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板凳
楼主 |
发表于 2019-9-19 16:46:42
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- #include "darknet.h"
- static int coco_ids[] = {1,2,3,4,5,6,7,8,9,10,11,13,14,15,16,17,18,19,20,21,22,23,24,25,27,28,31,32,33,34,35,36,37,38,39,40,41,42,43,44,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,67,70,72,73,74,75,76,77,78,79,80,81,82,84,85,86,87,88,89,90};
- void train_detector(char *datacfg, char *cfgfile, char *weightfile, int *gpus, int ngpus, int clear)
- {
- list *options = read_data_cfg(datacfg);
- char *train_images = option_find_str(options, "train", "data/train.list");
- char *backup_directory = option_find_str(options, "backup", "/backup/");
- srand(time(0));
- char *base = basecfg(cfgfile);
- printf("%s\n", base);
- float avg_loss = -1;
- network **nets = calloc(ngpus, sizeof(network));
- srand(time(0));
- int seed = rand();
- int i;
- for(i = 0; i < ngpus; ++i){
- srand(seed);
- #ifdef GPU
- cuda_set_device(gpus[i]);
- #endif
- nets[i] = load_network(cfgfile, weightfile, clear);
- nets[i]->learning_rate *= ngpus;
- }
- srand(time(0));
- network *net = nets[0];
- int imgs = net->batch * net->subdivisions * ngpus;
- printf("Learning Rate: %g, Momentum: %g, Decay: %g\n", net->learning_rate, net->momentum, net->decay);
- data train, buffer;
- layer l = net->layers[net->n - 1];
- int classes = l.classes;
- float jitter = l.jitter;
- list *plist = get_paths(train_images);
- //int N = plist->size;
- char **paths = (char **)list_to_array(plist);
- load_args args = get_base_args(net);
- args.coords = l.coords;
- args.paths = paths;
- args.n = imgs;
- args.m = plist->size;
- args.classes = classes;
- args.jitter = jitter;
- args.num_boxes = l.max_boxes;
- args.d = &buffer;
- args.type = DETECTION_DATA;
- //args.type = INSTANCE_DATA;
- args.threads = 64;
- pthread_t load_thread = load_data(args);
- double time;
- int count = 0;
- //while(i*imgs < N*120){
- while(get_current_batch(net) < net->max_batches){
- if(l.random && count++%10 == 0){
- printf("Resizing\n");
- int dim = (rand() % 10 + 10) * 32;
- if (get_current_batch(net)+200 > net->max_batches) dim = 608;
- //int dim = (rand() % 4 + 16) * 32;
- printf("%d\n", dim);
- args.w = dim;
- args.h = dim;
- pthread_join(load_thread, 0);
- train = buffer;
- free_data(train);
- load_thread = load_data(args);
- #pragma omp parallel for
- for(i = 0; i < ngpus; ++i){
- resize_network(nets[i], dim, dim);
- }
- net = nets[0];
- }
- time=what_time_is_it_now();
- pthread_join(load_thread, 0);
- train = buffer;
- load_thread = load_data(args);
- /*
- int k;
- for(k = 0; k < l.max_boxes; ++k){
- box b = float_to_box(train.y.vals[10] + 1 + k*5);
- if(!b.x) break;
- printf("loaded: %f %f %f %f\n", b.x, b.y, b.w, b.h);
- }
- */
- /*
- int zz;
- for(zz = 0; zz < train.X.cols; ++zz){
- image im = float_to_image(net->w, net->h, 3, train.X.vals[zz]);
- int k;
- for(k = 0; k < l.max_boxes; ++k){
- box b = float_to_box(train.y.vals[zz] + k*5, 1);
- printf("%f %f %f %f\n", b.x, b.y, b.w, b.h);
- draw_bbox(im, b, 1, 1,0,0);
- }
- show_image(im, "truth11");
- cvWaitKey(0);
- save_image(im, "truth11");
- }
- */
- printf("Loaded: %lf seconds\n", what_time_is_it_now()-time);
- time=what_time_is_it_now();
- float loss = 0;
- #ifdef GPU
- if(ngpus == 1){
- loss = train_network(net, train);
- } else {
- loss = train_networks(nets, ngpus, train, 4);
- }
- #else
- loss = train_network(net, train);
- #endif
- if (avg_loss < 0) avg_loss = loss;
