Merge pull request 'Bugfixes from dev' (#68) from Dev into Stable

Reviewed-on: #68

.gitignore

@@ -30,6 +30,6 @@ Thumbs.db
 gui/__pycache__
 __pycache__
 
-Testfiles
-Testfiles/
+
+Testfiles/TestOutputs
 Testfiles_

.roo/mcp.json

@@ -0,0 +1,45 @@
+{
+  "mcpServers": {
+    "conport": {
+      "command": "C:\\Users\\theis\\context-portal\\.venv\\Scripts\\python.exe",
+      "args": [
+        "C:\\Users\\theis\\context-portal\\src\\context_portal_mcp\\main.py",
+        "--mode",
+        "stdio",
+        "--workspace_id",
+        "${workspaceFolder}"
+      ],
+      "alwaysAllow": [
+        "get_product_context",
+        "update_product_context",
+        "get_active_context",
+        "update_active_context",
+        "log_decision",
+        "get_decisions",
+        "search_decisions_fts",
+        "log_progress",
+        "get_progress",
+        "update_progress",
+        "delete_progress_by_id",
+        "log_system_pattern",
+        "get_system_patterns",
+        "log_custom_data",
+        "get_custom_data",
+        "delete_custom_data",
+        "search_project_glossary_fts",
+        "export_conport_to_markdown",
+        "import_markdown_to_conport",
+        "link_conport_items",
+        "search_custom_data_value_fts",
+        "get_linked_items",
+        "batch_log_items",
+        "get_item_history",
+        "delete_decision_by_id",
+        "delete_system_pattern_by_id",
+        "get_conport_schema",
+        "get_recent_activity_summary",
+        "semantic_search_conport"
+      ]
+    }
+  }
+}

.roomodes

@@ -0,0 +1,3 @@
+{
+  "customModes": []
+}

AUTOTEST_GUI_PLAN.md

@@ -0,0 +1,112 @@
+# Plan for Autotest GUI Mode Implementation
+
+**I. Objective:**
+Create an `autotest.py` script that can launch the Asset Processor GUI headlessly, load a predefined asset (`.zip`), select a predefined preset, verify the predicted rule structure against an expected JSON, trigger processing to a predefined output directory, check the output, and analyze logs for errors or specific messages. This serves as a sanity check for core GUI-driven workflows.
+
+**II. `TestFiles` Directory:**
+A new directory named `TestFiles` will be created in the project root (`c:/Users/Theis/Assetprocessor/Asset-Frameworker/TestFiles/`). This directory will house:
+*   Sample asset `.zip` files for testing (e.g., `TestFiles/SampleAsset1.zip`).
+*   Expected rule structure JSON files (e.g., `TestFiles/SampleAsset1_PresetX_expected_rules.json`).
+*   A subdirectory for test outputs (e.g., `TestFiles/TestOutputs/`).
+
+**III. `autotest.py` Script:**
+
+1.  **Location:** `c:/Users/Theis/Assetprocessor/Asset-Frameworker/autotest.py` (or `scripts/autotest.py`).
+2.  **Command-Line Arguments (with defaults pointing to `TestFiles/`):**
+    *   `--zipfile`: Path to the test asset. Default: `TestFiles/default_test_asset.zip`.
+    *   `--preset`: Name of the preset. Default: `DefaultTestPreset`.
+    *   `--expectedrules`: Path to expected rules JSON. Default: `TestFiles/default_test_asset_rules.json`.
+    *   `--outputdir`: Path for processing output. Default: `TestFiles/TestOutputs/DefaultTestOutput`.
+    *   `--search` (optional): Log search term. Default: `None`.
+    *   `--additional-lines` (optional): Context lines for log search. Default: `0`.
+3.  **Core Structure:**
+    *   Imports necessary modules from the main application and PySide6.
+    *   Adds project root to `sys.path` for imports.
+    *   `AutoTester` class:
+        *   **`__init__(self, app_instance: App)`:**
+            *   Stores `app_instance` and `main_window`.
+            *   Initializes `QEventLoop`.
+            *   Connects `app_instance.all_tasks_finished` to `self._on_all_tasks_finished`.
+            *   Loads expected rules from the `--expectedrules` file.
+        *   **`run_test(self)`:** Orchestrates the test steps sequentially:
+            1.  Load ZIP (`main_window.add_input_paths()`).
+            2.  Select Preset (`main_window.preset_editor_widget.editor_preset_list.setCurrentItem()`).
+            3.  Await Prediction (using `QTimer` to poll `main_window._pending_predictions`, manage with `QEventLoop`).
+            4.  Retrieve & Compare Rulelist:
+                *   Get actual rules: `main_window.unified_model.get_all_source_rules()`.
+                *   Convert actual rules to comparable dict (`_convert_rules_to_comparable()`).
+                *   Compare with loaded expected rules (`_compare_rules()`). If mismatch, log and fail.
+            5.  Start Processing (emit `main_window.start_backend_processing` with rules and output settings).
+            6.  Await Processing (use `QEventLoop` waiting for `_on_all_tasks_finished`).
+            7.  Check Output Path (verify existence of output dir, list contents, basic sanity checks like non-emptiness or presence of key asset folders).
+            8.  Retrieve & Analyze Logs (`main_window.log_console.log_console_output.toPlainText()`, filter by `--search`, check for tracebacks).
+            9.  Report result and call `cleanup_and_exit()`.
+        *   **`_check_prediction_status(self)`:** Slot for prediction polling timer.
+        *   **`_on_all_tasks_finished(self, processed_count, skipped_count, failed_count)`:** Slot for `App.all_tasks_finished` signal.
+        *   **`_convert_rules_to_comparable(self, source_rules_list: List[SourceRule]) -> dict`:** Converts `SourceRule` objects to the JSON structure defined below.
+        *   **`_compare_rules(self, actual_rules_data: dict, expected_rules_data: dict) -> bool`:** Implements Option 1 comparison logic:
+            *   Errors if an expected field is missing or its value mismatches.
+            *   Logs (but doesn't error on) fields present in actual but not in expected.
+        *   **`_process_and_display_logs(self, logs_text: str)`:** Handles log filtering/display.
+        *   **`cleanup_and_exit(self, success=True)`:** Quits `QCoreApplication` and `sys.exit()`.
+    *   `main()` function:
+        *   Parses CLI arguments.
+        *   Initializes `QApplication`.
+        *   Instantiates `main.App()` (does *not* show the GUI).
+        *   Instantiates `AutoTester(app_instance)`.
+        *   Uses `QTimer.singleShot(0, tester.run_test)` to start the test.
+        *   Runs `q_app.exec()`.
+
+**IV. `expected_rules.json` Structure (Revised):**
+Located in `TestFiles/`. Example: `TestFiles/SampleAsset1_PresetX_expected_rules.json`.
+```json
+{
+  "source_rules": [
+    {
+      "input_path": "SampleAsset1.zip",
+      "supplier_identifier": "ExpectedSupplier",
+      "preset_name": "PresetX",
+      "assets": [
+        {
+          "asset_name": "AssetNameFromPrediction",
+          "asset_type": "Prop",
+          "files": [
+            {
+              "file_path": "relative/path/to/file1.png",
+              "item_type": "MAP_COL",
+              "target_asset_name_override": null
+            }
+          ]
+        }
+      ]
+    }
+  ]
+}
+```
+
+**V. Mermaid Diagram of Autotest Flow:**
+```mermaid
+graph TD
+    A[Start autotest.py with CLI Args (defaults to TestFiles/)] --> B{Setup Args & Logging};
+    B --> C[Init QApplication & main.App (GUI Headless)];
+    C --> D[Instantiate AutoTester(app_instance)];
+    D --> E[QTimer.singleShot -> AutoTester.run_test()];
+
+    subgraph AutoTester.run_test()
+        E --> F[Load Expected Rules from --expectedrules JSON];
+        F --> G[Load ZIP (--zipfile) via main_window.add_input_paths()];
+        G --> H[Select Preset (--preset) via main_window.preset_editor_widget];
+        H --> I[Await Prediction (Poll main_window._pending_predictions via QTimer & QEventLoop)];
+        I -- Prediction Done --> J[Get Actual Rules from main_window.unified_model];
+        J --> K[Convert Actual Rules to Comparable JSON Structure];
+        K --> L{Compare Actual vs Expected Rules (Option 1 Logic)};
+        L -- Match --> M[Start Processing (Emit main_window.start_backend_processing with --outputdir)];
+        L -- Mismatch --> ZFAIL[Log Mismatch & Call cleanup_and_exit(False)];
+        M --> N[Await Processing (QEventLoop for App.all_tasks_finished signal)];
+        N -- Processing Done --> O[Check Output Dir (--outputdir): Exists? Not Empty? Key Asset Folders?];
+        O --> P[Retrieve & Analyze Logs (Search, Tracebacks)];
+        P --> Q[Log Test Success & Call cleanup_and_exit(True)];
+    end
+
+    ZFAIL --> ZEND[AutoTester.cleanup_and_exit() -> QCoreApplication.quit() & sys.exit()];
+    Q --> ZEND;

Documentation/01_User_Guide/02_Features.md

@@ -16,6 +16,7 @@ This document outlines the key features of the Asset Processor Tool.
     *   Saves maps in appropriate formats (JPG, PNG, EXR) based on complex rules involving map type (`FORCE_LOSSLESS_MAP_TYPES`), resolution (`RESOLUTION_THRESHOLD_FOR_JPG`), bit depth, and source format.
     *   Calculates basic image statistics (Min/Max/Mean) for a reference resolution.
     *   Calculates and stores the relative aspect ratio change string in metadata (e.g., `EVEN`, `X150`, `Y125`).
+    *   **Low-Resolution Fallback:** If enabled (`ENABLE_LOW_RESOLUTION_FALLBACK`), automatically saves an additional "LOWRES" variant of source images if their largest dimension is below a configurable threshold (`LOW_RESOLUTION_THRESHOLD`). This "LOWRES" variant uses the original image dimensions and is saved in addition to any standard resolution outputs.
 *   **Channel Merging:** Combines channels from different maps into packed textures (e.g., NRMRGH) based on preset rules (`MAP_MERGE_RULES` in `config.py`).
 *   **Metadata Generation:** Creates a `metadata.json` file for each asset containing details about maps, category, archetype, aspect ratio change, processing settings, etc.
 *   **Output Organization:** Creates a clean, structured output directory (`<output_base>/<supplier>/<asset_name>/`).

Documentation/01_User_Guide/04_Configuration_and_Presets.md

@@ -13,6 +13,18 @@ The `app_settings.json` file is structured into several key sections, including:
 *   `ASSET_TYPE_DEFINITIONS`: Defines known asset types (like Surface, Model, Decal) and their properties.
 *   `MAP_MERGE_RULES`: Defines how multiple input maps can be merged into a single output map (e.g., combining Normal and Roughness into one).
 
+### Low-Resolution Fallback Settings
+
+These settings control the generation of low-resolution "fallback" variants for source images:
+
+*   `ENABLE_LOW_RESOLUTION_FALLBACK` (boolean, default: `true`):
+    *   If `true`, the tool will generate an additional "LOWRES" variant for source images whose largest dimension is smaller than the `LOW_RESOLUTION_THRESHOLD`.
+    *   This "LOWRES" variant uses the original dimensions of the source image and is saved in addition to any other standard resolution outputs (e.g., 1K, PREVIEW).
+    *   If `false`, this feature is disabled.
+*   `LOW_RESOLUTION_THRESHOLD` (integer, default: `512`):
+    *   Defines the pixel dimension (for the largest side of an image) below which the "LOWRES" fallback variant will be generated (if enabled).
+    *   For example, if set to `512`, any source image smaller than 512x512 (e.g., 256x512, 128x128) will have a "LOWRES" variant created.
+
 ### LLM Predictor Settings
 
 For users who wish to utilize the experimental LLM Predictor feature, the following settings are available in `config/llm_settings.json`:

Documentation/01_User_Guide/09_Output_Structure.md

@@ -58,6 +58,7 @@ The `<output_base_directory>` (the root folder where processing output starts) i
 Each asset directory contains the following:
 
 *   Processed texture maps (e.g., `WoodFloor_Albedo_4k.png`, `MetalPanel_Normal_2k.exr`). The exact filenames depend on the `OUTPUT_FILENAME_PATTERN`. These are the resized, format-converted, and bit-depth adjusted texture files.
+    *   **LOWRES Variants:** If the "Low-Resolution Fallback" feature is enabled and a source image's dimensions are below the configured threshold, an additional variant with "LOWRES" as its resolution token (e.g., `MyTexture_COL_LOWRES.png`) will be saved. This variant uses the original dimensions of the source image.
 *   Merged texture maps (e.g., `WoodFloor_Combined_4k.png`). The exact filenames depend on the `OUTPUT_FILENAME_PATTERN`. These are maps created by combining channels from different source maps based on the configured merge rules.
 *   Model files (if present in the source asset).
 *   `metadata.json`: A JSON file containing detailed information about the asset and the processing that was performed. This includes details about the maps (resolutions, formats, bit depths, and for roughness maps, a `derived_from_gloss_filename: true` flag if it was inverted from an original gloss map), merged map details, calculated image statistics, aspect ratio change information, asset category and archetype, the source preset used, and a list of ignored source files. This file is intended for use by downstream tools or scripts (like the Blender integration scripts).

