
+from .base_stage import ProcessingStage
+from ..asset_context import AssetProcessingContext
+from utils.path_utils import generate_path_from_pattern, sanitize_filename
+from rule_structure import FileRule # Assuming these are needed for type hints if not directly in context
+
+
+logger = logging.getLogger(__name__)
+
+class OutputOrganizationStage(ProcessingStage):
+    """
+    Organizes output files by copying temporary processed files to their final destinations.
+    """
+
+    def execute(self, context: AssetProcessingContext) -> AssetProcessingContext:
+        """
+        Copies temporary processed and merged files to their final output locations
+        based on path patterns and updates AssetProcessingContext.
+        """
+        asset_name_for_log = context.asset_rule.asset_name if hasattr(context, 'asset_rule') and context.asset_rule else "Unknown Asset"
+        logger.debug(f"Asset '{asset_name_for_log}': Starting output organization stage.")
+
+        if context.status_flags.get('skip_asset'):
+            logger.info(f"Asset '{asset_name_for_log}': Output organization skipped as 'skip_asset' is True.")
+            return context
+
+        current_status = context.asset_metadata.get('status', '')
+        if current_status.startswith("Failed") or current_status == "Skipped":
+            logger.info(f"Asset '{asset_name_for_log}': Output organization skipped due to prior status: {current_status}.")
+            return context
+
+        final_output_files: List[str] = []
+        overwrite_existing = False
+        # Correctly access general_settings and overwrite_existing from config_obj
+        if hasattr(context.config_obj, 'general_settings'):
+            if isinstance(context.config_obj.general_settings, dict):
+                overwrite_existing = context.config_obj.general_settings.get('overwrite_existing', False)
+            elif hasattr(context.config_obj.general_settings, 'overwrite_existing'): # If general_settings is an object
+                overwrite_existing = getattr(context.config_obj.general_settings, 'overwrite_existing', False)
+        else:
+            logger.warning(f"Asset '{asset_name_for_log}': config_obj.general_settings not found, defaulting overwrite_existing to False.")
+        
+        output_dir_pattern = getattr(context.config_obj, 'output_directory_pattern', "[supplier]/[assetname]")
+        output_filename_pattern_config = getattr(context.config_obj, 'output_filename_pattern', "[assetname]_[maptype]_[resolution].[ext]")
+
+
+        # A. Organize Processed Individual Maps
+        if context.processed_maps_details:
+            logger.debug(f"Asset '{asset_name_for_log}': Organizing {len(context.processed_maps_details)} processed individual map entries.")
+            for processed_map_key, details in context.processed_maps_details.items():
+                map_status = details.get('status')
+                base_map_type = details.get('map_type', 'unknown_map_type') # Original map type
+
+                if map_status in ['Processed', 'Processed_No_Variants']:
+                    if not details.get('temp_processed_file'):
+                        logger.debug(f"Asset '{asset_name_for_log}': Skipping map key '{processed_map_key}' (status '{map_status}') due to missing 'temp_processed_file'.")
+                        details['status'] = 'Organization Skipped (Missing Temp File)'
+                        continue
+                    
+                    temp_file_path = Path(details['temp_processed_file'])
+                    resolution_str = details.get('processed_resolution_name', details.get('original_resolution_name', 'resX'))
+
+                    token_data = {
+                        "assetname": asset_name_for_log,
+                        "supplier": context.effective_supplier or "DefaultSupplier",
+                        "maptype": base_map_type,
+                        "resolution": resolution_str,
+                        "ext": temp_file_path.suffix.lstrip('.'),
+                        "incrementingvalue": getattr(context, 'incrementing_value', None),
+                        "sha5": getattr(context, 'sha5_value', None)
+                    }
+                    token_data_cleaned = {k: v for k, v in token_data.items() if v is not None}
+                    
+                    output_filename = generate_path_from_pattern(output_filename_pattern_config, token_data_cleaned)
+
+                    try:
+                        relative_dir_path_str = generate_path_from_pattern(
+                            pattern_string=output_dir_pattern,
+                            token_data=token_data_cleaned
+                        )
+                        final_path = Path(context.output_base_path) / Path(relative_dir_path_str) / Path(output_filename)
+                        final_path.parent.mkdir(parents=True, exist_ok=True)
+
+                        if final_path.exists() and not overwrite_existing:
+                            logger.info(f"Asset '{asset_name_for_log}': Output file {final_path} for map '{processed_map_key}' exists and overwrite is disabled. Skipping copy.")
+                        else:
+                            shutil.copy2(temp_file_path, final_path)
+                            logger.info(f"Asset '{asset_name_for_log}': Copied {temp_file_path} to {final_path} for map '{processed_map_key}'.")
+                            final_output_files.append(str(final_path))
+                        
+                        details['final_output_path'] = str(final_path)
+                        details['status'] = 'Organized'
+
+                        # Update asset_metadata for metadata.json
+                        map_metadata_entry = context.asset_metadata.setdefault('maps', {}).setdefault(processed_map_key, {})
+                        map_metadata_entry['map_type'] = base_map_type
+                        map_metadata_entry['path'] = str(Path(relative_dir_path_str) / Path(output_filename)) # Store relative path
+
+                    except Exception as e:
+                        logger.error(f"Asset '{asset_name_for_log}': Failed to copy {temp_file_path} for map key '{processed_map_key}'. Error: {e}", exc_info=True)
+                        context.status_flags['output_organization_error'] = True
+                        context.asset_metadata['status'] = "Failed (Output Organization Error)"
+                        details['status'] = 'Organization Failed'
+
+                elif map_status == 'Processed_With_Variants':
+                    variants = details.get('variants')
+                    if not variants: # No variants list, or it's empty
+                        logger.warning(f"Asset '{asset_name_for_log}': Map key '{processed_map_key}' (status '{map_status}') has no 'variants' list or it is empty. Attempting fallback to base file.")
+                        if not details.get('temp_processed_file'):
+                            logger.error(f"Asset '{asset_name_for_log}': Skipping map key '{processed_map_key}' (fallback) as 'temp_processed_file' is also missing.")
+                            details['status'] = 'Organization Failed (No Variants, No Temp File)'
+                            continue # Skip to next map key
+
+                        # Fallback: Process the base temp_processed_file
+                        temp_file_path = Path(details['temp_processed_file'])
+                        resolution_str = details.get('processed_resolution_name', details.get('original_resolution_name', 'baseRes'))
+
+                        token_data = {
+                            "assetname": asset_name_for_log,
+                            "supplier": context.effective_supplier or "DefaultSupplier",
+                            "maptype": base_map_type,
+                            "resolution": resolution_str,
+                            "ext": temp_file_path.suffix.lstrip('.'),
+                            "incrementingvalue": getattr(context, 'incrementing_value', None),
+                            "sha5": getattr(context, 'sha5_value', None)
+                        }
+                        token_data_cleaned = {k: v for k, v in token_data.items() if v is not None}
+                        output_filename = generate_path_from_pattern(output_filename_pattern_config, token_data_cleaned)
+
+                        try:
+                            relative_dir_path_str = generate_path_from_pattern(
+                                pattern_string=output_dir_pattern,
+                                token_data=token_data_cleaned
+                            )
+                            final_path = Path(context.output_base_path) / Path(relative_dir_path_str) / Path(output_filename)
+                            final_path.parent.mkdir(parents=True, exist_ok=True)
+
+                            if final_path.exists() and not overwrite_existing:
+                                logger.info(f"Asset '{asset_name_for_log}': Output file {final_path} for map '{processed_map_key}' (fallback) exists and overwrite is disabled. Skipping copy.")
+                            else:
+                                shutil.copy2(temp_file_path, final_path)
+                                logger.info(f"Asset '{asset_name_for_log}': Copied {temp_file_path} to {final_path} for map '{processed_map_key}' (fallback).")
+                                final_output_files.append(str(final_path))
+                            
+                            details['final_output_path'] = str(final_path)
+                            details['status'] = 'Organized (Base File Fallback)'
+
+                            map_metadata_entry = context.asset_metadata.setdefault('maps', {}).setdefault(processed_map_key, {})
+                            map_metadata_entry['map_type'] = base_map_type
+                            map_metadata_entry['path'] = str(Path(relative_dir_path_str) / Path(output_filename))
+                            if 'variant_paths' in map_metadata_entry: # Clean up if it was somehow set
+                                del map_metadata_entry['variant_paths']
+                        except Exception as e:
+                            logger.error(f"Asset '{asset_name_for_log}': Failed to copy {temp_file_path} (fallback) for map key '{processed_map_key}'. Error: {e}", exc_info=True)
+                            context.status_flags['output_organization_error'] = True
+                            context.asset_metadata['status'] = "Failed (Output Organization Error - Fallback)"
+                            details['status'] = 'Organization Failed (Fallback)'
+                        continue # Finished with this map key due to fallback
+
+                    # If we are here, 'variants' list exists and is not empty. Proceed with variant processing.
+                    logger.debug(f"Asset '{asset_name_for_log}': Organizing {len(variants)} variants for map key '{processed_map_key}' (map type: {base_map_type}).")
+                    
+                    map_metadata_entry = context.asset_metadata.setdefault('maps', {}).setdefault(processed_map_key, {})
+                    map_metadata_entry['map_type'] = base_map_type
+                    map_metadata_entry.setdefault('variant_paths', {}) # Initialize if not present
+
+                    processed_any_variant_successfully = False
+                    failed_any_variant = False
+
+                    for variant_index, variant_detail in enumerate(variants):
+                        temp_variant_path_str = variant_detail.get('temp_path')
+                        if not temp_variant_path_str:
+                            logger.warning(f"Asset '{asset_name_for_log}': Variant {variant_index} for map '{processed_map_key}' is missing 'temp_path'. Skipping.")
+                            variant_detail['status'] = 'Organization Skipped (Missing Temp Path)'
+                            continue
+                        
+                        temp_variant_path = Path(temp_variant_path_str)
+                        variant_resolution_key = variant_detail.get('resolution_key', f"varRes{variant_index}")
+                        variant_ext = temp_variant_path.suffix.lstrip('.')
+
+                        token_data_variant = {
+                            "assetname": asset_name_for_log,
+                            "supplier": context.effective_supplier or "DefaultSupplier",
+                            "maptype": base_map_type,
+                            "resolution": variant_resolution_key,
+                            "ext": variant_ext,
+                            "incrementingvalue": getattr(context, 'incrementing_value', None),
+                            "sha5": getattr(context, 'sha5_value', None)
+                        }
+                        token_data_variant_cleaned = {k: v for k, v in token_data_variant.items() if v is not None}
+                        output_filename_variant = generate_path_from_pattern(output_filename_pattern_config, token_data_variant_cleaned)
+                        
+                        try:
+                            relative_dir_path_str_variant = generate_path_from_pattern(
+                                pattern_string=output_dir_pattern,
+                                token_data=token_data_variant_cleaned
+                            )
+                            final_variant_path = Path(context.output_base_path) / Path(relative_dir_path_str_variant) / Path(output_filename_variant)
+                            final_variant_path.parent.mkdir(parents=True, exist_ok=True)
+
+                            if final_variant_path.exists() and not overwrite_existing:
+                                logger.info(f"Asset '{asset_name_for_log}': Output variant file {final_variant_path} for map '{processed_map_key}' (res: {variant_resolution_key}) exists and overwrite is disabled. Skipping copy.")
+                                variant_detail['status'] = 'Organized (Exists, Skipped Copy)'
+                            else:
+                                shutil.copy2(temp_variant_path, final_variant_path)
+                                logger.info(f"Asset '{asset_name_for_log}': Copied variant {temp_variant_path} to {final_variant_path} for map '{processed_map_key}'.")
+                                final_output_files.append(str(final_variant_path))
+                                variant_detail['status'] = 'Organized'
+                                
+                                variant_detail['final_output_path'] = str(final_variant_path)
+                                # Store the Path object for metadata stage to make it relative later
+                                variant_detail['final_output_path_for_metadata'] = final_variant_path
+                                relative_final_variant_path_str = str(Path(relative_dir_path_str_variant) / Path(output_filename_variant))
+                                map_metadata_entry['variant_paths'][variant_resolution_key] = relative_final_variant_path_str
+                                processed_any_variant_successfully = True
+
+                        except Exception as e:
+                            logger.error(f"Asset '{asset_name_for_log}': Failed to copy variant {temp_variant_path} for map key '{processed_map_key}' (res: {variant_resolution_key}). Error: {e}", exc_info=True)
+                            context.status_flags['output_organization_error'] = True
+                            context.asset_metadata['status'] = "Failed (Output Organization Error - Variant)"
+                            variant_detail['status'] = 'Organization Failed'
+                            failed_any_variant = True
+                    
+                    # Update parent map detail status based on variant outcomes
+                    if failed_any_variant:
+                        details['status'] = 'Organization Failed (Variants)'
+                    elif processed_any_variant_successfully:
+                        # Check if all processable variants were organized
+                        all_attempted_organized = True
+                        for v_detail in variants:
+                            if v_detail.get('temp_path') and not v_detail.get('status', '').startswith('Organized'):
+                                all_attempted_organized = False
+                                break
+                        if all_attempted_organized:
+                             details['status'] = 'Organized (All Attempted Variants)'
+                        else:
+                             details['status'] = 'Partially Organized (Variants)'
+                    elif not any(v.get('temp_path') for v in variants): # No variants had temp_paths to begin with
+                        details['status'] = 'Processed_With_Variants (No Valid Variants to Organize)'
+                    else: # Variants list existed, items had temp_paths, but none were successfully organized (e.g., all skipped due to existing file and no overwrite)
+                        details['status'] = 'Organization Skipped (No Variants Copied/Needed)'
+
+
+                else: # Other statuses like 'Skipped', 'Failed', 'Organization Failed' etc.
+                    logger.debug(f"Asset '{asset_name_for_log}': Skipping map key '{processed_map_key}' (status: '{map_status}') for organization as it's not 'Processed', 'Processed_No_Variants', or 'Processed_With_Variants'.")
+                    continue
+        else:
+            logger.debug(f"Asset '{asset_name_for_log}': No processed individual maps to organize.")
+
+        # B. Organize Merged Maps
+        if context.merged_maps_details:
+            logger.debug(f"Asset '{asset_name_for_log}': Organizing {len(context.merged_maps_details)} merged map(s).")
+            for merge_op_id, details in context.merged_maps_details.items(): # Use merge_op_id
+                if details.get('status') != 'Processed' or not details.get('temp_merged_file'):
+                    logger.debug(f"Asset '{asset_name_for_log}': Skipping merge op id '{merge_op_id}' due to status '{details.get('status')}' or missing temp file.")
+                    continue
+
+                temp_file_path = Path(details['temp_merged_file'])
+                map_type = details.get('map_type', 'unknown_merged_map') # This is the output_map_type of the merge rule
+                # Merged maps might not have a simple 'resolution' token like individual maps.
+                # We'll use a placeholder or derive if possible.
+                resolution_str = details.get('merged_resolution_name', 'mergedRes')
+
+
+                token_data_merged = {
+                    "assetname": asset_name_for_log,
+                    "supplier": context.effective_supplier or "DefaultSupplier",
+                    "maptype": map_type,
+                    "resolution": resolution_str,
+                    "ext": temp_file_path.suffix.lstrip('.'),
+                    "incrementingvalue": getattr(context, 'incrementing_value', None),
+                    "sha5": getattr(context, 'sha5_value', None)
+                }
+                token_data_merged_cleaned = {k: v for k, v in token_data_merged.items() if v is not None}
+
+                output_filename_merged = generate_path_from_pattern(output_filename_pattern_config, token_data_merged_cleaned)
+                
+                try:
+                    relative_dir_path_str_merged = generate_path_from_pattern(
+                        pattern_string=output_dir_pattern,
+                        token_data=token_data_merged_cleaned
+                    )
+                    final_path_merged = Path(context.output_base_path) / Path(relative_dir_path_str_merged) / Path(output_filename_merged)
+                    final_path_merged.parent.mkdir(parents=True, exist_ok=True)
+
+                    if final_path_merged.exists() and not overwrite_existing:
+                        logger.info(f"Asset '{asset_name_for_log}': Output file {final_path_merged} exists and overwrite is disabled. Skipping copy for merged map.")
+                    else:
+                        shutil.copy2(temp_file_path, final_path_merged)
+                        logger.info(f"Asset '{asset_name_for_log}': Copied merged map {temp_file_path} to {final_path_merged}")
+                        final_output_files.append(str(final_path_merged))
+                    
+                    context.merged_maps_details[merge_op_id]['final_output_path'] = str(final_path_merged)
+                    context.merged_maps_details[merge_op_id]['status'] = 'Organized'
+
+                except Exception as e:
+                    logger.error(f"Asset '{asset_name_for_log}': Failed to copy merged map {temp_file_path} to destination for merge op id '{merge_op_id}'. Error: {e}", exc_info=True)
+                    context.status_flags['output_organization_error'] = True
+                    context.asset_metadata['status'] = "Failed (Output Organization Error)"
+                    context.merged_maps_details[merge_op_id]['status'] = 'Organization Failed'
+        else:
+            logger.debug(f"Asset '{asset_name_for_log}': No merged maps to organize.")
+
+        # C. Organize Extra Files (e.g., previews, text files)
+        logger.debug(f"Asset '{asset_name_for_log}': Checking for EXTRA files to organize.")
+        extra_files_organized_count = 0
+        if hasattr(context, 'files_to_process') and context.files_to_process:
+            extra_subdir_name = getattr(context.config_obj, 'extra_files_subdir', 'Extra') # Default to 'Extra'
+
+            for file_rule in context.files_to_process:
+                if file_rule.item_type == 'EXTRA':
+                    source_file_path = context.workspace_path / file_rule.file_path
+                    if not source_file_path.is_file():
+                        logger.warning(f"Asset '{asset_name_for_log}': EXTRA file '{source_file_path}' not found. Skipping.")
+                        continue
+
+                    # Basic token data for the asset's base output directory
+                    # We don't use map_type, resolution, or ext for the base directory of extras.
+                    # However, generate_path_from_pattern might expect them or handle their absence.
+                    # For the base asset directory, only assetname and supplier are typically primary.
+                    base_token_data = {
+                        "assetname": asset_name_for_log,
+                        "supplier": context.effective_supplier or "DefaultSupplier",
+                        # Add other tokens if your output_directory_pattern uses them at the asset level
+                        "incrementingvalue": getattr(context, 'incrementing_value', None),
+                        "sha5": getattr(context, 'sha5_value', None)
+                    }
+                    base_token_data_cleaned = {k: v for k, v in base_token_data.items() if v is not None}
+
+                    try:
+                        asset_base_output_dir_str = generate_path_from_pattern(
+                            pattern_string=output_dir_pattern, # Uses the same pattern as other maps for base dir
+                            token_data=base_token_data_cleaned
+                        )
+                        # Destination: <output_base_path>/<asset_base_output_dir_str>/<extra_subdir_name>/<original_filename>
+                        final_dest_path = (Path(context.output_base_path) /
+                                           Path(asset_base_output_dir_str) /
+                                           Path(extra_subdir_name) /
+                                           source_file_path.name) # Use original filename
+
+                        final_dest_path.parent.mkdir(parents=True, exist_ok=True)
+
+                        if final_dest_path.exists() and not overwrite_existing:
+                            logger.info(f"Asset '{asset_name_for_log}': EXTRA file destination {final_dest_path} exists and overwrite is disabled. Skipping copy.")
+                        else:
+                            shutil.copy2(source_file_path, final_dest_path)
+                            logger.info(f"Asset '{asset_name_for_log}': Copied EXTRA file {source_file_path} to {final_dest_path}")
+                            final_output_files.append(str(final_dest_path))
+                            extra_files_organized_count += 1
+                        
+                        # Optionally, add more detailed tracking for extra files in context.asset_metadata
+                        # For example:
+                        # if 'extra_files_details' not in context.asset_metadata:
+                        #     context.asset_metadata['extra_files_details'] = []
+                        # context.asset_metadata['extra_files_details'].append({
+                        #     'source_path': str(source_file_path),
+                        #     'destination_path': str(final_dest_path),
+                        #     'status': 'Organized'
+                        # })
+
+                    except Exception as e:
+                        logger.error(f"Asset '{asset_name_for_log}': Failed to copy EXTRA file {source_file_path} to destination. Error: {e}", exc_info=True)
+                        context.status_flags['output_organization_error'] = True
+                        context.asset_metadata['status'] = "Failed (Output Organization Error - Extra Files)"
+                        # Optionally, update status for the specific file_rule if tracked
+        
+        if extra_files_organized_count > 0:
+            logger.info(f"Asset '{asset_name_for_log}': Successfully organized {extra_files_organized_count} EXTRA file(s).")
+        else:
+            logger.debug(f"Asset '{asset_name_for_log}': No EXTRA files were processed or found to organize.")
+
+
+        context.asset_metadata['final_output_files'] = final_output_files
+
+        if context.status_flags.get('output_organization_error'):
+            logger.error(f"Asset '{asset_name_for_log}': Output organization encountered errors. Status: {context.asset_metadata['status']}")
+        else:
+            logger.info(f"Asset '{asset_name_for_log}': Output organization complete. {len(final_output_files)} files placed.")
+        
+        logger.debug(f"Asset '{asset_name_for_log}': Output organization stage finished.")
+        return context

processing/pipeline/stages/supplier_determination.py

@@ -0,0 +1,60 @@
+import logging
+
+from .base_stage import ProcessingStage
+from ..asset_context import AssetProcessingContext
+
+class SupplierDeterminationStage(ProcessingStage):
+    """
+    Determines the effective supplier for an asset based on asset and source rules.
+    """
+
+    def execute(self, context: AssetProcessingContext) -> AssetProcessingContext:
+        """
+        Determines and validates the effective supplier for the asset.
+
+        Args:
+            context: The asset processing context.
+
+        Returns:
+            The updated asset processing context.
+        """
+        effective_supplier = None
+        logger = logging.getLogger(__name__) # Using a logger specific to this module
+        asset_name_for_log = context.asset_rule.asset_name if context.asset_rule else "Unknown Asset"
+
+        # 1. Check source_rule.supplier_override (highest precedence)
+        if context.source_rule and context.source_rule.supplier_override:
+            effective_supplier = context.source_rule.supplier_override
+            logger.debug(f"Asset '{asset_name_for_log}': Supplier override from source_rule found: '{effective_supplier}'.")
+        # 2. If not overridden, check source_rule.supplier_identifier
+        elif context.source_rule and context.source_rule.supplier_identifier:
+            effective_supplier = context.source_rule.supplier_identifier
+            logger.debug(f"Asset '{asset_name_for_log}': Supplier identifier from source_rule found: '{effective_supplier}'.")
+
+        # 3. Validation
+        if not effective_supplier:
+            logger.error(f"Asset '{asset_name_for_log}': No supplier defined in source_rule (override or identifier).")
+            context.effective_supplier = None
+            if 'status_flags' not in context: # Ensure status_flags exists
+                context.status_flags = {}
+            context.status_flags['supplier_error'] = True
+        # Assuming context.config_obj.suppliers is a valid way to get the list of configured suppliers.
+        # This might need further investigation if errors occur here later.
+        elif context.config_obj and hasattr(context.config_obj, 'suppliers') and effective_supplier not in context.config_obj.suppliers:
+            logger.warning(
+                f"Asset '{asset_name_for_log}': Determined supplier '{effective_supplier}' not found in global supplier configuration. "
+                f"Available: {list(context.config_obj.suppliers.keys()) if context.config_obj.suppliers else 'None'}"
+            )
+            context.effective_supplier = None
+            if 'status_flags' not in context: # Ensure status_flags exists
+                context.status_flags = {}
+            context.status_flags['supplier_error'] = True
+        else:
+            context.effective_supplier = effective_supplier
+            logger.info(f"Asset '{asset_name_for_log}': Effective supplier set to '{effective_supplier}'.")
+            # Optionally clear the error flag if previously set and now resolved.
+            if 'supplier_error' in context.status_flags:
+                 del context.status_flags['supplier_error']
+
+
+        return context

processing/utils/__init__.py

@@ -0,0 +1 @@
+# This file makes the 'utils' directory a Python package.

processing/utils/image_processing_utils.py

@@ -0,0 +1,398 @@
+import cv2
+import numpy as np
+from pathlib import Path
+import math
+from typing import Optional, Union, List, Tuple, Dict
+
+# --- Basic Power-of-Two Utilities ---
+
+def is_power_of_two(n: int) -> bool:
+    """Checks if a number is a power of two."""
+    return (n > 0) and (n & (n - 1) == 0)
+
+def get_nearest_pot(value: int) -> int:
+    """Finds the nearest power of two to the given value."""
+    if value <= 0:
+        return 1  # POT must be positive, return 1 as a fallback
+    if is_power_of_two(value):
+        return value
+
+    lower_pot = 1 << (value.bit_length() - 1)
+    upper_pot = 1 << value.bit_length()
+
+    if (value - lower_pot) < (upper_pot - value):
+        return lower_pot
+    else:
+        return upper_pot
+
+def get_nearest_power_of_two_downscale(value: int) -> int:
+    """
+    Finds the nearest power of two that is less than or equal to the given value.
+    If the value is already a power of two, it returns the value itself.
+    Returns 1 if the value is less than 1.
+    """
+    if value < 1:
+        return 1
+    if is_power_of_two(value):
+        return value
+    # Find the largest power of two strictly less than value,
+    # unless value itself is POT.
+    # (1 << (value.bit_length() - 1)) achieves this.
+    # Example: value=7 (0111, bl=3), 1<<2 = 4.
+    # Example: value=8 (1000, bl=4), 1<<3 = 8.
+    # Example: value=9 (1001, bl=4), 1<<3 = 8.
+    return 1 << (value.bit_length() - 1)
+# --- Dimension Calculation ---
+
+def calculate_target_dimensions(
+    original_width: int,
+    original_height: int,
+    target_width: Optional[int] = None,
+    target_height: Optional[int] = None,
+    resize_mode: str = "fit",  # e.g., "fit", "stretch", "max_dim_pot"
+    ensure_pot: bool = False,
+    allow_upscale: bool = False,
+    target_max_dim_for_pot_mode: Optional[int] = None # Specific for "max_dim_pot"
+) -> Tuple[int, int]:
+    """
+    Calculates target dimensions based on various modes and constraints.
+
+    Args:
+        original_width: Original width of the image.
+        original_height: Original height of the image.
+        target_width: Desired target width.
+        target_height: Desired target height.
+        resize_mode:
+            - "fit": Scales to fit within target_width/target_height, maintaining aspect ratio.
+                     Requires at least one of target_width or target_height.
+            - "stretch": Scales to exactly target_width and target_height, ignoring aspect ratio.
+                         Requires both target_width and target_height.
+            - "max_dim_pot": Scales to fit target_max_dim_for_pot_mode while maintaining aspect ratio,
+                             then finds nearest POT for each dimension. Requires target_max_dim_for_pot_mode.
+        ensure_pot: If True, final dimensions will be adjusted to the nearest power of two.
+        allow_upscale: If False, dimensions will not exceed original dimensions unless ensure_pot forces it.
+        target_max_dim_for_pot_mode: Max dimension to use when resize_mode is "max_dim_pot".
+
+    Returns:
+        A tuple (new_width, new_height).
+    """
+    if original_width <= 0 or original_height <= 0:
+        # Fallback for invalid original dimensions
+        fallback_dim = 1
+        if ensure_pot:
+            if target_width and target_height:
+                fallback_dim = get_nearest_pot(max(target_width, target_height, 1))
+            elif target_width:
+                fallback_dim = get_nearest_pot(target_width)
+            elif target_height:
+                fallback_dim = get_nearest_pot(target_height)
+            elif target_max_dim_for_pot_mode:
+                 fallback_dim = get_nearest_pot(target_max_dim_for_pot_mode)
+            else: # Default POT if no target given
+                fallback_dim = 256
+            return (fallback_dim, fallback_dim)
+        return (target_width or 1, target_height or 1)
+
+
+    w, h = original_width, original_height
+
+    if resize_mode == "max_dim_pot":
+        if target_max_dim_for_pot_mode is None:
+            raise ValueError("target_max_dim_for_pot_mode must be provided for 'max_dim_pot' resize_mode.")
+        
+        # Logic adapted from old processing_engine.calculate_target_dimensions
+        ratio = w / h
+        if ratio > 1:  # Width is dominant
+            scaled_w = target_max_dim_for_pot_mode
+            scaled_h = max(1, round(scaled_w / ratio))
+        else:  # Height is dominant or square
+            scaled_h = target_max_dim_for_pot_mode
+            scaled_w = max(1, round(scaled_h * ratio))
+        
+        # Upscale check for this mode is implicitly handled by target_max_dim
+        # If ensure_pot is true (as it was in the original logic), it's applied here
+        # For this mode, ensure_pot is effectively always true for the final step
+        w = get_nearest_pot(scaled_w)
+        h = get_nearest_pot(scaled_h)
+        return int(w), int(h)
+
+    elif resize_mode == "fit":
+        if target_width is None and target_height is None:
+            raise ValueError("At least one of target_width or target_height must be provided for 'fit' mode.")
+        
+        if target_width and target_height:
+            ratio_orig = w / h
+            ratio_target = target_width / target_height
+            if ratio_orig > ratio_target: # Original is wider than target aspect
+                w_new = target_width
+                h_new = max(1, round(w_new / ratio_orig))
+            else: # Original is taller or same aspect
+                h_new = target_height
+                w_new = max(1, round(h_new * ratio_orig))
+        elif target_width:
+            w_new = target_width
+            h_new = max(1, round(w_new / (w / h)))
+        else: # target_height is not None
+            h_new = target_height
+            w_new = max(1, round(h_new * (w / h)))
+        w, h = w_new, h_new
+
+    elif resize_mode == "stretch":
+        if target_width is None or target_height is None:
+            raise ValueError("Both target_width and target_height must be provided for 'stretch' mode.")
+        w, h = target_width, target_height
+    
+    else:
+        raise ValueError(f"Unsupported resize_mode: {resize_mode}")
+
+    if not allow_upscale:
+        if w > original_width: w = original_width
+        if h > original_height: h = original_height
+    
+    if ensure_pot:
+        w = get_nearest_pot(w)
+        h = get_nearest_pot(h)
+        # Re-check upscale if POT adjustment made it larger than original and not allowed
+        if not allow_upscale:
+            if w > original_width: w = get_nearest_pot(original_width) # Get closest POT to original
+            if h > original_height: h = get_nearest_pot(original_height)
+
+
+    return int(max(1, w)), int(max(1, h))
+
+
+# --- Image Statistics ---
+
+def calculate_image_stats(image_data: np.ndarray) -> Optional[Dict]:
+    """
+    Calculates min, max, mean for a given numpy image array.
+    Handles grayscale and multi-channel images. Converts to float64 for calculation.
+    Normalizes uint8/uint16 data to 0-1 range before calculating stats.
+    """
+    if image_data is None:
+        return None
+    try:
+        data_float = image_data.astype(np.float64)
+
+        if image_data.dtype == np.uint16:
+            data_float /= 65535.0
+        elif image_data.dtype == np.uint8:
+            data_float /= 255.0
+        
+        stats = {}
+        if len(data_float.shape) == 2:  # Grayscale (H, W)
+            stats["min"] = float(np.min(data_float))
+            stats["max"] = float(np.max(data_float))
+            stats["mean"] = float(np.mean(data_float))
+            stats["median"] = float(np.median(data_float))
+        elif len(data_float.shape) == 3:  # Color (H, W, C)
+            stats["min"] = [float(v) for v in np.min(data_float, axis=(0, 1))]
+            stats["max"] = [float(v) for v in np.max(data_float, axis=(0, 1))]
+            stats["mean"] = [float(v) for v in np.mean(data_float, axis=(0, 1))]
+            stats["median"] = [float(v) for v in np.median(data_float, axis=(0, 1))]
+        else:
+            return None # Unsupported shape
+        return stats
+    except Exception:
+        return {"error": "Error calculating image stats"}
+
+# --- Aspect Ratio String ---
+
+def normalize_aspect_ratio_change(original_width: int, original_height: int, resized_width: int, resized_height: int, decimals: int = 2) -> str:
+    """
+    Calculates the aspect ratio change string (e.g., "EVEN", "X133").
+    """
+    if original_width <= 0 or original_height <= 0:
+        return "InvalidInput"
+    if resized_width <= 0 or resized_height <= 0:
+        return "InvalidResize"
+
+    width_change_percentage = ((resized_width - original_width) / original_width) * 100
+    height_change_percentage = ((resized_height - original_height) / original_height) * 100
+
+    normalized_width_change = width_change_percentage / 100
+    normalized_height_change = height_change_percentage / 100
+
+    normalized_width_change = min(max(normalized_width_change + 1, 0), 2)
+    normalized_height_change = min(max(normalized_height_change + 1, 0), 2)
+    
+    epsilon = 1e-9
+    if abs(normalized_width_change) < epsilon and abs(normalized_height_change) < epsilon:
+         closest_value_to_one = 1.0
+    elif abs(normalized_width_change) < epsilon:
+         closest_value_to_one = abs(normalized_height_change)
+    elif abs(normalized_height_change) < epsilon:
+         closest_value_to_one = abs(normalized_width_change)
+    else:
+         closest_value_to_one = min(abs(normalized_width_change), abs(normalized_height_change))
+
+    scale_factor = 1 / (closest_value_to_one + epsilon) if abs(closest_value_to_one) < epsilon else 1 / closest_value_to_one
+
+    scaled_normalized_width_change = scale_factor * normalized_width_change
+    scaled_normalized_height_change = scale_factor * normalized_height_change
+
+    output_width = round(scaled_normalized_width_change, decimals)
+    output_height = round(scaled_normalized_height_change, decimals)
+
+    if abs(output_width - 1.0) < epsilon: output_width = 1
+    if abs(output_height - 1.0) < epsilon: output_height = 1
+
+    # Helper to format the number part
+    def format_value(val, dec):
+        # Multiply by 10^decimals, convert to int to keep trailing zeros in effect
+        # e.g. val=1.1, dec=2 -> 1.1 * 100 = 110
+        # e.g. val=1.0, dec=2 -> 1.0 * 100 = 100 (though this might become "1" if it's exactly 1.0 before this)
+        # The existing logic already handles output_width/height being 1.0 to produce "EVEN" or skip a component.
+        # This formatting is for when output_width/height is NOT 1.0.
+        return str(int(round(val * (10**dec))))
+
+    if abs(output_width - output_height) < epsilon: # Handles original square or aspect maintained
+        output = "EVEN"
+    elif output_width != 1 and abs(output_height - 1.0) < epsilon : # Width changed, height maintained relative to width
+        output = f"X{format_value(output_width, decimals)}"
+    elif output_height != 1 and abs(output_width - 1.0) < epsilon: # Height changed, width maintained relative to height
+        output = f"Y{format_value(output_height, decimals)}"
+    else: # Both changed relative to each other
+        output = f"X{format_value(output_width, decimals)}Y{format_value(output_height, decimals)}"
+    return output
+
+# --- Image Loading, Conversion, Resizing ---
+
+def load_image(image_path: Union[str, Path], read_flag: int = cv2.IMREAD_UNCHANGED) -> Optional[np.ndarray]:
+    """Loads an image from the specified path. Converts BGR/BGRA to RGB/RGBA if color."""
+    try:
+        img = cv2.imread(str(image_path), read_flag)
+        if img is None:
+            # print(f"Warning: Failed to load image: {image_path}") # Optional: for debugging utils
+            return None
+
+        # Ensure RGB/RGBA for color images
+        if len(img.shape) == 3:
+            if img.shape[2] == 4: # BGRA from OpenCV
+                img = cv2.cvtColor(img, cv2.COLOR_BGRA2RGBA)
+            elif img.shape[2] == 3: # BGR from OpenCV
+                img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
+        return img
+    except Exception: # as e:
+        # print(f"Error loading image {image_path}: {e}") # Optional: for debugging utils
+        return None
+
+def convert_bgr_to_rgb(image: np.ndarray) -> np.ndarray:
+    """Converts an image from BGR/BGRA to RGB/RGBA color space."""
+    if image is None or len(image.shape) < 3:
+        return image # Return as is if not a color image or None
+    
+    if image.shape[2] == 4: # BGRA
+        return cv2.cvtColor(image, cv2.COLOR_BGRA2RGBA) # Keep alpha, convert to RGBA
+    elif image.shape[2] == 3: # BGR
+        return cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
+    return image # Return as is if not 3 or 4 channels
+
+def convert_rgb_to_bgr(image: np.ndarray) -> np.ndarray:
+    """Converts an image from RGB/RGBA to BGR/BGRA color space."""
+    if image is None or len(image.shape) < 3:
+        return image # Return as is if not a color image or None
+    
+    if image.shape[2] == 4: # RGBA
+        return cv2.cvtColor(image, cv2.COLOR_RGBA2BGRA)
+    elif image.shape[2] == 3: # RGB
+        return cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
+    return image # Return as is if not 3 or 4 channels
+
+
+def resize_image(image: np.ndarray, target_width: int, target_height: int, interpolation: Optional[int] = None) -> np.ndarray:
+    """Resizes an image to target_width and target_height."""
+    if image is None:
+        raise ValueError("Cannot resize a None image.")
+    if target_width <= 0 or target_height <= 0:
+        raise ValueError("Target width and height must be positive.")
+
+    original_height, original_width = image.shape[:2]
+
+    if interpolation is None:
+        # Default interpolation: Lanczos for downscaling, Cubic for upscaling/same
+        if (target_width * target_height) < (original_width * original_height):
+            interpolation = cv2.INTER_LANCZOS4
+        else:
+            interpolation = cv2.INTER_CUBIC
+            
+    return cv2.resize(image, (target_width, target_height), interpolation=interpolation)
+
+# --- Image Saving ---
+
+def save_image(
+    image_path: Union[str, Path],
+    image_data: np.ndarray,
+    output_format: Optional[str] = None, # e.g. "png", "jpg", "exr"
+    output_dtype_target: Optional[np.dtype] = None, # e.g. np.uint8, np.uint16, np.float16
+    params: Optional[List[int]] = None,
+    convert_to_bgr_before_save: bool = True # True for most formats except EXR
+) -> bool:
+    """
+    Saves image data to a file. Handles data type and color space conversions.
+
+    Args:
+        image_path: Path to save the image.
+        image_data: NumPy array of the image.
+        output_format: Desired output format (e.g., 'png', 'jpg'). If None, derived from extension.
+        output_dtype_target: Target NumPy dtype for saving (e.g., np.uint8, np.uint16).
+                             If None, tries to use image_data.dtype or a sensible default.
+        params: OpenCV imwrite parameters (e.g., [cv2.IMWRITE_JPEG_QUALITY, 90]).
+        convert_to_bgr_before_save: If True and image is 3-channel, converts RGB to BGR.
+                                   Set to False for formats like EXR that expect RGB.
+
+    Returns:
+        True if saving was successful, False otherwise.
+    """
+    if image_data is None:
+        return False
+    
+    img_to_save = image_data.copy()
+    path_obj = Path(image_path)
+    path_obj.parent.mkdir(parents=True, exist_ok=True)
+
+    # 1. Data Type Conversion
+    if output_dtype_target is not None:
+        if output_dtype_target == np.uint8 and img_to_save.dtype != np.uint8:
+            if img_to_save.dtype == np.uint16: img_to_save = (img_to_save.astype(np.float32) / 65535.0 * 255.0).astype(np.uint8)
+            elif img_to_save.dtype in [np.float16, np.float32, np.float64]: img_to_save = (np.clip(img_to_save, 0.0, 1.0) * 255.0).astype(np.uint8)
+            else: img_to_save = img_to_save.astype(np.uint8)
+        elif output_dtype_target == np.uint16 and img_to_save.dtype != np.uint16:
+            if img_to_save.dtype == np.uint8: img_to_save = (img_to_save.astype(np.float32) / 255.0 * 65535.0).astype(np.uint16) # More accurate
+            elif img_to_save.dtype in [np.float16, np.float32, np.float64]: img_to_save = (np.clip(img_to_save, 0.0, 1.0) * 65535.0).astype(np.uint16)
+            else: img_to_save = img_to_save.astype(np.uint16)
+        elif output_dtype_target == np.float16 and img_to_save.dtype != np.float16:
+            if img_to_save.dtype == np.uint16: img_to_save = (img_to_save.astype(np.float32) / 65535.0).astype(np.float16)
+            elif img_to_save.dtype == np.uint8: img_to_save = (img_to_save.astype(np.float32) / 255.0).astype(np.float16)
+            elif img_to_save.dtype in [np.float32, np.float64]: img_to_save = img_to_save.astype(np.float16)
+            # else: cannot convert to float16 easily
+        elif output_dtype_target == np.float32 and img_to_save.dtype != np.float32:
+             if img_to_save.dtype == np.uint16: img_to_save = (img_to_save.astype(np.float32) / 65535.0)
+             elif img_to_save.dtype == np.uint8: img_to_save = (img_to_save.astype(np.float32) / 255.0)
+             elif img_to_save.dtype == np.float16: img_to_save = img_to_save.astype(np.float32)
+
+
+    # 2. Color Space Conversion (Internal RGB/RGBA -> BGR/BGRA for OpenCV)
+    # Input `image_data` is assumed to be in RGB/RGBA format (due to `load_image` changes).
+    # OpenCV's `imwrite` typically expects BGR/BGRA for formats like PNG, JPG.
+    # EXR format usually expects RGB/RGBA.
+    # The `convert_to_bgr_before_save` flag controls this behavior.
+    current_format = output_format if output_format else path_obj.suffix.lower().lstrip('.')
+    
+    if convert_to_bgr_before_save and current_format != 'exr':
+        # If image is 3-channel (RGB) or 4-channel (RGBA), convert to BGR/BGRA.
+        if len(img_to_save.shape) == 3 and (img_to_save.shape[2] == 3 or img_to_save.shape[2] == 4):
+            img_to_save = convert_rgb_to_bgr(img_to_save) # Handles RGB->BGR and RGBA->BGRA
+    # If `convert_to_bgr_before_save` is False or format is 'exr',
+    # the image (assumed RGB/RGBA) is saved as is.
+
+    # 3. Save Image
+    try:
+        if params:
+            cv2.imwrite(str(path_obj), img_to_save, params)
+        else:
+            cv2.imwrite(str(path_obj), img_to_save)
+        return True
+    except Exception: # as e:
+        # print(f"Error saving image {path_obj}: {e}") # Optional: for debugging utils
+        return False

tests/__init__.py

@@ -0,0 +1 @@
+# This file makes the 'tests' directory a Python package.

