mirror of
https://github.com/pjreddie/darknet.git
synced 2023-08-10 21:13:14 +03:00
Fixed training, calculation mAP and anchors for Yolo v3
This commit is contained in:
AlexeyAB
533
src/detector.c
533
src/detector.c
@ -170,7 +170,7 @@ void train_detector(char *datacfg, char *cfgfile, char *weightfile, int *gpus, i
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avg_loss = avg_loss*.9 + loss*.1;
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i = get_current_batch(net);
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printf("%d: %f, %f avg, %f rate, %lf seconds, %d images\n", get_current_batch(net), loss, avg_loss, get_current_rate(net), sec(clock()-time), i*imgs);
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printf("\n %d: %f, %f avg, %f rate, %lf seconds, %d images\n", get_current_batch(net), loss, avg_loss, get_current_rate(net), sec(clock()-time), i*imgs);
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#ifdef OPENCV
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if(!dont_show)
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@ -204,301 +204,290 @@ void train_detector(char *datacfg, char *cfgfile, char *weightfile, int *gpus, i
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static int get_coco_image_id(char *filename)
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{
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char *p = strrchr(filename, '_');
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return atoi(p+1);
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char *p = strrchr(filename, '/');
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char *c = strrchr(filename, '_');
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if (c) p = c;
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return atoi(p + 1);
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}
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static void print_cocos(FILE *fp, char *image_path, box *boxes, float **probs, int num_boxes, int classes, int w, int h)
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static void print_cocos(FILE *fp, char *image_path, detection *dets, int num_boxes, int classes, int w, int h)
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{
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int i, j;
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int image_id = get_coco_image_id(image_path);
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for(i = 0; i < num_boxes; ++i){
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float xmin = boxes[i].x - boxes[i].w/2.;
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float xmax = boxes[i].x + boxes[i].w/2.;
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float ymin = boxes[i].y - boxes[i].h/2.;
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float ymax = boxes[i].y + boxes[i].h/2.;
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int i, j;
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int image_id = get_coco_image_id(image_path);
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for (i = 0; i < num_boxes; ++i) {
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float xmin = dets[i].bbox.x - dets[i].bbox.w / 2.;
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float xmax = dets[i].bbox.x + dets[i].bbox.w / 2.;
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float ymin = dets[i].bbox.y - dets[i].bbox.h / 2.;
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float ymax = dets[i].bbox.y + dets[i].bbox.h / 2.;
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if (xmin < 0) xmin = 0;
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if (ymin < 0) ymin = 0;
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if (xmax > w) xmax = w;
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if (ymax > h) ymax = h;
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if (xmin < 0) xmin = 0;
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if (ymin < 0) ymin = 0;
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if (xmax > w) xmax = w;
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if (ymax > h) ymax = h;
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float bx = xmin;
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float by = ymin;
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float bw = xmax - xmin;
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float bh = ymax - ymin;
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float bx = xmin;
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float by = ymin;
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float bw = xmax - xmin;
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float bh = ymax - ymin;
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for(j = 0; j < classes; ++j){
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if (probs[i][j]) fprintf(fp, "{\"image_id\":%d, \"category_id\":%d, \"bbox\":[%f, %f, %f, %f], \"score\":%f},\n", image_id, coco_ids[j], bx, by, bw, bh, probs[i][j]);
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}
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}
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for (j = 0; j < classes; ++j) {
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if (dets[i].prob[j]) fprintf(fp, "{\"image_id\":%d, \"category_id\":%d, \"bbox\":[%f, %f, %f, %f], \"score\":%f},\n", image_id, coco_ids[j], bx, by, bw, bh, dets[i].prob[j]);
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}
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}
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}
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void print_detector_detections(FILE **fps, char *id, box *boxes, float **probs, int total, int classes, int w, int h)
