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".vscode": true,
".vs": true,
".lh": true,
"__pycache__": true
"__pycache__": true,
}
}

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@ -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`) 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.
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.
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.
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.
## Table of Contents

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@ -6,19 +6,17 @@ 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 Initiation (`processing_engine.py`):** The `ProcessingEngine` class acts as the entry point for an asset processing task. It initializes and runs a `PipelineOrchestrator`.
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:
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.
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:
* **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.
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.
6. **Multiple Interfaces:** Provides different ways to interact with the tool:
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.
4. **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 `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.
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.
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.
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.
## Hierarchical Rule System
@ -28,14 +26,14 @@ 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 `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.
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.
## 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 pipeline.
* `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.
* `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.
@ -49,10 +47,7 @@ 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 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`).
* `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.
* `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.
@ -61,21 +56,19 @@ 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 Overview)
## Processing Pipeline (Simplified)
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:
The primary processing engine (`processing_engine.py`) executes a series of steps for each asset based on the provided `SourceRule` object and static configuration:
1. **Supplier Determination**: Identify the effective supplier.
2. **Asset Skip Logic**: Check if the asset should be skipped.
3. **Metadata Initialization**: Set up initial asset metadata.
4. **File Rule Filtering**: Determine which files to process.
5. **Pre-Map Processing**:
* Gloss-to-Roughness Conversion.
* Alpha Channel Extraction.
* Normal Map Green Channel Inversion.
6. **Individual Map Processing**: Handle individual maps (scaling, variants, stats, naming).
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.
1. Extraction of input to a temporary workspace (using `utils.workspace_utils.py`).
2. Classification of files (map, model, extra, ignored, unrecognised) based *only* on the provided `SourceRule` object (classification/prediction happens *before* the engine is called).
3. Determination of base metadata (asset name, category, archetype).
4. Skip check if output exists and overwrite is not forced.
5. Processing of maps (resize, format/bit depth conversion, inversion, stats calculation).
6. Merging of channels based on rules.
7. Generation of `metadata.json` file.
8. Organization of processed files into the final output structure.
9. Cleanup of the temporary workspace.
10. (Optional) Execution of Blender scripts (currently triggered directly, intended to use `utils.blender_utils.py`).
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.
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.

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This document describes the major classes and modules that form the core of the Asset Processor Tool.
## Core Processing Architecture
## `ProcessingEngine` (`processing_engine.py`)
The asset processing pipeline has been refactored into a staged architecture, managed by an orchestrator.
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:
### `ProcessingEngine` (`processing_engine.py`)
The `ProcessingEngine` class serves as the primary entry point for initiating an asset processing task. Its main responsibilities are:
* Initializing a `PipelineOrchestrator` instance.
* Providing the `PipelineOrchestrator` with the global `Configuration` object and a predefined list of processing stages.
* 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.
* Setting up and cleaning up a temporary workspace for processing (potentially using `utils.workspace_utils`).
* Extracting or copying input files to the workspace.
* Processing files based on the explicit rules and predicted values contained within the input `SourceRule`.
* 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`.
* Generating the `metadata.json` file containing details about the processed asset, incorporating information from the `SourceRule`.
* Organizing the final output files into the structured library directory.
## `Rule Structure` (`rule_structure.py`)
@ -70,19 +22,19 @@ 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`, `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`.
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.
## `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` (e.g., `FILE_TYPE_DEFINITIONS`, `ASSET_TYPE_DEFINITIONS`, `image_resolutions`, `map_merge_rules`, `output_filename_pattern`).
* Loading the core default settings defined in `config/app_settings.json`.
* 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 prediction handlers.
* Pre-compiling regular expression patterns defined in the preset for efficient file classification by the `PredictionHandler`.
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`.
An instance of the `Configuration` class is typically created once per application run (or per processing batch) and passed to the `ProcessingEngine`.
## GUI Components (`gui/`)
@ -239,10 +191,10 @@ 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` (which in turn uses the `PipelineOrchestrator`).
* Instantiates and runs the `ProcessingEngine`.
* 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. The processing core is now a staged pipeline managed by the `PipelineOrchestrator`, enhancing modularity and maintainability.
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.

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Cl# Developer Guide: Processing Pipeline
# Developer Guide: Processing Pipeline
This document details the step-by-step technical process executed by the asset processing pipeline, which is initiated by the [`ProcessingEngine`](processing_engine.py:73) class (`processing_engine.py`) and orchestrated by the [`PipelineOrchestrator`](processing/pipeline/orchestrator.py:36) (`processing/pipeline/orchestrator.py`).
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.
The [`ProcessingEngine.process()`](processing_engine.py:131) method serves as the main entry point. It initializes a [`PipelineOrchestrator`](processing/pipeline/orchestrator.py:36) instance, providing it with the application's [`Configuration`](configuration.py:68) object and predefined lists of pre-item and post-item processing stages. The [`PipelineOrchestrator.process_source_rule()`](processing/pipeline/orchestrator.py:95) method then manages the execution of these stages for each asset defined in the input [`SourceRule`](rule_structure.py:40).
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.
A crucial component in this architecture is the [`AssetProcessingContext`](processing/pipeline/asset_context.py:86) (`processing/pipeline/asset_context.py`). An instance of this dataclass is created for each [`AssetRule`](rule_structure.py:22) 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.
The pipeline steps are:
The pipeline execution for each asset follows this general flow:
1. **Workspace Preparation (External)**:
* 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.
1. **Pre-Item Stages:** A sequence of stages executed once per asset before the core item processing loop. These stages typically perform initial setup, filtering, and asset-level transformations.
2. **Core Item Processing Loop:** The [`PipelineOrchestrator`](processing/pipeline/orchestrator.py:36) iterates through a list of "processing items" (individual files or merge tasks) prepared by a dedicated stage. For each item, a sequence of core processing stages is executed.
3. **Post-Item Stages:** A sequence of stages executed once per asset after the core item processing loop is complete. These stages handle final tasks like organizing output files and saving metadata.
2. **Prediction and Rule Generation (External)**:
* Also handled before the `ProcessingEngine` is invoked.
* 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.
* 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.
## Pipeline Stages
3. **File Inventory (`_inventory_and_classify_files`)**:
* Scans the contents of the *already prepared* temporary workspace.
* 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`.
* Stores the file paths and their associated rules from the `SourceRule` in `self.classified_files`.
The stages are executed in the following order for each asset:
4. **Base Metadata Determination (`_determine_base_metadata`, `_determine_single_asset_metadata`)**:
* 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`.
### Pre-Item Stages
5. **Skip Check**:
* If the `overwrite` flag is `False`, checks if the final output directory already exists and contains `metadata.json`.
* If so, processing for this asset is skipped.
These stages are executed sequentially once for each asset before the core item processing loop begins.
6. **Map Processing (`_process_maps`)**:
* Iterates through files classified as maps in the `SourceRule`.
* Loads images (`cv2.imread`).
* **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.
1. **[`SupplierDeterminationStage`](processing/pipeline/stages/supplier_determination.py:6)** (`processing/pipeline/stages/supplier_determination.py`):
* **Responsibility**: Determines the effective supplier for the asset based on the [`SourceRule`](rule_structure.py:40)'s `supplier_override`, `supplier_identifier`, and validation against configured suppliers.
* **Context Interaction**: Sets `context.effective_supplier` and may set a `supplier_error` flag in `context.status_flags`.
7. **Map Merging (`_merge_maps_from_source`)**:
* Iterates through `MAP_MERGE_RULES` in `Configuration`.
* 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.
* 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.
2. **[`AssetSkipLogicStage`](processing/pipeline/stages/asset_skip_logic.py:5)** (`processing/pipeline/stages/asset_skip_logic.py`):
* **Responsibility**: Checks if the entire asset should be skipped based on conditions like a missing/invalid supplier, a "SKIP" status in asset metadata, or if the asset is already processed and overwrite is disabled.
* **Context Interaction**: Sets the `skip_asset` flag and `skip_reason` in `context.status_flags` if the asset should be skipped.
8. **Metadata File Generation (`_generate_metadata_file`)**:
* Collects asset metadata, processed/merged map details, ignored files list, etc., primarily from the `SourceRule` and internal processing results.
* Writes data to `metadata.json` in the temporary workspace.
3. **[`MetadataInitializationStage`](processing/pipeline/stages/metadata_initialization.py:81)** (`processing/pipeline/stages/metadata_initialization.py`):
* **Responsibility**: Initializes the `context.asset_metadata` dictionary with base information derived from the [`AssetRule`](rule_structure.py:22), [`SourceRule`](rule_structure.py:40), and [`Configuration`](configuration.py:68). This includes asset name, IDs, source/output paths, timestamps, and initial status.
* **Context Interaction**: Populates `context.asset_metadata`. Initializes `context.processed_maps_details` and `context.merged_maps_details` as empty dictionaries (these are used internally by subsequent stages but are not directly part of the final `metadata.json` in their original form).
9. **Output Organization (`_organize_output_files`)**:
* 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.
* Creates the final structured output directory (`<output_base_dir>/<supplier_name>/<asset_name>/`), using the supplier name from the `SourceRule`.
* Moves processed maps, merged maps, models, metadata, and other classified files from the temporary workspace to the final output directory.
4. **[`FileRuleFilterStage`](processing/pipeline/stages/file_rule_filter.py:10)** (`processing/pipeline/stages/file_rule_filter.py`):
* **Responsibility**: Filters the [`FileRule`](rule_structure.py:5) objects associated with the asset to determine which individual files should be considered for processing. It identifies and excludes files matching "FILE_IGNORE" rules based on their `item_type`.
* **Context Interaction**: Populates `context.files_to_process` with the list of [`FileRule`](rule_structure.py:5) objects that are not ignored.
10. **Workspace Cleanup (External)**:
* 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.
5. **[`GlossToRoughConversionStage`](processing/pipeline/stages/gloss_to_rough_conversion.py:15)** (`processing/pipeline/stages/gloss_to_rough_conversion.py`):
* **Responsibility**: Identifies processed maps in `context.processed_maps_details` whose `internal_map_type` starts with "MAP_GLOSS". If found, it loads the temporary image data, inverts it using the shared utility function [`apply_common_map_transformations`](processing/utils/image_processing_utils.py), saves a new temporary roughness map ("MAP_ROUGH"), and updates the corresponding details in `context.processed_maps_details` (setting `internal_map_type` to "MAP_ROUGH") and the relevant [`FileRule`](rule_structure.py:5) in `context.files_to_process` (setting `item_type` to "MAP_ROUGH").
* **Context Interaction**: Reads from and updates `context.processed_maps_details` (specifically `internal_map_type` and `temp_processed_file`) and `context.files_to_process` (specifically `item_type`).
11. **(Optional) Blender Script Execution (External)**:
* 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.
* *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.
6. **[`AlphaExtractionToMaskStage`](processing/pipeline/stages/alpha_extraction_to_mask.py:16)** (`processing/pipeline/stages/alpha_extraction_to_mask.py`):
* **Responsibility**: If no mask map is explicitly defined for the asset (as a [`FileRule`](rule_structure.py:5) with `item_type="MAP_MASK"`), this stage searches `context.processed_maps_details` for a suitable source map (e.g., a "MAP_COL" with an alpha channel, based on its `internal_map_type`). If found, it extracts the alpha channel, saves it as a new temporary mask map, and adds a new [`FileRule`](rule_structure.py:5) (with `item_type="MAP_MASK"`) and corresponding details (with `internal_map_type="MAP_MASK"`) to the context.
* **Context Interaction**: Reads from `context.processed_maps_details`, adds a new [`FileRule`](rule_structure.py:5) to `context.files_to_process`, and adds a new entry to `context.processed_maps_details` (setting `internal_map_type`).
7. **[`NormalMapGreenChannelStage`](processing/pipeline/stages/normal_map_green_channel.py:14)** (`processing/pipeline/stages/normal_map_green_channel.py`):
* **Responsibility**: Identifies processed normal maps in `context.processed_maps_details` (those with an `internal_map_type` starting with "MAP_NRM"). If the global `invert_normal_map_green_channel_globally` configuration is true, it loads the temporary image data, inverts the green channel using the shared utility function [`apply_common_map_transformations`](processing/utils/image_processing_utils.py), saves a new temporary modified normal map, and updates the `temp_processed_file` path in `context.processed_maps_details`.
* **Context Interaction**: Reads from and updates `context.processed_maps_details` (specifically `temp_processed_file` and `notes`).
### Core Item Processing Loop
The [`PipelineOrchestrator`](processing/pipeline/orchestrator.py:36) iterates through the `context.processing_items` list (populated by the [`PrepareProcessingItemsStage`](processing/pipeline/stages/prepare_processing_items.py:10)). For each item (either a [`FileRule`](rule_structure.py:5) for a regular map or a [`MergeTaskDefinition`](processing/pipeline/asset_context.py:16) for a merged map), the following stages are executed sequentially:
1. **[`PrepareProcessingItemsStage`](processing/pipeline/stages/prepare_processing_items.py:10)** (`processing/pipeline/stages/prepare_processing_items.py`):
* **Responsibility**: (Executed once before the loop) Creates the `context.processing_items` list by combining [`FileRule`](rule_structure.py:5)s from `context.files_to_process` and [`MergeTaskDefinition`](processing/pipeline/asset_context.py:16)s derived from the global `map_merge_rules` configuration. It correctly accesses `map_merge_rules` from `context.config_obj` and validates each merge rule for the presence of `output_map_type` and a dictionary for `inputs`. Initializes `context.intermediate_results`.
* **Context Interaction**: Reads from `context.files_to_process` and `context.config_obj` (accessing `map_merge_rules`). Populates `context.processing_items` and initializes `context.intermediate_results`.
2. **[`RegularMapProcessorStage`](processing/pipeline/stages/regular_map_processor.py:18)** (`processing/pipeline/stages/regular_map_processor.py`):
* **Responsibility**: (Executed per [`FileRule`](rule_structure.py:5) item) Checks if the `FileRule.item_type` starts with "MAP_". If not, the item is skipped. Otherwise, it loads the image data for the file, determines its potentially suffixed internal map type (e.g., "MAP_COL-1"), applies in-memory transformations (Gloss-to-Rough, Normal Green Invert) using the shared utility function [`apply_common_map_transformations`](processing/utils/image_processing_utils.py), and returns the processed image data and details in a [`ProcessedRegularMapData`](processing/pipeline/asset_context.py:23) object. The `internal_map_type` in the output reflects any transformations (e.g., "MAP_GLOSS" becomes "MAP_ROUGH").
* **Context Interaction**: Reads from the input [`FileRule`](rule_structure.py:5) (checking `item_type`) and [`Configuration`](configuration.py:68). Returns a [`ProcessedRegularMapData`](processing/pipeline/asset_context.py:23) object which is stored in `context.intermediate_results`.
3. **[`MergedTaskProcessorStage`](processing/pipeline/stages/merged_task_processor.py:68)** (`processing/pipeline/stages/merged_task_processor.py`):
* **Responsibility**: (Executed per [`MergeTaskDefinition`](processing/pipeline/asset_context.py:16) item) Validates that all input map types specified in the merge rule start with "MAP_". If not, the task is failed. It dynamically loads input images by looking up the required input map types (e.g., "MAP_NRM") in `context.processed_maps_details` and using the temporary file paths from their `saved_files_info`. It applies in-memory transformations to inputs using [`apply_common_map_transformations`](processing/utils/image_processing_utils.py), handles dimension mismatches (with fallback creation if configured and `source_dimensions` are available), performs the channel merging operation, and returns the merged image data and details in a [`ProcessedMergedMapData`](processing/pipeline/asset_context.py:35) object. The `output_map_type` of the merged map must also be "MAP_" prefixed in the configuration.
* **Context Interaction**: Reads from the input [`MergeTaskDefinition`](processing/pipeline/asset_context.py:16) (checking input map types), `context.workspace_path`, `context.processed_maps_details` (for input image data), and [`Configuration`](configuration.py:68). Returns a [`ProcessedMergedMapData`](processing/pipeline/asset_context.py:35) object which is stored in `context.intermediate_results`.
4. **[`InitialScalingStage`](processing/pipeline/stages/initial_scaling.py:14)** (`processing/pipeline/stages/initial_scaling.py`):
* **Responsibility**: (Executed per item) Applies initial scaling (e.g., Power-of-Two downscaling) to the image data from the previous processing stage based on the `initial_scaling_mode` configuration.