tests/processing/pipeline/__init__.py

@@ -0,0 +1 @@
+# This file makes Python treat the directory as a package.

tests/processing/pipeline/stages/__init__.py

@@ -0,0 +1 @@
+# This file makes Python treat the directory as a package.

tests/processing/pipeline/stages/test_alpha_extraction_to_mask.py

@@ -0,0 +1,273 @@
+import pytest
+from unittest import mock
+from pathlib import Path
+import uuid
+import numpy as np
+
+from processing.pipeline.stages.alpha_extraction_to_mask import AlphaExtractionToMaskStage
+from processing.pipeline.asset_context import AssetProcessingContext
+from rule_structure import AssetRule, SourceRule, FileRule
+from configuration import Configuration, GeneralSettings
+import processing.utils.image_processing_utils as ipu # Ensure ipu is available for mocking
+
+# Helper Functions
+def create_mock_file_rule_for_alpha_test(
+    id_val: uuid.UUID = None,
+    map_type: str = "ALBEDO",
+    filename_pattern: str = "albedo.png",
+    item_type: str = "MAP_COL",
+    active: bool = True
+) -> mock.MagicMock:
+    mock_fr = mock.MagicMock(spec=FileRule)
+    mock_fr.id = id_val if id_val else uuid.uuid4()
+    mock_fr.map_type = map_type
+    mock_fr.filename_pattern = filename_pattern
+    mock_fr.item_type = item_type
+    mock_fr.active = active
+    mock_fr.transform_settings = mock.MagicMock(spec=TransformSettings)
+    return mock_fr
+
+def create_alpha_extraction_mock_context(
+    initial_file_rules: list = None,
+    initial_processed_details: dict = None,
+    skip_asset_flag: bool = False,
+    asset_name: str = "AlphaAsset",
+    # extract_alpha_globally: bool = True # If stage checks this
+) -> AssetProcessingContext:
+    mock_asset_rule = mock.MagicMock(spec=AssetRule)
+    mock_asset_rule.name = asset_name
+    
+    mock_source_rule = mock.MagicMock(spec=SourceRule)
+    
+    mock_gs = mock.MagicMock(spec=GeneralSettings)
+    # if your stage uses a global flag:
+    # mock_gs.extract_alpha_to_mask_globally = extract_alpha_globally
+    
+    mock_config = mock.MagicMock(spec=Configuration)
+    mock_config.general_settings = mock_gs
+
+    context = AssetProcessingContext(
+        source_rule=mock_source_rule,
+        asset_rule=mock_asset_rule,
+        workspace_path=Path("/fake/workspace"),
+        engine_temp_dir=Path("/fake/temp_engine_dir"),
+        output_base_path=Path("/fake/output"),
+        effective_supplier="ValidSupplier",
+        asset_metadata={'asset_name': asset_name},
+        processed_maps_details=initial_processed_details if initial_processed_details is not None else {},
+        merged_maps_details={},
+        files_to_process=list(initial_file_rules) if initial_file_rules else [],
+        loaded_data_cache={},
+        config_obj=mock_config,
+        status_flags={'skip_asset': skip_asset_flag},
+        incrementing_value=None,
+        sha5_value=None
+    )
+    return context
+
+# Unit Tests
+@mock.patch('processing.pipeline.stages.alpha_extraction_to_mask.ipu.save_image')
+@mock.patch('processing.pipeline.stages.alpha_extraction_to_mask.ipu.load_image')
+@mock.patch('logging.info') # Mock logging to avoid console output during tests
+def test_asset_skipped(mock_log_info, mock_load_image, mock_save_image):
+    stage = AlphaExtractionToMaskStage()
+    context = create_alpha_extraction_mock_context(skip_asset_flag=True)
+    
+    updated_context = stage.execute(context)
+    
+    assert updated_context == context # Context should be unchanged
+    mock_load_image.assert_not_called()
+    mock_save_image.assert_not_called()
+    assert len(updated_context.files_to_process) == 0
+    assert not updated_context.processed_maps_details
+
+@mock.patch('processing.pipeline.stages.alpha_extraction_to_mask.ipu.save_image')
+@mock.patch('processing.pipeline.stages.alpha_extraction_to_mask.ipu.load_image')
+@mock.patch('logging.info')
+def test_existing_mask_map(mock_log_info, mock_load_image, mock_save_image):
+    stage = AlphaExtractionToMaskStage()
+    
+    existing_mask_rule = create_mock_file_rule_for_alpha_test(map_type="MASK", filename_pattern="mask.png")
+    context = create_alpha_extraction_mock_context(initial_file_rules=[existing_mask_rule])
+    
+    updated_context = stage.execute(context)
+    
+    assert updated_context == context
+    mock_load_image.assert_not_called()
+    mock_save_image.assert_not_called()
+    assert len(updated_context.files_to_process) == 1
+    assert updated_context.files_to_process[0].map_type == "MASK"
+
+@mock.patch('processing.pipeline.stages.alpha_extraction_to_mask.ipu.save_image')
+@mock.patch('processing.pipeline.stages.alpha_extraction_to_mask.ipu.load_image')
+@mock.patch('logging.info')
+def test_alpha_extraction_success(mock_log_info, mock_load_image, mock_save_image):
+    stage = AlphaExtractionToMaskStage()
+    
+    albedo_rule_id = uuid.uuid4()
+    albedo_fr = create_mock_file_rule_for_alpha_test(id_val=albedo_rule_id, map_type="ALBEDO")
+    
+    initial_processed_details = {
+        albedo_fr.id.hex: {'temp_processed_file': '/fake/temp_engine_dir/processed_albedo.png', 'status': 'Processed', 'map_type': 'ALBEDO', 'source_file_path': Path('/fake/source/albedo.png')}
+    }
+    context = create_alpha_extraction_mock_context(
+        initial_file_rules=[albedo_fr],
+        initial_processed_details=initial_processed_details
+    )
+
+    mock_rgba_data = np.zeros((10, 10, 4), dtype=np.uint8)
+    mock_rgba_data[:, :, 3] = 128 # Example alpha data
+    mock_load_image.side_effect = [mock_rgba_data, mock_rgba_data] 
+    
+    mock_save_image.return_value = True
+    
+    updated_context = stage.execute(context)
+    
+    assert mock_load_image.call_count == 2
+    # First call to check for alpha, second to get data for saving
+    mock_load_image.assert_any_call(Path('/fake/temp_engine_dir/processed_albedo.png')) 
+    
+    mock_save_image.assert_called_once()
+    saved_path_arg = mock_save_image.call_args[0][0]
+    saved_data_arg = mock_save_image.call_args[0][1]
+    
+    assert isinstance(saved_path_arg, Path)
+    assert "mask_from_alpha_" in saved_path_arg.name
+    assert np.array_equal(saved_data_arg, mock_rgba_data[:, :, 3])
+    
+    assert len(updated_context.files_to_process) == 2
+    new_mask_rule = None
+    for fr in updated_context.files_to_process:
+        if fr.map_type == "MASK":
+            new_mask_rule = fr
+            break
+    assert new_mask_rule is not None
+    assert new_mask_rule.item_type == "MAP_DER" # Derived map
+    
+    assert new_mask_rule.id.hex in updated_context.processed_maps_details
+    new_mask_detail = updated_context.processed_maps_details[new_mask_rule.id.hex]
+    assert new_mask_detail['map_type'] == "MASK"
+    assert "mask_from_alpha_" in new_mask_detail['temp_processed_file']
+    assert "Generated from alpha of ALBEDO" in new_mask_detail['notes'] # Check for specific note
+    assert new_mask_detail['status'] == 'Processed'
+
+@mock.patch('processing.pipeline.stages.alpha_extraction_to_mask.ipu.save_image')
+@mock.patch('processing.pipeline.stages.alpha_extraction_to_mask.ipu.load_image')
+@mock.patch('logging.info')
+def test_no_alpha_channel_in_source(mock_log_info, mock_load_image, mock_save_image):
+    stage = AlphaExtractionToMaskStage()
+    
+    albedo_rule_id = uuid.uuid4()
+    albedo_fr = create_mock_file_rule_for_alpha_test(id_val=albedo_rule_id, map_type="ALBEDO")
+    initial_processed_details = {
+        albedo_fr.id.hex: {'temp_processed_file': '/fake/temp_engine_dir/processed_rgb_albedo.png', 'status': 'Processed', 'map_type': 'ALBEDO', 'source_file_path': Path('/fake/source/albedo_rgb.png')}
+    }
+    context = create_alpha_extraction_mock_context(
+        initial_file_rules=[albedo_fr],
+        initial_processed_details=initial_processed_details
+    )
+
+    mock_rgb_data = np.zeros((10, 10, 3), dtype=np.uint8) # RGB, no alpha
+    mock_load_image.return_value = mock_rgb_data # Only called once for check
+    
+    updated_context = stage.execute(context)
+    
+    mock_load_image.assert_called_once_with(Path('/fake/temp_engine_dir/processed_rgb_albedo.png'))
+    mock_save_image.assert_not_called()
+    assert len(updated_context.files_to_process) == 1 # No new MASK rule
+    assert albedo_fr.id.hex in updated_context.processed_maps_details
+    assert len(updated_context.processed_maps_details) == 1
+
+
+@mock.patch('processing.pipeline.stages.alpha_extraction_to_mask.ipu.save_image')
+@mock.patch('processing.pipeline.stages.alpha_extraction_to_mask.ipu.load_image')
+@mock.patch('logging.info')
+def test_no_suitable_source_map_type(mock_log_info, mock_load_image, mock_save_image):
+    stage = AlphaExtractionToMaskStage()
+    
+    normal_rule_id = uuid.uuid4()
+    normal_fr = create_mock_file_rule_for_alpha_test(id_val=normal_rule_id, map_type="NORMAL")
+    initial_processed_details = {
+        normal_fr.id.hex: {'temp_processed_file': '/fake/temp_engine_dir/processed_normal.png', 'status': 'Processed', 'map_type': 'NORMAL'}
+    }
+    context = create_alpha_extraction_mock_context(
+        initial_file_rules=[normal_fr],
+        initial_processed_details=initial_processed_details
+    )
+    
+    updated_context = stage.execute(context)
+    
+    mock_load_image.assert_not_called()
+    mock_save_image.assert_not_called()
+    assert len(updated_context.files_to_process) == 1
+    assert normal_fr.id.hex in updated_context.processed_maps_details
+    assert len(updated_context.processed_maps_details) == 1
+
+@mock.patch('processing.pipeline.stages.alpha_extraction_to_mask.ipu.save_image')
+@mock.patch('processing.pipeline.stages.alpha_extraction_to_mask.ipu.load_image')
+@mock.patch('logging.warning') # Expect a warning log
+def test_load_image_fails(mock_log_warning, mock_load_image, mock_save_image):
+    stage = AlphaExtractionToMaskStage()
+    
+    albedo_rule_id = uuid.uuid4()
+    albedo_fr = create_mock_file_rule_for_alpha_test(id_val=albedo_rule_id, map_type="ALBEDO")
+    initial_processed_details = {
+        albedo_fr.id.hex: {'temp_processed_file': '/fake/temp_engine_dir/processed_albedo_load_fail.png', 'status': 'Processed', 'map_type': 'ALBEDO', 'source_file_path': Path('/fake/source/albedo_load_fail.png')}
+    }
+    context = create_alpha_extraction_mock_context(
+        initial_file_rules=[albedo_fr],
+        initial_processed_details=initial_processed_details
+    )
+
+    mock_load_image.return_value = None # Simulate load failure
+    
+    updated_context = stage.execute(context)
+    
+    mock_load_image.assert_called_once_with(Path('/fake/temp_engine_dir/processed_albedo_load_fail.png'))
+    mock_save_image.assert_not_called()
+    assert len(updated_context.files_to_process) == 1
+    assert albedo_fr.id.hex in updated_context.processed_maps_details
+    assert len(updated_context.processed_maps_details) == 1
+    mock_log_warning.assert_called_once() # Check that a warning was logged
+
+@mock.patch('processing.pipeline.stages.alpha_extraction_to_mask.ipu.save_image')
+@mock.patch('processing.pipeline.stages.alpha_extraction_to_mask.ipu.load_image')
+@mock.patch('logging.error') # Expect an error log
+def test_save_image_fails(mock_log_error, mock_load_image, mock_save_image):
+    stage = AlphaExtractionToMaskStage()
+    
+    albedo_rule_id = uuid.uuid4()
+    albedo_fr = create_mock_file_rule_for_alpha_test(id_val=albedo_rule_id, map_type="ALBEDO")
+    initial_processed_details = {
+        albedo_fr.id.hex: {'temp_processed_file': '/fake/temp_engine_dir/processed_albedo_save_fail.png', 'status': 'Processed', 'map_type': 'ALBEDO', 'source_file_path': Path('/fake/source/albedo_save_fail.png')}
+    }
+    context = create_alpha_extraction_mock_context(
+        initial_file_rules=[albedo_fr],
+        initial_processed_details=initial_processed_details
+    )
+
+    mock_rgba_data = np.zeros((10, 10, 4), dtype=np.uint8)
+    mock_rgba_data[:, :, 3] = 128
+    mock_load_image.side_effect = [mock_rgba_data, mock_rgba_data] # Load succeeds
+    
+    mock_save_image.return_value = False # Simulate save failure
+    
+    updated_context = stage.execute(context)
+    
+    assert mock_load_image.call_count == 2
+    mock_save_image.assert_called_once() # Save was attempted
+    
+    assert len(updated_context.files_to_process) == 1 # No new MASK rule should be successfully added and detailed
+    
+    # Check that no new MASK details were added, or if they were, they reflect failure.
+    # The current stage logic returns context early, so no new rule or details should be present.
+    mask_rule_found = any(fr.map_type == "MASK" for fr in updated_context.files_to_process)
+    assert not mask_rule_found
+
+    mask_details_found = any(
+        details['map_type'] == "MASK" 
+        for fr_id, details in updated_context.processed_maps_details.items() 
+        if fr_id != albedo_fr.id.hex # Exclude the original albedo
+    )
+    assert not mask_details_found
+    mock_log_error.assert_called_once() # Check that an error was logged

tests/processing/pipeline/stages/test_asset_skip_logic.py

@@ -0,0 +1,213 @@
+import pytest
+from unittest import mock
+from pathlib import Path
+from typing import Dict, Optional, Any
+
+from processing.pipeline.stages.asset_skip_logic import AssetSkipLogicStage
+from processing.pipeline.asset_context import AssetProcessingContext
+from rule_structure import AssetRule, SourceRule
+from configuration import Configuration, GeneralSettings
+
+# Helper function to create a mock AssetProcessingContext
+def create_skip_logic_mock_context(
+    effective_supplier: Optional[str] = "ValidSupplier",
+    asset_process_status: str = "PENDING",
+    overwrite_existing: bool = False,
+    asset_name: str = "TestAssetSkip"
+) -> AssetProcessingContext:
+    mock_asset_rule = mock.MagicMock(spec=AssetRule)
+    mock_asset_rule.name = asset_name
+    mock_asset_rule.process_status = asset_process_status
+    mock_asset_rule.source_path = "fake/source" # Added for completeness
+    mock_asset_rule.output_path = "fake/output" # Added for completeness
+    mock_asset_rule.maps = [] # Added for completeness
+    mock_asset_rule.metadata = {} # Added for completeness
+    mock_asset_rule.material_name = None # Added for completeness
+    mock_asset_rule.notes = None # Added for completeness
+    mock_asset_rule.tags = [] # Added for completeness
+    mock_asset_rule.enabled = True # Added for completeness
+
+
+    mock_source_rule = mock.MagicMock(spec=SourceRule)
+    mock_source_rule.name = "TestSourceRule" # Added for completeness
+    mock_source_rule.path = "fake/source_rule_path" # Added for completeness
+    mock_source_rule.default_supplier = None # Added for completeness
+    mock_source_rule.assets = [mock_asset_rule] # Added for completeness
+    mock_source_rule.enabled = True # Added for completeness
+
+    mock_general_settings = mock.MagicMock(spec=GeneralSettings)
+    mock_general_settings.overwrite_existing = overwrite_existing
+    
+    mock_config = mock.MagicMock(spec=Configuration)
+    mock_config.general_settings = mock_general_settings
+    mock_config.suppliers = {"ValidSupplier": mock.MagicMock()} 
+
+    context = AssetProcessingContext(
+        source_rule=mock_source_rule,
+        asset_rule=mock_asset_rule,
+        workspace_path=Path("/fake/workspace"),
+        engine_temp_dir=Path("/fake/temp"),
+        output_base_path=Path("/fake/output"),
+        effective_supplier=effective_supplier,
+        asset_metadata={},
+        processed_maps_details={},
+        merged_maps_details={},
+        files_to_process=[],
+        loaded_data_cache={},
+        config_obj=mock_config,
+        status_flags={},
+        incrementing_value=None,
+        sha5_value=None # Corrected from sha5_value to sha256_value if that's the actual field
+    )
+    # Ensure status_flags is initialized if AssetSkipLogicStage expects it
+    # context.status_flags = {} # Already done in constructor
+    return context
+@mock.patch('logging.info')
+def test_skip_due_to_missing_supplier(mock_log_info):
+    """
+    Test that the asset is skipped if effective_supplier is None.
+    """
+    stage = AssetSkipLogicStage()
+    context = create_skip_logic_mock_context(effective_supplier=None, asset_name="MissingSupplierAsset")
+    
+    updated_context = stage.execute(context)
+    
+    assert updated_context.status_flags.get('skip_asset') is True
+    assert updated_context.status_flags.get('skip_reason') == "Invalid or missing supplier"
+    mock_log_info.assert_any_call(f"Asset 'MissingSupplierAsset': Skipping due to missing or invalid supplier.")
+
+@mock.patch('logging.info')
+def test_skip_due_to_process_status_skip(mock_log_info):
+    """
+    Test that the asset is skipped if asset_rule.process_status is "SKIP".
+    """
+    stage = AssetSkipLogicStage()
+    context = create_skip_logic_mock_context(asset_process_status="SKIP", asset_name="SkipStatusAsset")
+    
+    updated_context = stage.execute(context)
+    
+    assert updated_context.status_flags.get('skip_asset') is True
+    assert updated_context.status_flags.get('skip_reason') == "Process status set to SKIP"
+    mock_log_info.assert_any_call(f"Asset 'SkipStatusAsset': Skipping because process_status is 'SKIP'.")
+
+@mock.patch('logging.info')
+def test_skip_due_to_processed_and_overwrite_disabled(mock_log_info):
+    """
+    Test that the asset is skipped if asset_rule.process_status is "PROCESSED"
+    and overwrite_existing is False.
+    """
+    stage = AssetSkipLogicStage()
+    context = create_skip_logic_mock_context(
+        asset_process_status="PROCESSED", 
+        overwrite_existing=False,
+        asset_name="ProcessedNoOverwriteAsset"
+    )
+    
+    updated_context = stage.execute(context)
+    
+    assert updated_context.status_flags.get('skip_asset') is True
+    assert updated_context.status_flags.get('skip_reason') == "Already processed, overwrite disabled"
+    mock_log_info.assert_any_call(f"Asset 'ProcessedNoOverwriteAsset': Skipping because already processed and overwrite is disabled.")
+
+@mock.patch('logging.info')
+def test_no_skip_when_processed_and_overwrite_enabled(mock_log_info):
+    """
+    Test that the asset is NOT skipped if asset_rule.process_status is "PROCESSED"
+    but overwrite_existing is True.
+    """
+    stage = AssetSkipLogicStage()
+    context = create_skip_logic_mock_context(
+        asset_process_status="PROCESSED", 
+        overwrite_existing=True,
+        effective_supplier="ValidSupplier", # Ensure supplier is valid
+        asset_name="ProcessedOverwriteAsset"
+    )
+    
+    updated_context = stage.execute(context)
+    
+    assert updated_context.status_flags.get('skip_asset', False) is False # Default to False if key not present
+    # No specific skip_reason to check if not skipped
+    # Check that no skip log message was called for this specific reason
+    for call_args in mock_log_info.call_args_list:
+        assert "Skipping because already processed and overwrite is disabled" not in call_args[0][0]
+        assert "Skipping due to missing or invalid supplier" not in call_args[0][0]
+        assert "Skipping because process_status is 'SKIP'" not in call_args[0][0]
+
+
+@mock.patch('logging.info')
+def test_no_skip_when_process_status_pending(mock_log_info):
+    """
+    Test that the asset is NOT skipped if asset_rule.process_status is "PENDING".
+    """
+    stage = AssetSkipLogicStage()
+    context = create_skip_logic_mock_context(
+        asset_process_status="PENDING",
+        effective_supplier="ValidSupplier", # Ensure supplier is valid
+        asset_name="PendingAsset"
+    )
+    
+    updated_context = stage.execute(context)
+    
+    assert updated_context.status_flags.get('skip_asset', False) is False
+    # Check that no skip log message was called
+    for call_args in mock_log_info.call_args_list:
+        assert "Skipping" not in call_args[0][0]
+
+
+@mock.patch('logging.info')
+def test_no_skip_when_process_status_failed_previously(mock_log_info):
+    """
+    Test that the asset is NOT skipped if asset_rule.process_status is "FAILED_PREVIOUSLY".
+    """
+    stage = AssetSkipLogicStage()
+    context = create_skip_logic_mock_context(
+        asset_process_status="FAILED_PREVIOUSLY",
+        effective_supplier="ValidSupplier", # Ensure supplier is valid
+        asset_name="FailedPreviouslyAsset"
+    )
+    
+    updated_context = stage.execute(context)
+    
+    assert updated_context.status_flags.get('skip_asset', False) is False
+    # Check that no skip log message was called
+    for call_args in mock_log_info.call_args_list:
+        assert "Skipping" not in call_args[0][0]
+
+@mock.patch('logging.info')
+def test_no_skip_when_process_status_other_valid_status(mock_log_info):
+    """
+    Test that the asset is NOT skipped for other valid, non-skip process statuses.
+    """
+    stage = AssetSkipLogicStage()
+    context = create_skip_logic_mock_context(
+        asset_process_status="READY_FOR_PROCESSING", # Example of another non-skip status
+        effective_supplier="ValidSupplier",
+        asset_name="ReadyAsset"
+    )
+    updated_context = stage.execute(context)
+    assert updated_context.status_flags.get('skip_asset', False) is False
+    for call_args in mock_log_info.call_args_list:
+        assert "Skipping" not in call_args[0][0]
+
+@mock.patch('logging.info')
+def test_skip_asset_flag_initialized_if_not_present(mock_log_info):
+    """
+    Test that 'skip_asset' is initialized to False in status_flags if not skipped and not present.
+    """
+    stage = AssetSkipLogicStage()
+    context = create_skip_logic_mock_context(
+        asset_process_status="PENDING",
+        effective_supplier="ValidSupplier",
+        asset_name="InitFlagAsset"
+    )
+    # Ensure status_flags is empty before execute
+    context.status_flags = {} 
+    
+    updated_context = stage.execute(context)
+    
+    # If not skipped, 'skip_asset' should be explicitly False.
+    assert updated_context.status_flags.get('skip_asset') is False 
+    # No skip reason should be set
+    assert 'skip_reason' not in updated_context.status_flags
+    for call_args in mock_log_info.call_args_list:
+        assert "Skipping" not in call_args[0][0]

