darknet运行测试命令代码详解

我们就按照代码运行的顺序来看，下面是我简化后的代码(原本的太多了，我只留下需要的)

作为一个萌新，写的注释肯定有错误的地方，欢迎大佬们指出。

main函数

#include 
#include 
#include 

#include "parser.h"
#include "utils.h"
#include "cuda.h"
#include "blas.h"
#include "connected_layer.h"

#ifdef OPENCV
#include "opencv2/highgui/highgui_c.h"
#endif
extern void run_detector(int argc, char **argv);
int main(int argc, char **argv)
{
/*如果定义了_DEBUG，就检测内存是否泄露*/
#ifdef _DEBUG
	_CrtSetDbgFlag(_CRTDBG_ALLOC_MEM_DF | _CRTDBG_LEAK_CHECK_DF);
#endif
    /*判断参数是否符合要求*/
    if(argc < 2){
        fprintf(stderr, "usage: %s \n", argv[0]);
        return 0;
    }
    /*find_int_arg:去掉-i和-i后面的一个参数，并返回后面的参数*/
    gpu_index = find_int_arg(argc, argv, "-i", 0);
    /*寻找argc是否有-nogpu*/
    if(find_arg(argc, argv, "-nogpu")) {
        gpu_index = -1;
    }
/*没有用GPU就把gpu_index设为-1*/
#ifndef GPU
    gpu_index = -1;
#else
    if(gpu_index >= 0){
        cuda_set_device(gpu_index);
    }
#endif
    
	run_detector(argc, argv);
    return 0;
}

int find_int_arg(int argc, char **argv, char *arg, int def)  /*寻找argc是否有arg,有则返回其位置i并且删除i和i+1上的参数，没有则返回def*/

int find_arg(int argc, char* argv[], char *arg) /*寻找argc是否有arg,有则返回1并且删除i上的参数，没有则返回0*/

run_detector函数

void run_detector(int argc, char **argv)
{
    //下面几行都是获得输入命令行的内容
	int dont_show = find_arg(argc, argv, "-dont_show");
	int show = find_arg(argc, argv, "-show");
	int http_stream_port = find_int_arg(argc, argv, "-http_port", -1);
	char *out_filename = find_char_arg(argc, argv, "-out_filename", 0);
	char *outfile = find_char_arg(argc, argv, "-out", 0);
    char *prefix = find_char_arg(argc, argv, "-prefix", 0);
    float thresh = find_float_arg(argc, argv, "-thresh", .25);	// 0.24
	float hier_thresh = find_float_arg(argc, argv, "-hier", .5);
    int cam_index = find_int_arg(argc, argv, "-c", 0);
    int frame_skip = find_int_arg(argc, argv, "-s", 0);
	int num_of_clusters = find_int_arg(argc, argv, "-num_of_clusters", 5);
	int width = find_int_arg(argc, argv, "-width", -1);
	int height = find_int_arg(argc, argv, "-height", -1);
	// extended output in test mode (output of rect bound coords)
	// and for recall mode (extended output table-like format with results for best_class fit)
	int ext_output = find_arg(argc, argv, "-ext_output");
	int save_labels = find_arg(argc, argv, "-save_labels");
    if(argc < 4){
        fprintf(stderr, "usage: %s %s [train/test/valid] [cfg] [weights (optional)]\n", argv[0], argv[1]);
        return;
    }
    char *gpu_list = find_char_arg(argc, argv, "-gpus", 0);
    int *gpus = 0;
    int gpu = 0;
    int ngpus = 0;
    if(gpu_list){
        printf("%s\n", gpu_list);
        int len = strlen(gpu_list);
        ngpus = 1;
        int i;
        for(i = 0; i < len; ++i){
            if (gpu_list[i] == ',') ++ngpus;
        }
        gpus = calloc(ngpus, sizeof(int));
        for(i = 0; i < ngpus; ++i){
            gpus[i] = atoi(gpu_list);
            gpu_list = strchr(gpu_list, ',')+1;
        }
    } else {
        gpu = gpu_index;
        gpus = &gpu;
        ngpus = 1;
    }