- avg_loss = avg_loss*.9 + loss*.1;
- i = get_current_batch(net);
- printf("%ld: %f, %f avg, %f rate, %lf seconds, %d images\n", get_current_batch(net), loss, avg_loss, get_current_rate(net), what_time_is_it_now()-time, i*imgs);
- if(i%100==0){
- #ifdef GPU
- if(ngpus != 1) sync_nets(nets, ngpus, 0);
- #endif
- char buff[256];
- sprintf(buff, "%s/%s.backup", backup_directory, base);
- save_weights(net, buff);
- }
- if(i%10000==0 || (i < 1000 && i%100 == 0)){
- #ifdef GPU
- if(ngpus != 1) sync_nets(nets, ngpus, 0);
- #endif
- char buff[256];
- sprintf(buff, "%s/%s_%d.weights", backup_directory, base, i);
- save_weights(net, buff);
- }
- free_data(train);
- }
- #ifdef GPU
- if(ngpus != 1) sync_nets(nets, ngpus, 0);
- #endif
- char buff[256];
- sprintf(buff, "%s/%s_final.weights", backup_directory, base);
- save_weights(net, buff);
- }
- static int get_coco_image_id(char *filename)
- {
- char *p = strrchr(filename, '/');
- char *c = strrchr(filename, '_');
- if(c) p = c;
- return atoi(p+1);
- }
- static void print_cocos(FILE *fp, char *image_path, detection *dets, int num_boxes, int classes, int w, int h)
- {
- int i, j;
- int image_id = get_coco_image_id(image_path);
- for(i = 0; i < num_boxes; ++i){
- float xmin = dets[i].bbox.x - dets[i].bbox.w/2.;
- float xmax = dets[i].bbox.x + dets[i].bbox.w/2.;
- float ymin = dets[i].bbox.y - dets[i].bbox.h/2.;
- float ymax = dets[i].bbox.y + dets[i].bbox.h/2.;
- if (xmin < 0) xmin = 0;
- if (ymin < 0) ymin = 0;
- if (xmax > w) xmax = w;
- if (ymax > h) ymax = h;
- float bx = xmin;
- float by = ymin;
- float bw = xmax - xmin;
- float bh = ymax - ymin;
- for(j = 0; j < classes; ++j){
- 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]);
- }
- }
- }
- void print_detector_detections(FILE **fps, char *id, detection *dets, int total, int classes, int w, int h)
- {
- int i, j;
- for(i = 0; i < total; ++i){
- float xmin = dets[i].bbox.x - dets[i].bbox.w/2. + 1;
- float xmax = dets[i].bbox.x + dets[i].bbox.w/2. + 1;
- float ymin = dets[i].bbox.y - dets[i].bbox.h/2. + 1;
- float ymax = dets[i].bbox.y + dets[i].bbox.h/2. + 1;
- if (xmin < 1) xmin = 1;
- if (ymin < 1) ymin = 1;
- if (xmax > w) xmax = w;
- if (ymax > h) ymax = h;
- for(j = 0; j < classes; ++j){
- if (dets[i].prob[j]) fprintf(fps[j], "%s %f %f %f %f %f\n", id, dets[i].prob[j],
- xmin, ymin, xmax, ymax);
- }
- }
- }
- void print_imagenet_detections(FILE *fp, int id, detection *dets, int total, int classes, int w, int h)
- {
- int i, j;
- for(i = 0; i < total; ++i){
- float xmin = dets[i].bbox.x - dets[i].bbox.w/2.;
- float xmax = dets[i].bbox.x + dets[i].bbox.w/2.;
- float ymin = dets[i].bbox.y - dets[i].bbox.h/2.;
- float ymax = dets[i].bbox.y + dets[i].bbox.h/2.;
- if (xmin < 0) xmin = 0;
- if (ymin < 0) ymin = 0;
- if (xmax > w) xmax = w;
- if (ymax > h) ymax = h;
- for(j = 0; j < classes; ++j){
- int class = j;
- if (dets[i].prob[class]) fprintf(fp, "%d %d %f %f %f %f %f\n", id, j+1, dets[i].prob[class],
- xmin, ymin, xmax, ymax);
- }
- }
- }
- void validate_detector_flip(char *datacfg, char *cfgfile, char *weightfile, char *outfile)
- {
- int j;
- list *options = read_data_cfg(datacfg);
- char *valid_images = option_find_str(options, "valid", "data/train.list");
- char *name_list = option_find_str(options, "names", "data/names.list");
- char *prefix = option_find_str(options, "results", "results");
- char **names = get_labels(name_list);
- char *mapf = option_find_str(options, "map", 0);
- int *map = 0;
- if (mapf) map = read_map(mapf);