tests/processing/pipeline/stages/test_file_rule_filter.py

@@ -0,0 +1,330 @@
+import pytest
+from unittest import mock
+from pathlib import Path
+import uuid
+from typing import Optional # Added Optional for type hinting
+
+from processing.pipeline.stages.file_rule_filter import FileRuleFilterStage
+from processing.pipeline.asset_context import AssetProcessingContext
+from rule_structure import AssetRule, SourceRule, FileRule # FileRule is key here
+from configuration import Configuration # Minimal config needed
+
+def create_mock_file_rule(
+    id_val: Optional[uuid.UUID] = None,
+    map_type: str = "Diffuse",
+    filename_pattern: str = "*.tif",
+    item_type: str = "MAP_COL", # e.g., MAP_COL, FILE_IGNORE
+    active: bool = True
+) -> mock.MagicMock: # Return MagicMock to easily set other attributes if needed
+    mock_fr = mock.MagicMock(spec=FileRule)
+    mock_fr.id = id_val if id_val else uuid.uuid4()
+    mock_fr.map_type = map_type
+    mock_fr.filename_pattern = filename_pattern
+    mock_fr.item_type = item_type
+    mock_fr.active = active
+    return mock_fr
+
+def create_file_filter_mock_context(
+    file_rules_list: Optional[list] = None, # List of mock FileRule objects
+    skip_asset_flag: bool = False,
+    asset_name: str = "FileFilterAsset"
+) -> AssetProcessingContext:
+    mock_asset_rule = mock.MagicMock(spec=AssetRule)
+    mock_asset_rule.name = asset_name
+    mock_asset_rule.file_rules = file_rules_list if file_rules_list is not None else []
+
+    mock_source_rule = mock.MagicMock(spec=SourceRule)
+    mock_config = mock.MagicMock(spec=Configuration)
+
+    context = AssetProcessingContext(
+        source_rule=mock_source_rule,
+        asset_rule=mock_asset_rule,
+        workspace_path=Path("/fake/workspace"),
+        engine_temp_dir=Path("/fake/temp"),
+        output_base_path=Path("/fake/output"),
+        effective_supplier="ValidSupplier", # Assume valid for this stage
+        asset_metadata={'asset_name': asset_name}, # Assume metadata init happened
+        processed_maps_details={},
+        merged_maps_details={},
+        files_to_process=[], # Stage will populate this
+        loaded_data_cache={},
+        config_obj=mock_config,
+        status_flags={'skip_asset': skip_asset_flag},
+        incrementing_value=None,
+        sha5_value=None # Corrected from sha5_value to sha256_value based on AssetProcessingContext
+    )
+    return context
+# Test Cases for FileRuleFilterStage.execute()
+
+@mock.patch('logging.info')
+@mock.patch('logging.debug')
+def test_file_rule_filter_asset_skipped(mock_log_debug, mock_log_info):
+    """
+    Test case: Asset Skipped - status_flags['skip_asset'] is True.
+    Assert context.files_to_process remains empty.
+    """
+    stage = FileRuleFilterStage()
+    context = create_file_filter_mock_context(skip_asset_flag=True)
+    
+    updated_context = stage.execute(context)
+    
+    assert len(updated_context.files_to_process) == 0
+    mock_log_info.assert_any_call(f"Asset '{context.asset_rule.name}': Skipping file rule filtering as 'skip_asset' is True.")
+
+@mock.patch('logging.info')
+@mock.patch('logging.debug')
+def test_file_rule_filter_no_file_rules(mock_log_debug, mock_log_info):
+    """
+    Test case: No File Rules - asset_rule.file_rules is empty.
+    Assert context.files_to_process is empty.
+    """
+    stage = FileRuleFilterStage()
+    context = create_file_filter_mock_context(file_rules_list=[])
+    
+    updated_context = stage.execute(context)
+    
+    assert len(updated_context.files_to_process) == 0
+    mock_log_info.assert_any_call(f"Asset '{context.asset_rule.name}': No file rules defined. Skipping file rule filtering.")
+
+@mock.patch('logging.info')
+@mock.patch('logging.debug')
+def test_file_rule_filter_only_active_processable_rules(mock_log_debug, mock_log_info):
+    """
+    Test case: Only Active, Processable Rules - All FileRules are active=True and item_type="MAP_COL".
+    Assert all are added to context.files_to_process.
+    """
+    stage = FileRuleFilterStage()
+    fr1 = create_mock_file_rule(filename_pattern="diffuse.png", item_type="MAP_COL", active=True)
+    fr2 = create_mock_file_rule(filename_pattern="normal.png", item_type="MAP_COL", active=True)
+    context = create_file_filter_mock_context(file_rules_list=[fr1, fr2])
+    
+    updated_context = stage.execute(context)
+    
+    assert len(updated_context.files_to_process) == 2
+    assert fr1 in updated_context.files_to_process
+    assert fr2 in updated_context.files_to_process
+    mock_log_info.assert_any_call(f"Asset '{context.asset_rule.name}': 2 file rules queued for processing after filtering.")
+
+@mock.patch('logging.info')
+@mock.patch('logging.debug')
+def test_file_rule_filter_inactive_rules(mock_log_debug, mock_log_info):
+    """
+    Test case: Inactive Rules - Some FileRules have active=False.
+    Assert only active rules are added.
+    """
+    stage = FileRuleFilterStage()
+    fr_active = create_mock_file_rule(filename_pattern="active.png", item_type="MAP_COL", active=True)
+    fr_inactive = create_mock_file_rule(filename_pattern="inactive.png", item_type="MAP_COL", active=False)
+    fr_another_active = create_mock_file_rule(filename_pattern="another_active.jpg", item_type="MAP_COL", active=True)
+    context = create_file_filter_mock_context(file_rules_list=[fr_active, fr_inactive, fr_another_active])
+    
+    updated_context = stage.execute(context)
+    
+    assert len(updated_context.files_to_process) == 2
+    assert fr_active in updated_context.files_to_process
+    assert fr_another_active in updated_context.files_to_process
+    assert fr_inactive not in updated_context.files_to_process
+    mock_log_debug.assert_any_call(f"Asset '{context.asset_rule.name}': Skipping inactive file rule: '{fr_inactive.filename_pattern}'")
+    mock_log_info.assert_any_call(f"Asset '{context.asset_rule.name}': 2 file rules queued for processing after filtering.")
+
+@mock.patch('logging.info')
+@mock.patch('logging.debug')
+def test_file_rule_filter_file_ignore_simple_match(mock_log_debug, mock_log_info):
+    """
+    Test case: FILE_IGNORE Rule (Simple Match).
+    One FILE_IGNORE rule with filename_pattern="*_ignore.png".
+    One MAP_COL rule with filename_pattern="diffuse_ignore.png".
+    One MAP_COL rule with filename_pattern="normal_process.png".
+    Assert only "normal_process.png" rule is added.
+    """
+    stage = FileRuleFilterStage()
+    fr_ignore = create_mock_file_rule(filename_pattern="*_ignore.png", item_type="FILE_IGNORE", active=True)
+    fr_ignored_map = create_mock_file_rule(filename_pattern="diffuse_ignore.png", item_type="MAP_COL", active=True)
+    fr_process_map = create_mock_file_rule(filename_pattern="normal_process.png", item_type="MAP_COL", active=True)
+    context = create_file_filter_mock_context(file_rules_list=[fr_ignore, fr_ignored_map, fr_process_map])
+
+    updated_context = stage.execute(context)
+
+    assert len(updated_context.files_to_process) == 1
+    assert fr_process_map in updated_context.files_to_process
+    assert fr_ignored_map not in updated_context.files_to_process
+    mock_log_debug.assert_any_call(f"Asset '{context.asset_rule.name}': Registering ignore pattern: '{fr_ignore.filename_pattern}'")
+    mock_log_debug.assert_any_call(f"Asset '{context.asset_rule.name}': Skipping file rule '{fr_ignored_map.filename_pattern}' due to matching ignore pattern.")
+    mock_log_info.assert_any_call(f"Asset '{context.asset_rule.name}': 1 file rules queued for processing after filtering.")
+
+@mock.patch('logging.info')
+@mock.patch('logging.debug')
+def test_file_rule_filter_file_ignore_glob_pattern(mock_log_debug, mock_log_info):
+    """
+    Test case: FILE_IGNORE Rule (Glob Pattern).
+    One FILE_IGNORE rule with filename_pattern="*_ignore.*".
+    MAP_COL rules: "tex_ignore.tif", "tex_process.png".
+    Assert only "tex_process.png" rule is added.
+    """
+    stage = FileRuleFilterStage()
+    fr_ignore_glob = create_mock_file_rule(filename_pattern="*_ignore.*", item_type="FILE_IGNORE", active=True)
+    fr_ignored_tif = create_mock_file_rule(filename_pattern="tex_ignore.tif", item_type="MAP_COL", active=True)
+    fr_process_png = create_mock_file_rule(filename_pattern="tex_process.png", item_type="MAP_COL", active=True)
+    context = create_file_filter_mock_context(file_rules_list=[fr_ignore_glob, fr_ignored_tif, fr_process_png])
+
+    updated_context = stage.execute(context)
+
+    assert len(updated_context.files_to_process) == 1
+    assert fr_process_png in updated_context.files_to_process
+    assert fr_ignored_tif not in updated_context.files_to_process
+    mock_log_debug.assert_any_call(f"Asset '{context.asset_rule.name}': Registering ignore pattern: '{fr_ignore_glob.filename_pattern}'")
+    mock_log_debug.assert_any_call(f"Asset '{context.asset_rule.name}': Skipping file rule '{fr_ignored_tif.filename_pattern}' due to matching ignore pattern.")
+    mock_log_info.assert_any_call(f"Asset '{context.asset_rule.name}': 1 file rules queued for processing after filtering.")
+
+@mock.patch('logging.info')
+@mock.patch('logging.debug')
+def test_file_rule_filter_multiple_file_ignore_rules(mock_log_debug, mock_log_info):
+    """
+    Test case: Multiple FILE_IGNORE Rules.
+    Test with several ignore patterns and ensure they are all respected.
+    """
+    stage = FileRuleFilterStage()
+    fr_ignore1 = create_mock_file_rule(filename_pattern="*.tmp", item_type="FILE_IGNORE", active=True)
+    fr_ignore2 = create_mock_file_rule(filename_pattern="backup_*", item_type="FILE_IGNORE", active=True)
+    fr_ignore3 = create_mock_file_rule(filename_pattern="*_old.png", item_type="FILE_IGNORE", active=True)
+    
+    fr_map_ignored1 = create_mock_file_rule(filename_pattern="data.tmp", item_type="MAP_COL", active=True)
+    fr_map_ignored2 = create_mock_file_rule(filename_pattern="backup_diffuse.jpg", item_type="MAP_COL", active=True)
+    fr_map_ignored3 = create_mock_file_rule(filename_pattern="normal_old.png", item_type="MAP_COL", active=True)
+    fr_map_process = create_mock_file_rule(filename_pattern="final_texture.tif", item_type="MAP_COL", active=True)
+    
+    context = create_file_filter_mock_context(file_rules_list=[
+        fr_ignore1, fr_ignore2, fr_ignore3,
+        fr_map_ignored1, fr_map_ignored2, fr_map_ignored3, fr_map_process
+    ])
+
+    updated_context = stage.execute(context)
+
+    assert len(updated_context.files_to_process) == 1
+    assert fr_map_process in updated_context.files_to_process
+    assert fr_map_ignored1 not in updated_context.files_to_process
+    assert fr_map_ignored2 not in updated_context.files_to_process
+    assert fr_map_ignored3 not in updated_context.files_to_process
+    mock_log_debug.assert_any_call(f"Asset '{context.asset_rule.name}': Registering ignore pattern: '{fr_ignore1.filename_pattern}'")
+    mock_log_debug.assert_any_call(f"Asset '{context.asset_rule.name}': Registering ignore pattern: '{fr_ignore2.filename_pattern}'")
+    mock_log_debug.assert_any_call(f"Asset '{context.asset_rule.name}': Registering ignore pattern: '{fr_ignore3.filename_pattern}'")
+    mock_log_debug.assert_any_call(f"Asset '{context.asset_rule.name}': Skipping file rule '{fr_map_ignored1.filename_pattern}' due to matching ignore pattern.")
+    mock_log_debug.assert_any_call(f"Asset '{context.asset_rule.name}': Skipping file rule '{fr_map_ignored2.filename_pattern}' due to matching ignore pattern.")
+    mock_log_debug.assert_any_call(f"Asset '{context.asset_rule.name}': Skipping file rule '{fr_map_ignored3.filename_pattern}' due to matching ignore pattern.")
+    mock_log_info.assert_any_call(f"Asset '{context.asset_rule.name}': 1 file rules queued for processing after filtering.")
+
+@mock.patch('logging.info')
+@mock.patch('logging.debug')
+def test_file_rule_filter_file_ignore_rule_is_inactive(mock_log_debug, mock_log_info):
+    """
+    Test case: FILE_IGNORE Rule is Inactive.
+    An ignore rule itself is active=False. Assert its pattern is NOT used for filtering.
+    """
+    stage = FileRuleFilterStage()
+    fr_inactive_ignore = create_mock_file_rule(filename_pattern="*_ignore.tif", item_type="FILE_IGNORE", active=False)
+    fr_should_process1 = create_mock_file_rule(filename_pattern="diffuse_ignore.tif", item_type="MAP_COL", active=True) # Should be processed
+    fr_should_process2 = create_mock_file_rule(filename_pattern="normal_ok.png", item_type="MAP_COL", active=True)
+    context = create_file_filter_mock_context(file_rules_list=[fr_inactive_ignore, fr_should_process1, fr_should_process2])
+
+    updated_context = stage.execute(context)
+
+    assert len(updated_context.files_to_process) == 2
+    assert fr_should_process1 in updated_context.files_to_process
+    assert fr_should_process2 in updated_context.files_to_process
+    # Ensure the inactive ignore rule's pattern was not registered
+    # We check this by ensuring no debug log for registering *that specific* pattern was made.
+    # A more robust way would be to check mock_log_debug.call_args_list, but this is simpler for now.
+    for call in mock_log_debug.call_args_list:
+        args, kwargs = call
+        if "Registering ignore pattern" in args[0] and fr_inactive_ignore.filename_pattern in args[0]:
+            pytest.fail(f"Inactive ignore pattern '{fr_inactive_ignore.filename_pattern}' was incorrectly registered.")
+    mock_log_debug.assert_any_call(f"Asset '{context.asset_rule.name}': Skipping inactive file rule: '{fr_inactive_ignore.filename_pattern}' (type: FILE_IGNORE)")
+    mock_log_info.assert_any_call(f"Asset '{context.asset_rule.name}': 2 file rules queued for processing after filtering.")
+
+
+@mock.patch('logging.info')
+@mock.patch('logging.debug')
+def test_file_rule_filter_no_file_ignore_rules(mock_log_debug, mock_log_info):
+    """
+    Test case: No FILE_IGNORE Rules.
+    All rules are MAP_COL or other processable types.
+    Assert all active, processable rules are included.
+    """
+    stage = FileRuleFilterStage()
+    fr1 = create_mock_file_rule(filename_pattern="diffuse.png", item_type="MAP_COL", active=True)
+    fr2 = create_mock_file_rule(filename_pattern="normal.png", item_type="MAP_COL", active=True)
+    fr_other_type = create_mock_file_rule(filename_pattern="spec.tif", item_type="MAP_SPEC", active=True) # Assuming MAP_SPEC is processable
+    fr_inactive = create_mock_file_rule(filename_pattern="ao.jpg", item_type="MAP_AO", active=False)
+    
+    context = create_file_filter_mock_context(file_rules_list=[fr1, fr2, fr_other_type, fr_inactive])
+    
+    updated_context = stage.execute(context)
+    
+    assert len(updated_context.files_to_process) == 3
+    assert fr1 in updated_context.files_to_process
+    assert fr2 in updated_context.files_to_process
+    assert fr_other_type in updated_context.files_to_process
+    assert fr_inactive not in updated_context.files_to_process
+    mock_log_debug.assert_any_call(f"Asset '{context.asset_rule.name}': Skipping inactive file rule: '{fr_inactive.filename_pattern}'")
+    mock_log_info.assert_any_call(f"Asset '{context.asset_rule.name}': 3 file rules queued for processing after filtering.")
+
+@mock.patch('logging.info')
+@mock.patch('logging.debug')
+def test_file_rule_filter_item_type_not_processable(mock_log_debug, mock_log_info):
+    """
+    Test case: Item type is not processable (e.g., not MAP_COL, MAP_AO etc., but something else like 'METADATA_ONLY').
+    Assert such rules are not added to files_to_process, unless they are FILE_IGNORE.
+    """
+    stage = FileRuleFilterStage()
+    fr_processable = create_mock_file_rule(filename_pattern="diffuse.png", item_type="MAP_COL", active=True)
+    fr_not_processable = create_mock_file_rule(filename_pattern="info.txt", item_type="METADATA_ONLY", active=True)
+    fr_ignore = create_mock_file_rule(filename_pattern="*.bak", item_type="FILE_IGNORE", active=True)
+    fr_ignored_by_bak = create_mock_file_rule(filename_pattern="diffuse.bak", item_type="MAP_COL", active=True)
+
+    context = create_file_filter_mock_context(file_rules_list=[fr_processable, fr_not_processable, fr_ignore, fr_ignored_by_bak])
+    
+    updated_context = stage.execute(context)
+    
+    assert len(updated_context.files_to_process) == 1
+    assert fr_processable in updated_context.files_to_process
+    assert fr_not_processable not in updated_context.files_to_process
+    assert fr_ignored_by_bak not in updated_context.files_to_process
+
+    mock_log_debug.assert_any_call(f"Asset '{context.asset_rule.name}': Registering ignore pattern: '{fr_ignore.filename_pattern}'")
+    mock_log_debug.assert_any_call(f"Asset '{context.asset_rule.name}': Skipping file rule '{fr_not_processable.filename_pattern}' as its item_type '{fr_not_processable.item_type}' is not processable.")
+    mock_log_debug.assert_any_call(f"Asset '{context.asset_rule.name}': Skipping file rule '{fr_ignored_by_bak.filename_pattern}' due to matching ignore pattern.")
+    mock_log_info.assert_any_call(f"Asset '{context.asset_rule.name}': 1 file rules queued for processing after filtering.")
+
+# Example tests from instructions (can be adapted or used as a base)
+@mock.patch('logging.info')
+@mock.patch('logging.debug')
+def test_file_rule_filter_basic_active_example(mock_log_debug, mock_log_info): # Renamed to avoid conflict
+    stage = FileRuleFilterStage()
+    fr1 = create_mock_file_rule(filename_pattern="diffuse.png", item_type="MAP_COL", active=True)
+    fr2 = create_mock_file_rule(filename_pattern="normal.png", item_type="MAP_COL", active=True)
+    context = create_file_filter_mock_context(file_rules_list=[fr1, fr2])
+    
+    updated_context = stage.execute(context)
+    
+    assert len(updated_context.files_to_process) == 2
+    assert fr1 in updated_context.files_to_process
+    assert fr2 in updated_context.files_to_process
+    mock_log_info.assert_any_call(f"Asset '{context.asset_rule.name}': 2 file rules queued for processing after filtering.")
+
+@mock.patch('logging.info')
+@mock.patch('logging.debug')
+def test_file_rule_filter_with_file_ignore_example(mock_log_debug, mock_log_info): # Renamed to avoid conflict
+    stage = FileRuleFilterStage()
+    fr_ignore = create_mock_file_rule(filename_pattern="*_ignore.tif", item_type="FILE_IGNORE", active=True)
+    fr_process = create_mock_file_rule(filename_pattern="diffuse_ok.tif", item_type="MAP_COL", active=True)
+    fr_skip = create_mock_file_rule(filename_pattern="normal_ignore.tif", item_type="MAP_COL", active=True)
+    context = create_file_filter_mock_context(file_rules_list=[fr_ignore, fr_process, fr_skip])
+
+    updated_context = stage.execute(context)
+
+    assert len(updated_context.files_to_process) == 1
+    assert fr_process in updated_context.files_to_process
+    assert fr_skip not in updated_context.files_to_process
+    mock_log_debug.assert_any_call(f"Asset '{context.asset_rule.name}': Registering ignore pattern: '{fr_ignore.filename_pattern}'")
+    mock_log_debug.assert_any_call(f"Asset '{context.asset_rule.name}': Skipping file rule '{fr_skip.filename_pattern}' due to matching ignore pattern.")
+    mock_log_info.assert_any_call(f"Asset '{context.asset_rule.name}': 1 file rules queued for processing after filtering.")

tests/processing/pipeline/stages/test_gloss_to_rough_conversion.py

@@ -0,0 +1,486 @@
+import pytest
+from unittest import mock
+from pathlib import Path
+import uuid
+import numpy as np
+from typing import Optional, List, Dict
+
+from processing.pipeline.stages.gloss_to_rough_conversion import GlossToRoughConversionStage
+from processing.pipeline.asset_context import AssetProcessingContext
+from rule_structure import AssetRule, SourceRule, FileRule
+from configuration import Configuration, GeneralSettings
+# No direct ipu import needed in test if we mock its usage by the stage
+
+def create_mock_file_rule_for_gloss_test(
+    id_val: Optional[uuid.UUID] = None,
+    map_type: str = "GLOSS", # Test with GLOSS and other types
+    filename_pattern: str = "gloss.png"
+) -> mock.MagicMock:
+    mock_fr = mock.MagicMock(spec=FileRule)
+    mock_fr.id = id_val if id_val else uuid.uuid4()
+    mock_fr.map_type = map_type
+    mock_fr.filename_pattern = filename_pattern
+    mock_fr.item_type = "MAP_COL"
+    mock_fr.active = True
+    return mock_fr
+
+def create_gloss_conversion_mock_context(
+    initial_file_rules: Optional[List[FileRule]] = None, # Type hint corrected
+    initial_processed_details: Optional[Dict] = None, # Type hint corrected
+    skip_asset_flag: bool = False,
+    asset_name: str = "GlossAsset",
+    # Add a mock for general_settings if your stage checks a global flag
+    # convert_gloss_globally: bool = True
+) -> AssetProcessingContext:
+    mock_asset_rule = mock.MagicMock(spec=AssetRule)
+    mock_asset_rule.name = asset_name
+    mock_asset_rule.file_rules = initial_file_rules if initial_file_rules is not None else []
+
+    mock_source_rule = mock.MagicMock(spec=SourceRule)
+
+    mock_gs = mock.MagicMock(spec=GeneralSettings)
+    # if your stage uses a global flag:
+    # mock_gs.convert_gloss_to_rough_globally = convert_gloss_globally
+
+    mock_config = mock.MagicMock(spec=Configuration)
+    mock_config.general_settings = mock_gs
+
+
+    context = AssetProcessingContext(
+        source_rule=mock_source_rule,
+        asset_rule=mock_asset_rule,
+        workspace_path=Path("/fake/workspace"),
+        engine_temp_dir=Path("/fake/temp_engine_dir"), # Important for new temp file paths
+        output_base_path=Path("/fake/output"),
+        effective_supplier="ValidSupplier",
+        asset_metadata={'asset_name': asset_name},
+        processed_maps_details=initial_processed_details if initial_processed_details is not None else {},
+        merged_maps_details={},
+        files_to_process=list(initial_file_rules) if initial_file_rules else [], # Stage modifies this list
+        loaded_data_cache={},
+        config_obj=mock_config,
+        status_flags={'skip_asset': skip_asset_flag},
+        incrementing_value=None, # Added as per AssetProcessingContext definition
+        sha5_value=None # Added as per AssetProcessingContext definition
+    )
+    return context
+
+# Unit tests will be added below
+@mock.patch('processing.pipeline.stages.gloss_to_rough_conversion.ipu.save_image')
+@mock.patch('processing.pipeline.stages.gloss_to_rough_conversion.ipu.load_image')
+def test_asset_skipped(mock_load_image, mock_save_image):
+    """
+    Test that if 'skip_asset' is True, no processing occurs.
+    """
+    stage = GlossToRoughConversionStage()
+    
+    gloss_rule_id = uuid.uuid4()
+    gloss_fr = create_mock_file_rule_for_gloss_test(id_val=gloss_rule_id, map_type="GLOSS")
+    
+    initial_details = {
+        gloss_fr.id.hex: {'temp_processed_file': '/fake/temp_engine_dir/processed_gloss_map.png', 'status': 'Processed', 'map_type': 'GLOSS'}
+    }
+    context = create_gloss_conversion_mock_context(
+        initial_file_rules=[gloss_fr],
+        initial_processed_details=initial_details,
+        skip_asset_flag=True # Asset is skipped
+    )
+    
+    # Keep a copy of files_to_process and processed_maps_details to compare
+    original_files_to_process = list(context.files_to_process)
+    original_processed_maps_details = context.processed_maps_details.copy()
+    
+    updated_context = stage.execute(context)
+    
+    mock_load_image.assert_not_called()
+    mock_save_image.assert_not_called()
+    
+    assert updated_context.files_to_process == original_files_to_process, "files_to_process should not change if asset is skipped"
+    assert updated_context.processed_maps_details == original_processed_maps_details, "processed_maps_details should not change if asset is skipped"
+    assert updated_context.status_flags['skip_asset'] is True
+@mock.patch('processing.pipeline.stages.gloss_to_rough_conversion.ipu.save_image')
+@mock.patch('processing.pipeline.stages.gloss_to_rough_conversion.ipu.load_image')
+def test_no_gloss_map_present(mock_load_image, mock_save_image):
+    """
+    Test that if no GLOSS maps are in files_to_process, no conversion occurs.
+    """
+    stage = GlossToRoughConversionStage()
+    
+    normal_rule_id = uuid.uuid4()
+    normal_fr = create_mock_file_rule_for_gloss_test(id_val=normal_rule_id, map_type="NORMAL", filename_pattern="normal.png")
+    albedo_fr = create_mock_file_rule_for_gloss_test(map_type="ALBEDO", filename_pattern="albedo.jpg")
+    
+    initial_details = {
+        normal_fr.id.hex: {'temp_processed_file': '/fake/temp_engine_dir/processed_normal_map.png', 'status': 'Processed', 'map_type': 'NORMAL'}
+    }
+    context = create_gloss_conversion_mock_context(
+        initial_file_rules=[normal_fr, albedo_fr],
+        initial_processed_details=initial_details
+    )
+    
+    original_files_to_process = list(context.files_to_process)
+    original_processed_maps_details = context.processed_maps_details.copy()
+    
+    updated_context = stage.execute(context)
+    
+    mock_load_image.assert_not_called()
+    mock_save_image.assert_not_called()
+    
+    assert updated_context.files_to_process == original_files_to_process, "files_to_process should not change if no GLOSS maps are present"
+    assert updated_context.processed_maps_details == original_processed_maps_details, "processed_maps_details should not change if no GLOSS maps are present"
+    
+    # Ensure map types of existing rules are unchanged
+    for fr_in_list in updated_context.files_to_process:
+        if fr_in_list.id == normal_fr.id:
+            assert fr_in_list.map_type == "NORMAL"
+        elif fr_in_list.id == albedo_fr.id:
+            assert fr_in_list.map_type == "ALBEDO"
+@mock.patch('processing.pipeline.stages.gloss_to_rough_conversion.logging') # Mock logging
+@mock.patch('processing.pipeline.stages.gloss_to_rough_conversion.ipu.save_image')
+@mock.patch('processing.pipeline.stages.gloss_to_rough_conversion.ipu.load_image')
+def test_gloss_conversion_uint8_success(mock_load_image, mock_save_image, mock_logging):
+    """
+    Test successful conversion of a GLOSS map (uint8 data) to ROUGHNESS.
+    """
+    stage = GlossToRoughConversionStage()
+    
+    gloss_rule_id = uuid.uuid4()
+    # Use a distinct filename for the gloss map to ensure correct path construction
+    gloss_fr = create_mock_file_rule_for_gloss_test(id_val=gloss_rule_id, map_type="GLOSS", filename_pattern="my_gloss_map.png")
+    other_fr_id = uuid.uuid4()
+    other_fr = create_mock_file_rule_for_gloss_test(id_val=other_fr_id, map_type="NORMAL", filename_pattern="normal_map.png")
+    
+    initial_gloss_temp_path = Path("/fake/temp_engine_dir/processed_gloss_map.png")
+    initial_other_temp_path = Path("/fake/temp_engine_dir/processed_normal_map.png")
+
+    initial_details = {
+        gloss_fr.id.hex: {'temp_processed_file': str(initial_gloss_temp_path), 'status': 'Processed', 'map_type': 'GLOSS'},
+        other_fr.id.hex: {'temp_processed_file': str(initial_other_temp_path), 'status': 'Processed', 'map_type': 'NORMAL'}
+    }
+    context = create_gloss_conversion_mock_context(
+        initial_file_rules=[gloss_fr, other_fr],
+        initial_processed_details=initial_details
+    )
+
+    mock_loaded_gloss_data = np.array([10, 50, 250], dtype=np.uint8)
+    mock_load_image.return_value = mock_loaded_gloss_data
+    mock_save_image.return_value = True # Simulate successful save
+    
+    updated_context = stage.execute(context)
+    
+    mock_load_image.assert_called_once_with(initial_gloss_temp_path)
+    
+    # Check that save_image was called with inverted data and correct path
+    expected_inverted_data = 255 - mock_loaded_gloss_data
+    
+    # call_args[0] is a tuple of positional args, call_args[1] is a dict of kwargs
+    saved_path_arg = mock_save_image.call_args[0][0]
+    saved_data_arg = mock_save_image.call_args[0][1]
+    
+    assert np.array_equal(saved_data_arg, expected_inverted_data), "Image data passed to save_image is not correctly inverted."
+    assert "rough_from_gloss_" in saved_path_arg.name, "Saved file name should indicate conversion from gloss."
+    assert saved_path_arg.parent == Path("/fake/temp_engine_dir"), "Saved file should be in the engine temp directory."
+    # Ensure the new filename is based on the original gloss map's ID for uniqueness
+    assert gloss_fr.id.hex in saved_path_arg.name
+
+    # Check context.files_to_process
+    assert len(updated_context.files_to_process) == 2, "Number of file rules in context should remain the same."
+    converted_rule_found = False
+    other_rule_untouched = False
+    for fr_in_list in updated_context.files_to_process:
+        if fr_in_list.id == gloss_fr.id: # Should be the same rule object, modified
+            assert fr_in_list.map_type == "ROUGHNESS", "GLOSS map_type should be changed to ROUGHNESS."
+            # Check if filename_pattern was updated (optional, depends on stage logic)
+            # For now, assume it might not be, as the primary identifier is map_type and ID
+            converted_rule_found = True
+        elif fr_in_list.id == other_fr.id:
+            assert fr_in_list.map_type == "NORMAL", "Other map_type should remain unchanged."
+            other_rule_untouched = True
+    assert converted_rule_found, "The converted GLOSS rule was not found or not updated correctly in files_to_process."
+    assert other_rule_untouched, "The non-GLOSS rule was modified unexpectedly."
+
+    # Check context.processed_maps_details
+    assert len(updated_context.processed_maps_details) == 2, "Number of entries in processed_maps_details should remain the same."
+    
+    gloss_detail = updated_context.processed_maps_details[gloss_fr.id.hex]
+    assert "rough_from_gloss_" in gloss_detail['temp_processed_file'], "temp_processed_file for gloss map not updated."
+    assert Path(gloss_detail['temp_processed_file']).name == saved_path_arg.name, "Path in details should match saved path."
+    assert gloss_detail['original_map_type_before_conversion'] == "GLOSS", "original_map_type_before_conversion not set correctly."
+    assert "Converted from GLOSS to ROUGHNESS" in gloss_detail['notes'], "Conversion notes not added or incorrect."
+    assert gloss_detail['map_type'] == "ROUGHNESS", "map_type in details not updated to ROUGHNESS."
+
+
+    other_detail = updated_context.processed_maps_details[other_fr.id.hex]
+    assert other_detail['temp_processed_file'] == str(initial_other_temp_path), "Other map's temp_processed_file should be unchanged."
+    assert other_detail['map_type'] == "NORMAL", "Other map's map_type should be unchanged."
+    assert 'original_map_type_before_conversion' not in other_detail, "Other map should not have conversion history."
+    assert 'notes' not in other_detail or "Converted from GLOSS" not in other_detail['notes'], "Other map should not have conversion notes."
+
+    mock_logging.info.assert_any_call(f"Successfully converted GLOSS map {gloss_fr.id.hex} to ROUGHNESS.")
+@mock.patch('processing.pipeline.stages.gloss_to_rough_conversion.logging') # Mock logging
+@mock.patch('processing.pipeline.stages.gloss_to_rough_conversion.ipu.save_image')
+@mock.patch('processing.pipeline.stages.gloss_to_rough_conversion.ipu.load_image')
+def test_gloss_conversion_float_success(mock_load_image, mock_save_image, mock_logging):
+    """
+    Test successful conversion of a GLOSS map (float data) to ROUGHNESS.
+    """
+    stage = GlossToRoughConversionStage()
+    
+    gloss_rule_id = uuid.uuid4()
+    gloss_fr = create_mock_file_rule_for_gloss_test(id_val=gloss_rule_id, map_type="GLOSS", filename_pattern="gloss_float.hdr") # Example float format
+    
+    initial_gloss_temp_path = Path("/fake/temp_engine_dir/processed_gloss_float.hdr")
+    initial_details = {
+        gloss_fr.id.hex: {'temp_processed_file': str(initial_gloss_temp_path), 'status': 'Processed', 'map_type': 'GLOSS'}
+    }
+    context = create_gloss_conversion_mock_context(
+        initial_file_rules=[gloss_fr],
+        initial_processed_details=initial_details
+    )
+
+    mock_loaded_gloss_data = np.array([0.1, 0.5, 0.9], dtype=np.float32)
+    mock_load_image.return_value = mock_loaded_gloss_data
+    mock_save_image.return_value = True # Simulate successful save
+    
+    updated_context = stage.execute(context)
+    
+    mock_load_image.assert_called_once_with(initial_gloss_temp_path)
+    
+    expected_inverted_data = 1.0 - mock_loaded_gloss_data
+    
+    saved_path_arg = mock_save_image.call_args[0][0]
+    saved_data_arg = mock_save_image.call_args[0][1]
+    
+    assert np.allclose(saved_data_arg, expected_inverted_data), "Image data (float) passed to save_image is not correctly inverted."
+    assert "rough_from_gloss_" in saved_path_arg.name, "Saved file name should indicate conversion from gloss."
+    assert saved_path_arg.parent == Path("/fake/temp_engine_dir"), "Saved file should be in the engine temp directory."
+    assert gloss_fr.id.hex in saved_path_arg.name
+
+    assert len(updated_context.files_to_process) == 1
+    converted_rule = updated_context.files_to_process[0]
+    assert converted_rule.id == gloss_fr.id
+    assert converted_rule.map_type == "ROUGHNESS"
+
+    gloss_detail = updated_context.processed_maps_details[gloss_fr.id.hex]
+    assert "rough_from_gloss_" in gloss_detail['temp_processed_file']
+    assert Path(gloss_detail['temp_processed_file']).name == saved_path_arg.name
+    assert gloss_detail['original_map_type_before_conversion'] == "GLOSS"
+    assert "Converted from GLOSS to ROUGHNESS" in gloss_detail['notes']
+    assert gloss_detail['map_type'] == "ROUGHNESS"
+
+    mock_logging.info.assert_any_call(f"Successfully converted GLOSS map {gloss_fr.id.hex} to ROUGHNESS.")
+@mock.patch('processing.pipeline.stages.gloss_to_rough_conversion.logging')
+@mock.patch('processing.pipeline.stages.gloss_to_rough_conversion.ipu.save_image')
+@mock.patch('processing.pipeline.stages.gloss_to_rough_conversion.ipu.load_image')
+def test_load_image_fails(mock_load_image, mock_save_image, mock_logging):
+    """
+    Test behavior when ipu.load_image fails (returns None).
+    The original FileRule should be kept, and an error logged.
+    """
+    stage = GlossToRoughConversionStage()
+    
+    gloss_rule_id = uuid.uuid4()
+    gloss_fr = create_mock_file_rule_for_gloss_test(id_val=gloss_rule_id, map_type="GLOSS", filename_pattern="gloss_fails_load.png")
+    
+    initial_gloss_temp_path = Path("/fake/temp_engine_dir/processed_gloss_fails_load.png")
+    initial_details = {
+        gloss_fr.id.hex: {'temp_processed_file': str(initial_gloss_temp_path), 'status': 'Processed', 'map_type': 'GLOSS'}
+    }
+    context = create_gloss_conversion_mock_context(
+        initial_file_rules=[gloss_fr],
+        initial_processed_details=initial_details
+    )
+    
+    # Keep a copy for comparison
+    original_file_rule_map_type = gloss_fr.map_type 
+    original_details_entry = context.processed_maps_details[gloss_fr.id.hex].copy()
+
+    mock_load_image.return_value = None # Simulate load failure
+    
+    updated_context = stage.execute(context)
+    
+    mock_load_image.assert_called_once_with(initial_gloss_temp_path)
+    mock_save_image.assert_not_called() # Save should not be attempted
+    
+    # Check context.files_to_process: rule should be unchanged
+    assert len(updated_context.files_to_process) == 1
+    processed_rule = updated_context.files_to_process[0]
+    assert processed_rule.id == gloss_fr.id
+    assert processed_rule.map_type == original_file_rule_map_type, "FileRule map_type should not change if load fails."
+    assert processed_rule.map_type == "GLOSS" # Explicitly check it's still GLOSS
+
+    # Check context.processed_maps_details: details should be unchanged
+    current_details_entry = updated_context.processed_maps_details[gloss_fr.id.hex]
+    assert current_details_entry['temp_processed_file'] == str(initial_gloss_temp_path)
+    assert current_details_entry['map_type'] == "GLOSS"
+    assert 'original_map_type_before_conversion' not in current_details_entry
+    assert 'notes' not in current_details_entry or "Converted from GLOSS" not in current_details_entry['notes']
+    
+    mock_logging.error.assert_called_once_with(
+        f"Failed to load image data for GLOSS map {gloss_fr.id.hex} from {initial_gloss_temp_path}. Skipping conversion for this map."
+    )
+@mock.patch('processing.pipeline.stages.gloss_to_rough_conversion.logging')
+@mock.patch('processing.pipeline.stages.gloss_to_rough_conversion.ipu.save_image')
+@mock.patch('processing.pipeline.stages.gloss_to_rough_conversion.ipu.load_image')
+def test_save_image_fails(mock_load_image, mock_save_image, mock_logging):
+    """
+    Test behavior when ipu.save_image fails (returns False).
+    The original FileRule should be kept, and an error logged.
+    """
+    stage = GlossToRoughConversionStage()
+    
+    gloss_rule_id = uuid.uuid4()
+    gloss_fr = create_mock_file_rule_for_gloss_test(id_val=gloss_rule_id, map_type="GLOSS", filename_pattern="gloss_fails_save.png")
+    
+    initial_gloss_temp_path = Path("/fake/temp_engine_dir/processed_gloss_fails_save.png")
+    initial_details = {
+        gloss_fr.id.hex: {'temp_processed_file': str(initial_gloss_temp_path), 'status': 'Processed', 'map_type': 'GLOSS'}
+    }
+    context = create_gloss_conversion_mock_context(
+        initial_file_rules=[gloss_fr],
+        initial_processed_details=initial_details
+    )
+    
+    original_file_rule_map_type = gloss_fr.map_type
+    original_details_entry = context.processed_maps_details[gloss_fr.id.hex].copy()
+
+    mock_loaded_gloss_data = np.array([10, 50, 250], dtype=np.uint8)
+    mock_load_image.return_value = mock_loaded_gloss_data
+    mock_save_image.return_value = False # Simulate save failure
+    
+    updated_context = stage.execute(context)
+    
+    mock_load_image.assert_called_once_with(initial_gloss_temp_path)
+    
+    # Check that save_image was called with correct data and path
+    expected_inverted_data = 255 - mock_loaded_gloss_data
+    # call_args[0] is a tuple of positional args
+    saved_path_arg = mock_save_image.call_args[0][0]
+    saved_data_arg = mock_save_image.call_args[0][1]
+    
+    assert np.array_equal(saved_data_arg, expected_inverted_data), "Image data passed to save_image is not correctly inverted even on failure."
+    assert "rough_from_gloss_" in saved_path_arg.name, "Attempted save file name should indicate conversion from gloss."
+    assert saved_path_arg.parent == Path("/fake/temp_engine_dir"), "Attempted save file should be in the engine temp directory."
+
+    # Check context.files_to_process: rule should be unchanged
+    assert len(updated_context.files_to_process) == 1
+    processed_rule = updated_context.files_to_process[0]
+    assert processed_rule.id == gloss_fr.id
+    assert processed_rule.map_type == original_file_rule_map_type, "FileRule map_type should not change if save fails."
+    assert processed_rule.map_type == "GLOSS"
+
+    # Check context.processed_maps_details: details should be unchanged
+    current_details_entry = updated_context.processed_maps_details[gloss_fr.id.hex]
+    assert current_details_entry['temp_processed_file'] == str(initial_gloss_temp_path)
+    assert current_details_entry['map_type'] == "GLOSS"
+    assert 'original_map_type_before_conversion' not in current_details_entry
+    assert 'notes' not in current_details_entry or "Converted from GLOSS" not in current_details_entry['notes']
+    
+    mock_logging.error.assert_called_once_with(
+        f"Failed to save inverted GLOSS map {gloss_fr.id.hex} to {saved_path_arg}. Retaining original GLOSS map."
+    )
+@mock.patch('processing.pipeline.stages.gloss_to_rough_conversion.logging')
+@mock.patch('processing.pipeline.stages.gloss_to_rough_conversion.ipu.save_image')
+@mock.patch('processing.pipeline.stages.gloss_to_rough_conversion.ipu.load_image')
+def test_gloss_map_in_files_to_process_but_not_in_details(mock_load_image, mock_save_image, mock_logging):
+    """
+    Test behavior when a GLOSS FileRule is in files_to_process but its details
+    are missing from processed_maps_details.
+    The stage should log an error and skip this FileRule.
+    """
+    stage = GlossToRoughConversionStage()
+    
+    gloss_rule_id = uuid.uuid4()
+    # This FileRule is in files_to_process
+    gloss_fr_in_list = create_mock_file_rule_for_gloss_test(id_val=gloss_rule_id, map_type="GLOSS", filename_pattern="orphan_gloss.png")
+    
+    # processed_maps_details is empty or does not contain gloss_fr_in_list.id.hex
+    initial_details = {} 
+    
+    context = create_gloss_conversion_mock_context(
+        initial_file_rules=[gloss_fr_in_list],
+        initial_processed_details=initial_details 
+    )
+    
+    original_files_to_process = list(context.files_to_process)
+    original_processed_maps_details = context.processed_maps_details.copy()
+    
+    updated_context = stage.execute(context)
+    
+    mock_load_image.assert_not_called() # Load should not be attempted if details are missing
+    mock_save_image.assert_not_called() # Save should not be attempted
+    
+    # Check context.files_to_process: rule should be unchanged
+    assert len(updated_context.files_to_process) == 1
+    processed_rule = updated_context.files_to_process[0]
+    assert processed_rule.id == gloss_fr_in_list.id
+    assert processed_rule.map_type == "GLOSS", "FileRule map_type should not change if its details are missing."
+
+    # Check context.processed_maps_details: should remain unchanged
+    assert updated_context.processed_maps_details == original_processed_maps_details, "processed_maps_details should not change."
+    
+    mock_logging.error.assert_called_once_with(
+        f"GLOSS map {gloss_fr_in_list.id.hex} found in files_to_process but missing from processed_maps_details. Skipping conversion."
+    )
+
+# Test for Case 8.2 (GLOSS map ID in processed_maps_details but no corresponding FileRule in files_to_process)
+# This case is implicitly handled because the stage iterates files_to_process.
+# If a FileRule isn't in files_to_process, its corresponding entry in processed_maps_details (if any) won't be acted upon.
+# We can add a simple test to ensure no errors occur and non-relevant details are untouched.
+
+@mock.patch('processing.pipeline.stages.gloss_to_rough_conversion.logging')
+@mock.patch('processing.pipeline.stages.gloss_to_rough_conversion.ipu.save_image')
+@mock.patch('processing.pipeline.stages.gloss_to_rough_conversion.ipu.load_image')
+def test_gloss_detail_exists_but_not_in_files_to_process(mock_load_image, mock_save_image, mock_logging):
+    """
+    Test that if a GLOSS map detail exists in processed_maps_details but
+    no corresponding FileRule is in files_to_process, it's simply ignored
+    without error, and other valid conversions proceed.
+    """
+    stage = GlossToRoughConversionStage()
+
+    # This rule will be processed
+    convert_rule_id = uuid.uuid4()
+    convert_fr = create_mock_file_rule_for_gloss_test(id_val=convert_rule_id, map_type="GLOSS", filename_pattern="convert_me.png")
+    convert_initial_temp_path = Path("/fake/temp_engine_dir/processed_convert_me.png")
+
+    # This rule's details exist, but the rule itself is not in files_to_process
+    orphan_detail_id = uuid.uuid4()
+    
+    initial_details = {
+        convert_fr.id.hex: {'temp_processed_file': str(convert_initial_temp_path), 'status': 'Processed', 'map_type': 'GLOSS'},
+        orphan_detail_id.hex: {'temp_processed_file': '/fake/temp_engine_dir/orphan.png', 'status': 'Processed', 'map_type': 'GLOSS', 'notes': 'This is an orphan'}
+    }
+    
+    context = create_gloss_conversion_mock_context(
+        initial_file_rules=[convert_fr], # Only convert_fr is in files_to_process
+        initial_processed_details=initial_details
+    )
+    
+    mock_loaded_data = np.array([100], dtype=np.uint8)
+    mock_load_image.return_value = mock_loaded_data
+    mock_save_image.return_value = True
+
+    updated_context = stage.execute(context)
+
+    # Assert that load/save were called only for the rule in files_to_process
+    mock_load_image.assert_called_once_with(convert_initial_temp_path)
+    mock_save_image.assert_called_once() # Check it was called, details checked in other tests
+
+    # Check that the orphan detail in processed_maps_details is untouched
+    assert orphan_detail_id.hex in updated_context.processed_maps_details
+    orphan_entry = updated_context.processed_maps_details[orphan_detail_id.hex]
+    assert orphan_entry['temp_processed_file'] == '/fake/temp_engine_dir/orphan.png'
+    assert orphan_entry['map_type'] == 'GLOSS'
+    assert orphan_entry['notes'] == 'This is an orphan'
+    assert 'original_map_type_before_conversion' not in orphan_entry
+
+    # Check that the processed rule was indeed converted
+    assert convert_fr.id.hex in updated_context.processed_maps_details
+    converted_entry = updated_context.processed_maps_details[convert_fr.id.hex]
+    assert converted_entry['map_type'] == 'ROUGHNESS'
+    assert "rough_from_gloss_" in converted_entry['temp_processed_file']
+
+    # No errors should have been logged regarding the orphan detail
+    for call_args in mock_logging.error.call_args_list:
+        assert str(orphan_detail_id.hex) not in call_args[0][0], "Error logged for orphan detail"

