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| # Image Preprocessing Module (Step 4) | |
| ## Overview | |
| The image preprocessing module enhances facial detection and emotion analysis by improving image quality before processing. This feature allows users to: | |
| 1. View a side-by-side comparison of original vs. processed images | |
| 2. Choose whether to use the original or improved image for analysis | |
| 3. Understand the technical benefits of preprocessing for facial analysis | |
| ## Key Components | |
| ### 1. Image Service (`services/image_service.py`) | |
| The core preprocessing functionality is implemented in the ImageService class: | |
| - **`preprocess_image()`**: Applies multiple image enhancements and returns both original and processed images | |
| - **`resize_image()`**: Resizes images to optimal dimensions while maintaining aspect ratio | |
| - **`adjust_brightness()`**: Increases or decreases image brightness based on analysis | |
| - **`adjust_contrast()`**: Adapts image contrast for better facial feature visibility | |
| - **`save_processed_image()`**: Saves processed images to temporary storage | |
| ### 2. Preprocessing UI (`utils/preprocessing_ui.py`) | |
| A modular UI component that handles the preprocessing interface: | |
| - **`display_preprocessing_comparison()`**: Shows side-by-side comparison of original vs. processed images | |
| - **`setup_preprocessing_controls()`**: Provides buttons for selecting which image to use | |
| - **`display_processing_status()`**: Shows status indicators for the current image selection | |
| - **`get_processing_image()`**: Retrieves the appropriate image based on user selection | |
| ### 3. Visual Agent Integration (`agent_framework/visual_agent.py`) | |
| The VisualAgent has been extended to support preprocessed images: | |
| - Updated `process()` method to handle preprocessed image paths | |
| - Added support for switching between original and processed images | |
| ## Technical Benefits for Facial Analysis | |
| The preprocessing steps provide several benefits for facial detection and emotion analysis: | |
| 1. **Balanced contrast**: Enhances visibility of facial features while reducing shadows and highlights | |
| 2. **Optimal brightness**: Ensures facial features are clearly distinguishable without over-exposure | |
| 3. **Proper sizing**: Maintains ideal dimensions for detection algorithms to recognize facial landmarks | |
| These improvements result in: | |
| - More accurate emotion classification | |
| - Better feature extraction (eyes, mouth, eyebrows) | |
| - Reduced noise and artifacts | |
| - More consistent performance across different lighting conditions | |
| ## Usage Flow | |
| 1. User uploads an image | |
| 2. System analyzes and applies appropriate preprocessing | |
| 3. User is shown a comparison of original vs. improved images | |
| 4. User selects which image to use for analysis | |
| 5. Analysis proceeds with the selected image | |
| 6. Status indicators keep the user informed about which image is being used | |
| The preprocessing is adaptive, applying only the enhancements needed for each specific image. | |