- network *net = load_network(cfgfile, weightfile, 0);
- set_batch_network(net, 2);
- 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);
- char **paths = (char **)list_to_array(plist);
- layer l = net->layers[net->n-1];
- int classes = l.classes;
- char buff[1024];
- char *type = option_find_str(options, "eval", "voc");
- FILE *fp = 0;
- FILE **fps = 0;
- int coco = 0;
- int imagenet = 0;
- if(0==strcmp(type, "coco")){
- if(!outfile) outfile = "coco_results";
- snprintf(buff, 1024, "%s/%s.json", prefix, outfile);
- fp = fopen(buff, "w");
- fprintf(fp, "[\n");
- coco = 1;
- } else if(0==strcmp(type, "imagenet")){
- if(!outfile) outfile = "imagenet-detection";
- snprintf(buff, 1024, "%s/%s.txt", prefix, outfile);
- fp = fopen(buff, "w");
- imagenet = 1;
- classes = 200;
- } else {
- if(!outfile) outfile = "comp4_det_test_";
- fps = calloc(classes, sizeof(FILE *));
- for(j = 0; j < classes; ++j){
- snprintf(buff, 1024, "%s/%s%s.txt", prefix, outfile, names[j]);
- fps[j] = fopen(buff, "w");
- }
- }
- int m = plist->size;
- int i=0;
- int t;
- float thresh = .005;
- float nms = .45;
- int nthreads = 4;
- image *val = calloc(nthreads, sizeof(image));
- image *val_resized = calloc(nthreads, sizeof(image));
- image *buf = calloc(nthreads, sizeof(image));
- image *buf_resized = calloc(nthreads, sizeof(image));
- pthread_t *thr = calloc(nthreads, sizeof(pthread_t));
- image input = make_image(net->w, net->h, net->c*2);
- load_args args = {0};
- args.w = net->w;
- args.h = net->h;
- //args.type = IMAGE_DATA;
- args.type = LETTERBOX_DATA;
- for(t = 0; t < nthreads; ++t){
- args.path = paths[i+t];
- args.im = &buf[t];
- args.resized = &buf_resized[t];
- thr[t] = load_data_in_thread(args);
- }
- double start = what_time_is_it_now();
- for(i = nthreads; i < m+nthreads; i += nthreads){
- fprintf(stderr, "%d\n", i);
- for(t = 0; t < nthreads && i+t-nthreads < m; ++t){
- pthread_join(thr[t], 0);
- val[t] = buf[t];
- val_resized[t] = buf_resized[t];
- }
- for(t = 0; t < nthreads && i+t < m; ++t){
- args.path = paths[i+t];
- args.im = &buf[t];
- args.resized = &buf_resized[t];
- thr[t] = load_data_in_thread(args);
- }
- for(t = 0; t < nthreads && i+t-nthreads < m; ++t){
- char *path = paths[i+t-nthreads];
- char *id = basecfg(path);
- copy_cpu(net->w*net->h*net->c, val_resized[t].data, 1, input.data, 1);
- flip_image(val_resized[t]);
- copy_cpu(net->w*net->h*net->c, val_resized[t].data, 1, input.data + net->w*net->h*net->c, 1);
- network_predict(net, input.data);
- int w = val[t].w;
- int h = val[t].h;
- int num = 0;
- detection *dets = get_network_boxes(net, w, h, thresh, .5, map, 0, &num);
- if (nms) do_nms_sort(dets, num, classes, nms);
- if (coco){
- print_cocos(fp, path, dets, num, classes, w, h);
- } else if (imagenet){
- print_imagenet_detections(fp, i+t-nthreads+1, dets, num, classes, w, h);
- } else {
- print_detector_detections(fps, id, dets, num, classes, w, h);
- }
- free_detections(dets, num);
- 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", what_time_is_it_now() - start);
- }
- void validate_detector(char *datacfg, char *cfgfile, char *weightfile, char *outfile)
- {
- int j;
- list *options = read_data_cfg(datacfg);
- char *valid_images = option_find_str(options, "valid", "data/train.list");
- char *name_list = option_find_str(options, "names", "data/names.list");
- char *prefix = option_find_str(options, "results", "results");
- char **names = get_labels(name_list);
- char *mapf = option_find_str(options, "map", 0);