Documentation/01_User_Guide/11_Usage_Autotest.md

@@ -0,0 +1,83 @@
+# User Guide: Usage - Automated GUI Testing (`autotest.py`)
+
+This document explains how to use the `autotest.py` script for automated sanity checks of the Asset Processor Tool's GUI-driven workflow.
+
+## Overview
+
+The `autotest.py` script provides a way to run predefined test scenarios headlessly (without displaying the GUI). It simulates the core user actions: loading an asset, selecting a preset, allowing rules to be predicted, processing the asset, and then checks the results against expectations. This is primarily intended as a developer tool for regression testing and ensuring core functionality remains stable.
+
+## Running the Autotest Script
+
+From the project root directory, you can run the script using Python:
+
+```bash
+python autotest.py [OPTIONS]
+```
+
+### Command-Line Options
+
+The script accepts several command-line arguments to configure the test run. If not provided, they use predefined default values.
+
+*   `--zipfile PATH_TO_ZIP`:
+    *   Specifies the path to the input asset `.zip` file to be used for the test.
+    *   Default: `TestFiles/BoucleChunky001.zip`
+*   `--preset PRESET_NAME`:
+    *   Specifies the name of the preset to be selected and used for rule prediction and processing.
+    *   Default: `Dinesen`
+*   `--expectedrules PATH_TO_JSON`:
+    *   Specifies the path to a JSON file containing the expected rule structure that should be generated after the preset is applied to the input asset.
+    *   Default: `TestFiles/test-BoucleChunky001.json`
+*   `--outputdir PATH_TO_DIR`:
+    *   Specifies the directory where the processed assets will be written.
+    *   Default: `TestFiles/TestOutputs/DefaultTestOutput`
+*   `--search "SEARCH_TERM"` (optional):
+    *   A string to search for within the application logs generated during the test run. If found, matching log lines (with context) will be highlighted.
+    *   Default: None
+*   `--additional-lines NUM_LINES` (optional):
+    *   When using `--search`, this specifies how many lines of context before and after each matching log line should be displayed.
+    *   Default: `0`
+
+**Example Usage:**
+
+```bash
+# Run with default test files and settings
+python autotest.py
+
+# Run with specific test files and search for a log message
+python autotest.py --zipfile TestFiles/MySpecificAsset.zip --preset MyPreset --expectedrules TestFiles/MySpecificAsset_rules.json --outputdir TestFiles/TestOutputs/MySpecificOutput --search "Processing complete for asset"
+```
+
+## `TestFiles` Directory
+
+The autotest script relies on a directory named `TestFiles` located in the project root. This directory should contain:
+
+*   **Test Asset `.zip` files:** The actual asset archives used as input for tests (e.g., `default_test_asset.zip`, `MySpecificAsset.zip`).
+*   **Expected Rules `.json` files:** JSON files defining the expected rule structure for a given asset and preset combination (e.g., `default_test_asset_rules.json`, `MySpecificAsset_rules.json`). The structure of this file is detailed in the main autotest plan (`AUTOTEST_GUI_PLAN.md`).
+*   **`TestOutputs/` subdirectory:** This is the default parent directory where the autotest script will create specific output folders for each test run (e.g., `TestFiles/TestOutputs/DefaultTestOutput/`).
+
+## Test Workflow
+
+When executed, `autotest.py` performs the following steps:
+
+1.  **Initialization:** Parses command-line arguments and initializes the main application components headlessly.
+2.  **Load Expected Rules:** Loads the `expected_rules.json` file.
+3.  **Load Asset:** Loads the specified `.zip` file into the application.
+4.  **Select Preset:** Selects the specified preset. This triggers the internal rule prediction process.
+5.  **Await Prediction:** Waits for the rule prediction to complete.
+6.  **Compare Rules:** Retrieves the predicted rules from the application and compares them against the loaded expected rules. If there's a mismatch, the test typically fails at this point.
+7.  **Start Processing:** If the rules match, it initiates the asset processing pipeline, directing output to the specified output directory.
+8.  **Await Processing:** Waits for all backend processing tasks to complete.
+9.  **Check Output:** Verifies the existence of the output directory and lists its contents. Basic checks ensure some output was generated.
+10. **Analyze Logs:** Retrieves logs from the application. If a search term was provided, it filters and displays relevant log portions. It also checks for Python tracebacks, which usually indicate a failure.
+11. **Report Result:** Prints a summary of the test outcome (success or failure) and exits with an appropriate status code (0 for success, 1 for failure).
+
+## Interpreting Results
+
+*   **Console Output:** The script will log its progress and the results of each step to the console.
+*   **Log Analysis:** Pay attention to the log output, especially if a `--search` term was used or if any tracebacks are reported.
+*   **Exit Code:**
+    *   `0`: Test completed successfully.
+    *   `1`: Test failed at some point (e.g., rule mismatch, processing error, traceback found).
+*   **Output Directory:** Inspect the contents of the specified output directory to manually verify the processed assets if needed.
+
+This automated test helps ensure the stability of the core processing logic when driven by GUI-equivalent actions.

Documentation/02_Developer_Guide/04_Configuration_System_and_Presets.md

@@ -12,6 +12,9 @@ The tool's configuration is loaded from several JSON files, providing a layered
 
 1.  **Application Settings (`config/app_settings.json`):** This JSON file defines the core global default settings, constants, and rules that apply generally across different asset sources (e.g., the global `OUTPUT_DIRECTORY_PATTERN` and `OUTPUT_FILENAME_PATTERN`, standard image resolutions, map merge rules, output format rules, Blender paths, temporary directory prefix, initial scaling mode, merge dimension mismatch strategy). See the [User Guide: Output Structure](../01_User_Guide/09_Output_Structure.md#available-tokens) for a list of available tokens for these patterns.
     *   *Note:* `ASSET_TYPE_DEFINITIONS` and `FILE_TYPE_DEFINITIONS` are no longer stored here; they have been moved to dedicated files.
+    *   It also includes settings for new features like the "Low-Resolution Fallback":
+        *   `ENABLE_LOW_RESOLUTION_FALLBACK` (boolean): Enables or disables the generation of "LOWRES" variants for small source images. Defaults to `true`.
+        *   `LOW_RESOLUTION_THRESHOLD` (integer): The pixel dimension threshold (largest side) below which a "LOWRES" variant is created if the feature is enabled. Defaults to `512`.
 
 2.  **User Settings (`config/user_settings.json`):** This optional JSON file allows users to override specific settings defined in `config/app_settings.json`. If this file exists, its values for corresponding keys will take precedence over the base application settings. This file is primarily managed through the GUI's Application Preferences Editor.
 

Documentation/02_Developer_Guide/05_Processing_Pipeline.md

@@ -50,27 +50,44 @@ These stages are executed sequentially once for each asset before the core item
 
 ### 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:
+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)). Each `item` in this list is now either a [`ProcessingItem`](rule_structure.py:0) (representing a specific variant of a source map, e.g., Color at 1K, or Color at LOWRES) or a [`MergeTaskDefinition`](processing/pipeline/asset_context.py:16).
 
 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`.
+    *   **Responsibility**: (Executed once before the loop) This stage is now responsible for "exploding" each relevant [`FileRule`](rule_structure.py:5) into one or more [`ProcessingItem`](rule_structure.py:0) objects.
+        *   For each [`FileRule`](rule_structure.py:5) that represents an image map:
+            *   It loads the source image data and determines its original dimensions and bit depth.
+            *   It creates standard [`ProcessingItem`](rule_structure.py:0)s for each required output resolution (e.g., "1K", "PREVIEW"), populating them with a copy of the source image data and the respective `resolution_key`.
+            *   If the "Low-Resolution Fallback" feature is enabled (`ENABLE_LOW_RESOLUTION_FALLBACK` in config) and the source image's largest dimension is below `LOW_RESOLUTION_THRESHOLD`, it creates an additional [`ProcessingItem`](rule_structure.py:0) with `resolution_key="LOWRES"`, using the original image data and dimensions.
+        *   It also adds [`MergeTaskDefinition`](processing/pipeline/asset_context.py:16)s derived from global `map_merge_rules`.
+    *   **Context Interaction**: Reads `context.files_to_process` and `context.config_obj`. Populates `context.processing_items` with a list of [`ProcessingItem`](rule_structure.py:0) and [`MergeTaskDefinition`](processing/pipeline/asset_context.py:16) objects. 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`.
+For each `item` in `context.processing_items`:
+
+2.  **Transformations (Implicit or via a dedicated stage - formerly `RegularMapProcessorStage` logic):**
+    *   **Responsibility**: If the `item` is a [`ProcessingItem`](rule_structure.py:0), its `image_data` (loaded by `PrepareProcessingItemsStage`) may need transformations (Gloss-to-Rough, Normal Green Invert). This logic, previously in `RegularMapProcessorStage`, might be integrated into `PrepareProcessingItemsStage` before `ProcessingItem` creation, or handled by a new dedicated transformation stage that operates on `ProcessingItem.image_data`. The `item.map_type_identifier` would be updated if a transformation like Gloss-to-Rough occurs.
+    *   **Context Interaction**: Modifies `item.image_data` and `item.map_type_identifier` within the [`ProcessingItem`](rule_structure.py:0) object.
 
 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`.
+    *   **Responsibility**: (Executed if `item` is a [`MergeTaskDefinition`](processing/pipeline/asset_context.py:16)) Same as before: validates inputs, loads source map data (likely from `ProcessingItem`s in `context.processing_items` or a cache populated from them), applies transformations, merges channels, and returns [`ProcessedMergedMapData`](processing/pipeline/asset_context.py:35).
+    *   **Context Interaction**: Reads [`MergeTaskDefinition`](processing/pipeline/asset_context.py:16), potentially `context.processing_items` (or a cache derived from it) for input image data. Returns [`ProcessedMergedMapData`](processing/pipeline/asset_context.py:35).
 
 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`.
+    *   **Responsibility**: (Executed per item)
+        *   If `item` is a [`ProcessingItem`](rule_structure.py:0): Takes `item.image_data`, `item.current_dimensions`, and `item.resolution_key` as input. If `item.resolution_key` is "LOWRES", POT scaling is skipped. Otherwise, applies POT scaling if configured.
+        *   If `item` is from a `MergeTaskDefinition` (i.e., `processed_data` from `MergedTaskProcessorStage`): Applies POT scaling as before.
+    *   **Context Interaction**: Takes [`InitialScalingInput`](processing/pipeline/asset_context.py:46) (now including `resolution_key`). Returns [`InitialScalingOutput`](processing/pipeline/asset_context.py:54) (also including `resolution_key`), which updates `context.intermediate_results`. The `current_image_data` and `current_dimensions` for saving are taken from this output.
 
 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.
+    *   **Responsibility**: (Executed per item) Saves the (potentially scaled) `current_image_data`.
+    *   **Context Interaction**:
+        *   Takes [`SaveVariantsInput`](processing/pipeline/asset_context.py:61).
+        *   `internal_map_type` is set from `item.map_type_identifier` (for `ProcessingItem`) or `processed_data.output_map_type` (for merged).
+        *   `output_filename_pattern_tokens['resolution']` is set to the `resolution_key` obtained from `scaled_data_output.resolution_key` (which originates from `item.resolution_key` for `ProcessingItem`s, or is `None` for merged items that get all standard resolutions).
+        *   `image_resolutions` argument for `SaveVariantsInput`:
+            *   If `resolution_key == "LOWRES"`: Set to `{"LOWRES": width_of_lowres_data}`.
+            *   If `resolution_key` is a standard key (e.g., "1K"): Set to `{resolution_key: configured_dimension}`.
+            *   For merged items (where `resolution_key` from scaling is likely `None`): Set to the full `config.image_resolutions` map to generate all applicable standard sizes.
+        *   Returns [`SaveVariantsOutput`](processing/pipeline/asset_context.py:79). Orchestrator stores details in `context.processed_maps_details`.
 
 ### Post-Item Stages
 

TestFiles/Test-BoucleChunky001.json

@@ -0,0 +1,57 @@
+{
+  "source_rules": [
+    {
+      "input_path": "BoucleChunky001.zip",
+      "supplier_identifier": "Dinesen",
+      "preset_name": null,
+      "assets": [
+        {
+          "asset_name": "BoucleChunky001",
+          "asset_type": "Surface",
+          "files": [
+            {
+              "file_path": "BoucleChunky001_AO_1K_METALNESS.png",
+              "item_type": "MAP_AO",
+              "target_asset_name_override": "BoucleChunky001"
+            },
+            {
+              "file_path": "BoucleChunky001_COL_1K_METALNESS.png",
+              "item_type": "MAP_COL",
+              "target_asset_name_override": "BoucleChunky001"
+            },
+            {
+              "file_path": "BoucleChunky001_DISP16_1K_METALNESS.png",
+              "item_type": "MAP_DISP",
+              "target_asset_name_override": "BoucleChunky001"
+            },
+            {
+              "file_path": "BoucleChunky001_DISP_1K_METALNESS.png",
+              "item_type": "EXTRA", 
+              "target_asset_name_override": "BoucleChunky001"
+            },
+            {
+              "file_path": "BoucleChunky001_Fabric.png",
+              "item_type": "EXTRA",
+              "target_asset_name_override": "BoucleChunky001"
+            },
+            {
+              "file_path": "BoucleChunky001_METALNESS_1K_METALNESS.png",
+              "item_type": "MAP_METAL",
+              "target_asset_name_override": "BoucleChunky001"
+            },
+            {
+              "file_path": "BoucleChunky001_NRM_1K_METALNESS.png",
+              "item_type": "MAP_NRM",
+              "target_asset_name_override": "BoucleChunky001"
+            },
+            {
+              "file_path": "BoucleChunky001_ROUGHNESS_1K_METALNESS.png",
+              "item_type": "MAP_ROUGH",
+              "target_asset_name_override": "BoucleChunky001"
+            }
+          ]
+        }
+      ]
+    }
+  ]
+}

