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.

Depth-Anything-V1

Depth-Anything-V2

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

[!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

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:

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 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.

3. Export the model to onnx format using export_v1.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
   cd Depth-Anything
   python export_v1.py --encoder vitb --load_from depth_anything_vitb14.pth --image_shape 3 518 518

Depth-Anything-V2

1. Clone Depth-Anything-V2

   git clone https://github.com/DepthAnything/Depth-Anything-V2.git
   cd Depth-Anything-v2
   pip install -r requirements.txt

2. Download the pretrained models from the readme and put them in checkpoints folder:
3. 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.
4. 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.