- int *map = 0;
- if (mapf) map = read_map(mapf);
- network *net = load_network(cfgfile, weightfile, 0);
- 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);
- char **paths = (char **)list_to_array(plist);
- layer l = net->layers[net->n-1];
- int classes = l.classes;
- char buff[1024];
- char *type = option_find_str(options, "eval", "voc");
- FILE *fp = 0;
- FILE **fps = 0;
- int coco = 0;
- int imagenet = 0;
- if(0==strcmp(type, "coco")){
- if(!outfile) outfile = "coco_results";
- snprintf(buff, 1024, "%s/%s.json", prefix, outfile);
- fp = fopen(buff, "w");
- fprintf(fp, "[\n");
- coco = 1;
- } else if(0==strcmp(type, "imagenet")){
- if(!outfile) outfile = "imagenet-detection";
- snprintf(buff, 1024, "%s/%s.txt", prefix, outfile);
- fp = fopen(buff, "w");
- imagenet = 1;
- classes = 200;
- } else {
- if(!outfile) outfile = "comp4_det_test_";
- fps = calloc(classes, sizeof(FILE *));
- for(j = 0; j < classes; ++j){
- snprintf(buff, 1024, "%s/%s%s.txt", prefix, outfile, names[j]);
- fps[j] = fopen(buff, "w");
- }
- }
- int m = plist->size;
- int i=0;
- int t;
- float thresh = .005;
- float nms = .45;
- int nthreads = 4;
- image *val = calloc(nthreads, sizeof(image));
- image *val_resized = calloc(nthreads, sizeof(image));
- image *buf = calloc(nthreads, sizeof(image));
- image *buf_resized = calloc(nthreads, sizeof(image));
- pthread_t *thr = calloc(nthreads, sizeof(pthread_t));
- load_args args = {0};
- args.w = net->w;
- args.h = net->h;
- //args.type = IMAGE_DATA;
- args.type = LETTERBOX_DATA;
- for(t = 0; t < nthreads; ++t){
- args.path = paths[i+t];
- args.im = &buf[t];
- args.resized = &buf_resized[t];
- thr[t] = load_data_in_thread(args);
- }
- double start = what_time_is_it_now();
- for(i = nthreads; i < m+nthreads; i += nthreads){
- fprintf(stderr, "%d\n", i);
- for(t = 0; t < nthreads && i+t-nthreads < m; ++t){
- pthread_join(thr[t], 0);
- val[t] = buf[t];
- val_resized[t] = buf_resized[t];
- }
- for(t = 0; t < nthreads && i+t < m; ++t){
- args.path = paths[i+t];
- args.im = &buf[t];
- args.resized = &buf_resized[t];
- thr[t] = load_data_in_thread(args);
- }
- for(t = 0; t < nthreads && i+t-nthreads < m; ++t){
- char *path = paths[i+t-nthreads];
- char *id = basecfg(path);
- float *X = val_resized[t].data;
- network_predict(net, X);
- int w = val[t].w;
- int h = val[t].h;
- int nboxes = 0;
- detection *dets = get_network_boxes(net, w, h, thresh, .5, map, 0, &nboxes);
- if (nms) do_nms_sort(dets, nboxes, classes, nms);
- if (coco){
- print_cocos(fp, path, dets, nboxes, classes, w, h);
- } 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", what_time_is_it_now() - start);
- }
- void validate_detector_recall(char *cfgfile, char *weightfile)
- {
- network *net = load_network(cfgfile, weightfile, 0);
- 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("data/coco_val_5k.list");
- char **paths = (char **)list_to_array(plist);
- layer l = net->layers[net->n-1];
- int j, k;
- int m = plist->size;
- int i=0;
- float thresh = .001;
- float iou_thresh = .5;
- float nms = .4;
- 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;
- detection *dets = get_network_boxes(net, sized.w, sized.h, thresh, .5, 0, 1, &nboxes);
- if (nms) do_nms_obj(dets, nboxes, 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);
- 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(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;
- }
- }
- 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);
- }
