| | |
| | | return alphabets; |
| | | } |
| | | |
| | | void draw_detections_v3(image im, detection *dets, int num, float thresh, char **names, image **alphabet, int classes) |
| | | { |
| | | int i, j; |
| | | |
| | | for (i = 0; i < num; ++i) { |
| | | char labelstr[4096] = { 0 }; |
| | | int class_id = -1; |
| | | for (j = 0; j < classes; ++j) { |
| | | |
| | | // Creates array of detections with prob > thresh and fills best_class for them |
| | | detection** get_actual_detections(detection *dets, int dets_num, int classes, float thresh, int* selected_detections_num) |
| | | { |
| | | int selected_num = 0; |
| | | detection** result_arr = calloc(dets_num, sizeof(detection*)); |
| | | for (int i = 0; i < dets_num; ++i) { |
| | | dets[i].best_class = -1; |
| | | for (int j = 0; j < classes; ++j) { |
| | | if (dets[i].prob[j] > thresh) { |
| | | if (class_id < 0) { |
| | | strcat(labelstr, names[j]); |
| | | class_id = j; |
| | | if (dets[i].best_class < 0 || dets[i].prob[dets[i].best_class] < dets[i].prob[j]) { |
| | | dets[i].best_class = j; |
| | | } |
| | | else { |
| | | strcat(labelstr, ", "); |
| | | strcat(labelstr, names[j]); |
| | | } |
| | | printf("%s: %.0f%%\n", names[j], dets[i].prob[j] * 100); |
| | | } |
| | | } |
| | | if (class_id >= 0) { |
| | | if (dets[i].best_class >= 0) { |
| | | result_arr[selected_num] = &(dets[i]); |
| | | ++selected_num; |
| | | } |
| | | } |
| | | if (selected_detections_num) |
| | | *selected_detections_num = selected_num; |
| | | return result_arr; |
| | | } |
| | | |
| | | // compare to sort detection** by bbox.x |
| | | int compare_by_lefts(const void *a, const void *b) { |
| | | const float delta = ((*(detection**)a)->bbox.x - (*(detection**)a)->bbox.w / 2) - ((*(detection**)b)->bbox.x - (*(detection**)b)->bbox.w / 2); |
| | | return delta < 0 ? -1 : delta > 0 ? 1 : 0; |
| | | } |
| | | |
| | | // compare to sort detection** by best_class probability |
| | | int compare_by_probs(const void *a_ptr, const void *b_ptr) { |
| | | const detection* a = *(detection**)a_ptr; |
| | | const detection* b = *(detection**)b_ptr; |
| | | float delta = a->prob[a->best_class] - b->prob[b->best_class]; |
| | | return delta < 0 ? -1 : delta > 0 ? 1 : 0; |
| | | } |
| | | |
| | | void draw_detections_v3(image im, detection *dets, int num, float thresh, char **names, image **alphabet, int classes) |
| | | { |
| | | int selected_detections_num; |
| | | detection** selected_detections = get_actual_detections(dets, num, classes, thresh, &selected_detections_num); |
| | | |
| | | // text output |
| | | qsort(selected_detections, selected_detections_num, sizeof(*selected_detections), compare_by_lefts); |
| | | for (int i = 0; i < selected_detections_num; ++i) { |
| | | const int best_class = selected_detections[i]->best_class; |
| | | printf("%s: %.0f%%\t(left: %.0f\ttop: %.0f\tw: %0.f\th: %0.f)\n", names[best_class], |
| | | selected_detections[i]->prob[best_class] * 100, |
| | | (selected_detections[i]->bbox.x - selected_detections[i]->bbox.w / 2)*im.w, |
| | | (selected_detections[i]->bbox.y - selected_detections[i]->bbox.h / 2)*im.h, |
| | | selected_detections[i]->bbox.w*im.w, selected_detections[i]->bbox.h*im.h); |
| | | for (int j = 0; j < classes; ++j) { |
| | | if (selected_detections[i]->prob[j] > thresh && j != best_class) { |
| | | printf("%s: %.0f%%\n", names[j], selected_detections[i]->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) { |
| | | int width = im.h * .006; |
| | | if (width < 1) |
| | | width = 1; |
| | |
| | | } |
| | | */ |
| | | |
| | | //printf("%d %s: %.0f%%\n", i, names[class_id], prob*100); |
| | | int offset = class_id * 123457 % classes; |
| | | //printf("%d %s: %.0f%%\n", i, names[dets[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); |
| | |
| | | rgb[0] = red; |
| | | rgb[1] = green; |
| | | rgb[2] = blue; |
| | | box b = dets[i].bbox; |
| | | box b = selected_detections[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; |
| | |
| | | |
| | | draw_box_width(im, left, top, right, bot, width, red, green, blue); |
| | | if (alphabet) { |
| | | char labelstr[4096] = { 0 }; |
| | | strcat(labelstr, names[selected_detections[i]->best_class]); |
| | | for (int j = 0; j < classes; ++j) { |
| | | if (selected_detections[i]->prob[j] > thresh && j != selected_detections[i]->best_class) { |
| | | strcat(labelstr, ", "); |
| | | strcat(labelstr, names[j]); |
| | | } |
| | | } |
| | | image label = get_label_v3(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); |
| | | if (selected_detections[i]->mask) { |
| | | image mask = float_to_image(14, 14, 1, selected_detections[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(resized_mask); |
| | | free_image(tmask); |
| | | } |
| | | } |
| | | } |
| | | free(selected_detections); |
| | | } |
| | | |
| | | void draw_detections(image im, int num, float thresh, box *boxes, float **probs, char **names, image **alphabet, int classes) |