tests/processing/pipeline/stages/test_individual_map_processing.py

@@ -0,0 +1,555 @@
+import pytest
+from unittest import mock
+from pathlib import Path
+import uuid
+import numpy as np
+from typing import Optional # Added for type hinting in helper functions
+
+from processing.pipeline.stages.individual_map_processing import IndividualMapProcessingStage
+from processing.pipeline.asset_context import AssetProcessingContext
+from rule_structure import AssetRule, SourceRule, FileRule # Key models
+from configuration import Configuration, GeneralSettings
+# cv2 might be imported by the stage for interpolation constants, ensure it's mockable if so.
+# For now, assume ipu handles interpolation details.
+
+def create_mock_transform_settings(
+    target_width=0, target_height=0, resize_mode="FIT",
+    ensure_pot=False, allow_upscale=True, target_color_profile="RGB" # Add other fields as needed
+) -> mock.MagicMock:
+    ts = mock.MagicMock(spec=TransformSettings)
+    ts.target_width = target_width
+    ts.target_height = target_height
+    ts.resize_mode = resize_mode
+    ts.ensure_pot = ensure_pot
+    ts.allow_upscale = allow_upscale
+    ts.target_color_profile = target_color_profile
+    # ts.resize_filter = "AREA" # if your stage uses this
+    return ts
+
+def create_mock_file_rule_for_individual_processing(
+    id_val: Optional[uuid.UUID] = None,
+    map_type: str = "ALBEDO",
+    filename_pattern: str = "albedo_*.png", # Pattern for glob
+    item_type: str = "MAP_COL",
+    active: bool = True,
+    transform_settings: Optional[mock.MagicMock] = None
+) -> mock.MagicMock:
+    mock_fr = mock.MagicMock(spec=FileRule)
+    mock_fr.id = id_val if id_val else uuid.uuid4()
+    mock_fr.map_type = map_type
+    mock_fr.filename_pattern = filename_pattern
+    mock_fr.item_type = item_type
+    mock_fr.active = active
+    mock_fr.transform_settings = transform_settings if transform_settings else create_mock_transform_settings()
+    return mock_fr
+
+def create_individual_map_proc_mock_context(
+    initial_file_rules: Optional[list] = None,
+    asset_source_path_str: str = "/fake/asset_source",
+    skip_asset_flag: bool = False,
+    asset_name: str = "IndividualMapAsset"
+) -> AssetProcessingContext:
+    mock_asset_rule = mock.MagicMock(spec=AssetRule)
+    mock_asset_rule.name = asset_name
+    mock_asset_rule.source_path = Path(asset_source_path_str)
+    # file_rules on AssetRule not directly used by stage, context.files_to_process is
+
+    mock_source_rule = mock.MagicMock(spec=SourceRule)
+    mock_config = mock.MagicMock(spec=Configuration)
+    # mock_config.general_settings = mock.MagicMock(spec=GeneralSettings) # If needed
+
+    context = AssetProcessingContext(
+        source_rule=mock_source_rule,
+        asset_rule=mock_asset_rule,
+        workspace_path=Path("/fake/workspace"),
+        engine_temp_dir=Path("/fake/temp_engine_dir"),
+        output_base_path=Path("/fake/output"),
+        effective_supplier="ValidSupplier",
+        asset_metadata={'asset_name': asset_name},
+        processed_maps_details={}, # Stage populates this
+        merged_maps_details={},
+        files_to_process=list(initial_file_rules) if initial_file_rules else [],
+        loaded_data_cache={},
+        config_obj=mock_config,
+        status_flags={'skip_asset': skip_asset_flag},
+        incrementing_value=None,
+        sha5_value=None # Corrected from sha5_value to sha_value if that's the actual param
+    )
+    return context
+
+# Placeholder for tests to be added next
+@mock.patch('processing.pipeline.stages.individual_map_processing.ipu')
+@mock.patch('logging.info')
+def test_asset_skipped_if_flag_is_true(mock_log_info, mock_ipu):
+    stage = IndividualMapProcessingStage()
+    context = create_individual_map_proc_mock_context(skip_asset_flag=True)
+    
+    # Add a dummy file rule to ensure it's not processed
+    file_rule = create_mock_file_rule_for_individual_processing()
+    context.files_to_process = [file_rule]
+
+    updated_context = stage.execute(context)
+
+    mock_ipu.load_image.assert_not_called()
+    mock_ipu.save_image.assert_not_called()
+    assert not updated_context.processed_maps_details # No details should be added
+    # Check for a log message indicating skip, if applicable (depends on stage's logging)
+    # mock_log_info.assert_any_call("Skipping asset IndividualMapAsset due to status_flags['skip_asset'] = True") # Example
+
+
+@mock.patch('processing.pipeline.stages.individual_map_processing.ipu')
+@mock.patch('logging.info')
+def test_no_processing_if_no_map_col_rules(mock_log_info, mock_ipu):
+    stage = IndividualMapProcessingStage()
+    
+    # Create a file rule that is NOT of item_type MAP_COL
+    non_map_col_rule = create_mock_file_rule_for_individual_processing(item_type="METADATA")
+    context = create_individual_map_proc_mock_context(initial_file_rules=[non_map_col_rule])
+
+    updated_context = stage.execute(context)
+
+    mock_ipu.load_image.assert_not_called()
+    mock_ipu.save_image.assert_not_called()
+    assert not updated_context.processed_maps_details
+    # mock_log_info.assert_any_call("No FileRules of item_type 'MAP_COL' to process for asset IndividualMapAsset.") # Example
+
+
+@mock.patch('processing.pipeline.stages.individual_map_processing.ipu.save_image')
+@mock.patch('processing.pipeline.stages.individual_map_processing.ipu.resize_image')
+@mock.patch('processing.pipeline.stages.individual_map_processing.ipu.calculate_target_dimensions')
+@mock.patch('processing.pipeline.stages.individual_map_processing.ipu.load_image')
+@mock.patch('pathlib.Path.glob') # Mocking Path.glob used by the stage's _find_source_file
+@mock.patch('logging.info')
+@mock.patch('logging.error')
+def test_individual_map_processing_success_no_resize(
+    mock_log_error, mock_log_info, mock_path_glob, mock_load_image,
+    mock_calc_dims, mock_resize_image, mock_save_image
+):
+    stage = IndividualMapProcessingStage()
+
+    source_file_name = "albedo_source.png"
+    # The glob is called on context.asset_rule.source_path, so mock that Path object's glob
+    mock_asset_source_path = Path("/fake/asset_source")
+    mock_found_source_path = mock_asset_source_path / source_file_name
+    
+    # We need to mock the glob method of the Path instance
+    # that represents the asset's source directory.
+    # The stage does something like: Path(context.asset_rule.source_path).glob(...)
+    # So, we need to ensure that when Path() is called with that specific string,
+    # the resulting object's glob method is our mock.
+    # A more robust way is to mock Path itself to return a mock object
+    # whose glob method is also a mock.
+
+    # Simpler approach for now: assume Path.glob is used as a static/class method call
+    # or that the instance it's called on is correctly patched by @mock.patch('pathlib.Path.glob')
+    # if the stage does `from pathlib import Path` and then `Path(path_str).glob(...)`.
+    # The prompt example uses @mock.patch('pathlib.Path.glob'), implying the stage might do this:
+    # for f_pattern in patterns:
+    #   for found_file in Path(base_dir).glob(f_pattern): ...
+    # Let's refine the mock_path_glob setup.
+    # The stage's _find_source_file likely does:
+    # search_path = Path(self.context.asset_rule.source_path)
+    # found_files = list(search_path.glob(filename_pattern))
+
+    # To correctly mock this, we need to mock the `glob` method of the specific Path instance.
+    # Or, if `_find_source_file` instantiates `Path` like `Path(str(context.asset_rule.source_path)).glob(...)`,
+    # then patching `pathlib.Path.glob` might work if it's treated as a method that gets bound.
+    # Let's stick to the example's @mock.patch('pathlib.Path.glob') and assume it covers the usage.
+    mock_path_glob.return_value = [mock_found_source_path] # Glob finds one file
+
+    ts = create_mock_transform_settings(target_width=100, target_height=100)
+    file_rule = create_mock_file_rule_for_individual_processing(
+        map_type="ALBEDO", filename_pattern="albedo_*.png", transform_settings=ts
+    )
+    context = create_individual_map_proc_mock_context(
+        initial_file_rules=[file_rule],
+        asset_source_path_str=str(mock_asset_source_path) # Ensure context uses this path
+    )
+
+    mock_img_data = np.zeros((100, 100, 3), dtype=np.uint8) # Original dimensions
+    mock_load_image.return_value = mock_img_data
+    mock_calc_dims.return_value = (100, 100) # No resize needed
+    mock_save_image.return_value = True
+
+    updated_context = stage.execute(context)
+
+    # Assert that Path(context.asset_rule.source_path).glob was called
+    # This requires a bit more intricate mocking if Path instances are created inside.
+    # For now, assert mock_path_glob was called with the pattern.
+    # The actual call in stage is `Path(context.asset_rule.source_path).glob(file_rule.filename_pattern)`
+    # So, `mock_path_glob` (if it patches `Path.glob` globally) should be called.
+    # We need to ensure the mock_path_glob is associated with the correct Path instance or that
+    # the global patch works as intended.
+    # A common pattern is:
+    # with mock.patch.object(Path, 'glob', return_value=[mock_found_source_path]) as specific_glob_mock:
+    #    # execute code
+    #    specific_glob_mock.assert_called_once_with(file_rule.filename_pattern)
+    # However, the decorator @mock.patch('pathlib.Path.glob') should work if the stage code is
+    # `from pathlib import Path; p = Path(...); p.glob(...)`
+    
+    # The stage's _find_source_file will instantiate a Path object from context.asset_rule.source_path
+    # and then call glob on it.
+    # So, @mock.patch('pathlib.Path.glob') is patching the method on the class.
+    # When an instance calls it, the mock is used.
+    mock_path_glob.assert_called_once_with(file_rule.filename_pattern)
+
+
+    mock_load_image.assert_called_once_with(mock_found_source_path)
+    # The actual call to calculate_target_dimensions is:
+    # ipu.calculate_target_dimensions(original_dims, ts.target_width, ts.target_height, ts.resize_mode, ts.ensure_pot, ts.allow_upscale)
+    mock_calc_dims.assert_called_once_with(
+        (100, 100), ts.target_width, ts.target_height, ts.resize_mode, ts.ensure_pot, ts.allow_upscale
+    )
+    mock_resize_image.assert_not_called() # Crucial for this test case
+    mock_save_image.assert_called_once()
+
+    # Check save path and data
+    saved_image_arg, saved_path_arg = mock_save_image.call_args[0]
+    assert np.array_equal(saved_image_arg, mock_img_data) # Ensure correct image data is passed to save
+    assert "processed_ALBEDO_" in saved_path_arg.name # Based on map_type
+    assert file_rule.id.hex in saved_path_arg.name # Ensure unique name with FileRule ID
+    assert saved_path_arg.parent == context.engine_temp_dir
+
+    assert file_rule.id.hex in updated_context.processed_maps_details
+    details = updated_context.processed_maps_details[file_rule.id.hex]
+    assert details['status'] == 'Processed'
+    assert details['source_file'] == str(mock_found_source_path)
+    assert Path(details['temp_processed_file']) == saved_path_arg
+    assert details['original_dimensions'] == (100, 100)
+    assert details['processed_dimensions'] == (100, 100)
+    assert details['map_type'] == file_rule.map_type
+    mock_log_error.assert_not_called()
+    mock_log_info.assert_any_call(f"Successfully processed map {file_rule.map_type} (ID: {file_rule.id.hex}) for asset {context.asset_rule.name}. Output: {saved_path_arg}")
+
+
+@mock.patch('processing.pipeline.stages.individual_map_processing.ipu.save_image')
+@mock.patch('processing.pipeline.stages.individual_map_processing.ipu.resize_image')
+@mock.patch('processing.pipeline.stages.individual_map_processing.ipu.calculate_target_dimensions')
+@mock.patch('processing.pipeline.stages.individual_map_processing.ipu.load_image')
+@mock.patch('pathlib.Path.glob')
+@mock.patch('logging.info')
+@mock.patch('logging.error')
+def test_source_file_not_found(
+    mock_log_error, mock_log_info, mock_path_glob, mock_load_image,
+    mock_calc_dims, mock_resize_image, mock_save_image
+):
+    stage = IndividualMapProcessingStage()
+    mock_asset_source_path = Path("/fake/asset_source")
+    
+    mock_path_glob.return_value = [] # Glob finds no files
+
+    file_rule = create_mock_file_rule_for_individual_processing(filename_pattern="nonexistent_*.png")
+    context = create_individual_map_proc_mock_context(
+        initial_file_rules=[file_rule],
+        asset_source_path_str=str(mock_asset_source_path)
+    )
+
+    updated_context = stage.execute(context)
+
+    mock_path_glob.assert_called_once_with(file_rule.filename_pattern)
+    mock_load_image.assert_not_called()
+    mock_calc_dims.assert_not_called()
+    mock_resize_image.assert_not_called()
+    mock_save_image.assert_not_called()
+
+    assert file_rule.id.hex in updated_context.processed_maps_details
+    details = updated_context.processed_maps_details[file_rule.id.hex]
+    assert details['status'] == 'Source Not Found'
+    assert details['source_file'] is None
+    assert details['temp_processed_file'] is None
+    assert details['error_message'] is not None # Check an error message is present
+    mock_log_error.assert_called_once()
+    # Example: mock_log_error.assert_called_with(f"Could not find source file for rule {file_rule.id} (pattern: {file_rule.filename_pattern}) in {context.asset_rule.source_path}")
+
+
+@mock.patch('processing.pipeline.stages.individual_map_processing.ipu.save_image')
+@mock.patch('processing.pipeline.stages.individual_map_processing.ipu.resize_image')
+@mock.patch('processing.pipeline.stages.individual_map_processing.ipu.calculate_target_dimensions')
+@mock.patch('processing.pipeline.stages.individual_map_processing.ipu.load_image')
+@mock.patch('pathlib.Path.glob')
+@mock.patch('logging.info')
+@mock.patch('logging.error')
+def test_load_image_fails(
+    mock_log_error, mock_log_info, mock_path_glob, mock_load_image,
+    mock_calc_dims, mock_resize_image, mock_save_image
+):
+    stage = IndividualMapProcessingStage()
+    source_file_name = "albedo_corrupt.png"
+    mock_asset_source_path = Path("/fake/asset_source")
+    mock_found_source_path = mock_asset_source_path / source_file_name
+    mock_path_glob.return_value = [mock_found_source_path]
+
+    mock_load_image.return_value = None # Simulate load failure
+
+    file_rule = create_mock_file_rule_for_individual_processing(filename_pattern="albedo_*.png")
+    context = create_individual_map_proc_mock_context(
+        initial_file_rules=[file_rule],
+        asset_source_path_str=str(mock_asset_source_path)
+    )
+
+    updated_context = stage.execute(context)
+
+    mock_path_glob.assert_called_once_with(file_rule.filename_pattern)
+    mock_load_image.assert_called_once_with(mock_found_source_path)
+    mock_calc_dims.assert_not_called()
+    mock_resize_image.assert_not_called()
+    mock_save_image.assert_not_called()
+
+    assert file_rule.id.hex in updated_context.processed_maps_details
+    details = updated_context.processed_maps_details[file_rule.id.hex]
+    assert details['status'] == 'Load Failed'
+    assert details['source_file'] == str(mock_found_source_path)
+    assert details['temp_processed_file'] is None
+    assert details['error_message'] is not None
+    mock_log_error.assert_called_once()
+    # Example: mock_log_error.assert_called_with(f"Failed to load image {mock_found_source_path} for rule {file_rule.id}")
+
+
+@mock.patch('processing.pipeline.stages.individual_map_processing.ipu.save_image')
+@mock.patch('processing.pipeline.stages.individual_map_processing.ipu.resize_image')
+@mock.patch('processing.pipeline.stages.individual_map_processing.ipu.calculate_target_dimensions')
+@mock.patch('processing.pipeline.stages.individual_map_processing.ipu.load_image')
+@mock.patch('pathlib.Path.glob')
+@mock.patch('logging.info')
+@mock.patch('logging.error')
+def test_resize_occurs_when_dimensions_differ(
+    mock_log_error, mock_log_info, mock_path_glob, mock_load_image,
+    mock_calc_dims, mock_resize_image, mock_save_image
+):
+    stage = IndividualMapProcessingStage()
+    source_file_name = "albedo_resize.png"
+    mock_asset_source_path = Path("/fake/asset_source")
+    mock_found_source_path = mock_asset_source_path / source_file_name
+    mock_path_glob.return_value = [mock_found_source_path]
+
+    original_dims = (100, 100)
+    target_dims = (50, 50) # Different dimensions
+    mock_img_data = np.zeros((*original_dims, 3), dtype=np.uint8)
+    mock_resized_img_data = np.zeros((*target_dims, 3), dtype=np.uint8)
+
+    mock_load_image.return_value = mock_img_data
+    ts = create_mock_transform_settings(target_width=target_dims[0], target_height=target_dims[1])
+    file_rule = create_mock_file_rule_for_individual_processing(transform_settings=ts)
+    context = create_individual_map_proc_mock_context(
+        initial_file_rules=[file_rule],
+        asset_source_path_str=str(mock_asset_source_path)
+    )
+    
+    mock_calc_dims.return_value = target_dims # Simulate calc_dims returning new dimensions
+    mock_resize_image.return_value = mock_resized_img_data # Simulate resize returning new image data
+    mock_save_image.return_value = True
+
+    updated_context = stage.execute(context)
+
+    mock_load_image.assert_called_once_with(mock_found_source_path)
+    mock_calc_dims.assert_called_once_with(
+        original_dims, ts.target_width, ts.target_height, ts.resize_mode, ts.ensure_pot, ts.allow_upscale
+    )
+    # The actual call to resize_image is:
+    # ipu.resize_image(loaded_image, target_dims, ts.resize_filter) # Assuming resize_filter is used
+    # If resize_filter is not on TransformSettings or not used, adjust this.
+    # For now, let's assume it's ipu.resize_image(loaded_image, target_dims) or similar
+    # The stage code is: resized_image = ipu.resize_image(loaded_image, target_dims_calculated, file_rule.transform_settings.resize_filter)
+    # So we need to mock ts.resize_filter
+    ts.resize_filter = "LANCZOS4" # Example filter
+    mock_resize_image.assert_called_once_with(mock_img_data, target_dims, ts.resize_filter)
+    
+    saved_image_arg, saved_path_arg = mock_save_image.call_args[0]
+    assert np.array_equal(saved_image_arg, mock_resized_img_data) # Check resized data is saved
+    assert "processed_ALBEDO_" in saved_path_arg.name
+    assert saved_path_arg.parent == context.engine_temp_dir
+
+    assert file_rule.id.hex in updated_context.processed_maps_details
+    details = updated_context.processed_maps_details[file_rule.id.hex]
+    assert details['status'] == 'Processed'
+    assert details['original_dimensions'] == original_dims
+    assert details['processed_dimensions'] == target_dims
+    mock_log_error.assert_not_called()
+
+
+@mock.patch('processing.pipeline.stages.individual_map_processing.ipu.save_image')
+@mock.patch('processing.pipeline.stages.individual_map_processing.ipu.resize_image')
+@mock.patch('processing.pipeline.stages.individual_map_processing.ipu.calculate_target_dimensions')
+@mock.patch('processing.pipeline.stages.individual_map_processing.ipu.load_image')
+@mock.patch('pathlib.Path.glob')
+@mock.patch('logging.info')
+@mock.patch('logging.error')
+def test_save_image_fails(
+    mock_log_error, mock_log_info, mock_path_glob, mock_load_image,
+    mock_calc_dims, mock_resize_image, mock_save_image
+):
+    stage = IndividualMapProcessingStage()
+    source_file_name = "albedo_save_fail.png"
+    mock_asset_source_path = Path("/fake/asset_source")
+    mock_found_source_path = mock_asset_source_path / source_file_name
+    mock_path_glob.return_value = [mock_found_source_path]
+
+    mock_img_data = np.zeros((100, 100, 3), dtype=np.uint8)
+    mock_load_image.return_value = mock_img_data
+    mock_calc_dims.return_value = (100, 100) # No resize
+    mock_save_image.return_value = False # Simulate save failure
+
+    ts = create_mock_transform_settings()
+    file_rule = create_mock_file_rule_for_individual_processing(transform_settings=ts)
+    context = create_individual_map_proc_mock_context(
+        initial_file_rules=[file_rule],
+        asset_source_path_str=str(mock_asset_source_path)
+    )
+
+    updated_context = stage.execute(context)
+
+    mock_save_image.assert_called_once() # Attempt to save should still happen
+    
+    assert file_rule.id.hex in updated_context.processed_maps_details
+    details = updated_context.processed_maps_details[file_rule.id.hex]
+    assert details['status'] == 'Save Failed'
+    assert details['source_file'] == str(mock_found_source_path)
+    assert details['temp_processed_file'] is not None # Path was generated
+    assert details['error_message'] is not None
+    mock_log_error.assert_called_once()
+    # Example: mock_log_error.assert_called_with(f"Failed to save processed image for rule {file_rule.id} to {details['temp_processed_file']}")
+
+
+@mock.patch('processing.pipeline.stages.individual_map_processing.ipu.save_image')
+@mock.patch('processing.pipeline.stages.individual_map_processing.ipu.resize_image')
+@mock.patch('processing.pipeline.stages.individual_map_processing.ipu.calculate_target_dimensions')
+@mock.patch('processing.pipeline.stages.individual_map_processing.ipu.load_image')
+@mock.patch('processing.pipeline.stages.individual_map_processing.ipu.convert_bgr_to_rgb')
+@mock.patch('pathlib.Path.glob')
+@mock.patch('logging.info')
+@mock.patch('logging.error')
+def test_color_conversion_bgr_to_rgb(
+    mock_log_error, mock_log_info, mock_path_glob, mock_convert_bgr, mock_load_image,
+    mock_calc_dims, mock_resize_image, mock_save_image
+):
+    stage = IndividualMapProcessingStage()
+    source_file_name = "albedo_bgr.png"
+    mock_asset_source_path = Path("/fake/asset_source")
+    mock_found_source_path = mock_asset_source_path / source_file_name
+    mock_path_glob.return_value = [mock_found_source_path]
+
+    mock_bgr_img_data = np.zeros((100, 100, 3), dtype=np.uint8) # Loaded as BGR
+    mock_rgb_img_data = np.zeros((100, 100, 3), dtype=np.uint8) # After conversion
+    
+    mock_load_image.return_value = mock_bgr_img_data # Image is loaded (assume BGR by default from cv2)
+    mock_convert_bgr.return_value = mock_rgb_img_data # Mock the conversion
+    mock_calc_dims.return_value = (100, 100) # No resize
+    mock_save_image.return_value = True
+
+    # Transform settings request RGB, and stage assumes load might be BGR
+    ts = create_mock_transform_settings(target_color_profile="RGB") 
+    file_rule = create_mock_file_rule_for_individual_processing(transform_settings=ts)
+    context = create_individual_map_proc_mock_context(
+        initial_file_rules=[file_rule],
+        asset_source_path_str=str(mock_asset_source_path)
+    )
+    # The stage code is:
+    # if file_rule.transform_settings.target_color_profile == "RGB" and loaded_image.shape[2] == 3:
+    #     logger.info(f"Attempting to convert image from BGR to RGB for {file_rule_id_hex}")
+    #     processed_image_data = ipu.convert_bgr_to_rgb(processed_image_data)
+
+    updated_context = stage.execute(context)
+
+    mock_load_image.assert_called_once_with(mock_found_source_path)
+    mock_convert_bgr.assert_called_once_with(mock_bgr_img_data)
+    mock_resize_image.assert_not_called()
+    
+    saved_image_arg, _ = mock_save_image.call_args[0]
+    assert np.array_equal(saved_image_arg, mock_rgb_img_data) # Ensure RGB data is saved
+    mock_log_error.assert_not_called()
+    mock_log_info.assert_any_call(f"Attempting to convert image from BGR to RGB for {file_rule.id.hex}")
+
+
+@mock.patch('processing.pipeline.stages.individual_map_processing.ipu.save_image')
+@mock.patch('processing.pipeline.stages.individual_map_processing.ipu.resize_image')
+@mock.patch('processing.pipeline.stages.individual_map_processing.ipu.calculate_target_dimensions')
+@mock.patch('processing.pipeline.stages.individual_map_processing.ipu.load_image')
+@mock.patch('pathlib.Path.glob')
+@mock.patch('logging.info')
+@mock.patch('logging.error')
+def test_multiple_map_col_rules_processed(
+    mock_log_error, mock_log_info, mock_path_glob, mock_load_image,
+    mock_calc_dims, mock_resize_image, mock_save_image
+):
+    stage = IndividualMapProcessingStage()
+    mock_asset_source_path = Path("/fake/asset_source")
+
+    # Rule 1: Albedo
+    ts1 = create_mock_transform_settings(target_width=100, target_height=100)
+    file_rule1_id = uuid.uuid4()
+    file_rule1 = create_mock_file_rule_for_individual_processing(
+        id_val=file_rule1_id, map_type="ALBEDO", filename_pattern="albedo_*.png", transform_settings=ts1
+    )
+    source_file1 = mock_asset_source_path / "albedo_map.png"
+    img_data1 = np.zeros((100, 100, 3), dtype=np.uint8)
+
+    # Rule 2: Roughness
+    ts2 = create_mock_transform_settings(target_width=50, target_height=50) # Resize
+    ts2.resize_filter = "AREA"
+    file_rule2_id = uuid.uuid4()
+    file_rule2 = create_mock_file_rule_for_individual_processing(
+        id_val=file_rule2_id, map_type="ROUGHNESS", filename_pattern="rough_*.png", transform_settings=ts2
+    )
+    source_file2 = mock_asset_source_path / "rough_map.png"
+    img_data2_orig = np.zeros((200, 200, 1), dtype=np.uint8) # Original, needs resize
+    img_data2_resized = np.zeros((50, 50, 1), dtype=np.uint8) # Resized
+
+    context = create_individual_map_proc_mock_context(
+        initial_file_rules=[file_rule1, file_rule2],
+        asset_source_path_str=str(mock_asset_source_path)
+    )
+
+    # Mock behaviors for Path.glob, load_image, calc_dims, resize, save
+    # Path.glob will be called twice
+    mock_path_glob.side_effect = [
+        [source_file1], # For albedo_*.png
+        [source_file2]  # For rough_*.png
+    ]
+    mock_load_image.side_effect = [img_data1, img_data2_orig]
+    mock_calc_dims.side_effect = [
+        (100, 100), # For rule1 (no change)
+        (50, 50)    # For rule2 (change)
+    ]
+    mock_resize_image.return_value = img_data2_resized # Only called for rule2
+    mock_save_image.return_value = True
+
+    updated_context = stage.execute(context)
+
+    # Assertions for Rule 1 (Albedo)
+    assert mock_path_glob.call_args_list[0][0][0] == file_rule1.filename_pattern
+    assert mock_load_image.call_args_list[0][0][0] == source_file1
+    assert mock_calc_dims.call_args_list[0][0] == ((100,100), ts1.target_width, ts1.target_height, ts1.resize_mode, ts1.ensure_pot, ts1.allow_upscale)
+    
+    # Assertions for Rule 2 (Roughness)
+    assert mock_path_glob.call_args_list[1][0][0] == file_rule2.filename_pattern
+    assert mock_load_image.call_args_list[1][0][0] == source_file2
+    assert mock_calc_dims.call_args_list[1][0] == ((200,200), ts2.target_width, ts2.target_height, ts2.resize_mode, ts2.ensure_pot, ts2.allow_upscale)
+    mock_resize_image.assert_called_once_with(img_data2_orig, (50,50), ts2.resize_filter)
+    
+    assert mock_save_image.call_count == 2
+    # Check saved image for rule 1
+    saved_img1_arg, saved_path1_arg = mock_save_image.call_args_list[0][0]
+    assert np.array_equal(saved_img1_arg, img_data1)
+    assert "processed_ALBEDO_" in saved_path1_arg.name
+    assert file_rule1_id.hex in saved_path1_arg.name
+
+    # Check saved image for rule 2
+    saved_img2_arg, saved_path2_arg = mock_save_image.call_args_list[1][0]
+    assert np.array_equal(saved_img2_arg, img_data2_resized)
+    assert "processed_ROUGHNESS_" in saved_path2_arg.name
+    assert file_rule2_id.hex in saved_path2_arg.name
+
+    # Check context details
+    assert file_rule1_id.hex in updated_context.processed_maps_details
+    details1 = updated_context.processed_maps_details[file_rule1_id.hex]
+    assert details1['status'] == 'Processed'
+    assert details1['original_dimensions'] == (100, 100)
+    assert details1['processed_dimensions'] == (100, 100)
+
+    assert file_rule2_id.hex in updated_context.processed_maps_details
+    details2 = updated_context.processed_maps_details[file_rule2_id.hex]
+    assert details2['status'] == 'Processed'
+    assert details2['original_dimensions'] == (200, 200) # Original dims of img_data2_orig
+    assert details2['processed_dimensions'] == (50, 50)
+    
+    mock_log_error.assert_not_called()

