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# ByteTrack-TensorRT in C++
## Installation
Install opencv with ```sudo apt-get install libopencv-dev``` (we don't need a higher version of opencv like v3.3+).
Install eigen-3.3.9 [[google]](https://drive.google.com/file/d/1rqO74CYCNrmRAg8Rra0JP3yZtJ-rfket/view?usp=sharing), [[baidu(code:ueq4)]](https://pan.baidu.com/s/15kEfCxpy-T7tz60msxxExg).
```shell
unzip eigen-3.3.9.zip
cd eigen-3.3.9
mkdir build
cd build
cmake ..
sudo make install
```
## Prepare serialized engine file
Follow the TensorRT Python demo to convert and save the serialized engine file.
Check the 'model_trt.engine' file, which will be automatically saved at the YOLOX_output dir.
## Build the demo
You should set the TensorRT path and CUDA path in CMakeLists.txt.
For bytetrack_s model, we set the input frame size 1088 x 608. For bytetrack_m, bytetrack_l, bytetrack_x models, we set the input frame size 1440 x 800. You can modify the INPUT_W and INPUT_H in src/bytetrack.cpp
```c++
static const int INPUT_W = 1088;
static const int INPUT_H = 608;
```
You can first build the demo:
```shell
cd <ByteTrack_HOME>/deploy/TensorRT/cpp
mkdir build
cd build
cmake ..
make
```
Then you can run the demo with **200 FPS**:
```shell
./bytetrack ../../../../YOLOX_outputs/yolox_s_mix_det/model_trt.engine -i ../../../../videos/palace.mp4
```
(If you find the output video lose some frames, you can convert the input video by running:
```shell
cd <ByteTrack_HOME>
python3 tools/convert_video.py
```
to generate an appropriate input video for TensorRT C++ demo. )
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