https://github.com/hilbertcube/darknet-yolov4-cpp
Darknet YOLOv4 C++ command-line wrapper with OpenCV-cpp and CUDA cmake setup
https://github.com/hilbertcube/darknet-yolov4-cpp
darknet opencv-dnn yolov4
Last synced: 5 months ago
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Darknet YOLOv4 C++ command-line wrapper with OpenCV-cpp and CUDA cmake setup
- Host: GitHub
- URL: https://github.com/hilbertcube/darknet-yolov4-cpp
- Owner: hilbertcube
- Created: 2025-01-14T11:36:47.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2025-01-17T09:40:24.000Z (over 1 year ago)
- Last Synced: 2025-06-10T11:52:52.135Z (about 1 year ago)
- Topics: darknet, opencv-dnn, yolov4
- Language: C++
- Homepage:
- Size: 1.31 MB
- Stars: 3
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# 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 `.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
```