# ByteTrack-CPP-ncnn ## Installation Clone [ncnn](https://github.com/Tencent/ncnn) first, then please following [build tutorial of ncnn](https://github.com/Tencent/ncnn/wiki/how-to-build) to build on your own device. 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 ``` ## Generate onnx file Use provided tools to generate onnx file. For example, if you want to generate onnx file of bytetrack_s_mot17.pth, please run the following command: ```shell cd python3 tools/export_onnx.py -f exps/example/mot/yolox_s_mix_det.py -c pretrained/bytetrack_s_mot17.pth.tar ``` Then, a bytetrack_s.onnx file is generated under . ## Generate ncnn param and bin file Put bytetrack_s.onnx under ncnn/build/tools/onnx and then run: ```shell cd ncnn/build/tools/onnx ./onnx2ncnn bytetrack_s.onnx bytetrack_s.param bytetrack_s.bin ``` Since Focus module is not supported in ncnn. Warnings like: ```shell Unsupported slice step ! ``` will be printed. However, don't worry! C++ version of Focus layer is already implemented in src/bytetrack.cpp. ## Modify param file Open **bytetrack_s.param**, and modify it. Before (just an example): ``` 235 268 Input images 0 1 images Split splitncnn_input0 1 4 images images_splitncnn_0 images_splitncnn_1 images_splitncnn_2 images_splitncnn_3 Crop Slice_4 1 1 images_splitncnn_3 467 -23309=1,0 -23310=1,2147483647 -23311=1,1 Crop Slice_9 1 1 467 472 -23309=1,0 -23310=1,2147483647 -23311=1,2 Crop Slice_14 1 1 images_splitncnn_2 477 -23309=1,0 -23310=1,2147483647 -23311=1,1 Crop Slice_19 1 1 477 482 -23309=1,1 -23310=1,2147483647 -23311=1,2 Crop Slice_24 1 1 images_splitncnn_1 487 -23309=1,1 -23310=1,2147483647 -23311=1,1 Crop Slice_29 1 1 487 492 -23309=1,0 -23310=1,2147483647 -23311=1,2 Crop Slice_34 1 1 images_splitncnn_0 497 -23309=1,1 -23310=1,2147483647 -23311=1,1 Crop Slice_39 1 1 497 502 -23309=1,1 -23310=1,2147483647 -23311=1,2 Concat Concat_40 4 1 472 492 482 502 503 0=0 ... ``` * Change first number for 235 to 235 - 9 = 226(since we will remove 10 layers and add 1 layers, total layers number should minus 9). * Then remove 10 lines of code from Split to Concat, but remember the last but 2nd number: 503. * Add YoloV5Focus layer After Input (using previous number 503): ``` YoloV5Focus focus 1 1 images 503 ``` After(just an exmaple): ``` 226 328 Input images 0 1 images YoloV5Focus focus 1 1 images 503 ... ``` ## Use ncnn_optimize to generate new param and bin ```shell # suppose you are still under ncnn/build/tools/onnx dir. ../ncnnoptimize bytetrack_s.param bytetrack_s.bin bytetrack_s_op.param bytetrack_s_op.bin 65536 ``` ## Copy files and build ByteTrack Copy or move 'src', 'include' folders and 'CMakeLists.txt' file into ncnn/examples. Copy bytetrack_s_op.param, bytetrack_s_op.bin and /videos/palace.mp4 into ncnn/build/examples. Then, build ByteTrack: ```shell cd ncnn/build/examples cmake .. make ``` ## Run the demo You can run the ncnn demo with **5 FPS** (96-core Intel(R) Xeon(R) Platinum 8163 CPU @ 2.50GHz): ```shell ./bytetrack palace.mp4 ``` You can modify 'num_threads' to optimize the running speed in [bytetrack.cpp](https://github.com/ifzhang/ByteTrack/blob/2e9a67895da6b47b948015f6861bba0bacd4e72f/deploy/ncnn/cpp/src/bytetrack.cpp#L309) according to the number of your CPU cores: ``` yolox.opt.num_threads = 20; ``` ## Acknowledgement * [ncnn](https://github.com/Tencent/ncnn)