- }
- 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;
- while(1){
- 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);
- draw_detections(im, dets, nboxes, thresh, names, alphabet, l.classes);
- free_detections(dets, nboxes);
- if(outfile){
- save_image(im, outfile);
- }
- else{
- save_image(im, "predictions");
- #ifdef OPENCV
- make_window("predictions", 512, 512, 0);
- show_image(im, "predictions", 0);
- #endif
- }
- free_image(im);
- free_image(sized);
- if (filename) break;
- }
- }
- /*
- void censor_detector(char *datacfg, char *cfgfile, char *weightfile, int cam_index, const char *filename, int class, float thresh, int skip)
- {
- #ifdef OPENCV
- char *base = basecfg(cfgfile);
- network *net = load_network(cfgfile, weightfile, 0);
- set_batch_network(net, 1);
- srand(2222222);
- CvCapture * cap;
- int w = 1280;
- int h = 720;
- if(filename){
- cap = cvCaptureFromFile(filename);
- }else{
- cap = cvCaptureFromCAM(cam_index);
- }
- if(w){
- cvSetCaptureProperty(cap, CV_CAP_PROP_FRAME_WIDTH, w);
- }
- if(h){
- cvSetCaptureProperty(cap, CV_CAP_PROP_FRAME_HEIGHT, h);
- }
- if(!cap) error("Couldn't connect to webcam.\n");
- cvNamedWindow(base, CV_WINDOW_NORMAL);
- cvResizeWindow(base, 512, 512);
- float fps = 0;
- int i;
- float nms = .45;
- while(1){
- image in = get_image_from_stream(cap);
- //image in_s = resize_image(in, net->w, net->h);
- image in_s = letterbox_image(in, net->w, net->h);
- layer l = net->layers[net->n-1];
- float *X = in_s.data;
- network_predict(net, X);
- int nboxes = 0;
- detection *dets = get_network_boxes(net, in.w, in.h, thresh, 0, 0, 0, &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);
- for(i = 0; i < nboxes; ++i){
- if(dets[i].prob[class] > thresh){
- box b = dets[i].bbox;
- int left = b.x-b.w/2.;
- int top = b.y-b.h/2.;
- censor_image(in, left, top, b.w, b.h);
- }
- }
- show_image(in, base);
- cvWaitKey(10);
- free_detections(dets, nboxes);
- free_image(in_s);
- free_image(in);
- float curr = 0;
- fps = .9*fps + .1*curr;
- for(i = 0; i < skip; ++i){
- image in = get_image_from_stream(cap);
- free_image(in);
- }
- }
- #endif
- }
- void extract_detector(char *datacfg, char *cfgfile, char *weightfile, int cam_index, const char *filename, int class, float thresh, int skip)
- {
- #ifdef OPENCV
- char *base = basecfg(cfgfile);
- network *net = load_network(cfgfile, weightfile, 0);
- set_batch_network(net, 1);
- srand(2222222);
- CvCapture * cap;
- int w = 1280;
- int h = 720;
- if(filename){
- cap = cvCaptureFromFile(filename);
- }else{
- cap = cvCaptureFromCAM(cam_index);
- }
- if(w){
- cvSetCaptureProperty(cap, CV_CAP_PROP_FRAME_WIDTH, w);
- }
- if(h){
- cvSetCaptureProperty(cap, CV_CAP_PROP_FRAME_HEIGHT, h);
- }
- if(!cap) error("Couldn't connect to webcam.\n");
- cvNamedWindow(base, CV_WINDOW_NORMAL);
- cvResizeWindow(base, 512, 512);
- float fps = 0;
- int i;
- int count = 0;
- float nms = .45;
- while(1){
- image in = get_image_from_stream(cap);
- //image in_s = resize_image(in, net->w, net->h);
- image in_s = letterbox_image(in, net->w, net->h);
- layer l = net->layers[net->n-1];
- show_image(in, base);
- int nboxes = 0;
- float *X = in_s.data;
- network_predict(net, X);
- detection *dets = get_network_boxes(net, in.w, in.h, thresh, 0, 0, 1, &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);
- for(i = 0; i < nboxes; ++i){
- if(dets[i].prob[class] > thresh){
- box b = dets[i].bbox;
- int size = b.w*in.w > b.h*in.h ? b.w*in.w : b.h*in.h;
- int dx = b.x*in.w-size/2.;
