Update README.md

README.md

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+# Wound Analysis LE
+
+## 🩹 Project Overview
+Wound Analysis LE is an advanced medical imaging tool for automated wound assessment using deep learning. It provides:
+- **Wound classification** (type identification)
+- **Depth estimation** (3D wound structure)
+- **Segmentation** (precise wound area extraction)
+- **Severity analysis** (quantitative and AI-powered clinical assessment)
+
+The system is built for research and educational purposes, integrating state-of-the-art computer vision models and a user-friendly Gradio interface.
+
+---
+
+## 🚀 Features & Workflow
+1. **Wound Classification**: Identifies wound type using a vision transformer model.
+2. **Depth Estimation**: Generates depth maps and 3D visualizations from 2D images using DepthAnythingV2 (DINOv2 + DPT).
+3. **Segmentation**: Extracts wound regions using deep learning models (Deeplabv3+, FCN, SegNet, Unet).
+4. **Severity Analysis**: Computes wound area, depth, volume, and provides AI-powered medical assessment (Gemini AI integration).
+5. **Interactive Gradio App**: Step-by-step workflow with visualization, overlays, and downloadable results.
+
+---
+
+## 🏗️ Model Architecture
+### Segmentation Models
+- **Deeplabv3+**: Encoder-decoder with atrous convolutions for semantic segmentation.
+- **FCN (VGG16-16s)**: Fully convolutional network for pixel-wise segmentation.
+- **SegNet**: Encoder-decoder architecture for efficient segmentation.
+- **Unet (multiple variants)**: U-shaped architecture for biomedical image segmentation.
+
+### Depth Estimation
+- **DepthAnythingV2**: Combines DINOv2 vision transformer backbone with DPT head for monocular depth prediction.
+- **DINOv2**: Self-supervised vision transformer for feature extraction.
+- **DPT**: Dense Prediction Transformer for pixel-wise depth estimation.
+
+### Classification
+- **Vision Transformer (ViT)**: Used for wound type classification (via HuggingFace Transformers).
+
+---
+
+## 🛠️ Installation & Requirements
+1. **Clone the repository**
+   ```bash
+   git clone <repo-url>
+   cd Wound-Analysis-LE
+   ```
+2. **Install dependencies**
+   ```bash
+   pip install -r requirements.txt
+   ```
+   - Key dependencies: `gradio`, `torch`, `tensorflow`, `opencv-python`, `transformers`, `open3d`, `plotly`, `google-generativeai`, etc.
+3. **Download model weights**
+   - The app will auto-download required weights (e.g., DINOv2, segmentation models) on first run if not present.
+
+---
+
+## 💻 Usage Instructions
+### Run the Gradio App
+```bash
+python app.py
+```
+- Access the app at: [http://localhost:7860](http://localhost:7860)
+
+### Segmentation Tool (Standalone)
+```bash
+python temp_files/segmentation_app.py
+```
+
+### Workflow
+1. **Upload a wound image**
+2. **Classify**: Get wound type and initial AI analysis
+3. **Depth Estimation**: Generate depth map and 3D visualization
+4. **Segmentation**: Auto-segment wound area
+5. **Severity Analysis**: Quantitative and AI-powered report
+6. **Download**: Export masks, overlays, and 3D data
+
+---
+
+## 📊 Training & Evaluation
+- **Training scripts**: See `temp_files/train.py`
+- **Metrics**: Dice coefficient, precision, recall, loss (see `utils/learning/metrics.py`)
+- **Results**: Training history and model checkpoints in `training_history/`
+  - Example: Dice coefficient > 0.98 on training set (see `2025-08-07_16-25-27.json`)
+
+---
+
+## 📁 Code Structure
+- `app.py` — Main Gradio app (classification, depth, segmentation, severity)
+- `models/` — Segmentation model definitions (Deeplab, FCN, SegNet, Unet)
+- `depth_anything_v2/` — Depth estimation (DINOv2, DPT, utility layers)
+- `utils/` — Data loading, augmentation, metrics, postprocessing
+- `temp_files/` — Standalone scripts, experiments, and legacy tools
+- `training_history/` — Model checkpoints and training logs
+
+---
+
+## 📚 References
+- [Deeplabv3+ Paper](https://arxiv.org/pdf/1802.02611.pdf)
+- [DINOv2 (Meta AI)](https://github.com/facebookresearch/dinov2)
+- [DPT: Vision Transformers for Dense Prediction](https://arxiv.org/abs/2103.13413)
+- [HuggingFace Transformers](https://huggingface.co/docs/transformers/index)
+- [Gradio](https://gradio.app/)
+- [Open3D](http://www.open3d.org/)
+- [Augmentor](https://github.com/mdbloice/Augmentor)
+- Datasets: Custom wound datasets (not included)
+
+---
+
+## ⚠️ Disclaimer
+This tool is for research and educational purposes only. It does **not** provide medical advice or diagnosis. Always consult a medical professional for clinical decisions.
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference