计算机视觉：人体识别与跟踪（People Recognition and Tracking）

<h1 align='center'>Tracklite</h1> ## Introduction This repo using TensorRT to speed up yolov3 backbone and work with [deep_sort torch](https://github.com/ZQPei/deep_sort_pytorch). mainly run on **Nvidia Jetson Nano** but x64 may also works. haven't tried yet. note that it is a inference pipeline not for training model. Thanks for [ZQPei](https://github.com/ZQPei)'s great work. and also thanks to [jkjung-avt](https://github.com/jkjung-avt) for his [tensorrt_demos](https://github.com/jkjung-avt/tensorrt_demos), which give me a a lot to learn. ------ ## Update 2020.4.12 > Release yolov3-tiny416 inference 2020.4.11 > first upload the project ------ ## Speed Whole process time from read image to finished deepsort (include every img preprocess and postprocess) | Backbone | before TensorRT | TensorRT(detection + tracking) | FPS(detection + tracking) | | :-------------- | --------------- | ------------------------------ | ------------------------- | | Yolov3_416 | 750ms | 450ms | 1.5 ~ 2 | | Yolov3-tiny-416 | N/A | 100-150ms | 8 ~ 9 | ------ ## Install #### Environment - Jetson nano with TensorRT 5.1.6.1 - Onnx 1.4.0 (or onnx 1.4.1, cannot be higher or lower) follow my step to set up everything 1. clone this repo ``` git clone xxxx ``` 2. Download YOLOv3 parameters ``` cd detector/YOLOv3/weight/ wget https://pjreddie.com/media/files/yolov3.weights ``` 3. Download deepsort parameters ckpt.t7 ``` cd deep_sort/deep/checkpoint # download ckpt.t7 from https://drive.google.com/drive/folders/1xhG0kRH1EX5B9_Iz8gQJb7UNnn_riXi6 to this folder ``` 4. Compile nms ``` cd detector/YOLOv3/nms sh build.sh ``` ------ ## Convert yolov3 weights to onnx to tensorrt 1. firstly check the yolo weights under **weights directory** and just simply command like below to convert yolov3.weights file to onnx, and onnx will be yielded at the same dir ( ./weights/yolov3_416.onnx ) ```shell #if yolov3 python3 yolov3_to_onnx.py #else yolov3_tiny python3 yolov3_tiny_to_onnx.py ``` 2. convert yolov3_416.onnx to tensorrt engine ```shell #if yolov3 python3 onnx_to_tensorrt --onnx /path/to/yolov3_416.onnx --output_engine /path/to/yolov3_416.engine #else yolov3_tiny python3 onnx_to_tensorrt_tiny --onnx /path/to/yolov3_tiny_416.onnx --output_engine /path/to/yolov3_tiny_416.engine ``` **Note**: In `onnx_to_tensorrt.py` , you can set `max_workspace_size` = 1 << 30 in `get_engine` function and delete ` builder.fp16_mode = True` if you are using x86 arch for better performance (both mAP and frames per second) ------ ## Demo support video and webcam demo for now 1. Make sure everything is settled down - Yolov3_416 engine file - demo video you want to test on 2. Let's do demo ! support 1. onboard camera webcam / usb camera. 2. Video track - Webcam demo - onboard camera, csi camera ```shell #yolov3 python3 run_tracker.py #yolov3 tiny python3 run_tracker_tiny.py ``` - Webcam demo - usb camera ```shell #yolov3 python3 run_tracker.py --usb #yolov3 tiny python3 run_tracker_tiny.py --usb ``` - Video demo ```shell #yolov3 python3 run_tracker.py --file --filename your_test.mp4 --output_file ./output.mp4 #yolov3 tiny python3 run_tracker_tiny.py --file --filename your_test.mp4 --output_file ./output.mp4 ```   ------ ## Issue I had a hard time on saving video, now the VideoWriter works for me, but it might not work for you, issue me if you have any problem.