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.

⏱️ Performance

The inference time includes the pre-preprocessing and post-processing stages:

[!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++

• Usage 1: Create an engine from an onnx model and save it

depth-anything-tensorrt.exe <onnx model> <input image or video>

• Usage 2: Deserialize an engine and run it

depth-anything-tensorrt.exe <engine> <input image or video>

Example:

# infer image
depth-anything-tensorrt.exe depth_anything_vitb14.engine test.jpg
# infer folder(images)
depth-anything-tensorrt.exe depth_anything_vitb14.engine data
# infer video
depth-anything-tensorrt.exe depth_anything_vitb14.engine test.mp4 # the video path

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

Perform the following steps to create an onnx model:

1. Download the pretrained model and install Depth-Anything:

   git clone https://github.com/LiheYoung/Depth-Anything
   cd Depth-Anything
   pip install -r requirements.txt
   

2. Copy and paste dpt.py in this repo to <depth_anything_installpath>/depth_anything folder. Then, copy export.py in this repo to <depth_anything_installpath>. Note that I've only removed a squeeze operation at the end of model's forward function in dpt.py to avoid conflicts with TensorRT.

3. Export the model to onnx format using export.py. You will get an onnx file named depth_anything_vit{}14.onnx, such as depth_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
   python export.py --encoder vitb --load_from depth_anything_vitb14.pth --image_shape 3 518 518
   

[!TIP] The width and height of the model input should be divisible by 14, the patch height.

4. Finally, copy the opencv dll files such as opencv_world490.dll and opencv_videoio_ffmpeg490_64.dll into the <depth_anything_installpath>/build/Release folder.

👏 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.