autotest.py

@@ -0,0 +1,855 @@
+import argparse
+import sys
+import logging
+import logging.handlers
+import time
+import json
+import shutil # Import shutil for directory operations
+from pathlib import Path
+from typing import List, Dict, Any
+
+from PySide6.QtCore import QCoreApplication, QTimer, Slot, QEventLoop, QObject, Signal
+from PySide6.QtWidgets import QApplication, QListWidgetItem
+
+# Add project root to sys.path
+project_root = Path(__file__).resolve().parent
+if str(project_root) not in sys.path:
+    sys.path.insert(0, str(project_root))
+
+try:
+    from main import App
+    from gui.main_window import MainWindow
+    from rule_structure import SourceRule # Assuming SourceRule is in rule_structure.py
+except ImportError as e:
+    print(f"Error importing project modules: {e}")
+    print(f"Ensure that the script is run from the project root or that the project root is in PYTHONPATH.")
+    print(f"Current sys.path: {sys.path}")
+    sys.exit(1)
+
+# Global variable for the memory log handler
+autotest_memory_handler = None
+
+# Custom Log Filter for Concise Output
+class InfoSummaryFilter(logging.Filter):
+    # Keywords that identify INFO messages to *allow* for concise output
+    SUMMARY_KEYWORDS_PRECISE = [
+        "Test run completed",
+        "Test succeeded",
+        "Test failed",
+        "Rule comparison successful",
+        "Rule comparison failed",
+        "ProcessingEngine finished. Summary:",
+        "Autotest Context:",
+        "Parsed CLI arguments:",
+        "Prediction completed successfully.",
+        "Processing completed.",
+        "Signal 'all_tasks_finished' received",
+        "final status:",  # To catch "Asset '...' final status:"
+        "User settings file not found:",
+        "MainPanelWidget: Default output directory set to:",
+        # Search related (as per original filter)
+        "Searching logs for term",
+        "Search term ",
+        "Found ",
+        "No tracebacks found in the logs.",
+        "--- End Log Analysis ---",
+        "Log analysis completed.",
+    ]
+    # Patterns for case-insensitive rejection
+    REJECT_PATTERNS_LOWER = [
+        # Original debug prefixes (ensure these are still relevant or merge if needed)
+        "debug:", "orchestrator_trace:", "configuration_debug:", "app_debug:", "output_org_debug:",
+        # Iterative / Per-item / Per-file details / Intermediate steps
+        ": item ",  # Catches "Asset '...', Item X/Y"
+        "item successfully processed and saved",
+        ", file '", # Catches "Asset '...', File '...'"
+        ": processing regular map",
+        ": found source file:",
+        ": determined source bit depth:",
+        "successfully processed regular map",
+        "successfully created mergetaskdefinition",
+        ": preparing processing items",
+        ": finished preparing items. found",
+        ": starting core item processing loop",
+        ", task '",
+        ": processing merge task",
+        "loaded from context:",
+        "using dimensions from first loaded input",
+        "successfully merged inputs into image",
+        "successfully processed merge task",
+        "mergedtaskprocessorstage result",
+        "calling savevariantsstage",
+        "savevariantsstage result",
+        "adding final details to context",
+        ": finished core item processing loop",
+        ": copied variant",
+        ": copied extra file",
+        ": successfully organized",
+        ": output organization complete.",
+        ": metadata saved to",
+        "worker thread: starting processing for rule:",
+        "preparing workspace for input:",
+        "input is a supported archive",
+        "calling processingengine.process with rule",
+        "calculated sha5 for",
+        "calculated next incrementing value for",
+        "verify: processingengine.process called",
+        ": effective supplier set to",
+        ": metadata initialized.",
+        "path",
+        "\\asset_processor",
+        ": file rules queued for processing",
+        "successfully loaded base application settings",
+        "successfully loaded and merged asset_type_definitions",
+        "successfully loaded and merged file_type_definitions",
+        "starting rule-based prediction for:",
+        "rule-based prediction finished successfully for",
+        "finished rule-based prediction run for",
+        "updating model with rule-based results for source:",
+        "debug task ",
+        "worker thread: finished processing for rule:",
+        "task finished signal received for",
+        # Autotest step markers (not global summaries)
+    ]
+
+    def filter(self, record):
+        # Allow CRITICAL, ERROR, WARNING unconditionally
+        if record.levelno >= logging.WARNING:
+            return True
+
+        if record.levelno == logging.INFO:
+            msg = record.getMessage()
+            msg_lower = msg.lower() # For case-insensitive pattern rejection
+
+            # 1. Explicitly REJECT if message contains verbose patterns (case-insensitive)
+            for pattern in self.REJECT_PATTERNS_LOWER: # Use the new list
+                if pattern in msg_lower:
+                    return False # Reject
+
+            # 2. Then, if not rejected, ALLOW only if message contains precise summary keywords
+            for keyword in self.SUMMARY_KEYWORDS_PRECISE: # Use the new list
+                if keyword in msg: # Original message for case-sensitive summary keywords if needed
+                    return True # Allow
+
+            # 3. Reject all other INFO messages that don't match precise summary keywords
+            return False
+
+        # Reject levels below INFO (e.g., DEBUG) by default for this handler
+        return False
+
+# --- Root Logger Configuration for Concise Console Output ---
+def setup_autotest_logging():
+    """
+    Configures the root logger for concise console output for autotest.py.
+    This ensures that only essential summary information, warnings, and errors
+    are displayed on the console by default.
+    """
+    root_logger = logging.getLogger()
+
+    # 1. Remove all existing handlers from the root logger.
+    # This prevents interference from other logging configurations.
+    for handler in root_logger.handlers[:]:
+        root_logger.removeHandler(handler)
+        handler.close() # Close handler before removing
+
+    # 2. Set the root logger's level to DEBUG to capture everything for the memory handler.
+    # The console handler will still filter down to INFO/selected.
+    root_logger.setLevel(logging.DEBUG) # Changed from INFO to DEBUG
+
+    # 3. Create a new StreamHandler for sys.stdout (for concise console output).
+    console_handler = logging.StreamHandler(sys.stdout)
+
+    # 4. Set this console handler's level to INFO.
+    # The filter will then decide which INFO messages to display on console.
+    console_handler.setLevel(logging.INFO)
+
+    # 5. Apply the enhanced InfoSummaryFilter to the console handler.
+    info_filter = InfoSummaryFilter()
+    console_handler.addFilter(info_filter)
+
+    # 6. Set a concise formatter for the console handler.
+    formatter = logging.Formatter('[%(levelname)s] %(message)s')
+    console_handler.setFormatter(formatter)
+
+    # 7. Add this newly configured console handler to the root_logger.
+    root_logger.addHandler(console_handler)
+
+    # 8. Setup the MemoryHandler
+    global autotest_memory_handler # Declare usage of global
+    autotest_memory_handler = logging.handlers.MemoryHandler(
+        capacity=20000,  # Increased capacity
+        flushLevel=logging.CRITICAL + 1, # Prevent automatic flushing
+        target=None # Does not flush to another handler
+    )
+    autotest_memory_handler.setLevel(logging.DEBUG) # Capture all logs from DEBUG up
+    # Not adding a formatter here, will format in _process_and_display_logs
+
+    # 9. Add the memory handler to the root logger.
+    root_logger.addHandler(autotest_memory_handler)
+
+# Call the setup function early in the script's execution.
+setup_autotest_logging()
+
+# Logger for autotest.py's own messages.
+# Messages from this logger will propagate to the root logger and be filtered
+# by the console_handler configured above.
+# Setting its level to DEBUG allows autotest.py to generate DEBUG messages,
+# which won't appear on the concise console (due to handler's INFO level)
+# but can be captured by other handlers (e.g., the GUI's log console).
+logger = logging.getLogger(__name__)
+logger.setLevel(logging.DEBUG) # Ensure autotest.py can generate DEBUGs for other handlers
+
+# Note: The GUI's log console (e.g., self.main_window.log_console.log_console_output)
+# is assumed to capture all logs (including DEBUG) from various modules.
+# The _process_and_display_logs function then uses these comprehensive logs for the --search feature.
+# This root logger setup primarily makes autotest.py's direct console output concise,
+# ensuring that only filtered, high-level information appears on stdout by default.
+# --- End of Root Logger Configuration ---
+
+# --- Argument Parsing ---
+def parse_arguments():
+    """Parses command-line arguments for the autotest script."""
+    parser = argparse.ArgumentParser(description="Automated test script for Asset Processor GUI.")
+    parser.add_argument(
+        "--zipfile",
+        type=Path,
+        default=project_root / "TestFiles" / "BoucleChunky001.zip",
+        help="Path to the test asset ZIP file. Default: TestFiles/BoucleChunky001.zip"
+    )
+    parser.add_argument(
+        "--preset",
+        type=str,
+        default="Dinesen", # This should match a preset name in the application
+        help="Name of the preset to use. Default: Dinesen"
+    )
+    parser.add_argument(
+        "--expectedrules",
+        type=Path,
+        default=project_root / "TestFiles" / "Test-BoucleChunky001.json",
+        help="Path to the JSON file with expected rules. Default: TestFiles/Test-BoucleChunky001.json"
+    )
+    parser.add_argument(
+        "--outputdir",
+        type=Path,
+        default=project_root / "TestFiles" / "TestOutputs" / "BoucleChunkyOutput",
+        help="Path for processing output. Default: TestFiles/TestOutputs/BoucleChunkyOutput"
+    )
+    parser.add_argument(
+        "--search",
+        type=str,
+        default=None,
+        help="Optional log search term. Default: None"
+    )
+    parser.add_argument(
+        "--additional-lines",
+        type=int,
+        default=0,
+        help="Context lines for log search. Default: 0"
+    )
+    return parser.parse_args()
+
+class AutoTester(QObject):
+    """
+    Handles the automated testing process for the Asset Processor GUI.
+    """
+    # Define signals if needed, e.g., for specific test events
+    # test_step_completed = Signal(str)
+
+    def __init__(self, app_instance: App, cli_args: argparse.Namespace):
+        super().__init__()
+        self.app_instance: App = app_instance
+        self.main_window: MainWindow = app_instance.main_window
+        self.cli_args: argparse.Namespace = cli_args
+        self.event_loop = QEventLoop(self)
+        self.prediction_poll_timer = QTimer(self)
+        self.expected_rules_data: Dict[str, Any] = {}
+        self.test_step: str = "INIT"  # Possible values: INIT, LOADING_ZIP, SELECTING_PRESET, AWAITING_PREDICTION, PREDICTION_COMPLETE, COMPARING_RULES, STARTING_PROCESSING, AWAITING_PROCESSING, PROCESSING_COMPLETE, CHECKING_OUTPUT, ANALYZING_LOGS, DONE
+
+        if not self.main_window:
+            logger.error("MainWindow instance not found in App. Cannot proceed.")
+            self.cleanup_and_exit(success=False)
+            return
+
+        # Connect signals
+        if hasattr(self.app_instance, 'all_tasks_finished') and isinstance(self.app_instance.all_tasks_finished, Signal):
+            self.app_instance.all_tasks_finished.connect(self._on_all_tasks_finished)
+        else:
+            logger.warning("App instance does not have 'all_tasks_finished' signal or it's not a Signal. Processing completion might not be detected.")
+
+        self._load_expected_rules()
+
+    def _load_expected_rules(self) -> None:
+        """Loads the expected rules from the JSON file specified by cli_args."""
+        self.test_step = "LOADING_EXPECTED_RULES"
+        logger.debug(f"Loading expected rules from: {self.cli_args.expectedrules}")
+        try:
+            with open(self.cli_args.expectedrules, 'r') as f:
+                self.expected_rules_data = json.load(f)
+            logger.debug("Expected rules loaded successfully.")
+        except FileNotFoundError:
+            logger.error(f"Expected rules file not found: {self.cli_args.expectedrules}")
+            self.cleanup_and_exit(success=False)
+        except json.JSONDecodeError as e:
+            logger.error(f"Error decoding expected rules JSON: {e}")
+            self.cleanup_and_exit(success=False)
+        except Exception as e:
+            logger.error(f"An unexpected error occurred while loading expected rules: {e}")
+            self.cleanup_and_exit(success=False)
+
+    def run_test(self) -> None:
+        """Orchestrates the test steps."""
+        logger.info("Starting test run...")
+
+        if not self.expected_rules_data: # Ensure rules were loaded
+            logger.error("Expected rules not loaded. Aborting test.")
+            self.cleanup_and_exit(success=False)
+            return
+
+        # Add a specific summary log for essential context
+        logger.info(f"Autotest Context: Input='{self.cli_args.zipfile.name}', Preset='{self.cli_args.preset}', Output='{self.cli_args.outputdir}'")
+
+        # Step 1: Load ZIP
+        self.test_step = "LOADING_ZIP"
+        logger.info(f"Step 1: Loading ZIP file: {self.cli_args.zipfile}") # KEEP INFO - Passes filter
+        if not self.cli_args.zipfile.exists():
+            logger.error(f"ZIP file not found: {self.cli_args.zipfile}")
+            self.cleanup_and_exit(success=False)
+            return
+        try:
+            # Assuming add_input_paths can take a list of strings or Path objects
+            self.main_window.add_input_paths([str(self.cli_args.zipfile)])
+            logger.debug("ZIP file loading initiated.")
+        except Exception as e:
+            logger.error(f"Error during ZIP file loading: {e}")
+            self.cleanup_and_exit(success=False)
+            return
+
+        # Step 2: Select Preset
+        self.test_step = "SELECTING_PRESET"
+        logger.info(f"Step 2: Selecting preset: {self.cli_args.preset}") # KEEP INFO - Passes filter
+        preset_found = False
+        preset_list_widget = self.main_window.preset_editor_widget.editor_preset_list
+        for i in range(preset_list_widget.count()):
+            item = preset_list_widget.item(i)
+            if item and item.text() == self.cli_args.preset:
+                preset_list_widget.setCurrentItem(item)
+                logger.debug(f"Preset '{self.cli_args.preset}' selected.")
+                preset_found = True
+                break
+        if not preset_found:
+            logger.error(f"Preset '{self.cli_args.preset}' not found in the list.")
+            available_presets = [preset_list_widget.item(i).text() for i in range(preset_list_widget.count())]
+            logger.debug(f"Available presets: {available_presets}")
+            self.cleanup_and_exit(success=False)
+            return
+
+        # Step 3: Await Prediction Completion
+        self.test_step = "AWAITING_PREDICTION"
+        logger.debug("Step 3: Awaiting prediction completion...")
+        self.prediction_poll_timer.timeout.connect(self._check_prediction_status)
+        self.prediction_poll_timer.start(500) # Poll every 500ms
+
+        # Use a QTimer to allow event loop to process while waiting for this step
+        # This ensures that the _check_prediction_status can be called.
+        # We will exit this event_loop from _check_prediction_status when prediction is done.
+        logger.debug("Starting event loop for prediction...")
+        self.event_loop.exec() # This loop is quit by _check_prediction_status
+        self.prediction_poll_timer.stop()
+        logger.debug("Event loop for prediction finished.")
+
+
+        if self.test_step != "PREDICTION_COMPLETE":
+            logger.error(f"Prediction did not complete as expected. Current step: {self.test_step}")
+            # Check if there were any pending predictions that never cleared
+            if hasattr(self.main_window, '_pending_predictions'):
+                 logger.error(f"Pending predictions at timeout: {self.main_window._pending_predictions}")
+            self.cleanup_and_exit(success=False)
+            return
+        logger.info("Prediction completed successfully.") # KEEP INFO - Passes filter
+
+        # Step 4: Retrieve & Compare Rulelist
+        self.test_step = "COMPARING_RULES"
+        logger.info("Step 4: Retrieving and Comparing Rules...") # KEEP INFO - Passes filter
+        actual_source_rules_list: List[SourceRule] = self.main_window.unified_model.get_all_source_rules()
+        actual_rules_obj = actual_source_rules_list # Keep the SourceRule list for processing
+        
+        comparable_actual_rules = self._convert_rules_to_comparable(actual_source_rules_list)
+
+        if not self._compare_rules(comparable_actual_rules, self.expected_rules_data):
+            logger.error("Rule comparison failed. See logs for details.")
+            self.cleanup_and_exit(success=False)
+            return
+        logger.info("Rule comparison successful.") # KEEP INFO - Passes filter
+
+        # Step 5: Start Processing
+        self.test_step = "START_PROCESSING"
+        logger.info("Step 5: Starting Processing...") # KEEP INFO - Passes filter
+        processing_settings = {
+            "output_dir": str(self.cli_args.outputdir), # Ensure it's a string for JSON/config
+            "overwrite": True,
+            "workers": 1,
+            "blender_enabled": False # Basic test, no Blender
+        }
+        try:
+            Path(self.cli_args.outputdir).mkdir(parents=True, exist_ok=True)
+            logger.debug(f"Ensured output directory exists: {self.cli_args.outputdir}")
+        except Exception as e:
+            logger.error(f"Could not create output directory {self.cli_args.outputdir}: {e}")
+            self.cleanup_and_exit(success=False)
+            return
+
+        if hasattr(self.main_window, 'start_backend_processing') and isinstance(self.main_window.start_backend_processing, Signal):
+            logger.debug(f"Emitting start_backend_processing with rules count: {len(actual_rules_obj)} and settings: {processing_settings}")
+            self.main_window.start_backend_processing.emit(actual_rules_obj, processing_settings)
+        else:
+            logger.error("'start_backend_processing' signal not found on MainWindow. Cannot start processing.")
+            self.cleanup_and_exit(success=False)
+            return
+        
+        # Step 6: Await Processing Completion
+        self.test_step = "AWAIT_PROCESSING"
+        logger.debug("Step 6: Awaiting processing completion...")
+        self.event_loop.exec() # This loop is quit by _on_all_tasks_finished
+
+        if self.test_step != "PROCESSING_COMPLETE":
+            logger.error(f"Processing did not complete as expected. Current step: {self.test_step}")
+            self.cleanup_and_exit(success=False)
+            return
+        logger.info("Processing completed.") # KEEP INFO - Passes filter
+
+        # Step 7: Check Output Path
+        self.test_step = "CHECK_OUTPUT"
+        logger.info(f"Step 7: Checking output path: {self.cli_args.outputdir}") # KEEP INFO - Passes filter
+        output_path = Path(self.cli_args.outputdir)
+        if not output_path.exists() or not output_path.is_dir():
+            logger.error(f"Output directory {output_path} does not exist or is not a directory.")
+            self.cleanup_and_exit(success=False)
+            return
+
+        output_items = list(output_path.iterdir())
+        if not output_items:
+            logger.warning(f"Output directory {output_path} is empty. This might be a test failure depending on the case.")
+            # For a more specific check, one might iterate through actual_rules_obj
+            # and verify if subdirectories matching asset_name exist.
+            # e.g. for asset_rule in source_rule.assets:
+            #   expected_asset_dir = output_path / asset_rule.asset_name
+            #   if not expected_asset_dir.is_dir(): logger.error(...)
+        else:
+            logger.debug(f"Found {len(output_items)} item(s) in output directory:")
+            for item in output_items:
+                logger.debug(f"  - {item.name} ({'dir' if item.is_dir() else 'file'})")
+        logger.info("Output path check completed.") # KEEP INFO - Passes filter
+
+        # Step 8: Retrieve & Analyze Logs
+        self.test_step = "CHECK_LOGS"
+        logger.debug("Step 8: Retrieving and Analyzing Logs...")
+        all_logs_text = ""
+        if self.main_window.log_console and self.main_window.log_console.log_console_output:
+            all_logs_text = self.main_window.log_console.log_console_output.toPlainText()
+        else:
+            logger.warning("Log console or output widget not found. Cannot retrieve logs.")
+        
+        self._process_and_display_logs(all_logs_text)
+        logger.info("Log analysis completed.")
+
+        # Final Step
+        logger.info("Test run completed successfully.") # KEEP INFO - Passes filter
+        self.cleanup_and_exit(success=True)
+
+    @Slot()
+    def _check_prediction_status(self) -> None:
+        """Polls the main window for pending predictions."""
+        # logger.debug(f"Checking prediction status. Pending: {self.main_window._pending_predictions if hasattr(self.main_window, '_pending_predictions') else 'N/A'}")
+        if hasattr(self.main_window, '_pending_predictions'):
+            if not self.main_window._pending_predictions: # Assuming _pending_predictions is a list/dict that's empty when done
+                logger.debug("No pending predictions. Prediction assumed complete.")