- int dy = b.y*in.h-size/2.;
- image bim = crop_image(in, dx, dy, size, size);
- char buff[2048];
- sprintf(buff, "results/extract/%07d", count);
- ++count;
- save_image(bim, buff);
- free_image(bim);
- }
- }
- free_detections(dets, nboxes);
- free_image(in_s);
- free_image(in);
- float curr = 0;
- fps = .9*fps + .1*curr;
- for(i = 0; i < skip; ++i){
- image in = get_image_from_stream(cap);
- free_image(in);
- }
- }
- #endif
- }
- */
- /*
- void network_detect(network *net, image im, float thresh, float hier_thresh, float nms, detection *dets)
- {
- network_predict_image(net, im);
- layer l = net->layers[net->n-1];
- int nboxes = num_boxes(net);
- fill_network_boxes(net, im.w, im.h, thresh, hier_thresh, 0, 0, dets);
- if (nms) do_nms_sort(dets, nboxes, l.classes, nms);
- }
- */
- void run_detector(int argc, char **argv)
- {
- char *prefix = find_char_arg(argc, argv, "-prefix", 0);
- float thresh = find_float_arg(argc, argv, "-thresh", .5);
- 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 avg = find_int_arg(argc, argv, "-avg", 3);
- if(argc < 4){
- fprintf(stderr, "usage: %s %s [train/test/valid] [cfg] [weights (optional)]\n", argv[0], argv[1]);
- return;
- }
- char *gpu_list = find_char_arg(argc, argv, "-gpus", 0);
- char *outfile = find_char_arg(argc, argv, "-out", 0);
- int *gpus = 0;
- int gpu = 0;
- int ngpus = 0;
- if(gpu_list){
- printf("%s\n", gpu_list);
- int len = strlen(gpu_list);
- ngpus = 1;
- int i;
- for(i = 0; i < len; ++i){
- if (gpu_list[i] == ',') ++ngpus;
- }
- gpus = calloc(ngpus, sizeof(int));
- for(i = 0; i < ngpus; ++i){
- gpus[i] = atoi(gpu_list);
- gpu_list = strchr(gpu_list, ',')+1;
- }
- } else {
- gpu = gpu_index;
- gpus = &gpu;
- ngpus = 1;
- }
- int clear = find_arg(argc, argv, "-clear");
- int fullscreen = find_arg(argc, argv, "-fullscreen");
- int width = find_int_arg(argc, argv, "-w", 0);
- int height = find_int_arg(argc, argv, "-h", 0);
- int fps = find_int_arg(argc, argv, "-fps", 0);
- //int class = find_int_arg(argc, argv, "-class", 0);
- char *datacfg = argv[3];
- char *cfg = argv[4];
- char *weights = (argc > 5) ? argv[5] : 0;
- char *filename = (argc > 6) ? argv[6]: 0;
- if(0==strcmp(argv[2], "test")) test_detector(datacfg, cfg, weights, filename, thresh, hier_thresh, outfile, fullscreen);
- else if(0==strcmp(argv[2], "train")) train_detector(datacfg, cfg, weights, gpus, ngpus, clear);
- else if(0==strcmp(argv[2], "valid")) validate_detector(datacfg, cfg, weights, outfile);
- else if(0==strcmp(argv[2], "valid2")) validate_detector_flip(datacfg, cfg, weights, outfile);
- else if(0==strcmp(argv[2], "recall")) validate_detector_recall(cfg, weights);
- else if(0==strcmp(argv[2], "demo")) {
- list *options = read_data_cfg(datacfg);
- int classes = option_find_int(options, "classes", 20);
- char *name_list = option_find_str(options, "names", "data/names.list");
- char **names = get_labels(name_list);
- demo(cfg, weights, thresh, cam_index, filename, names, classes, frame_skip, prefix, avg, hier_thresh, width, height, fps, fullscreen);
- }
- //else if(0==strcmp(argv[2], "extract")) extract_detector(datacfg, cfg, weights, cam_index, filename, class, thresh, frame_skip);
- //else if(0==strcmp(argv[2], "censor")) censor_detector(datacfg, cfg, weights, cam_index, filename, class, thresh, frame_skip);
- }
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|Archiver|手机版|小黑屋|人工智能工程师的摇篮
( 湘ICP备2020019608号-1 )
发表于 2019-9-19 10:36:55
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