yolov3源码darknet在vscode下调试

1. 安装配置：

• https://pjreddie.com/darknet/yolo/
• darknet文件夹下make命令搞定；

2. 配置vscode

打开安装好的vscode并安装扩展C/C++(ms-vscode.cpptools)；在开始处打开文件夹打开darknet文件夹；

首先分析一下目录结构：

├── backup
├── cfg
├── darknet //编译的可执行文件
├── data
├── examples   //重要：examples/darknet.c exmapels/detector.c等文件是主要执行文件的入口；调用src下的源码
├── ILSVRC2015_train_00755001.mp4
├── include
├── libdarknet.a
├── libdarknet.so
├── LICENSE
├── LICENSE.fuck
├── LICENSE.gen
├── LICENSE.gpl
├── LICENSE.meta
├── LICENSE.mit
├── LICENSE.v1
├── Makefile
├── obj
├── predictions.jpg
├── python
├── README.md
├── results
├── scripts
├── src   // 重要代码;各个层的实现，src/demo实现摄像头实时检测
├── yolov3-tiny.weights
└── yolov3.weights

- vscode默认从系统和${workspaceFolder}（当前打开的文件夹，即darknet目录）下找头文件，所以我们要将工作区的include文件夹加入到配置文件中去。vscode下的C或C++项目的配置文件是c_cpp_properties.json，用快捷键ctrl+shift+p调出vscode命令搜索框，搜索Edit Configurations并点击就会跳转到c_cpp_properties.json文件的编辑界面，将include加入到includePath即可：

{
    "configurations": [
        {
            "name": "Linux",
            "includePath": [
                "${workspaceFolder}/**",
                "${workspaceFolder}/include"
            ],
            "defines": [],
            "compilerPath": "/usr/bin/gcc",
            "cStandard": "c11",
            "cppStandard": "c++17",
            "intelliSenseMode": "clang-x64"
        }
    ],
    "version": 4
}

3. 调试

要启动调试，vscode需要知道编译的可调式的可执行文件的目录，以及带进去的参数，这些内容需要配置到launch.json中。按F5启动调试，选择GDB环境，编辑launch.json如下所示：主要修改program和args，具体参数请根据自己具体的情况配置；

{
    // Use IntelliSense to learn about possible attributes.
    // Hover to view descriptions of existing attributes.
    // For more information, visit: https://go.microsoft.com/fwlink/?linkid=830387
    "version": "0.2.0",
    "configurations": [
        {
            "name": "(gdb) Launch",
            "type": "cppdbg",
            "request": "launch",
            "program": "${workspaceFolder}/darknet",
            "args": ["detector","test"," cfg/coco.data","cfg/yolov3.cfg","yolov3.weights","data/dog.jpg"],
            "stopAtEntry": false,
            "cwd": "${workspaceFolder}",
            "environment": [],
            "externalConsole": true,
            "MIMode": "gdb",
            "setupCommands": [
                {
                    "description": "Enable pretty-printing for gdb",
                    "text": "-enable-pretty-printing",
                    "ignoreFailures": true
                }
            ]
        }
    ]
}

- 调试主要进入darknet.c文件，选择进入相应的子函数：

int main(int argc, char **argv)
{
    //test_resize("data/bad.jpg");
    //test_box();
    //test_convolutional_layer();
    if(argc < ){
        fprintf(stderr, "usage: %s <function>\n", argv[]);
        return ;
    }
    gpu_index = find_int_arg(argc, argv, "-i", );
    if(find_arg(argc, argv, "-nogpu")) {
        gpu_index = -;
    }
 
#ifndef GPU
    gpu_index = -;
#else
    if(gpu_index >= ){
        cuda_set_device(gpu_index);
    }
#endif
 