+                self.test_step = "PREDICTION_COMPLETE"
+                if self.event_loop.isRunning():
+                    self.event_loop.quit()
+            # else:
+                # logger.debug(f"Still awaiting predictions: {len(self.main_window._pending_predictions)} remaining.")
+        else:
+            logger.warning("'_pending_predictions' attribute not found on MainWindow. Cannot check prediction status automatically.")
+            # As a fallback, if the attribute is missing, we might assume prediction is instant or needs manual check.
+            # For now, let's assume it means it's done if the attribute is missing, but this is risky.
+            # A better approach would be to have a clear signal from MainWindow when predictions are done.
+            self.test_step = "PREDICTION_COMPLETE" # Risky assumption
+            if self.event_loop.isRunning():
+                self.event_loop.quit()
+
+
+    @Slot(int, int, int)
+    def _on_all_tasks_finished(self, processed_count: int, skipped_count: int, failed_count: int) -> None:
+        """Slot for App.all_tasks_finished signal."""
+        logger.info(f"Signal 'all_tasks_finished' received: Processed={processed_count}, Skipped={skipped_count}, Failed={failed_count}") # KEEP INFO - Passes filter
+        
+        if self.test_step == "AWAIT_PROCESSING":
+            logger.debug("Processing completion signal received.") # Covered by the summary log above
+            if failed_count > 0:
+                logger.error(f"Processing finished with {failed_count} failed task(s).")
+            # Even if tasks failed, the test might pass based on output checks.
+            # The error is logged for information.
+            self.test_step = "PROCESSING_COMPLETE"
+            if self.event_loop.isRunning():
+                self.event_loop.quit()
+        else:
+            logger.warning(f"Signal 'all_tasks_finished' received at an unexpected test step: '{self.test_step}'. Counts: P={processed_count}, S={skipped_count}, F={failed_count}")
+
+
+    def _convert_rules_to_comparable(self, source_rules_list: List[SourceRule]) -> Dict[str, Any]:
+        """
+        Converts a list of SourceRule objects to a dictionary structure
+        suitable for comparison with the expected_rules.json.
+        """
+        logger.debug(f"Converting {len(source_rules_list)} SourceRule objects to comparable dictionary...")
+        comparable_sources_list = []
+        for source_rule_obj in source_rules_list:
+            comparable_asset_list = []
+            # source_rule_obj.assets is List[AssetRule]
+            for asset_rule_obj in source_rule_obj.assets:
+                comparable_file_list = []
+                # asset_rule_obj.files is List[FileRule]
+                for file_rule_obj in asset_rule_obj.files:
+                    comparable_file_list.append({
+                        "file_path": file_rule_obj.file_path,
+                        "item_type": file_rule_obj.item_type,
+                        "target_asset_name_override": file_rule_obj.target_asset_name_override
+                    })
+                comparable_asset_list.append({
+                    "asset_name": asset_rule_obj.asset_name,
+                    "asset_type": asset_rule_obj.asset_type,
+                    "files": comparable_file_list
+                })
+            comparable_sources_list.append({
+                "input_path": Path(source_rule_obj.input_path).name, # Use only the filename
+                "supplier_identifier": source_rule_obj.supplier_identifier,
+                "preset_name": source_rule_obj.preset_name,
+                "assets": comparable_asset_list
+            })
+        logger.debug("Conversion to comparable dictionary finished.")
+        return {"source_rules": comparable_sources_list}
+
+    def _compare_rule_item(self, actual_item: Dict[str, Any], expected_item: Dict[str, Any], item_type_name: str, parent_context: str = "") -> bool:
+        """
+        Recursively compares an individual actual rule item dictionary with an expected rule item dictionary.
+        Logs differences and returns True if they match, False otherwise.
+        """
+        item_match = True
+        
+        identifier = ""
+        if item_type_name == "SourceRule":
+            identifier = expected_item.get('input_path', f'UnknownSource_at_{parent_context}')
+        elif item_type_name == "AssetRule":
+            identifier = expected_item.get('asset_name', f'UnknownAsset_at_{parent_context}')
+        elif item_type_name == "FileRule":
+            identifier = expected_item.get('file_path', f'UnknownFile_at_{parent_context}')
+        
+        current_context = f"{parent_context}/{identifier}" if parent_context else identifier
+
+        # Log Extra Fields: Iterate through keys in actual_item.
+        # If a key is in actual_item but not in expected_item (and is not a list container like "assets" or "files"),
+        # log this as an informational message.
+        for key in actual_item.keys():
+            if key not in expected_item and key not in ["assets", "files"]:
+                logger.debug(f"Field '{key}' present in actual {item_type_name} ({current_context}) but not specified in expected. Value: '{actual_item[key]}'")
+
+        # Check Expected Fields: Iterate through keys in expected_item.
+        for key, expected_value in expected_item.items():
+            if key not in actual_item:
+                logger.error(f"Missing expected field '{key}' in actual {item_type_name} ({current_context}).")
+                item_match = False
+                continue # Continue to check other fields in the expected_item
+
+            actual_value = actual_item[key]
+
+            if key == "assets": # List of AssetRule dictionaries
+                if not self._compare_list_of_rules(actual_value, expected_value, "AssetRule", current_context, "asset_name"):
+                    item_match = False
+            elif key == "files": # List of FileRule dictionaries
+                if not self._compare_list_of_rules(actual_value, expected_value, "FileRule", current_context, "file_path"):
+                    item_match = False
+            else: # Regular field comparison
+                if actual_value != expected_value:
+                    # Handle None vs "None" string for preset_name specifically if it's a common issue
+                    if key == "preset_name" and actual_value is None and expected_value == "None":
+                        logger.debug(f"Field '{key}' in {item_type_name} ({current_context}): Actual is None, Expected is string \"None\". Treating as match for now.")
+                    elif key == "target_asset_name_override" and actual_value is not None and expected_value is None:
+                         # If actual has a value (e.g. parent asset name) and expected is null/None,
+                         # this is a mismatch according to strict comparison.
+                         # For a more lenient check, this logic could be adjusted here.
+                         # Current strict comparison will flag this as error, which is what the logs show.
+                         logger.error(f"Value mismatch for field '{key}' in {item_type_name} ({current_context}): Actual='{actual_value}', Expected='{expected_value}'.")
+                         item_match = False
+                    else:
+                        logger.error(f"Value mismatch for field '{key}' in {item_type_name} ({current_context}): Actual='{actual_value}', Expected='{expected_value}'.")
+                        item_match = False
+            
+        return item_match
+    
+    def _compare_list_of_rules(self, actual_list: List[Dict[str, Any]], expected_list: List[Dict[str, Any]], item_type_name: str, parent_context: str, item_key_field: str) -> bool:
+        """
+        Compares a list of actual rule items against a list of expected rule items.
+        Items are matched by a key field (e.g., 'asset_name' or 'file_path').
+        Order independent for matching, but logs count mismatches.
+        """
+        list_match = True
+        if not isinstance(actual_list, list) or not isinstance(expected_list, list):
+            logger.error(f"Type mismatch for list of {item_type_name}s in {parent_context}. Expected lists.")
+            return False
+
+        if len(actual_list) != len(expected_list):
+            logger.error(f"Mismatch in number of {item_type_name}s for {parent_context}. Actual: {len(actual_list)}, Expected: {len(expected_list)}.")
+            list_match = False # Count mismatch is an error
+            # If counts differ, we still try to match what we can to provide more detailed feedback,
+            # but the overall list_match will remain False.
+
+        actual_items_map = {item.get(item_key_field): item for item in actual_list if item.get(item_key_field) is not None}
+        
+        # Keep track of expected items that found a match to identify missing ones more easily
+        matched_expected_keys = set()
+
+        for expected_item in expected_list:
+            expected_key_value = expected_item.get(item_key_field)
+            if expected_key_value is None:
+                logger.error(f"Expected {item_type_name} in {parent_context} is missing key field '{item_key_field}'. Cannot compare this item: {expected_item}")
+                list_match = False # This specific expected item cannot be processed
+                continue
+
+            actual_item = actual_items_map.get(expected_key_value)
+            if actual_item:
+                matched_expected_keys.add(expected_key_value)
+                if not self._compare_rule_item(actual_item, expected_item, item_type_name, parent_context):
+                    list_match = False # Individual item comparison failed
+            else:
+                logger.error(f"Expected {item_type_name} with {item_key_field} '{expected_key_value}' not found in actual items for {parent_context}.")
+                list_match = False
+        
+        # Identify actual items that were not matched by any expected item
+        # This is useful if len(actual_list) >= len(expected_list) but some actual items are "extra"
+        for actual_key_value, actual_item_data in actual_items_map.items():
+            if actual_key_value not in matched_expected_keys:
+                logger.debug(f"Extra actual {item_type_name} with {item_key_field} '{actual_key_value}' found in {parent_context} (not in expected list or already matched).")
+                if len(actual_list) != len(expected_list): # If counts already flagged a mismatch, this is just detail
+                    pass
+                else: # Counts matched, but content didn't align perfectly by key
+                    list_match = False
+
+
+        return list_match
+    
+
+    def _compare_rules(self, actual_rules_data: Dict[str, Any], expected_rules_data: Dict[str, Any]) -> bool:
+        """
+        Compares the actual rule data (converted from live SourceRule objects)
+        with the expected rule data (loaded from JSON).
+        """
+        logger.debug("Comparing actual rules with expected rules...")
+
+        actual_source_rules = actual_rules_data.get("source_rules", []) if actual_rules_data else []
+        expected_source_rules = expected_rules_data.get("source_rules", []) if expected_rules_data else []
+
+        if not isinstance(actual_source_rules, list):
+            logger.error(f"Actual 'source_rules' is not a list. Found type: {type(actual_source_rules)}. Comparison aborted.")
+            return False # Cannot compare if actual data is malformed
+        if not isinstance(expected_source_rules, list):
+            logger.error(f"Expected 'source_rules' is not a list. Found type: {type(expected_source_rules)}. Test configuration error. Comparison aborted.")
+            return False # Test setup error
+
+        if not expected_source_rules and not actual_source_rules:
+            logger.debug("Both expected and actual source rules lists are empty. Considered a match.")
+            return True
+        
+        if len(actual_source_rules) != len(expected_source_rules):
+            logger.error(f"Mismatch in the number of source rules. Actual: {len(actual_source_rules)}, Expected: {len(expected_source_rules)}.")
+            # Optionally, log more details about which list is longer/shorter or identifiers if available
+            return False
+
+        overall_match_status = True
+        for i in range(len(expected_source_rules)):
+            actual_sr = actual_source_rules[i]
+            expected_sr = expected_source_rules[i]
+            
+            # For context, use input_path or an index
+            source_rule_context = expected_sr.get('input_path', f"SourceRule_index_{i}")
+
+            if not self._compare_rule_item(actual_sr, expected_sr, "SourceRule", parent_context=source_rule_context):
+                overall_match_status = False
+                # Continue checking other source rules to log all discrepancies
+
+        if overall_match_status:
+            logger.debug("All rules match the expected criteria.") # Covered by "Rule comparison successful" summary
+        else:
+            logger.warning("One or more rules did not match the expected criteria. See logs above for details.")
+            
+        return overall_match_status
+
+    def _process_and_display_logs(self, logs_text: str) -> None: # logs_text is no longer the primary source for search
+        """
+        Processes and displays logs, potentially filtering them if --search is used.
+        Also checks for tracebacks.
+        Sources logs from the in-memory handler for search and detailed analysis.
+        """
+        logger.debug("--- Log Analysis ---")
+        global autotest_memory_handler # Access the global handler
+        log_records = []
+        if autotest_memory_handler and autotest_memory_handler.buffer:
+            log_records = autotest_memory_handler.buffer
+
+        formatted_log_lines = []
+        # Define a consistent formatter, similar to what might be expected or useful for search
+        record_formatter = logging.Formatter('%(asctime)s [%(levelname)s] %(name)s: %(message)s')
+        # Default asctime format includes milliseconds.
+
+
+        for record in log_records:
+            formatted_log_lines.append(record_formatter.format(record))
+        
+        lines_for_search_and_traceback = formatted_log_lines
+        
+        if not lines_for_search_and_traceback:
+            logger.warning("No log records found in memory handler. No analysis to perform.")
+            # Still check the console logs_text for tracebacks if it exists, as a fallback
+            # or if some critical errors didn't make it to the memory handler (unlikely with DEBUG level)
+            if logs_text:
+                logger.debug("Checking provided logs_text (from console) for tracebacks as a fallback.")
+                console_lines = logs_text.splitlines()
+                traceback_found_console = False
+                for i, line in enumerate(console_lines):
+                    if line.strip().startswith("Traceback (most recent call last):"):
+                        logger.error(f"!!! TRACEBACK DETECTED in console logs_text around line {i+1} !!!")
+                        traceback_found_console = True
+                if traceback_found_console:
+                     logger.warning("A traceback was found in the console logs_text.")
+                else:
+                    logger.info("No tracebacks found in the console logs_text either.")
+            logger.info("--- End Log Analysis ---")
+            return
+
+        traceback_found = False
+
+        if self.cli_args.search:
+            logger.info(f"Searching {len(lines_for_search_and_traceback)} in-memory log lines for term '{self.cli_args.search}' with {self.cli_args.additional_lines} context lines.")
+            matched_line_indices = [i for i, line in enumerate(lines_for_search_and_traceback) if self.cli_args.search in line]
+            
+            if not matched_line_indices:
+                logger.info(f"Search term '{self.cli_args.search}' not found in in-memory logs.")
+            else:
+                logger.info(f"Found {len(matched_line_indices)} match(es) for '{self.cli_args.search}' in in-memory logs:")
+                collected_lines_to_print = set()
+                for match_idx in matched_line_indices:
+                    start_idx = max(0, match_idx - self.cli_args.additional_lines)
+                    end_idx = min(len(lines_for_search_and_traceback), match_idx + self.cli_args.additional_lines + 1)
+                    for i in range(start_idx, end_idx):
+                        # Use i directly as index for lines_for_search_and_traceback, line number is for display
+                        collected_lines_to_print.add(f"L{i+1:05d}: {lines_for_search_and_traceback[i]}")
+                
+                print("--- Filtered Log Output (from Memory Handler) ---")
+                for line_to_print in sorted(list(collected_lines_to_print)):
+                    print(line_to_print)
+                print("--- End Filtered Log Output ---")
+        # Removed: else block that showed last N lines by default (as per original instruction for this section)
+
+        # Traceback Check (on lines_for_search_and_traceback)
+        for i, line in enumerate(lines_for_search_and_traceback):
+            if line.strip().startswith("Traceback (most recent call last):") or "Traceback (most recent call last):" in line : # More robust check
+                logger.error(f"!!! TRACEBACK DETECTED in in-memory logs around line index {i} !!!")
+                logger.error(f"Line content: {line}")
+                traceback_found = True
+        
+        if traceback_found:
+            logger.warning("A traceback was found in the in-memory logs. This usually indicates a significant issue.")
+        else:
+            logger.info("No tracebacks found in the in-memory logs.") # This refers to the comprehensive memory logs
+            
+        logger.info("--- End Log Analysis ---")
+
+    def cleanup_and_exit(self, success: bool = True) -> None:
+        """Cleans up and exits the application."""
+        global autotest_memory_handler
+        if autotest_memory_handler:
+            logger.debug("Clearing memory log handler buffer and removing handler.")
+            autotest_memory_handler.buffer = [] # Clear buffer
+            logging.getLogger().removeHandler(autotest_memory_handler) # Remove handler
+            autotest_memory_handler.close() # MemoryHandler close is a no-op but good practice
+            autotest_memory_handler = None
+
+        logger.info(f"Test {'succeeded' if success else 'failed'}. Cleaning up and exiting...") # KEEP INFO - Passes filter
+        q_app = QCoreApplication.instance()
+        if q_app:
+            q_app.quit()
+        sys.exit(0 if success else 1)
+
+# --- Main Execution ---
+def main():
+    """Main function to run the autotest script."""
+    cli_args = parse_arguments()
+    # Logger is configured above, this will now use the new filtered setup
+    logger.info(f"Parsed CLI arguments: {cli_args}") # KEEP INFO - Passes filter
+
+    # Clean and ensure output directory exists
+    output_dir_path = Path(cli_args.outputdir)
+    logger.debug(f"Preparing output directory: {output_dir_path}")
+    try:
+        if output_dir_path.exists():
+            logger.debug(f"Output directory {output_dir_path} exists. Cleaning its contents...")
+            for item in output_dir_path.iterdir():
+                if item.is_dir():
+                    shutil.rmtree(item)
+                    logger.debug(f"Removed directory: {item}")
+                else:
+                    item.unlink()
+                    logger.debug(f"Removed file: {item}")
+            logger.debug(f"Contents of {output_dir_path} cleaned.")
+        else:
+            logger.debug(f"Output directory {output_dir_path} does not exist. Creating it.")
+        
+        output_dir_path.mkdir(parents=True, exist_ok=True) # Ensure it exists after cleaning/if it didn't exist
+        logger.debug(f"Output directory {output_dir_path} is ready.")
+
+    except Exception as e:
+        logger.error(f"Could not prepare output directory {output_dir_path}: {e}", exc_info=True)
+        sys.exit(1)
+
+    # Initialize QApplication
+    # Use QCoreApplication if no GUI elements are directly interacted with by the test logic itself,
+    # but QApplication is needed if MainWindow or its widgets are constructed and used.
+    # Since MainWindow is instantiated by App, QApplication is appropriate.
+    q_app = QApplication.instance()
+    if not q_app:
+        q_app = QApplication(sys.argv)
+    if not q_app: # Still no app
+        logger.error("Failed to initialize QApplication.")
+        sys.exit(1)
+
+    logger.debug("Initializing main.App()...")
+    try:
+        # Instantiate main.App() - this should create MainWindow but not show it by default
+        # if App is designed to not show GUI unless app.main_window.show() is called.
+        app_instance = App()
+    except Exception as e:
+        logger.error(f"Failed to initialize main.App: {e}", exc_info=True)
+        sys.exit(1)
+    
+    if not app_instance.main_window:
+        logger.error("main.App initialized, but main_window is None. Cannot proceed with test.")
+        sys.exit(1)
+    
+    logger.debug("Initializing AutoTester...")
+    try:
+        tester = AutoTester(app_instance, cli_args)
+    except Exception as e:
+        logger.error(f"Failed to initialize AutoTester: {e}", exc_info=True)
+        sys.exit(1)
+
+    # Use QTimer.singleShot to start the test after the Qt event loop has started.
+    # This ensures that the Qt environment is fully set up.
+    logger.debug("Scheduling test run...")
+    QTimer.singleShot(0, tester.run_test)
+
+    logger.debug("Starting Qt application event loop...")
+    exit_code = q_app.exec()
+    logger.debug(f"Qt application event loop finished with exit code: {exit_code}")
+    sys.exit(exit_code)
+
+if __name__ == "__main__":
+    main()

