vid23d_scallop is a Python application designed to transform 2D video frames into 3D stereo images using depth estimation and stereo pair generation. This project leverages deep learning models for depth perception and computer vision techniques to create enhanced visual experiences.
- Depth map estimation from 2D images.
- Generation of stereo image pairs from single frames.
- Video processing to extract frames and concatenate them back to video format.
- Audio extraction and merging with processed video.
Before you begin, ensure you have the following installed:
- Python 3.8 or higher
- OpenCV
- Torch
- tqdm
- moviepy
- matplotlib (optional, for debugging depth maps)
Clone the repository to your local machine:
git clone https://github.com/yourusername/vid23d_scallop.git
cd vid23d_scallopInstall the required Python libraries:
pip install -r requirements.txtConfiguring Your Device
Edit conf.py to configure the processing device (CPU, CUDA, MPS):
import torch
device = 'cpu'
if torch.backends.mps.is_available():
device = torch.device('mps')
if torch.cuda.is_available():
device = torch.device('cuda')
print(f'Using device: {device}', flush=True)Use main.py to process videos through the command line. The script supports various operations including frame extraction, depth map creation, and stereo pair generation.
python main.py --input_video path/to/your/video.mp4 --output_dir path/to/outputimg_depth.py and stereo.py are used internally to generate depth maps and stereo pairs, but can be used standalone for testing and development purposes.
Contributions are welcome! Please fork the repository and submit a pull request with your new features or fixes.
This project is licensed under the MIT License - see the LICENSE file for details.
Our work uses code from MiDaS. We'd like to thank the authors for making this library available.