* **Context Interaction**: Takes a [`InitialScalingInput`](processing/pipeline/asset_context.py:46) (containing image data and config) and returns an [`InitialScalingOutput`](processing/pipeline/asset_context.py:54) object, which updates the item's entry in `context.intermediate_results`.
5. **[`SaveVariantsStage`](processing/pipeline/stages/save_variants.py:15)** (`processing/pipeline/stages/save_variants.py`):
* **Responsibility**: (Executed per item) Takes the final processed image data (potentially scaled) and configuration, and calls a utility to save the image to temporary files in various resolutions and formats as defined by the configuration.
* **Context Interaction**: Takes a [`SaveVariantsInput`](processing/pipeline/asset_context.py:61) object (which includes the "MAP_" prefixed `internal_map_type`). It uses the `get_filename_friendly_map_type` utility to convert this to a "standard type" (e.g., "COL") for output naming. Returns a [`SaveVariantsOutput`](processing/pipeline/asset_context.py:79) object containing details about the saved temporary files. The orchestrator stores these details, including the original "MAP_" prefixed `internal_map_type`, in `context.processed_maps_details` for the item.
### Post-Item Stages
These stages are executed sequentially once for each asset after the core item processing loop has finished for all items.
1. **[`OutputOrganizationStage`](processing/pipeline/stages/output_organization.py:14)** (`processing/pipeline/stages/output_organization.py`):
* **Responsibility**: Determines the final output paths for all processed maps (including variants) and extra files based on configured patterns. It copies the temporary files generated by the core stages to these final destinations, creating directories as needed and respecting overwrite settings.
* **Context Interaction**: Reads from `context.processed_maps_details`, `context.files_to_process` (for 'EXTRA' files), `context.output_base_path`, and [`Configuration`](configuration.py:68). Updates entries in `context.processed_maps_details` with organization status. Populates `context.asset_metadata['maps']` with the final map structure:
* The `maps` object is a dictionary where keys are standard map types (e.g., "COL", "REFL").
* Each entry contains a `variant_paths` dictionary, where keys are resolution strings (e.g., "8K", "4K") and values are the filenames of the map variants (relative to the asset's output directory).
It also populates `context.asset_metadata['final_output_files']` with a list of absolute paths to all generated files (this list itself is not saved in the final `metadata.json`).
2. **[`MetadataFinalizationAndSaveStage`](processing/pipeline/stages/metadata_finalization_save.py:14)** (`processing/pipeline/stages/metadata_finalization_save.py`):
* **Responsibility**: Finalizes the `context.asset_metadata` (setting final status based on flags). It determines the save path for the metadata file based on configuration and patterns, serializes the `context.asset_metadata` (which now contains the structured `maps` data from `OutputOrganizationStage`) to JSON, and saves the `metadata.json` file.
* **Context Interaction**: Reads from `context.asset_metadata` (including the `maps` structure), `context.output_base_path`, and [`Configuration`](configuration.py:68). Before saving, it explicitly removes the `final_output_files` key from `context.asset_metadata`. The `processing_end_time` is also no longer added. The `metadata.json` file is written, and `context.asset_metadata` is updated with its final path and status. The older `processed_maps_details` and `merged_maps_details` from the context are not directly included in the JSON.
## External Steps
Certain steps are integral to the overall asset processing workflow but are handled outside the [`PipelineOrchestrator`](processing/pipeline/orchestrator.py:36)'s direct execution loop:
* **Workspace Preparation and Cleanup**: Handled by the code that invokes [`ProcessingEngine.process()`](processing_engine.py:131) (e.g., `main.ProcessingTask`, `monitor._process_archive_task`), typically involving extracting archives and setting up temporary directories. The engine itself manages a sub-temporary directory (`engine_temp_dir`) for intermediate processing files.
* **Prediction and Rule Generation**: Performed before the [`ProcessingEngine`](processing_engine.py:73) is called. This involves analyzing source files and generating the [`SourceRule`](rule_structure.py:40) object with its nested [`AssetRule`](rule_structure.py:22)s and [`FileRule`](rule_structure.py:5)s, often involving prediction logic (potentially using LLMs).
* **Optional Blender Script Execution**: Can be triggered externally after successful processing to perform tasks like material setup in Blender using the generated output files and metadata.
This staged pipeline provides a modular and extensible architecture for asset processing, with clear separation of concerns for each step. The [`AssetProcessingContext`](processing/pipeline/asset_context.py:86) ensures that data flows consistently between these stages.
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.

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]
},
{
"target_type": "MAP_GLOSS",
"target_type": "MAP_ROUGH",
"keywords": [
"GLOSS"
]

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]
},
{
"target_type": "MAP_GLOSS",
"target_type": "MAP_ROUGH",
"keywords": [
"GLOSS"
],

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# Plan: Enforcing "MAP_" Prefix for Internal Processing and Standard Type for Output Naming
**Date:** 2025-05-13
**I. Goal:**
The primary goal is to ensure that for all internal processing, the system *exclusively* uses `FileRule.item_type` values that start with the "MAP_" prefix (e.g., "MAP_COL", "MAP_NRM"). The "standard type" (e.g., "COL", "NRM") associated with these "MAP_" types (as defined in `config/app_settings.json`) should *only* be used during the file saving stages for output naming. Any `FileRule` whose `item_type` does not start with "MAP_" (and isn't a special type like "EXTRA" or "MODEL") should be skipped by the relevant map processing stages.
**II. Current State Analysis Summary:**
* **Output Naming:** The use of "standard type" for output filenames via the `get_filename_friendly_map_type` utility in `SaveVariantsStage` and `OutputOrganizationStage` is **correct** and already meets the requirement.
* **Internal "MAP_" Prefix Usage:**
* Some stages like `GlossToRoughConversionStage` correctly check for "MAP_" prefixes (e.g., `processing_map_type.startswith("MAP_GLOSS")`).
* Other stages like `RegularMapProcessorStage` and `MergedTaskProcessorStage` (and its helpers) implicitly expect "MAP_" prefixed types for their internal regex-based logic but lack explicit checks to skip items if the prefix is missing.
* Stages like `AlphaExtractionToMaskStage` and `NormalMapGreenChannelStage` currently use non-"MAP_" prefixed "standard types" (e.g., "NORMAL", "ALBEDO") when reading from `context.processed_maps_details` for their decision-making logic.
* The `PrepareProcessingItemsStage` adds `FileRule`s to the processing queue without filtering based on the "MAP_" prefix in `item_type`.
* **Data Consistency in `AssetProcessingContext`:**
* `FileRule.item_type` is the field that should hold the "MAP_" prefixed type from the initial rule generation.
* `context.processed_maps_details` entries can contain various map type representations:
* `map_type`: Often stores the "standard type" (e.g., "Roughness", "MASK", "NORMAL").
* `processing_map_type` / `internal_map_type`: Generally seem to store the "MAP_" prefixed type. This needs to be consistent.
* **Configuration (`config/app_settings.json`):**
* `FILE_TYPE_DEFINITIONS` correctly use "MAP_" prefixed keys.
* `MAP_MERGE_RULES` need to be reviewed to ensure their `output_map_type` and input map types are "MAP_" prefixed.
**III. Proposed Changes (Code Identification & Recommendations):**
**A. Enforce "MAP_" Prefix for Processing Items (Skipping Logic):**
The core requirement is that processing stages should skip `FileRule` items if their `item_type` doesn't start with "MAP_".
1. **`RegularMapProcessorStage` (`processing/pipeline/stages/regular_map_processor.py`):**
* **Identify:** In the `execute` method, `initial_internal_map_type` is derived from `file_rule.item_type_override` or `file_rule.item_type`.
* **Recommend:** Add an explicit check after determining `initial_internal_map_type`. If `initial_internal_map_type` does not start with `"MAP_"`, the stage should log a warning, set the `result.status` to "Skipped (Invalid Type)" or similar, and return `result` early, effectively skipping processing for this item.
2. **`MergedTaskProcessorStage` (`processing/pipeline/stages/merged_task_processor.py`):**
* **Identify:** This stage processes `MergeTaskDefinition`s. The definitions for these tasks (input types, output type) come from `MAP_MERGE_RULES` in `config/app_settings.json`. The stage uses `required_map_type_from_rule` for its inputs.
* **Recommend:**
* **Configuration First:** Review all entries in `MAP_MERGE_RULES` in `config/app_settings.json`.
* Ensure the `output_map_type` for each rule (e.g., "MAP_NRMRGH") starts with "MAP_".
* Ensure all map type values within the `inputs` dictionary (e.g., `"R": "MAP_NRM"`) start with "MAP_".
* **Stage Logic:** In the `execute` method, when iterating through `merge_inputs_config.items()`, check if `required_map_type_from_rule` starts with `"MAP_"`. If not, log a warning and either:
* Skip loading/processing this specific input channel (potentially using its fallback if the overall merge can still proceed).
* Or, if a non-"MAP_" input is critical, fail the entire merge task for this asset.
* The helper `_apply_in_memory_transformations` already uses regex expecting "MAP_" prefixes; this will naturally fail or misbehave if inputs are not "MAP_" prefixed, reinforcing the need for the check above.
**B. Standardize Map Type Fields and Usage in `context.processed_maps_details`:**
Ensure consistency in how "MAP_" prefixed types are stored and accessed within `context.processed_maps_details` for internal logic (not naming).
1. **Recommendation:** Establish a single, consistent field name within `context.processed_maps_details` to store the definitive "MAP_" prefixed internal map type (e.g., `internal_map_type` or `processing_map_type`). All stages that perform logic based on the specific *kind* of map (e.g., transformations, source selection) should read from this standardized field. The `map_type` field can continue to store the "standard type" (e.g., "Roughness") primarily for informational/metadata purposes if needed, but not for core processing logic.
2. **`AlphaExtractionToMaskStage` (`processing/pipeline/stages/alpha_extraction_to_mask.py`):**
* **Identify:**
* Checks for existing MASK map using `file_rule.map_type == "MASK"`. (Discrepancy: `FileRule` uses `item_type`).
* Searches for suitable source maps using `details.get('map_type') in self.SUITABLE_SOURCE_MAP_TYPES` where `SUITABLE_SOURCE_MAP_TYPES` are standard types like "ALBEDO".
* When adding new details, it sets `map_type: "MASK"` and the new `FileRule` gets `item_type="MAP_MASK"`.
* **Recommend:**
* Change the check for an existing MASK map to `file_rule.item_type == "MAP_MASK"`.
* Modify the source map search to use the standardized "MAP_" prefixed field from `details` (e.g., `details.get('internal_map_type')`) and update `SUITABLE_SOURCE_MAP_TYPES` to be "MAP_" prefixed (e.g., "MAP_COL", "MAP_ALBEDO").
* When adding new details for the created MASK map to `context.processed_maps_details`, ensure the standardized "MAP_" prefixed field is set to "MAP_MASK", and `map_type` (if kept) is "MASK".
3. **`NormalMapGreenChannelStage` (`processing/pipeline/stages/normal_map_green_channel.py`):**
* **Identify:** Checks `map_details.get('map_type') == "NORMAL"`.
* **Recommend:** Change this check to use the standardized "MAP_" prefixed field from `map_details` (e.g., `map_details.get('internal_map_type')`) and verify if it `startswith("MAP_NRM")`.
4. **`GlossToRoughConversionStage` (`processing/pipeline/stages/gloss_to_rough_conversion.py`):**
* **Identify:** This stage already uses `processing_map_type.startswith("MAP_GLOSS")` and updates `processing_map_type` to "MAP_ROUGH" in `map_details`. It also updates the `FileRule.item_type` correctly.
* **Recommend:** This stage is largely consistent. Ensure the field it reads/writes (`processing_map_type`) aligns with the chosen standardized "MAP_" prefixed field for `processed_maps_details`.
**C. Review Orchestration Logic (Conceptual):**
* When the orchestrator populates `context.processed_maps_details` after stages like `SaveVariantsStage`, ensure it stores the "MAP_" prefixed `internal_map_type` (from `SaveVariantsInput`) into the chosen standardized field in `processed_maps_details`.
**IV. Testing Recommendations:**
* Create test cases with `AssetRule`s containing `FileRule`s where `item_type` is intentionally set to a non-"MAP_" prefixed value (e.g., "COLOR_MAP", "TEXTURE_ROUGH"). Verify that `RegularMapProcessorStage` skips these.
* Modify `MAP_MERGE_RULES` in a test configuration:
* Set an `output_map_type` to a non-"MAP_" value.
* Set an input map type (e.g., for channel "R") to a non-"MAP_" value.
* Verify that `MergedTaskProcessorStage` correctly handles these (e.g., fails the task, skips the input, logs warnings).
* Test `AlphaExtractionToMaskStage`:
* With an existing `FileRule` having `item_type="MAP_MASK"` to ensure extraction is skipped.
* With source maps having "MAP_COL" (with alpha) as their `internal_map_type` in `processed_maps_details` to ensure they are correctly identified as sources.
* Test `NormalMapGreenChannelStage` with a normal map having "MAP_NRM" as its `internal_map_type` in `processed_maps_details` to ensure it's processed.
* Verify that output filenames continue to use the "standard type" (e.g., "COL", "ROUGH", "NRM") correctly.
**V. Mermaid Diagram (Illustrative Flow for `FileRule` Processing):**
```mermaid
graph TD
A[AssetRule with FileRules] --> B{FileRuleFilterStage};
B -- files_to_process --> C{PrepareProcessingItemsStage};
C -- processing_items (FileRule) --> D{PipelineOrchestrator};
D -- FileRule --> E(RegularMapProcessorStage);
E --> F{Check FileRule.item_type};
F -- Starts with "MAP_"? --> G[Process Map];
F -- No --> H[Skip Map / Log Warning];
G --> I[...subsequent stages...];
H --> I;

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# Processing Pipeline Refactoring Plan
## 1. Problem Summary
The current processing pipeline, particularly the `IndividualMapProcessingStage`, exhibits maintainability challenges:
* **High Complexity:** The stage handles too many responsibilities (loading, merging, transformations, scaling, saving).
* **Duplicated Logic:** Image transformations (Gloss-to-Rough, Normal Green Invert) are duplicated within the stage instead of relying solely on dedicated stages or being handled consistently.
* **Tight Coupling:** Heavy reliance on the large, mutable `AssetProcessingContext` object creates implicit dependencies and makes isolated testing difficult.
## 2. Refactoring Goals
* Improve code readability and understanding.
* Enhance maintainability by localizing changes and removing duplication.
* Increase testability through smaller, focused components with clear interfaces.
* Clarify data dependencies between pipeline stages.
* Adhere more closely to the Single Responsibility Principle (SRP).
## 3. Proposed New Pipeline Stages
Replace the existing `IndividualMapProcessingStage` with the following sequence of smaller, focused stages, executed by the `PipelineOrchestrator` for each processing item:
1. **`PrepareProcessingItemsStage`:**
* **Responsibility:** Identifies and lists all items (`FileRule`, `MergeTaskDefinition`) to be processed from the main context.
* **Output:** Updates `context.processing_items`.
2. **`RegularMapProcessorStage`:** (Handles `FileRule` items)
* **Responsibility:** Loads source image, determines internal map type (with suffix), applies relevant transformations (Gloss-to-Rough, Normal Green Invert), determines original metadata.
* **Output:** `ProcessedRegularMapData` object containing transformed image data and metadata.
3. **`MergedTaskProcessorStage`:** (Handles `MergeTaskDefinition` items)
* **Responsibility:** Loads input images, applies transformations to inputs, handles fallbacks/resizing, performs merge operation.
* **Output:** `ProcessedMergedMapData` object containing merged image data and metadata.
4. **`InitialScalingStage`:** (Optional)
* **Responsibility:** Applies configured scaling (e.g., POT downscale) to the processed image data received from the previous stage.
* **Output:** Scaled image data.
5. **`SaveVariantsStage`:**
* **Responsibility:** Takes the final processed (and potentially scaled) image data and orchestrates saving variants using the `save_image_variants` utility.
* **Output:** List of saved file details (`saved_files_details`).
## 4. Proposed Data Flow
* **Input/Output Objects:** Key stages (`RegularMapProcessor`, `MergedTaskProcessor`, `InitialScaling`, `SaveVariants`) will use specific Input and Output dataclasses for clearer interfaces.