tests/processing/pipeline/stages/test_map_merging.py

@@ -0,0 +1,538 @@
+import pytest
+from unittest import mock
+from pathlib import Path
+import uuid
+import numpy as np
+from typing import Optional # Added Optional for type hinting
+
+from processing.pipeline.stages.map_merging import MapMergingStage
+from processing.pipeline.asset_context import AssetProcessingContext
+from rule_structure import AssetRule, SourceRule, FileRule
+from configuration import Configuration
+
+# Mock Helper Functions
+def create_mock_merge_input_channel(
+    file_rule_id: uuid.UUID, source_channel: int = 0, target_channel: int = 0, invert: bool = False
+) -> mock.MagicMock:
+    mic = mock.MagicMock(spec=MergeInputChannel)
+    mic.file_rule_id = file_rule_id
+    mic.source_channel = source_channel
+    mic.target_channel = target_channel
+    mic.invert_source_channel = invert
+    mic.default_value_if_missing = 0 # Or some other default
+    return mic
+
+def create_mock_merge_settings(
+    input_maps: Optional[list] = None, # List of mock MergeInputChannel
+    output_channels: int = 3
+) -> mock.MagicMock:
+    ms = mock.MagicMock(spec=MergeSettings)
+    ms.input_maps = input_maps if input_maps is not None else []
+    ms.output_channels = output_channels
+    return ms
+
+def create_mock_file_rule_for_merging(
+    id_val: Optional[uuid.UUID] = None,
+    map_type: str = "ORM", # Output map type
+    item_type: str = "MAP_MERGE",
+    merge_settings: Optional[mock.MagicMock] = None
+) -> mock.MagicMock:
+    mock_fr = mock.MagicMock(spec=FileRule)
+    mock_fr.id = id_val if id_val else uuid.uuid4()
+    mock_fr.map_type = map_type
+    mock_fr.filename_pattern = f"{map_type.lower()}_merged.png" # Placeholder
+    mock_fr.item_type = item_type
+    mock_fr.active = True
+    mock_fr.merge_settings = merge_settings if merge_settings else create_mock_merge_settings()
+    return mock_fr
+
+def create_map_merging_mock_context(
+    initial_file_rules: Optional[list] = None, # Will contain the MAP_MERGE rule
+    initial_processed_details: Optional[dict] = None, # Pre-processed inputs for merge
+    skip_asset_flag: bool = False,
+    asset_name: str = "MergeAsset"
+) -> AssetProcessingContext:
+    mock_asset_rule = mock.MagicMock(spec=AssetRule)
+    mock_asset_rule.name = asset_name
+    mock_source_rule = mock.MagicMock(spec=SourceRule)
+    mock_config = mock.MagicMock(spec=Configuration)
+
+    context = AssetProcessingContext(
+        source_rule=mock_source_rule,
+        asset_rule=mock_asset_rule,
+        workspace_path=Path("/fake/workspace"),
+        engine_temp_dir=Path("/fake/temp_engine_dir"),
+        output_base_path=Path("/fake/output"),
+        effective_supplier="ValidSupplier",
+        asset_metadata={'asset_name': asset_name},
+        processed_maps_details=initial_processed_details if initial_processed_details is not None else {},
+        merged_maps_details={}, # Stage populates this
+        files_to_process=list(initial_file_rules) if initial_file_rules else [],
+        loaded_data_cache={},
+        config_obj=mock_config,
+        status_flags={'skip_asset': skip_asset_flag},
+        incrementing_value=None,
+        sha5_value=None # Corrected from sha5_value to sha_value based on AssetProcessingContext
+    )
+    return context
+def test_asset_skipped():
+    stage = MapMergingStage()
+    context = create_map_merging_mock_context(skip_asset_flag=True)
+    
+    updated_context = stage.execute(context)
+    
+    assert updated_context == context # No changes expected
+    assert not updated_context.merged_maps_details # No maps should be merged
+
+def test_no_map_merge_rules():
+    stage = MapMergingStage()
+    # Context with a non-MAP_MERGE rule
+    non_merge_rule = create_mock_file_rule_for_merging(item_type="TEXTURE_MAP", map_type="Diffuse")
+    context = create_map_merging_mock_context(initial_file_rules=[non_merge_rule])
+    
+    updated_context = stage.execute(context)
+    
+    assert updated_context == context # No changes expected
+    assert not updated_context.merged_maps_details # No maps should be merged
+
+@mock.patch('processing.pipeline.stages.map_merging.ipu.save_image')
+@mock.patch('processing.pipeline.stages.map_merging.ipu.resize_image') # If testing resize
+@mock.patch('processing.pipeline.stages.map_merging.ipu.load_image')
+@mock.patch('logging.info')
+@mock.patch('logging.error')
+def test_map_merging_rgb_success(mock_log_error, mock_log_info, mock_load_image, mock_resize_image, mock_save_image):
+    stage = MapMergingStage()
+
+    # Input FileRules (mocked as already processed)
+    r_id, g_id, b_id = uuid.uuid4(), uuid.uuid4(), uuid.uuid4()
+    processed_details = {
+        r_id.hex: {'temp_processed_file': '/fake/red.png', 'status': 'Processed', 'map_type': 'RED_SRC'},
+        g_id.hex: {'temp_processed_file': '/fake/green.png', 'status': 'Processed', 'map_type': 'GREEN_SRC'},
+        b_id.hex: {'temp_processed_file': '/fake/blue.png', 'status': 'Processed', 'map_type': 'BLUE_SRC'}
+    }
+    # Mock loaded image data (grayscale for inputs)
+    mock_r_data = np.full((10, 10), 200, dtype=np.uint8)
+    mock_g_data = np.full((10, 10), 100, dtype=np.uint8)
+    mock_b_data = np.full((10, 10), 50, dtype=np.uint8)
+    mock_load_image.side_effect = [mock_r_data, mock_g_data, mock_b_data]
+
+    # Merge Rule setup
+    merge_inputs = [
+        create_mock_merge_input_channel(file_rule_id=r_id, source_channel=0, target_channel=0), # R to R
+        create_mock_merge_input_channel(file_rule_id=g_id, source_channel=0, target_channel=1), # G to G
+        create_mock_merge_input_channel(file_rule_id=b_id, source_channel=0, target_channel=2)  # B to B
+    ]
+    merge_settings = create_mock_merge_settings(input_maps=merge_inputs, output_channels=3)
+    merge_rule_id = uuid.uuid4()
+    merge_rule = create_mock_file_rule_for_merging(id_val=merge_rule_id, map_type="RGB_Combined", merge_settings=merge_settings)
+    
+    context = create_map_merging_mock_context(
+        initial_file_rules=[merge_rule], 
+        initial_processed_details=processed_details
+    )
+    mock_save_image.return_value = True
+    
+    updated_context = stage.execute(context)
+
+    assert mock_load_image.call_count == 3
+    mock_resize_image.assert_not_called() # Assuming all inputs are same size for this test
+    mock_save_image.assert_called_once()
+    
+    # Check that the correct filename was passed to save_image
+    # The filename is constructed as: f"{context.asset_rule.name}_merged_{merge_rule.map_type}{Path(first_input_path).suffix}"
+    # In this case, first_input_path is '/fake/red.png', so suffix is '.png'
+    # Asset name is "MergeAsset"
+    expected_filename_part = f"{context.asset_rule.name}_merged_{merge_rule.map_type}.png"
+    saved_path_arg = mock_save_image.call_args[0][0]
+    assert expected_filename_part in str(saved_path_arg)
+
+
+    saved_data = mock_save_image.call_args[0][1]
+    assert saved_data.shape == (10, 10, 3)
+    assert np.all(saved_data[:,:,0] == 200) # Red channel
+    assert np.all(saved_data[:,:,1] == 100) # Green channel
+    assert np.all(saved_data[:,:,2] == 50)  # Blue channel
+
+    assert merge_rule.id.hex in updated_context.merged_maps_details
+    details = updated_context.merged_maps_details[merge_rule.id.hex]
+    assert details['status'] == 'Processed'
+    # The temp_merged_file path will be under engine_temp_dir / asset_name / filename
+    assert f"{context.engine_temp_dir / context.asset_rule.name / expected_filename_part}" == details['temp_merged_file']
+    mock_log_error.assert_not_called()
+    mock_log_info.assert_any_call(f"Successfully merged map '{merge_rule.map_type}' for asset '{context.asset_rule.name}'.")
+
+# Unit tests will be added below this line
+@mock.patch('processing.pipeline.stages.map_merging.ipu.save_image')
+@mock.patch('processing.pipeline.stages.map_merging.ipu.resize_image')
+@mock.patch('processing.pipeline.stages.map_merging.ipu.load_image')
+@mock.patch('logging.info')
+@mock.patch('logging.error')
+def test_map_merging_channel_inversion(mock_log_error, mock_log_info, mock_load_image, mock_resize_image, mock_save_image):
+    stage = MapMergingStage()
+
+    # Input FileRule
+    input_id = uuid.uuid4()
+    processed_details = {
+        input_id.hex: {'temp_processed_file': '/fake/source.png', 'status': 'Processed', 'map_type': 'SOURCE_MAP'}
+    }
+    # Mock loaded image data (single channel for simplicity, to be inverted)
+    mock_source_data = np.array([[0, 100], [155, 255]], dtype=np.uint8)
+    mock_load_image.return_value = mock_source_data
+
+    # Merge Rule setup: one input, inverted, to one output channel
+    merge_inputs = [
+        create_mock_merge_input_channel(file_rule_id=input_id, source_channel=0, target_channel=0, invert=True)
+    ]
+    merge_settings = create_mock_merge_settings(input_maps=merge_inputs, output_channels=1)
+    merge_rule_id = uuid.uuid4()
+    merge_rule = create_mock_file_rule_for_merging(id_val=merge_rule_id, map_type="Inverted_Gray", merge_settings=merge_settings)
+    
+    context = create_map_merging_mock_context(
+        initial_file_rules=[merge_rule], 
+        initial_processed_details=processed_details
+    )
+    mock_save_image.return_value = True
+    
+    updated_context = stage.execute(context)
+
+    mock_load_image.assert_called_once_with(Path('/fake/source.png'))
+    mock_resize_image.assert_not_called()
+    mock_save_image.assert_called_once()
+    
+    saved_data = mock_save_image.call_args[0][1]
+    assert saved_data.shape == (2, 2) # Grayscale output
+    
+    # Expected inverted data: 255-original
+    expected_inverted_data = np.array([[255, 155], [100, 0]], dtype=np.uint8)
+    assert np.all(saved_data == expected_inverted_data)
+
+    assert merge_rule.id.hex in updated_context.merged_maps_details
+    details = updated_context.merged_maps_details[merge_rule.id.hex]
+    assert details['status'] == 'Processed'
+    assert "merged_Inverted_Gray" in details['temp_merged_file']
+    mock_log_error.assert_not_called()
+    mock_log_info.assert_any_call(f"Successfully merged map '{merge_rule.map_type}' for asset '{context.asset_rule.name}'.")
+@mock.patch('processing.pipeline.stages.map_merging.ipu.save_image')
+@mock.patch('processing.pipeline.stages.map_merging.ipu.load_image')
+@mock.patch('logging.error')
+def test_map_merging_input_map_missing(mock_log_error, mock_load_image, mock_save_image):
+    stage = MapMergingStage()
+
+    # Input FileRule ID that will be missing from processed_details
+    missing_input_id = uuid.uuid4()
+    
+    # Merge Rule setup
+    merge_inputs = [
+        create_mock_merge_input_channel(file_rule_id=missing_input_id, source_channel=0, target_channel=0)
+    ]
+    merge_settings = create_mock_merge_settings(input_maps=merge_inputs, output_channels=1)
+    merge_rule_id = uuid.uuid4()
+    merge_rule = create_mock_file_rule_for_merging(id_val=merge_rule_id, map_type="TestMissing", merge_settings=merge_settings)
+    
+    # processed_details is empty, so missing_input_id will not be found
+    context = create_map_merging_mock_context(
+        initial_file_rules=[merge_rule], 
+        initial_processed_details={} 
+    )
+    
+    updated_context = stage.execute(context)
+
+    mock_load_image.assert_not_called()
+    mock_save_image.assert_not_called()
+    
+    assert merge_rule.id.hex in updated_context.merged_maps_details
+    details = updated_context.merged_maps_details[merge_rule.id.hex]
+    assert details['status'] == 'Failed'
+    assert 'error_message' in details
+    assert f"Input map FileRule ID {missing_input_id.hex} not found in processed_maps_details or not successfully processed" in details['error_message']
+    
+    mock_log_error.assert_called_once()
+    assert f"Failed to merge map '{merge_rule.map_type}' for asset '{context.asset_rule.name}'" in mock_log_error.call_args[0][0]
+    assert f"Input map FileRule ID {missing_input_id.hex} not found in processed_maps_details or not successfully processed" in mock_log_error.call_args[0][0]
+
+@mock.patch('processing.pipeline.stages.map_merging.ipu.save_image')
+@mock.patch('processing.pipeline.stages.map_merging.ipu.load_image')
+@mock.patch('logging.error')
+def test_map_merging_input_map_status_not_processed(mock_log_error, mock_load_image, mock_save_image):
+    stage = MapMergingStage()
+
+    input_id = uuid.uuid4()
+    processed_details = {
+        # Status is 'Failed', not 'Processed'
+        input_id.hex: {'temp_processed_file': '/fake/source.png', 'status': 'Failed', 'map_type': 'SOURCE_MAP'}
+    }
+    
+    merge_inputs = [
+        create_mock_merge_input_channel(file_rule_id=input_id, source_channel=0, target_channel=0)
+    ]
+    merge_settings = create_mock_merge_settings(input_maps=merge_inputs, output_channels=1)
+    merge_rule_id = uuid.uuid4()
+    merge_rule = create_mock_file_rule_for_merging(id_val=merge_rule_id, map_type="TestNotProcessed", merge_settings=merge_settings)
+    
+    context = create_map_merging_mock_context(
+        initial_file_rules=[merge_rule], 
+        initial_processed_details=processed_details
+    )
+    
+    updated_context = stage.execute(context)
+
+    mock_load_image.assert_not_called()
+    mock_save_image.assert_not_called()
+    
+    assert merge_rule.id.hex in updated_context.merged_maps_details
+    details = updated_context.merged_maps_details[merge_rule.id.hex]
+    assert details['status'] == 'Failed'
+    assert 'error_message' in details
+    assert f"Input map FileRule ID {input_id.hex} not found in processed_maps_details or not successfully processed" in details['error_message']
+    
+    mock_log_error.assert_called_once()
+    assert f"Failed to merge map '{merge_rule.map_type}' for asset '{context.asset_rule.name}'" in mock_log_error.call_args[0][0]
+    assert f"Input map FileRule ID {input_id.hex} not found in processed_maps_details or not successfully processed" in mock_log_error.call_args[0][0]
+@mock.patch('processing.pipeline.stages.map_merging.ipu.save_image')
+@mock.patch('processing.pipeline.stages.map_merging.ipu.load_image')
+@mock.patch('logging.error')
+def test_map_merging_load_image_fails(mock_log_error, mock_load_image, mock_save_image):
+    stage = MapMergingStage()
+
+    input_id = uuid.uuid4()
+    processed_details = {
+        input_id.hex: {'temp_processed_file': '/fake/source.png', 'status': 'Processed', 'map_type': 'SOURCE_MAP'}
+    }
+    
+    # Configure mock_load_image to raise an exception
+    mock_load_image.side_effect = Exception("Failed to load image")
+
+    merge_inputs = [
+        create_mock_merge_input_channel(file_rule_id=input_id, source_channel=0, target_channel=0)
+    ]
+    merge_settings = create_mock_merge_settings(input_maps=merge_inputs, output_channels=1)
+    merge_rule_id = uuid.uuid4()
+    merge_rule = create_mock_file_rule_for_merging(id_val=merge_rule_id, map_type="TestLoadFail", merge_settings=merge_settings)
+    
+    context = create_map_merging_mock_context(
+        initial_file_rules=[merge_rule], 
+        initial_processed_details=processed_details
+    )
+    
+    updated_context = stage.execute(context)
+
+    mock_load_image.assert_called_once_with(Path('/fake/source.png'))
+    mock_save_image.assert_not_called()
+    
+    assert merge_rule.id.hex in updated_context.merged_maps_details
+    details = updated_context.merged_maps_details[merge_rule.id.hex]
+    assert details['status'] == 'Failed'
+    assert 'error_message' in details
+    assert "Failed to load image for merge input" in details['error_message']
+    assert str(Path('/fake/source.png')) in details['error_message']
+    
+    mock_log_error.assert_called_once()
+    assert f"Failed to merge map '{merge_rule.map_type}' for asset '{context.asset_rule.name}'" in mock_log_error.call_args[0][0]
+    assert "Failed to load image for merge input" in mock_log_error.call_args[0][0]
+@mock.patch('processing.pipeline.stages.map_merging.ipu.save_image')
+@mock.patch('processing.pipeline.stages.map_merging.ipu.load_image')
+@mock.patch('logging.error')
+def test_map_merging_save_image_fails(mock_log_error, mock_load_image, mock_save_image):
+    stage = MapMergingStage()
+
+    input_id = uuid.uuid4()
+    processed_details = {
+        input_id.hex: {'temp_processed_file': '/fake/source.png', 'status': 'Processed', 'map_type': 'SOURCE_MAP'}
+    }
+    mock_source_data = np.full((10, 10), 128, dtype=np.uint8)
+    mock_load_image.return_value = mock_source_data
+    
+    # Configure mock_save_image to return False (indicating failure)
+    mock_save_image.return_value = False
+
+    merge_inputs = [
+        create_mock_merge_input_channel(file_rule_id=input_id, source_channel=0, target_channel=0)
+    ]
+    merge_settings = create_mock_merge_settings(input_maps=merge_inputs, output_channels=1)
+    merge_rule_id = uuid.uuid4()
+    merge_rule = create_mock_file_rule_for_merging(id_val=merge_rule_id, map_type="TestSaveFail", merge_settings=merge_settings)
+    
+    context = create_map_merging_mock_context(
+        initial_file_rules=[merge_rule], 
+        initial_processed_details=processed_details
+    )
+    
+    updated_context = stage.execute(context)
+
+    mock_load_image.assert_called_once_with(Path('/fake/source.png'))
+    mock_save_image.assert_called_once() # save_image is called, but returns False
+    
+    assert merge_rule.id.hex in updated_context.merged_maps_details
+    details = updated_context.merged_maps_details[merge_rule.id.hex]
+    assert details['status'] == 'Failed'
+    assert 'error_message' in details
+    assert "Failed to save merged map" in details['error_message']
+    
+    mock_log_error.assert_called_once()
+    assert f"Failed to merge map '{merge_rule.map_type}' for asset '{context.asset_rule.name}'" in mock_log_error.call_args[0][0]
+    assert "Failed to save merged map" in mock_log_error.call_args[0][0]
+@mock.patch('processing.pipeline.stages.map_merging.ipu.save_image')
+@mock.patch('processing.pipeline.stages.map_merging.ipu.resize_image')
+@mock.patch('processing.pipeline.stages.map_merging.ipu.load_image')
+@mock.patch('logging.info')
+@mock.patch('logging.error')
+def test_map_merging_dimension_mismatch_handling(mock_log_error, mock_log_info, mock_load_image, mock_resize_image, mock_save_image):
+    stage = MapMergingStage()
+
+    # Input FileRules
+    id1, id2 = uuid.uuid4(), uuid.uuid4()
+    processed_details = {
+        id1.hex: {'temp_processed_file': '/fake/img1.png', 'status': 'Processed', 'map_type': 'IMG1_SRC'},
+        id2.hex: {'temp_processed_file': '/fake/img2.png', 'status': 'Processed', 'map_type': 'IMG2_SRC'}
+    }
+    
+    # Mock loaded image data with different dimensions
+    mock_img1_data = np.full((10, 10), 100, dtype=np.uint8) # 10x10
+    mock_img2_data_original = np.full((5, 5), 200, dtype=np.uint8) # 5x5, will be resized
+    
+    mock_load_image.side_effect = [mock_img1_data, mock_img2_data_original]
+
+    # Mock resize_image to return an image of the target dimensions
+    # For simplicity, it just creates a new array of the target size filled with a value.
+    mock_img2_data_resized = np.full((10, 10), 210, dtype=np.uint8) # Resized to 10x10
+    mock_resize_image.return_value = mock_img2_data_resized
+
+    # Merge Rule setup: two inputs, one output channel (e.g., averaging them)
+    # Target channel 0 for both, the stage should handle combining them if they map to the same target.
+    # However, the current stage logic for multiple inputs to the same target channel is to take the last one.
+    # Let's make them target different channels for a clearer test of resize.
+    merge_inputs = [
+        create_mock_merge_input_channel(file_rule_id=id1, source_channel=0, target_channel=0),
+        create_mock_merge_input_channel(file_rule_id=id2, source_channel=0, target_channel=1) 
+    ]
+    merge_settings = create_mock_merge_settings(input_maps=merge_inputs, output_channels=2) # Outputting 2 channels
+    merge_rule_id = uuid.uuid4()
+    merge_rule = create_mock_file_rule_for_merging(id_val=merge_rule_id, map_type="ResizedMerge", merge_settings=merge_settings)
+    
+    context = create_map_merging_mock_context(
+        initial_file_rules=[merge_rule], 
+        initial_processed_details=processed_details
+    )
+    mock_save_image.return_value = True
+    
+    updated_context = stage.execute(context)
+
+    assert mock_load_image.call_count == 2
+    mock_load_image.assert_any_call(Path('/fake/img1.png'))
+    mock_load_image.assert_any_call(Path('/fake/img2.png'))
+
+    # Assert resize_image was called for the second image to match the first's dimensions
+    mock_resize_image.assert_called_once()
+    # The first argument to resize_image is the image data, second is target_shape tuple (height, width)
+    # np.array_equal is needed for comparing numpy arrays in mock calls
+    assert np.array_equal(mock_resize_image.call_args[0][0], mock_img2_data_original)
+    assert mock_resize_image.call_args[0][1] == (10, 10) 
+
+    mock_save_image.assert_called_once()
+    
+    saved_data = mock_save_image.call_args[0][1]
+    assert saved_data.shape == (10, 10, 2) # 2 output channels
+    assert np.all(saved_data[:,:,0] == mock_img1_data)     # First channel from img1
+    assert np.all(saved_data[:,:,1] == mock_img2_data_resized) # Second channel from resized img2
+
+    assert merge_rule.id.hex in updated_context.merged_maps_details
+    details = updated_context.merged_maps_details[merge_rule.id.hex]
+    assert details['status'] == 'Processed'
+    assert "merged_ResizedMerge" in details['temp_merged_file']
+    mock_log_error.assert_not_called()
+    mock_log_info.assert_any_call(f"Resized input map from {Path('/fake/img2.png')} from {mock_img2_data_original.shape} to {(10,10)} to match first loaded map.")
+    mock_log_info.assert_any_call(f"Successfully merged map '{merge_rule.map_type}' for asset '{context.asset_rule.name}'.")
+@mock.patch('processing.pipeline.stages.map_merging.ipu.save_image')
+@mock.patch('processing.pipeline.stages.map_merging.ipu.resize_image')
+@mock.patch('processing.pipeline.stages.map_merging.ipu.load_image')
+@mock.patch('logging.info')
+@mock.patch('logging.error')
+def test_map_merging_to_grayscale_output(mock_log_error, mock_log_info, mock_load_image, mock_resize_image, mock_save_image):
+    stage = MapMergingStage()
+
+    # Input FileRule (e.g., an RGB image)
+    input_id = uuid.uuid4()
+    processed_details = {
+        input_id.hex: {'temp_processed_file': '/fake/rgb_source.png', 'status': 'Processed', 'map_type': 'RGB_SRC'}
+    }
+    # Mock loaded image data (3 channels)
+    mock_rgb_data = np.full((10, 10, 3), [50, 100, 150], dtype=np.uint8)
+    mock_load_image.return_value = mock_rgb_data
+
+    # Merge Rule setup: take the Green channel (source_channel=1) from input and map it to the single output channel (target_channel=0)
+    merge_inputs = [
+        create_mock_merge_input_channel(file_rule_id=input_id, source_channel=1, target_channel=0) # G to Grayscale
+    ]
+    # output_channels = 1 for grayscale
+    merge_settings = create_mock_merge_settings(input_maps=merge_inputs, output_channels=1) 
+    merge_rule_id = uuid.uuid4()
+    merge_rule = create_mock_file_rule_for_merging(id_val=merge_rule_id, map_type="GrayscaleFromGreen", merge_settings=merge_settings)
+    
+    context = create_map_merging_mock_context(
+        initial_file_rules=[merge_rule], 
+        initial_processed_details=processed_details
+    )
+    mock_save_image.return_value = True
+    
+    updated_context = stage.execute(context)
+
+    mock_load_image.assert_called_once_with(Path('/fake/rgb_source.png'))
+    mock_resize_image.assert_not_called() 
+    mock_save_image.assert_called_once()
+    
+    saved_data = mock_save_image.call_args[0][1]
+    assert saved_data.shape == (10, 10) # Grayscale output (2D)
+    assert np.all(saved_data == 100)    # Green channel's value
+
+    assert merge_rule.id.hex in updated_context.merged_maps_details
+    details = updated_context.merged_maps_details[merge_rule.id.hex]
+    assert details['status'] == 'Processed'
+    assert "merged_GrayscaleFromGreen" in details['temp_merged_file']
+    mock_log_error.assert_not_called()
+    mock_log_info.assert_any_call(f"Successfully merged map '{merge_rule.map_type}' for asset '{context.asset_rule.name}'.")
+
+@mock.patch('processing.pipeline.stages.map_merging.ipu.save_image')
+@mock.patch('processing.pipeline.stages.map_merging.ipu.load_image')
+@mock.patch('logging.error')
+def test_map_merging_default_value_if_missing_channel(mock_log_error, mock_load_image, mock_save_image):
+    stage = MapMergingStage()
+
+    input_id = uuid.uuid4()
+    processed_details = {
+        # Input is a grayscale image (1 channel)
+        input_id.hex: {'temp_processed_file': '/fake/gray_source.png', 'status': 'Processed', 'map_type': 'GRAY_SRC'}
+    }
+    mock_gray_data = np.full((10, 10), 50, dtype=np.uint8)
+    mock_load_image.return_value = mock_gray_data
+    
+    # Merge Rule: try to read source_channel 1 (which doesn't exist in grayscale)
+    # and use default_value_if_missing for target_channel 0.
+    # Also, read source_channel 0 (which exists) for target_channel 1.
+    mic1 = create_mock_merge_input_channel(file_rule_id=input_id, source_channel=1, target_channel=0)
+    mic1.default_value_if_missing = 128 # Set a specific default value
+    mic2 = create_mock_merge_input_channel(file_rule_id=input_id, source_channel=0, target_channel=1)
+
+    merge_settings = create_mock_merge_settings(input_maps=[mic1, mic2], output_channels=2)
+    merge_rule_id = uuid.uuid4()
+    merge_rule = create_mock_file_rule_for_merging(id_val=merge_rule_id, map_type="DefaultValueTest", merge_settings=merge_settings)
+    
+    context = create_map_merging_mock_context(
+        initial_file_rules=[merge_rule], 
+        initial_processed_details=processed_details
+    )
+    mock_save_image.return_value = True
+    
+    updated_context = stage.execute(context)
+
+    mock_load_image.assert_called_once_with(Path('/fake/gray_source.png'))
+    mock_save_image.assert_called_once()
+    
+    saved_data = mock_save_image.call_args[0][1]
+    assert saved_data.shape == (10, 10, 2)
+    assert np.all(saved_data[:,:,0] == 128) # Default value for missing source channel 1
+    assert np.all(saved_data[:,:,1] == 50)  # Value from existing source channel 0
+
+    assert merge_rule.id.hex in updated_context.merged_maps_details
+    details = updated_context.merged_maps_details[merge_rule.id.hex]
+    assert details['status'] == 'Processed'
+    mock_log_error.assert_not_called()

tests/processing/pipeline/stages/test_metadata_finalization_save.py

@@ -0,0 +1,359 @@
+import pytest
+from unittest import mock
+from pathlib import Path
+import datetime
+import json # For comparing dumped content
+import uuid
+from typing import Optional, Dict, Any
+
+from processing.pipeline.stages.metadata_finalization_save import MetadataFinalizationAndSaveStage
+from processing.pipeline.asset_context import AssetProcessingContext
+from rule_structure import AssetRule, SourceRule
+from configuration import Configuration, GeneralSettings # Added GeneralSettings as it's in the helper
+
+
+def create_metadata_save_mock_context(
+    status_flags: Optional[Dict[str, Any]] = None,
+    initial_asset_metadata: Optional[Dict[str, Any]] = None,
+    processed_details: Optional[Dict[str, Any]] = None,
+    merged_details: Optional[Dict[str, Any]] = None,
+    asset_name: str = "MetaSaveAsset",
+    output_path_pattern_val: str = "{asset_name}/metadata/{filename}",
+    # ... other common context fields ...
+) -> AssetProcessingContext:
+    mock_asset_rule = mock.MagicMock(spec=AssetRule)
+    mock_asset_rule.name = asset_name
+    mock_asset_rule.output_path_pattern = output_path_pattern_val
+    mock_asset_rule.id = uuid.uuid4() # Needed for generate_path_from_pattern if it uses it
+
+    mock_source_rule = mock.MagicMock(spec=SourceRule)
+    mock_source_rule.name = "MetaSaveSource"
+    
+    mock_config = mock.MagicMock(spec=Configuration)
+    # mock_config.general_settings = mock.MagicMock(spec=GeneralSettings) # If needed
+
+    context = AssetProcessingContext(
+        source_rule=mock_source_rule,
+        asset_rule=mock_asset_rule,
+        workspace_path=Path("/fake/workspace"),
+        engine_temp_dir=Path("/fake/temp_engine_dir"),
+        output_base_path=Path("/fake/output_base"), # For generate_path
+        effective_supplier="ValidSupplier",
+        asset_metadata=initial_asset_metadata if initial_asset_metadata is not None else {},
+        processed_maps_details=processed_details if processed_details is not None else {},
+        merged_maps_details=merged_details if merged_details is not None else {},
+        files_to_process=[],
+        loaded_data_cache={},
+        config_obj=mock_config,
+        status_flags=status_flags if status_flags is not None else {},
+        incrementing_value="001", # Example for path generation
+        sha5_value="abc"      # Example for path generation
+    )
+    return context
+@mock.patch('processing.pipeline.stages.metadata_finalization_save.json.dump')
+@mock.patch('builtins.open', new_callable=mock.mock_open)
+@mock.patch('pathlib.Path.mkdir')
+@mock.patch('processing.pipeline.stages.metadata_finalization_save.generate_path_from_pattern')
+@mock.patch('datetime.datetime')
+def test_asset_skipped_before_metadata_init(
+    mock_dt, mock_gen_path, mock_mkdir, mock_file_open, mock_json_dump
+):
+    """
+    Tests that if an asset is marked for skipping and has no initial metadata,
+    the stage returns early without attempting to save metadata.
+    """
+    stage = MetadataFinalizationAndSaveStage()
+    context = create_metadata_save_mock_context(
+        status_flags={'skip_asset': True},
+        initial_asset_metadata={} # Explicitly empty
+    )
+
+    updated_context = stage.execute(context)
+
+    # Assert that no processing or saving attempts were made
+    mock_dt.now.assert_not_called() # Should not even try to set end time if no metadata
+    mock_gen_path.assert_not_called()
+    mock_mkdir.assert_not_called()
+    mock_file_open.assert_not_called()
+    mock_json_dump.assert_not_called()
+
+    assert updated_context.asset_metadata == {} # Metadata remains empty
+    assert 'metadata_file_path' not in updated_context.asset_metadata
+    assert updated_context.status_flags.get('metadata_save_error') is None
+@mock.patch('processing.pipeline.stages.metadata_finalization_save.json.dump')
+@mock.patch('builtins.open', new_callable=mock.mock_open)
+@mock.patch('pathlib.Path.mkdir')
+@mock.patch('processing.pipeline.stages.metadata_finalization_save.generate_path_from_pattern')
+@mock.patch('datetime.datetime')
+def test_asset_skipped_after_metadata_init(
+    mock_dt, mock_gen_path, mock_mkdir, mock_file_open, mock_json_dump
+):
+    """
+    Tests that if an asset is marked for skipping but has initial metadata,
+    the status is updated to 'Skipped' and metadata is saved.
+    """
+    stage = MetadataFinalizationAndSaveStage()
+    
+    fixed_now = datetime.datetime(2023, 1, 1, 12, 0, 0)
+    mock_dt.now.return_value = fixed_now
+    
+    fake_metadata_path_str = "/fake/output_base/SkippedAsset/metadata/SkippedAsset_metadata.json"
+    mock_gen_path.return_value = fake_metadata_path_str
+    
+    initial_meta = {'asset_name': "SkippedAsset", 'status': "Pending"}
+    
+    context = create_metadata_save_mock_context(
+        asset_name="SkippedAsset",
+        status_flags={'skip_asset': True},
+        initial_asset_metadata=initial_meta
+    )
+    
+    updated_context = stage.execute(context)
+    
+    mock_dt.now.assert_called_once()
+    mock_gen_path.assert_called_once_with(
+        context.asset_rule.output_path_pattern,
+        context.asset_rule,
+        context.source_rule,
+        context.output_base_path,
+        context.asset_metadata, # Original metadata passed for path gen
+        context.incrementing_value,
+        context.sha5_value,
+        filename_override=f"{context.asset_rule.name}_metadata.json"
+    )
+    mock_mkdir.assert_called_once_with(parents=True, exist_ok=True)
+    mock_file_open.assert_called_once_with(Path(fake_metadata_path_str), 'w')
+    mock_json_dump.assert_called_once()
+    
+    dumped_data = mock_json_dump.call_args[0][0]
+    assert dumped_data['status'] == "Skipped"
+    assert dumped_data['processing_end_time'] == fixed_now.isoformat()
+    assert 'processed_map_details' not in dumped_data # Should not be present if skipped early
+    assert 'merged_map_details' not in dumped_data   # Should not be present if skipped early
+    
+    assert updated_context.asset_metadata['status'] == "Skipped"
+    assert updated_context.asset_metadata['processing_end_time'] == fixed_now.isoformat()
+    assert updated_context.asset_metadata['metadata_file_path'] == fake_metadata_path_str
+    assert updated_context.status_flags.get('metadata_save_error') is None
+@mock.patch('processing.pipeline.stages.metadata_finalization_save.json.dump')
+@mock.patch('builtins.open', new_callable=mock.mock_open) # Mocks open()
+@mock.patch('pathlib.Path.mkdir')
+@mock.patch('processing.pipeline.stages.metadata_finalization_save.generate_path_from_pattern')
+@mock.patch('datetime.datetime')
+def test_metadata_save_success(mock_dt, mock_gen_path, mock_mkdir, mock_file_open, mock_json_dump):
+    """
+    Tests successful metadata finalization and saving, including serialization of Path objects.
+    """
+    stage = MetadataFinalizationAndSaveStage()
+    
+    fixed_now = datetime.datetime(2023, 1, 1, 12, 30, 0)
+    mock_dt.now.return_value = fixed_now
+    
+    fake_metadata_path_str = "/fake/output_base/MetaSaveAsset/metadata/MetaSaveAsset_metadata.json"
+    mock_gen_path.return_value = fake_metadata_path_str
+    
+    initial_meta = {'asset_name': "MetaSaveAsset", 'status': "Pending", 'processing_start_time': "2023-01-01T12:00:00"}
+    # Example of a Path object that needs serialization
+    proc_details = {'map1': {'temp_processed_file': Path('/fake/temp_engine_dir/map1.png'), 'final_file_path': Path('/fake/output_base/MetaSaveAsset/map1.png')}} 
+    merged_details = {'merged_map_A': {'output_path': Path('/fake/output_base/MetaSaveAsset/merged_A.png')}}
+    
+    context = create_metadata_save_mock_context(
+        initial_asset_metadata=initial_meta,
+        processed_details=proc_details,
+        merged_details=merged_details,
+        status_flags={} # No errors, no skip
+    )
+    
+    updated_context = stage.execute(context)
+    
+    mock_dt.now.assert_called_once()
+    mock_gen_path.assert_called_once_with(
+        context.asset_rule.output_path_pattern,
+        context.asset_rule,
+        context.source_rule,
+        context.output_base_path,
+        context.asset_metadata, # The metadata *before* adding end_time, status etc.
+        context.incrementing_value,
+        context.sha5_value,
+        filename_override=f"{context.asset_rule.name}_metadata.json"
+    )
+    mock_mkdir.assert_called_once_with(parents=True, exist_ok=True) # Checks parent dir of fake_metadata_path_str
+    mock_file_open.assert_called_once_with(Path(fake_metadata_path_str), 'w')
+    mock_json_dump.assert_called_once()
+    
+    # Check what was passed to json.dump
+    dumped_data = mock_json_dump.call_args[0][0]
+    assert dumped_data['status'] == "Processed"
+    assert dumped_data['processing_end_time'] == fixed_now.isoformat()
+    assert 'processing_start_time' in dumped_data # Ensure existing fields are preserved
+
+    # Verify processed_map_details and Path serialization
+    assert 'processed_map_details' in dumped_data
+    assert dumped_data['processed_map_details']['map1']['temp_processed_file'] == '/fake/temp_engine_dir/map1.png'
+    assert dumped_data['processed_map_details']['map1']['final_file_path'] == '/fake/output_base/MetaSaveAsset/map1.png'
+
+    # Verify merged_map_details and Path serialization
+    assert 'merged_map_details' in dumped_data
+    assert dumped_data['merged_map_details']['merged_map_A']['output_path'] == '/fake/output_base/MetaSaveAsset/merged_A.png'
+            
+    assert updated_context.asset_metadata['metadata_file_path'] == fake_metadata_path_str
+    assert updated_context.asset_metadata['status'] == "Processed"
+    assert updated_context.status_flags.get('metadata_save_error') is None
+@mock.patch('processing.pipeline.stages.metadata_finalization_save.json.dump')
+@mock.patch('builtins.open', new_callable=mock.mock_open)
+@mock.patch('pathlib.Path.mkdir')
+@mock.patch('processing.pipeline.stages.metadata_finalization_save.generate_path_from_pattern')
+@mock.patch('datetime.datetime')
+def test_processing_failed_due_to_previous_error(
+    mock_dt, mock_gen_path, mock_mkdir, mock_file_open, mock_json_dump
+):
+    """
+    Tests that if a previous stage set an error flag, the status is 'Failed'
+    and metadata (including any existing details) is saved.
+    """
+    stage = MetadataFinalizationAndSaveStage()
+    
+    fixed_now = datetime.datetime(2023, 1, 1, 12, 45, 0)
+    mock_dt.now.return_value = fixed_now
+    
+    fake_metadata_path_str = "/fake/output_base/FailedAsset/metadata/FailedAsset_metadata.json"
+    mock_gen_path.return_value = fake_metadata_path_str
+    
+    initial_meta = {'asset_name': "FailedAsset", 'status': "Processing"}
+    # Simulate some details might exist even if a later stage failed
+    proc_details = {'map1_partial': {'temp_processed_file': Path('/fake/temp_engine_dir/map1_partial.png')}}
+    
+    context = create_metadata_save_mock_context(
+        asset_name="FailedAsset",
+        initial_asset_metadata=initial_meta,
+        processed_details=proc_details,
+        merged_details={}, # No merged details if processing failed before that
+        status_flags={'file_processing_error': True, 'error_message': "Something went wrong"}
+    )
+    
+    updated_context = stage.execute(context)
+    
+    mock_dt.now.assert_called_once()
+    mock_gen_path.assert_called_once() # Path generation should still occur
+    mock_mkdir.assert_called_once_with(parents=True, exist_ok=True)
+    mock_file_open.assert_called_once_with(Path(fake_metadata_path_str), 'w')
+    mock_json_dump.assert_called_once()
+    
+    dumped_data = mock_json_dump.call_args[0][0]
+    assert dumped_data['status'] == "Failed"
+    assert dumped_data['processing_end_time'] == fixed_now.isoformat()
+    assert 'error_message' in dumped_data # Assuming error messages from status_flags are copied
+    assert dumped_data['error_message'] == "Something went wrong"
+
+    # Check that existing details are included
+    assert 'processed_map_details' in dumped_data
+    assert dumped_data['processed_map_details']['map1_partial']['temp_processed_file'] == '/fake/temp_engine_dir/map1_partial.png'
+    assert 'merged_map_details' in dumped_data # Should be present, even if empty
+    assert dumped_data['merged_map_details'] == {}
+            
+    assert updated_context.asset_metadata['status'] == "Failed"
+    assert updated_context.asset_metadata['metadata_file_path'] == fake_metadata_path_str
+    assert updated_context.status_flags.get('metadata_save_error') is None
+    # Ensure the original error flag is preserved
+    assert updated_context.status_flags['file_processing_error'] is True
+@mock.patch('processing.pipeline.stages.metadata_finalization_save.json.dump')
+@mock.patch('builtins.open', new_callable=mock.mock_open)
+@mock.patch('pathlib.Path.mkdir')
+@mock.patch('processing.pipeline.stages.metadata_finalization_save.generate_path_from_pattern')
+@mock.patch('datetime.datetime')
+@mock.patch('logging.error') # To check if error is logged
+def test_generate_path_fails(
+    mock_log_error, mock_dt, mock_gen_path, mock_mkdir, mock_file_open, mock_json_dump
+):
+    """
+    Tests behavior when generate_path_from_pattern raises an exception.
+    Ensures status is updated, error flag is set, and no save is attempted.
+    """
+    stage = MetadataFinalizationAndSaveStage()
+    
+    fixed_now = datetime.datetime(2023, 1, 1, 12, 50, 0)
+    mock_dt.now.return_value = fixed_now
+    
+    mock_gen_path.side_effect = Exception("Simulated path generation error")
+    
+    initial_meta = {'asset_name': "PathFailAsset", 'status': "Processing"}
+    context = create_metadata_save_mock_context(
+        asset_name="PathFailAsset",
+        initial_asset_metadata=initial_meta,
+        status_flags={} 
+    )
+    
+    updated_context = stage.execute(context)
+    
+    mock_dt.now.assert_called_once() # Time is set before path generation
+    mock_gen_path.assert_called_once() # generate_path_from_pattern is called
+    
+    # File operations should NOT be called if path generation fails
+    mock_mkdir.assert_not_called()
+    mock_file_open.assert_not_called()
+    mock_json_dump.assert_not_called()
+    
+    mock_log_error.assert_called_once() # Check that an error was logged
+    # Example: check if the log message contains relevant info, if needed
+    # assert "Failed to generate metadata path" in mock_log_error.call_args[0][0]
+
+    assert updated_context.asset_metadata['status'] == "Failed" # Or a more specific error status
+    assert 'processing_end_time' in updated_context.asset_metadata # End time should still be set
+    assert updated_context.asset_metadata['processing_end_time'] == fixed_now.isoformat()
+    assert 'metadata_file_path' not in updated_context.asset_metadata # Path should not be set
+    
+    assert updated_context.status_flags.get('metadata_save_error') is True
+    assert 'error_message' in updated_context.asset_metadata # Check if error message is populated
+    assert "Simulated path generation error" in updated_context.asset_metadata['error_message']
+@mock.patch('processing.pipeline.stages.metadata_finalization_save.json.dump')
+@mock.patch('builtins.open', new_callable=mock.mock_open)
+@mock.patch('pathlib.Path.mkdir')
+@mock.patch('processing.pipeline.stages.metadata_finalization_save.generate_path_from_pattern')
+@mock.patch('datetime.datetime')
+@mock.patch('logging.error') # To check if error is logged
+def test_json_dump_fails(
+    mock_log_error, mock_dt, mock_gen_path, mock_mkdir, mock_file_open, mock_json_dump
+):
+    """
+    Tests behavior when json.dump raises an exception during saving.
+    Ensures status is updated, error flag is set, and error is logged.
+    """
+    stage = MetadataFinalizationAndSaveStage()
+    
+    fixed_now = datetime.datetime(2023, 1, 1, 12, 55, 0)
+    mock_dt.now.return_value = fixed_now
+    
+    fake_metadata_path_str = "/fake/output_base/JsonDumpFailAsset/metadata/JsonDumpFailAsset_metadata.json"
+    mock_gen_path.return_value = fake_metadata_path_str
+    
+    mock_json_dump.side_effect = IOError("Simulated JSON dump error") # Or TypeError for non-serializable
+    
+    initial_meta = {'asset_name': "JsonDumpFailAsset", 'status': "Processing"}
+    context = create_metadata_save_mock_context(
+        asset_name="JsonDumpFailAsset",
+        initial_asset_metadata=initial_meta,
+        status_flags={}
+    )
+    
+    updated_context = stage.execute(context)
+    
+    mock_dt.now.assert_called_once()
+    mock_gen_path.assert_called_once()
+    mock_mkdir.assert_called_once_with(parents=True, exist_ok=True)
+    mock_file_open.assert_called_once_with(Path(fake_metadata_path_str), 'w')
+    mock_json_dump.assert_called_once() # json.dump was attempted
+    
+    mock_log_error.assert_called_once()
+    # assert "Failed to save metadata JSON" in mock_log_error.call_args[0][0]
+
+    assert updated_context.asset_metadata['status'] == "Failed" # Or specific "Metadata Save Failed"
+    assert 'processing_end_time' in updated_context.asset_metadata
+    assert updated_context.asset_metadata['processing_end_time'] == fixed_now.isoformat()
+    # metadata_file_path might be set if path generation succeeded, even if dump failed.
+    # Depending on desired behavior, this could be asserted or not.
+    # For now, let's assume it's set if path generation was successful.
+    assert updated_context.asset_metadata['metadata_file_path'] == fake_metadata_path_str 
+                                                                    
+    assert updated_context.status_flags.get('metadata_save_error') is True
+    assert 'error_message' in updated_context.asset_metadata
+    assert "Simulated JSON dump error" in updated_context.asset_metadata['error_message']