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void print_detector_detections(FILE **fps, char *id, detection *dets, int total, int classes, int w, int h)
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{
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int i, j;
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for(i = 0; i < total; ++i){
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float xmin = boxes[i].x - boxes[i].w/2.;
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float xmax = boxes[i].x + boxes[i].w/2.;
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float ymin = boxes[i].y - boxes[i].h/2.;
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float ymax = boxes[i].y + boxes[i].h/2.;
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int i, j;
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for (i = 0; i < total; ++i) {
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float xmin = dets[i].bbox.x - dets[i].bbox.w / 2. + 1;
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float xmax = dets[i].bbox.x + dets[i].bbox.w / 2. + 1;
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float ymin = dets[i].bbox.y - dets[i].bbox.h / 2. + 1;
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float ymax = dets[i].bbox.y + dets[i].bbox.h / 2. + 1;
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if (xmin < 0) xmin = 0;
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if (ymin < 0) ymin = 0;
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if (xmax > w) xmax = w;
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if (ymax > h) ymax = h;
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if (xmin < 1) xmin = 1;
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if (ymin < 1) ymin = 1;
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if (xmax > w) xmax = w;
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if (ymax > h) ymax = h;
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for(j = 0; j < classes; ++j){
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if (probs[i][j]) fprintf(fps[j], "%s %f %f %f %f %f\n", id, probs[i][j],
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xmin, ymin, xmax, ymax);
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}
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}
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for (j = 0; j < classes; ++j) {
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if (dets[i].prob[j]) fprintf(fps[j], "%s %f %f %f %f %f\n", id, dets[i].prob[j],
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xmin, ymin, xmax, ymax);
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}
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}
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}
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void print_imagenet_detections(FILE *fp, int id, box *boxes, float **probs, int total, int classes, int w, int h)
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void print_imagenet_detections(FILE *fp, int id, detection *dets, int total, int classes, int w, int h)
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{
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int i, j;
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for(i = 0; i < total; ++i){
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float xmin = boxes[i].x - boxes[i].w/2.;
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float xmax = boxes[i].x + boxes[i].w/2.;
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float ymin = boxes[i].y - boxes[i].h/2.;
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float ymax = boxes[i].y + boxes[i].h/2.;
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int i, j;
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for (i = 0; i < total; ++i) {
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float xmin = dets[i].bbox.x - dets[i].bbox.w / 2.;
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float xmax = dets[i].bbox.x + dets[i].bbox.w / 2.;
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float ymin = dets[i].bbox.y - dets[i].bbox.h / 2.;
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float ymax = dets[i].bbox.y + dets[i].bbox.h / 2.;
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if (xmin < 0) xmin = 0;
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if (ymin < 0) ymin = 0;
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if (xmax > w) xmax = w;
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if (ymax > h) ymax = h;
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if (xmin < 0) xmin = 0;
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if (ymin < 0) ymin = 0;
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if (xmax > w) xmax = w;
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if (ymax > h) ymax = h;
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for(j = 0; j < classes; ++j){
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int class_id = j;
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if (probs[i][class_id]) fprintf(fp, "%d %d %f %f %f %f %f\n", id, j+1, probs[i][class_id],
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xmin, ymin, xmax, ymax);
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}
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}
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for (j = 0; j < classes; ++j) {
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int class = j;
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if (dets[i].prob[class]) fprintf(fp, "%d %d %f %f %f %f %f\n", id, j + 1, dets[i].prob[class],
