🗺️ Depth Map Generator

Transform your images into depth maps using AI! This tool analyzes photos and creates grayscale depth maps showing how far objects are from the camera.

🎯 What Does This Do?

This Python script takes regular photos and generates depth maps - images where brightness represents distance:

• Bright/White pixels = Objects close to the camera
• Dark/Black pixels = Objects far from the camera

Real-World Example:

Input Image:              Depth Map Output:
🏔️ Mountain (far)   →    🖤 Dark Gray
🌳 Trees (medium)   →    ⚪ Light Gray  
👤 Person (close)   →    ⬜ White

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🚀 How to Use

1️⃣ Install Requirements

First, install the necessary Python packages:

pip install -r requirements.txt

Or manually:

pip install torch torchvision opencv-python matplotlib

2️⃣ Set Your Paths

Open generate_depth.py and edit these lines:

dataset_folder = r"C:\your\images\folder"  # Your input images
output_folder = r"C:\your\output\folder"   # Where to save depth maps

3️⃣ Run the Script

python generate_depth.py

4️⃣ Wait and Done! ✨

The script will:

• Find all images in your input folder (including subfolders)
• Process each image using the MiDaS AI model
• Save depth maps with the same filename and folder structure
• Show progress in the terminal

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📊 What Gets Created?

Input/Output Structure:

📁 Input Folder                    📁 Output Folder
├── photo1.jpg           →         ├── photo1.png (depth map)
├── photo2.png           →         ├── photo2.png (depth map)
└── 📁 vacation                    └── 📁 vacation
    ├── beach.jpg        →             ├── beach.png (depth map)
    └── sunset.jpg       →             └── sunset.png (depth map)

Key Points:

• ✅ Preserves your folder structure
• ✅ Converts all formats to .png
• ✅ Same filename (original extension replaced)
• ✅ Normalized to 0-255 grayscale values

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🤖 Model Comparison: MiDaS_small vs DPT_Large

The script supports two AI models. Choose based on your needs:

Feature	MiDaS_small (Current)	DPT_Large
Speed	⚡ Very Fast	🐢 Slower (3-5x)
Accuracy	✅ Good	🎯 Excellent
GPU Memory	💚 Low (~2GB)	🔴 High (~8GB)
Best For	Batch processing thousands of images	High-quality, detailed depth maps
File Size	📦 ~100MB	📦 ~1.2GB

🔄 How to Switch Models:

Change line 11 in generate_depth.py:

# For speed:
model_type = "MiDaS_small"

# For accuracy:
model_type = "DPT_Large"

# For balance:
model_type = "DPT_Hybrid"

📈 Performance Example:

• 1000 images with MiDaS_small: ~15-20 minutes (with GPU)
• 1000 images with DPT_Large: ~60-90 minutes (with GPU)

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🛠️ How to Contribute & Improve

Want to make this tool even better? Here are some ideas:

🌟 Easy Contributions:

• Add a progress bar (using tqdm library)
• Create a GUI interface (using tkinter or gradio)
• Add batch size options for faster processing
• Include a config file instead of hardcoded paths
• Add colored depth maps (heatmap visualization)

🚀 Medium Difficulty:

• Support video depth map generation
• Add depth map quality comparison tool
• Implement multi-GPU support for faster processing
• Create before/after preview window
• Add command-line arguments (argparse)

💎 Advanced Features:

• 3D point cloud generation from depth maps
• Depth map refinement using multiple models
• Real-time webcam depth estimation
• Export to 3D formats (.obj, .ply)
• API/web service deployment

📝 How to Contribute:

1. Fork this repository
2. Create a feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

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🐛 Troubleshooting

❌ "CUDA out of memory"

Solution: Switch to MiDaS_small or process fewer images at once

❌ "Could not load image"

Solution: Check if the image file is corrupted or in an unsupported format

❌ Slow processing without GPU

Solution: Install CUDA-enabled PyTorch: https://pytorch.org/get-started/locally/

❌ Import errors

Solution: Make sure all dependencies are installed:

pip install --upgrade torch torchvision opencv-python matplotlib

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📄 License

This project uses the MiDaS model, which is released under the MIT license. Please refer to the MiDaS repository for more details.

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🙏 Acknowledgments

• Intel ISL for creating the MiDaS depth estimation model
• PyTorch for the deep learning framework
• OpenCV for image processing capabilities

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📞 Need Help?

• Check the MiDaS GitHub Issues
• Review PyTorch documentation for GPU setup
• Ensure your paths use raw strings (r"path\to\folder") on Windows

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