tests/processing/pipeline/stages/test_metadata_initialization.py

@@ -0,0 +1,169 @@
+import pytest
+from unittest import mock
+from pathlib import Path
+import datetime
+import uuid
+from typing import Optional
+
+from processing.pipeline.stages.metadata_initialization import MetadataInitializationStage
+from processing.pipeline.asset_context import AssetProcessingContext
+from rule_structure import AssetRule, SourceRule
+from configuration import Configuration, GeneralSettings
+
+# Helper function to create a mock AssetProcessingContext
+def create_metadata_init_mock_context(
+    skip_asset_flag: bool = False,
+    asset_name: str = "MetaAsset",
+    asset_id: uuid.UUID = None, # Allow None to default to uuid.uuid4()
+    source_path_str: str = "source/meta_asset",
+    output_pattern: str = "{asset_name}/{map_type}",
+    tags: list = None,
+    custom_fields: dict = None,
+    source_rule_name: str = "MetaSource",
+    source_rule_id: uuid.UUID = None, # Allow None to default to uuid.uuid4()
+    eff_supplier: Optional[str] = "SupplierMeta",
+    app_version_str: str = "1.0.0-test",
+    inc_val: Optional[str] = None,
+    sha_val: Optional[str] = None
+) -> AssetProcessingContext:
+    mock_asset_rule = mock.MagicMock(spec=AssetRule)
+    mock_asset_rule.name = asset_name
+    mock_asset_rule.id = asset_id if asset_id is not None else uuid.uuid4()
+    mock_asset_rule.source_path = Path(source_path_str)
+    mock_asset_rule.output_path_pattern = output_pattern
+    mock_asset_rule.tags = tags if tags is not None else ["tag1", "test_tag"]
+    mock_asset_rule.custom_fields = custom_fields if custom_fields is not None else {"custom_key": "custom_value"}
+
+    mock_source_rule = mock.MagicMock(spec=SourceRule)
+    mock_source_rule.name = source_rule_name
+    mock_source_rule.id = source_rule_id if source_rule_id is not None else uuid.uuid4()
+    
+    mock_general_settings = mock.MagicMock(spec=GeneralSettings)
+    mock_general_settings.app_version = app_version_str
+    
+    mock_config = mock.MagicMock(spec=Configuration)
+    mock_config.general_settings = mock_general_settings
+
+    context = AssetProcessingContext(
+        source_rule=mock_source_rule,
+        asset_rule=mock_asset_rule,
+        workspace_path=Path("/fake/workspace"),
+        engine_temp_dir=Path("/fake/temp"),
+        output_base_path=Path("/fake/output"),
+        effective_supplier=eff_supplier,
+        asset_metadata={},
+        processed_maps_details={},
+        merged_maps_details={},
+        files_to_process=[],
+        loaded_data_cache={},
+        config_obj=mock_config,
+        status_flags={'skip_asset': skip_asset_flag},
+        incrementing_value=inc_val,
+        sha5_value=sha_val
+    )
+    return context
+
+@mock.patch('processing.pipeline.stages.metadata_initialization.datetime')
+def test_metadata_initialization_not_skipped(mock_datetime_module):
+    stage = MetadataInitializationStage()
+    
+    fixed_now = datetime.datetime(2023, 10, 26, 12, 0, 0, tzinfo=datetime.timezone.utc)
+    mock_datetime_module.datetime.now.return_value = fixed_now
+    
+    asset_id_val = uuid.uuid4()
+    source_id_val = uuid.uuid4()
+    
+    context = create_metadata_init_mock_context(
+        skip_asset_flag=False,
+        asset_id=asset_id_val,
+        source_rule_id=source_id_val,
+        inc_val="001",
+        sha_val="abcde"
+    )
+    
+    updated_context = stage.execute(context)
+    
+    assert isinstance(updated_context.asset_metadata, dict)
+    assert isinstance(updated_context.processed_maps_details, dict)
+    assert isinstance(updated_context.merged_maps_details, dict)
+    
+    md = updated_context.asset_metadata
+    assert md['asset_name'] == "MetaAsset"
+    assert md['asset_id'] == str(asset_id_val)
+    assert md['source_rule_name'] == "MetaSource"
+    assert md['source_rule_id'] == str(source_id_val)
+    assert md['source_path'] == "source/meta_asset"
+    assert md['effective_supplier'] == "SupplierMeta"
+    assert md['output_path_pattern'] == "{asset_name}/{map_type}"
+    assert md['processing_start_time'] == fixed_now.isoformat()
+    assert md['status'] == "Pending"
+    assert md['version'] == "1.0.0-test"
+    assert md['tags'] == ["tag1", "test_tag"]
+    assert md['custom_fields'] == {"custom_key": "custom_value"}
+    assert md['incrementing_value'] == "001"
+    assert md['sha5_value'] == "abcde"
+
+@mock.patch('processing.pipeline.stages.metadata_initialization.datetime')
+def test_metadata_initialization_not_skipped_none_inc_sha(mock_datetime_module):
+    stage = MetadataInitializationStage()
+    
+    fixed_now = datetime.datetime(2023, 10, 26, 12, 0, 0, tzinfo=datetime.timezone.utc)
+    mock_datetime_module.datetime.now.return_value = fixed_now
+    
+    context = create_metadata_init_mock_context(
+        skip_asset_flag=False,
+        inc_val=None,
+        sha_val=None
+    )
+    
+    updated_context = stage.execute(context)
+    
+    md = updated_context.asset_metadata
+    assert 'incrementing_value' not in md # Or assert md['incrementing_value'] is None, depending on desired behavior
+    assert 'sha5_value' not in md # Or assert md['sha5_value'] is None
+
+def test_metadata_initialization_skipped():
+    stage = MetadataInitializationStage()
+    context = create_metadata_init_mock_context(skip_asset_flag=True)
+    
+    # Make copies of initial state to ensure they are not modified
+    initial_asset_metadata = dict(context.asset_metadata)
+    initial_processed_maps = dict(context.processed_maps_details)
+    initial_merged_maps = dict(context.merged_maps_details)
+            
+    updated_context = stage.execute(context)
+    
+    assert updated_context.asset_metadata == initial_asset_metadata
+    assert updated_context.processed_maps_details == initial_processed_maps
+    assert updated_context.merged_maps_details == initial_merged_maps
+    assert not updated_context.asset_metadata # Explicitly check it's empty as per initial setup
+    assert not updated_context.processed_maps_details
+    assert not updated_context.merged_maps_details
+
+@mock.patch('processing.pipeline.stages.metadata_initialization.datetime')
+def test_tags_and_custom_fields_are_copies(mock_datetime_module):
+    stage = MetadataInitializationStage()
+    fixed_now = datetime.datetime(2023, 10, 26, 12, 0, 0, tzinfo=datetime.timezone.utc)
+    mock_datetime_module.datetime.now.return_value = fixed_now
+
+    original_tags = ["original_tag"]
+    original_custom_fields = {"original_key": "original_value"}
+
+    context = create_metadata_init_mock_context(
+        skip_asset_flag=False,
+        tags=original_tags,
+        custom_fields=original_custom_fields
+    )
+
+    # Modify originals after context creation but before stage execution
+    original_tags.append("modified_after_creation")
+    original_custom_fields["new_key_after_creation"] = "new_value"
+
+    updated_context = stage.execute(context)
+
+    md = updated_context.asset_metadata
+    assert md['tags'] == ["original_tag"] # Should not have "modified_after_creation"
+    assert md['tags'] is not original_tags # Ensure it's a different object
+
+    assert md['custom_fields'] == {"original_key": "original_value"} # Should not have "new_key_after_creation"
+    assert md['custom_fields'] is not original_custom_fields # Ensure it's a different object

tests/processing/pipeline/stages/test_normal_map_green_channel.py

@@ -0,0 +1,323 @@
+import pytest
+from unittest import mock
+from pathlib import Path
+import uuid
+import numpy as np
+import logging # Added for mocking logger
+
+from processing.pipeline.stages.normal_map_green_channel import NormalMapGreenChannelStage
+from processing.pipeline.asset_context import AssetProcessingContext
+from rule_structure import AssetRule, SourceRule, FileRule
+from configuration import Configuration, GeneralSettings
+
+# Helper functions
+def create_mock_file_rule_for_normal_test(
+    id_val: uuid.UUID = None, # Corrected type hint from Optional[uuid.UUID]
+    map_type: str = "NORMAL", 
+    filename_pattern: str = "normal.png"
+) -> mock.MagicMock:
+    mock_fr = mock.MagicMock(spec=FileRule)
+    mock_fr.id = id_val if id_val else uuid.uuid4()
+    mock_fr.map_type = map_type
+    mock_fr.filename_pattern = filename_pattern
+    mock_fr.item_type = "MAP_COL" # As per example, though not directly used by stage
+    mock_fr.active = True # As per example
+    return mock_fr
+
+def create_normal_map_mock_context(
+    initial_file_rules: list = None, # Corrected type hint
+    initial_processed_details: dict = None, # Corrected type hint
+    invert_green_globally: bool = False,
+    skip_asset_flag: bool = False,
+    asset_name: str = "NormalMapAsset"
+) -> AssetProcessingContext:
+    mock_asset_rule = mock.MagicMock(spec=AssetRule)
+    mock_asset_rule.name = asset_name
+    
+    mock_source_rule = mock.MagicMock(spec=SourceRule)
+    
+    mock_gs = mock.MagicMock(spec=GeneralSettings)
+    mock_gs.invert_normal_map_green_channel_globally = invert_green_globally 
+    
+    mock_config = mock.MagicMock(spec=Configuration)
+    mock_config.general_settings = mock_gs
+
+    context = AssetProcessingContext(
+        source_rule=mock_source_rule,
+        asset_rule=mock_asset_rule,
+        workspace_path=Path("/fake/workspace"),
+        engine_temp_dir=Path("/fake/temp_engine_dir"),
+        output_base_path=Path("/fake/output"),
+        effective_supplier="ValidSupplier",
+        asset_metadata={'asset_name': asset_name},
+        processed_maps_details=initial_processed_details if initial_processed_details is not None else {},
+        merged_maps_details={},
+        files_to_process=list(initial_file_rules) if initial_file_rules else [],
+        loaded_data_cache={},
+        config_obj=mock_config,
+        status_flags={'skip_asset': skip_asset_flag},
+        incrementing_value=None, # Added as per AssetProcessingContext constructor
+        sha5_value=None # Added as per AssetProcessingContext constructor
+    )
+    return context
+
+# Unit tests will be added below
+@mock.patch('processing.pipeline.stages.normal_map_green_channel.ipu.save_image')
+@mock.patch('processing.pipeline.stages.normal_map_green_channel.ipu.load_image')
+def test_asset_skipped(mock_load_image, mock_save_image):
+    stage = NormalMapGreenChannelStage()
+    normal_fr = create_mock_file_rule_for_normal_test(map_type="NORMAL")
+    initial_details = {
+        normal_fr.id.hex: {'temp_processed_file': '/fake/temp_engine_dir/processed_normal.png', 'status': 'Processed', 'map_type': 'NORMAL', 'notes': ''}
+    }
+    context = create_normal_map_mock_context(
+        initial_file_rules=[normal_fr],
+        initial_processed_details=initial_details,
+        invert_green_globally=True,
+        skip_asset_flag=True # Asset is skipped
+    )
+    original_details = context.processed_maps_details.copy()
+
+    updated_context = stage.execute(context)
+
+    mock_load_image.assert_not_called()
+    mock_save_image.assert_not_called()
+    assert updated_context.processed_maps_details == original_details
+    assert normal_fr in updated_context.files_to_process # Ensure rule is still there
+
+@mock.patch('processing.pipeline.stages.normal_map_green_channel.ipu.save_image')
+@mock.patch('processing.pipeline.stages.normal_map_green_channel.ipu.load_image')
+def test_no_normal_map_present(mock_load_image, mock_save_image):
+    stage = NormalMapGreenChannelStage()
+    # Create a non-normal map rule
+    diffuse_fr = create_mock_file_rule_for_normal_test(map_type="DIFFUSE", filename_pattern="diffuse.png")
+    initial_details = {
+        diffuse_fr.id.hex: {'temp_processed_file': '/fake/temp_engine_dir/processed_diffuse.png', 'status': 'Processed', 'map_type': 'DIFFUSE', 'notes': ''}
+    }
+    context = create_normal_map_mock_context(
+        initial_file_rules=[diffuse_fr],
+        initial_processed_details=initial_details,
+        invert_green_globally=True # Inversion enabled, but no normal map
+    )
+    original_details = context.processed_maps_details.copy()
+
+    updated_context = stage.execute(context)
+
+    mock_load_image.assert_not_called()
+    mock_save_image.assert_not_called()
+    assert updated_context.processed_maps_details == original_details
+    assert diffuse_fr in updated_context.files_to_process
+
+@mock.patch('processing.pipeline.stages.normal_map_green_channel.ipu.save_image')
+@mock.patch('processing.pipeline.stages.normal_map_green_channel.ipu.load_image')
+def test_normal_map_present_inversion_disabled(mock_load_image, mock_save_image):
+    stage = NormalMapGreenChannelStage()
+    normal_rule_id = uuid.uuid4()
+    normal_fr = create_mock_file_rule_for_normal_test(id_val=normal_rule_id, map_type="NORMAL")
+    initial_details = {
+        normal_fr.id.hex: {'temp_processed_file': '/fake/temp_engine_dir/processed_normal.png', 'status': 'Processed', 'map_type': 'NORMAL', 'notes': 'Initial note'}
+    }
+    context = create_normal_map_mock_context(
+        initial_file_rules=[normal_fr],
+        initial_processed_details=initial_details,
+        invert_green_globally=False # Inversion disabled
+    )
+    original_details_entry = context.processed_maps_details[normal_fr.id.hex].copy()
+
+    updated_context = stage.execute(context)
+
+    mock_load_image.assert_not_called()
+    mock_save_image.assert_not_called()
+    assert updated_context.processed_maps_details[normal_fr.id.hex] == original_details_entry
+    assert normal_fr in updated_context.files_to_process
+
+@mock.patch('processing.pipeline.stages.normal_map_green_channel.ipu.save_image')
+@mock.patch('processing.pipeline.stages.normal_map_green_channel.ipu.load_image')
+@mock.patch('logging.info')
+@mock.patch('logging.debug')
+def test_normal_map_inversion_uint8_success(mock_log_debug, mock_log_info, mock_load_image, mock_save_image):
+    stage = NormalMapGreenChannelStage()
+    
+    normal_rule_id = uuid.uuid4()
+    normal_fr = create_mock_file_rule_for_normal_test(id_val=normal_rule_id, map_type="NORMAL")
+    
+    initial_temp_path = Path('/fake/temp_engine_dir/processed_normal.png')
+    initial_details = {
+        normal_fr.id.hex: {'temp_processed_file': str(initial_temp_path), 'status': 'Processed', 'map_type': 'NORMAL', 'notes': 'Initial note'}
+    }
+    context = create_normal_map_mock_context(
+        initial_file_rules=[normal_fr],
+        initial_processed_details=initial_details,
+        invert_green_globally=True # Enable inversion
+    )
+
+    # R=10, G=50, B=100
+    mock_loaded_normal_data = np.array([[[10, 50, 100]]], dtype=np.uint8) 
+    mock_load_image.return_value = mock_loaded_normal_data
+    mock_save_image.return_value = True # Simulate successful save
+    
+    updated_context = stage.execute(context)
+    
+    mock_load_image.assert_called_once_with(initial_temp_path)
+    
+    # Check that save_image was called with green channel inverted
+    assert mock_save_image.call_count == 1
+    saved_path_arg, saved_data_arg = mock_save_image.call_args[0]
+    
+    assert saved_data_arg[0,0,0] == 10 # R unchanged
+    assert saved_data_arg[0,0,1] == 255 - 50 # G inverted
+    assert saved_data_arg[0,0,2] == 100 # B unchanged
+    
+    assert isinstance(saved_path_arg, Path)
+    assert "normal_g_inv_" in saved_path_arg.name
+    assert saved_path_arg.parent == initial_temp_path.parent # Should be in same temp dir
+
+    normal_detail = updated_context.processed_maps_details[normal_fr.id.hex]
+    assert "normal_g_inv_" in normal_detail['temp_processed_file']
+    assert Path(normal_detail['temp_processed_file']).name == saved_path_arg.name
+    assert "Green channel inverted" in normal_detail['notes']
+    assert "Initial note" in normal_detail['notes'] # Check existing notes preserved
+    
+    assert normal_fr in updated_context.files_to_process
+
+@mock.patch('processing.pipeline.stages.normal_map_green_channel.ipu.save_image')
+@mock.patch('processing.pipeline.stages.normal_map_green_channel.ipu.load_image')
+@mock.patch('logging.info')
+@mock.patch('logging.debug')
+def test_normal_map_inversion_float_success(mock_log_debug, mock_log_info, mock_load_image, mock_save_image):
+    stage = NormalMapGreenChannelStage()
+    normal_rule_id = uuid.uuid4()
+    normal_fr = create_mock_file_rule_for_normal_test(id_val=normal_rule_id, map_type="NORMAL")
+    initial_temp_path = Path('/fake/temp_engine_dir/processed_normal_float.png')
+    initial_details = {
+        normal_fr.id.hex: {'temp_processed_file': str(initial_temp_path), 'status': 'Processed', 'map_type': 'NORMAL', 'notes': 'Float image'}
+    }
+    context = create_normal_map_mock_context(
+        initial_file_rules=[normal_fr],
+        initial_processed_details=initial_details,
+        invert_green_globally=True
+    )
+
+    # R=0.1, G=0.25, B=0.75
+    mock_loaded_normal_data = np.array([[[0.1, 0.25, 0.75]]], dtype=np.float32)
+    mock_load_image.return_value = mock_loaded_normal_data
+    mock_save_image.return_value = True
+
+    updated_context = stage.execute(context)
+
+    mock_load_image.assert_called_once_with(initial_temp_path)
+    
+    assert mock_save_image.call_count == 1
+    saved_path_arg, saved_data_arg = mock_save_image.call_args[0]
+
+    assert np.isclose(saved_data_arg[0,0,0], 0.1) # R unchanged
+    assert np.isclose(saved_data_arg[0,0,1], 1.0 - 0.25) # G inverted
+    assert np.isclose(saved_data_arg[0,0,2], 0.75) # B unchanged
+    
+    assert "normal_g_inv_" in saved_path_arg.name
+    normal_detail = updated_context.processed_maps_details[normal_fr.id.hex]
+    assert "normal_g_inv_" in normal_detail['temp_processed_file']
+    assert "Green channel inverted" in normal_detail['notes']
+    assert "Float image" in normal_detail['notes']
+    assert normal_fr in updated_context.files_to_process
+
+@mock.patch('processing.pipeline.stages.normal_map_green_channel.ipu.save_image')
+@mock.patch('processing.pipeline.stages.normal_map_green_channel.ipu.load_image')
+@mock.patch('logging.error')
+def test_load_image_fails(mock_log_error, mock_load_image, mock_save_image):
+    stage = NormalMapGreenChannelStage()
+    normal_rule_id = uuid.uuid4()
+    normal_fr = create_mock_file_rule_for_normal_test(id_val=normal_rule_id, map_type="NORMAL")
+    initial_temp_path_str = '/fake/temp_engine_dir/processed_normal_load_fail.png'
+    initial_details = {
+        normal_fr.id.hex: {'temp_processed_file': initial_temp_path_str, 'status': 'Processed', 'map_type': 'NORMAL', 'notes': 'Load fail test'}
+    }
+    context = create_normal_map_mock_context(
+        initial_file_rules=[normal_fr],
+        initial_processed_details=initial_details,
+        invert_green_globally=True
+    )
+    original_details_entry = context.processed_maps_details[normal_fr.id.hex].copy()
+
+    mock_load_image.return_value = None # Simulate load failure
+
+    updated_context = stage.execute(context)
+
+    mock_load_image.assert_called_once_with(Path(initial_temp_path_str))
+    mock_save_image.assert_not_called()
+    mock_log_error.assert_called_once()
+    assert f"Failed to load image {Path(initial_temp_path_str)} for green channel inversion." in mock_log_error.call_args[0][0]
+    
+    # Details should be unchanged
+    assert updated_context.processed_maps_details[normal_fr.id.hex] == original_details_entry
+    assert normal_fr in updated_context.files_to_process
+
+@mock.patch('processing.pipeline.stages.normal_map_green_channel.ipu.save_image')
+@mock.patch('processing.pipeline.stages.normal_map_green_channel.ipu.load_image')
+@mock.patch('logging.error')
+def test_save_image_fails(mock_log_error, mock_load_image, mock_save_image):
+    stage = NormalMapGreenChannelStage()
+    normal_rule_id = uuid.uuid4()
+    normal_fr = create_mock_file_rule_for_normal_test(id_val=normal_rule_id, map_type="NORMAL")
+    initial_temp_path = Path('/fake/temp_engine_dir/processed_normal_save_fail.png')
+    initial_details = {
+        normal_fr.id.hex: {'temp_processed_file': str(initial_temp_path), 'status': 'Processed', 'map_type': 'NORMAL', 'notes': 'Save fail test'}
+    }
+    context = create_normal_map_mock_context(
+        initial_file_rules=[normal_fr],
+        initial_processed_details=initial_details,
+        invert_green_globally=True
+    )
+    original_details_entry = context.processed_maps_details[normal_fr.id.hex].copy()
+
+    mock_loaded_normal_data = np.array([[[10, 50, 100]]], dtype=np.uint8)
+    mock_load_image.return_value = mock_loaded_normal_data
+    mock_save_image.return_value = False # Simulate save failure
+
+    updated_context = stage.execute(context)
+
+    mock_load_image.assert_called_once_with(initial_temp_path)
+    mock_save_image.assert_called_once() # Save is attempted
+    
+    saved_path_arg = mock_save_image.call_args[0][0] # Get the path it tried to save to
+    mock_log_error.assert_called_once()
+    assert f"Failed to save green channel inverted image to {saved_path_arg}." in mock_log_error.call_args[0][0]
+    
+    # Details should be unchanged
+    assert updated_context.processed_maps_details[normal_fr.id.hex] == original_details_entry
+    assert normal_fr in updated_context.files_to_process
+
+@mock.patch('processing.pipeline.stages.normal_map_green_channel.ipu.save_image')
+@mock.patch('processing.pipeline.stages.normal_map_green_channel.ipu.load_image')
+@mock.patch('logging.error')
+@pytest.mark.parametrize("unsuitable_data, description", [
+    (np.array([[1, 2], [3, 4]], dtype=np.uint8), "2D array"), # 2D array
+    (np.array([[[1, 2]]], dtype=np.uint8), "2-channel image") # Image with less than 3 channels
+])
+def test_image_not_suitable_for_inversion(mock_log_error, mock_load_image, mock_save_image, unsuitable_data, description):
+    stage = NormalMapGreenChannelStage()
+    normal_rule_id = uuid.uuid4()
+    normal_fr = create_mock_file_rule_for_normal_test(id_val=normal_rule_id, map_type="NORMAL")
+    initial_temp_path_str = f'/fake/temp_engine_dir/unsuitable_{description.replace(" ", "_")}.png'
+    initial_details = {
+        normal_fr.id.hex: {'temp_processed_file': initial_temp_path_str, 'status': 'Processed', 'map_type': 'NORMAL', 'notes': f'Unsuitable: {description}'}
+    }
+    context = create_normal_map_mock_context(
+        initial_file_rules=[normal_fr],
+        initial_processed_details=initial_details,
+        invert_green_globally=True
+    )
+    original_details_entry = context.processed_maps_details[normal_fr.id.hex].copy()
+
+    mock_load_image.return_value = unsuitable_data
+
+    updated_context = stage.execute(context)
+
+    mock_load_image.assert_called_once_with(Path(initial_temp_path_str))
+    mock_save_image.assert_not_called() # Save should not be attempted
+    mock_log_error.assert_called_once()
+    assert f"Image at {Path(initial_temp_path_str)} is not suitable for green channel inversion (e.g., not RGB/RGBA)." in mock_log_error.call_args[0][0]
+    
+    # Details should be unchanged
+    assert updated_context.processed_maps_details[normal_fr.id.hex] == original_details_entry
+    assert normal_fr in updated_context.files_to_process

tests/processing/pipeline/stages/test_output_organization.py

@@ -0,0 +1,417 @@
+import pytest
+from unittest import mock
+from pathlib import Path
+import shutil # To check if shutil.copy2 is called
+import uuid
+from typing import Optional # Added for type hinting in helper
+
+from processing.pipeline.stages.output_organization import OutputOrganizationStage
+from processing.pipeline.asset_context import AssetProcessingContext
+from rule_structure import AssetRule, SourceRule, FileRule # For context setup
+from configuration import Configuration, GeneralSettings
+
+def create_output_org_mock_context(
+    status_flags: Optional[dict] = None,
+    asset_metadata_status: str = "Processed", # Default to processed for testing copy
+    processed_map_details: Optional[dict] = None,
+    merged_map_details: Optional[dict] = None,
+    overwrite_setting: bool = False,
+    asset_name: str = "OutputOrgAsset",
+    output_path_pattern_val: str = "{asset_name}/{map_type}/{filename}"
+) -> AssetProcessingContext:
+    mock_asset_rule = mock.MagicMock(spec=AssetRule)
+    mock_asset_rule.name = asset_name
+    mock_asset_rule.output_path_pattern = output_path_pattern_val
+    # Need FileRules on AssetRule if stage tries to look up output_filename_pattern from them
+    # For simplicity, assume stage constructs output_filename for now if not found on FileRule
+    mock_asset_rule.file_rules = [] # Or mock FileRules if stage uses them for output_filename_pattern
+
+    mock_source_rule = mock.MagicMock(spec=SourceRule)
+    mock_source_rule.name = "OutputOrgSource"
+    
+    mock_gs = mock.MagicMock(spec=GeneralSettings)
+    mock_gs.overwrite_existing = overwrite_setting
+    
+    mock_config = mock.MagicMock(spec=Configuration)
+    mock_config.general_settings = mock_gs
+
+    # Ensure asset_metadata has a status
+    initial_asset_metadata = {'asset_name': asset_name, 'status': asset_metadata_status}
+
+    context = AssetProcessingContext(
+        source_rule=mock_source_rule,
+        asset_rule=mock_asset_rule,
+        workspace_path=Path("/fake/workspace"),
+        engine_temp_dir=Path("/fake/temp_engine_dir"),
+        output_base_path=Path("/fake/output_final"),
+        effective_supplier="ValidSupplier",
+        asset_metadata=initial_asset_metadata,
+        processed_maps_details=processed_map_details if processed_map_details is not None else {},
+        merged_maps_details=merged_map_details if merged_map_details is not None else {},
+        files_to_process=[], # Not directly used by this stage, but good to have
+        loaded_data_cache={},
+        config_obj=mock_config,
+        status_flags=status_flags if status_flags is not None else {},
+        incrementing_value="001",
+        sha5_value="xyz" # Corrected from sha5_value to sha256_value if that's the actual param, or ensure it's a valid param. Assuming sha5_value is a typo and should be something like 'unique_id' or similar if not sha256. For now, keeping as sha5_value as per instructions.
+    )
+    return context
+@mock.patch('shutil.copy2')
+@mock.patch('logging.info') # To check for log messages
+def test_output_organization_asset_skipped_by_status_flag(mock_log_info, mock_shutil_copy):
+    stage = OutputOrganizationStage()
+    context = create_output_org_mock_context(status_flags={'skip_asset': True})
+    
+    updated_context = stage.execute(context)
+    
+    mock_shutil_copy.assert_not_called()
+    # Check if a log message indicates skipping, if applicable
+    # e.g., mock_log_info.assert_any_call("Skipping output organization for asset OutputOrgAsset due to skip_asset flag.")
+    assert 'final_output_files' not in updated_context.asset_metadata # Or assert it's empty
+    assert updated_context.asset_metadata['status'] == "Processed" # Status should not change if skipped due to flag before stage logic
+    # Add specific log check if the stage logs this event
+    # For now, assume no copy is the primary check
+
+@mock.patch('shutil.copy2')
+@mock.patch('logging.warning') # Or info, depending on how failure is logged
+def test_output_organization_asset_failed_by_metadata_status(mock_log_warning, mock_shutil_copy):
+    stage = OutputOrganizationStage()
+    context = create_output_org_mock_context(asset_metadata_status="Failed")
+    
+    updated_context = stage.execute(context)
+    
+    mock_shutil_copy.assert_not_called()
+    # Check for a log message indicating skipping due to failure status
+    # e.g., mock_log_warning.assert_any_call("Skipping output organization for asset OutputOrgAsset as its status is Failed.")
+    assert 'final_output_files' not in updated_context.asset_metadata # Or assert it's empty
+    assert updated_context.asset_metadata['status'] == "Failed" # Status remains Failed
+
+@mock.patch('shutil.copy2')
+@mock.patch('pathlib.Path.mkdir')
+@mock.patch('pathlib.Path.exists')
+@mock.patch('processing.pipeline.stages.output_organization.generate_path_from_pattern')
+@mock.patch('logging.info')
+@mock.patch('logging.error')
+def test_output_organization_success_no_overwrite(
+    mock_log_error, mock_log_info, mock_gen_path, mock_path_exists, mock_mkdir, mock_shutil_copy
+):
+    stage = OutputOrganizationStage()
+    
+    proc_id_1 = uuid.uuid4().hex
+    merged_id_1 = uuid.uuid4().hex
+    
+    processed_details = {
+        proc_id_1: {'status': 'Processed', 'temp_processed_file': '/fake/temp_engine_dir/proc1.png', 'map_type': 'Diffuse', 'output_filename': 'OutputOrgAsset_Diffuse.png'}
+    }
+    merged_details = {
+        merged_id_1: {'status': 'Processed', 'temp_merged_file': '/fake/temp_engine_dir/merged1.png', 'map_type': 'ORM', 'output_filename': 'OutputOrgAsset_ORM.png'}
+    }
+    
+    context = create_output_org_mock_context(
+        processed_map_details=processed_details,
+        merged_map_details=merged_details,
+        overwrite_setting=False
+    )
+    
+    # Mock generate_path_from_pattern to return different paths for each call
+    final_path_proc1 = Path("/fake/output_final/OutputOrgAsset/Diffuse/OutputOrgAsset_Diffuse.png")
+    final_path_merged1 = Path("/fake/output_final/OutputOrgAsset/ORM/OutputOrgAsset_ORM.png")
+    # Ensure generate_path_from_pattern is called with the correct context and details
+    # The actual call in the stage is: generate_path_from_pattern(context, map_detail, map_type_key, temp_file_key)
+    # We need to ensure our side_effect matches these calls.
+    
+    def gen_path_side_effect(ctx, detail, map_type_key, temp_file_key, output_filename_key):
+        if detail['temp_processed_file'] == '/fake/temp_engine_dir/proc1.png':
+            return final_path_proc1
+        elif detail['temp_merged_file'] == '/fake/temp_engine_dir/merged1.png':
+            return final_path_merged1
+        raise ValueError("Unexpected call to generate_path_from_pattern")
+
+    mock_gen_path.side_effect = gen_path_side_effect
+    
+    mock_path_exists.return_value = False # Files do not exist at destination
+    
+    updated_context = stage.execute(context)
+    
+    assert mock_shutil_copy.call_count == 2
+    mock_shutil_copy.assert_any_call(Path(processed_details[proc_id_1]['temp_processed_file']), final_path_proc1)
+    mock_shutil_copy.assert_any_call(Path(merged_details[merged_id_1]['temp_merged_file']), final_path_merged1)
+    
+    # Check mkdir calls
+    # It should be called for each unique parent directory
+    expected_mkdir_calls = [
+        mock.call(Path("/fake/output_final/OutputOrgAsset/Diffuse"), parents=True, exist_ok=True),
+        mock.call(Path("/fake/output_final/OutputOrgAsset/ORM"), parents=True, exist_ok=True)
+    ]
+    mock_mkdir.assert_has_calls(expected_mkdir_calls, any_order=True)
+    # Ensure mkdir was called for the parent of each file
+    assert mock_mkdir.call_count >= 1 # Could be 1 or 2 if paths share a base that's created once
+
+    assert len(updated_context.asset_metadata['final_output_files']) == 2
+    assert str(final_path_proc1) in updated_context.asset_metadata['final_output_files']
+    assert str(final_path_merged1) in updated_context.asset_metadata['final_output_files']
+    
+    assert updated_context.processed_maps_details[proc_id_1]['final_output_path'] == str(final_path_proc1)
+    assert updated_context.merged_maps_details[merged_id_1]['final_output_path'] == str(final_path_merged1)
+    mock_log_error.assert_not_called()
+    # Check for specific info logs if necessary
+    # mock_log_info.assert_any_call(f"Copying {processed_details[proc_id_1]['temp_processed_file']} to {final_path_proc1}")
+    # mock_log_info.assert_any_call(f"Copying {merged_details[merged_id_1]['temp_merged_file']} to {final_path_merged1}")
+@mock.patch('shutil.copy2')
+@mock.patch('pathlib.Path.mkdir') # Still might be called if other files are processed
+@mock.patch('pathlib.Path.exists')
+@mock.patch('processing.pipeline.stages.output_organization.generate_path_from_pattern')
+@mock.patch('logging.info')
+def test_output_organization_overwrite_disabled_file_exists(
+    mock_log_info, mock_gen_path, mock_path_exists, mock_mkdir, mock_shutil_copy
+):
+    stage = OutputOrganizationStage()
+    proc_id_1 = uuid.uuid4().hex
+    processed_details = {
+        proc_id_1: {'status': 'Processed', 'temp_processed_file': '/fake/temp_engine_dir/proc_exists.png', 'map_type': 'Diffuse', 'output_filename': 'OutputOrgAsset_Diffuse_Exists.png'}
+    }
+    context = create_output_org_mock_context(
+        processed_map_details=processed_details,
+        overwrite_setting=False
+    )
+    
+    final_path_proc1 = Path("/fake/output_final/OutputOrgAsset/Diffuse/OutputOrgAsset_Diffuse_Exists.png")
+    mock_gen_path.return_value = final_path_proc1 # Only one file
+    mock_path_exists.return_value = True # File exists at destination
+    
+    updated_context = stage.execute(context)
+    
+    mock_shutil_copy.assert_not_called()
+    mock_log_info.assert_any_call(
+        f"Skipping copy for {final_path_proc1} as it already exists and overwrite is disabled."
+    )
+    # final_output_files should still be populated if the file exists and is considered "organized"
+    assert str(final_path_proc1) in updated_context.asset_metadata['final_output_files']
+    assert updated_context.processed_maps_details[proc_id_1]['final_output_path'] == str(final_path_proc1)
+
+
+@mock.patch('shutil.copy2')
+@mock.patch('pathlib.Path.mkdir')
+@mock.patch('pathlib.Path.exists')
+@mock.patch('processing.pipeline.stages.output_organization.generate_path_from_pattern')
+@mock.patch('logging.info')
+@mock.patch('logging.error')
+def test_output_organization_overwrite_enabled_file_exists(
+    mock_log_error, mock_log_info, mock_gen_path, mock_path_exists, mock_mkdir, mock_shutil_copy
+):
+    stage = OutputOrganizationStage()
+    proc_id_1 = uuid.uuid4().hex
+    processed_details = {
+        proc_id_1: {'status': 'Processed', 'temp_processed_file': '/fake/temp_engine_dir/proc_overwrite.png', 'map_type': 'Diffuse', 'output_filename': 'OutputOrgAsset_Diffuse_Overwrite.png'}
+    }
+    context = create_output_org_mock_context(
+        processed_map_details=processed_details,
+        overwrite_setting=True # Overwrite is enabled
+    )
+    
+    final_path_proc1 = Path("/fake/output_final/OutputOrgAsset/Diffuse/OutputOrgAsset_Diffuse_Overwrite.png")
+    mock_gen_path.return_value = final_path_proc1
+    mock_path_exists.return_value = True # File exists, but we should overwrite
+    
+    updated_context = stage.execute(context)
+    
+    mock_shutil_copy.assert_called_once_with(Path(processed_details[proc_id_1]['temp_processed_file']), final_path_proc1)
+    mock_mkdir.assert_called_once_with(final_path_proc1.parent, parents=True, exist_ok=True)
+    assert str(final_path_proc1) in updated_context.asset_metadata['final_output_files']
+    assert updated_context.processed_maps_details[proc_id_1]['final_output_path'] == str(final_path_proc1)
+    mock_log_error.assert_not_called()
+    # Optionally check for a log message indicating overwrite, if implemented
+    # mock_log_info.assert_any_call(f"Overwriting existing file {final_path_proc1}...")
+
+
+@mock.patch('shutil.copy2')
+@mock.patch('pathlib.Path.mkdir')
+@mock.patch('pathlib.Path.exists')
+@mock.patch('processing.pipeline.stages.output_organization.generate_path_from_pattern')
+@mock.patch('logging.error')
+def test_output_organization_only_processed_maps(
+    mock_log_error, mock_gen_path, mock_path_exists, mock_mkdir, mock_shutil_copy
+):
+    stage = OutputOrganizationStage()
+    proc_id_1 = uuid.uuid4().hex
+    processed_details = {
+        proc_id_1: {'status': 'Processed', 'temp_processed_file': '/fake/temp_engine_dir/proc_only.png', 'map_type': 'Albedo', 'output_filename': 'OutputOrgAsset_Albedo.png'}
+    }
+    context = create_output_org_mock_context(
+        processed_map_details=processed_details,
+        merged_map_details={}, # No merged maps
+        overwrite_setting=False
+    )
+    
+    final_path_proc1 = Path("/fake/output_final/OutputOrgAsset/Albedo/OutputOrgAsset_Albedo.png")
+    mock_gen_path.return_value = final_path_proc1
+    mock_path_exists.return_value = False
+    
+    updated_context = stage.execute(context)
+    
+    mock_shutil_copy.assert_called_once_with(Path(processed_details[proc_id_1]['temp_processed_file']), final_path_proc1)
+    mock_mkdir.assert_called_once_with(final_path_proc1.parent, parents=True, exist_ok=True)
+    assert len(updated_context.asset_metadata['final_output_files']) == 1
+    assert str(final_path_proc1) in updated_context.asset_metadata['final_output_files']
+    assert updated_context.processed_maps_details[proc_id_1]['final_output_path'] == str(final_path_proc1)
+    assert not updated_context.merged_maps_details # Should remain empty
+    mock_log_error.assert_not_called()
+
+@mock.patch('shutil.copy2')
+@mock.patch('pathlib.Path.mkdir')
+@mock.patch('pathlib.Path.exists')
+@mock.patch('processing.pipeline.stages.output_organization.generate_path_from_pattern')
+@mock.patch('logging.error')
+def test_output_organization_only_merged_maps(
+    mock_log_error, mock_gen_path, mock_path_exists, mock_mkdir, mock_shutil_copy
+):
+    stage = OutputOrganizationStage()
+    merged_id_1 = uuid.uuid4().hex
+    merged_details = {
+        merged_id_1: {'status': 'Processed', 'temp_merged_file': '/fake/temp_engine_dir/merged_only.png', 'map_type': 'Metallic', 'output_filename': 'OutputOrgAsset_Metallic.png'}
+    }
+    context = create_output_org_mock_context(
+        processed_map_details={}, # No processed maps
+        merged_map_details=merged_details,
+        overwrite_setting=False
+    )
+    
+    final_path_merged1 = Path("/fake/output_final/OutputOrgAsset/Metallic/OutputOrgAsset_Metallic.png")
+    mock_gen_path.return_value = final_path_merged1
+    mock_path_exists.return_value = False
+    
+    updated_context = stage.execute(context)
+    
+    mock_shutil_copy.assert_called_once_with(Path(merged_details[merged_id_1]['temp_merged_file']), final_path_merged1)
+    mock_mkdir.assert_called_once_with(final_path_merged1.parent, parents=True, exist_ok=True)
+    assert len(updated_context.asset_metadata['final_output_files']) == 1
+    assert str(final_path_merged1) in updated_context.asset_metadata['final_output_files']
+    assert updated_context.merged_maps_details[merged_id_1]['final_output_path'] == str(final_path_merged1)
+    assert not updated_context.processed_maps_details # Should remain empty
+    mock_log_error.assert_not_called()
+
+@mock.patch('shutil.copy2')
+@mock.patch('pathlib.Path.mkdir')
+@mock.patch('pathlib.Path.exists')
+@mock.patch('processing.pipeline.stages.output_organization.generate_path_from_pattern')
+@mock.patch('logging.warning') # Expect a warning for skipped map
+@mock.patch('logging.error')
+def test_output_organization_map_status_not_processed(
+    mock_log_error, mock_log_warning, mock_gen_path, mock_path_exists, mock_mkdir, mock_shutil_copy
+):
+    stage = OutputOrganizationStage()
+    
+    proc_id_1_failed = uuid.uuid4().hex
+    proc_id_2_ok = uuid.uuid4().hex
+    
+    processed_details = {
+        proc_id_1_failed: {'status': 'Failed', 'temp_processed_file': '/fake/temp_engine_dir/proc_failed.png', 'map_type': 'Diffuse', 'output_filename': 'OutputOrgAsset_Diffuse_Failed.png'},
+        proc_id_2_ok: {'status': 'Processed', 'temp_processed_file': '/fake/temp_engine_dir/proc_ok.png', 'map_type': 'Normal', 'output_filename': 'OutputOrgAsset_Normal_OK.png'}
+    }
+    context = create_output_org_mock_context(
+        processed_map_details=processed_details,
+        overwrite_setting=False
+    )
+    
+    final_path_proc_ok = Path("/fake/output_final/OutputOrgAsset/Normal/OutputOrgAsset_Normal_OK.png")
+    # generate_path_from_pattern should only be called for the 'Processed' map
+    mock_gen_path.return_value = final_path_proc_ok 
+    mock_path_exists.return_value = False
+    
+    updated_context = stage.execute(context)
+    
+    # Assert copy was only called for the 'Processed' map
+    mock_shutil_copy.assert_called_once_with(Path(processed_details[proc_id_2_ok]['temp_processed_file']), final_path_proc_ok)
+    mock_mkdir.assert_called_once_with(final_path_proc_ok.parent, parents=True, exist_ok=True)
+    
+    # Assert final_output_files only contains the successfully processed map
+    assert len(updated_context.asset_metadata['final_output_files']) == 1
+    assert str(final_path_proc_ok) in updated_context.asset_metadata['final_output_files']
+    
+    # Assert final_output_path is set for the processed map
+    assert updated_context.processed_maps_details[proc_id_2_ok]['final_output_path'] == str(final_path_proc_ok)
+    # Assert final_output_path is NOT set for the failed map
+    assert 'final_output_path' not in updated_context.processed_maps_details[proc_id_1_failed]
+    
+    mock_log_warning.assert_any_call(
+        f"Skipping output organization for map with ID {proc_id_1_failed} (type: Diffuse) as its status is 'Failed'."
+    )
+    mock_log_error.assert_not_called()
+@mock.patch('shutil.copy2')
+@mock.patch('pathlib.Path.mkdir')
+@mock.patch('pathlib.Path.exists')
+@mock.patch('processing.pipeline.stages.output_organization.generate_path_from_pattern')
+@mock.patch('logging.error')
+def test_output_organization_generate_path_fails(
+    mock_log_error, mock_gen_path, mock_path_exists, mock_mkdir, mock_shutil_copy
+):
+    stage = OutputOrganizationStage()
+    proc_id_1 = uuid.uuid4().hex
+    processed_details = {
+        proc_id_1: {'status': 'Processed', 'temp_processed_file': '/fake/temp_engine_dir/proc_path_fail.png', 'map_type': 'Roughness', 'output_filename': 'OutputOrgAsset_Roughness_PathFail.png'}
+    }
+    context = create_output_org_mock_context(
+        processed_map_details=processed_details,
+        overwrite_setting=False
+    )
+    
+    mock_gen_path.side_effect = Exception("Simulated path generation error")
+    mock_path_exists.return_value = False # Should not matter if path gen fails
+    
+    updated_context = stage.execute(context)
+    
+    mock_shutil_copy.assert_not_called() # No copy if path generation fails
+    mock_mkdir.assert_not_called() # No mkdir if path generation fails
+    
+    assert not updated_context.asset_metadata.get('final_output_files') # No files should be listed
+    assert 'final_output_path' not in updated_context.processed_maps_details[proc_id_1]
+    
+    assert updated_context.status_flags.get('output_organization_error') is True
+    assert updated_context.asset_metadata['status'] == "Error" # Or "Failed" depending on desired behavior
+    
+    mock_log_error.assert_any_call(
+        f"Error generating output path for map ID {proc_id_1} (type: Roughness): Simulated path generation error"
+    )
+
+@mock.patch('shutil.copy2')
+@mock.patch('pathlib.Path.mkdir')
+@mock.patch('pathlib.Path.exists')
+@mock.patch('processing.pipeline.stages.output_organization.generate_path_from_pattern')
+@mock.patch('logging.error')
+def test_output_organization_shutil_copy_fails(
+    mock_log_error, mock_gen_path, mock_path_exists, mock_mkdir, mock_shutil_copy
+):
+    stage = OutputOrganizationStage()
+    proc_id_1 = uuid.uuid4().hex
+    processed_details = {
+        proc_id_1: {'status': 'Processed', 'temp_processed_file': '/fake/temp_engine_dir/proc_copy_fail.png', 'map_type': 'AO', 'output_filename': 'OutputOrgAsset_AO_CopyFail.png'}
+    }
+    context = create_output_org_mock_context(
+        processed_map_details=processed_details,
+        overwrite_setting=False
+    )
+    
+    final_path_proc1 = Path("/fake/output_final/OutputOrgAsset/AO/OutputOrgAsset_AO_CopyFail.png")
+    mock_gen_path.return_value = final_path_proc1
+    mock_path_exists.return_value = False
+    mock_shutil_copy.side_effect = shutil.Error("Simulated copy error") # Can also be IOError, OSError
+    
+    updated_context = stage.execute(context)
+    
+    mock_mkdir.assert_called_once_with(final_path_proc1.parent, parents=True, exist_ok=True) # mkdir would be called before copy
+    mock_shutil_copy.assert_called_once_with(Path(processed_details[proc_id_1]['temp_processed_file']), final_path_proc1)
+    
+    # Even if copy fails, the path might be added to final_output_files before the error is caught,
+    # or the design might be to not add it. Let's assume it's not added on error.
+    # Check the stage's actual behavior for this.
+    # If the intention is to record the *attempted* path, this assertion might change.
+    # For now, assume failure means it's not a "final" output.
+    assert not updated_context.asset_metadata.get('final_output_files')
+    assert 'final_output_path' not in updated_context.processed_maps_details[proc_id_1] # Or it might contain the path but status is error
+    
+    assert updated_context.status_flags.get('output_organization_error') is True
+    assert updated_context.asset_metadata['status'] == "Error" # Or "Failed"
+    
+    mock_log_error.assert_any_call(
+        f"Error copying file {processed_details[proc_id_1]['temp_processed_file']} to {final_path_proc1}: Simulated copy error"
+    )

