YOLOv3使用笔记——批处理图片并返回检测数量

1.测试单张图

./darknet detector test cfg/coco.data cfg/yolov3.cfg yolov3.weights data/dog.jpg 

主要调用到的detector.c中的test_detector函数

2.修改批处理

test_detector（）

void test_detector(char *datacfg, char *cfgfile, char *weightfile, char *filename, float thresh, float hier_thresh, char *outfile, int fullscreen)
{
    list *options = read_data_cfg(datacfg);
    char *name_list = option_find_str(options, "names", "data/names.list");
    char **names = get_labels(name_list);

    image **alphabet = load_alphabet();
    network *net = load_network(cfgfile, weightfile, 0);
    set_batch_network(net, 1);
    srand(2222222);
    double time;
    char buff[256];
    char *input = buff;
    float nms=.45;
    char **filelist = get_labels("name.txt");
    //while(1){
    int index = 0;
    while(filelist[index] != NULL){
            filename = filelist[index];
            printf("filename: %s\n", filename);
        if(filename){
            strncpy(input, filename, 256);
        } else {
            printf("Enter Image Path: ");
            fflush(stdout);
            input = fgets(input, 256, stdin);
            if(!input) return;
            strtok(input, "\n");
        }
        image im = load_image_color(input,0,0);
        image sized = letterbox_image(im, net->w, net->h);
        //image sized = resize_image(im, net->w, net->h);
        //image sized2 = resize_max(im, net->w);
        //image sized = crop_image(sized2, -((net->w - sized2.w)/2), -((net->h - sized2.h)/2), net->w, net->h);
        //resize_network(net, sized.w, sized.h);
        layer l = net->layers[net->n-1];

        float *X = sized.data;
        time=what_time_is_it_now();
        network_predict(net, X);
        printf("%s: Predicted in %f seconds.\n", input, what_time_is_it_now()-time);
        int nboxes = 0;
        detection *dets = get_network_boxes(net, im.w, im.h, thresh, hier_thresh, 0, 1, &nboxes);
        //printf("%d\n", nboxes);
        //if (nms) do_nms_obj(boxes, probs, l.w*l.h*l.n, l.classes, nms);
        if (nms) do_nms_sort(dets, nboxes, l.classes, nms);
        //printf("classes=%d\n", l.classes);
        draw_detections(im, dets, nboxes, thresh, names, alphabet, l.classes);
        free_detections(dets, nboxes);
/*  original
        if(outfile){
            save_image(im, outfile);
        }
        else{
            save_image(im, "predictions");
#ifdef OPENCV
            cvNamedWindow("predictions", CV_WINDOW_NORMAL); 
            if(fullscreen){
                cvSetWindowProperty("predictions", CV_WND_PROP_FULLSCREEN, CV_WINDOW_FULLSCREEN);
            }
            show_image(im, "predictions");
            cvWaitKey(0);
            cvDestroyAllWindows();
#endif
        }
*/

        if(outfile){
            save_image(im, outfile);
        }
        else{
            save_image(im, filename);
#ifdef OPENCV
            cvNamedWindow("detect_drawing", CV_WINDOW_NORMAL); 
            if(fullscreen){
                cvSetWindowProperty("detect_drawing", CV_WND_PROP_FULLSCREEN, CV_WINDOW_FULLSCREEN);
            }
            show_image(im, "detect_drawing");
            //cvWaitKey(0);
            cvDestroyAllWindows();
#endif
        }

        free_image(im);
        free_image(sized);
        //if (filename) break;
        index++;
    }
}

批处理的图片用根目录的相对路径写在name.txt中。

3.返回每张图的检测目标数量

修改image.c，draw_detections函数，增加返回值

int draw_detections(image im, detection *dets, int num, float thresh, char **names, image **alphabet, int classes)
{
    int i,j;
    int n_t=0;//add detect_num

    for(i = 0; i < num; ++i){
        char labelstr[4096] = {0};
        int class = -1;
        for(j = 0; j < classes; ++j){
            if (dets[i].prob[j] > thresh){
            ++n_t;//add
                if (class < 0) {
                    strcat(labelstr, names[j]);
                    class = j;
                } else {
                    strcat(labelstr, ", ");
                    strcat(labelstr, names[j]);
                }
                printf("%s: %.0f%%\n", names[j], dets[i].prob[j]*100);
            }
        }
        if(class >= 0){
            int width = im.h * .006;

            /*
               if(0){
               width = pow(prob, 1./2.)*10+1;
               alphabet = 0;
               }
             */

            //printf("%d %s: %.0f%%\n", i, names[class], prob*100);
            int offset = class*123457 % classes;
            float red = get_color(2,offset,classes);
            float green = get_color(1,offset,classes);
            float blue = get_color(0,offset,classes);
            float rgb[3];