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xmin, ymin, xmax, ymax);
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}
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}
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}
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void validate_detector(char *datacfg, char *cfgfile, char *weightfile)
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void validate_detector(char *datacfg, char *cfgfile, char *weightfile, char *outfile)
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{
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int j;
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list *options = read_data_cfg(datacfg);
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char *valid_images = option_find_str(options, "valid", "data/train.list");
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char *name_list = option_find_str(options, "names", "data/names.list");
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char *prefix = option_find_str(options, "results", "results");
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char **names = get_labels(name_list);
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char *mapf = option_find_str(options, "map", 0);
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int *map = 0;
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if (mapf) map = read_map(mapf);
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int j;
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list *options = read_data_cfg(datacfg);
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char *valid_images = option_find_str(options, "valid", "data/train.list");
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char *name_list = option_find_str(options, "names", "data/names.list");
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char *prefix = option_find_str(options, "results", "results");
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char **names = get_labels(name_list);
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char *mapf = option_find_str(options, "map", 0);
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int *map = 0;
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if (mapf) map = read_map(mapf);
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network net = parse_network_cfg_custom(cfgfile, 1);
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if(weightfile){
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load_weights(&net, weightfile);
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}
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set_batch_network(&net, 1);
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fprintf(stderr, "Learning Rate: %g, Momentum: %g, Decay: %g\n", net.learning_rate, net.momentum, net.decay);
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srand(time(0));
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network net = parse_network_cfg_custom(cfgfile, 1);
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if (weightfile) {
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load_weights(&net, weightfile);
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}
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set_batch_network(&net, 1);
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fprintf(stderr, "Learning Rate: %g, Momentum: %g, Decay: %g\n", net.learning_rate, net.momentum, net.decay);
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srand(time(0));
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char *base = "comp4_det_test_";
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list *plist = get_paths(valid_images);
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char **paths = (char **)list_to_array(plist);
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list *plist = get_paths(valid_images);
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char **paths = (char **)list_to_array(plist);
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layer l = net.layers[net.n-1];
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int classes = l.classes;
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layer l = net.layers[net.n - 1];
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int classes = l.classes;
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char buff[1024];
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char *type = option_find_str(options, "eval", "voc");
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FILE *fp = 0;
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FILE **fps = 0;
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int coco = 0;
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int imagenet = 0;
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if(0==strcmp(type, "coco")){
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snprintf(buff, 1024, "%s/coco_results.json", prefix);
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fp = fopen(buff, "w");
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fprintf(fp, "[\n");
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coco = 1;
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} else if(0==strcmp(type, "imagenet")){
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snprintf(buff, 1024, "%s/imagenet-detection.txt", prefix);
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fp = fopen(buff, "w");
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imagenet = 1;
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classes = 200;
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} else {
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fps = calloc(classes, sizeof(FILE *));