* **Orchestrator Role:** The `PipelineOrchestrator` manages the overall flow. It calls stages, passes necessary data (extracting image data references and metadata from previous stage outputs to create inputs for the next), receives output objects, and integrates final results (like saved file details) back into the main `AssetProcessingContext`.
* **Image Data Handling:** Large image arrays (`np.ndarray`) are passed primarily via stage return values (Output objects) and used as inputs to subsequent stages, managed by the Orchestrator. They are not stored long-term in the main `AssetProcessingContext`.
* **Main Context:** The `AssetProcessingContext` remains for overall state (rules, paths, configuration access, final status tracking) and potentially for simpler stages with minimal side effects.
## 5. Visualization (Conceptual)
```mermaid
graph TD
subgraph Proposed Pipeline Stages
Start --> Prep[PrepareProcessingItemsStage]
Prep --> ItemLoop{Loop per Item}
ItemLoop -- FileRule --> RegProc[RegularMapProcessorStage]
ItemLoop -- MergeTask --> MergeProc[MergedTaskProcessorStage]
RegProc --> Scale(InitialScalingStage)
MergeProc --> Scale
Scale --> Save[SaveVariantsStage]
Save --> UpdateContext[Update Main Context w/ Results]
UpdateContext --> ItemLoop
end
```
## 6. Benefits
* Improved Readability & Understanding.
* Enhanced Maintainability & Reduced Risk.
* Better Testability.
* Clearer Dependencies.

20
config/app_settings.json
View File

@ -246,7 +246,7 @@
],
"EXTRA_FILES_SUBDIR": "Extra",
"OUTPUT_BASE_DIR": "../Asset_Processor_Output_Tests",
"OUTPUT_DIRECTORY_PATTERN": "[supplier]_[assetname]",
"OUTPUT_DIRECTORY_PATTERN": "[supplier]/[sha5]_[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",
@ -259,8 +259,7 @@
"8K": 8192,
"4K": 4096,
"2K": 2048,
"1K": 1024,
"PREVIEW": 128
"1K": 1024
},
"ASPECT_RATIO_DECIMALS": 2,
"OUTPUT_FORMAT_16BIT_PRIMARY": "exr",
@ -268,11 +267,11 @@
"OUTPUT_FORMAT_8BIT": "png",
"MAP_MERGE_RULES": [
{
"output_map_type": "MAP_NRMRGH",
"output_map_type": "NRMRGH",
"inputs": {
"R": "MAP_NRM",
"G": "MAP_NRM",
"B": "MAP_ROUGH"
"R": "NRM",
"G": "NRM",
"B": "ROUGH"
},
"defaults": {
"R": 0.5,
@ -284,10 +283,5 @@
],
"CALCULATE_STATS_RESOLUTION": "1K",
"DEFAULT_ASSET_CATEGORY": "Surface",
"TEMP_DIR_PREFIX": "_PROCESS_ASSET_",
"INITIAL_SCALING_MODE": "POT_DOWNSCALE",
"MERGE_DIMENSION_MISMATCH_STRATEGY": "USE_LARGEST",
"general_settings": {
"invert_normal_map_green_channel_globally": false
}
"TEMP_DIR_PREFIX": "_PROCESS_ASSET_"
}

25
configuration.py
View File

@ -379,33 +379,10 @@ class Configuration:
"""Gets the configured JPG quality level."""
return self._core_settings.get('JPG_QUALITY', 95)
@property
def invert_normal_green_globally(self) -> bool:
"""Gets the global setting for inverting the green channel of normal maps."""
# Default to False if the setting is missing in the core config
return self._core_settings.get('invert_normal_map_green_channel_globally', False)
@property
def overwrite_existing(self) -> bool:
"""Gets the setting for overwriting existing files from core settings."""
return self._core_settings.get('overwrite_existing', False)
@property
def png_compression_level(self) -> int:
"""Gets the PNG compression level from core settings."""
return self._core_settings.get('PNG_COMPRESSION', 6) # Default to 6 if not found
@property
def resolution_threshold_for_jpg(self) -> int:
"""Gets the pixel dimension threshold for using JPG for 8-bit images."""
value = self._core_settings.get('RESOLUTION_THRESHOLD_FOR_JPG', 4096)
log.info(f"CONFIGURATION_DEBUG: resolution_threshold_for_jpg property returning: {value} (type: {type(value)})")
# Ensure it's an int, as downstream might expect it.
# The .get() default is an int, but if the JSON had null or a string, it might be different.
if not isinstance(value, int):
log.warning(f"CONFIGURATION_DEBUG: RESOLUTION_THRESHOLD_FOR_JPG was not an int, got {type(value)}. Defaulting to 4096.")
return 4096
return value
return self._core_settings.get('RESOLUTION_THRESHOLD_FOR_JPG', 4096)
@property
def respect_variant_map_types(self) -> list:

28
main.py
View File

@ -21,43 +21,15 @@ 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.")

106
processing/pipeline/asset_context.py
View File

@ -1,106 +0,0 @@
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
# Imports needed for new dataclasses
import numpy as np
from typing import Any, Tuple, Union
# --- Stage Input/Output Dataclasses ---
# Item types for PrepareProcessingItemsStage output
@dataclass
class MergeTaskDefinition:
"""Represents a merge task identified by PrepareProcessingItemsStage."""
task_data: Dict # The original task data from context.merged_image_tasks
task_key: str # e.g., "merged_task_0"
# Output for RegularMapProcessorStage
@dataclass
class ProcessedRegularMapData:
processed_image_data: np.ndarray
final_internal_map_type: str
source_file_path: Path
original_bit_depth: Optional[int]
original_dimensions: Optional[Tuple[int, int]] # (width, height)
transformations_applied: List[str]
status: str = "Processed"
error_message: Optional[str] = None
# Output for MergedTaskProcessorStage
@dataclass
class ProcessedMergedMapData:
merged_image_data: np.ndarray
output_map_type: str # Internal type
source_bit_depths: List[int]
final_dimensions: Optional[Tuple[int, int]] # (width, height)
transformations_applied_to_inputs: Dict[str, List[str]] # Map type -> list of transforms
status: str = "Processed"
error_message: Optional[str] = None
# Input for InitialScalingStage
@dataclass
class InitialScalingInput:
image_data: np.ndarray
original_dimensions: Optional[Tuple[int, int]] # (width, height)
# Configuration needed
initial_scaling_mode: str
# Output for InitialScalingStage
@dataclass
class InitialScalingOutput:
scaled_image_data: np.ndarray
scaling_applied: bool
final_dimensions: Tuple[int, int] # (width, height)
# Input for SaveVariantsStage
@dataclass
class SaveVariantsInput:
image_data: np.ndarray # Final data (potentially scaled)
internal_map_type: str # Final internal type (e.g., MAP_ROUGH, MAP_COL-1)
source_bit_depth_info: List[int]
# Configuration needed
output_filename_pattern_tokens: Dict[str, Any]
image_resolutions: List[int]
file_type_defs: Dict[str, Dict]
output_format_8bit: str
output_format_16bit_primary: str
output_format_16bit_fallback: str
png_compression_level: int
jpg_quality: int
output_filename_pattern: str
resolution_threshold_for_jpg: Optional[int] # Added for JPG conversion
# Output for SaveVariantsStage
@dataclass
class SaveVariantsOutput:
saved_files_details: List[Dict]
status: str = "Processed"
error_message: Optional[str] = None
# Add a field to AssetProcessingContext for the prepared items
@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] # Will store final results per item_key
merged_maps_details: Dict[str, Dict] # This might become redundant? Keep for now.
files_to_process: List[FileRule]
loaded_data_cache: Dict
config_obj: Configuration
status_flags: Dict
incrementing_value: Optional[str]
sha5_value: Optional[str] # Keep existing fields
# New field for prepared items
processing_items: Optional[List[Union[FileRule, MergeTaskDefinition]]] = None
# Temporary storage during pipeline execution (managed by orchestrator)
# Keys could be FileRule object hash/id or MergeTaskDefinition task_key
intermediate_results: Optional[Dict[Any, Union[ProcessedRegularMapData, ProcessedMergedMapData, InitialScalingOutput]]] = None

439
processing/pipeline/orchestrator.py
View File

@ -1,439 +0,0 @@
# --- Imports ---
import logging
import shutil
import tempfile
from pathlib import Path
from typing import List, Dict, Optional, Any, Union # Added Any, Union
import numpy as np # Added numpy
from configuration import Configuration
from rule_structure import SourceRule, AssetRule, FileRule # Added FileRule
# Import new context classes and stages
from .asset_context import (
AssetProcessingContext,
MergeTaskDefinition,
ProcessedRegularMapData,
ProcessedMergedMapData,
InitialScalingInput,
InitialScalingOutput,
SaveVariantsInput,
SaveVariantsOutput,
)
from .stages.base_stage import ProcessingStage
# Import the new stages we created
from .stages.prepare_processing_items import PrepareProcessingItemsStage
from .stages.regular_map_processor import RegularMapProcessorStage
from .stages.merged_task_processor import MergedTaskProcessorStage
from .stages.initial_scaling import InitialScalingStage
from .stages.save_variants import SaveVariantsStage
log = logging.getLogger(__name__)
# --- PipelineOrchestrator Class ---
class PipelineOrchestrator:
"""
Orchestrates the processing of assets based on source rules and a series of processing stages.
Manages the overall flow, including the core item processing sequence.
"""
def __init__(self, config_obj: Configuration,
pre_item_stages: List[ProcessingStage],
post_item_stages: List[ProcessingStage]):
"""
Initializes the PipelineOrchestrator.
Args:
config_obj: The main configuration object.
pre_item_stages: Stages to run before the core item processing loop.
post_item_stages: Stages to run after the core item processing loop.
"""
self.config_obj: Configuration = config_obj
self.pre_item_stages: List[ProcessingStage] = pre_item_stages
self.post_item_stages: List[ProcessingStage] = post_item_stages
# Instantiate the core item processing stages internally
self._prepare_stage = PrepareProcessingItemsStage()
self._regular_processor_stage = RegularMapProcessorStage()
self._merged_processor_stage = MergedTaskProcessorStage()
self._scaling_stage = InitialScalingStage()
self._save_stage = SaveVariantsStage()
def _execute_specific_stages(
self, context: AssetProcessingContext,
stages_to_run: List[ProcessingStage],
stage_group_name: str,
stop_on_skip: bool = True
) -> AssetProcessingContext:
"""Executes a specific list of stages."""
asset_name = context.asset_rule.asset_name if context.asset_rule else "Unknown"
log.debug(f"Asset '{asset_name}': Executing {stage_group_name} stages...")
for stage in stages_to_run:
stage_name = stage.__class__.__name__
log.debug(f"Asset '{asset_name}': Executing {stage_group_name} stage: {stage_name}")
try:
# Check if stage expects context directly or specific input
# For now, assume outer stages take context directly
# This might need refinement if outer stages also adopt Input/Output pattern
context = stage.execute(context)
except Exception as e:
log.error(f"Asset '{asset_name}': Error during outer stage '{stage_name}': {e}", exc_info=True)
context.status_flags["asset_failed"] = True
context.status_flags["asset_failed_stage"] = stage_name
context.status_flags["asset_failed_reason"] = str(e)
# Update overall metadata immediately on outer stage failure
context.asset_metadata["status"] = f"Failed: Error in stage {stage_name}"
context.asset_metadata["error_message"] = str(e)
break # Stop processing outer stages for this asset on error
if stop_on_skip and context.status_flags.get("skip_asset"):
log.info(f"Asset '{asset_name}': Skipped by outer stage '{stage_name}'. Reason: {context.status_flags.get('skip_reason', 'N/A')}")
break # Skip remaining outer stages for this asset
return context
def process_source_rule(
self,
source_rule: SourceRule,
workspace_path: Path,
output_base_path: Path,
overwrite: bool,
incrementing_value: Optional[str],
sha5_value: Optional[str] # Keep param name consistent for now
) -> Dict[str, List[str]]:
"""
Processes a single source rule, applying pre-processing stages,
the core item processing loop (Prepare, Process, Scale, Save),
and post-processing stages.
"""
overall_status: Dict[str, List[str]] = {
"processed": [],
"skipped": [],
"failed": [],
}
engine_temp_dir_path: Optional[Path] = None
try:
# --- Setup Temporary Directory ---
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}")
# --- Process Each Asset Rule ---
for asset_rule in source_rule.assets:
asset_name = asset_rule.asset_name
log.info(f"Orchestrator: Processing asset '{asset_name}'")
# --- Initialize Asset Context ---
context = AssetProcessingContext(
source_rule=source_rule,
asset_rule=asset_rule,
workspace_path=workspace_path,
engine_temp_dir=engine_temp_dir_path,
output_base_path=output_base_path,
effective_supplier=None,
asset_metadata={},
processed_maps_details={}, # Final results per item
merged_maps_details={}, # Keep for potential backward compat or other uses?
files_to_process=[], # Populated by FileRuleFilterStage (assumed in outer_stages)
loaded_data_cache={},
config_obj=self.config_obj,
status_flags={"skip_asset": False, "asset_failed": False},
incrementing_value=incrementing_value,
sha5_value=sha5_value,
processing_items=[], # Initialize new fields
intermediate_results={}
)
# --- Execute Pre-Item-Processing Outer Stages ---
# (e.g., MetadataInit, SupplierDet, FileRuleFilter, GlossToRough, NormalInvert)
# Identify which outer stages run before the item loop
# This requires knowing the intended order. Assume all run before for now.
context = self._execute_specific_stages(context, self.pre_item_stages, "pre-item", stop_on_skip=True)
# Check if asset should be skipped or failed after pre-processing
if context.status_flags.get("asset_failed"):
log.error(f"Asset '{asset_name}': Failed during pre-processing stage '{context.status_flags.get('asset_failed_stage', 'Unknown')}'. Skipping item processing.")
overall_status["failed"].append(f"{asset_name} (Failed in {context.status_flags.get('asset_failed_stage', 'Pre-Processing')})")
continue # Move to the next asset rule
if context.status_flags.get("skip_asset"):
log.info(f"Asset '{asset_name}': Skipped during pre-processing. Skipping item processing.")
overall_status["skipped"].append(asset_name)
continue # Move to the next asset rule
# --- Prepare Processing Items ---
log.debug(f"Asset '{asset_name}': Preparing processing items...")
try:
log.info(f"ORCHESTRATOR_TRACE: Asset '{asset_name}': Attempting to call _prepare_stage.execute(). Current context.status_flags: {context.status_flags}")
# Prepare stage modifies context directly
context = self._prepare_stage.execute(context)
log.info(f"ORCHESTRATOR_TRACE: Asset '{asset_name}': Successfully RETURNED from _prepare_stage.execute(). context.processing_items count: {len(context.processing_items) if context.processing_items is not None else 'None'}. context.status_flags: {context.status_flags}")
except Exception as e:
log.error(f"ORCHESTRATOR_TRACE: Asset '{asset_name}': EXCEPTION during _prepare_stage.execute(): {e}", exc_info=True)
context.status_flags["asset_failed"] = True
context.status_flags["asset_failed_stage"] = "PrepareProcessingItemsStage"
context.status_flags["asset_failed_reason"] = str(e)
overall_status["failed"].append(f"{asset_name} (Failed in Prepare Items)")
continue # Move to next asset
if context.status_flags.get('prepare_items_failed'):
log.error(f"Asset '{asset_name}': Failed during item preparation. Reason: {context.status_flags.get('prepare_items_failed_reason', 'Unknown')}. Skipping item processing loop.")
overall_status["failed"].append(f"{asset_name} (Failed Prepare Items: {context.status_flags.get('prepare_items_failed_reason', 'Unknown')})")
continue # Move to next asset
if not context.processing_items:
log.info(f"Asset '{asset_name}': No items to process after preparation stage.")