            //width = prob*20+2;

            rgb[0] = red;
            rgb[1] = green;
            rgb[2] = blue;
            box b = dets[i].bbox;
            //printf("%f %f %f %f\n", b.x, b.y, b.w, b.h);

            int left  = (b.x-b.w/2.)*im.w;
            int right = (b.x+b.w/2.)*im.w;
            int top   = (b.y-b.h/2.)*im.h;
            int bot   = (b.y+b.h/2.)*im.h;

            if(left < 0) left = 0;
            if(right > im.w-1) right = im.w-1;
            if(top < 0) top = 0;
            if(bot > im.h-1) bot = im.h-1;

            draw_box_width(im, left, top, right, bot, width, red, green, blue);
            if (alphabet) {
                image label = get_label(alphabet, labelstr, (im.h*.03));
                draw_label(im, top + width, left, label, rgb);
                free_image(label);
            }
            if (dets[i].mask){
                image mask = float_to_image(14, 14, 1, dets[i].mask);
                image resized_mask = resize_image(mask, b.w*im.w, b.h*im.h);
                image tmask = threshold_image(resized_mask, .5);
                embed_image(tmask, im, left, top);
                free_image(mask);
                free_image(resized_mask);
                free_image(tmask);
            }
        }
    }
    return n_t;//add
}

修改darknet.h

int draw_detections(image im, detection *dets, int num, float thresh, char **names, image **alphabet, int classes);//modify type

修改detector.c

void test_detector(char *datacfg, char *cfgfile, char *weightfile, char *filename, float thresh, float hier_thresh, char *outfile, int fullscreen)
{
    int person_num=0;
    list *options = read_data_cfg(datacfg);
    char *name_list = option_find_str(options, "names", "data/names.list");
    char **names = get_labels(name_list);

    image **alphabet = load_alphabet();
    network *net = load_network(cfgfile, weightfile, 0);
    set_batch_network(net, 1);
    srand(2222222);
    double time;
    char buff[256];
    char *input = buff;
    float nms=.45;
    char **filelist = get_labels("name.txt");
    //while(1){
    int index = 0;
    while(filelist[index] != NULL){
            filename = filelist[index];
            printf("filename: %s\n", filename);
        if(filename){
            strncpy(input, filename, 256);
        } else {
            printf("Enter Image Path: ");
            fflush(stdout);
            input = fgets(input, 256, stdin);
            if(!input) return;
            strtok(input, "\n");
        }
        image im = load_image_color(input,0,0);
        image sized = letterbox_image(im, net->w, net->h);
        //image sized = resize_image(im, net->w, net->h);
        //image sized2 = resize_max(im, net->w);
        //image sized = crop_image(sized2, -((net->w - sized2.w)/2), -((net->h - sized2.h)/2), net->w, net->h);
        //resize_network(net, sized.w, sized.h);
        layer l = net->layers[net->n-1];

        float *X = sized.data;
        time=what_time_is_it_now();
        network_predict(net, X);
        printf("%s: Predicted in %f seconds.\n", input, what_time_is_it_now()-time);
        int nboxes = 0;
        detection *dets = get_network_boxes(net, im.w, im.h, thresh, hier_thresh, 0, 1, &nboxes);
        //printf("%d\n", nboxes);
        //if (nms) do_nms_obj(boxes, probs, l.w*l.h*l.n, l.classes, nms);
        if (nms) do_nms_sort(dets, nboxes, l.classes, nms);
        //printf("classes=%d\n", l.classes);
        person_num=draw_detections(im, dets, nboxes, thresh, names, alphabet, l.classes);
        free_detections(dets, nboxes);
        printf("%d\n", person_num);
/*  original
        if(outfile){
            save_image(im, outfile);
        }
        else{
            save_image(im, "predictions");
#ifdef OPENCV
            cvNamedWindow("predictions", CV_WINDOW_NORMAL); 
            if(fullscreen){
                cvSetWindowProperty("predictions", CV_WND_PROP_FULLSCREEN, CV_WINDOW_FULLSCREEN);
            }
            show_image(im, "predictions");
            cvWaitKey(0);
            cvDestroyAllWindows();
#endif
        }
*/

        if(outfile){
            save_image(im, outfile);
        }
        else{
            save_image(im, filename);
#ifdef OPENCV
            cvNamedWindow("detect_drawing", CV_WINDOW_NORMAL); 
            if(fullscreen){
                cvSetWindowProperty("detect_drawing", CV_WND_PROP_FULLSCREEN, CV_WINDOW_FULLSCREEN);
            }
            show_image(im, "detect_drawing");
            //cvWaitKey(0);
            cvDestroyAllWindows();
#endif
        }

        free_image(im);
        free_image(sized);
        //if (filename) break;
        index++;
    }
}

重新编译darknet，测试图像会在最后打印一行检测数量。

参考

https://blog.csdn.net/v1_vivian/article/details/78419059

https://blog.csdn.net/fffupeng/article/details/55050441