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for(j = 0; j < classes; ++j){
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snprintf(buff, 1024, "%s/%s%s.txt", prefix, base, names[j]);
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fps[j] = fopen(buff, "w");
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}
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}
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char buff[1024];
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char *type = option_find_str(options, "eval", "voc");
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FILE *fp = 0;
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FILE **fps = 0;
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int coco = 0;
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int imagenet = 0;
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if (0 == strcmp(type, "coco")) {
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if (!outfile) outfile = "coco_results";
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snprintf(buff, 1024, "%s/%s.json", prefix, outfile);
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fp = fopen(buff, "w");
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fprintf(fp, "[\n");
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coco = 1;
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}
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else if (0 == strcmp(type, "imagenet")) {
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if (!outfile) outfile = "imagenet-detection";
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snprintf(buff, 1024, "%s/%s.txt", prefix, outfile);
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fp = fopen(buff, "w");
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imagenet = 1;
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classes = 200;
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}
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else {
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if (!outfile) outfile = "comp4_det_test_";
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fps = calloc(classes, sizeof(FILE *));
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for (j = 0; j < classes; ++j) {
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snprintf(buff, 1024, "%s/%s%s.txt", prefix, outfile, names[j]);
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fps[j] = fopen(buff, "w");
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}
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}
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box *boxes = calloc(l.w*l.h*l.n, sizeof(box));
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float **probs = calloc(l.w*l.h*l.n, sizeof(float *));
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for(j = 0; j < l.w*l.h*l.n; ++j) probs[j] = calloc(classes, sizeof(float *));
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int m = plist->size;
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int i = 0;
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int t;
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int m = plist->size;
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int i=0;
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int t;
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float thresh = .005;
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float nms = .45;
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float thresh = .005;
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float nms = .45;
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int nthreads = 4;
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image *val = calloc(nthreads, sizeof(image));
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image *val_resized = calloc(nthreads, sizeof(image));
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image *buf = calloc(nthreads, sizeof(image));
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image *buf_resized = calloc(nthreads, sizeof(image));
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pthread_t *thr = calloc(nthreads, sizeof(pthread_t));
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int detection_count = 0;
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load_args args = { 0 };
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args.w = net.w;
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args.h = net.h;
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args.type = IMAGE_DATA;
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//args.type = LETTERBOX_DATA;
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int nthreads = 4;
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image *val = calloc(nthreads, sizeof(image));
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image *val_resized = calloc(nthreads, sizeof(image));
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image *buf = calloc(nthreads, sizeof(image));
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image *buf_resized = calloc(nthreads, sizeof(image));
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pthread_t *thr = calloc(nthreads, sizeof(pthread_t));
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load_args args = {0};
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args.w = net.w;
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args.h = net.h;
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args.type = IMAGE_DATA;
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for(t = 0; t < nthreads; ++t){
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args.path = paths[i+t];