# Status will be determined at the end
# --- Core Item Processing Loop ---
log.info("ORCHESTRATOR: Starting processing items loop for asset '%s'", asset_name) # Corrected indentation and message
log.info(f"Asset '{asset_name}': Starting core item processing loop for {len(context.processing_items)} items...")
asset_had_item_errors = False
for item_index, item in enumerate(context.processing_items):
item_key: Any = None # Key for storing results (FileRule object or task_key string)
item_log_prefix = f"Asset '{asset_name}', Item {item_index + 1}/{len(context.processing_items)}"
processed_data: Optional[Union[ProcessedRegularMapData, ProcessedMergedMapData]] = None
scaled_data_output: Optional[InitialScalingOutput] = None # Store output object
saved_data: Optional[SaveVariantsOutput] = None
item_status = "Failed" # Default item status
current_image_data: Optional[np.ndarray] = None # Track current image data ref
try:
# 1. Process (Load/Merge + Transform)
if isinstance(item, FileRule):
if item.item_type == 'EXTRA':
log.debug(f"{item_log_prefix}: Skipping image processing for EXTRA FileRule '{item.file_path}'.")
# Add a basic entry to processed_maps_details to acknowledge it was seen
context.processed_maps_details[item.file_path] = {
"status": "Skipped (EXTRA file)",
"internal_map_type": "EXTRA",
"source_file": str(item.file_path)
}
continue # Skip to the next item
item_key = item.file_path # Use file_path string as key
log.debug(f"{item_log_prefix}: Processing FileRule '{item.file_path}'...")
processed_data = self._regular_processor_stage.execute(context, item)
elif isinstance(item, MergeTaskDefinition):
item_key = item.task_key # Use task_key string as key
log.info(f"{item_log_prefix}: Executing MergedTaskProcessorStage for MergeTask '{item_key}'...") # Log call
processed_data = self._merged_processor_stage.execute(context, item)
# Log status/error from merge processor
if processed_data:
log.info(f"{item_log_prefix}: MergedTaskProcessorStage result - Status: {processed_data.status}, Error: {processed_data.error_message}")
else:
log.warning(f"{item_log_prefix}: MergedTaskProcessorStage returned None for MergeTask '{item_key}'.")
else:
log.warning(f"{item_log_prefix}: Unknown item type '{type(item)}'. Skipping.")
item_key = f"unknown_item_{item_index}"
context.processed_maps_details[item_key] = {"status": "Skipped", "notes": f"Unknown item type {type(item)}"}
asset_had_item_errors = True
continue # Next item
# Check for processing failure
if not processed_data or processed_data.status != "Processed":
error_msg = processed_data.error_message if processed_data else "Processor returned None"
log.error(f"{item_log_prefix}: Failed during processing stage. Error: {error_msg}")
context.processed_maps_details[item_key] = {"status": "Failed", "notes": f"Processing Error: {error_msg}", "stage": processed_data.__class__.__name__ if processed_data else "UnknownProcessor"}
asset_had_item_errors = True
continue # Next item
# Store intermediate result & get current image data
context.intermediate_results[item_key] = processed_data
current_image_data = processed_data.processed_image_data if isinstance(processed_data, ProcessedRegularMapData) else processed_data.merged_image_data
current_dimensions = processed_data.original_dimensions if isinstance(processed_data, ProcessedRegularMapData) else processed_data.final_dimensions
# 2. Scale (Optional)
scaling_mode = getattr(context.config_obj, "INITIAL_SCALING_MODE", "NONE")
if scaling_mode != "NONE" and current_image_data is not None and current_image_data.size > 0:
if isinstance(item, MergeTaskDefinition): # Log scaling call for merge tasks
log.info(f"{item_log_prefix}: Calling InitialScalingStage for MergeTask '{item_key}' (Mode: {scaling_mode})...")
log.debug(f"{item_log_prefix}: Applying initial scaling (Mode: {scaling_mode})...")
scale_input = InitialScalingInput(
image_data=current_image_data,
original_dimensions=current_dimensions, # Pass original/merged dims
initial_scaling_mode=scaling_mode
)
scaled_data_output = self._scaling_stage.execute(scale_input)
# Update intermediate result and current image data reference
context.intermediate_results[item_key] = scaled_data_output # Overwrite previous intermediate
current_image_data = scaled_data_output.scaled_image_data # Use scaled data for saving
log.debug(f"{item_log_prefix}: Scaling applied: {scaled_data_output.scaling_applied}. New Dims: {scaled_data_output.final_dimensions}")
else:
log.debug(f"{item_log_prefix}: Initial scaling skipped (Mode: NONE or empty image).")
# Create dummy output if scaling skipped, using current dims
final_dims = current_dimensions if current_dimensions else (current_image_data.shape[1], current_image_data.shape[0]) if current_image_data is not None else (0,0)
scaled_data_output = InitialScalingOutput(scaled_image_data=current_image_data, scaling_applied=False, final_dimensions=final_dims)
# 3. Save Variants
if current_image_data is None or current_image_data.size == 0:
log.warning(f"{item_log_prefix}: Skipping save stage because image data is empty.")
context.processed_maps_details[item_key] = {"status": "Skipped", "notes": "No image data to save", "stage": "SaveVariantsStage"}
# Don't mark as asset error, just skip this item's saving
continue # Next item
if isinstance(item, MergeTaskDefinition): # Log save call for merge tasks
log.info(f"{item_log_prefix}: Calling SaveVariantsStage for MergeTask '{item_key}'...")
log.debug(f"{item_log_prefix}: Saving variants...")
# Prepare input for save stage
internal_map_type = processed_data.final_internal_map_type if isinstance(processed_data, ProcessedRegularMapData) else processed_data.output_map_type
source_bit_depth = [processed_data.original_bit_depth] if isinstance(processed_data, ProcessedRegularMapData) and processed_data.original_bit_depth is not None else processed_data.source_bit_depths if isinstance(processed_data, ProcessedMergedMapData) else [8] # Default bit depth if unknown
# Construct filename tokens (ensure temp dir is used)
output_filename_tokens = {
'asset_name': asset_name,
'output_base_directory': context.engine_temp_dir, # Save variants to temp dir
# Add other tokens from context/config as needed by the pattern
'supplier': context.effective_supplier or 'UnknownSupplier',
}
# Log the value being read for the threshold before creating the input object
log.info(f"ORCHESTRATOR_DEBUG: Reading RESOLUTION_THRESHOLD_FOR_JPG from config for SaveVariantsInput: {getattr(context.config_obj, 'RESOLUTION_THRESHOLD_FOR_JPG', None)}")
save_input = SaveVariantsInput(
image_data=current_image_data, # Use potentially scaled data
internal_map_type=internal_map_type,
source_bit_depth_info=source_bit_depth,
output_filename_pattern_tokens=output_filename_tokens,
# Pass config values needed by save stage
image_resolutions=context.config_obj.image_resolutions,
file_type_defs=getattr(context.config_obj, "FILE_TYPE_DEFINITIONS", {}),
output_format_8bit=context.config_obj.get_8bit_output_format(),
output_format_16bit_primary=context.config_obj.get_16bit_output_formats()[0],
output_format_16bit_fallback=context.config_obj.get_16bit_output_formats()[1],
png_compression_level=context.config_obj.png_compression_level,
jpg_quality=context.config_obj.jpg_quality,
output_filename_pattern=context.config_obj.output_filename_pattern,
resolution_threshold_for_jpg=getattr(context.config_obj, "resolution_threshold_for_jpg", None) # Corrected case
)
saved_data = self._save_stage.execute(save_input)
# Log saved_data for merge tasks
if isinstance(item, MergeTaskDefinition):
log.info(f"{item_log_prefix}: SaveVariantsStage result for MergeTask '{item_key}' - Status: {saved_data.status if saved_data else 'N/A'}, Saved Files: {len(saved_data.saved_files_details) if saved_data else 0}")
# Check save status and finalize item result
if saved_data and saved_data.status.startswith("Processed"):
item_status = saved_data.status # e.g., "Processed" or "Processed (No Output)"
log.info(f"{item_log_prefix}: Item successfully processed and saved. Status: {item_status}")
# Populate final details for this item
final_details = {
"status": item_status,
"saved_files_info": saved_data.saved_files_details, # List of dicts from save util
"internal_map_type": internal_map_type,
"original_dimensions": processed_data.original_dimensions if isinstance(processed_data, ProcessedRegularMapData) else None,
"final_dimensions": scaled_data_output.final_dimensions if scaled_data_output else current_dimensions,
"transformations": processed_data.transformations_applied if isinstance(processed_data, ProcessedRegularMapData) else processed_data.transformations_applied_to_inputs,
# Add source file if regular map
"source_file": str(processed_data.source_file_path) if isinstance(processed_data, ProcessedRegularMapData) else None,
}
# Log final details addition for merge tasks
if isinstance(item, MergeTaskDefinition):
log.info(f"{item_log_prefix}: Adding final details to context.processed_maps_details for MergeTask '{item_key}'. Details: {final_details}")
context.processed_maps_details[item_key] = final_details
else:
error_msg = saved_data.error_message if saved_data else "Save stage returned None"
log.error(f"{item_log_prefix}: Failed during save stage. Error: {error_msg}")
context.processed_maps_details[item_key] = {"status": "Failed", "notes": f"Save Error: {error_msg}", "stage": "SaveVariantsStage"}
asset_had_item_errors = True
item_status = "Failed" # Ensure item status reflects failure
except Exception as e:
log.exception(f"{item_log_prefix}: Unhandled exception during item processing loop: {e}")
# Ensure details are recorded even on unhandled exception
if item_key is not None:
context.processed_maps_details[item_key] = {"status": "Failed", "notes": f"Unhandled Loop Error: {e}", "stage": "OrchestratorLoop"}
else:
log.error(f"Asset '{asset_name}': Unhandled exception in item loop before item key was set.")
asset_had_item_errors = True
item_status = "Failed"
# Optionally break loop or continue? Continue for now to process other items.
log.info("ORCHESTRATOR: Finished processing items loop for asset '%s'", asset_name)
log.info(f"Asset '{asset_name}': Finished core item processing loop.")
# --- Execute Post-Item-Processing Outer Stages ---
# (e.g., OutputOrganization, MetadataFinalizationSave)
# Identify which outer stages run after the item loop
# This needs better handling based on stage purpose. Assume none run after for now.
if not context.status_flags.get("asset_failed"):
log.info("ORCHESTRATOR: Executing post-item-processing outer stages for asset '%s'", asset_name)
context = self._execute_specific_stages(context, self.post_item_stages, "post-item", stop_on_skip=False)
# --- Final Asset Status Determination ---
final_asset_status = "Unknown"
fail_reason = ""
if context.status_flags.get("asset_failed"):
final_asset_status = "Failed"
fail_reason = f"(Failed in {context.status_flags.get('asset_failed_stage', 'Unknown Stage')}: {context.status_flags.get('asset_failed_reason', 'Unknown Reason')})"
elif context.status_flags.get("skip_asset"):
final_asset_status = "Skipped"
fail_reason = f"(Skipped: {context.status_flags.get('skip_reason', 'Unknown Reason')})"
elif asset_had_item_errors:
final_asset_status = "Failed"
fail_reason = "(One or more items failed)"
elif not context.processing_items:
# No items prepared, no errors -> consider skipped or processed based on definition?
final_asset_status = "Skipped" # Or "Processed (No Items)"
fail_reason = "(No items to process)"
elif not context.processed_maps_details and context.processing_items:
# Items were prepared, but none resulted in processed_maps_details entry
final_asset_status = "Skipped" # Or Failed?
fail_reason = "(All processing items skipped or failed internally)"
elif context.processed_maps_details:
# Check if all items in processed_maps_details are actually processed successfully
all_processed_ok = all(
str(details.get("status", "")).startswith("Processed")
for details in context.processed_maps_details.values()
)
some_processed_ok = any(
str(details.get("status", "")).startswith("Processed")
for details in context.processed_maps_details.values()
)
if all_processed_ok:
final_asset_status = "Processed"
elif some_processed_ok:
final_asset_status = "Partial" # Introduce a partial status? Or just Failed?
fail_reason = "(Some items failed)"
final_asset_status = "Failed" # Treat partial as Failed for overall status
else: # No items processed successfully
final_asset_status = "Failed"
fail_reason = "(All items failed)"
else:
# Should not happen if processing_items existed
final_asset_status = "Failed"
fail_reason = "(Unknown state after item processing)"
# Update overall status list
if final_asset_status == "Processed":
overall_status["processed"].append(asset_name)
elif final_asset_status == "Skipped":
overall_status["skipped"].append(f"{asset_name} {fail_reason}")
else: # Failed or Unknown
overall_status["failed"].append(f"{asset_name} {fail_reason}")
log.info(f"Asset '{asset_name}' final status: {final_asset_status} {fail_reason}")
# Clean up intermediate results for the asset to save memory
context.intermediate_results = {}
except Exception as e:
log.error(f"PipelineOrchestrator.process_source_rule failed critically: {e}", exc_info=True)
# Mark all assets from this source rule that weren't finished as failed
processed_or_skipped_or_failed = set(overall_status["processed"]) | \
set(name.split(" ")[0] for name in overall_status["skipped"]) | \
set(name.split(" ")[0] for name in 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: {e})")
finally:
# --- Cleanup Temporary Directory ---
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

179
processing/pipeline/stages/alpha_extraction_to_mask.py
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@ -1,179 +0,0 @@
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.
"""
# Use MAP_ prefixed types for internal logic checks
SUITABLE_SOURCE_MAP_TYPES = ["MAP_COL", "MAP_ALBEDO", "MAP_BASECOLOR"] # 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)
# Check for existing MASK map using the correct item_type field and MAP_ prefix
if file_rule.item_type == "MAP_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():
# Check for suitable source map using the standardized internal_map_type field
internal_map_type = details.get('internal_map_type') # Use the standardized field
if details.get('status') == 'Processed' and \
internal_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] = {
'internal_map_type': "MAP_MASK", # Use the standardized MAP_ prefixed field
'map_type': "MASK", # Keep standard type for metadata/naming consistency if needed
'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.get('internal_map_type', 'unknown type')} " # Use internal_map_type for notes
f"(Source Detail ID: {source_file_rule_id_for_alpha})"
),
# '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

55
processing/pipeline/stages/asset_skip_logic.py
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@ -1,55 +0,0 @@
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

22
processing/pipeline/stages/base_stage.py
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@ -1,22 +0,0 @@
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

90
processing/pipeline/stages/file_rule_filter.py
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@ -1,90 +0,0 @@
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

195
processing/pipeline/stages/gloss_to_rough_conversion.py
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@ -1,195 +0,0 @@
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():
# Use the standardized internal_map_type field
internal_map_type = map_details.get('internal_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"
# Check if the map is a GLOSS map using the standardized internal_map_type
if not internal_map_type.startswith("MAP_GLOSS"):
# logger.debug(f"Asset '{asset_name_for_log}', Map Key Index {map_key_index}: Type '{internal_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: {internal_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'] = internal_map_type # Store the original internal type
map_details['internal_map_type'] = "MAP_ROUGH" # Use the standardized MAP_ prefixed field
map_details['map_type'] = "Roughness" # Keep standard type for metadata/naming consistency if needed
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

83
processing/pipeline/stages/initial_scaling.py
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import logging
from typing import Tuple
import cv2 # Assuming cv2 is available for interpolation flags
import numpy as np
from .base_stage import ProcessingStage
# Import necessary context classes and utils
from ..asset_context import InitialScalingInput, InitialScalingOutput
from ...utils import image_processing_utils as ipu
log = logging.getLogger(__name__)
class InitialScalingStage(ProcessingStage):
"""
Applies initial scaling (e.g., Power-of-Two downscaling) to image data
if configured via the InitialScalingInput.
"""
def execute(self, input_data: InitialScalingInput) -> InitialScalingOutput:
"""
Applies scaling based on input_data.initial_scaling_mode.
"""
log.debug(f"Initial Scaling Stage: Mode '{input_data.initial_scaling_mode}'.")
image_to_scale = input_data.image_data
original_dims_wh = input_data.original_dimensions
scaling_mode = input_data.initial_scaling_mode
scaling_applied = False
final_image_data = image_to_scale # Default to original if no scaling happens
if image_to_scale is None or image_to_scale.size == 0:
log.warning("Initial Scaling Stage: Input image data is None or empty. Skipping.")