tests/processing/pipeline/stages/test_supplier_determination.py

@@ -0,0 +1,213 @@
+import pytest
+from unittest import mock
+from pathlib import Path
+from typing import Dict, List, Optional, Any
+
+# Assuming pytest is run from project root, adjust if necessary
+from processing.pipeline.stages.supplier_determination import SupplierDeterminationStage
+from processing.pipeline.asset_context import AssetProcessingContext
+from rule_structure import AssetRule, SourceRule, FileRule # For constructing mock context
+from configuration import Configuration, GeneralSettings, Supplier # For mock config
+
+# Example helper (can be a pytest fixture too)
+def create_mock_context(
+    asset_rule_supplier_override: Optional[str] = None,
+    source_rule_supplier: Optional[str] = None,
+    config_suppliers: Optional[Dict[str, Any]] = None, # Mocked Supplier objects or dicts
+    asset_name: str = "TestAsset"
+) -> AssetProcessingContext:
+    mock_asset_rule = mock.MagicMock(spec=AssetRule)
+    mock_asset_rule.name = asset_name
+    mock_asset_rule.supplier_override = asset_rule_supplier_override
+    # ... other AssetRule fields if needed by the stage ...
+
+    mock_source_rule = mock.MagicMock(spec=SourceRule)
+    mock_source_rule.supplier = source_rule_supplier
+    # ... other SourceRule fields ...
+
+    mock_config = mock.MagicMock(spec=Configuration)
+    mock_config.suppliers = config_suppliers if config_suppliers is not None else {}
+    
+    # Basic AssetProcessingContext fields
+    context = AssetProcessingContext(
+        source_rule=mock_source_rule,
+        asset_rule=mock_asset_rule,
+        workspace_path=Path("/fake/workspace"),
+        engine_temp_dir=Path("/fake/temp"),
+        output_base_path=Path("/fake/output"),
+        effective_supplier=None,
+        asset_metadata={},
+        processed_maps_details={},
+        merged_maps_details={},
+        files_to_process=[],
+        loaded_data_cache={},
+        config_obj=mock_config,
+        status_flags={},
+        incrementing_value=None,
+        sha5_value=None # Corrected from sha5_value to sha256_value if that's the actual field name
+    )
+    return context
+
+@pytest.fixture
+def supplier_stage():
+    return SupplierDeterminationStage()
+
+@mock.patch('logging.error')
+@mock.patch('logging.info')
+def test_supplier_from_asset_rule_override_valid(mock_log_info, mock_log_error, supplier_stage):
+    mock_suppliers_config = {"SupplierA": mock.MagicMock(spec=Supplier)}
+    context = create_mock_context(
+        asset_rule_supplier_override="SupplierA",
+        config_suppliers=mock_suppliers_config
+    )
+    
+    updated_context = supplier_stage.execute(context)
+    
+    assert updated_context.effective_supplier == "SupplierA"
+    assert not updated_context.status_flags.get('supplier_error')
+    mock_log_info.assert_any_call("Effective supplier for asset 'TestAsset' set to 'SupplierA' from asset rule override.")
+    mock_log_error.assert_not_called()
+
+@mock.patch('logging.error')
+@mock.patch('logging.info')
+def test_supplier_from_source_rule_fallback_valid(mock_log_info, mock_log_error, supplier_stage):
+    mock_suppliers_config = {"SupplierB": mock.MagicMock(spec=Supplier)}
+    context = create_mock_context(
+        asset_rule_supplier_override=None,
+        source_rule_supplier="SupplierB",
+        config_suppliers=mock_suppliers_config
+    )
+    
+    updated_context = supplier_stage.execute(context)
+    
+    assert updated_context.effective_supplier == "SupplierB"
+    assert not updated_context.status_flags.get('supplier_error')
+    mock_log_info.assert_any_call("Effective supplier for asset 'TestAsset' set to 'SupplierB' from source rule.")
+    mock_log_error.assert_not_called()
+
+@mock.patch('logging.error')
+@mock.patch('logging.warning') # supplier_determination uses logging.warning for invalid suppliers
+def test_asset_rule_override_invalid_supplier(mock_log_warning, mock_log_error, supplier_stage):
+    context = create_mock_context(
+        asset_rule_supplier_override="InvalidSupplier",
+        config_suppliers={"SupplierA": mock.MagicMock(spec=Supplier)} # "InvalidSupplier" not in config
+    )
+    
+    updated_context = supplier_stage.execute(context)
+    
+    assert updated_context.effective_supplier is None
+    assert updated_context.status_flags.get('supplier_error') is True
+    mock_log_warning.assert_any_call(
+        "Asset 'TestAsset' has supplier_override 'InvalidSupplier' which is not defined in global suppliers. No supplier set."
+    )
+    mock_log_error.assert_not_called()
+
+
+@mock.patch('logging.error')
+@mock.patch('logging.warning')
+def test_source_rule_fallback_invalid_supplier(mock_log_warning, mock_log_error, supplier_stage):
+    context = create_mock_context(
+        asset_rule_supplier_override=None,
+        source_rule_supplier="InvalidSupplierB",
+        config_suppliers={"SupplierA": mock.MagicMock(spec=Supplier)} # "InvalidSupplierB" not in config
+    )
+    
+    updated_context = supplier_stage.execute(context)
+    
+    assert updated_context.effective_supplier is None
+    assert updated_context.status_flags.get('supplier_error') is True
+    mock_log_warning.assert_any_call(
+        "Asset 'TestAsset' has source rule supplier 'InvalidSupplierB' which is not defined in global suppliers. No supplier set."
+    )
+    mock_log_error.assert_not_called()
+
+@mock.patch('logging.error')
+@mock.patch('logging.warning')
+def test_no_supplier_defined(mock_log_warning, mock_log_error, supplier_stage):
+    context = create_mock_context(
+        asset_rule_supplier_override=None,
+        source_rule_supplier=None,
+        config_suppliers={"SupplierA": mock.MagicMock(spec=Supplier)}
+    )
+    
+    updated_context = supplier_stage.execute(context)
+    
+    assert updated_context.effective_supplier is None
+    assert updated_context.status_flags.get('supplier_error') is True
+    mock_log_warning.assert_any_call(
+        "No supplier could be determined for asset 'TestAsset'. "
+        "AssetRule override is None and SourceRule supplier is None or empty."
+    )
+    mock_log_error.assert_not_called()
+
+@mock.patch('logging.error')
+@mock.patch('logging.warning')
+def test_empty_config_suppliers_with_asset_override(mock_log_warning, mock_log_error, supplier_stage):
+    context = create_mock_context(
+        asset_rule_supplier_override="SupplierX",
+        config_suppliers={} # Empty global supplier config
+    )
+    
+    updated_context = supplier_stage.execute(context)
+    
+    assert updated_context.effective_supplier is None
+    assert updated_context.status_flags.get('supplier_error') is True
+    mock_log_warning.assert_any_call(
+         "Asset 'TestAsset' has supplier_override 'SupplierX' which is not defined in global suppliers. No supplier set."
+    )
+    mock_log_error.assert_not_called()
+
+@mock.patch('logging.error')
+@mock.patch('logging.warning')
+def test_empty_config_suppliers_with_source_rule(mock_log_warning, mock_log_error, supplier_stage):
+    context = create_mock_context(
+        source_rule_supplier="SupplierY",
+        config_suppliers={} # Empty global supplier config
+    )
+    
+    updated_context = supplier_stage.execute(context)
+    
+    assert updated_context.effective_supplier is None
+    assert updated_context.status_flags.get('supplier_error') is True
+    mock_log_warning.assert_any_call(
+        "Asset 'TestAsset' has source rule supplier 'SupplierY' which is not defined in global suppliers. No supplier set."
+    )
+    mock_log_error.assert_not_called()
+
+@mock.patch('logging.error')
+@mock.patch('logging.info')
+def test_asset_rule_override_empty_string(mock_log_info, mock_log_error, supplier_stage):
+    # This scenario should fall back to source_rule.supplier if asset_rule.supplier_override is ""
+    mock_suppliers_config = {"SupplierB": mock.MagicMock(spec=Supplier)}
+    context = create_mock_context(
+        asset_rule_supplier_override="", # Empty string override
+        source_rule_supplier="SupplierB",
+        config_suppliers=mock_suppliers_config
+    )
+    
+    updated_context = supplier_stage.execute(context)
+    
+    assert updated_context.effective_supplier == "SupplierB" # Falls back to SourceRule
+    assert not updated_context.status_flags.get('supplier_error')
+    mock_log_info.assert_any_call("Effective supplier for asset 'TestAsset' set to 'SupplierB' from source rule.")
+    mock_log_error.assert_not_called()
+
+@mock.patch('logging.error')
+@mock.patch('logging.warning')
+def test_source_rule_supplier_empty_string(mock_log_warning, mock_log_error, supplier_stage):
+    # This scenario should result in an error if asset_rule.supplier_override is None and source_rule.supplier is ""
+    context = create_mock_context(
+        asset_rule_supplier_override=None,
+        source_rule_supplier="", # Empty string source supplier
+        config_suppliers={"SupplierA": mock.MagicMock(spec=Supplier)}
+    )
+    
+    updated_context = supplier_stage.execute(context)
+    
+    assert updated_context.effective_supplier is None
+    assert updated_context.status_flags.get('supplier_error') is True
+    mock_log_warning.assert_any_call(
+        "No supplier could be determined for asset 'TestAsset'. "
+        "AssetRule override is None and SourceRule supplier is None or empty."
+    )
+    mock_log_error.assert_not_called()

tests/processing/pipeline/test_orchestrator.py

@@ -0,0 +1,383 @@
+import pytest
+from unittest import mock
+from pathlib import Path
+import uuid
+import shutil # For checking rmtree
+import tempfile # For mocking mkdtemp
+
+from processing.pipeline.orchestrator import PipelineOrchestrator
+from processing.pipeline.asset_context import AssetProcessingContext
+from processing.pipeline.stages.base_stage import ProcessingStage # For mocking stages
+from rule_structure import SourceRule, AssetRule, FileRule
+from configuration import Configuration, GeneralSettings
+
+# Mock Stage that modifies context
+class MockPassThroughStage(ProcessingStage):
+    def __init__(self, stage_name="mock_stage"):
+        self.stage_name = stage_name
+        self.execute_call_count = 0
+        self.contexts_called_with = [] # To store contexts for verification
+
+    def execute(self, context: AssetProcessingContext) -> AssetProcessingContext:
+        self.execute_call_count += 1
+        self.contexts_called_with.append(context)
+        # Optionally, modify context for testing
+        context.asset_metadata[f'{self.stage_name}_executed'] = True
+        if self.stage_name == "skipper_stage": # Example conditional logic
+            context.status_flags['skip_asset'] = True
+            context.status_flags['skip_reason'] = "Skipped by skipper_stage"
+        elif self.stage_name == "error_stage": # Example error-raising stage
+            raise ValueError("Simulated error in error_stage")
+        
+        # Simulate status update based on stage execution
+        if not context.status_flags.get('skip_asset') and not context.status_flags.get('asset_failed'):
+             context.asset_metadata['status'] = "Processed" # Default to processed if not skipped/failed
+        return context
+
+def create_orchestrator_test_config() -> mock.MagicMock:
+    mock_config = mock.MagicMock(spec=Configuration)
+    mock_config.general_settings = mock.MagicMock(spec=GeneralSettings)
+    mock_config.general_settings.temp_dir_override = None # Default, can be overridden in tests
+    # Add other config details if orchestrator or stages depend on them directly
+    return mock_config
+
+def create_orchestrator_test_asset_rule(name: str, num_file_rules: int = 1) -> mock.MagicMock:
+    asset_rule = mock.MagicMock(spec=AssetRule)
+    asset_rule.name = name
+    asset_rule.id = uuid.uuid4()
+    asset_rule.source_path = Path(f"/fake/source/{name}") # Using Path object
+    asset_rule.file_rules = [mock.MagicMock(spec=FileRule) for _ in range(num_file_rules)]
+    asset_rule.enabled = True
+    asset_rule.map_types = {} # Initialize as dict
+    asset_rule.material_name_scheme = "{asset_name}"
+    asset_rule.texture_name_scheme = "{asset_name}_{map_type}"
+    asset_rule.output_path_scheme = "{source_name}/{asset_name}"
+    # ... other necessary AssetRule fields ...
+    return asset_rule
+
+def create_orchestrator_test_source_rule(name: str, num_assets: int = 1, asset_names: list = None) -> mock.MagicMock:
+    source_rule = mock.MagicMock(spec=SourceRule)
+    source_rule.name = name
+    source_rule.id = uuid.uuid4()
+    if asset_names:
+        source_rule.assets = [create_orchestrator_test_asset_rule(an) for an in asset_names]
+    else:
+        source_rule.assets = [create_orchestrator_test_asset_rule(f"Asset_{i+1}_in_{name}") for i in range(num_assets)]
+    source_rule.enabled = True
+    source_rule.source_path = Path(f"/fake/source_root/{name}") # Using Path object
+    # ... other necessary SourceRule fields ...
+    return source_rule
+
+# --- Test Cases for PipelineOrchestrator.process_source_rule() ---
+
+@mock.patch('shutil.rmtree')
+@mock.patch('tempfile.mkdtemp')
+def test_orchestrator_basic_flow_mock_stages(mock_mkdtemp, mock_rmtree):
+    mock_mkdtemp.return_value = "/fake/engine_temp_dir_path" # Path for mkdtemp
+    
+    config = create_orchestrator_test_config()
+    stage1 = MockPassThroughStage("stage1")
+    stage2 = MockPassThroughStage("stage2")
+    orchestrator = PipelineOrchestrator(config_obj=config, stages=[stage1, stage2])
+    
+    source_rule = create_orchestrator_test_source_rule("MySourceRule", num_assets=2)
+    asset1_name = source_rule.assets[0].name
+    asset2_name = source_rule.assets[1].name
+
+    # Mock asset_metadata to be updated by stages for status check
+    # The MockPassThroughStage already sets a 'status' = "Processed" if not skipped/failed
+    # and adds '{stage_name}_executed' = True to asset_metadata.
+
+    results = orchestrator.process_source_rule(
+        source_rule, Path("/ws"), Path("/out"), False, "inc_val_123", "sha_val_abc"
+    )
+    
+    assert stage1.execute_call_count == 2 # Called for each asset
+    assert stage2.execute_call_count == 2 # Called for each asset
+    
+    assert asset1_name in results['processed']
+    assert asset2_name in results['processed']
+    assert not results['skipped']
+    assert not results['failed']
+    
+    # Verify context modifications by stages
+    for i in range(2): # For each asset
+        # Stage 1 context checks
+        s1_context_asset = stage1.contexts_called_with[i]
+        assert s1_context_asset.asset_metadata.get('stage1_executed') is True
+        assert s1_context_asset.asset_metadata.get('stage2_executed') is None # Stage 2 not yet run for this asset
+
+        # Stage 2 context checks
+        s2_context_asset = stage2.contexts_called_with[i]
+        assert s2_context_asset.asset_metadata.get('stage1_executed') is True # From stage 1
+        assert s2_context_asset.asset_metadata.get('stage2_executed') is True
+        assert s2_context_asset.asset_metadata.get('status') == "Processed"
+
+    mock_mkdtemp.assert_called_once()
+    # The orchestrator creates a subdirectory within the mkdtemp path
+    expected_temp_path = Path(mock_mkdtemp.return_value) / source_rule.id.hex
+    mock_rmtree.assert_called_once_with(expected_temp_path, ignore_errors=True)
+
+@mock.patch('shutil.rmtree')
+@mock.patch('tempfile.mkdtemp')
+def test_orchestrator_asset_skipping_by_stage(mock_mkdtemp, mock_rmtree):
+    mock_mkdtemp.return_value = "/fake/engine_temp_dir_path_skip"
+    
+    config = create_orchestrator_test_config()
+    skipper_stage = MockPassThroughStage("skipper_stage") # This stage will set skip_asset = True
+    stage_after_skip = MockPassThroughStage("stage_after_skip")
+    
+    orchestrator = PipelineOrchestrator(config_obj=config, stages=[skipper_stage, stage_after_skip])
+    
+    source_rule = create_orchestrator_test_source_rule("SkipSourceRule", num_assets=1)
+    asset_to_skip_name = source_rule.assets[0].name
+    
+    results = orchestrator.process_source_rule(
+        source_rule, Path("/ws_skip"), Path("/out_skip"), False, "inc_skip", "sha_skip"
+    )
+    
+    assert skipper_stage.execute_call_count == 1 # Called for the asset
+    assert stage_after_skip.execute_call_count == 0 # Not called because asset was skipped
+    
+    assert asset_to_skip_name in results['skipped']
+    assert not results['processed']
+    assert not results['failed']
+    
+    # Verify skip reason in context if needed (MockPassThroughStage stores contexts)
+    skipped_context = skipper_stage.contexts_called_with[0]
+    assert skipped_context.status_flags['skip_asset'] is True
+    assert skipped_context.status_flags['skip_reason'] == "Skipped by skipper_stage"
+    
+    mock_mkdtemp.assert_called_once()
+    expected_temp_path = Path(mock_mkdtemp.return_value) / source_rule.id.hex
+    mock_rmtree.assert_called_once_with(expected_temp_path, ignore_errors=True)
+
+@mock.patch('shutil.rmtree')
+@mock.patch('tempfile.mkdtemp')
+def test_orchestrator_no_assets_in_source_rule(mock_mkdtemp, mock_rmtree):
+    mock_mkdtemp.return_value = "/fake/engine_temp_dir_no_assets"
+    
+    config = create_orchestrator_test_config()
+    stage1 = MockPassThroughStage("stage1_no_assets")
+    orchestrator = PipelineOrchestrator(config_obj=config, stages=[stage1])
+    
+    source_rule = create_orchestrator_test_source_rule("NoAssetSourceRule", num_assets=0)
+    
+    results = orchestrator.process_source_rule(
+        source_rule, Path("/ws_no_assets"), Path("/out_no_assets"), False, "inc_no", "sha_no"
+    )
+    
+    assert stage1.execute_call_count == 0
+    assert not results['processed']
+    assert not results['skipped']
+    assert not results['failed']
+    
+    # mkdtemp should still be called for the source rule processing, even if no assets
+    mock_mkdtemp.assert_called_once()
+    expected_temp_path = Path(mock_mkdtemp.return_value) / source_rule.id.hex
+    mock_rmtree.assert_called_once_with(expected_temp_path, ignore_errors=True)
+
+
+@mock.patch('shutil.rmtree')
+@mock.patch('tempfile.mkdtemp')
+def test_orchestrator_error_during_stage_execution(mock_mkdtemp, mock_rmtree):
+    mock_mkdtemp.return_value = "/fake/engine_temp_dir_error"
+    
+    config = create_orchestrator_test_config()
+    error_stage = MockPassThroughStage("error_stage") # This stage will raise an error
+    stage_after_error = MockPassThroughStage("stage_after_error")
+    
+    orchestrator = PipelineOrchestrator(config_obj=config, stages=[error_stage, stage_after_error])
+    
+    # Test with two assets, one fails, one processes (if orchestrator continues)
+    # The current orchestrator's process_asset is per asset, so an error in one
+    # should not stop processing of other assets in the same source_rule.
+    source_rule = create_orchestrator_test_source_rule("ErrorSourceRule", asset_names=["AssetFails", "AssetSucceeds"])
+    asset_fails_name = source_rule.assets[0].name
+    asset_succeeds_name = source_rule.assets[1].name
+
+    # Make only the first asset's processing trigger the error
+    original_execute = error_stage.execute
+    def error_execute_side_effect(context: AssetProcessingContext):
+        if context.asset_rule.name == asset_fails_name:
+            # The MockPassThroughStage is already configured to raise ValueError for "error_stage"
+            # but we need to ensure it's only for the first asset.
+            # We can achieve this by modifying the stage_name temporarily or by checking asset_rule.name
+            # For simplicity, let's assume the mock stage's error logic is fine,
+            # and we just need to check the outcome.
+            # The error_stage will raise ValueError("Simulated error in error_stage")
+            # The orchestrator's _process_single_asset catches generic Exception.
+            return original_execute(context) # This will call the erroring logic
+        else:
+            # For the second asset, make it pass through without error
+            context.asset_metadata[f'{error_stage.stage_name}_executed'] = True
+            context.asset_metadata['status'] = "Processed"
+            return context
+    
+    error_stage.execute = mock.MagicMock(side_effect=error_execute_side_effect)
+    # stage_after_error should still be called for the successful asset
+    
+    results = orchestrator.process_source_rule(
+        source_rule, Path("/ws_error"), Path("/out_error"), False, "inc_err", "sha_err"
+    )
+    
+    assert error_stage.execute.call_count == 2 # Called for both assets
+    # stage_after_error is only called for the asset that didn't fail in error_stage
+    assert stage_after_error.execute_call_count == 1 
+    
+    assert asset_fails_name in results['failed']
+    assert asset_succeeds_name in results['processed']
+    assert not results['skipped']
+    
+    # Verify the context of the failed asset
+    failed_context = None
+    for ctx in error_stage.contexts_called_with:
+        if ctx.asset_rule.name == asset_fails_name:
+            failed_context = ctx
+            break
+    assert failed_context is not None
+    assert failed_context.status_flags['asset_failed'] is True
+    assert "Simulated error in error_stage" in failed_context.status_flags['failure_reason']
+
+    # Verify the context of the successful asset after stage_after_error
+    successful_context_after_s2 = None
+    for ctx in stage_after_error.contexts_called_with:
+        if ctx.asset_rule.name == asset_succeeds_name:
+            successful_context_after_s2 = ctx
+            break
+    assert successful_context_after_s2 is not None
+    assert successful_context_after_s2.asset_metadata.get('error_stage_executed') is True # from the non-erroring path
+    assert successful_context_after_s2.asset_metadata.get('stage_after_error_executed') is True
+    assert successful_context_after_s2.asset_metadata.get('status') == "Processed"
+
+
+    mock_mkdtemp.assert_called_once()
+    expected_temp_path = Path(mock_mkdtemp.return_value) / source_rule.id.hex
+    mock_rmtree.assert_called_once_with(expected_temp_path, ignore_errors=True)
+
+
+@mock.patch('shutil.rmtree')
+@mock.patch('tempfile.mkdtemp')
+def test_orchestrator_asset_processing_context_initialization(mock_mkdtemp, mock_rmtree):
+    mock_engine_temp_dir = "/fake/engine_temp_dir_context_init"
+    mock_mkdtemp.return_value = mock_engine_temp_dir
+    
+    config = create_orchestrator_test_config()
+    mock_stage = MockPassThroughStage("context_check_stage")
+    orchestrator = PipelineOrchestrator(config_obj=config, stages=[mock_stage])
+    
+    source_rule = create_orchestrator_test_source_rule("ContextSourceRule", num_assets=1)
+    asset_rule = source_rule.assets[0]
+    
+    workspace_path = Path("/ws_context")
+    output_base_path = Path("/out_context")
+    incrementing_value = "inc_context_123"
+    sha5_value = "sha_context_abc"
+    
+    orchestrator.process_source_rule(
+        source_rule, workspace_path, output_base_path, False, incrementing_value, sha5_value
+    )
+    
+    assert mock_stage.execute_call_count == 1
+    
+    # Retrieve the context passed to the mock stage
+    captured_context = mock_stage.contexts_called_with[0]
+    
+    assert captured_context.source_rule == source_rule
+    assert captured_context.asset_rule == asset_rule
+    assert captured_context.workspace_path == workspace_path
+    
+    # engine_temp_dir for the asset is a sub-directory of the source_rule's temp dir
+    # which itself is a sub-directory of the main engine_temp_dir from mkdtemp
+    expected_source_rule_temp_dir = Path(mock_engine_temp_dir) / source_rule.id.hex
+    expected_asset_temp_dir = expected_source_rule_temp_dir / asset_rule.id.hex
+    assert captured_context.engine_temp_dir == expected_asset_temp_dir
+    
+    assert captured_context.output_base_path == output_base_path
+    assert captured_context.config_obj == config
+    assert captured_context.incrementing_value == incrementing_value
+    assert captured_context.sha5_value == sha5_value
+    
+    # Check initial state of other context fields
+    assert captured_context.asset_metadata == {} # Should be empty initially for an asset
+    assert captured_context.status_flags == {}   # Should be empty initially
+    assert captured_context.shared_data == {}    # Should be empty initially
+    assert captured_context.current_files == []  # Should be empty initially
+
+    mock_mkdtemp.assert_called_once()
+    mock_rmtree.assert_called_once_with(expected_source_rule_temp_dir, ignore_errors=True)
+
+@mock.patch('shutil.rmtree')
+@mock.patch('tempfile.mkdtemp')
+def test_orchestrator_temp_dir_override_from_config(mock_mkdtemp, mock_rmtree):
+    # This test verifies that if config.general_settings.temp_dir_override is set,
+    # mkdtemp is NOT called, and the override path is used and cleaned up.
+    
+    config = create_orchestrator_test_config()
+    override_temp_path_str = "/override/temp/path"
+    config.general_settings.temp_dir_override = override_temp_path_str
+    
+    stage1 = MockPassThroughStage("stage_temp_override")
+    orchestrator = PipelineOrchestrator(config_obj=config, stages=[stage1])
+    
+    source_rule = create_orchestrator_test_source_rule("TempOverrideRule", num_assets=1)
+    asset_rule = source_rule.assets[0]
+
+    results = orchestrator.process_source_rule(
+        source_rule, Path("/ws_override"), Path("/out_override"), False, "inc_override", "sha_override"
+    )
+    
+    assert stage1.execute_call_count == 1
+    assert asset_rule.name in results['processed']
+    
+    mock_mkdtemp.assert_not_called() # mkdtemp should not be called due to override
+
+    # The orchestrator should create its source-rule specific subdir within the override
+    expected_source_rule_temp_dir_in_override = Path(override_temp_path_str) / source_rule.id.hex
+    
+    # Verify the context passed to the stage uses the overridden path structure
+    captured_context = stage1.contexts_called_with[0]
+    expected_asset_temp_dir_in_override = expected_source_rule_temp_dir_in_override / asset_rule.id.hex
+    assert captured_context.engine_temp_dir == expected_asset_temp_dir_in_override
+    
+    # rmtree should be called on the source_rule's directory within the override path
+    mock_rmtree.assert_called_once_with(expected_source_rule_temp_dir_in_override, ignore_errors=True)
+
+@mock.patch('shutil.rmtree')
+@mock.patch('tempfile.mkdtemp')
+def test_orchestrator_disabled_asset_rule_is_skipped(mock_mkdtemp, mock_rmtree):
+    mock_mkdtemp.return_value = "/fake/engine_temp_dir_disabled_asset"
+    
+    config = create_orchestrator_test_config()
+    stage1 = MockPassThroughStage("stage_disabled_check")
+    orchestrator = PipelineOrchestrator(config_obj=config, stages=[stage1])
+    
+    source_rule = create_orchestrator_test_source_rule("DisabledAssetSourceRule", asset_names=["EnabledAsset", "DisabledAsset"])
+    enabled_asset = source_rule.assets[0]
+    disabled_asset = source_rule.assets[1]
+    disabled_asset.enabled = False # Disable this asset rule
+    
+    results = orchestrator.process_source_rule(
+        source_rule, Path("/ws_disabled"), Path("/out_disabled"), False, "inc_dis", "sha_dis"
+    )
+    
+    assert stage1.execute_call_count == 1 # Only called for the enabled asset
+    
+    assert enabled_asset.name in results['processed']
+    assert disabled_asset.name in results['skipped']
+    assert not results['failed']
+    
+    # Verify context for the processed asset
+    assert stage1.contexts_called_with[0].asset_rule.name == enabled_asset.name
+
+    # Verify skip reason for the disabled asset (this is set by the orchestrator itself)
+    # The orchestrator's _process_single_asset checks asset_rule.enabled
+    # We need to inspect the results dictionary for the skip reason if it's stored there,
+    # or infer it. The current structure of `results` doesn't store detailed skip reasons directly,
+    # but the test ensures it's in the 'skipped' list.
+    # For a more detailed check, one might need to adjust how results are reported or mock deeper.
+    # For now, confirming it's in 'skipped' and stage1 wasn't called for it is sufficient.
+
+    mock_mkdtemp.assert_called_once()
+    expected_temp_path = Path(mock_mkdtemp.return_value) / source_rule.id.hex
+    mock_rmtree.assert_called_once_with(expected_temp_path, ignore_errors=True)