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args.im = &buf[t];
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args.resized = &buf_resized[t];
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thr[t] = load_data_in_thread(args);
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}
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time_t start = time(0);
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for(i = nthreads; i < m+nthreads; i += nthreads){
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fprintf(stderr, "%d\n", i);
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for(t = 0; t < nthreads && i+t-nthreads < m; ++t){
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pthread_join(thr[t], 0);
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val[t] = buf[t];
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val_resized[t] = buf_resized[t];
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}
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for(t = 0; t < nthreads && i+t < m; ++t){
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args.path = paths[i+t];
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args.im = &buf[t];
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args.resized = &buf_resized[t];
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thr[t] = load_data_in_thread(args);
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}
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for(t = 0; t < nthreads && i+t-nthreads < m; ++t){
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char *path = paths[i+t-nthreads];
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char *id = basecfg(path);
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float *X = val_resized[t].data;
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network_predict(net, X);
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int w = val[t].w;
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int h = val[t].h;
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get_region_boxes(l, w, h, thresh, probs, boxes, 0, map);
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if (nms) do_nms_sort(boxes, probs, l.w*l.h*l.n, classes, nms);
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int x, y;
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for (x = 0; x < (l.w*l.h*l.n); ++x) {
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for (y = 0; y < classes; ++y)
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{
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if (probs[x][y]) ++detection_count;
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}
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for (t = 0; t < nthreads; ++t) {
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args.path = paths[i + t];
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args.im = &buf[t];
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args.resized = &buf_resized[t];
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thr[t] = load_data_in_thread(args);
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}
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time_t start = time(0);
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for (i = nthreads; i < m + nthreads; i += nthreads) {
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fprintf(stderr, "%d\n", i);
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for (t = 0; t < nthreads && i + t - nthreads < m; ++t) {
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pthread_join(thr[t], 0);
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val[t] = buf[t];
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val_resized[t] = buf_resized[t];
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}
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for (t = 0; t < nthreads && i + t < m; ++t) {
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args.path = paths[i + t];
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args.im = &buf[t];
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args.resized = &buf_resized[t];
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thr[t] = load_data_in_thread(args);
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}
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for (t = 0; t < nthreads && i + t - nthreads < m; ++t) {
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char *path = paths[i + t - nthreads];
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char *id = basecfg(path);
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float *X = val_resized[t].data;
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network_predict(net, X);
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int w = val[t].w;
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int h = val[t].h;
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int nboxes = 0;
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int letterbox = (args.type == LETTERBOX_DATA);
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detection *dets = get_network_boxes(&net, w, h, thresh, .5, map, 0, &nboxes, letterbox);
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if (nms) do_nms_sort_v3(dets, nboxes, classes, nms);
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if (coco) {
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print_cocos(fp, path, dets, nboxes, classes, w, h);
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}
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if (coco){
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print_cocos(fp, path, boxes, probs, l.w*l.h*l.n, classes, w, h);
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} else if (imagenet){