# Return original (empty) data and indicate no scaling
return InitialScalingOutput(
scaled_image_data=np.array([]),
scaling_applied=False,
final_dimensions=(0, 0)
)
if original_dims_wh is None:
log.warning("Initial Scaling Stage: Original dimensions not provided. Using current image shape.")
h_pre_scale, w_pre_scale = image_to_scale.shape[:2]
original_dims_wh = (w_pre_scale, h_pre_scale)
else:
w_pre_scale, h_pre_scale = original_dims_wh
if scaling_mode == "POT_DOWNSCALE":
pot_w = ipu.get_nearest_power_of_two_downscale(w_pre_scale)
pot_h = ipu.get_nearest_power_of_two_downscale(h_pre_scale)
if (pot_w, pot_h) != (w_pre_scale, h_pre_scale):
log.info(f"Initial Scaling: Applying POT Downscale from ({w_pre_scale},{h_pre_scale}) to ({pot_w},{pot_h}).")
# Use INTER_AREA for downscaling generally
resized_img = ipu.resize_image(image_to_scale, pot_w, pot_h, interpolation=cv2.INTER_AREA)
if resized_img is not None:
final_image_data = resized_img
scaling_applied = True
log.debug("Initial Scaling: POT Downscale applied successfully.")
else:
log.warning("Initial Scaling: POT Downscale resize failed. Using original data.")
# final_image_data remains image_to_scale
else:
log.info("Initial Scaling: POT Downscale - Image already POT or smaller. No scaling needed.")
# final_image_data remains image_to_scale
elif scaling_mode == "NONE":
log.info("Initial Scaling: Mode is NONE. No scaling applied.")
# final_image_data remains image_to_scale
else:
log.warning(f"Initial Scaling: Unknown INITIAL_SCALING_MODE '{scaling_mode}'. Defaulting to NONE.")
# final_image_data remains image_to_scale
# Determine final dimensions
final_h, final_w = final_image_data.shape[:2]
final_dims_wh = (final_w, final_h)
return InitialScalingOutput(
scaled_image_data=final_image_data,
scaling_applied=scaling_applied,
final_dimensions=final_dims_wh
)

329
processing/pipeline/stages/merged_task_processor.py
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@ -1,329 +0,0 @@
import logging
import re
from pathlib import Path
from typing import List, Optional, Tuple, Dict, Any
import cv2
import numpy as np
from .base_stage import ProcessingStage
# Import necessary context classes and utils
from ..asset_context import AssetProcessingContext, MergeTaskDefinition, ProcessedMergedMapData
from ...utils import image_processing_utils as ipu
log = logging.getLogger(__name__)
class MergedTaskProcessorStage(ProcessingStage):
"""
Processes a single merge task defined in the configuration.
Loads inputs, applies transformations to inputs, handles fallbacks/resizing,
performs the merge, and returns the merged data.
"""
def _find_input_map_details_in_context(
self,
required_map_type: str,
processed_map_details_context: Dict[str, Dict[str, Any]],
log_prefix_for_find: str
) -> Optional[Dict[str, Any]]:
"""
Finds the details of a required input map from the context's processed_maps_details.
Prefers exact match for full types (e.g. MAP_TYPE-1), or base type / base type + "-1" for base types (e.g. MAP_TYPE).
Returns the details dictionary for the found map if it has saved_files_info.
"""
# Try exact match first (e.g., rule asks for "MAP_NRM-1" or "MAP_NRM" if that's how it was processed)
for item_key, details in processed_map_details_context.items():
if details.get('internal_map_type') == required_map_type:
if details.get('saved_files_info') and isinstance(details['saved_files_info'], list) and len(details['saved_files_info']) > 0:
log.debug(f"{log_prefix_for_find}: Found exact match for '{required_map_type}' with key '{item_key}'.")
return details
log.warning(f"{log_prefix_for_find}: Found exact match for '{required_map_type}' (key '{item_key}') but no saved_files_info.")
return None # Found type but no usable files
# If exact match not found, and required_map_type is a base type (e.g. "MAP_NRM")
# try to find the primary suffixed version "MAP_NRM-1" or the base type itself if it was processed without a suffix.
if not re.search(r'-\d+$', required_map_type): # if it's a base type like MAP_XXX
# Prefer "MAP_XXX-1" as the primary variant if suffixed types exist
primary_suffixed_type = f"{required_map_type}-1"
for item_key, details in processed_map_details_context.items():
if details.get('internal_map_type') == primary_suffixed_type:
if details.get('saved_files_info') and isinstance(details['saved_files_info'], list) and len(details['saved_files_info']) > 0:
log.debug(f"{log_prefix_for_find}: Found primary suffixed match '{primary_suffixed_type}' for base '{required_map_type}' with key '{item_key}'.")
return details
log.warning(f"{log_prefix_for_find}: Found primary suffixed match '{primary_suffixed_type}' (key '{item_key}') but no saved_files_info.")
return None # Found type but no usable files
log.debug(f"{log_prefix_for_find}: No suitable match found for '{required_map_type}' via exact or primary suffixed type search.")
return None
def execute(
self,
context: AssetProcessingContext,
merge_task: MergeTaskDefinition # Specific item passed by orchestrator
) -> ProcessedMergedMapData:
"""
Processes the given MergeTaskDefinition item.
"""
asset_name_for_log = context.asset_rule.asset_name if context.asset_rule else "Unknown Asset"
task_key = merge_task.task_key
task_data = merge_task.task_data
log_prefix = f"Asset '{asset_name_for_log}', Task '{task_key}'"
log.info(f"{log_prefix}: Processing Merge Task.")
# Initialize output object with default failure state
result = ProcessedMergedMapData(
merged_image_data=np.array([]), # Placeholder
output_map_type=task_data.get('output_map_type', 'UnknownMergeOutput'),
source_bit_depths=[],
final_dimensions=None,
transformations_applied_to_inputs={},
status="Failed",
error_message="Initialization error"
)
try:
# --- Configuration & Task Data ---
config = context.config_obj
file_type_definitions = getattr(config, "FILE_TYPE_DEFINITIONS", {})
invert_normal_green = config.invert_normal_green_globally
merge_dimension_mismatch_strategy = getattr(config, "MERGE_DIMENSION_MISMATCH_STRATEGY", "USE_LARGEST")
workspace_path = context.workspace_path # Base for resolving relative input paths
# input_map_sources_from_task is no longer used for paths. Paths are sourced from context.processed_maps_details.
target_dimensions_hw = task_data.get('source_dimensions') # Expected dimensions (h, w) for fallback creation, must be in config.
merge_inputs_config = task_data.get('inputs', {}) # e.g., {'R': 'MAP_AO', 'G': 'MAP_ROUGH', ...}
merge_defaults = task_data.get('defaults', {}) # e.g., {'R': 255, 'G': 255, ...}
merge_channels_order = task_data.get('channel_order', 'RGB') # e.g., 'RGB', 'RGBA'
# Target dimensions are crucial if fallbacks are needed.
# Merge inputs config is essential.
# Merge inputs config is essential. Check directly in task_data.
inputs_from_task_data = task_data.get('inputs')
if not isinstance(inputs_from_task_data, dict) or not inputs_from_task_data:
result.error_message = "Merge task data is incomplete (missing or invalid 'inputs' dictionary in task_data)."
log.error(f"{log_prefix}: {result.error_message}")
return result
if not target_dimensions_hw and any(merge_defaults.get(ch) is not None for ch in merge_inputs_config.keys()):
log.warning(f"{log_prefix}: Merge task has defaults defined, but 'source_dimensions' (target_dimensions_hw) is missing in task_data. Fallback image creation might fail if needed.")
# Not returning error yet, as fallbacks might not be triggered.
loaded_inputs_for_merge: Dict[str, np.ndarray] = {} # Channel char -> image data
actual_input_dimensions: List[Tuple[int, int]] = [] # List of (h, w) for loaded files
input_source_bit_depths: Dict[str, int] = {} # Channel char -> bit depth
all_transform_notes: Dict[str, List[str]] = {} # Channel char -> list of transform notes
# --- Load, Transform, and Prepare Inputs ---
log.debug(f"{log_prefix}: Loading and preparing inputs...")
for channel_char, required_map_type_from_rule in merge_inputs_config.items():
# Validate that the required input map type starts with "MAP_"
if not required_map_type_from_rule.startswith("MAP_"):
result.error_message = (
f"Invalid input map type '{required_map_type_from_rule}' for channel '{channel_char}'. "
f"Input map types for merging must start with 'MAP_'."
)
log.error(f"{log_prefix}: {result.error_message}")
return result # Fail the task if an input type is invalid
input_image_data: Optional[np.ndarray] = None
input_source_desc = f"Fallback for {required_map_type_from_rule}"
input_log_prefix = f"{log_prefix}, Input '{required_map_type_from_rule}' (Channel '{channel_char}')"
channel_transform_notes: List[str] = []
# 1. Attempt to load from context.processed_maps_details
found_input_map_details = self._find_input_map_details_in_context(
required_map_type_from_rule, context.processed_maps_details, input_log_prefix
)
if found_input_map_details:
# Assuming the first saved file is the primary one for merging.
# This might need refinement if specific variants (resolutions/formats) are required.
primary_saved_file_info = found_input_map_details['saved_files_info'][0]
input_file_path_str = primary_saved_file_info.get('path')
if input_file_path_str:
input_file_path = Path(input_file_path_str) # Path is absolute from SaveVariantsStage
if input_file_path.is_file():
try:
input_image_data = ipu.load_image(str(input_file_path))
if input_image_data is not None:
log.info(f"{input_log_prefix}: Loaded from context: {input_file_path}")
actual_input_dimensions.append(input_image_data.shape[:2]) # (h, w)
input_source_desc = str(input_file_path)
# Bit depth from the saved variant info
input_source_bit_depths[channel_char] = primary_saved_file_info.get('bit_depth', 8)
else:
log.warning(f"{input_log_prefix}: Failed to load image from {input_file_path} (found in context). Attempting fallback.")
input_image_data = None # Ensure fallback is triggered
except Exception as e:
log.warning(f"{input_log_prefix}: Error loading image from {input_file_path} (found in context): {e}. Attempting fallback.")
input_image_data = None # Ensure fallback is triggered
else:
log.warning(f"{input_log_prefix}: Input file path '{input_file_path}' (from context) not found. Attempting fallback.")
input_image_data = None # Ensure fallback is triggered
else:
log.warning(f"{input_log_prefix}: Found map type '{required_map_type_from_rule}' in context, but 'path' is missing in saved_files_info. Attempting fallback.")
input_image_data = None # Ensure fallback is triggered
else:
log.info(f"{input_log_prefix}: Input map type '{required_map_type_from_rule}' not found in context.processed_maps_details. Attempting fallback.")
input_image_data = None # Ensure fallback is triggered
# 2. Apply Fallback if needed
if input_image_data is None:
fallback_value = merge_defaults.get(channel_char)
if fallback_value is not None:
try:
if not target_dimensions_hw:
result.error_message = f"Cannot create fallback for channel '{channel_char}': 'source_dimensions' (target_dimensions_hw) not defined in task_data."
log.error(f"{log_prefix}: {result.error_message}")
return result # Critical failure if dimensions for fallback are missing
h, w = target_dimensions_hw
# Infer shape/dtype for fallback (simplified)
num_channels = 1 if isinstance(fallback_value, (int, float)) else len(fallback_value) if isinstance(fallback_value, (list, tuple)) else 1
dtype = np.uint8 # Default dtype
shape = (h, w) if num_channels == 1 else (h, w, num_channels)
input_image_data = np.full(shape, fallback_value, dtype=dtype)
log.warning(f"{input_log_prefix}: Using fallback value {fallback_value} (Target Dims: {target_dimensions_hw}).")
input_source_desc = f"Fallback value {fallback_value}"
input_source_bit_depths[channel_char] = 8 # Assume 8-bit for fallbacks
channel_transform_notes.append(f"Used fallback value {fallback_value}")
except Exception as e:
result.error_message = f"Error creating fallback for channel '{channel_char}': {e}"
log.error(f"{log_prefix}: {result.error_message}")
return result # Critical failure
else:
result.error_message = f"Missing input '{required_map_type_from_rule}' and no fallback default provided for channel '{channel_char}'."
log.error(f"{log_prefix}: {result.error_message}")
return result # Critical failure
# 3. Apply Transformations to the loaded/fallback input
if input_image_data is not None:
input_image_data, _, transform_notes = ipu.apply_common_map_transformations(
input_image_data.copy(), # Transform a copy
required_map_type_from_rule, # Use the type required by the rule
invert_normal_green,
file_type_definitions,
input_log_prefix
)
channel_transform_notes.extend(transform_notes)
else:
# This case should be prevented by fallback logic, but as a safeguard:
result.error_message = f"Input data for channel '{channel_char}' is None after load/fallback attempt."
log.error(f"{log_prefix}: {result.error_message} This indicates an internal logic error.")
return result
loaded_inputs_for_merge[channel_char] = input_image_data
all_transform_notes[channel_char] = channel_transform_notes
result.transformations_applied_to_inputs = all_transform_notes # Store notes
# --- Handle Dimension Mismatches (using transformed inputs) ---
log.debug(f"{log_prefix}: Handling dimension mismatches...")
unique_dimensions = set(actual_input_dimensions)
target_merge_dims_hw = target_dimensions_hw # Default
if len(unique_dimensions) > 1:
log.warning(f"{log_prefix}: Mismatched dimensions found among loaded inputs: {unique_dimensions}. Applying strategy: {merge_dimension_mismatch_strategy}")
mismatch_note = f"Mismatched input dimensions ({unique_dimensions}), applied {merge_dimension_mismatch_strategy}"
# Add note to all relevant inputs? Or just a general note? Add general for now.
# result.status_notes.append(mismatch_note) # Need a place for general notes
if merge_dimension_mismatch_strategy == "ERROR_SKIP":
result.error_message = "Dimension mismatch and strategy is ERROR_SKIP."
log.error(f"{log_prefix}: {result.error_message}")
return result
elif merge_dimension_mismatch_strategy == "USE_LARGEST":
max_h = max(h for h, w in unique_dimensions)
max_w = max(w for h, w in unique_dimensions)
target_merge_dims_hw = (max_h, max_w)
elif merge_dimension_mismatch_strategy == "USE_FIRST":
target_merge_dims_hw = actual_input_dimensions[0] if actual_input_dimensions else target_dimensions_hw
# Add other strategies or default to USE_LARGEST
log.info(f"{log_prefix}: Resizing inputs to target merge dimensions: {target_merge_dims_hw}")
# Resize loaded inputs (not fallbacks unless they were treated as having target dims)
for channel_char, img_data in loaded_inputs_for_merge.items():
# Only resize if it was a loaded input that contributed to the mismatch check
if img_data.shape[:2] in unique_dimensions and img_data.shape[:2] != target_merge_dims_hw:
resized_img = ipu.resize_image(img_data, target_merge_dims_hw[1], target_merge_dims_hw[0]) # w, h
if resized_img is None:
result.error_message = f"Failed to resize input for channel '{channel_char}' to {target_merge_dims_hw}."
log.error(f"{log_prefix}: {result.error_message}")
return result
loaded_inputs_for_merge[channel_char] = resized_img
log.debug(f"{log_prefix}: Resized input for channel '{channel_char}'.")