config/app_settings.json

@@ -46,7 +46,10 @@
     "TEMP_DIR_PREFIX": "_PROCESS_ASSET_",
     "INITIAL_SCALING_MODE": "POT_DOWNSCALE",
     "MERGE_DIMENSION_MISMATCH_STRATEGY": "USE_LARGEST",
+    "ENABLE_LOW_RESOLUTION_FALLBACK": true,
+    "LOW_RESOLUTION_THRESHOLD": 512,
     "general_settings": {
-        "invert_normal_map_green_channel_globally": false
+        "invert_normal_map_green_channel_globally": false,
+        "app_version": "Pre-Alpha"
     }
 }

config/file_type_definitions.json

@@ -190,7 +190,7 @@
             ],
             "is_grayscale": false,
             "keybind": "E",
-            "standard_type": ""
+            "standard_type": "EXTRA"
         },
         "FILE_IGNORE": {
             "bit_depth_rule": "",

configuration.py

@@ -4,6 +4,7 @@ from pathlib import Path
 import logging
 import re
 import collections.abc
+from typing import Optional
 
 log = logging.getLogger(__name__)
 
@@ -12,7 +13,7 @@ APP_SETTINGS_PATH = BASE_DIR / "config" / "app_settings.json"
 LLM_SETTINGS_PATH = BASE_DIR / "config" / "llm_settings.json"
 ASSET_TYPE_DEFINITIONS_PATH = BASE_DIR / "config" / "asset_type_definitions.json"
 FILE_TYPE_DEFINITIONS_PATH = BASE_DIR / "config" / "file_type_definitions.json"
-USER_SETTINGS_PATH = BASE_DIR / "config" / "user_settings.json" # New path for user settings
+USER_SETTINGS_PATH = BASE_DIR / "config" / "user_settings.json"
 SUPPLIERS_CONFIG_PATH = BASE_DIR / "config" / "suppliers.json"
 PRESETS_DIR = BASE_DIR / "Presets"
 
@@ -649,6 +650,24 @@ class Configuration:
         """Returns the LLM request timeout in seconds from LLM settings."""
         return self._llm_settings.get('llm_request_timeout', 120)
 
+    @property
+    def app_version(self) -> Optional[str]:
+        """Returns the application version from general_settings."""
+        gs = self._core_settings.get('general_settings')
+        if isinstance(gs, dict):
+            return gs.get('app_version')
+        return None
+
+    @property
+    def enable_low_resolution_fallback(self) -> bool:
+        """Gets the setting for enabling low-resolution fallback."""
+        return self._core_settings.get('ENABLE_LOW_RESOLUTION_FALLBACK', True)
+
+    @property
+    def low_resolution_threshold(self) -> int:
+        """Gets the pixel dimension threshold for low-resolution fallback."""
+        return self._core_settings.get('LOW_RESOLUTION_THRESHOLD', 512)
+
     @property
     def FILE_TYPE_DEFINITIONS(self) -> dict:
         return self._file_type_definitions

gui/delegates.py

@@ -126,12 +126,15 @@ class SupplierSearchDelegate(QStyledItemDelegate):
         """Loads the list of known suppliers from the JSON config file."""
         try:
             with open(SUPPLIERS_CONFIG_PATH, 'r') as f:
-                suppliers = json.load(f)
-                if isinstance(suppliers, list):
+                suppliers_data = json.load(f) # Renamed variable for clarity
+                if isinstance(suppliers_data, list):
                     # Ensure all items are strings
-                    return sorted([str(s) for s in suppliers if isinstance(s, str)])
-                else:
-                    log.warning(f"'{SUPPLIERS_CONFIG_PATH}' does not contain a valid list. Starting fresh.")
+                    return sorted([str(s) for s in suppliers_data if isinstance(s, str)])
+                elif isinstance(suppliers_data, dict): # ADDED: Handle dictionary case
+                    # If it's a dictionary, extract keys as supplier names
+                    return sorted([str(key) for key in suppliers_data.keys() if isinstance(key, str)])
+                else: # MODIFIED: Updated warning message
+                    log.warning(f"'{SUPPLIERS_CONFIG_PATH}' does not contain a valid list or dictionary of suppliers. Starting fresh.")
                     return []
         except FileNotFoundError:
             log.info(f"'{SUPPLIERS_CONFIG_PATH}' not found. Starting with an empty supplier list.")

gui/preset_editor_widget.py

@@ -20,7 +20,8 @@ script_dir = Path(__file__).parent
 project_root = script_dir.parent
 PRESETS_DIR = project_root / "Presets"
 TEMPLATE_PATH = PRESETS_DIR / "_template.json"
-APP_SETTINGS_PATH_LOCAL = project_root / "config" / "app_settings.json"
+APP_SETTINGS_PATH_LOCAL = project_root / "config" / "app_settings.json" # Retain for other settings if used elsewhere
+FILE_TYPE_DEFINITIONS_PATH = project_root / "config" / "file_type_definitions.json"
 
 
 log = logging.getLogger(__name__)
@@ -63,18 +64,19 @@ class PresetEditorWidget(QWidget):
         """Loads FILE_TYPE_DEFINITIONS keys from app_settings.json."""
         keys = []
         try:
-            if APP_SETTINGS_PATH_LOCAL.is_file():
-                with open(APP_SETTINGS_PATH_LOCAL, 'r', encoding='utf-8') as f:
+            if FILE_TYPE_DEFINITIONS_PATH.is_file():
+                with open(FILE_TYPE_DEFINITIONS_PATH, 'r', encoding='utf-8') as f:
                     settings = json.load(f)
+                # The FILE_TYPE_DEFINITIONS key is at the root of file_type_definitions.json
                 ftd = settings.get("FILE_TYPE_DEFINITIONS", {})
                 keys = list(ftd.keys())
-                log.debug(f"Successfully loaded {len(keys)} FILE_TYPE_DEFINITIONS keys.")
+                log.debug(f"Successfully loaded {len(keys)} FILE_TYPE_DEFINITIONS keys from {FILE_TYPE_DEFINITIONS_PATH}.")
             else:
-                log.error(f"app_settings.json not found at {APP_SETTINGS_PATH_LOCAL} for PresetEditorWidget.")
+                log.error(f"file_type_definitions.json not found at {FILE_TYPE_DEFINITIONS_PATH} for PresetEditorWidget.")
         except json.JSONDecodeError as e:
-            log.error(f"Failed to parse app_settings.json in PresetEditorWidget: {e}")
+            log.error(f"Failed to parse file_type_definitions.json in PresetEditorWidget: {e}")
         except Exception as e:
-            log.error(f"Error loading FILE_TYPE_DEFINITIONS keys in PresetEditorWidget: {e}")
+            log.error(f"Error loading FILE_TYPE_DEFINITIONS keys from {FILE_TYPE_DEFINITIONS_PATH} in PresetEditorWidget: {e}")
         return keys
 
     def _init_ui(self):

gui/unified_view_model.py

@@ -552,6 +552,13 @@ class UnifiedViewModel(QAbstractItemModel):
                      supplier_col_index = self.createIndex(existing_source_row, self.COL_SUPPLIER, existing_source_rule)
                      self.dataChanged.emit(supplier_col_index, supplier_col_index, [Qt.DisplayRole, Qt.EditRole])
 
+            # Always update the preset_name from the new_source_rule, as this reflects the latest prediction context
+            if existing_source_rule.preset_name != new_source_rule.preset_name:
+                log.debug(f"  Updating preset_name for SourceRule '{source_path}' from '{existing_source_rule.preset_name}' to '{new_source_rule.preset_name}'")
+                existing_source_rule.preset_name = new_source_rule.preset_name
+                # Note: preset_name is not directly displayed in the view, so no dataChanged needed for a specific column,
+                # but if it influenced other display elements, dataChanged would be emitted for those.
+
 
             # --- Merge AssetRules ---
             existing_assets_dict = {asset.asset_name: asset for asset in existing_source_rule.assets}

main.py

@@ -4,6 +4,7 @@ import time
 import os
 import logging
 from pathlib import Path
+import re # Added for checking incrementing token
 from concurrent.futures import ProcessPoolExecutor, as_completed
 import subprocess
 import shutil
@@ -238,9 +239,14 @@ class ProcessingTask(QRunnable):
                 # output_dir should already be a Path object
                 pattern = getattr(config, 'output_directory_pattern', None)
                 if pattern:
-                    log.debug(f"Calculating next incrementing value for dir: {output_dir} using pattern: {pattern}")
-                    next_increment_str = get_next_incrementing_value(output_dir, pattern)
-                    log.info(f"Calculated next incrementing value for {output_dir}: {next_increment_str}")
+                    # Only call get_next_incrementing_value if the pattern contains an incrementing token
+                    if re.search(r"\[IncrementingValue\]|#+", pattern):
+                        log.debug(f"Incrementing token found in pattern '{pattern}'. Calculating next value for dir: {output_dir}")
+                        next_increment_str = get_next_incrementing_value(output_dir, pattern)
+                        log.info(f"Calculated next incrementing value for {output_dir}: {next_increment_str}")
+                    else:
+                        log.debug(f"No incrementing token found in pattern '{pattern}'. Skipping increment calculation.")
+                        next_increment_str = None # Or a default like "00" if downstream expects a string, but None is cleaner if handled.
                 else:
                     log.warning(f"Cannot calculate incrementing value: 'output_directory_pattern' not found in configuration for preset {config.preset_name}")
             except Exception as e:
@@ -401,11 +407,13 @@ class App(QObject):
 
         # --- Get paths needed for ProcessingTask ---
         try:
-            # Access output path via MainPanelWidget
-            output_base_path_str = self.main_window.main_panel_widget.output_path_edit.text().strip()
+            # Get output_dir from processing_settings passed from autotest.py
+            output_base_path_str = processing_settings.get("output_dir")
+            log.info(f"APP_DEBUG: Received output_dir in processing_settings: {output_base_path_str}")
+
             if not output_base_path_str:
-                log.error("Cannot queue tasks: Output directory path is empty in the GUI.")
-                self.main_window.statusBar().showMessage("Error: Output directory cannot be empty.", 5000)
+                log.error("Cannot queue tasks: Output directory path is empty in processing_settings.")
+                # self.main_window.statusBar().showMessage("Error: Output directory cannot be empty.", 5000) # GUI specific
                 return
             output_base_path = Path(output_base_path_str)
             # Basic validation - check if it's likely a valid path structure (doesn't guarantee existence/writability here)
@@ -477,8 +485,9 @@ class App(QObject):
                     engine=task_engine,
                     rule=rule,
                     workspace_path=workspace_path,
-                    output_base_path=output_base_path
+                    output_base_path=output_base_path # This is Path(output_base_path_str)
                 )
+                log.info(f"APP_DEBUG: Passing to ProcessingTask: output_base_path = {output_base_path}")
                 task.signals.finished.connect(self._on_task_finished)
                 log.debug(f"DEBUG: Calling thread_pool.start() for task {i+1}")
                 self.thread_pool.start(task)

monitor.py

@@ -195,17 +195,25 @@ def _process_archive_task(archive_path: Path, output_dir: Path, processed_dir: P
             # Assuming config object has 'output_directory_pattern' attribute/key
             pattern = getattr(config, 'output_directory_pattern', None) # Use getattr for safety
             if pattern:
-                log.debug(f"[Task:{archive_path.name}] Calculating next incrementing value for dir: {output_dir} using pattern: {pattern}")
-                next_increment_str = get_next_incrementing_value(output_dir, pattern)
-                log.info(f"[Task:{archive_path.name}] Calculated next incrementing value: {next_increment_str}")
+                if re.search(r"\[IncrementingValue\]|#+", pattern):
+                    log.debug(f"[Task:{archive_path.name}] Incrementing token found in pattern '{pattern}'. Calculating next value for dir: {output_dir}")
+                    next_increment_str = get_next_incrementing_value(output_dir, pattern)
+                    log.info(f"[Task:{archive_path.name}] Calculated next incrementing value: {next_increment_str}")
+                else:
+                    log.debug(f"[Task:{archive_path.name}] No incrementing token found in pattern '{pattern}'. Skipping increment calculation.")
+                    next_increment_str = None
             else:
                 # Check if config is a dict as fallback (depends on load_config implementation)
                 if isinstance(config, dict):
                     pattern = config.get('output_directory_pattern')
                     if pattern:
-                        log.debug(f"[Task:{archive_path.name}] Calculating next incrementing value for dir: {output_dir} using pattern (from dict): {pattern}")
-                        next_increment_str = get_next_incrementing_value(output_dir, pattern)
-                        log.info(f"[Task:{archive_path.name}] Calculated next incrementing value (from dict): {next_increment_str}")
+                        if re.search(r"\[IncrementingValue\]|#+", pattern):
+                            log.debug(f"[Task:{archive_path.name}] Incrementing token found in pattern '{pattern}' (from dict). Calculating next value for dir: {output_dir}")
+                            next_increment_str = get_next_incrementing_value(output_dir, pattern)
+                            log.info(f"[Task:{archive_path.name}] Calculated next incrementing value (from dict): {next_increment_str}")
+                        else:
+                            log.debug(f"[Task:{archive_path.name}] No incrementing token found in pattern '{pattern}' (from dict). Skipping increment calculation.")
+                            next_increment_str = None
                     else:
                         log.warning(f"[Task:{archive_path.name}] Cannot calculate incrementing value: 'output_directory_pattern' not found in configuration dictionary.")
                 else:

processing/pipeline/asset_context.py

@@ -1,3 +1,4 @@
+import dataclasses # Added import
 from dataclasses import dataclass
 from pathlib import Path
 from typing import Dict, List, Optional
@@ -27,6 +28,7 @@ class ProcessedRegularMapData:
     original_bit_depth: Optional[int]
     original_dimensions: Optional[Tuple[int, int]] # (width, height)
     transformations_applied: List[str]
+    resolution_key: Optional[str] = None # Added field
     status: str = "Processed"
     error_message: Optional[str] = None
 