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print_imagenet_detections(fp, i+t-nthreads+1, boxes, probs, l.w*l.h*l.n, classes, w, h);
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} else {
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print_detector_detections(fps, id, boxes, probs, l.w*l.h*l.n, classes, w, h);
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}
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free(id);
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free_image(val[t]);
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free_image(val_resized[t]);
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}
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}
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for(j = 0; j < classes; ++j){
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if(fps) fclose(fps[j]);
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}
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if(coco){
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fseek(fp, -2, SEEK_CUR);
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fprintf(fp, "\n]\n");
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fclose(fp);
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}
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printf("\n detection_count = %d \n", detection_count);
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fprintf(stderr, "Total Detection Time: %f Seconds\n", (double)(time(0) - start));
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else if (imagenet) {
|
||||
print_imagenet_detections(fp, i + t - nthreads + 1, dets, nboxes, classes, w, h);
|
||||
}
|
||||
else {
|
||||
print_detector_detections(fps, id, dets, nboxes, classes, w, h);
|
||||
}
|
||||
free_detections(dets, nboxes);
|
||||
free(id);
|
||||
free_image(val[t]);
|
||||
free_image(val_resized[t]);
|
||||
}
|
||||
}
|
||||
for (j = 0; j < classes; ++j) {
|
||||
if (fps) fclose(fps[j]);
|
||||
}
|
||||
if (coco) {
|
||||
fseek(fp, -2, SEEK_CUR);
|
||||
fprintf(fp, "\n]\n");
|
||||
fclose(fp);
|
||||
}
|
||||
fprintf(stderr, "Total Detection Time: %f Seconds\n", time(0) - start);
|
||||
}
|
||||
|
||||
void validate_detector_recall(char *datacfg, char *cfgfile, char *weightfile)
|
||||
{
|
||||
network net = parse_network_cfg_custom(cfgfile, 1);
|
||||
if(weightfile){
|
||||
load_weights(&net, weightfile);
|
||||
}
|
||||
set_batch_network(&net, 1);
|
||||
fprintf(stderr, "Learning Rate: %g, Momentum: %g, Decay: %g\n", net.learning_rate, net.momentum, net.decay);
|
||||
srand(time(0));
|
||||
network net = parse_network_cfg_custom(cfgfile, 1);
|
||||
if (weightfile) {
|
||||
load_weights(&net, weightfile);
|
||||
}
|
||||
set_batch_network(&net, 1);
|
||||
srand(time(0));
|
||||
|
||||
//list *plist = get_paths("data/coco_val_5k.list");
|
||||
list *options = read_data_cfg(datacfg);
|
||||
char *valid_images = option_find_str(options, "valid", "data/train.txt");
|
||||
list *plist = get_paths(valid_images);
|
||||
char **paths = (char **)list_to_array(plist);
|
||||
list *plist = get_paths(valid_images);
|
||||
char **paths = (char **)list_to_array(plist);
|
||||
|
||||
layer l = net.layers[net.n-1];
|
||||
int classes = l.classes;
|
||||
layer l = net.layers[net.n - 1];
|
||||
|
||||
int j, k;
|
||||
box *boxes = calloc(l.w*l.h*l.n, sizeof(box));
|
||||
float **probs = calloc(l.w*l.h*l.n, sizeof(float *));
|
||||
for(j = 0; j < l.w*l.h*l.n; ++j) probs[j] = calloc(classes, sizeof(float *));
|
||||
int j, k;
|
||||
|
||||
int m = plist->size;
|
||||
int i=0;
|
||||
int m = plist->size;
|
||||
int i = 0;
|
||||
|
||||
float thresh = .001;// .001; // .2;
|
||||
float iou_thresh = .5;
|
||||
float nms = .4;
|
||||
float thresh = .001;
|
||||
float iou_thresh = .5;
|
||||
float nms = .4;
|
||||
|
||||
int detection_count = 0, truth_count = 0;
|
||||
int total = 0;
|
||||
int correct = 0;
|
||||
int proposals = 0;
|
||||
float avg_iou = 0;
|
||||
|
||||
int total = 0;
|
||||
int correct = 0;
|
||||
int proposals = 0;
|
||||
float avg_iou = 0;
|
||||
for (i = 0; i < m; ++i) {
|
||||
char *path = paths[i];
|
||||
image orig = load_image_color(path, 0, 0);
|
||||
image sized = resize_image(orig, net.w, net.h);
|
||||
char *id = basecfg(path);
|
||||
network_predict(net, sized.data);
|
||||
int nboxes = 0;
|
||||
int letterbox = 0;
|
||||
detection *dets = get_network_boxes(&net, sized.w, sized.h, thresh, .5, 0, 1, &nboxes, letterbox);
|
||||
if (nms) do_nms_obj_v3(dets, nboxes, 1, nms);
|
||||
|
||||
for(i = 0; i < m; ++i){
|
||||
char *path = paths[i];
|
||||
image orig = load_image_color(path, 0, 0);
|
||||
image sized = resize_image(orig, net.w, net.h);
|
||||
char *id = basecfg(path);
|
||||
network_predict(net, sized.data);
|
||||
get_region_boxes(l, 1, 1, thresh, probs, boxes, 1, 0);
|
||||
if (nms) do_nms(boxes, probs, l.w*l.h*l.n, 1, nms);
|
||||
char labelpath[4096];
|
||||
find_replace(path, "images", "labels", labelpath);
|
||||
find_replace(labelpath, "JPEGImages", "labels", labelpath);
|
||||
find_replace(labelpath, ".jpg", ".txt", labelpath);
|
||||
find_replace(labelpath, ".JPEG", ".txt", labelpath);
|
||||
|
||||
char labelpath[4096];
|
||||
find_replace(path, "images", "labels", labelpath);
|
||||
find_replace(labelpath, "JPEGImages", "labels", labelpath);
|
||||
find_replace(labelpath, ".jpg", ".txt", labelpath);
|
||||
find_replace(labelpath, ".JPEG", ".txt", labelpath);
|
||||
find_replace(labelpath, ".png", ".txt", labelpath);
|
||||
|
||||
int num_labels = 0;
|
||||
box_label *truth = read_boxes(labelpath, &num_labels);
|
||||
truth_count += num_labels;
|
||||
for(k = 0; k < l.w*l.h*l.n; ++k){
|
||||
if(probs[k][0] > thresh){
|
||||