    if ( == strcmp(argv[], "average")){
        average(argc, argv);
    } else if ( == strcmp(argv[], "yolo")){
        run_yolo(argc, argv);
    } else if ( == strcmp(argv[], "super")){
        run_super(argc, argv);
    } else if ( == strcmp(argv[], "lsd")){
        run_lsd(argc, argv);
    } else if ( == strcmp(argv[], "detector")){
        run_detector(argc, argv);
    } else if ( == strcmp(argv[], "detect")){
        float thresh = find_float_arg(argc, argv, "-thresh", .);
        char *filename = (argc > ) ? argv[]: ;
        char *outfile = find_char_arg(argc, argv, "-out", );
        int fullscreen = find_arg(argc, argv, "-fullscreen");
        test_detector("cfg/coco.data", argv[], argv[], filename, thresh, ., outfile, fullscreen);
    } else if ( == strcmp(argv[], "cifar")){
        run_cifar(argc, argv);
    } else if ( == strcmp(argv[], "go")){
        run_go(argc, argv);
    } else if ( == strcmp(argv[], "rnn")){
        run_char_rnn(argc, argv);
    } else if ( == strcmp(argv[], "coco")){
        run_coco(argc, argv);
    } else if ( == strcmp(argv[], "classify")){
        predict_classifier("cfg/imagenet1k.data", argv[], argv[], argv[], );
    } else if ( == strcmp(argv[], "classifier")){
        run_classifier(argc, argv);
    } else if ( == strcmp(argv[], "regressor")){
        run_regressor(argc, argv);
    } else if ( == strcmp(argv[], "isegmenter")){
        run_isegmenter(argc, argv);
    } else if ( == strcmp(argv[], "segmenter")){
        run_segmenter(argc, argv);
    } else if ( == strcmp(argv[], "art")){
        run_art(argc, argv);
    } else if ( == strcmp(argv[], "tag")){
        run_tag(argc, argv);
    } else if ( == strcmp(argv[], "3d")){
        composite_3d(argv[], argv[], argv[], (argc > ) ? atof(argv[]) : );
    } else if ( == strcmp(argv[], "test")){
        test_resize(argv[]);
    } else if ( == strcmp(argv[], "nightmare")){
        run_nightmare(argc, argv);
    } else if ( == strcmp(argv[], "rgbgr")){
        rgbgr_net(argv[], argv[], argv[]);
    } else if ( == strcmp(argv[], "reset")){
        reset_normalize_net(argv[], argv[], argv[]);
    } else if ( == strcmp(argv[], "denormalize")){
        denormalize_net(argv[], argv[], argv[]);
    } else if ( == strcmp(argv[], "statistics")){
        statistics_net(argv[], argv[]);
    } else if ( == strcmp(argv[], "normalize")){
        normalize_net(argv[], argv[], argv[]);
    } else if ( == strcmp(argv[], "rescale")){
        rescale_net(argv[], argv[], argv[]);
    } else if ( == strcmp(argv[], "ops")){
        operations(argv[]);
    } else if ( == strcmp(argv[], "speed")){
        speed(argv[], (argc >  && argv[]) ? atoi(argv[]) : );
    } else if ( == strcmp(argv[], "oneoff")){
        oneoff(argv[], argv[], argv[]);
    } else if ( == strcmp(argv[], "oneoff2")){
        oneoff2(argv[], argv[], argv[], atoi(argv[]));
    } else if ( == strcmp(argv[], "print")){
        print_weights(argv[], argv[], atoi(argv[]));
    } else if ( == strcmp(argv[], "partial")){
        partial(argv[], argv[], argv[], atoi(argv[]));
    } else if ( == strcmp(argv[], "average")){
        average(argc, argv);
    } else if ( == strcmp(argv[], "visualize")){
        visualize(argv[], (argc > ) ? argv[] : );
    } else if ( == strcmp(argv[], "mkimg")){
        mkimg(argv[], argv[], atoi(argv[]), atoi(argv[]), atoi(argv[]), argv[]);
    } else if ( == strcmp(argv[], "imtest")){
        test_resize(argv[]);
    } else {
        fprintf(stderr, "Not an option: %s\n", argv[]);
    }
    return ;
}

4. 测试Real-Time Detection on a Webcam

Running YOLO on test data isn't very interesting if you can't see the result. Instead of running it on a bunch of images let's run it on the input from a webcam!

To run this demo you will need to compile Darknet with CUDA and OpenCV. Then run the command:

./darknet detector demo cfg/coco.data cfg/yolov3.cfg yolov3.weights

YOLO will display the current FPS and predicted classes as well as the image with bounding boxes drawn on top of it.

You will need a webcam connected to the computer that OpenCV can connect to or it won't work. If you have multiple webcams connected and want to select which one to use you can pass the flag -c <num> to pick (OpenCV uses webcam 0 by default).

You can also run it on a video file if OpenCV can read the video:

./darknet detector demo cfg/coco.data cfg/yolov3.cfg yolov3.weights <video file>

That's how we made the YouTube video above.