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--- |
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license: cc-by-nc-4.0 |
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language: |
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- en |
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pipeline_tag: depth-estimation |
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tags: |
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- depth |
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- relative depth |
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--- |
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# Depth-Anything-V2-Large (Transformers version) |
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## Introduction |
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Depth Anything V2 is trained from 595K synthetic labeled images & 62M+ real unlabeled images, providing the most capable monocular depth estimation (MDE) model with the following features: |
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- more fine-grained details than Depth Anything V1 |
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- more robust than Depth Anything V1 & SD-based models (e.g., Marigold, Geowizard) |
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- more efficient (10x faster) & more lightweight than SD-based models |
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- impressive fine-tuned performance with our pre-trained models |
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## Installation |
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```bash |
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git clone https://github.com/MackinationsAi/Upgraded-Depth-Anything-V2.git |
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cd Upgraded-Depth-Anything-V2 |
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one_click_install.bat |
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``` |
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## Usage |
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Please refer to the [README.md](https://github.com/MackinationsAi/Upgraded-Depth-Anything-V2/blob/main/README.md) for actual usage. |
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## Test Code |
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```bash |
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cd Upgraded-Depth-Anything-V2 |
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venv\scripts\activate |
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python test.py /path/to/your/image.jpg (or .png) |
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``` |
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Create a test.py script using the code below: |
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```python |
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import cv2 |
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import torch |
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import numpy as np |
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import os |
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import argparse |
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from safetensors.torch import load_file |
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from depth_anything_v2.dpt import DepthAnythingV2 |
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# Argument parser for input image path |
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parser = argparse.ArgumentParser(description="Depth map inference using DepthAnythingV2 model.") |
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parser.add_argument("input_image_path", type=str, help="Path to the input image") |
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args = parser.parse_args() |
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# Determine the directory of this script |
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script_dir = os.path.dirname(os.path.abspath(__file__)) |
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# Set output path relative to the script directory |
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output_image_path = os.path.join(script_dir, "base_udav2_hf-code-test.png") |
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checkpoint_path = os.path.join(script_dir, "checkpoints", "depth_anything_v2_vitl.safetensors") |
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# Device selection: CUDA, MPS, or CPU |
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if torch.cuda.is_available(): |
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device = torch.device('cuda') |
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elif torch.backends.mps.is_available(): |
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device = torch.device('mps') |
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else: |
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device = torch.device('cpu') |
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model = DepthAnythingV2(encoder='vitl', features=256, out_channels=[256, 512, 1024, 1024]) |
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state_dict = load_file(checkpoint_path, device='cpu') |
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model.load_state_dict(state_dict) |
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model.to(device) |
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model.eval() |
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# Load the input image |
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raw_img = cv2.imread(args.input_image_path) |
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# Infer the depth map |
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depth = model.infer_image(raw_img) # HxW raw depth map |
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# Normalize the depth map to 0-255 for saving as an image |
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depth_normalized = cv2.normalize(depth, None, 0, 255, cv2.NORM_MINMAX) |
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depth_normalized = depth_normalized.astype(np.uint8) |
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cv2.imwrite(output_image_path, depth_normalized) |
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print(f"Depth map saved at {output_image_path}") |
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``` |
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## Citation |
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If you find this project useful, please consider citing [MackinationsAi](https://github.com/MackinationsAi/) & the following: |
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```bibtex |
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@article{depth_anything_v2, |
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title={Depth Anything V2}, |
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author={Yang, Lihe & Kang, Bingyi & Huang, Zilong & Zhao, Zhen & Xu, Xiaogang & Feng, Jiashi & Zhao, Hengshuang}, |
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journal={arXiv:2406.09414}, |
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year={2024} |
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} |
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@inproceedings{depth_anything_v1, |
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title={Depth Anything: Unleashing the Power of Large-Scale Unlabeled Data}, |
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author={Yang, Lihe & Kang, Bingyi & Huang, Zilong & Xu, Xiaogang & Feng, Jiashi & Zhao, Hengshuang}, |
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booktitle={CVPR}, |
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year={2024} |
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} |
