README-PREPROCESSING.md

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.