++proposals;
|
||||
}
|
||||
}
|
||||
int num_labels = 0;
|
||||
box_label *truth = read_boxes(labelpath, &num_labels);
|
||||
for (k = 0; k < nboxes; ++k) {
|
||||
if (dets[k].objectness > thresh) {
|
||||
++proposals;
|
||||
}
|
||||
}
|
||||
for (j = 0; j < num_labels; ++j) {
|
||||
++total;
|
||||
box t = { truth[j].x, truth[j].y, truth[j].w, truth[j].h };
|
||||
float best_iou = 0;
|
||||
for (k = 0; k < l.w*l.h*l.n; ++k) {
|
||||
float iou = box_iou(boxes[k], t);
|
||||
if (probs[k][0] > thresh && iou > best_iou) {
|
||||
float iou = box_iou(dets[k].bbox, t);
|
||||
if (dets[k].objectness > thresh && iou > best_iou) {
|
||||
best_iou = iou;
|
||||
}
|
||||
}
|
||||
avg_iou += best_iou;
|
||||
if(best_iou > iou_thresh){
|
||||
++correct;
|
||||
}
|
||||
}
|
||||
avg_iou += best_iou;
|
||||
if (best_iou > iou_thresh) {
|
||||
++correct;
|
||||
}
|
||||
}
|
||||
|
||||
fprintf(stderr, "%5d %5d %5d\tRPs/Img: %.2f\tIOU: %.2f%%\tRecall:%.2f%%\n", i, correct, total, (float)proposals/(i+1), avg_iou*100/total, 100.*correct/total);
|
||||
free(id);
|
||||
free_image(orig);
|
||||
free_image(sized);
|
||||
}
|
||||
printf("\n truth_count = %d \n", truth_count);
|
||||
fprintf(stderr, "%5d %5d %5d\tRPs/Img: %.2f\tIOU: %.2f%%\tRecall:%.2f%%\n", i, correct, total, (float)proposals / (i + 1), avg_iou * 100 / total, 100.*correct / total);
|
||||
free(id);
|
||||
free_image(orig);
|
||||
free_image(sized);
|
||||
}
|
||||
}
|
||||
|
||||
typedef struct {
|
||||
@ -537,7 +526,6 @@ void validate_detector_map(char *datacfg, char *cfgfile, char *weightfile, float
|
||||
load_weights(&net, weightfile);
|
||||
}
|
||||
set_batch_network(&net, 1);
|
||||
fprintf(stderr, "Learning Rate: %g, Momentum: %g, Decay: %g\n", net.learning_rate, net.momentum, net.decay);
|
||||
srand(time(0));
|
||||
|
||||
list *plist = get_paths(valid_images);
|
||||
@ -553,10 +541,6 @@ void validate_detector_map(char *datacfg, char *cfgfile, char *weightfile, float
|
||||
layer l = net.layers[net.n - 1];
|
||||
int classes = l.classes;
|
||||
|
||||
box *boxes = calloc(l.w*l.h*l.n, sizeof(box));
|
||||
float **probs = calloc(l.w*l.h*l.n, sizeof(float *));
|
||||
for (j = 0; j < l.w*l.h*l.n; ++j) probs[j] = calloc(classes, sizeof(float *));
|
||||
|
||||
int m = plist->size;
|
||||
int i = 0;
|
||||
int t;
|
||||
@ -576,6 +560,7 @@ void validate_detector_map(char *datacfg, char *cfgfile, char *weightfile, float
|
||||
args.w = net.w;
|
||||
args.h = net.h;
|
||||
args.type = IMAGE_DATA;
|
||||
//args.type = LETTERBOX_DATA;
|
||||
|
||||
//const float thresh_calc_avg_iou = 0.24;
|
||||
float avg_iou = 0;
|
||||
@ -614,8 +599,12 @@ void validate_detector_map(char *datacfg, char *cfgfile, char *weightfile, float
|
||||
char *id = basecfg(path);
|
||||
float *X = val_resized[t].data;
|
||||
network_predict(net, X);
|
||||
get_region_boxes(l, 1, 1, thresh, probs, boxes, 0, map);
|
||||
if (nms) do_nms_sort(boxes, probs, l.w*l.h*l.n, classes, nms);
|
||||
|
||||
int nboxes = 0;
|
||||
int letterbox = (args.type == LETTERBOX_DATA);
|
||||
float hier_thresh = 0;
|
||||
detection *dets = get_network_boxes(&net, 1, 1, thresh, hier_thresh, 0, 1, &nboxes, letterbox);
|
||||
if (nms) do_nms_sort_v3(dets, nboxes, l.classes, nms);
|
||||
|
||||
char labelpath[4096];
|
||||
find_replace(path, "images", "labels", labelpath);
|
||||
@ -646,15 +635,15 @@ void validate_detector_map(char *datacfg, char *cfgfile, char *weightfile, float
|
||||
truth_dif = read_boxes(labelpath_dif, &num_labels_dif);
|
||||
}
|
||||
|
||||
for (i = 0; i < (l.w*l.h*l.n); ++i) {
|
||||
for (i = 0; i < nboxes; ++i) {
|
||||
|
||||
int class_id;
|
||||
for (class_id = 0; class_id < classes; ++class_id) {
|
||||
float prob = probs[i][class_id];
|
||||
float prob = dets[i].prob[class_id];
|
||||
if (prob > 0) {
|
||||
detections_count++;
|
||||
detections = realloc(detections, detections_count * sizeof(box_prob));
|
||||
detections[detections_count - 1].b = boxes[i];
|
||||
detections[detections_count - 1].b = dets[i].bbox;
|
||||
detections[detections_count - 1].p = prob;
|
||||
detections[detections_count - 1].image_index = image_index;
|
||||
detections[detections_count - 1].class_id = class_id;
|
||||
@ -667,8 +656,8 @@ void validate_detector_map(char *datacfg, char *cfgfile, char *weightfile, float
|
||||
{
|
||||
box t = { truth[j].x, truth[j].y, truth[j].w, truth[j].h };
|
||||
//printf(" IoU = %f, prob = %f, class_id = %d, truth[j].id = %d \n",
|
||||
// box_iou(boxes[i], t), prob, class_id, truth[j].id);
|
||||
float current_iou = box_iou(boxes[i], t);
|
||||
// box_iou(dets[i].bbox, t), prob, class_id, truth[j].id);
|
||||
float current_iou = box_iou(dets[i].bbox, t);
|
||||
if (current_iou > iou_thresh && class_id == truth[j].id) {
|
||||
if (current_iou > max_iou) {
|
||||
max_iou = current_iou;
|
||||
@ -686,7 +675,7 @@ void validate_detector_map(char *datacfg, char *cfgfile, char *weightfile, float
|
||||
// if object is difficult then remove detection
|
||||
for (j = 0; j < num_labels_dif; ++j) {
|
||||
box t = { truth_dif[j].x, truth_dif[j].y, truth_dif[j].w, truth_dif[j].h };
|
||||
float current_iou = box_iou(boxes[i], t);
|
||||
float current_iou = box_iou(dets[i].bbox, t);
|
||||
if (current_iou > iou_thresh && class_id == truth_dif[j].id) {
|
||||
--detections_count;
|
||||
break;
|
||||
@ -709,6 +698,7 @@ void validate_detector_map(char *datacfg, char *cfgfile, char *weightfile, float
|
||||
|
||||
unique_truth_count += num_labels;
|
||||
|
||||
free_detections(dets, nboxes);
|
||||
free(id);
|
||||
free_image(val[t]);
|
||||
free_image(val_resized[t]);
|
||||
@ -830,9 +820,9 @@ void validate_detector_map(char *datacfg, char *cfgfile, char *weightfile, float
|
||||
}
|
||||
|
||||
#ifdef OPENCV
|
