| | |
| | | { |
| | | int selected_num = 0; |
| | | detection_with_class* result_arr = calloc(dets_num, sizeof(detection_with_class)); |
| | | for (int i = 0; i < dets_num; ++i) { |
| | | int i; |
| | | for (i = 0; i < dets_num; ++i) { |
| | | int best_class = -1; |
| | | float best_class_prob = thresh; |
| | | for (int j = 0; j < dets[i].classes; ++j) { |
| | | int j; |
| | | for (j = 0; j < dets[i].classes; ++j) { |
| | | if (dets[i].prob[j] > best_class_prob ) { |
| | | best_class = j; |
| | | best_class_prob = dets[i].prob[j]; |
| | |
| | | |
| | | // text output |
| | | qsort(selected_detections, selected_detections_num, sizeof(*selected_detections), compare_by_lefts); |
| | | for (int i = 0; i < selected_detections_num; ++i) { |
| | | int i; |
| | | for (i = 0; i < selected_detections_num; ++i) { |
| | | const int best_class = selected_detections[i].best_class; |
| | | printf("%s: %.0f%%", names[best_class], selected_detections[i].det.prob[best_class] * 100); |
| | | if (ext_output) |
| | | printf("\t(left: %.0f\ttop: %.0f\tw: %0.f\th: %0.f)\n", |
| | | printf("\t(left: %.0f \ttop: %.0f \tw: %0.f \th: %0.f)\n", |
| | | (selected_detections[i].det.bbox.x - selected_detections[i].det.bbox.w / 2)*im.w, |
| | | (selected_detections[i].det.bbox.y - selected_detections[i].det.bbox.h / 2)*im.h, |
| | | selected_detections[i].det.bbox.w*im.w, selected_detections[i].det.bbox.h*im.h); |
| | | else |
| | | printf("\n"); |
| | | for (int j = 0; j < classes; ++j) { |
| | | int j; |
| | | for (j = 0; j < classes; ++j) { |
| | | if (selected_detections[i].det.prob[j] > thresh && j != best_class) { |
| | | printf("%s: %.0f%%\n", names[j], selected_detections[i].det.prob[j] * 100); |
| | | } |
| | |
| | | |
| | | // image output |
| | | qsort(selected_detections, selected_detections_num, sizeof(*selected_detections), compare_by_probs); |
| | | for (int i = 0; i < selected_detections_num; ++i) { |
| | | for (i = 0; i < selected_detections_num; ++i) { |
| | | int width = im.h * .006; |
| | | if (width < 1) |
| | | width = 1; |
| | |
| | | if (alphabet) { |
| | | char labelstr[4096] = { 0 }; |
| | | strcat(labelstr, names[selected_detections[i].best_class]); |
| | | for (int j = 0; j < classes; ++j) { |
| | | 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]); |
| | |
| | | sprintf(buff, "echo %s >> bad.list", filename); |
| | | system(buff); |
| | | return make_image(10,10,3); |
| | | //exit(0); |
| | | //exit(EXIT_FAILURE); |
| | | } |
| | | image out = ipl_to_image(src); |
| | | cvReleaseImage(&src); |
| | |
| | | unsigned char *data = stbi_load(filename, &w, &h, &c, channels); |
| | | if (!data) { |
| | | fprintf(stderr, "Cannot load image \"%s\"\nSTB Reason: %s\n", filename, stbi_failure_reason()); |
| | | exit(0); |
| | | char buff[256]; |
| | | sprintf(buff, "echo %s >> bad.list", filename); |
| | | system(buff); |
| | | return make_image(10, 10, 3); |
| | | //exit(EXIT_FAILURE); |
| | | } |
| | | if(channels) c = channels; |
| | | int i,j,k; |