@@ -45,9 +47,10 @@ class ProcessedMergedMapData:
 @dataclass
 class InitialScalingInput:
     image_data: np.ndarray
+    initial_scaling_mode: str # Moved before fields with defaults
     original_dimensions: Optional[Tuple[int, int]] # (width, height)
+    resolution_key: Optional[str] = None # Added field
     # Configuration needed
-    initial_scaling_mode: str
 
 # Output for InitialScalingStage
 @dataclass
@@ -55,6 +58,7 @@ class InitialScalingOutput:
     scaled_image_data: np.ndarray
     scaling_applied: bool
     final_dimensions: Tuple[int, int] # (width, height)
+    resolution_key: Optional[str] = None # Added field
 
 # Input for SaveVariantsStage
 @dataclass

processing/pipeline/orchestrator.py

@@ -8,7 +8,7 @@ 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
+from rule_structure import SourceRule, AssetRule, FileRule, ProcessingItem # Added ProcessingItem
 
 # Import new context classes and stages
 from .asset_context import (
@@ -200,145 +200,224 @@ class PipelineOrchestrator:
                     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
+                        # The 'item' is now expected to be a ProcessingItem or MergeTaskDefinition
+                        
+                        if isinstance(item, ProcessingItem):
+                            item_key = f"{item.source_file_info_ref}_{item.map_type_identifier}_{item.resolution_key}"
+                            item_log_prefix = f"Asset '{asset_name}', ProcItem '{item_key}'"
+                            log.info(f"{item_log_prefix}: Starting processing.")
 
-                        # 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
+                            # Data for ProcessingItem is already loaded by PrepareProcessingItemsStage
+                            current_image_data = item.image_data
+                            current_dimensions = item.current_dimensions
+                            item_resolution_key = item.resolution_key
+                            
+                            # Transformations (like gloss to rough, normal invert) are assumed to be applied
+                            # by RegularMapProcessorStage if it's still used, or directly in PrepareProcessingItemsStage
+                            # before creating the ProcessingItem, or a new dedicated transformation stage.
+                            # For now, assume item.image_data is ready for scaling/saving.
+                            
+                            # Store initial ProcessingItem data as "processed_data" for consistency if RegularMapProcessor is bypassed
+                            # This is a simplification; a dedicated transformation stage would be cleaner.
+                            # For now, we assume transformations happened before or within PrepareProcessingItemsStage.
+                            # The 'processed_data' variable here is more of a placeholder for what would feed into scaling.
+                            
+                            # Create a simple ProcessedRegularMapData-like structure for logging/details if needed,
+                            # or adapt the final_details population later.
+                            # For now, we'll directly use 'item' fields.
 
-                        # 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})...")
+                            # 2. Scale (Optional)
+                            scaling_mode = getattr(context.config_obj, "INITIAL_SCALING_MODE", "NONE")
+                            # Pass the item's resolution_key to InitialScalingInput
                             scale_input = InitialScalingInput(
                                 image_data=current_image_data,
-                                original_dimensions=current_dimensions, # Pass original/merged dims
-                                initial_scaling_mode=scaling_mode
+                                original_dimensions=current_dimensions,
+                                initial_scaling_mode=scaling_mode,
+                                resolution_key=item_resolution_key # Pass the key
                             )
+                            # Add _source_file_path for logging within InitialScalingStage if available
+                            setattr(scale_input, '_source_file_path', item.source_file_info_ref)
+
+                            log.debug(f"{item_log_prefix}: Calling InitialScalingStage. Input res_key: {scale_input.resolution_key}")
                             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)
+                            current_image_data = scaled_data_output.scaled_image_data
+                            current_dimensions = scaled_data_output.final_dimensions # Dimensions after scaling
+                            # The resolution_key from item is passed through by InitialScalingOutput
+                            output_resolution_key = scaled_data_output.resolution_key
+                            log.debug(f"{item_log_prefix}: InitialScalingStage output. Scaled: {scaled_data_output.scaling_applied}, New Dims: {current_dimensions}, Output ResKey: {output_resolution_key}")
+                            context.intermediate_results[item_key] = scaled_data_output
 
 
-                        # 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
+                            # 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"}
+                                continue
 
-                        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.debug(f"{item_log_prefix}: Preparing to save variant with resolution key '{output_resolution_key}'...")
+                            
+                            output_filename_tokens = {
+                                'asset_name': asset_name,
+                                'output_base_directory': context.engine_temp_dir,
+                                'supplier': context.effective_supplier or 'UnknownSupplier',
+                                'resolution': output_resolution_key # Use the key from the item/scaling stage
                             }
-                            # 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
+                            
+                            # Determine image_resolutions argument for save_image_variants
+                            save_specific_resolutions = {}
+                            if output_resolution_key == "LOWRES":
+                                # For LOWRES, the "resolution value" is its actual dimension.
+                                # image_saving_utils needs a dict like {"LOWRES": 64} if current_dim is 64x64
+                                # Assuming current_dimensions[0] is width.
+                                save_specific_resolutions = {"LOWRES": current_dimensions[0] if current_dimensions else 0}
+                                log.debug(f"{item_log_prefix}: Preparing to save LOWRES variant. Dimensions: {current_dimensions}. Save resolutions arg: {save_specific_resolutions}")
+                            elif output_resolution_key in context.config_obj.image_resolutions:
+                                save_specific_resolutions = {output_resolution_key: context.config_obj.image_resolutions[output_resolution_key]}
+                            else:
+                                log.warning(f"{item_log_prefix}: Resolution key '{output_resolution_key}' not found in config.image_resolutions and not LOWRES. Saving might fail or use full res.")
+                                # Fallback: pass all configured resolutions, image_saving_utils will try to match by size.
+                                # This might not be ideal if the key is truly unknown.
+                                # Or, more strictly, fail here if key is unknown and not LOWRES.
+                                # For now, let image_saving_utils handle it by passing all.
+                                save_specific_resolutions = context.config_obj.image_resolutions
+
+
+                            save_input = SaveVariantsInput(
+                                image_data=current_image_data,
+                                internal_map_type=item.map_type_identifier,
+                                source_bit_depth_info=[item.bit_depth] if item.bit_depth is not None else [8], # Default to 8 if not set
+                                output_filename_pattern_tokens=output_filename_tokens,
+                                image_resolutions=save_specific_resolutions, # Pass the specific resolution(s)
+                                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)
+                            )
+                            saved_data = self._save_stage.execute(save_input)
+                            
+                            if saved_data and saved_data.status.startswith("Processed"):
+                                item_status = saved_data.status
+                                log.info(f"{item_log_prefix}: Item successfully processed and saved. Status: {item_status}")
+                                context.processed_maps_details[item_key] = {
+                                    "status": item_status,
+                                    "saved_files_info": saved_data.saved_files_details,
+                                    "internal_map_type": item.map_type_identifier,
+                                    "resolution_key": output_resolution_key,
+                                    "original_dimensions": item.original_dimensions,
+                                    "final_dimensions": current_dimensions, # Dimensions after scaling
+                                    "source_file": item.source_file_info_ref,
+                                }
+                            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"
+
+                        elif isinstance(item, MergeTaskDefinition):
+                            # --- This part needs similar refactoring for resolution_key if merged outputs can be LOWRES ---
+                            # --- For now, assume merged tasks always produce standard resolutions ---
+                            item_key = item.task_key
+                            item_log_prefix = f"Asset '{asset_name}', MergeTask '{item_key}'"
+                            log.info(f"{item_log_prefix}: Processing MergeTask.")
+
+                            # 1. Process Merge Task
+                            processed_data = self._merged_processor_stage.execute(context, item)
+                            if not processed_data or processed_data.status != "Processed":
+                                error_msg = processed_data.error_message if processed_data else "Merge processor returned None"
+                                log.error(f"{item_log_prefix}: Failed during merge processing. Error: {error_msg}")
+                                context.processed_maps_details[item_key] = {"status": "Failed", "notes": f"Merge Error: {error_msg}", "stage": "MergedTaskProcessorStage"}
+                                asset_had_item_errors = True
+                                continue
+                            
+                            context.intermediate_results[item_key] = processed_data
+                            current_image_data = processed_data.merged_image_data
+                            current_dimensions = processed_data.final_dimensions
+
+                            # 2. Scale Merged Output (Optional)
+                            # Merged tasks typically don't have a single "resolution_key" like LOWRES from source.
+                            # They produce an image that then gets downscaled to 1K, PREVIEW etc.
+                            # So, resolution_key for InitialScalingInput here would be None or a default.
+                            scaling_mode = getattr(context.config_obj, "INITIAL_SCALING_MODE", "NONE")
+                            scale_input = InitialScalingInput(
+                                image_data=current_image_data,
+                                original_dimensions=current_dimensions,
+                                initial_scaling_mode=scaling_mode,
+                                resolution_key=None # Merged outputs are not "LOWRES" themselves before this scaling
+                            )
+                            setattr(scale_input, '_source_file_path', f"MergeTask_{item_key}") # For logging
+                            
+                            log.debug(f"{item_log_prefix}: Calling InitialScalingStage for merged data.")
+                            scaled_data_output = self._scaling_stage.execute(scale_input)
+                            current_image_data = scaled_data_output.scaled_image_data
+                            current_dimensions = scaled_data_output.final_dimensions
+                            # Merged items don't have a specific output_resolution_key from source,
+                            # they will be saved to all applicable resolutions from config.
+                            # So scaled_data_output.resolution_key will be None here.
+                            context.intermediate_results[item_key] = scaled_data_output
+
+                            # 3. Save Merged Variants
+                            if current_image_data is None or current_image_data.size == 0:
+                                log.warning(f"{item_log_prefix}: Skipping save for merged task, image data is empty.")
+                                context.processed_maps_details[item_key] = {"status": "Skipped", "notes": "No merged image data to save", "stage": "SaveVariantsStage"}
+                                continue
+
+                            output_filename_tokens = {
+                                'asset_name': asset_name,
+                                'output_base_directory': context.engine_temp_dir,
+                                'supplier': context.effective_supplier or 'UnknownSupplier',
+                                # 'resolution' token will be filled by image_saving_utils for each variant
+                            }
+                            
+                            # For merged tasks, we usually want to generate all standard resolutions.
+                            # The `resolution_key` from the item itself is not applicable here for the `resolution` token.
+                            # The `image_saving_utils.save_image_variants` will iterate through `context.config_obj.image_resolutions`.
+                            save_input = SaveVariantsInput(
+                                image_data=current_image_data,
+                                internal_map_type=processed_data.output_map_type,
+                                source_bit_depth_info=processed_data.source_bit_depths,
+                                output_filename_pattern_tokens=output_filename_tokens,
+                                image_resolutions=context.config_obj.image_resolutions, # Pass all configured 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)
+                            )
+                            saved_data = self._save_stage.execute(save_input)
+
+                            if saved_data and saved_data.status.startswith("Processed"):
+                                item_status = saved_data.status
+                                log.info(f"{item_log_prefix}: Merged task successfully processed and saved. Status: {item_status}")
+                                context.processed_maps_details[item_key] = {
+                                    "status": item_status,
+                                    "saved_files_info": saved_data.saved_files_details,
+                                    "internal_map_type": processed_data.output_map_type,
+                                    "final_dimensions": current_dimensions,
+                                }
+                            else:
+                                error_msg = saved_data.error_message if saved_data else "Save stage for merged task returned None"
+                                log.error(f"{item_log_prefix}: Failed during save stage for merged task. Error: {error_msg}")
+                                context.processed_maps_details[item_key] = {"status": "Failed", "notes": f"Save Error (Merged): {error_msg}", "stage": "SaveVariantsStage"}
+                                asset_had_item_errors = True
+                                item_status = "Failed"
                         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"}
+                            log.warning(f"{item_log_prefix}: Unknown item type in loop: {type(item)}. Skipping.")
+                            # Ensure some key exists to prevent KeyError if item_key was not set
+                            unknown_item_key = f"unknown_item_at_index_{item_index}"
+                            context.processed_maps_details[unknown_item_key] = {"status": "Skipped", "notes": f"Unknown item type {type(item)}"}
                             asset_had_item_errors = True
-                            item_status = "Failed" # Ensure item status reflects failure
+                            continue
 
                     except Exception as e:
-                        log.exception(f"{item_log_prefix}: Unhandled exception during item processing loop: {e}")
+                        log.exception(f"Asset '{asset_name}', Item Loop Index {item_index}: Unhandled exception: {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"}

processing/pipeline/stages/initial_scaling.py

@@ -1,5 +1,5 @@
 import logging
-from typing import Tuple
+from typing import Tuple, Optional # Added Optional
 
 import cv2 # Assuming cv2 is available for interpolation flags
 import numpy as np
@@ -7,77 +7,93 @@ import numpy as np
 from .base_stage import ProcessingStage
 # Import necessary context classes and utils
 from ..asset_context import InitialScalingInput, InitialScalingOutput
+# ProcessingItem is no longer created here, so its import can be removed if not used otherwise.
+# For now, keep rule_structure import if other elements from it might be needed,
+# but ProcessingItem itself is not directly instantiated by this stage anymore.
+# from rule_structure import ProcessingItem
 from ...utils import image_processing_utils as ipu
+import numpy as np
+import cv2 # Added cv2 for interpolation flags (already used implicitly by ipu.resize_image)
 
 log = logging.getLogger(__name__)
 
 class InitialScalingStage(ProcessingStage):
     """
-    Applies initial scaling (e.g., Power-of-Two downscaling) to image data
-    if configured via the InitialScalingInput.
+    Applies initial Power-of-Two (POT) downscaling to image data if configured
+    and if the item is not already a 'LOWRES' variant.
     """
 
     def execute(self, input_data: InitialScalingInput) -> InitialScalingOutput:
         """
-        Applies scaling based on input_data.initial_scaling_mode.
+        Applies POT scaling based on input_data.initial_scaling_mode,
+        unless input_data.resolution_key is 'LOWRES'.
+        Passes through the resolution_key.
         """
-        log.debug(f"Initial Scaling Stage: Mode '{input_data.initial_scaling_mode}'.")
+        # Safely access source_file_path for logging, if provided by orchestrator via underscore attribute
+        source_file_path = getattr(input_data, '_source_file_path', "UnknownSourcePath")
+        log_prefix = f"InitialScalingStage (Source: {source_file_path}, ResKey: {input_data.resolution_key})"
+
+        log.debug(f"{log_prefix}: Mode '{input_data.initial_scaling_mode}'. Received resolution_key: '{input_data.resolution_key}'")
 
         image_to_scale = input_data.image_data
-        original_dims_wh = input_data.original_dimensions
+        current_dimensions_wh = input_data.original_dimensions # Dimensions of the image_to_scale
         scaling_mode = input_data.initial_scaling_mode
-        scaling_applied = False
-        final_image_data = image_to_scale # Default to original if no scaling happens
+        
+        output_resolution_key = input_data.resolution_key # Pass through the resolution key
 