||||
void calc_anchors(char *datacfg, int num_of_clusters, int final_width, int final_height, int show)
|
||||
void calc_anchors(char *datacfg, int num_of_clusters, int width, int height, int show)
|
||||
{
|
||||
printf("\n num_of_clusters = %d, final_width = %d, final_height = %d \n", num_of_clusters, final_width, final_height);
|
||||
printf("\n num_of_clusters = %d, width = %d, height = %d \n", num_of_clusters, width, height);
|
||||
|
||||
//float pointsdata[] = { 1,1, 2,2, 6,6, 5,5, 10,10 };
|
||||
float *rel_width_height_array = calloc(1000, sizeof(float));
|
||||
@ -862,8 +852,8 @@ void calc_anchors(char *datacfg, int num_of_clusters, int final_width, int final
|
||||
{
|
||||
number_of_boxes++;
|
||||
rel_width_height_array = realloc(rel_width_height_array, 2 * number_of_boxes * sizeof(float));
|
||||
rel_width_height_array[number_of_boxes * 2 - 2] = truth[j].w * final_width;
|
||||
rel_width_height_array[number_of_boxes * 2 - 1] = truth[j].h * final_height;
|
||||
rel_width_height_array[number_of_boxes * 2 - 2] = truth[j].w * width;
|
||||
rel_width_height_array[number_of_boxes * 2 - 1] = truth[j].h * height;
|
||||
printf("\r loaded \t image: %d \t box: %d", i+1, number_of_boxes);
|
||||
}
|
||||
}
|
||||
@ -967,15 +957,15 @@ void calc_anchors(char *datacfg, int num_of_clusters, int final_width, int final
|
||||
for (j = 0; j < num_of_clusters; ++j) {
|
||||
CvPoint pt1, pt2;
|
||||
pt1.x = pt1.y = 0;
|
||||
pt2.x = centers->data.fl[j * 2] * img_size / final_width;
|
||||
pt2.y = centers->data.fl[j * 2 + 1] * img_size / final_height;
|
||||
pt2.x = centers->data.fl[j * 2] * img_size / width;
|
||||
pt2.y = centers->data.fl[j * 2 + 1] * img_size / height;
|
||||
cvRectangle(img, pt1, pt2, CV_RGB(255, 255, 255), 1, 8, 0);
|
||||
}
|
||||
|
||||
for (i = 0; i < number_of_boxes; ++i) {
|
||||
CvPoint pt;
|
||||
pt.x = points->data.fl[i * 2] * img_size / final_width;
|
||||
pt.y = points->data.fl[i * 2 + 1] * img_size / final_height;
|
||||
pt.x = points->data.fl[i * 2] * img_size / width;
|
||||
pt.y = points->data.fl[i * 2 + 1] * img_size / height;
|
||||
int cluster_idx = labels->data.i[i];
|
||||
int red_id = (cluster_idx * (uint64_t)123 + 55) % 255;
|
||||
int green_id = (cluster_idx * (uint64_t)321 + 33) % 255;
|
||||
@ -995,7 +985,7 @@ void calc_anchors(char *datacfg, int num_of_clusters, int final_width, int final
|
||||
cvReleaseMat(&labels);
|
||||
}
|
||||
#else
|
||||
void calc_anchors(char *datacfg, int num_of_clusters, int final_width, int final_height, int show) {
|
||||
void calc_anchors(char *datacfg, int num_of_clusters, int width, int height, int show) {
|
||||
printf(" k-means++ can't be used without OpenCV, because there is used cvKMeans2 implementation \n");
|
||||
}
|
||||
#endif // OPENCV
|
||||
@ -1030,9 +1020,9 @@ void test_detector(char *datacfg, char *cfgfile, char *weightfile, char *filenam
|
||||
strtok(input, "\n");
|
||||
}
|
||||
image im = load_image_color(input,0,0);
|
||||
int letter = 0;
|
||||
int letterbox = 0;
|
||||
image sized = resize_image(im, net.w, net.h);
|
||||
//image sized = letterbox_image(im, net.w, net.h); letter = 1;
|
||||
//image sized = letterbox_image(im, net.w, net.h); letterbox = 1;
|
||||
layer l = net.layers[net.n-1];
|
||||
|
||||
//box *boxes = calloc(l.w*l.h*l.n, sizeof(box));
|
||||
@ -1047,7 +1037,7 @@ void test_detector(char *datacfg, char *cfgfile, char *weightfile, char *filenam
|
||||
// if (nms) do_nms_sort(boxes, probs, l.w*l.h*l.n, l.classes, nms);
|
||||
//draw_detections(im, l.w*l.h*l.n, thresh, boxes, probs, names, alphabet, l.classes);
|
||||
int nboxes = 0;
|
||||
detection *dets = get_network_boxes(&net, im.w, im.h, thresh, hier_thresh, 0, 1, &nboxes, letter);
|
||||
detection *dets = get_network_boxes(&net, im.w, im.h, thresh, hier_thresh, 0, 1, &nboxes, letterbox);
|
||||
if (nms) do_nms_sort_v3(dets, nboxes, l.classes, nms);
|
||||
draw_detections_v3(im, dets, nboxes, thresh, names, alphabet, l.classes);
|
||||
free_detections(dets, nboxes);
|
||||
@ -1076,14 +1066,15 @@ void run_detector(int argc, char **argv)
|
||||
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 final_width = find_int_arg(argc, argv, "-final_width", 13);
|
||||
int final_heigh = find_int_arg(argc, argv, "-final_heigh", 13);
|
||||
int width = find_int_arg(argc, argv, "-width", 13);
|
||||
int heigh = find_int_arg(argc, argv, "-heigh", 13);
|
||||
if(argc < 4){
|
||||
fprintf(stderr, "usage: %s %s [train/test/valid] [cfg] [weights (optional)]\n", argv[0], argv[1]);
|
||||
return;
|
||||
@ -1121,10 +1112,10 @@ void run_detector(int argc, char **argv)
|
||||
char *filename = (argc > 6) ? argv[6]: 0;
|
||||
if(0==strcmp(argv[2], "test")) test_detector(datacfg, cfg, weights, filename, thresh, hier_thresh, dont_show);
|
||||
else if(0==strcmp(argv[2], "train")) train_detector(datacfg, cfg, weights, gpus, ngpus, clear, dont_show);
|
||||
else if(0==strcmp(argv[2], "valid")) validate_detector(datacfg, cfg, weights);
|
||||
else if(0==strcmp(argv[2], "valid")) validate_detector(datacfg, cfg, weights, outfile);
|
||||
else if(0==strcmp(argv[2], "recall")) validate_detector_recall(datacfg, cfg, weights);
|
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else if(0==strcmp(argv[2], "map")) validate_detector_map(datacfg, cfg, weights, thresh);
|
||||
else if(0==strcmp(argv[2], "calc_anchors")) calc_anchors(datacfg, num_of_clusters, final_width, final_heigh, show);
|
||||
else if(0==strcmp(argv[2], "calc_anchors")) calc_anchors(datacfg, num_of_clusters, width, heigh, show);
|
||||
else if(0==strcmp(argv[2], "demo")) {
|
||||
list *options = read_data_cfg(datacfg);
|
||||
int classes = option_find_int(options, "classes", 20);
|
||||
|
||||
Reference in New Issue
Block a user