# If target_merge_dims_hw is still None (no source_dimensions and no mismatch), use first loaded input's dimensions
if target_merge_dims_hw is None and actual_input_dimensions:
target_merge_dims_hw = actual_input_dimensions[0]
log.info(f"{log_prefix}: Using dimensions from first loaded input: {target_merge_dims_hw}")
# --- Perform Merge ---
log.debug(f"{log_prefix}: Performing merge operation for channels '{merge_channels_order}'.")
try:
# Final check for valid dimensions before unpacking
if not isinstance(target_merge_dims_hw, tuple) or len(target_merge_dims_hw) != 2:
result.error_message = "Could not determine valid target dimensions for merge operation."
log.error(f"{log_prefix}: {result.error_message} (target_merge_dims_hw: {target_merge_dims_hw})")
return result
output_channels = len(merge_channels_order)
h, w = target_merge_dims_hw # Use the potentially adjusted dimensions
# Determine output dtype (e.g., based on inputs or config) - Assume uint8 for now
output_dtype = np.uint8
if output_channels == 1:
# Assume the first channel in order is the one to use
channel_char_to_use = merge_channels_order[0]
source_img = loaded_inputs_for_merge[channel_char_to_use]
# Ensure it's grayscale (take first channel if it's multi-channel)
if len(source_img.shape) == 3:
merged_image = source_img[:, :, 0].copy().astype(output_dtype)
else:
merged_image = source_img.copy().astype(output_dtype)
elif output_channels > 1:
merged_image = np.zeros((h, w, output_channels), dtype=output_dtype)
for i, channel_char in enumerate(merge_channels_order):
source_img = loaded_inputs_for_merge.get(channel_char)
if source_img is not None:
# Extract the correct channel (e.g., R from RGB, or use grayscale directly)
if len(source_img.shape) == 3:
# Simple approach: take the first channel if source is color. Needs refinement if specific channel mapping (R->R, G->G etc.) is needed.
merged_image[:, :, i] = source_img[:, :, 0]
else: # Grayscale source
merged_image[:, :, i] = source_img
else:
# This case should have been caught by fallback logic earlier
result.error_message = f"Internal error: Missing prepared input for channel '{channel_char}' during final merge assembly."
log.error(f"{log_prefix}: {result.error_message}")
return result
else:
result.error_message = f"Invalid channel_order '{merge_channels_order}' in merge config."
log.error(f"{log_prefix}: {result.error_message}")
return result
result.merged_image_data = merged_image
result.final_dimensions = (merged_image.shape[1], merged_image.shape[0]) # w, h
result.source_bit_depths = list(input_source_bit_depths.values()) # Collect bit depths used
log.info(f"{log_prefix}: Successfully merged inputs into image with shape {result.merged_image_data.shape}")
except Exception as e:
log.exception(f"{log_prefix}: Error during merge operation: {e}")
result.error_message = f"Merge operation failed: {e}"
return result
# --- Success ---
result.status = "Processed"
result.error_message = None
log.info(f"{log_prefix}: Successfully processed merge task.")
except Exception as e:
log.exception(f"{log_prefix}: Unhandled exception during processing: {e}")
result.status = "Failed"
result.error_message = f"Unhandled exception: {e}"
# Ensure image data is empty on failure
if result.merged_image_data is None or result.merged_image_data.size == 0:
result.merged_image_data = np.array([])
return result

219
processing/pipeline/stages/metadata_finalization_save.py
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@ -1,219 +0,0 @@
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
# final_output_files is populated by OutputOrganizationStage. Explicitly remove it as per user request.
context.asset_metadata.pop('final_output_files', None)
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

173
processing/pipeline/stages/metadata_initialization.py
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@ -1,173 +0,0 @@
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

155
processing/pipeline/stages/normal_map_green_channel.py
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@ -1,155 +0,0 @@
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():
# Check if the map is a processed normal map using the standardized internal_map_type
internal_map_type = map_details.get('internal_map_type')
if internal_map_type and internal_map_type.startswith("MAP_NRM") 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

293
processing/pipeline/stages/output_organization.py
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@ -1,293 +0,0 @@
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, get_filename_friendly_map_type # Absolute import
from rule_structure import FileRule # Assuming these are needed for type hints if not directly in context
log = logging.getLogger(__name__)
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:
log.info("OUTPUT_ORG: Stage execution started for asset '%s'", context.asset_rule.asset_name)
log.info(f"OUTPUT_ORG: context.processed_maps_details at start: {context.processed_maps_details}")
"""
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 = context.config_obj.overwrite_existing
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')
# Retrieve the internal map type first
internal_map_type = details.get('internal_map_type', 'unknown_map_type')
# Convert internal type to filename-friendly type using the helper
file_type_definitions = getattr(context.config_obj, "FILE_TYPE_DEFINITIONS", {})
base_map_type = get_filename_friendly_map_type(internal_map_type, file_type_definitions) # Final filename-friendly type
# --- Handle maps processed by the SaveVariantsStage (identified by having saved_files_info) ---
saved_files_info = details.get('saved_files_info') # This is a list of dicts from SaveVariantsOutput
# Check if 'saved_files_info' exists and is a non-empty list.
# This indicates the item was processed by SaveVariantsStage.
if saved_files_info and isinstance(saved_files_info, list) and len(saved_files_info) > 0:
logger.debug(f"Asset '{asset_name_for_log}': Organizing {len(saved_files_info)} variants for map key '{processed_map_key}' (map type: {base_map_type}) from SaveVariantsStage.")
# Use base_map_type (e.g., "COL") as the key for the map entry
map_metadata_entry = context.asset_metadata.setdefault('maps', {}).setdefault(base_map_type, {})
# map_type is now the key, so no need to store it inside the entry
# 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(saved_files_info):
# Extract info from the save utility's output structure
temp_variant_path_str = variant_detail.get('path') # Key is '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 'path' in saved_files_info. Skipping.")
# Optionally update variant_detail status if it's mutable and tracked, otherwise just skip
continue
temp_variant_path = Path(temp_variant_path_str)
if not temp_variant_path.is_file():
logger.warning(f"Asset '{asset_name_for_log}': Temporary variant file '{temp_variant_path}' for map '{processed_map_key}' not found. Skipping.")
continue
variant_resolution_key = variant_detail.get('resolution_key', f"varRes{variant_index}")
variant_ext = variant_detail.get('format', temp_variant_path.suffix.lstrip('.')) # Use 'format' key
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.")
# Optionally update variant_detail status if needed
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))
# Optionally update variant_detail status if needed
# Store relative path in metadata
# Store only the filename, as it's relative to the metadata.json location
map_metadata_entry['variant_paths'][variant_resolution_key] = output_filename_variant
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)"
# Optionally update variant_detail status if needed
failed_any_variant = True
# Update parent map detail status based on variant outcomes
if failed_any_variant:
details['status'] = 'Organization Failed (Save Utility Variants)'
elif processed_any_variant_successfully:
details['status'] = 'Organized (Save Utility Variants)'
else: # No variants were successfully copied (e.g., all skipped due to existing file or missing temp file)
details['status'] = 'Organization Skipped (No Save Utility Variants Copied/Needed)'
# --- Handle older/other processing statuses (like single file processing) ---
elif map_status in ['Processed', 'Processed_No_Variants', 'Converted_To_Rough']: # Add other single-file statuses if needed
temp_file_path_str = details.get('temp_processed_file')
if not temp_file_path_str:
logger.warning(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(temp_file_path_str)
if not temp_file_path.is_file():
logger.warning(f"Asset '{asset_name_for_log}': Temporary file '{temp_file_path}' for map '{processed_map_key}' not found. Skipping.")
details['status'] = 'Organization Skipped (Temp File Not Found)'
continue
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.")
details['status'] = 'Organized (Exists, Skipped 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['status'] = 'Organized'
details['final_output_path'] = str(final_path)
# Update asset_metadata for metadata.json
# Use base_map_type (e.g., "COL") as the key for the map entry
map_metadata_entry = context.asset_metadata.setdefault('maps', {}).setdefault(base_map_type, {})
# map_type is now the key, so no need to store it inside the entry
# map_metadata_entry['map_type'] = base_map_type
# Store single path in variant_paths, keyed by its resolution string
# Store only the filename, as it's relative to the metadata.json location
map_metadata_entry.setdefault('variant_paths', {})[resolution_str] = output_filename
# Remove old cleanup logic, as variant_paths is now the standard
# if 'variant_paths' in map_metadata_entry:
# del map_metadata_entry['variant_paths']
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'
# --- Handle other statuses (Skipped, Failed, etc.) ---
else: # Catches statuses not explicitly handled above
logger.debug(f"Asset '{asset_name_for_log}': Skipping map key '{processed_map_key}' (status: '{map_status}') for organization as it's not a recognized final processed state or variant state.")
continue
else:
logger.debug(f"Asset '{asset_name_for_log}': No processed individual maps to organize.")
# B. Organize Merged Maps (OBSOLETE BLOCK - Merged maps are handled by the main loop processing context.processed_maps_details)
# The log "No merged maps to organize" will no longer appear from here.
# If merged maps are not appearing, the issue is likely that they are not being added
# to context.processed_maps_details with 'saved_files_info' by the orchestrator/SaveVariantsStage.
# 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

105
processing/pipeline/stages/prepare_processing_items.py
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@ -1,105 +0,0 @@
import logging
from typing import List, Union, Optional
from .base_stage import ProcessingStage
from ..asset_context import AssetProcessingContext, MergeTaskDefinition
from rule_structure import FileRule # Assuming FileRule is imported correctly
log = logging.getLogger(__name__)
class PrepareProcessingItemsStage(ProcessingStage):
"""
Identifies and prepares a unified list of items (FileRule, MergeTaskDefinition)
to be processed in subsequent stages. Performs initial validation.
"""
def execute(self, context: AssetProcessingContext) -> AssetProcessingContext:
"""
Populates context.processing_items with FileRule and MergeTaskDefinition objects.
"""
asset_name_for_log = context.asset_rule.asset_name if context.asset_rule else "Unknown Asset"
log.info(f"Asset '{asset_name_for_log}': Preparing processing items...")
if context.status_flags.get('skip_asset', False):
log.info(f"Asset '{asset_name_for_log}': Skipping item preparation due to skip_asset flag.")
context.processing_items = []
return context
items_to_process: List[Union[FileRule, MergeTaskDefinition]] = []
preparation_failed = False
# --- Add regular files ---
if context.files_to_process:
# Validate source path early for regular files
source_path_valid = True
if not context.source_rule or not context.source_rule.input_path:
log.error(f"Asset '{asset_name_for_log}': SourceRule or SourceRule.input_path is not set. Cannot process regular files.")
source_path_valid = False
preparation_failed = True # Mark as failed if source path is missing
context.status_flags['prepare_items_failed_reason'] = "SourceRule.input_path missing"
elif not context.workspace_path or not context.workspace_path.is_dir():
log.error(f"Asset '{asset_name_for_log}': Workspace path '{context.workspace_path}' is not a valid directory. Cannot process regular files.")
source_path_valid = False
preparation_failed = True # Mark as failed if workspace path is bad
context.status_flags['prepare_items_failed_reason'] = "Workspace path invalid"
if source_path_valid:
for file_rule in context.files_to_process:
# Basic validation for FileRule itself
if not file_rule.file_path:
log.warning(f"Asset '{asset_name_for_log}': Skipping FileRule with empty file_path.")
continue # Skip this specific rule, but don't fail the whole stage
items_to_process.append(file_rule)
log.debug(f"Asset '{asset_name_for_log}': Added {len(context.files_to_process)} potential FileRule items.")
else:
log.warning(f"Asset '{asset_name_for_log}': Skipping addition of all FileRule items due to invalid source/workspace path.")
# --- Add merged tasks ---
# --- Add merged tasks from global configuration ---
# merged_image_tasks are expected to be loaded into context.config_obj
# by the Configuration class from app_settings.json.
merged_tasks_list = getattr(context.config_obj, 'map_merge_rules', None)
if merged_tasks_list and isinstance(merged_tasks_list, list):
log.debug(f"Asset '{asset_name_for_log}': Found {len(merged_tasks_list)} merge tasks in global config.")
for task_idx, task_data in enumerate(merged_tasks_list):
if isinstance(task_data, dict):
task_key = f"merged_task_{task_idx}"
# Basic validation for merge task data: requires output_map_type and an inputs dictionary
if not task_data.get('output_map_type') or not isinstance(task_data.get('inputs'), dict):
log.warning(f"Asset '{asset_name_for_log}', Task Index {task_idx}: Skipping merge task due to missing 'output_map_type' or valid 'inputs' dictionary. Task data: {task_data}")
continue # Skip this specific task
log.debug(f"Asset '{asset_name_for_log}', Preparing Merge Task Index {task_idx}: Raw task_data: {task_data}")
merge_def = MergeTaskDefinition(task_data=task_data, task_key=task_key)
log.debug(f"Asset '{asset_name_for_log}': Created MergeTaskDefinition object: {merge_def}")
log.info(f"Asset '{asset_name_for_log}': Successfully CREATED MergeTaskDefinition: Key='{merge_def.task_key}', OutputType='{merge_def.task_data.get('output_map_type', 'N/A')}'")
items_to_process.append(merge_def)
else:
log.warning(f"Asset '{asset_name_for_log}': Item at index {task_idx} in config_obj.merged_image_tasks is not a dictionary. Skipping. Item: {task_data}")
# The log for "Added X potential MergeTaskDefinition items" will be covered by the final log.
elif merged_tasks_list is None:
log.debug(f"Asset '{asset_name_for_log}': 'merged_image_tasks' not found in config_obj. No global merge tasks to add.")
elif not isinstance(merged_tasks_list, list):
log.warning(f"Asset '{asset_name_for_log}': 'merged_image_tasks' in config_obj is not a list. Skipping global merge tasks. Type: {type(merged_tasks_list)}")
else: # Empty list
log.debug(f"Asset '{asset_name_for_log}': 'merged_image_tasks' in config_obj is empty. No global merge tasks to add.")
if not items_to_process:
log.info(f"Asset '{asset_name_for_log}': No valid items found to process after preparation.")
log.debug(f"Asset '{asset_name_for_log}': Final items_to_process before assigning to context: {items_to_process}")
context.processing_items = items_to_process
context.intermediate_results = {} # Initialize intermediate results storage
if preparation_failed:
# Set a flag indicating failure during preparation, even if some items might have been added before failure
context.status_flags['prepare_items_failed'] = True
log.error(f"Asset '{asset_name_for_log}': Item preparation failed. Reason: {context.status_flags.get('prepare_items_failed_reason', 'Unknown')}")
# Optionally, clear items if failure means nothing should proceed
# context.processing_items = []
log.info(f"Asset '{asset_name_for_log}': Finished preparing items. Found {len(context.processing_items)} valid items.")
return context

213
processing/pipeline/stages/regular_map_processor.py
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@ -1,213 +0,0 @@
import logging
import re
from pathlib import Path
from typing import List, Optional, Tuple, Dict
import cv2
import numpy as np
from .base_stage import ProcessingStage # Assuming base_stage is in the same directory
from ..asset_context import AssetProcessingContext, ProcessedRegularMapData
from rule_structure import FileRule, AssetRule
from processing.utils import image_processing_utils as ipu # Absolute import
from utils.path_utils import get_filename_friendly_map_type # Absolute import
log = logging.getLogger(__name__)
class RegularMapProcessorStage(ProcessingStage):
"""
Processes a single regular texture map defined by a FileRule.
Loads the image, determines map type, applies transformations,
and returns the processed data.
"""
# --- Helper Methods (Adapted from IndividualMapProcessingStage) ---
def _get_suffixed_internal_map_type(
self,
asset_rule: Optional[AssetRule],
current_file_rule: FileRule,
initial_internal_map_type: str,
respect_variant_map_types: List[str],
asset_name_for_log: str
) -> str:
"""
Determines the potentially suffixed internal map type (e.g., MAP_COL-1).
"""
final_internal_map_type = initial_internal_map_type # Default
base_map_type_match = re.match(r"(MAP_[A-Z]{3})", initial_internal_map_type)
if not base_map_type_match or not asset_rule or not asset_rule.files:
return final_internal_map_type # Cannot determine suffix without base type or asset rule files
true_base_map_type = base_map_type_match.group(1) # This is "MAP_XXX"
# Find all FileRules in the asset with the same base map type
peers_of_same_base_type = []
for fr_asset in asset_rule.files:
fr_asset_item_type = fr_asset.item_type_override or fr_asset.item_type or "UnknownMapType"
fr_asset_base_match = re.match(r"(MAP_[A-Z]{3})", fr_asset_item_type)
if fr_asset_base_match and fr_asset_base_match.group(1) == true_base_map_type:
peers_of_same_base_type.append(fr_asset)
num_occurrences = len(peers_of_same_base_type)
current_instance_index = 0 # 1-based index
try:
# Find the index based on the FileRule object itself (requires object identity)
current_instance_index = peers_of_same_base_type.index(current_file_rule) + 1
except ValueError:
# Fallback: try matching by file_path if object identity fails (less reliable)
try:
current_instance_index = [fr.file_path for fr in peers_of_same_base_type].index(current_file_rule.file_path) + 1
log.warning(f"Asset '{asset_name_for_log}', FileRule path '{current_file_rule.file_path}': Found peer index using file_path fallback for suffixing.")
except (ValueError, AttributeError): # Catch AttributeError if file_path is None
log.warning(
f"Asset '{asset_name_for_log}', FileRule path '{current_file_rule.file_path}' (Initial Type: '{initial_internal_map_type}', Base: '{true_base_map_type}'): "
f"Could not find its own instance in the list of {num_occurrences} peers from asset_rule.files using object identity or path. Suffixing may be incorrect."