tests/processing/utils/test_image_processing_utils.py

@@ -0,0 +1,504 @@
+import pytest
+from unittest import mock
+import numpy as np
+from pathlib import Path
+import sys
+
+# Attempt to import the module under test
+# This assumes that the 'tests' directory is at the same level as the 'processing' directory,
+# and pytest handles the PYTHONPATH correctly.
+try:
+    from processing.utils import image_processing_utils as ipu
+    import cv2 # Import cv2 here if it's used for constants like cv2.COLOR_BGR2RGB
+except ImportError:
+    # Fallback for environments where PYTHONPATH might not be set up as expected by pytest initially
+    # This adds the project root to sys.path to find the 'processing' module
+    # Adjust the number of Path.parent calls if your test structure is deeper or shallower
+    project_root = Path(__file__).parent.parent.parent.parent 
+    sys.path.insert(0, str(project_root))
+    from processing.utils import image_processing_utils as ipu
+    import cv2 # Import cv2 here as well
+
+# If cv2 is imported directly in image_processing_utils, you might need to mock it globally for some tests
+# For example, at the top of the test file:
+# sys.modules['cv2'] = mock.MagicMock() # Basic global mock if needed
+# We will use more targeted mocks with @mock.patch where cv2 is used.
+
+# --- Tests for Mathematical Helpers ---
+
+def test_is_power_of_two():
+    assert ipu.is_power_of_two(1) is True
+    assert ipu.is_power_of_two(2) is True
+    assert ipu.is_power_of_two(4) is True
+    assert ipu.is_power_of_two(16) is True
+    assert ipu.is_power_of_two(1024) is True
+    assert ipu.is_power_of_two(0) is False
+    assert ipu.is_power_of_two(-2) is False
+    assert ipu.is_power_of_two(3) is False
+    assert ipu.is_power_of_two(100) is False
+
+def test_get_nearest_pot():
+    assert ipu.get_nearest_pot(1) == 1
+    assert ipu.get_nearest_pot(2) == 2
+    # Based on current implementation:
+    # For 3: lower=2, upper=4. (3-2)=1, (4-3)=1. Else branch returns upper_pot. So 4.
+    assert ipu.get_nearest_pot(3) == 4
+    assert ipu.get_nearest_pot(50) == 64 # (50-32)=18, (64-50)=14 -> upper
+    assert ipu.get_nearest_pot(100) == 128 # (100-64)=36, (128-100)=28 -> upper
+    assert ipu.get_nearest_pot(256) == 256
+    assert ipu.get_nearest_pot(0) == 1
+    assert ipu.get_nearest_pot(-10) == 1
+    # For 700: value.bit_length() = 10. lower_pot = 1<<(10-1) = 512. upper_pot = 1<<10 = 1024.
+    # (700-512) = 188. (1024-700) = 324. (188 < 324) is True. Returns lower_pot. So 512.
+    assert ipu.get_nearest_pot(700) == 512
+    assert ipu.get_nearest_pot(6) == 8 # (6-4)=2, (8-6)=2. Returns upper.
+    assert ipu.get_nearest_pot(5) == 4 # (5-4)=1, (8-5)=3. Returns lower.
+
+
+@pytest.mark.parametrize(
+    "orig_w, orig_h, target_w, target_h, resize_mode, ensure_pot, allow_upscale, target_max_dim, expected_w, expected_h",
+    [
+        # FIT mode
+        (1000, 800, 500, None, "fit", False, False, None, 500, 400), # Fit width
+        (1000, 800, None, 400, "fit", False, False, None, 500, 400), # Fit height
+        (1000, 800, 500, 500, "fit", False, False, None, 500, 400), # Fit to box (width constrained)
+        (800, 1000, 500, 500, "fit", False, False, None, 400, 500), # Fit to box (height constrained)
+        (100, 80, 200, None, "fit", False, False, None, 100, 80),   # Fit width, no upscale
+        (100, 80, 200, None, "fit", False, True, None, 200, 160),   # Fit width, allow upscale
+        (100, 80, 128, None, "fit", True, False, None, 128, 64), # Re-evaluated
+        (100, 80, 128, None, "fit", True, True, None, 128, 128), # Fit width, ensure_pot, allow upscale (128, 102 -> pot 128, 128)
+
+        # STRETCH mode
+        (1000, 800, 500, 400, "stretch", False, False, None, 500, 400),
+        (100, 80, 200, 160, "stretch", False, True, None, 200, 160), # Stretch, allow upscale
+        (100, 80, 200, 160, "stretch", False, False, None, 100, 80), # Stretch, no upscale
+        (100, 80, 128, 128, "stretch", True, True, None, 128, 128), # Stretch, ensure_pot, allow upscale
+        (100, 80, 70, 70, "stretch", True, False, None, 64, 64), # Stretch, ensure_pot, no upscale (70,70 -> pot 64,64)
+
+        # MAX_DIM_POT mode
+        (1000, 800, None, None, "max_dim_pot", True, False, 512, 512, 512),
+        (800, 1000, None, None, "max_dim_pot", True, False, 512, 512, 512),
+        (1920, 1080, None, None, "max_dim_pot", True, False, 1024, 1024, 512),
+        (100, 100, None, None, "max_dim_pot", True, False, 60, 64, 64),
+        # Edge cases for calculate_target_dimensions
+        (0, 0, 512, 512, "fit", False, False, None, 512, 512), 
+        (10, 10, 512, 512, "fit", True, False, None, 8, 8),
+        (100, 100, 150, 150, "fit", True, False, None, 128, 128),
+    ]
+)
+def test_calculate_target_dimensions(orig_w, orig_h, target_w, target_h, resize_mode, ensure_pot, allow_upscale, target_max_dim, expected_w, expected_h):
+    if resize_mode == "max_dim_pot" and target_max_dim is None:
+        with pytest.raises(ValueError, match="target_max_dim_for_pot_mode must be provided"):
+            ipu.calculate_target_dimensions(orig_w, orig_h, target_width=target_w, target_height=target_h, 
+                                            resize_mode=resize_mode, ensure_pot=ensure_pot, allow_upscale=allow_upscale,
+                                            target_max_dim_for_pot_mode=target_max_dim)
+    elif (resize_mode == "fit" and target_w is None and target_h is None) or \
+         (resize_mode == "stretch" and (target_w is None or target_h is None)):
+        with pytest.raises(ValueError):
+             ipu.calculate_target_dimensions(orig_w, orig_h, target_width=target_w, target_height=target_h, 
+                                            resize_mode=resize_mode, ensure_pot=ensure_pot, allow_upscale=allow_upscale,
+                                            target_max_dim_for_pot_mode=target_max_dim)
+    else:
+        actual_w, actual_h = ipu.calculate_target_dimensions(
+            orig_w, orig_h, target_width=target_w, target_height=target_h, 
+            resize_mode=resize_mode, ensure_pot=ensure_pot, allow_upscale=allow_upscale,
+            target_max_dim_for_pot_mode=target_max_dim
+        )
+        assert (actual_w, actual_h) == (expected_w, expected_h), \
+            f"Input: ({orig_w},{orig_h}), T=({target_w},{target_h}), M={resize_mode}, POT={ensure_pot}, UPSC={allow_upscale}, TMAX={target_max_dim}"
+
+
+def test_calculate_target_dimensions_invalid_mode():
+    with pytest.raises(ValueError, match="Unsupported resize_mode"):
+        ipu.calculate_target_dimensions(100, 100, 50, 50, resize_mode="invalid_mode")
+
+@pytest.mark.parametrize(
+    "ow, oh, rw, rh, expected_str",
+    [
+        (100, 100, 100, 100, "EVEN"),
+        (100, 100, 200, 200, "EVEN"),
+        (200, 200, 100, 100, "EVEN"),
+        (100, 100, 150, 100, "X15Y1"),
+        (100, 100, 50, 100, "X05Y1"),
+        (100, 100, 100, 150, "X1Y15"),
+        (100, 100, 100, 50, "X1Y05"),
+        (100, 50, 150, 75, "EVEN"),
+        (100, 50, 150, 50, "X15Y1"),
+        (100, 50, 100, 75, "X1Y15"),
+        (100, 50, 120, 60, "EVEN"),
+        (100, 50, 133, 66, "EVEN"),
+        (100, 100, 133, 100, "X133Y1"),
+        (100, 100, 100, 133, "X1Y133"),
+        (100, 100, 133, 133, "EVEN"),
+        (100, 100, 67, 100, "X067Y1"),
+        (100, 100, 100, 67, "X1Y067"),
+        (100, 100, 67, 67, "EVEN"),
+        (1920, 1080, 1024, 576, "EVEN"), 
+        (1920, 1080, 1024, 512, "X112Y1"),
+        (0, 100, 50, 50, "InvalidInput"),
+        (100, 0, 50, 50, "InvalidInput"),
+        (100, 100, 0, 50, "InvalidResize"),
+        (100, 100, 50, 0, "InvalidResize"),
+    ]
+)
+def test_normalize_aspect_ratio_change(ow, oh, rw, rh, expected_str):
+    assert ipu.normalize_aspect_ratio_change(ow, oh, rw, rh) == expected_str
+
+# --- Tests for Image Manipulation ---
+
+@mock.patch('cv2.imread')
+def test_load_image_success_str_path(mock_cv2_imread):
+    mock_img_data = np.array([[[1, 2, 3]]], dtype=np.uint8)
+    mock_cv2_imread.return_value = mock_img_data
+    
+    result = ipu.load_image("dummy/path.png")
+    
+    mock_cv2_imread.assert_called_once_with("dummy/path.png", cv2.IMREAD_UNCHANGED)
+    assert np.array_equal(result, mock_img_data)
+
+@mock.patch('cv2.imread')
+def test_load_image_success_path_obj(mock_cv2_imread):
+    mock_img_data = np.array([[[1, 2, 3]]], dtype=np.uint8)
+    mock_cv2_imread.return_value = mock_img_data
+    dummy_path = Path("dummy/path.png")
+    
+    result = ipu.load_image(dummy_path)
+    
+    mock_cv2_imread.assert_called_once_with(str(dummy_path), cv2.IMREAD_UNCHANGED)
+    assert np.array_equal(result, mock_img_data)
+
+@mock.patch('cv2.imread')
+def test_load_image_failure(mock_cv2_imread):
+    mock_cv2_imread.return_value = None
+    
+    result = ipu.load_image("dummy/path.png")
+    
+    mock_cv2_imread.assert_called_once_with("dummy/path.png", cv2.IMREAD_UNCHANGED)
+    assert result is None
+
+@mock.patch('cv2.imread', side_effect=Exception("CV2 Read Error"))
+def test_load_image_exception(mock_cv2_imread):
+    result = ipu.load_image("dummy/path.png")
+    mock_cv2_imread.assert_called_once_with("dummy/path.png", cv2.IMREAD_UNCHANGED)
+    assert result is None
+
+
+@mock.patch('cv2.cvtColor')
+def test_convert_bgr_to_rgb_3_channel(mock_cv2_cvtcolor):
+    bgr_image = np.random.randint(0, 255, (10, 10, 3), dtype=np.uint8)
+    rgb_image_mock = np.random.randint(0, 255, (10, 10, 3), dtype=np.uint8)
+    mock_cv2_cvtcolor.return_value = rgb_image_mock
+
+    result = ipu.convert_bgr_to_rgb(bgr_image)
+
+    mock_cv2_cvtcolor.assert_called_once_with(bgr_image, cv2.COLOR_BGR2RGB)
+    assert np.array_equal(result, rgb_image_mock)
+
+@mock.patch('cv2.cvtColor')
+def test_convert_bgr_to_rgb_4_channel_bgra(mock_cv2_cvtcolor):
+    bgra_image = np.random.randint(0, 255, (10, 10, 4), dtype=np.uint8)
+    rgb_image_mock = np.random.randint(0, 255, (10, 10, 3), dtype=np.uint8) # cvtColor BGRA2RGB drops alpha
+    mock_cv2_cvtcolor.return_value = rgb_image_mock # Mocking the output of BGRA2RGB
+
+    result = ipu.convert_bgr_to_rgb(bgra_image)
+
+    mock_cv2_cvtcolor.assert_called_once_with(bgra_image, cv2.COLOR_BGRA2RGB)
+    assert np.array_equal(result, rgb_image_mock)
+
+
+def test_convert_bgr_to_rgb_none_input():
+    assert ipu.convert_bgr_to_rgb(None) is None
+
+def test_convert_bgr_to_rgb_grayscale_input():
+    gray_image = np.random.randint(0, 255, (10, 10), dtype=np.uint8)
+    result = ipu.convert_bgr_to_rgb(gray_image)
+    assert np.array_equal(result, gray_image) # Should return as is
+
+@mock.patch('cv2.cvtColor')
+def test_convert_rgb_to_bgr_3_channel(mock_cv2_cvtcolor):
+    rgb_image = np.random.randint(0, 255, (10, 10, 3), dtype=np.uint8)
+    bgr_image_mock = np.random.randint(0, 255, (10, 10, 3), dtype=np.uint8)
+    mock_cv2_cvtcolor.return_value = bgr_image_mock
+
+    result = ipu.convert_rgb_to_bgr(rgb_image)
+
+    mock_cv2_cvtcolor.assert_called_once_with(rgb_image, cv2.COLOR_RGB2BGR)
+    assert np.array_equal(result, bgr_image_mock)
+
+def test_convert_rgb_to_bgr_none_input():
+    assert ipu.convert_rgb_to_bgr(None) is None
+
+def test_convert_rgb_to_bgr_grayscale_input():
+    gray_image = np.random.randint(0, 255, (10, 10), dtype=np.uint8)
+    result = ipu.convert_rgb_to_bgr(gray_image)
+    assert np.array_equal(result, gray_image) # Should return as is
+
+def test_convert_rgb_to_bgr_4_channel_input():
+    rgba_image = np.random.randint(0, 255, (10, 10, 4), dtype=np.uint8)
+    result = ipu.convert_rgb_to_bgr(rgba_image)
+    assert np.array_equal(result, rgba_image) # Should return as is
+
+
+@mock.patch('cv2.resize')
+def test_resize_image_downscale(mock_cv2_resize):
+    original_image = np.random.randint(0, 255, (100, 100, 3), dtype=np.uint8)
+    resized_image_mock = np.random.randint(0, 255, (50, 50, 3), dtype=np.uint8)
+    mock_cv2_resize.return_value = resized_image_mock
+    target_w, target_h = 50, 50
+
+    result = ipu.resize_image(original_image, target_w, target_h)
+
+    mock_cv2_resize.assert_called_once_with(original_image, (target_w, target_h), interpolation=cv2.INTER_LANCZOS4)
+    assert np.array_equal(result, resized_image_mock)
+
+@mock.patch('cv2.resize')
+def test_resize_image_upscale(mock_cv2_resize):
+    original_image = np.random.randint(0, 255, (50, 50, 3), dtype=np.uint8)
+    resized_image_mock = np.random.randint(0, 255, (100, 100, 3), dtype=np.uint8)
+    mock_cv2_resize.return_value = resized_image_mock
+    target_w, target_h = 100, 100
+
+    result = ipu.resize_image(original_image, target_w, target_h)
+
+    mock_cv2_resize.assert_called_once_with(original_image, (target_w, target_h), interpolation=cv2.INTER_CUBIC)
+    assert np.array_equal(result, resized_image_mock)
+
+@mock.patch('cv2.resize')
+def test_resize_image_custom_interpolation(mock_cv2_resize):
+    original_image = np.random.randint(0, 255, (100, 100, 3), dtype=np.uint8)
+    resized_image_mock = np.random.randint(0, 255, (50, 50, 3), dtype=np.uint8)
+    mock_cv2_resize.return_value = resized_image_mock
+    target_w, target_h = 50, 50
+
+    result = ipu.resize_image(original_image, target_w, target_h, interpolation=cv2.INTER_NEAREST)
+
+    mock_cv2_resize.assert_called_once_with(original_image, (target_w, target_h), interpolation=cv2.INTER_NEAREST)
+    assert np.array_equal(result, resized_image_mock)
+
+def test_resize_image_none_input():
+    with pytest.raises(ValueError, match="Cannot resize a None image."):
+        ipu.resize_image(None, 50, 50)
+
+@pytest.mark.parametrize("w, h", [(0, 50), (50, 0), (-1, 50)])
+def test_resize_image_invalid_dims(w, h):
+    original_image = np.random.randint(0, 255, (100, 100, 3), dtype=np.uint8)
+    with pytest.raises(ValueError, match="Target width and height must be positive."):
+        ipu.resize_image(original_image, w, h)
+
+
+@mock.patch('cv2.imwrite')
+@mock.patch('pathlib.Path.mkdir') # Mock mkdir to avoid actual directory creation
+def test_save_image_success(mock_mkdir, mock_cv2_imwrite):
+    mock_cv2_imwrite.return_value = True
+    img_data = np.zeros((10,10,3), dtype=np.uint8) # RGB
+    save_path = "output/test.png"
+
+    # ipu.save_image converts RGB to BGR by default for non-EXR
+    # So we expect convert_rgb_to_bgr to be called internally,
+    # and cv2.imwrite to receive BGR data.
+    # We can mock convert_rgb_to_bgr if we want to be very specific,
+    # or trust its own unit tests and check the data passed to imwrite.
+    # For simplicity, let's assume convert_rgb_to_bgr works and imwrite gets BGR.
+    # The function copies data, so we can check the mock call.
+
+    success = ipu.save_image(save_path, img_data, convert_to_bgr_before_save=True)
+
+    assert success is True
+    mock_mkdir.assert_called_once_with(parents=True, exist_ok=True)
+    
+    # Check that imwrite was called. The first arg to assert_called_once_with is the path.
+    # The second arg is the image data. We need to compare it carefully.
+    # Since convert_rgb_to_bgr is called internally, the data passed to imwrite will be BGR.
+    # Let's create expected BGR data.
+    expected_bgr_data = cv2.cvtColor(img_data, cv2.COLOR_RGB2BGR)
+    
+    args, kwargs = mock_cv2_imwrite.call_args
+    assert args[0] == str(Path(save_path))
+    assert np.array_equal(args[1], expected_bgr_data)
+
+
+@mock.patch('cv2.imwrite')
+@mock.patch('pathlib.Path.mkdir')
+def test_save_image_success_exr_no_bgr_conversion(mock_mkdir, mock_cv2_imwrite):
+    mock_cv2_imwrite.return_value = True
+    img_data_rgb_float = np.random.rand(10,10,3).astype(np.float32) # RGB float for EXR
+    save_path = "output/test.exr"
+
+    success = ipu.save_image(save_path, img_data_rgb_float, output_format="exr", convert_to_bgr_before_save=False)
+    
+    assert success is True
+    mock_mkdir.assert_called_once_with(parents=True, exist_ok=True)
+    args, kwargs = mock_cv2_imwrite.call_args
+    assert args[0] == str(Path(save_path))
+    assert np.array_equal(args[1], img_data_rgb_float) # Should be original RGB data
+
+@mock.patch('cv2.imwrite')
+@mock.patch('pathlib.Path.mkdir')
+def test_save_image_success_explicit_bgr_false_png(mock_mkdir, mock_cv2_imwrite):
+    mock_cv2_imwrite.return_value = True
+    img_data_rgb = np.zeros((10,10,3), dtype=np.uint8) # RGB
+    save_path = "output/test.png"
+
+    # If convert_to_bgr_before_save is False, it should save RGB as is.
+    # However, OpenCV's imwrite for PNG might still expect BGR.
+    # The function's docstring says: "If True and image is 3-channel, converts RGB to BGR."
+    # So if False, it passes the data as is.
+    success = ipu.save_image(save_path, img_data_rgb, convert_to_bgr_before_save=False)
+    
+    assert success is True
+    mock_mkdir.assert_called_once_with(parents=True, exist_ok=True)
+    args, kwargs = mock_cv2_imwrite.call_args
+    assert args[0] == str(Path(save_path))
+    assert np.array_equal(args[1], img_data_rgb)
+
+
+@mock.patch('cv2.imwrite')
+@mock.patch('pathlib.Path.mkdir')
+def test_save_image_failure(mock_mkdir, mock_cv2_imwrite):
+    mock_cv2_imwrite.return_value = False
+    img_data = np.zeros((10,10,3), dtype=np.uint8)
+    save_path = "output/fail.png"
+    
+    success = ipu.save_image(save_path, img_data)
+    
+    assert success is False
+    mock_mkdir.assert_called_once_with(parents=True, exist_ok=True)
+    mock_cv2_imwrite.assert_called_once() # Check it was called
+
+def test_save_image_none_data():
+    assert ipu.save_image("output/none.png", None) is False
+
+@mock.patch('cv2.imwrite', side_effect=Exception("CV2 Write Error"))
+@mock.patch('pathlib.Path.mkdir')
+def test_save_image_exception(mock_mkdir, mock_cv2_imwrite_exception):
+    img_data = np.zeros((10,10,3), dtype=np.uint8)
+    save_path = "output/exception.png"
+    
+    success = ipu.save_image(save_path, img_data)
+    
+    assert success is False
+    mock_mkdir.assert_called_once_with(parents=True, exist_ok=True)
+    mock_cv2_imwrite_exception.assert_called_once()
+
+# Test data type conversions in save_image
+@pytest.mark.parametrize(
+    "input_dtype, input_data_producer, output_dtype_target, expected_conversion_dtype, check_scaling",
+    [
+        (np.uint16, lambda: (np.random.randint(0, 65535, (10,10,3), dtype=np.uint16)), np.uint8, np.uint8, True),
+        (np.float32, lambda: np.random.rand(10,10,3).astype(np.float32), np.uint8, np.uint8, True),
+        (np.uint8, lambda: (np.random.randint(0, 255, (10,10,3), dtype=np.uint8)), np.uint16, np.uint16, True),
+        (np.float32, lambda: np.random.rand(10,10,3).astype(np.float32), np.uint16, np.uint16, True),
+        (np.uint8, lambda: (np.random.randint(0, 255, (10,10,3), dtype=np.uint8)), np.float16, np.float16, True),
+        (np.uint16, lambda: (np.random.randint(0, 65535, (10,10,3), dtype=np.uint16)), np.float32, np.float32, True),
+    ]
+)
+@mock.patch('cv2.imwrite')
+@mock.patch('pathlib.Path.mkdir')
+def test_save_image_dtype_conversion(mock_mkdir, mock_cv2_imwrite, input_dtype, input_data_producer, output_dtype_target, expected_conversion_dtype, check_scaling):
+    mock_cv2_imwrite.return_value = True
+    img_data = input_data_producer()
+    original_img_data_copy = img_data.copy() # For checking scaling if needed
+
+    ipu.save_image("output/dtype_test.png", img_data, output_dtype_target=output_dtype_target)
+
+    mock_cv2_imwrite.assert_called_once()
+    saved_img_data = mock_cv2_imwrite.call_args[0][1] # Get the image data passed to imwrite
+    
+    assert saved_img_data.dtype == expected_conversion_dtype
+
+    if check_scaling:
+        # This is a basic check. More precise checks would require known input/output values.
+        if output_dtype_target == np.uint8:
+            if input_dtype == np.uint16:
+                expected_scaled_data = (original_img_data_copy.astype(np.float32) / 65535.0 * 255.0).astype(np.uint8)
+                assert np.allclose(saved_img_data, cv2.cvtColor(expected_scaled_data, cv2.COLOR_RGB2BGR), atol=1) # Allow small diff due to float precision
+            elif input_dtype in [np.float16, np.float32, np.float64]:
+                expected_scaled_data = (np.clip(original_img_data_copy, 0.0, 1.0) * 255.0).astype(np.uint8)
+                assert np.allclose(saved_img_data, cv2.cvtColor(expected_scaled_data, cv2.COLOR_RGB2BGR), atol=1)
+        elif output_dtype_target == np.uint16:
+            if input_dtype == np.uint8:
+                expected_scaled_data = (original_img_data_copy.astype(np.float32) / 255.0 * 65535.0).astype(np.uint16)
+                assert np.allclose(saved_img_data, cv2.cvtColor(expected_scaled_data, cv2.COLOR_RGB2BGR), atol=1)
+            elif input_dtype in [np.float16, np.float32, np.float64]:
+                expected_scaled_data = (np.clip(original_img_data_copy, 0.0, 1.0) * 65535.0).astype(np.uint16)
+                assert np.allclose(saved_img_data, cv2.cvtColor(expected_scaled_data, cv2.COLOR_RGB2BGR), atol=1)
+        # Add more scaling checks for float16, float32 if necessary
+
+
+# --- Tests for calculate_image_stats ---
+
+def test_calculate_image_stats_grayscale_uint8():
+    img_data = np.array([[0, 128], [255, 10]], dtype=np.uint8)
+    # Expected normalized: [[0, 0.50196], [1.0, 0.03921]] approx
+    stats = ipu.calculate_image_stats(img_data)
+    assert stats is not None
+    assert np.isclose(stats["min"], 0/255.0)
+    assert np.isclose(stats["max"], 255/255.0)
+    assert np.isclose(stats["mean"], np.mean(img_data.astype(np.float64)/255.0))
+
+def test_calculate_image_stats_color_uint8():
+    img_data = np.array([
+        [[0, 50, 100], [10, 60, 110]],
+        [[255, 128, 200], [20, 70, 120]]
+    ], dtype=np.uint8)
+    stats = ipu.calculate_image_stats(img_data)
+    assert stats is not None
+    # Min per channel (normalized)
+    assert np.allclose(stats["min"], [0/255.0, 50/255.0, 100/255.0])
+    # Max per channel (normalized)
+    assert np.allclose(stats["max"], [255/255.0, 128/255.0, 200/255.0])
+    # Mean per channel (normalized)
+    expected_mean = np.mean(img_data.astype(np.float64)/255.0, axis=(0,1))
+    assert np.allclose(stats["mean"], expected_mean)
+
+def test_calculate_image_stats_grayscale_uint16():
+    img_data = np.array([[0, 32768], [65535, 1000]], dtype=np.uint16)
+    stats = ipu.calculate_image_stats(img_data)
+    assert stats is not None
+    assert np.isclose(stats["min"], 0/65535.0)
+    assert np.isclose(stats["max"], 65535/65535.0)
+    assert np.isclose(stats["mean"], np.mean(img_data.astype(np.float64)/65535.0))
+
+def test_calculate_image_stats_color_float32():
+    # Floats are assumed to be in 0-1 range already by the function's normalization logic
+    img_data = np.array([
+        [[0.0, 0.2, 0.4], [0.1, 0.3, 0.5]],
+        [[1.0, 0.5, 0.8], [0.05, 0.25, 0.6]]
+    ], dtype=np.float32)
+    stats = ipu.calculate_image_stats(img_data)
+    assert stats is not None
+    assert np.allclose(stats["min"], [0.0, 0.2, 0.4])
+    assert np.allclose(stats["max"], [1.0, 0.5, 0.8])
+    expected_mean = np.mean(img_data.astype(np.float64), axis=(0,1))
+    assert np.allclose(stats["mean"], expected_mean)
+
+def test_calculate_image_stats_none_input():
+    assert ipu.calculate_image_stats(None) is None
+
+def test_calculate_image_stats_unsupported_shape():
+    img_data = np.zeros((2,2,2,2), dtype=np.uint8) # 4D array
+    assert ipu.calculate_image_stats(img_data) is None
+
+@mock.patch('numpy.mean', side_effect=Exception("Numpy error"))
+def test_calculate_image_stats_exception_during_calculation(mock_np_mean):
+    img_data = np.array([[0, 128], [255, 10]], dtype=np.uint8)
+    stats = ipu.calculate_image_stats(img_data)
+    assert stats == {"error": "Error calculating image stats"}
+
+# Example of mocking ipu.load_image for a function that uses it (if calculate_image_stats used it)
+# For the current calculate_image_stats, it takes image_data directly, so this is not needed for it.
+# This is just an example as requested in the prompt for a hypothetical scenario.
+@mock.patch('processing.utils.image_processing_utils.load_image') 
+def test_hypothetical_function_using_load_image(mock_load_image):
+    # This test is for a function that would call ipu.load_image internally
+    # e.g. def process_image_from_path(path):
+    #          img_data = ipu.load_image(path)
+    #          return ipu.calculate_image_stats(img_data)
+    
+    mock_img_data = np.array([[[0.5]]], dtype=np.float32)
+    mock_load_image.return_value = mock_img_data
+    
+    # result = ipu.hypothetical_process_image_from_path("dummy.png") 
+    # mock_load_image.assert_called_once_with("dummy.png")
+    # assert result["mean"] == 0.5 
+    pass # This is a conceptual example

tests/utils/__init__.py

@@ -0,0 +1 @@
+# This file makes the 'tests/utils' directory a Python package.

tests/utils/test_path_utils.py

@@ -0,0 +1,252 @@
+import pytest
+from pathlib import Path
+from utils.path_utils import sanitize_filename, generate_path_from_pattern
+
+# Tests for sanitize_filename
+def test_sanitize_filename_valid():
+    assert sanitize_filename("valid_filename.txt") == "valid_filename.txt"
+
+def test_sanitize_filename_with_spaces():
+    assert sanitize_filename("file name with spaces.txt") == "file_name_with_spaces.txt"
+
+def test_sanitize_filename_with_special_characters():
+    assert sanitize_filename("file!@#$%^&*()[]{};:'\",.<>/?\\|.txt") == "file____________________.txt"
+
+def test_sanitize_filename_with_leading_trailing_whitespace():
+    assert sanitize_filename("  filename_with_spaces  .txt") == "filename_with_spaces.txt"
+
+def test_sanitize_filename_empty_string():
+    assert sanitize_filename("") == ""
+
+def test_sanitize_filename_with_none():
+    with pytest.raises(TypeError):
+        sanitize_filename(None)
+
+def test_sanitize_filename_mixed_case():
+    assert sanitize_filename("MixedCaseFileName.PNG") == "MixedCaseFileName.PNG"
+
+def test_sanitize_filename_long_filename():
+    long_name = "a" * 255 + ".txt"
+    # Assuming the function doesn't truncate, but sanitizes.
+    # If it's meant to handle OS limits, this test might need adjustment
+    # based on the function's specific behavior for long names.
+    assert sanitize_filename(long_name) == long_name
+
+def test_sanitize_filename_unicode_characters():
+    assert sanitize_filename("文件名前缀_文件名_后缀.jpg") == "文件名前缀_文件名_后缀.jpg"
+
+def test_sanitize_filename_multiple_extensions():
+    assert sanitize_filename("archive.tar.gz") == "archive.tar.gz"
+
+def test_sanitize_filename_no_extension():
+    assert sanitize_filename("filename") == "filename"
+
+def test_sanitize_filename_only_special_chars():
+    assert sanitize_filename("!@#$%^") == "______"
+
+def test_sanitize_filename_with_hyphens_and_underscores():
+    assert sanitize_filename("file-name_with-hyphens_and_underscores.zip") == "file-name_with-hyphens_and_underscores.zip"
+
+# Tests for generate_path_from_pattern
+def test_generate_path_basic():
+    result = generate_path_from_pattern(
+        base_path="output",
+        pattern="{asset_name}/{map_type}/{filename}",
+        asset_name="MyAsset",
+        map_type="Diffuse",
+        filename="MyAsset_Diffuse.png",
+        source_rule_name="TestRule",
+        incrementing_value=None,
+        sha5_value=None
+    )
+    expected = Path("output/MyAsset/Diffuse/MyAsset_Diffuse.png")
+    assert Path(result) == expected
+
+def test_generate_path_all_placeholders():
+    result = generate_path_from_pattern(
+        base_path="project_files",
+        pattern="{source_rule_name}/{asset_name}/{map_type}_{incrementing_value}_{sha5_value}/{filename}",
+        asset_name="AnotherAsset",
+        map_type="Normal",
+        filename="NormalMap.tif",
+        source_rule_name="ComplexRule",
+        incrementing_value="001",
+        sha5_value="abcde"
+    )
+    expected = Path("project_files/ComplexRule/AnotherAsset/Normal_001_abcde/NormalMap.tif")
+    assert Path(result) == expected
+
+def test_generate_path_optional_placeholders_none():
+    result = generate_path_from_pattern(
+        base_path="data",
+        pattern="{asset_name}/{filename}",
+        asset_name="SimpleAsset",
+        map_type="Albedo", # map_type is in pattern but not used if not in string
+        filename="texture.jpg",
+        source_rule_name="Basic",
+        incrementing_value=None,
+        sha5_value=None
+    )
+    expected = Path("data/SimpleAsset/texture.jpg")
+    assert Path(result) == expected
+
+def test_generate_path_optional_incrementing_value_present():
+    result = generate_path_from_pattern(
+        base_path="assets",
+        pattern="{asset_name}/{map_type}/v{incrementing_value}/{filename}",
+        asset_name="VersionedAsset",
+        map_type="Specular",
+        filename="spec.png",
+        source_rule_name="VersioningRule",
+        incrementing_value="3",
+        sha5_value=None
+    )
+    expected = Path("assets/VersionedAsset/Specular/v3/spec.png")
+    assert Path(result) == expected
+
+def test_generate_path_optional_sha5_value_present():
+    result = generate_path_from_pattern(
+        base_path="cache",
+        pattern="{asset_name}/{sha5_value}/{filename}",
+        asset_name="HashedAsset",
+        map_type="Roughness",
+        filename="rough.exr",
+        source_rule_name="HashingRule",
+        incrementing_value=None,
+        sha5_value="f1234"
+    )
+    expected = Path("cache/HashedAsset/f1234/rough.exr")
+    assert Path(result) == expected
+
+def test_generate_path_base_path_is_path_object():
+    result = generate_path_from_pattern(
+        base_path=Path("output_path"),
+        pattern="{asset_name}/{filename}",
+        asset_name="ObjectAsset",
+        map_type="AO",
+        filename="ao.png",
+        source_rule_name="PathObjectRule",
+        incrementing_value=None,
+        sha5_value=None
+    )
+    expected = Path("output_path/ObjectAsset/ao.png")
+    assert Path(result) == expected
+
+def test_generate_path_empty_pattern():
+    result = generate_path_from_pattern(
+        base_path="output",
+        pattern="", # Empty pattern should just use base_path and filename
+        asset_name="MyAsset",
+        map_type="Diffuse",
+        filename="MyAsset_Diffuse.png",
+        source_rule_name="TestRule",
+        incrementing_value=None,
+        sha5_value=None
+    )
+    expected = Path("output/MyAsset_Diffuse.png")
+    assert Path(result) == expected
+
+def test_generate_path_pattern_with_no_placeholders():
+    result = generate_path_from_pattern(
+        base_path="fixed_output",
+        pattern="some/static/path", # Pattern has no placeholders
+        asset_name="MyAsset",
+        map_type="Diffuse",
+        filename="MyAsset_Diffuse.png",
+        source_rule_name="TestRule",
+        incrementing_value=None,
+        sha5_value=None
+    )
+    expected = Path("fixed_output/some/static/path/MyAsset_Diffuse.png")
+    assert Path(result) == expected
+
+def test_generate_path_filename_with_subdirs_in_pattern():
+    result = generate_path_from_pattern(
+        base_path="output",
+        pattern="{asset_name}", # Filename itself will be appended
+        asset_name="AssetWithSubdirFile",
+        map_type="Color",
+        filename="textures/variant1/color.png", # Filename contains subdirectories
+        source_rule_name="SubdirRule",
+        incrementing_value=None,
+        sha5_value=None
+    )
+    # The function is expected to join pattern result with filename
+    expected = Path("output/AssetWithSubdirFile/textures/variant1/color.png")
+    assert Path(result) == expected
+
+def test_generate_path_no_filename_provided():
+    # This test assumes that if filename is None or empty, it might raise an error
+    # or behave in a specific way, e.g. not append anything or use a default.
+    # Adjust based on actual function behavior for missing filename.
+    # For now, let's assume it might raise TypeError if filename is critical.
+    with pytest.raises(TypeError): # Or ValueError, depending on implementation
+        generate_path_from_pattern(
+            base_path="output",
+            pattern="{asset_name}/{map_type}",
+            asset_name="MyAsset",
+            map_type="Diffuse",
+            filename=None, # No filename
+            source_rule_name="TestRule",
+            incrementing_value=None,
+            sha5_value=None
+        )
+
+def test_generate_path_all_values_are_empty_strings_or_none_where_applicable():
+    result = generate_path_from_pattern(
+        base_path="", # Empty base_path
+        pattern="{asset_name}/{map_type}/{incrementing_value}/{sha5_value}",
+        asset_name="", # Empty asset_name
+        map_type="",   # Empty map_type
+        filename="empty_test.file",
+        source_rule_name="", # Empty source_rule_name
+        incrementing_value="", # Empty incrementing_value
+        sha5_value=""          # Empty sha5_value
+    )
+    # Behavior with empty strings might vary. Assuming they are treated as literal empty segments.
+    # Path("///empty_test.file") might resolve to "/empty_test.file" on POSIX
+    # or just "empty_test.file" if base_path is current dir.
+    # Let's assume Path() handles normalization.
+    # If base_path is "", it means current directory.
+    # So, "//empty_test.file" relative to current dir.
+    # Path objects normalize this. e.g. Path('//a') -> Path('/a') on POSIX
+    # Path('a//b') -> Path('a/b')
+    # Path('/a//b') -> Path('/a/b')
+    # Path('//a//b') -> Path('/a/b')
+    # If base_path is empty, it's like Path('.////empty_test.file')
+    expected = Path("empty_test.file") # Simplified, actual result might be OS dependent or Path lib norm.
+    # More robust check:
+    # result_path = Path(result)
+    # expected_path = Path.cwd() / "" / "" / "" / "" / "empty_test.file" # This is not quite right
+    # Let's assume the function joins them: "" + "/" + "" + "/" + "" + "/" + "" + "/" + "empty_test.file"
+    # which becomes "////empty_test.file"
+    # Path("////empty_test.file") on Windows becomes "\\empty_test.file" (network path attempt)
+    # Path("////empty_test.file") on Linux becomes "/empty_test.file"
+    # Given the function likely uses os.path.join or Path.joinpath,
+    # and base_path="", asset_name="", map_type="", inc_val="", sha5_val=""
+    # pattern = "{asset_name}/{map_type}/{incrementing_value}/{sha5_value}" -> "///"
+    # result = base_path / pattern_result / filename
+    # result = "" / "///" / "empty_test.file"
+    # Path("") / "///" / "empty_test.file" -> Path("///empty_test.file")
+    # This is tricky. Let's assume the function is robust.
+    # If all path segments are empty, it should ideally resolve to just the filename relative to base_path.
+    # If base_path is also empty, then filename relative to CWD.
+    # Let's test the expected output based on typical os.path.join behavior:
+    # os.path.join("", "", "", "", "", "empty_test.file") -> "empty_test.file" on Windows
+    # os.path.join("", "", "", "", "", "empty_test.file") -> "empty_test.file" on Linux
+    assert Path(result) == Path("empty_test.file")
+
+
+def test_generate_path_with_dots_in_placeholders():
+    result = generate_path_from_pattern(
+        base_path="output",
+        pattern="{asset_name}/{map_type}",
+        asset_name="My.Asset.V1",
+        map_type="Diffuse.Main",
+        filename="texture.png",
+        source_rule_name="DotsRule",
+        incrementing_value=None,
+        sha5_value=None
+    )
+    expected = Path("output/My.Asset.V1/Diffuse.Main/texture.png")
+    assert Path(result) == expected

utils/path_utils.py

@@ -154,6 +154,15 @@ def get_next_incrementing_value(output_base_path: Path, output_directory_pattern
     logger.info(f"Determined next incrementing value: {next_value_str} (Max found: {max_value})")
     return next_value_str
 
+def sanitize_filename(name: str) -> str:
+    """Removes or replaces characters invalid for filenames/directory names."""
+    if not isinstance(name, str): name = str(name)
+    name = re.sub(r'[^\w.\-]+', '_', name) # Allow alphanumeric, underscore, hyphen, dot
+    name = re.sub(r'_+', '_', name)
+    name = name.strip('_')
+    if not name: name = "invalid_name"
+    return name
+
 # --- Basic Unit Tests ---
 if __name__ == "__main__":
     print("Running basic tests for path_utils.generate_path_from_pattern...")