darknetYolov3图片的分类计数 置信度以及输出xmin xmax ymin ymax的结果

之前一直没找到c语言的darknet的计数代码，就自己改了。就是一个计数器再显示在识别框上，输出xmin,xmax,ymin,ymax的坐标是因为比赛需要，然而大佬们都不用yolo了根本拼不过。。。很久以前改的可能有的地方做了修

改忘记了，如有问题在评论里问一下。

具体修改就在src文件夹的image.c里，找到draw_detections_v3这个函数修改就行。代码如下，修改部分有中文注释。

void draw_detections_v3(image im, detection* dets, int num, float thresh, char** names, image** alphabet, int classes, int ext_output, char* impath){static int frame_id = 0;frame_id++;int selected_detections_num;detection_with_class* selected_detections = get_actual_detections(dets, num, thresh, &selected_detections_num, names);// text outputqsort(selected_detections, selected_detections_num, sizeof(*selected_detections), compare_by_lefts);int i;for (i = 0; i < selected_detections_num; ++i){//计算xmin，xmax,ymin,ymaxfloat xmin = round((selected_detections[i].det.bbox.x - selected_detections[i].det.bbox.w / 2) * im.w);float ymin = round((selected_detections[i].det.bbox.y - selected_detections[i].det.bbox.h / 2) * im.h);float xmax = round((selected_detections[i].det.bbox.x + selected_detections[i].det.bbox.w / 2) * im.w);float ymax = round((selected_detections[i].det.bbox.y + selected_detections[i].det.bbox.h / 2) * im.h);if (xmin < 0){xmin = 0;}if (ymin < 0){ymin = 0;}if (xmax < 0){xmax = 0;}if (ymax < 0){ymax = 0;}const int best_class = selected_detections[i].best_class;printf("%s,%s,%.15f%,", names[best_class], impath, selected_detections[i].det.prob[best_class]);//改输出函数if (ext_output)/* printf("\t(left_x: %4.0f top_y: %4.0f width: %4.0f height: %4.0f)\n",round((selected_detections[i].det.bbox.x - selected_detections[i].det.bbox.w / 2)*im.w),round((selected_detections[i].det.bbox.y - selected_detections[i].det.bbox.h / 2)*im.h),round(selected_detections[i].det.bbox.w*im.w), round(selected_detections[i].det.bbox.h*im.h));printf("\txmin: %4.0f ymin: %4.0f xmax: %4.0f ymax: %4.0f\n",*/printf("%4.0f,%4.0f,%4.0f,%4.0f\n",xmin,ymin,xmax,ymax);elseprintf("\n");int j;for (j = 0; j < classes; ++j) {if (selected_detections[i].det.prob[j] > thresh&& j != best_class) {printf("%s,%s,%.15f%", names[j], impath, selected_detections[i].det.prob[j]);if (ext_output)/* printf("\t(left_x: %4.0f top_y: %4.0f width: %4.0f height: %4.0f)\n",round((selected_detections[i].det.bbox.x - selected_detections[i].det.bbox.w / 2)*im.w),round((selected_detections[i].det.bbox.y - selected_detections[i].det.bbox.h / 2)*im.h),round(selected_detections[i].det.bbox.w*im.w), round(selected_detections[i].det.bbox.h*im.h));*///printf("xmin: %4.0f ymin: %4.0f xmax: %4.0f ymax: %4.0f\n",printf(",%4.0f,%4.0f,%4.0f,%4.0f\n",xmin,ymin,xmax,ymax);elseprintf("\n");}}}// image outputqsort(selected_detections, selected_detections_num, sizeof(*selected_detections), compare_by_probs);//记录识别框IDint imageID_1 = 0;//海胆int imageID_2 = 0;//海参int imageID_3 = 0;//扇贝int imageID_4 = 0;//海星for (i = 0; i < selected_detections_num; ++i){int width = im.h * .006;if (width < 1)width = 1;/*if(0){width = pow(prob, 1./2.)*10+1;alphabet = 0;}*///printf("%d %s: %.0f%%\n", i, names[selected_detections[i].best_class], prob*100);int offset = selected_detections[i].best_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 = selected_detections[i].det.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;//int b_x_center = (left + right) / 2;//int b_y_center = (top + bot) / 2;//int b_width = right - left;//int b_height = bot - top;//sprintf(labelstr, "%d x %d - w: %d, h: %d", b_x_center, b_y_center, b_width, b_height);// you should create directory: result_img//static int copied_frame_id = -1;//static image copy_img;//if (copied_frame_id != frame_id) {// copied_frame_id = frame_id;// if (copy_img.data) free_image(copy_img);// copy_img = copy_image(im);//}//image cropped_im = crop_image(copy_img, left, top, right - left, bot - top);//static int img_id = 0;//img_id++;//char image_name[1024];//int best_class_id = selected_detections[i].best_class;//sprintf(image_name, "result_img/img_%d_%d_%d_%s.jpg", frame_id, img_id, best_class_id, names[best_class_id]);//save_image(cropped_im, image_name);//free_image(cropped_im);if (im.c == 1){draw_box_width_bw(im, left, top, right, bot, width, 0.8); // 1 channel Black-White}else{draw_box_width(im, left, top, right, bot, width, red, green, blue); // 3 channels RGB}if (alphabet){char echinus[7] = "echinus";char holothurian[11] = "holothurian";char scallop[7] = "scallop";char starfish[8] = "starfish";char labelstr[4096] = {0 };strcat(labelstr, names[selected_detections[i].best_class]);int j;for (j = 0; j < classes; ++j){if (selected_detections[i].det.prob[j] > thresh&& j != selected_detections[i].best_class){strcat(labelstr, ", ");strcat(labelstr, names[j]);}}//添加置信度char buff[10];//_gcvt((selected_detections[i].det.prob[selected_detections[i].best_class] * 100), 5, buff);sprintf(buff, "%.2f", selected_detections[i].det.prob[selected_detections[i].best_class] * 100);char prob[] = ":";strcat(labelstr, prob);strcat(labelstr, buff);strcat(labelstr, "%");char prob2[] = " NO:";strcat(labelstr, prob2);//计数并编号if (!strcmp(names[selected_detections[i].best_class], echinus)){imageID_1++;char buff2[5];strcpy(buff2, "");strcat(labelstr, _itoa(imageID_1, buff2, 10));}else if (!strcmp(names[selected_detections[i].best_class], holothurian)){imageID_2++;char buff2[5];strcpy(buff2, "");strcat(labelstr, _itoa(imageID_2, buff2, 10));}else if (!strcmp(names[selected_detections[i].best_class], scallop)){imageID_3++;char buff2[5];strcpy(buff2, "");strcat(labelstr, _itoa(imageID_3, buff2, 10));}else{imageID_4++;char buff2[5];strcpy(buff2, "");strcat(labelstr, _itoa(imageID_4, buff2, 10));}image label = get_label_v3(alphabet, labelstr, (im.h * .03));draw_label(im, top + width, left, label, rgb);free_image(label);}if (selected_detections[i].det.mask) {image mask = float_to_image(14, 14, 1, selected_detections[i].det.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);}}free(selected_detections);}

附上测试结果（批量测试并保存图片，看了几篇博客，找不到链接了）

输出检测结果（图片、置信度、位置坐标）到txt文档（这个改的地方有点杂，回来整理整理再发出来）