https://github.com/luis-kr/depthmap
Depth map estimation tool using Depth-Anything-V2. Generate accurate depth maps from images with support for both relative and metric depth measurements.
https://github.com/luis-kr/depthmap
cuda depth-anything depth-estimation depth-map image-processing python pytorch
Last synced: 3 months ago
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Depth map estimation tool using Depth-Anything-V2. Generate accurate depth maps from images with support for both relative and metric depth measurements.
- Host: GitHub
- URL: https://github.com/luis-kr/depthmap
- Owner: Luis-Kr
- License: mit
- Created: 2024-11-06T12:03:39.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2024-11-14T08:19:30.000Z (over 1 year ago)
- Last Synced: 2025-01-14T11:17:49.219Z (over 1 year ago)
- Topics: cuda, depth-anything, depth-estimation, depth-map, image-processing, python, pytorch
- Language: Python
- Homepage:
- Size: 22.7 MB
- Stars: 0
- Watchers: 2
- Forks: 2
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
A Python workflow for depth map estimation using [Depth-Anything](https://github.com/DepthAnything/Depth-Anything-V2?tab=readme-ov-file). This tool processes images to generate depth maps using the Depth-Anything-V2 model, providing both visual depth maps and numerical depth data. It automatically handles model downloading, environment setup, and provides an easy-to-use interface for batch processing images.
## Features
- Automatic download of pre-trained Depth-Anything-V2 models (large version only)
- Batch processing of images
- Outputs both visual depth maps and raw depth data
- Statistical analysis of depth values
- CSV export of depth statistics
## Installation
Make sure you have Conda installed. If not, download it from [Miniconda](https://docs.anaconda.com/miniconda/)
To set up the project, follow these steps:
```bash
# Clone the repository
git clone https://github.com/Luis-Kr/DepthMap.git
# Navigate into the directory
cd DepthMap
# Run setup script (this will download large models >2GB)
python setup.py
```
## Usage
Run the depth estimation with default settings:
```bash
# Activate the conda environment
conda activate depthmap
# Run the main script to create the depth maps with the default configurations
python -m DepthMap.main
```
### Mac-specific issues (Apple Silicon)
I case the error message `Unexpected error: The operator 'aten::upsample_bicubic2d.out' is not currently implemented for the MPS device` appears when running the script. This occurs because PyTorch's Metal Performance Shaders (MPS) backend for Apple Silicon doesn't yet support all operations. To work around this:
```bash
# Set this environment variable before running
export PYTORCH_ENABLE_MPS_FALLBACK=1
# Or: alternatively, you can add this line to your .zshrc or .bash_profile file to make it persistent across terminal sessions:
echo 'export PYTORCH_ENABLE_MPS_FALLBACK=1' >> ~/.zshrc #same applies for .bash_profile
source ~/.zshrc
```
**Note**: Using this fallback will cause these specific operations to run on CPU instead of GPU, which may result in slightly slower performance.
### Configuration
The default configuration can be found in config/main.yaml. You can override any of these settings via command line:
- `input_folder`: Directory containing input images
- `output.dir`: Directory for saving depth maps
- `depth_measurement`: Choose between "relative" or "metric" depth
- `relative`: Provides relative depth values (better for general use)
- `metric`: Fine-tuned version that attempts to predict actual depth in meters
- `encoder`: Model encoder type (currently only "vitl" supported)
- `dataset`: For metric depth, choose between:
- `hypersim`: Optimized for indoor scenes
- `vkitti`: Optimized for outdoor scenes
- `max_depth`: Maximum depth (in meters) to be expected in the input image (only for metric depth)
Example with multiple parameters:
```bash
python -m DepthMap.main \
input_folder=my_images \
output.dir=results \
depth_measurement=metric \
dataset=hypersim \
max_depth=10
```
## Output
The tool generates:
1. Colored depth maps in `/images/`
2. Raw depth arrays in `/arrays/`
3. CSV file with depth statistics including:
- 5th percentile depth
- 95th percentile depth
- Mean depth
- Median depth
## Requirements
- Python 3.8+
- CUDA-capable GPU (recommended)
- Conda package manager
## License
This project has two licensing components:
1. **Depth-Anything Model**: The Depth-Anything model is governed by its own license terms. You must comply with the [Depth-Anything License](https://github.com/DepthAnything/Depth-Anything-V2/blob/main/LICENSE) for any use of the model.
2. **This Project**: The DepthMap workflow code (excluding the Depth-Anything model) is released under the MIT License. See the [LICENSE](LICENSE) file for details.