Adding Report

README.MD

@@ -1,6 +1,82 @@
-# 3D Person Segmentation App
+# 3D Person Segmentation and Anaglyph Generation
 
-This app segments a person from an image using SegFormer and creates a 3D red-cyan anaglyph image.
+## Lab Report
+
+### Introduction
+This project implements a sophisticated 3D image processing system that combines person segmentation with stereoscopic and anaglyph image generation. The main objectives were to:
+1. Accurately segment people from images using advanced AI models
+2. Generate stereoscopic 3D effects from 2D images
+3. Create red-cyan anaglyph images for 3D viewing
+4. Provide an interactive web interface for real-time processing
+
+### Methodology
+
+#### Tools and Technologies Used
+- **SegFormer (nvidia/segformer-b0)**: State-of-the-art transformer-based model for semantic segmentation
+- **PyTorch**: Deep learning framework for running the SegFormer model
+- **OpenCV**: Image processing operations and mask refinement
+- **Gradio**: Web interface development
+- **NumPy**: Efficient array operations for image manipulation
+- **PIL (Python Imaging Library)**: Image loading and basic transformations
+
+#### Implementation Steps
+
+1. **Person Segmentation**
+   - Utilized SegFormer model fine-tuned on ADE20K dataset
+   - Applied post-processing with erosion and Gaussian blur for mask refinement
+   - Implemented mask scaling and centering for various input sizes
+
+2. **Stereoscopic Processing**
+   - Created depth simulation through horizontal pixel shifting
+   - Implemented parallel view stereo pair generation
+   - Added configurable interaxial distance for 3D effect adjustment
+
+3. **Anaglyph Generation**
+   - Combined left and right eye views into red-cyan anaglyph
+   - Implemented color channel separation and recombination
+   - Added background image support with proper masking
+
+4. **User Interface**
+   - Developed interactive web interface using Gradio
+   - Added real-time parameter adjustment capabilities
+   - Implemented support for custom background images
+
+### Results
+
+The system produces three main outputs:
+1. Segmentation mask showing the isolated person
+2. Side-by-side stereo pair for parallel viewing
+3. Red-cyan anaglyph image for 3D glasses viewing
+
+Key Features:
+- Adjustable person size (10-200%)
+- Configurable interaxial distance (0-10 pixels)
+- Optional custom background support
+- Real-time processing and preview
+
+### Discussion
+
+#### Technical Challenges
+1. **Mask Alignment**: Ensuring proper alignment between segmentation masks and background images required careful consideration of image dimensions and aspect ratios.
+2. **Stereo Effect Quality**: Balancing the interaxial distance for comfortable viewing while maintaining the 3D effect.
+3. **Performance Optimization**: Efficient processing of large images while maintaining real-time interaction.
+
+#### Learning Outcomes
+- Deep understanding of stereoscopic image generation
+- Experience with state-of-the-art segmentation models
+- Practical knowledge of image processing techniques
+- Web interface development for ML applications
+
+### Conclusion
+
+This project successfully demonstrates the integration of modern AI-powered segmentation with classical stereoscopic image processing techniques. The system provides an accessible way to create 3D effects from regular 2D images.
+
+#### Future Work
+- Implementation of depth-aware 3D effect generation
+- Support for video processing
+- Additional 3D viewing formats (side-by-side, over-under)
+- Enhanced background replacement options
+- Mobile device optimization
 
 ## Setup
 
@@ -8,10 +84,23 @@ This app segments a person from an image using SegFormer and creates a 3D red-cy
 pip install -r requirements.txt
 ```
 
-
 ## Usage
 
 ```bash
 cd src
 python app.py
-```
+```
+
+## Parameters
+
+- **Person Image**: Upload an image containing a person
+- **Background Image**: (Optional) Custom background image
+- **Interaxial Distance**: Adjust the 3D effect strength (0-10)
+- **Person Size**: Adjust the size of the person in the output (10-200%)
+
+## Output Types
+
+1. **Segmentation Mask**: Shows the isolated person
+2. **Stereo Pair**: Side-by-side stereo image for parallel viewing
+3. **Anaglyph**: Red-cyan 3D image viewable with anaglyph glasses
+