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<div align="left">Depth Anything TensorRT CLI
</div>Depth estimation is the task of measuring the distance of each pixel relative to the camera. This repo provides a TensorRT implementation of the Depth-Anything depth estimation model in both C++ and Python, enabling efficient real-time inference.
<p align="center"> Depth-Anything-V1 <img src="assets/davis_dolphins_result.gif" height="225px" width="720px" /> </p> <p align="center"> Depth-Anything-V2 <img src="assets/ferris_wheel_result.gif" height="225px" width="720px" /> </p>News
- 2024-08-20: Added CLI (xa1on)
- 2024-06-20: Added support for TensorRT 10.
- 2024-06-17: Depth Anything V2 has been integrated.
- 2024-01-23: The Depth Anything TensorRT version has been created.
⏱️ Performance
The inference time includes the pre-preprocessing and post-processing stages:
Device | Model | Model Input (WxH) | Image Resolution (WxH) | Inference Time(ms) |
---|---|---|---|---|
RTX4090 | Depth-Anything-S | 518x518 | 1280x720 | 3 |
RTX4090 | Depth-Anything-B | 518x518 | 1280x720 | 6 |
RTX4090 | Depth-Anything-L | 518x518 | 1280x720 | 12 |
[!NOTE] Inference was conducted using
FP16
precision, with a warm-up period of 10 frames. The reported time corresponds to the last inference.
🚀 Quick Start
C++
- Step 1: Create an engine from an onnx model and save it:
depth-anything-tensorrt.exe -model <onnx model>
- Step 2: Deserialize an engine. Once you've built your engine, the next time you run it, simply use your engine file:
depth-anything-tensorrt.exe -model <engine file> -input <input image or video>
- Alternatively, you can skip immediately to running the model with just an onnx file, however, it will still generate a engine file.
depth-anything-tensorrt.exe -model <onnx model> -input <input image or video>
Example:
# infer image
depth-anything-tensorrt.exe -model depth_anything_vitb14.engine -input test.jpg
# infer folder(images/videos)
depth-anything-tensorrt.exe -model depth_anything_vitb14.engine -input data # folder containing videos/images
# infer video
depth-anything-tensorrt.exe -model depth_anything_vitb14.engine -input test.mp4 # the video path
# specify output location
depth-anything-tensorrt.exe -model depth_anything_vitb14.engine -input test.mp4 -output result # rendered depth maps will go into the "results" directory
# display progress in one line rather than multiple
depth-anything-tensorrt.exe -model depth_anything_vitb14.engine -input test.mp4 -one-line
# modify prefix of generated files (default: "depth_")
depth-anything-tensorrt.exe -model depth_anything_vitb14.engine -input test.mp4 -prefix "depthify_" # rendered depth map will have the name "depthify_test.mp4"
# show preview including before and after (may slow down performance)
depth-anything-tensorrt.exe -preview -model depth_anything_vitb14.engine -input test.mp4
# modify fps of footage (does not interpolate, will speed up or slow down footage if original video file has a different fps value)
depth-anything-tensorrt.exe -model depth_anything_vitb14.engine -input test.mp4 -fps 60
# use an existing engine file if found
depth-anything-tensorrt.exe -model depth_anything_vitb14.onnx -input test.mp4 -find-engine
<p align="center">
<img src="assets/usage-example.png"/>
</p>
Python
cd depth-anything-tensorrt/python
# infer image
python trt_infer.py --engine <path to trt engine> --img <single-img> --outdir <outdir> [--grayscale]
🛠️ Build
C++
Refer to our docs/INSTALL.md for C++ environment installation.
Python
cd <tensorrt installation path>/python
pip install cuda-python
pip install tensorrt-8.6.0-cp310-none-win_amd64.whl
pip install opencv-python
🤖 Model Preparation
Depth-Anything-V1
Perform the following steps to create an onnx model:
-
Download the pretrained model and install Depth-Anything:
git clone https://github.com/LiheYoung/Depth-Anything cd Depth-Anything pip install -r requirements.txt
-
Copy dpt.py in depth_anything_v1 from this repo to
<Depth-Anything>/depth_anything
folder. And, Copy export_v1.py in depth_anything_v1 from this repo to<Depth-Anything>
folder. -
Export the model to onnx format using export_v1.py. You will get an onnx file named
depth_anything_vit{}14.onnx
, such asdepth_anything_vitb14.onnx
. Note that I used torch cpu version for exporting the onnx model as it is not necessary to deploy the model on GPU when exporting.conda create -n depth-anything python=3.8 conda activate depth-anything pip install torch torchvision pip install opencv-python pip install onnx cd Depth-Anything python export_v1.py --encoder vitb --load_from depth_anything_vitb14.pth --image_shape 3 518 518
Depth-Anything-V2
- Clone Depth-Anything-V2
git clone https://github.com/DepthAnything/Depth-Anything-V2.git cd Depth-Anything-v2 pip install -r requirements.txt
- Download the pretrained models from the readme and put them in checkpoints folder:
- Copy dpt.py in depth_anything_v2 from this repo to
<Depth-Anything-V2>/depth_anything_v2
folder. And, Copy export_v2.py in depth_anything_v2 from this repo to<Depth-Anything-V2>
folder. - Run the following to export the model:
conda create -n depth-anything python=3.8 conda activate depth-anything pip install torch torchvision pip install opencv-python pip install onnx cd Depth-Anything-V2 python export_v2.py --encoder vitb --input-size 518
[!TIP] The width and height of the model input should be divisible by 14, the patch height.
👏 Acknowledgement
This project is based on the following projects:
- Depth-Anything - Unleashing the Power of Large-Scale Unlabeled Data.
- TensorRT - TensorRT samples and api documentation.