- Linux or macOS with Python ≥ 3.6
- PyTorch ≥ 1.8 and torchvision that matches the PyTorch installation. Install them together at pytorch.org to make sure of this
- OpenCV is optional but needed by demo and visualization
Step 1: Install Pytroch: following the instruction in https://pytorch.org/ to install the latest version of pytorch.
Step 2: Following the corresponding structure, clone the code, and run:
cd ./angel_system/berkeley
python -m pip install.
or run
cd ./angel_system
python -m pip install -e berkeley
Download our model and save it to ./weights folder
python test_integration.py
Please ignore the reading images algorithm in test_integration.py, and directly use the predict function in it.
You will get a dict including the frame level preditions, with the structure of
├── frame_id
│ ├── object1
│ │ ├── class
│ │ └── confidence score
| | └── bbox
│ ├── object2
│ │ ├── ......
│ │ └── ......
│ ├── object3
│ │ ├── ......
│ │ ├── ......
│ ├ └── ......
│── frame_id
You will also get an array with a dimension of N * H * W * 3 of the visualized frames with predicted bounding box in it.
Notice that the keys of the dict should be equal or less than the input number of frames, including no empty predictions, but the visualized images should be the same number of the input images.