)
# Keep index 0, suffix logic below will handle it
# 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 > 1:
if current_instance_index > 0:
suffix_to_append = f"-{current_instance_index}"
else:
# If index is still 0 (not found), don't add suffix to avoid ambiguity
log.warning(f"Asset '{asset_name_for_log}', FileRule path '{current_file_rule.file_path}': Index for multi-occurrence map type '{true_base_map_type}' (count: {num_occurrences}) not determined. Omitting numeric suffix.")
elif num_occurrences == 1 and is_in_respect_list:
suffix_to_append = "-1" # Add suffix even for single instance if in respect list
if suffix_to_append:
final_internal_map_type = true_base_map_type + suffix_to_append
if final_internal_map_type != initial_internal_map_type:
log.debug(f"Asset '{asset_name_for_log}', FileRule path '{current_file_rule.file_path}': Suffixed internal map type determined: '{initial_internal_map_type}' -> '{final_internal_map_type}'")
return final_internal_map_type
# --- Execute Method ---
def execute(
self,
context: AssetProcessingContext,
file_rule: FileRule # Specific item passed by orchestrator
) -> ProcessedRegularMapData:
"""
Processes the given FileRule item.
"""
asset_name_for_log = context.asset_rule.asset_name if context.asset_rule else "Unknown Asset"
log_prefix = f"Asset '{asset_name_for_log}', File '{file_rule.file_path}'"
log.info(f"{log_prefix}: Processing Regular Map.")
# Initialize output object with default failure state
result = ProcessedRegularMapData(
processed_image_data=np.array([]), # Placeholder
final_internal_map_type="Unknown",
source_file_path=Path(file_rule.file_path or "InvalidPath"),
original_bit_depth=None,
original_dimensions=None,
transformations_applied=[],
status="Failed",
error_message="Initialization error"
)
try:
# --- Configuration ---
config = context.config_obj
file_type_definitions = getattr(config, "FILE_TYPE_DEFINITIONS", {})
respect_variant_map_types = getattr(config, "respect_variant_map_types", [])
invert_normal_green = config.invert_normal_green_globally
# --- Determine Map Type (with suffix) ---
initial_internal_map_type = file_rule.item_type_override or file_rule.item_type or "UnknownMapType"
if not initial_internal_map_type or initial_internal_map_type == "UnknownMapType":
result.error_message = "Map type (item_type) not defined in FileRule."
log.error(f"{log_prefix}: {result.error_message}")
return result # Early exit
# Explicitly skip if the determined type doesn't start with "MAP_"
if not initial_internal_map_type.startswith("MAP_"):
result.status = "Skipped (Invalid Type)"
result.error_message = f"FileRule item_type '{initial_internal_map_type}' does not start with 'MAP_'. Skipping processing."
log.warning(f"{log_prefix}: {result.error_message}")
return result # Early exit
processing_map_type = self._get_suffixed_internal_map_type(
context.asset_rule, file_rule, initial_internal_map_type, respect_variant_map_types, asset_name_for_log
)
result.final_internal_map_type = processing_map_type # Store initial suffixed type
# --- Find and Load Source File ---
if not file_rule.file_path: # Should have been caught by Prepare stage, but double-check
result.error_message = "FileRule has empty file_path."
log.error(f"{log_prefix}: {result.error_message}")
return result
source_base_path = context.workspace_path
potential_source_path = source_base_path / file_rule.file_path
source_file_path_found: Optional[Path] = None
if potential_source_path.is_file():
source_file_path_found = potential_source_path
log.info(f"{log_prefix}: Found source file: {source_file_path_found}")
else:
# Optional: Add globbing fallback if needed, similar to original stage
log.warning(f"{log_prefix}: Source file not found directly at '{potential_source_path}'. Add globbing if necessary.")
result.error_message = f"Source file not found at '{potential_source_path}'"
log.error(f"{log_prefix}: {result.error_message}")
return result
result.source_file_path = source_file_path_found # Update result with found path
# Load image
source_image_data = ipu.load_image(str(source_file_path_found))
if source_image_data is None:
result.error_message = f"Failed to load image from '{source_file_path_found}'."
log.error(f"{log_prefix}: {result.error_message}")
return result
original_height, original_width = source_image_data.shape[:2]
result.original_dimensions = (original_width, original_height)
log.debug(f"{log_prefix}: Loaded image {result.original_dimensions[0]}x{result.original_dimensions[1]}.")
# Get original bit depth
try:
result.original_bit_depth = ipu.get_image_bit_depth(str(source_file_path_found))
log.info(f"{log_prefix}: Determined source bit depth: {result.original_bit_depth}")
except Exception as e:
log.warning(f"{log_prefix}: Could not determine source bit depth for {source_file_path_found}: {e}. Setting to None.")
result.original_bit_depth = None # Indicate failure to determine
# --- Apply Transformations ---
transformed_image_data, final_map_type, transform_notes = ipu.apply_common_map_transformations(
source_image_data.copy(), # Pass a copy to avoid modifying original load
processing_map_type,
invert_normal_green,
file_type_definitions,
log_prefix
)
result.processed_image_data = transformed_image_data
result.final_internal_map_type = final_map_type # Update if Gloss->Rough changed it
result.transformations_applied = transform_notes
# --- Success ---
result.status = "Processed"
result.error_message = None
log.info(f"{log_prefix}: Successfully processed regular map. Final type: '{result.final_internal_map_type}'.")
except Exception as e:
log.exception(f"{log_prefix}: Unhandled exception during processing: {e}")
result.status = "Failed"
result.error_message = f"Unhandled exception: {e}"
# Ensure image data is empty on failure if it wasn't set
if result.processed_image_data is None or result.processed_image_data.size == 0:
result.processed_image_data = np.array([])
return result

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processing/pipeline/stages/save_variants.py
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import logging
from typing import List, Dict, Optional # Added Optional
import numpy as np
from .base_stage import ProcessingStage
# Import necessary context classes and utils
from ..asset_context import SaveVariantsInput, SaveVariantsOutput
from processing.utils import image_saving_utils as isu # Absolute import
from utils.path_utils import get_filename_friendly_map_type # Absolute import
log = logging.getLogger(__name__)
class SaveVariantsStage(ProcessingStage):
"""
Takes final processed image data and configuration, calls the
save_image_variants utility, and returns the results.
"""
def execute(self, input_data: SaveVariantsInput) -> SaveVariantsOutput:
"""
Calls isu.save_image_variants with data from input_data.
"""
internal_map_type = input_data.internal_map_type
log_prefix = f"Save Variants Stage (Type: {internal_map_type})"
log.info(f"{log_prefix}: Starting.")
# Initialize output object with default failure state
result = SaveVariantsOutput(
saved_files_details=[],
status="Failed",
error_message="Initialization error"
)
if input_data.image_data is None or input_data.image_data.size == 0:
result.error_message = "Input image data is None or empty."
log.error(f"{log_prefix}: {result.error_message}")
return result
try:
# --- Prepare arguments for save_image_variants ---
# Get the filename-friendly base map type using the helper
# This assumes the save utility expects the friendly type. Adjust if needed.
base_map_type_friendly = get_filename_friendly_map_type(
internal_map_type, input_data.file_type_defs
)
log.debug(f"{log_prefix}: Using filename-friendly base type '{base_map_type_friendly}' for saving.")
save_args = {
"source_image_data": input_data.image_data,
"base_map_type": base_map_type_friendly, # Use the friendly type
"source_bit_depth_info": input_data.source_bit_depth_info,
"image_resolutions": input_data.image_resolutions,
"file_type_defs": input_data.file_type_defs,
"output_format_8bit": input_data.output_format_8bit,
"output_format_16bit_primary": input_data.output_format_16bit_primary,
"output_format_16bit_fallback": input_data.output_format_16bit_fallback,
"png_compression_level": input_data.png_compression_level,
"jpg_quality": input_data.jpg_quality,
"output_filename_pattern_tokens": input_data.output_filename_pattern_tokens,
"output_filename_pattern": input_data.output_filename_pattern,
"resolution_threshold_for_jpg": input_data.resolution_threshold_for_jpg, # Added
}
log.debug(f"{log_prefix}: Calling save_image_variants utility.")
saved_files_details: List[Dict] = isu.save_image_variants(**save_args)
if saved_files_details:
log.info(f"{log_prefix}: Save utility completed successfully. Saved {len(saved_files_details)} variants.")
result.saved_files_details = saved_files_details
result.status = "Processed"
result.error_message = None
else:
# This might not be an error, maybe no variants were configured?
log.warning(f"{log_prefix}: Save utility returned no saved file details. This might be expected if no resolutions/formats matched.")
result.saved_files_details = []
result.status = "Processed (No Output)" # Indicate processing happened but nothing saved
result.error_message = "Save utility reported no files saved (check configuration/resolutions)."
except Exception as e:
log.exception(f"{log_prefix}: Error calling or executing save_image_variants: {e}")
result.status = "Failed"
result.error_message = f"Save utility call failed: {e}"
result.saved_files_details = [] # Ensure empty list on error
return result

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processing/pipeline/stages/supplier_determination.py
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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']
# merged_image_tasks are loaded from app_settings.json into Configuration object,
# not from supplier-specific presets.
# Ensure the attribute exists on context for PrepareProcessingItemsStage,
# which will get it from context.config_obj.
if not hasattr(context, 'merged_image_tasks'):
context.merged_image_tasks = []
return context

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processing/utils/__init__.py
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# This file makes the 'utils' directory a Python package.

515
processing/utils/image_processing_utils.py
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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 get_image_bit_depth(image_path_str: str) -> Optional[int]:
"""
Determines the bit depth of an image file.
"""
try:
# Use IMREAD_UNCHANGED to preserve original bit depth
img = cv2.imread(image_path_str, cv2.IMREAD_UNCHANGED)
if img is None:
# logger.error(f"Failed to read image for bit depth: {image_path_str}") # Use print for utils
print(f"Warning: Failed to read image for bit depth: {image_path_str}")
return None
dtype_to_bit_depth = {
np.dtype('uint8'): 8,
np.dtype('uint16'): 16,
np.dtype('float32'): 32, # Typically for EXR etc.
np.dtype('int8'): 8, # Unlikely for images but good to have
np.dtype('int16'): 16, # Unlikely
# Add other dtypes if necessary
}
bit_depth = dtype_to_bit_depth.get(img.dtype)
if bit_depth is None:
# logger.warning(f"Unknown dtype {img.dtype} for image {image_path_str}, cannot determine bit depth.") # Use print for utils
print(f"Warning: Unknown dtype {img.dtype} for image {image_path_str}, cannot determine bit depth.")
pass # Return None
return bit_depth
except Exception as e:
# logger.error(f"Error getting bit depth for {image_path_str}: {e}") # Use print for utils
print(f"Error getting bit depth for {image_path_str}: {e}")
return None
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
# --- Common Map Transformations ---
import re
import logging
ipu_log = logging.getLogger(__name__)
def apply_common_map_transformations(
image_data: np.ndarray,
processing_map_type: str, # The potentially suffixed internal type
invert_normal_green: bool,
file_type_definitions: Dict[str, Dict],
log_prefix: str
) -> Tuple[np.ndarray, str, List[str]]:
"""
Applies common in-memory transformations (Gloss-to-Rough, Normal Green Invert).
Returns potentially transformed image data, potentially updated map type, and notes.
"""
transformation_notes = []
current_image_data = image_data # Start with original data
updated_processing_map_type = processing_map_type # Start with original type
# Gloss-to-Rough
# Check if the base type is Gloss (before suffix)
base_map_type_match = re.match(r"(MAP_GLOSS)", processing_map_type)
if base_map_type_match:
ipu_log.info(f"{log_prefix}: Applying Gloss-to-Rough conversion.")
inversion_succeeded = False
if np.issubdtype(current_image_data.dtype, np.floating):
current_image_data = 1.0 - current_image_data
current_image_data = np.clip(current_image_data, 0.0, 1.0)
ipu_log.debug(f"{log_prefix}: Inverted float image data for Gloss->Rough.")
inversion_succeeded = True
elif np.issubdtype(current_image_data.dtype, np.integer):
max_val = np.iinfo(current_image_data.dtype).max
current_image_data = max_val - current_image_data
ipu_log.debug(f"{log_prefix}: Inverted integer image data (max_val: {max_val}) for Gloss->Rough.")
inversion_succeeded = True
else:
ipu_log.error(f"{log_prefix}: Unsupported image data type {current_image_data.dtype} for GLOSS map. Cannot invert.")
transformation_notes.append("Gloss-to-Rough FAILED (unsupported dtype)")
if inversion_succeeded:
# Update the type string itself (e.g., MAP_GLOSS-1 -> MAP_ROUGH-1)
updated_processing_map_type = processing_map_type.replace("GLOSS", "ROUGH")
ipu_log.info(f"{log_prefix}: Map type updated: '{processing_map_type}' -> '{updated_processing_map_type}'")
transformation_notes.append("Gloss-to-Rough applied")
# Normal Green Invert
# Check if the base type is Normal (before suffix)
base_map_type_match_nrm = re.match(r"(MAP_NRM)", processing_map_type)
if base_map_type_match_nrm and invert_normal_green:
ipu_log.info(f"{log_prefix}: Applying Normal Map Green Channel Inversion (Global Setting).")
current_image_data = invert_normal_map_green_channel(current_image_data)
transformation_notes.append("Normal Green Inverted (Global)")
return current_image_data, updated_processing_map_type, transformation_notes
# --- Normal Map Utilities ---
def invert_normal_map_green_channel(normal_map: np.ndarray) -> np.ndarray:
"""
Inverts the green channel of a normal map.
Assumes the normal map is in RGB or RGBA format (channel order R, G, B, A).
"""
if normal_map is None or len(normal_map.shape) < 3 or normal_map.shape[2] < 3:
# Not a valid color image with at least 3 channels
return normal_map
# Ensure data is mutable
inverted_map = normal_map.copy()
# Invert the green channel (index 1)
# Handle different data types
if np.issubdtype(inverted_map.dtype, np.floating):
inverted_map[:, :, 1] = 1.0 - inverted_map[:, :, 1]
elif np.issubdtype(inverted_map.dtype, np.integer):
max_val = np.iinfo(inverted_map.dtype).max
inverted_map[:, :, 1] = max_val - inverted_map[:, :, 1]
else:
# Unsupported dtype, return original
print(f"Warning: Unsupported dtype {inverted_map.dtype} for normal map green channel inversion.")
return normal_map
return inverted_map

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processing/utils/image_saving_utils.py
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import logging
import cv2
import numpy as np
from pathlib import Path
from typing import List, Dict, Any, Tuple, Optional
# Potentially import ipu from ...utils import image_processing_utils as ipu
# Assuming ipu is available in the same utils directory or parent
try:
from . import image_processing_utils as ipu
except ImportError:
# Fallback for different import structures if needed, adjust based on actual project structure
# For this project structure, the relative import should work.
logging.warning("Could not import image_processing_utils using relative path. Attempting absolute import.")
try:
from processing.utils import image_processing_utils as ipu
except ImportError:
logging.error("Could not import image_processing_utils.")
ipu = None # Handle case where ipu is not available
logger = logging.getLogger(__name__)
def save_image_variants(
source_image_data: np.ndarray,
base_map_type: str, # Filename-friendly map type
source_bit_depth_info: List[Optional[int]],
image_resolutions: Dict[str, int],
file_type_defs: Dict[str, Dict[str, Any]],
output_format_8bit: str,
output_format_16bit_primary: str,
output_format_16bit_fallback: str,
png_compression_level: int,
jpg_quality: int,
output_filename_pattern_tokens: Dict[str, Any], # Must include 'output_base_directory': Path and 'asset_name': str
output_filename_pattern: str,
resolution_threshold_for_jpg: Optional[int] = None, # Added
# Consider adding ipu or relevant parts of it if not importing globally
) -> List[Dict[str, Any]]:
"""
Centralizes image saving logic, generating and saving various resolution variants
according to configuration.
Args:
source_image_data (np.ndarray): High-res image data (in memory, potentially transformed).
base_map_type (str): Final map type (e.g., "COL", "ROUGH", "NORMAL", "MAP_NRMRGH").