         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
+            log.warning(f"{log_prefix}: Input image data is None or empty. Skipping POT scaling.")
             return InitialScalingOutput(
                 scaled_image_data=np.array([]),
                 scaling_applied=False,
-                final_dimensions=(0, 0)
+                final_dimensions=(0, 0),
+                resolution_key=output_resolution_key
             )
 
-        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)
+        if not current_dimensions_wh:
+            log.warning(f"{log_prefix}: Original dimensions not provided for POT scaling. Using current image shape.")
+            h_pre_pot_scale, w_pre_pot_scale = image_to_scale.shape[:2]
         else:
-            w_pre_scale, h_pre_scale = original_dims_wh
+            w_pre_pot_scale, h_pre_pot_scale = current_dimensions_wh
+        
+        final_image_data = image_to_scale # Default to original if no scaling happens
+        scaling_applied = False
 
+        # Skip POT scaling if the item is already a LOWRES variant or scaling mode is NONE
+        if output_resolution_key == "LOWRES":
+            log.info(f"{log_prefix}: Item is a 'LOWRES' variant. Skipping POT downscaling.")
+        elif scaling_mode == "NONE":
+            log.info(f"{log_prefix}: Mode is NONE. No POT scaling applied.")
+        elif scaling_mode == "POT_DOWNSCALE":
+            pot_w = ipu.get_nearest_power_of_two_downscale(w_pre_pot_scale)
+            pot_h = ipu.get_nearest_power_of_two_downscale(h_pre_pot_scale)
 
-        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
+            if (pot_w, pot_h) != (w_pre_pot_scale, h_pre_pot_scale):
+                log.info(f"{log_prefix}: Applying POT Downscale from ({w_pre_pot_scale},{h_pre_pot_scale}) to ({pot_w},{pot_h}).")
                 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.")
+                    log.debug(f"{log_prefix}: POT Downscale applied successfully.")
                 else:
-                    log.warning("Initial Scaling: POT Downscale resize failed. Using original data.")
-                    # final_image_data remains image_to_scale
+                    log.warning(f"{log_prefix}: POT Downscale resize failed. Using pre-POT-scaled data.")
             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
+                log.info(f"{log_prefix}: Image already POT or smaller. No POT scaling needed.")
         else:
-            log.warning(f"Initial Scaling: Unknown INITIAL_SCALING_MODE '{scaling_mode}'. Defaulting to NONE.")
-            # final_image_data remains image_to_scale
+            log.warning(f"{log_prefix}: Unknown INITIAL_SCALING_MODE '{scaling_mode}'. Defaulting to NONE (no scaling).")
 
         # Determine final dimensions
-        final_h, final_w = final_image_data.shape[:2]
-        final_dims_wh = (final_w, final_h)
+        if final_image_data is not None and final_image_data.size > 0:
+            final_h, final_w = final_image_data.shape[:2]
+            final_dims_wh = (final_w, final_h)
+        else:
+            final_dims_wh = (0,0)
+            if final_image_data is None: # Ensure it's an empty array for consistency if None
+                 final_image_data = np.array([])
 
         return InitialScalingOutput(
             scaled_image_data=final_image_data,
             scaling_applied=scaling_applied,
-            final_dimensions=final_dims_wh
+            final_dimensions=final_dims_wh,
+            resolution_key=output_resolution_key # Pass through the resolution key
         )

processing/pipeline/stages/metadata_initialization.py

@@ -85,6 +85,7 @@ class MetadataInitializationStage(ProcessingStage):
     merged_maps_details.
     """
     def execute(self, context: AssetProcessingContext) -> AssetProcessingContext:
+        logger.debug(f"METADATA_INIT_DEBUG: Entry - context.output_base_path = {context.output_base_path}") # Added
         """
         Executes the metadata initialization logic.
 
@@ -147,12 +148,15 @@ class MetadataInitializationStage(ProcessingStage):
         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
+        app_version_value = None
+        if context.config_obj and hasattr(context.config_obj, 'app_version'):
+            app_version_value = context.config_obj.app_version
+
+        if app_version_value:
+            context.asset_metadata['version'] = app_version_value
         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
+            logger.warning("App version not found using config_obj.app_version. Setting version to 'N/A'.")
+            context.asset_metadata['version'] = "N/A"
 
         if context.incrementing_value is not None:
             context.asset_metadata['incrementing_value'] = context.incrementing_value
@@ -170,4 +174,5 @@ class MetadataInitializationStage(ProcessingStage):
         # Example of how you might log the full metadata for debugging:
         # logger.debug(f"Initialized metadata: {context.asset_metadata}")
 
+        logger.debug(f"METADATA_INIT_DEBUG: Exit - context.output_base_path = {context.output_base_path}") # Added
         return context

processing/pipeline/stages/output_organization.py

@@ -17,8 +17,16 @@ class OutputOrganizationStage(ProcessingStage):
     """
 
     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}")
+        asset_name_for_log_early = context.asset_rule.asset_name if hasattr(context, 'asset_rule') and context.asset_rule else "Unknown Asset (early)"
+        log.info(f"OUTPUT_ORG_DEBUG: Stage execution started for asset '{asset_name_for_log_early}'.")
+        logger.debug(f"OUTPUT_ORG_DEBUG: Entry - context.output_base_path = {context.output_base_path}") # Modified
+        log.info(f"OUTPUT_ORG_DEBUG: Received context.config_obj.output_directory_base (raw from config) = {getattr(context.config_obj, 'output_directory_base', 'N/A')}")
+        # resolved_base = "N/A"
+        # if hasattr(context.config_obj, '_settings') and context.config_obj._settings.get('OUTPUT_BASE_DIR'):
+        #     base_dir_from_settings = context.config_obj._settings.get('OUTPUT_BASE_DIR')
+            # Path resolution logic might be complex
+        # log.info(f"OUTPUT_ORG_DEBUG: Received context.config_obj._settings.OUTPUT_BASE_DIR (resolved guess) = {resolved_base}")
+        log.info(f"OUTPUT_ORG_DEBUG: 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.
@@ -103,7 +111,9 @@ class OutputOrganizationStage(ProcessingStage):
                                 pattern_string=output_dir_pattern,
                                 token_data=token_data_variant_cleaned
                             )
+                            logger.debug(f"OUTPUT_ORG_DEBUG: Variants - Using context.output_base_path = {context.output_base_path} for final_variant_path construction.") # Added
                             final_variant_path = Path(context.output_base_path) / Path(relative_dir_path_str_variant) / Path(output_filename_variant)
+                            logger.debug(f"OUTPUT_ORG_DEBUG: Variants - Constructed final_variant_path = {final_variant_path}") # Added
                             final_variant_path.parent.mkdir(parents=True, exist_ok=True)
 
                             if final_variant_path.exists() and not overwrite_existing:
@@ -169,7 +179,9 @@ class OutputOrganizationStage(ProcessingStage):
                             pattern_string=output_dir_pattern,
                             token_data=token_data_cleaned
                         )
+                        logger.debug(f"OUTPUT_ORG_DEBUG: SingleFile - Using context.output_base_path = {context.output_base_path} for final_path construction.") # Added
                         final_path = Path(context.output_base_path) / Path(relative_dir_path_str) / Path(output_filename)
+                        logger.debug(f"OUTPUT_ORG_DEBUG: SingleFile - Constructed final_path = {final_path}") # Added
                         final_path.parent.mkdir(parents=True, exist_ok=True)
 
                         if final_path.exists() and not overwrite_existing:
@@ -245,10 +257,12 @@ class OutputOrganizationStage(ProcessingStage):
                             token_data=base_token_data_cleaned
                         )
                         # Destination: <output_base_path>/<asset_base_output_dir_str>/<extra_subdir_name>/<original_filename>
+                        logger.debug(f"OUTPUT_ORG_DEBUG: ExtraFiles - Using context.output_base_path = {context.output_base_path} for final_dest_path construction.") # Added
                         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
+                        logger.debug(f"OUTPUT_ORG_DEBUG: ExtraFiles - Constructed final_dest_path = {final_dest_path}") # Added
 
                         final_dest_path.parent.mkdir(parents=True, exist_ok=True)
 

processing/pipeline/stages/prepare_processing_items.py

@@ -1,21 +1,69 @@
 import logging
-from typing import List, Union, Optional
+from typing import List, Union, Optional, Tuple, Dict # Added Dict
+from pathlib import Path # Added Path
 
 from .base_stage import ProcessingStage
 from ..asset_context import AssetProcessingContext, MergeTaskDefinition
-from rule_structure import FileRule # Assuming FileRule is imported correctly
+from rule_structure import FileRule, ProcessingItem # Added ProcessingItem
+from processing.utils import image_processing_utils as ipu # Added ipu
 
 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.
+    Identifies and prepares a unified list of ProcessingItem and MergeTaskDefinition objects
+    to be processed in subsequent stages. Performs initial validation and explodes
+    FileRules into specific ProcessingItems for each required output variant.
     """
 
+    def _get_target_resolutions(self, source_w: int, source_h: int, config_resolutions: dict, file_rule: FileRule) -> Dict[str, int]:
+        """
+        Determines the target output resolutions for a given source image.
+        Placeholder logic: Uses all config resolutions smaller than or equal to source, plus PREVIEW if smaller.
+        Needs to be refined to consider FileRule.resolution_override and actual project requirements.
+        """
+        # For now, very basic logic:
+        # If FileRule has a resolution_override (e.g., (1024,1024)), that might be the *only* target.
+        # This needs to be clarified. Assuming override means *only* that size.
+        if file_rule.resolution_override and isinstance(file_rule.resolution_override, tuple) and len(file_rule.resolution_override) == 2:
+            # How to get a "key" for an arbitrary override? For now, skip if overridden.
+            # This part of the design (how overrides interact with standard resolutions) is unclear.
+            # Let's assume for now that if resolution_override is set, we don't generate standard named resolutions.
+            # This is likely incorrect for a full implementation.
+            log.warning(f"FileRule '{file_rule.file_path}' has resolution_override. Standard resolution key generation skipped (needs design refinement).")
+            return {}
+
+
+        target_res = {}
+        max_source_dim = max(source_w, source_h)
+
+        for key, res_val in config_resolutions.items():
+            if key == "PREVIEW": # Always consider PREVIEW if its value is smaller
+                if res_val < max_source_dim : # Or just always include PREVIEW? For now, if smaller.
+                     target_res[key] = res_val
+            elif res_val <= max_source_dim:
+                target_res[key] = res_val
+        
+        # Ensure PREVIEW is included if it's defined and smaller than the smallest other target, or if no other targets.
+        # This logic is still a bit naive.
+        if "PREVIEW" in config_resolutions and config_resolutions["PREVIEW"] < max_source_dim:
+            if not target_res or config_resolutions["PREVIEW"] < min(v for k,v in target_res.items() if k != "PREVIEW" and isinstance(v,int)):
+                 target_res["PREVIEW"] = config_resolutions["PREVIEW"]
+        elif "PREVIEW" in config_resolutions and not target_res : # if only preview is applicable
+             if config_resolutions["PREVIEW"] <= max_source_dim:
+                  target_res["PREVIEW"] = config_resolutions["PREVIEW"]
+
+
+        if not target_res and max_source_dim > 0 : # If no standard res is smaller, but image exists
+             log.debug(f"No standard resolutions from config are <= source dimension {max_source_dim}. Only LOWRES (if applicable) or PREVIEW (if smaller) might be generated.")
+        
+        log.debug(f"Determined target resolutions for source {source_w}x{source_h}: {target_res}")
+        return target_res
+
+
     def execute(self, context: AssetProcessingContext) -> AssetProcessingContext:
         """
-        Populates context.processing_items with FileRule and MergeTaskDefinition objects.
+        Populates context.processing_items with ProcessingItem 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...")
@@ -25,72 +73,135 @@ class PrepareProcessingItemsStage(ProcessingStage):
             context.processing_items = []
             return context
 
-        items_to_process: List[Union[FileRule, MergeTaskDefinition]] = []
+        # Output list will now be List[Union[ProcessingItem, MergeTaskDefinition]]
+        items_to_process: List[Union[ProcessingItem, MergeTaskDefinition]] = []
         preparation_failed = False
+        config = context.config_obj
 
-        # --- Add regular files ---
+        # --- Process FileRules into ProcessingItems ---
         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.")
+                log.error(f"Asset '{asset_name_for_log}': SourceRule or SourceRule.input_path is not set.")
                 source_path_valid = False
-                preparation_failed = True # Mark as failed if source path is missing
+                preparation_failed = True
                 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.")
+                 log.error(f"Asset '{asset_name_for_log}': Workspace path '{context.workspace_path}' is invalid.")
                  source_path_valid = False
-                 preparation_failed = True # Mark as failed if workspace path is bad
+                 preparation_failed = True
                  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
+                    log_prefix_fr = f"Asset '{asset_name_for_log}', FileRule '{file_rule.file_path}'"
                     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.")
+                         log.warning(f"{log_prefix_fr}: Skipping FileRule with empty file_path.")
+                         continue
+                    
+                    item_type = file_rule.item_type_override or file_rule.item_type
+                    if not item_type or item_type == "EXTRA" or not item_type.startswith("MAP_"):
+                        log.debug(f"{log_prefix_fr}: Item type is '{item_type}'. Not creating map ProcessingItems.")
+                        # Optionally, create a different kind of ProcessingItem for EXTRAs if they need pipeline processing
+                        continue
+
+                    source_image_path = context.workspace_path / file_rule.file_path
+                    if not source_image_path.is_file():
+                        log.error(f"{log_prefix_fr}: Source image file not found at '{source_image_path}'. Skipping this FileRule.")
+                        preparation_failed = True # Individual file error can contribute to overall stage failure
+                        context.status_flags.setdefault('prepare_items_file_errors', []).append(str(source_image_path))
+                        continue
+                    
+                    # Load image data to get dimensions and for LOWRES variant
+                    # This data will be passed to subsequent stages via ProcessingItem.
+                    # Consider caching this load if RegularMapProcessorStage also loads.
+                    # For now, load here as dimensions are needed for LOWRES decision.
+                    log.debug(f"{log_prefix_fr}: Loading image from '{source_image_path}' to determine dimensions and prepare items.")
+                    source_image_data = ipu.load_image(str(source_image_path))
+                    if source_image_data is None:
+                        log.error(f"{log_prefix_fr}: Failed to load image from '{source_image_path}'. Skipping this FileRule.")
+                        preparation_failed = True
+                        context.status_flags.setdefault('prepare_items_file_errors', []).append(f"Failed to load {source_image_path}")
+                        continue
+                    
+                    orig_h, orig_w = source_image_data.shape[:2]
+                    original_dimensions_wh = (orig_w, orig_h)
+                    source_bit_depth = ipu.get_image_bit_depth(str(source_image_path)) # Get bit depth from file
+                    source_channels = ipu.get_image_channels(source_image_data)
 
