Darknet YOLOv4 C++ command-line wrapper with OpenCV-cpp and CUDA

Minimum requirements: C++17

Lists of command lines:

• Yolo v4 COCO - image: ./darknet detector test cfg/coco.data cfg/yolov4.cfg yolov4.weights -thresh 0.25
• Output coordinates of objects: ./darknet detector test cfg/coco.data yolov4.cfg yolov4.weights -ext_output dog.jpg
• Yolo v4 COCO - video: ./darknet detector demo cfg/coco.data cfg/yolov4.cfg yolov4.weights -ext_output test.mp4
• Yolo v4 COCO - WebCam 0: ./darknet detector demo cfg/coco.data cfg/yolov4.cfg yolov4.weights -c 0
• Yolo v4 COCO for net-videocam - Smart WebCam: ./darknet detector demo cfg/coco.data cfg/yolov4.cfg yolov4.weights http://192.168.0.80:8080/video?dummy=param.mjpg
• Yolo v4 - save result videofile res.avi: ./darknet detector demo cfg/coco.data cfg/yolov4.cfg yolov4.weights test.mp4 -out_filename res.avi
• Yolo v3 Tiny COCO - video: ./darknet detector demo cfg/coco.data cfg/yolov3-tiny.cfg yolov3-tiny.weights test.mp4
• JSON and MJPEG server that allows multiple connections from your soft or Web-browser ip-address:8070 and 8090: ./darknet detector demo ./cfg/coco.data ./cfg/yolov3.cfg ./yolov3.weights test50.mp4 -json_port 8070 -mjpeg_port 8090 -ext_output
• Yolo v3 Tiny on GPU #1: ./darknet detector demo cfg/coco.data cfg/yolov3-tiny.cfg yolov3-tiny.weights -i 1 test.mp4
• Alternative method Yolo v3 COCO - image: ./darknet detect cfg/yolov4.cfg yolov4.weights -i 0 -thresh 0.25
• Train on Amazon EC2, to see mAP & Loss-chart using URL like: http://ec2-35-160-228-91.us-west-2.compute.amazonaws.com:8090 in the Chrome/Firefox (Darknet should be compiled with OpenCV): ./darknet detector train cfg/coco.data yolov4.cfg yolov4.conv.137 -dont_show -mjpeg_port 8090 -map
• 186 MB Yolo9000 - image: ./darknet detector test cfg/combine9k.data cfg/yolo9000.cfg yolo9000.weights
• Remember to put data/9k.tree and data/coco9k.map under the same folder of your app if you use the cpp api to build an app
• To process a list of images data/train.txt and save results of detection to result.json file use: ./darknet detector test cfg/coco.data cfg/yolov4.cfg yolov4.weights -ext_output -dont_show -out result.json < data/train.txt
• To process a list of images data/train.txt and save results of detection to result.txt use: ./darknet detector test cfg/coco.data cfg/yolov4.cfg yolov4.weights -dont_show -ext_output < data/train.txt > result.txt
• To process a video and output results to a json file use: darknet.exe detector demo cfg/coco.data cfg/yolov3.cfg yolov3.weights file.mp4 -dont_show -json_file_output results.json
• Pseudo-labelling - to process a list of images data/new_train.txt and save results of detection in Yolo training format for each image as label <image_name>.txt (in this way you can increase the amount of training data) use: ./darknet detector test cfg/coco.data cfg/yolov4.cfg yolov4.weights -thresh 0.25 -dont_show -save_labels < data/new_train.txt
• To calculate anchors: ./darknet detector calc_anchors data/obj.data -num_of_clusters 9 -width 416 -height 416
• To check accuracy mAP@IoU=50: ./darknet detector map data/obj.data yolo-obj.cfg backup\yolo-obj_7000.weights
• To check accuracy mAP@IoU=75: ./darknet detector map data/obj.data yolo-obj.cfg backup\yolo-obj_7000.weights -iou_thresh 0.75

To run the wrapper,

Go to the src folder, which contains some codes at webcam.cpp and simply run

g++ webcam.cpp -o ./bin/webcam.exe; ./bin/webcam.exe

or if you use OpenCV, build the folder with CMake and run

cmake --build .\build\ --config Debug; .\build\Debug\webcam.exe

# or,

cmake --build .\build\ --config Release; .\build\Release\webcam.exe

on Linux,

you still have to specify the C++ version in your .json setting file, and to run, use

g++ -std=c++17 webcam.cpp -o ./bin/webcam; ./bin/webcam