This is the filename-friendly map type.
source_bit_depth_info (List[Optional[int]]): List of original source bit depth(s)
(e.g., [8], [16], [8, 16]). Can contain None.
image_resolutions (Dict[str, int]): Dictionary mapping resolution keys (e.g., "4K")
to max dimensions (e.g., 4096).
file_type_defs (Dict[str, Dict[str, Any]]): Dictionary defining properties for map types,
including 'bit_depth_rule'.
output_format_8bit (str): File extension for 8-bit output (e.g., "jpg", "png").
output_format_16bit_primary (str): Primary file extension for 16-bit output (e.g., "png", "tif").
output_format_16bit_fallback (str): Fallback file extension for 16-bit output.
png_compression_level (int): Compression level for PNG output (0-9).
jpg_quality (int): Quality level for JPG output (0-100).
output_filename_pattern_tokens (Dict[str, Any]): Dictionary of tokens for filename
pattern replacement. Must include
'output_base_directory' (Path) and
'asset_name' (str).
output_filename_pattern (str): Pattern string for generating output filenames
(e.g., "[assetname]_[maptype]_[resolution].[ext]").
Returns:
List[Dict[str, Any]]: A list of dictionaries, each containing details about a saved file.
Example: [{'path': str, 'resolution_key': str, 'format': str,
'bit_depth': int, 'dimensions': (w,h)}, ...]
"""
if ipu is None:
logger.error("image_processing_utils is not available. Cannot save images.")
return []
saved_file_details = []
source_h, source_w = source_image_data.shape[:2]
source_max_dim = max(source_h, source_w)
# 1. Use provided configuration inputs (already available as function arguments)
logger.info(f"SaveImageVariants: Starting for map type: {base_map_type}. Source shape: {source_image_data.shape}, Source bit depths: {source_bit_depth_info}")
logger.debug(f"SaveImageVariants: Resolutions: {image_resolutions}, File Type Defs: {file_type_defs.keys()}, Output Formats: 8bit={output_format_8bit}, 16bit_pri={output_format_16bit_primary}, 16bit_fall={output_format_16bit_fallback}")
logger.debug(f"SaveImageVariants: PNG Comp: {png_compression_level}, JPG Qual: {jpg_quality}")
logger.debug(f"SaveImageVariants: Output Tokens: {output_filename_pattern_tokens}, Output Pattern: {output_filename_pattern}")
logger.debug(f"SaveImageVariants: Received resolution_threshold_for_jpg: {resolution_threshold_for_jpg}") # Log received threshold
# 2. Determine Target Bit Depth
target_bit_depth = 8 # Default
bit_depth_rule = file_type_defs.get(base_map_type, {}).get('bit_depth_rule', 'force_8bit')
if bit_depth_rule not in ['force_8bit', 'respect_inputs']:
logger.warning(f"Unknown bit_depth_rule '{bit_depth_rule}' for map type '{base_map_type}'. Defaulting to 'force_8bit'.")
bit_depth_rule = 'force_8bit'
if bit_depth_rule == 'respect_inputs':
# Check if any source bit depth is > 8, ignoring None
if any(depth is not None and depth > 8 for depth in source_bit_depth_info):
target_bit_depth = 16
else:
target_bit_depth = 8
logger.info(f"Bit depth rule 'respect_inputs' applied. Source bit depths: {source_bit_depth_info}. Target bit depth: {target_bit_depth}")
else: # force_8bit
target_bit_depth = 8
logger.info(f"Bit depth rule 'force_8bit' applied. Target bit depth: {target_bit_depth}")
# 3. Determine Output File Format(s)
if target_bit_depth == 8:
output_ext = output_format_8bit.lstrip('.').lower()
elif target_bit_depth == 16:
# Prioritize primary, fallback to fallback if primary is not supported/desired
# For now, just use primary. More complex logic might be needed later.
output_ext = output_format_16bit_primary.lstrip('.').lower()
# Basic fallback logic example (can be expanded)
if output_ext not in ['png', 'tif']: # Assuming common 16-bit formats
output_ext = output_format_16bit_fallback.lstrip('.').lower()
logger.warning(f"Primary 16-bit format '{output_format_16bit_primary}' might not be suitable. Using fallback '{output_format_16bit_fallback}'.")
else:
logger.error(f"Unsupported target bit depth: {target_bit_depth}. Defaulting to 8-bit format.")
output_ext = output_format_8bit.lstrip('.').lower()
current_output_ext = output_ext # Store the initial extension based on bit depth
logger.info(f"SaveImageVariants: Determined target bit depth: {target_bit_depth}, Initial output format: {current_output_ext} for map type {base_map_type}")
# 4. Generate and Save Resolution Variants
# Sort resolutions by max dimension descending
sorted_resolutions = sorted(image_resolutions.items(), key=lambda item: item[1], reverse=True)
for res_key, res_max_dim in sorted_resolutions:
logger.info(f"SaveImageVariants: Processing variant {res_key} ({res_max_dim}px) for {base_map_type}")
# --- Prevent Upscaling ---
# Skip this resolution variant if its target dimension is larger than the source image's largest dimension.
if res_max_dim > source_max_dim:
logger.info(f"SaveImageVariants: Skipping variant {res_key} ({res_max_dim}px) for {base_map_type} because target resolution is larger than source ({source_max_dim}px).")
continue # Skip to the next resolution
# Calculate target dimensions for valid variants (equal or smaller than source)
if source_max_dim == res_max_dim:
# Use source dimensions if target is equal
target_w_res, target_h_res = source_w, source_h
logger.info(f"SaveImageVariants: Using source resolution ({source_w}x{source_h}) for {res_key} variant of {base_map_type} as target matches source.")
else: # Downscale (source_max_dim > res_max_dim)
# Downscale, maintaining aspect ratio
aspect_ratio = source_w / source_h
if source_w >= source_h: # Use >= to handle square images correctly
target_w_res = res_max_dim
target_h_res = max(1, int(res_max_dim / aspect_ratio)) # Ensure height is at least 1
else:
target_h_res = res_max_dim
target_w_res = max(1, int(res_max_dim * aspect_ratio)) # Ensure width is at least 1
logger.info(f"SaveImageVariants: Calculated downscale for {base_map_type} {res_key}: from ({source_w}x{source_h}) to ({target_w_res}x{target_h_res})")
# Resize source_image_data (only if necessary)
if (target_w_res, target_h_res) == (source_w, source_h):
# No resize needed if dimensions match
variant_data = source_image_data.copy() # Copy to avoid modifying original if needed later
logger.debug(f"SaveImageVariants: No resize needed for {base_map_type} {res_key}, using copy of source data.")
else:
# Perform resize only if dimensions differ (i.e., downscaling)
interpolation_method = cv2.INTER_AREA # Good for downscaling
try:
variant_data = ipu.resize_image(source_image_data, target_w_res, target_h_res, interpolation=interpolation_method)
if variant_data is None: # Check if resize failed
raise ValueError("ipu.resize_image returned None")
logger.debug(f"SaveImageVariants: Resized variant data shape for {base_map_type} {res_key}: {variant_data.shape}")
except Exception as e:
logger.error(f"SaveImageVariants: Error resizing image for {base_map_type} {res_key} variant: {e}")
continue # Skip this variant if resizing fails
# Filename Construction
current_tokens = output_filename_pattern_tokens.copy()
current_tokens['maptype'] = base_map_type
current_tokens['resolution'] = res_key
# Determine final extension for this variant, considering JPG threshold
final_variant_ext = current_output_ext
# --- Start JPG Threshold Logging ---
logger.debug(f"SaveImageVariants: JPG Threshold Check for {base_map_type} {res_key}:")
logger.debug(f" - target_bit_depth: {target_bit_depth}")
logger.debug(f" - resolution_threshold_for_jpg: {resolution_threshold_for_jpg}")
logger.debug(f" - target_w_res: {target_w_res}, target_h_res: {target_h_res}")
logger.debug(f" - max(target_w_res, target_h_res): {max(target_w_res, target_h_res)}")
logger.debug(f" - current_output_ext: {current_output_ext}")
cond_bit_depth = target_bit_depth == 8
cond_threshold_not_none = resolution_threshold_for_jpg is not None
cond_res_exceeded = False
if cond_threshold_not_none: # Avoid comparison if threshold is None
cond_res_exceeded = max(target_w_res, target_h_res) > resolution_threshold_for_jpg
cond_is_png = current_output_ext == 'png'
logger.debug(f" - Condition (target_bit_depth == 8): {cond_bit_depth}")
logger.debug(f" - Condition (resolution_threshold_for_jpg is not None): {cond_threshold_not_none}")
logger.debug(f" - Condition (max(res) > threshold): {cond_res_exceeded}")
logger.debug(f" - Condition (current_output_ext == 'png'): {cond_is_png}")
# --- End JPG Threshold Logging ---
if cond_bit_depth and cond_threshold_not_none and cond_res_exceeded and cond_is_png:
final_variant_ext = 'jpg'
logger.info(f"SaveImageVariants: Overriding 8-bit PNG to JPG for {base_map_type} {res_key} due to resolution {max(target_w_res, target_h_res)}px > threshold {resolution_threshold_for_jpg}px.")
current_tokens['ext'] = final_variant_ext
try:
# Replace placeholders in the pattern
filename = output_filename_pattern
for token, value in current_tokens.items():
# Ensure value is string for replacement, handle Path objects later
filename = filename.replace(f"[{token}]", str(value))
# Construct full output path
output_base_directory = current_tokens.get('output_base_directory')
if not isinstance(output_base_directory, Path):
logger.error(f"'output_base_directory' token is missing or not a Path object: {output_base_directory}. Cannot save file.")
continue # Skip this variant
output_path = output_base_directory / filename
logger.info(f"SaveImageVariants: Constructed output path for {base_map_type} {res_key}: {output_path}")
# Ensure parent directory exists
output_path.parent.mkdir(parents=True, exist_ok=True)
logger.debug(f"SaveImageVariants: Ensured directory exists for {base_map_type} {res_key}: {output_path.parent}")
except Exception as e:
logger.error(f"SaveImageVariants: Error constructing filepath for {base_map_type} {res_key} variant: {e}")
continue # Skip this variant if path construction fails
# Prepare Save Parameters
save_params_cv2 = []
if final_variant_ext == 'jpg': # Check against final_variant_ext
save_params_cv2.append(cv2.IMWRITE_JPEG_QUALITY)
save_params_cv2.append(jpg_quality)
logger.debug(f"SaveImageVariants: Using JPG quality: {jpg_quality} for {base_map_type} {res_key}")
elif final_variant_ext == 'png': # Check against final_variant_ext
save_params_cv2.append(cv2.IMWRITE_PNG_COMPRESSION)
save_params_cv2.append(png_compression_level)
logger.debug(f"SaveImageVariants: Using PNG compression level: {png_compression_level} for {base_map_type} {res_key}")
# Add other format specific parameters if needed (e.g., TIFF compression)
# Bit Depth Conversion is handled by ipu.save_image via output_dtype_target
image_data_for_save = variant_data # Use the resized variant data directly
# Determine the target dtype for ipu.save_image
output_dtype_for_save: Optional[np.dtype] = None
if target_bit_depth == 8:
output_dtype_for_save = np.uint8
elif target_bit_depth == 16:
output_dtype_for_save = np.uint16
# Add other target bit depths like float16/float32 if necessary
# elif target_bit_depth == 32: # Assuming float32 for EXR etc.
# output_dtype_for_save = np.float32
# Saving
try:
# ipu.save_image is expected to handle the actual cv2.imwrite call
logger.debug(f"SaveImageVariants: Attempting to save {base_map_type} {res_key} to {output_path} with params {save_params_cv2}, target_dtype: {output_dtype_for_save}")
success = ipu.save_image(
str(output_path),
image_data_for_save,
output_dtype_target=output_dtype_for_save, # Pass the target dtype
params=save_params_cv2
)
if success:
logger.info(f"SaveImageVariants: Successfully saved {base_map_type} {res_key} variant to {output_path}")
# Collect details for the returned list
saved_file_details.append({
'path': str(output_path),
'resolution_key': res_key,
'format': final_variant_ext, # Log the actual saved format
'bit_depth': target_bit_depth,
'dimensions': (target_w_res, target_h_res)
})
else:
logger.error(f"SaveImageVariants: Failed to save {base_map_type} {res_key} variant to {output_path} (ipu.save_image returned False)")
except Exception as e:
logger.error(f"SaveImageVariants: Error during ipu.save_image for {base_map_type} {res_key} variant to {output_path}: {e}", exc_info=True)
# Continue to next variant even if one fails
# Discard in-memory variant after saving (Python's garbage collection handles this)
del variant_data
del image_data_for_save
# 5. Return List of Saved File Details
logger.info(f"Finished saving variants for map type: {base_map_type}. Saved {len(saved_file_details)} variants.")
return saved_file_details
# Optional Helper Functions (can be added here if needed)
# def _determine_target_bit_depth(...): ...
# def _determine_output_format(...): ...
# def _construct_variant_filepath(...): ...

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processing_engine.py
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tests/__init__.py
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# This file makes the 'tests' directory a Python package.

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tests/processing/pipeline/__init__.py
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tests/processing/pipeline/stages/test_alpha_extraction_to_mask.py
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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

213
tests/processing/pipeline/stages/test_asset_skip_logic.py
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@ -1,213 +0,0 @@
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]

330
tests/processing/pipeline/stages/test_file_rule_filter.py
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@ -1,330 +0,0 @@
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.")

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tests/processing/pipeline/stages/test_gloss_to_rough_conversion.py
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@ -1,486 +0,0 @@
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"

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tests/processing/pipeline/stages/test_individual_map_processing.py
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@ -1,555 +0,0 @@
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()

538
tests/processing/pipeline/stages/test_map_merging.py
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@ -1,538 +0,0 @@
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()

359
tests/processing/pipeline/stages/test_metadata_finalization_save.py
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@ -1,359 +0,0 @@
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']

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tests/processing/pipeline/stages/test_metadata_initialization.py
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@ -1,169 +0,0 @@
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

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tests/processing/pipeline/stages/test_normal_map_green_channel.py
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@ -1,323 +0,0 @@
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

417
tests/processing/pipeline/stages/test_output_organization.py
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@ -1,417 +0,0 @@
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"
)

213
tests/processing/pipeline/stages/test_supplier_determination.py
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@ -1,213 +0,0 @@
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()

383
tests/processing/pipeline/test_orchestrator.py
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@ -1,383 +0,0 @@
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)

504
tests/processing/utils/test_image_processing_utils.py
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@ -1,504 +0,0 @@
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

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

252
tests/utils/test_path_utils.py
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@ -1,252 +0,0 @@
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

42
utils/path_utils.py
View File

@ -154,48 +154,6 @@ 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
def get_filename_friendly_map_type(internal_map_type: str, file_type_definitions: Optional[Dict[str, Dict]]) -> str:
"""Derives a filename-friendly map type from the internal map type."""
filename_friendly_map_type = internal_map_type # Fallback
if not file_type_definitions or not isinstance(file_type_definitions, dict) or not file_type_definitions:
logger.warning(f"Filename-friendly lookup: FILE_TYPE_DEFINITIONS not available or invalid. Falling back to internal type: {internal_map_type}")
return filename_friendly_map_type
base_map_key_val = None
suffix_part = ""
# Sort keys by length descending to match longest prefix first (e.g., MAP_ROUGHNESS before MAP_ROUGH)
sorted_known_base_keys = sorted(list(file_type_definitions.keys()), key=len, reverse=True)
for known_key in sorted_known_base_keys:
if internal_map_type.startswith(known_key):
base_map_key_val = known_key
suffix_part = internal_map_type[len(known_key):]
break
if base_map_key_val:
definition = file_type_definitions.get(base_map_key_val)
if definition and isinstance(definition, dict):
standard_type_alias = definition.get("standard_type")
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.debug(f"Filename-friendly lookup: Transformed '{internal_map_type}' -> '{filename_friendly_map_type}'")
else:
logger.warning(f"Filename-friendly lookup: Standard type alias for '{base_map_key_val}' is missing or invalid. Falling back.")
else:
logger.warning(f"Filename-friendly lookup: No valid definition for '{base_map_key_val}'. Falling back.")
else:
logger.warning(f"Filename-friendly lookup: Could not parse base key from '{internal_map_type}'. Falling back.")
return filename_friendly_map_type
# --- Basic Unit Tests ---
if __name__ == "__main__":
print("Running basic tests for path_utils.generate_path_from_pattern...")