 
-        # --- 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)
+                    # Determine standard resolutions to generate
+                    # This logic needs to be robust and consider file_rule.resolution_override, etc.
+                    # Using a placeholder _get_target_resolutions for now.
+                    target_resolutions = self._get_target_resolutions(orig_w, orig_h, config.image_resolutions, file_rule)
 
+                    for res_key, _res_val in target_resolutions.items():
+                        pi = ProcessingItem(
+                            source_file_info_ref=str(source_image_path), # Using full path as ref
+                            map_type_identifier=item_type,
+                            resolution_key=res_key,
+                            image_data=source_image_data.copy(), # Give each PI its own copy
+                            original_dimensions=original_dimensions_wh,
+                            current_dimensions=original_dimensions_wh,
+                            bit_depth=source_bit_depth,
+                            channels=source_channels,
+                            status="Pending"
+                        )
+                        items_to_process.append(pi)
+                        log.debug(f"{log_prefix_fr}: Created standard ProcessingItem: {pi.map_type_identifier}_{pi.resolution_key}")
+
+                    # Create LOWRES variant if applicable
+                    if config.enable_low_resolution_fallback and max(orig_w, orig_h) < config.low_resolution_threshold:
+                        # Check if a LOWRES item for this source_file_info_ref already exists (e.g. if target_resolutions was empty)
+                        # This check is important if _get_target_resolutions might return empty for small images.
+                        # A more robust way is to ensure LOWRES is distinct from standard resolutions.
+                        
+                        # Avoid duplicate LOWRES if _get_target_resolutions somehow already made one (unlikely with current placeholder)
+                        is_lowres_already_added = any(p.resolution_key == "LOWRES" and p.source_file_info_ref == str(source_image_path) for p in items_to_process if isinstance(p, ProcessingItem))
+
+                        if not is_lowres_already_added:
+                            pi_lowres = ProcessingItem(
+                                source_file_info_ref=str(source_image_path),
+                                map_type_identifier=item_type,
+                                resolution_key="LOWRES",
+                                image_data=source_image_data.copy(), # Fresh copy for LOWRES
+                                original_dimensions=original_dimensions_wh,
+                                current_dimensions=original_dimensions_wh,
+                                bit_depth=source_bit_depth,
+                                channels=source_channels,
+                                status="Pending"
+                            )
+                            items_to_process.append(pi_lowres)
+                            log.info(f"{log_prefix_fr}: Created LOWRES ProcessingItem because {orig_w}x{orig_h} < {config.low_resolution_threshold}px threshold.")
+                        else:
+                            log.debug(f"{log_prefix_fr}: LOWRES item for this source already added by target resolution logic. Skipping duplicate LOWRES creation.")
+                    elif config.enable_low_resolution_fallback:
+                         log.debug(f"{log_prefix_fr}: Image {orig_w}x{orig_h} not below LOWRES threshold {config.low_resolution_threshold}px.")
+
+
+            else: # Source path not valid
+                 log.warning(f"Asset '{asset_name_for_log}': Skipping creation of ProcessingItems from FileRules due to invalid source/workspace path.")
+
+        # --- Add MergeTaskDefinitions --- (This part remains largely the same)
+        merged_tasks_list = getattr(config, '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}")
+                        log.warning(f"Asset '{asset_name_for_log}', Task Index {task_idx}: Skipping merge task due to missing 'output_map_type' or valid 'inputs'. Task data: {task_data}")
+                        continue
                     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)
+                    log.info(f"Asset '{asset_name_for_log}': Added MergeTaskDefinition: Key='{merge_def.task_key}', OutputType='{merge_def.task_data.get('output_map_type', 'N/A')}'")
                 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.")
+                    log.warning(f"Asset '{asset_name_for_log}': Item at index {task_idx} in config.map_merge_rules is not a dict. Skipping. Item: {task_data}")
+        # ... (rest of merge task handling) ...
 
+        if not items_to_process and not preparation_failed: # Check preparation_failed too
+             log.info(f"Asset '{asset_name_for_log}': No valid items (ProcessingItem or MergeTaskDefinition) found to process.")
 
-        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
 

processing/pipeline/stages/regular_map_processor.py

@@ -37,7 +37,7 @@ class RegularMapProcessorStage(ProcessingStage):
         """
         final_internal_map_type = initial_internal_map_type # Default
 
-        base_map_type_match = re.match(r"(MAP_[A-Z]{3})", initial_internal_map_type)
+        base_map_type_match = re.match(r"(MAP_[A-Z]+)", 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
 
@@ -47,7 +47,7 @@ class RegularMapProcessorStage(ProcessingStage):
         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)
+            fr_asset_base_match = re.match(r"(MAP_[A-Z]+)", 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)
 
@@ -197,10 +197,17 @@ class RegularMapProcessorStage(ProcessingStage):
             result.final_internal_map_type = final_map_type # Update if Gloss->Rough changed it
             result.transformations_applied = transform_notes
 
+            # --- Determine Resolution Key for LOWRES ---
+            if config.enable_low_resolution_fallback and result.original_dimensions:
+                w, h = result.original_dimensions
+                if max(w, h) < config.low_resolution_threshold:
+                    result.resolution_key = "LOWRES"
+                    log.info(f"{log_prefix}: Image dimensions ({w}x{h}) are below threshold ({config.low_resolution_threshold}px). Flagging as LOWRES.")
+
             # --- Success ---
             result.status = "Processed"
             result.error_message = None
-            log.info(f"{log_prefix}: Successfully processed regular map. Final type: '{result.final_internal_map_type}'.")
+            log.info(f"{log_prefix}: Successfully processed regular map. Final type: '{result.final_internal_map_type}', ResolutionKey: {result.resolution_key}.")
 
         except Exception as e:
             log.exception(f"{log_prefix}: Unhandled exception during processing: {e}")

processing/pipeline/stages/save_variants.py

@@ -23,8 +23,17 @@ class SaveVariantsStage(ProcessingStage):
         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})"
+        # The input_data for SaveVariantsStage doesn't directly contain the ProcessingItem.
+        # It receives data *derived* from a ProcessingItem by previous stages.
+        # For debugging, we'd need to pass more context or rely on what's in output_filename_pattern_tokens.
+        resolution_key_from_tokens = input_data.output_filename_pattern_tokens.get('resolution', 'UnknownResKey')
+        log_prefix = f"Save Variants Stage (Type: {internal_map_type}, ResKey: {resolution_key_from_tokens})"
+        
         log.info(f"{log_prefix}: Starting.")
+        log.debug(f"{log_prefix}: Input image_data shape: {input_data.image_data.shape if input_data.image_data is not None else 'None'}")
+        log.debug(f"{log_prefix}: Input source_bit_depth_info: {input_data.source_bit_depth_info}")
+        log.debug(f"{log_prefix}: Configured image_resolutions for saving: {input_data.image_resolutions}")
+        log.debug(f"{log_prefix}: Output filename pattern tokens: {input_data.output_filename_pattern_tokens}")
 
         # Initialize output object with default failure state
         result = SaveVariantsOutput(
@@ -64,11 +73,11 @@ class SaveVariantsStage(ProcessingStage):
                 "resolution_threshold_for_jpg": input_data.resolution_threshold_for_jpg, # Added
             }
 
-            log.debug(f"{log_prefix}: Calling save_image_variants utility.")
+            log.debug(f"{log_prefix}: Calling save_image_variants utility with args: {save_args}")
             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.")
+                log.info(f"{log_prefix}: Save utility completed successfully. Saved {len(saved_files_details)} variants: {[details.get('filepath') for details in saved_files_details]}")
                 result.saved_files_details = saved_files_details
                 result.status = "Processed"
                 result.error_message = None

processing/utils/image_processing_utils.py

@@ -194,6 +194,16 @@ def get_image_bit_depth(image_path_str: str) -> Optional[int]:
         print(f"Error getting bit depth for {image_path_str}: {e}")
         return None
 
+def get_image_channels(image_data: np.ndarray) -> Optional[int]:
+    """Determines the number of channels in an image."""
+    if image_data is None:
+        return None
+    if len(image_data.shape) == 2: # Grayscale
+        return 1
+    elif len(image_data.shape) == 3: # Color
+        return image_data.shape[2]
+    return None # Unknown shape
+
 def calculate_image_stats(image_data: np.ndarray) -> Optional[Dict]:
     """
     Calculates min, max, mean for a given numpy image array.

projectBrief.md

@@ -0,0 +1,44 @@
+# Project Brief: Asset Processor Tool
+
+## 1. Main Goal & Purpose
+
+The primary goal of the Asset Processor Tool is to provide **CG artists and 3D content teams with a friendly, fast, and flexible interface to process and organize 3D asset source files into a standardized library format.** It automates repetitive and complex tasks involved in preparing assets from various suppliers for use in production pipelines.
+
+## 2. Key Features & Components
+
+*   **Automated Asset Processing:** Ingests 3D asset source files (texture sets, models, etc.) from `.zip`, `.rar`, `.7z` archives, or folders.
+*   **Preset-Driven Workflow:** Utilizes configurable JSON presets to interpret different asset sources (e.g., from various online vendors or internal standards), defining rules for file classification and processing.
+*   **Comprehensive File Operations:**
+    *   **Classification:** Automatically identifies map types (Color, Normal, Roughness, etc.), models, and other file categories based on preset rules.
+    *   **Image Processing:** Performs tasks like image resizing (to standard resolutions like 1K, 2K, 4K, avoiding upscaling), glossiness-to-roughness conversion, normal map green channel inversion (OpenGL/DirectX handling), alpha channel extraction, bit-depth adjustments, and low-resolution fallback generation for small source images.
+    *   **Channel Merging:** Combines channels from different source maps into packed textures (e.g., Normal + Roughness + Metallic into a single NRMRGH map).
+*   **Metadata Generation:** Creates a detailed `metadata.json` file for each processed asset, containing information about maps, categories, processing settings, and more, for downstream tool integration.
+*   **Flexible Output Organization:** Generates a clean, structured output directory based on user-configurable naming patterns and tokens.
+*   **Multiple User Interfaces:**
+    *   **Graphical User Interface (GUI):** The primary interface, designed to be user-friendly, offering drag-and-drop functionality, an integrated preset editor, a live preview table for rule validation and overrides, and clear processing controls.
+    *   **Directory Monitor:** An automated script that watches a specified folder for new asset archives and processes them based on preset names embedded in the archive filename.
+    *   **Command-Line Interface (CLI):** Intended for batch processing and scripting (currently with limited core functionality).
+*   **Optional Blender Integration:** Can automatically run Blender scripts post-processing to create PBR node groups and materials in specified `.blend` files, linking to the newly processed textures.
+*   **Hierarchical Rule System:** Allows for dynamic, granular overrides of preset configurations at the source, asset, or individual file level via the GUI.
+*   **Experimental LLM Prediction:** Includes an option to use a Large Language Model for file interpretation and rule prediction.
+
+## 3. Target Audience
+
+*   **CG Artists:** Individual artists looking for an efficient way to manage and prepare their personal or downloaded asset libraries.
+*   **3D Content Creation Teams:** Studios or groups needing a standardized pipeline for processing and organizing assets from multiple sources.
+*   **Technical Artists/Pipeline Developers:** Who may extend or integrate the tool into broader production workflows.
+
+## 4. Overall Architectural Style & Key Technologies
+
+*   **Core Language:** Python
+*   **GUI Framework:** PySide6
+*   **Configuration:** Primarily JSON-based (application settings, user overrides, type definitions, supplier settings, presets, LLM settings).
+*   **Processing Architecture:** A modular, staged processing pipeline orchestrated by a central engine. Each stage performs a discrete task on an `AssetProcessingContext` object.
+*   **Key Libraries:** OpenCV (image processing), NumPy (numerical operations), py7zr/rarfile (archive handling), watchdog (directory monitoring).
+*   **Design Principles:** Modularity, configurability, and user-friendliness (especially for the GUI).
+
+## 5. Foundational Information
+
+*   The tool aims to significantly reduce manual effort and ensure consistency in asset preparation.
+*   It is designed to be adaptable to various asset sources and pipeline requirements through its extensive configuration options and preset system.
+*   The output `metadata.json` is key for enabling further automation and integration with other tools or digital content creation (DCC) applications.

rule_structure.py

@@ -1,6 +1,7 @@
 import dataclasses
 import json
 from typing import List, Dict, Any, Tuple, Optional
+import numpy as np # Added for ProcessingItem
 @dataclasses.dataclass
 class FileRule:
     file_path: str = None
@@ -10,8 +11,12 @@ class FileRule:
     resolution_override: Tuple[int, int] = None
     channel_merge_instructions: Dict[str, Any] = dataclasses.field(default_factory=dict)
     output_format_override: str = None
+    processing_items: List['ProcessingItem'] = dataclasses.field(default_factory=list) # Added field
 
     def to_json(self) -> str:
+        # Need to handle ProcessingItem serialization if it contains non-serializable types like np.ndarray
+        # For now, assume asdict handles it or it's handled before calling to_json for persistence.
+        # A custom asdict_factory might be needed for robust serialization.
         return json.dumps(dataclasses.asdict(self), indent=4)
 
     @classmethod
@@ -54,4 +59,43 @@ class SourceRule:
         data = json.loads(json_string)
         # Manually deserialize nested AssetRule objects
         data['assets'] = [AssetRule.from_json(json.dumps(asset_data)) for asset_data in data.get('assets', [])]
-        return cls(**data)
+        # Need to handle ProcessingItem deserialization if it was serialized
+        # For now, from_json for FileRule doesn't explicitly handle processing_items from JSON.
+        return cls(**data)
+
+@dataclasses.dataclass
+class ProcessingItem:
+    """
+    Represents a specific version of an image map to be processed and saved.
+    This could be a standard resolution (1K, 2K), a preview, or a special
+    variant like 'LOWRES'.
+    """
+    source_file_info_ref: str  # Reference to the original SourceFileInfo or unique ID of the source image
+    map_type_identifier: str   # The internal map type (e.g., "MAP_COL", "MAP_ROUGH")
+    resolution_key: str        # The resolution identifier (e.g., "1K", "PREVIEW", "LOWRES")
+    image_data: np.ndarray     # The actual image data for this item
+    original_dimensions: Tuple[int, int] # (width, height) of the source image for this item
+    current_dimensions: Tuple[int, int]  # (width, height) of the image_data in this item
+    target_filename: str = ""  # Will be populated by SaveVariantsStage
+    is_extra: bool = False     # If this item should be treated as an 'extra' file
+    bit_depth: Optional[int] = None
+    channels: Optional[int] = None
+    file_extension: Optional[str] = None # Determined during saving based on format
+    processing_applied_log: List[str] = dataclasses.field(default_factory=list)
+    status: str = "Pending" # e.g., Pending, Processed, Failed
+    error_message: Optional[str] = None
+
+    # __getstate__ and __setstate__ might be needed if we pickle these objects
+    # and np.ndarray causes issues. For JSON, image_data would typically not be serialized.
+    def __getstate__(self):
+        state = self.__dict__.copy()
+        # Don't pickle image_data if it's large or not needed for state
+        if 'image_data' in state: # Or a more sophisticated check
+            del state['image_data'] # Example: remove it
+        return state
+
+    def __setstate__(self, state):
+        self.__dict__.update(state)
+        # Potentially re-initialize or handle missing 'image_data'
+        if 'image_data' not in self.__dict__:
+             self.image_data = None # Or load it if a path was stored instead