    int clear = find_arg(argc, argv, "-clear");

    char *datacfg = argv[3];
    char *cfg = argv[4];
    char *weights = (argc > 5) ? argv[5] : 0;
	if(weights)
		if(strlen(weights) > 0)
			if (weights[strlen(weights) - 1] == 0x0d) weights[strlen(weights) - 1] = 0;
    char *filename = (argc > 6) ? argv[6]: 0;
    test_detector(datacfg, cfg, weights, filename, thresh, hier_thresh, dont_show, ext_output, save_labels);
}

test_detector函数(detector.c 1231行)

void test_detector(char *datacfg, char *cfgfile, char *weightfile, char *filename, float thresh,
    float hier_thresh, int dont_show, int ext_output, int save_labels, char *outfile, int letter_box, int benchmark_layers)
{
    //获取data/coco.data里的内容制成列表options，key是等号前的字符串,val是等号后的(key='class', val='80)
    list *options = read_data_cfg(datacfg);
    char *name_list = option_find_str(options, "names", "data/names.list");
    int names_size = 0;
    //得到coco的类别
    char **names = get_labels_custom(name_list, &names_size); //get_labels(name_list);
    //用来画标签的
    image **alphabet = load_alphabet();
    //加载模型结构
    network net = parse_network_cfg_custom(cfgfile, 1, 1); // set batch=1
    //加载参数
    if (weightfile) {
        load_weights(&net, weightfile);
    }
    //对net里的conv层的w和b进行归一化操作
    fuse_conv_batchnorm(net);
    //计算二进制权重
    calculate_binary_weights(net);
    //判断类别数量是否错误
    if (net.layers[net.n - 1].classes != names_size) {
        printf(" Error: in the file %s number of names %d that isn't equal to classes=%d in the file %s \n",
            name_list, names_size, net.layers[net.n - 1].classes, cfgfile);
        if (net.layers[net.n - 1].classes > names_size) getchar();
    }
    srand(2222222);
    double time;
    char buff[256];
    char *input = buff;
    char *json_buf = NULL;
    int json_image_id = 0;
    FILE* json_file = NULL;
    if (outfile) {
        json_file = fopen(outfile, "wb");
        char *tmp = "[\n";
        fwrite(tmp, sizeof(char), strlen(tmp), json_file);
    }
    int j;
    float nms = .45;    // 0.4F
    //读取图片并进行识别
    while (1) {
        if (filename) {
            strncpy(input, filename, 256);
            if (strlen(input) > 0)
                if (input[strlen(input) - 1] == 0x0d) input[strlen(input) - 1] = 0;
        }
        else {
            printf("Enter Image Path: ");
            fflush(stdout);
            input = fgets(input, 256, stdin);
            if (!input) break;
            strtok(input, "\n");
        }

        //加载图片并且resize成(416, 416, 3)
        image im = load_image(input, 0, 0, net.c);
        image sized = resize_image(im, net.w, net.h);
        int letterbox = 0;
        layer l = net.layers[net.n - 1];

        float *X = sized.data;

        double time = get_time_point();
        //把数据输入网络
        network_predict(net, X);
        printf("%s: Predicted in %lf milli-seconds.\n", input, ((double)get_time_point() - time) / 1000);

        int nboxes = 0;
        //获得bbbox,可以利用vs的查看定义看det里包含了什么
        detection *dets = get_network_boxes(&net, im.w, im.h, thresh, hier_thresh, 0, 1, &nboxes, letterbox);
        //非极大值抑制
        if (nms) do_nms_sort(dets, nboxes, l.classes, nms);
        //画框
        draw_detections_v3(im, dets, nboxes, thresh, names, alphabet, l.classes, ext_output);
        save_image(im, "predictions");
        if (!dont_show) {
            show_image(im, "predictions");
        }

}