在 DeepStream 上使用 Yolo v4

參考 Using YOLOv4 on NVIDIA DeepStream 5.0

參考 NVIDIA-AI-IOT / yolov4_deepstream

到 darknet 下載 yolov4.cfg, yolov4.weights, yolov4-tiny.cfg, yolov4-tiny.weights

可修改 cfg 檔的 width, height

$ pip3 install torch

$ pip3 install torchvision

$ git clone https://github.com/Tianxiaomo/pytorch-YOLOv4.git

$ cd pytorch-YOLOv4

$ pip3 install onnxruntime

$ python3 demo_darknet2onnx.py yolov4.cfg yolov4.weights ./data/giraffe.jpg 1

產生 yolov4_1_3_416_416_static.onnx

/usr/src/tensorrt/bin/trtexec --onnx=yolov4_1_3_416_416_static.onnx \

--explicitBatch --saveEngine=yolov4_1_3_416_416_fp16.engine \

--workspace=4096 --fp16

產生 yolov4_1_3_416_416_fp16.engine

$ cd ..

$ git clone https://github.com/NVIDIA-AI-IOT/yolov4_deepstream

$ cd yolov4_deepstream

$ sudo cp -r deepstream_yolov4 /opt/nvidia/deepstream/deepstream-5.0/sources

$ cd /opt/nvidia/deepstream/deepstream-5.0/sources

$ sudo chown user.group deepstream_yolov4

$ cd deepstream_yolov4/nvdsinfer_custom_impl_Yolo/

$ make

$ cd ..

拷貝 yolov4_1_3_416_416_fp16.engine 到此

修改 config_infer_primary_yoloV4.txt

model-engine-file=yolov4-tiny_1_3_416_416_fp16.engine

修改 deepstream_app_config_yoloV4.txt

# 不存檔

[sink0]

enable=0

# 螢幕顯示

[sink1]

enable=1

type=2

sync=0

display-id=0

offset-x=0

offset-y=0

width=0

height=0

overlay-id=1

source-id=0

# 不要在此使用 model-engine-file

[primary-gie]

enable=1

#model-engine-file=yolov4_1_3_320_320_fp16.engine

# 開啟 tracker

[tracker]

enable=1

執行

$ deepstream-app -c deepstream_app_config_yoloV4.txt