mirror of
https://github.com/pjreddie/darknet.git
synced 2023-08-10 21:13:14 +03:00
this'll teach me to mess with maxpooling
This commit is contained in:
Joseph Redmon
@ -24,7 +24,6 @@ void demo_art(char *cfgfile, char *weightfile, int cam_index)
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while(1){
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image in = get_image_from_stream(cap);
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image in_s = resize_image(in, net->w, net->h);
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show_image(in, window);
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float *p = network_predict(net, in_s.data);
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@ -45,10 +44,9 @@ void demo_art(char *cfgfile, char *weightfile, int cam_index)
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}
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printf("]\n");
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show_image(in, window, 1);
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free_image(in_s);
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free_image(in);
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cvWaitKey(1);
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}
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#endif
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}
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@ -645,6 +645,45 @@ void label_classifier(char *datacfg, char *filename, char *weightfile)
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}
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}
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void csv_classifier(char *datacfg, char *cfgfile, char *weightfile)
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{
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int i,j;
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network *net = load_network(cfgfile, weightfile, 0);
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srand(time(0));
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list *options = read_data_cfg(datacfg);
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char *test_list = option_find_str(options, "test", "data/test.list");
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int top = option_find_int(options, "top", 1);
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list *plist = get_paths(test_list);
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char **paths = (char **)list_to_array(plist);
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int m = plist->size;
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free_list(plist);
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int *indexes = calloc(top, sizeof(int));
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for(i = 0; i < m; ++i){
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double time = what_time_is_it_now();
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char *path = paths[i];
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image im = load_image_color(path, 0, 0);
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image r = letterbox_image(im, net->w, net->h);
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float *predictions = network_predict(net, r.data);
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if(net->hierarchy) hierarchy_predictions(predictions, net->outputs, net->hierarchy, 1, 1);
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top_k(predictions, net->outputs, top, indexes);
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printf("%s", path);
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for(j = 0; j < top; ++j){
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printf("\t%d", indexes[j]);
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}
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printf("\n");
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free_image(im);
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free_image(r);
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fprintf(stderr, "%lf seconds, %d images, %d total\n", what_time_is_it_now() - time, i+1, m);
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}
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}
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void test_classifier(char *datacfg, char *cfgfile, char *weightfile, int target_layer)
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{
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@ -869,8 +908,7 @@ void threat_classifier(char *datacfg, char *cfgfile, char *weightfile, int cam_i
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}
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if(1){
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show_image(out, "Threat");
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cvWaitKey(10);
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show_image(out, "Threat", 10);
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}
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free_image(in_s);
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free_image(in);
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@ -922,7 +960,6 @@ void gun_classifier(char *datacfg, char *cfgfile, char *weightfile, int cam_inde
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image in = get_image_from_stream(cap);
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image in_s = resize_image(in, net->w, net->h);
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show_image(in, "Threat Detection");
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float *predictions = network_predict(net, in_s.data);
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top_predictions(net, top, indexes);
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@ -947,11 +984,10 @@ void gun_classifier(char *datacfg, char *cfgfile, char *weightfile, int cam_inde
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}
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}
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show_image(in, "Threat Detection", 10);
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free_image(in_s);
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free_image(in);
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cvWaitKey(10);
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gettimeofday(&tval_after, NULL);
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timersub(&tval_after, &tval_before, &tval_result);
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float curr = 1000000.f/((long int)tval_result.tv_usec);
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@ -1036,12 +1072,10 @@ void demo_classifier(char *datacfg, char *cfgfile, char *weightfile, int cam_ind
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free_image(label);
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}
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show_image(in, base);
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show_image(in, base, 10);
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free_image(in_s);
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free_image(in);
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cvWaitKey(10);
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gettimeofday(&tval_after, NULL);
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timersub(&tval_after, &tval_before, &tval_result);
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float curr = 1000000.f/((long int)tval_result.tv_usec);
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@ -1080,6 +1114,7 @@ void run_classifier(int argc, char **argv)
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else if(0==strcmp(argv[2], "gun")) gun_classifier(data, cfg, weights, cam_index, filename);
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else if(0==strcmp(argv[2], "threat")) threat_classifier(data, cfg, weights, cam_index, filename);
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else if(0==strcmp(argv[2], "test")) test_classifier(data, cfg, weights, layer);
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else if(0==strcmp(argv[2], "csv")) csv_classifier(data, cfg, weights);
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else if(0==strcmp(argv[2], "label")) label_classifier(data, cfg, weights);
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else if(0==strcmp(argv[2], "valid")) validate_classifier_single(data, cfg, weights);
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else if(0==strcmp(argv[2], "validmulti")) validate_classifier_multi(data, cfg, weights);
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@ -325,14 +325,10 @@ void test_coco(char *cfgfile, char *weightfile, char *filename, float thresh)
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draw_detections(im, dets, l.side*l.side*l.n, thresh, coco_classes, alphabet, 80);
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save_image(im, "prediction");
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show_image(im, "predictions");
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show_image(im, "predictions", 0);
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free_detections(dets, nboxes);
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free_image(im);
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free_image(sized);
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#ifdef OPENCV
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cvWaitKey(0);
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cvDestroyAllWindows();
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#endif
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if (filename) break;
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}
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}
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@ -14,6 +14,7 @@ extern void run_nightmare(int argc, char **argv);
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extern void run_classifier(int argc, char **argv);
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extern void run_regressor(int argc, char **argv);
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extern void run_segmenter(int argc, char **argv);
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extern void run_isegmenter(int argc, char **argv);
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extern void run_char_rnn(int argc, char **argv);
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extern void run_tag(int argc, char **argv);
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extern void run_cifar(int argc, char **argv);
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@ -452,6 +453,8 @@ int main(int argc, char **argv)
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run_classifier(argc, argv);
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} else if (0 == strcmp(argv[1], "regressor")){
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run_regressor(argc, argv);
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} else if (0 == strcmp(argv[1], "isegmenter")){
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run_isegmenter(argc, argv);
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} else if (0 == strcmp(argv[1], "segmenter")){
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run_segmenter(argc, argv);
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} else if (0 == strcmp(argv[1], "art")){
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@ -613,9 +613,7 @@ void test_detector(char *datacfg, char *cfgfile, char *weightfile, char *filenam
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if(fullscreen){
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cvSetWindowProperty("predictions", CV_WND_PROP_FULLSCREEN, CV_WINDOW_FULLSCREEN);
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}
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show_image(im, "predictions");
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cvWaitKey(0);
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cvDestroyAllWindows();
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show_image(im, "predictions", 0);
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#endif
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}
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265
examples/instance-segmenter.c
Normal file
265
examples/instance-segmenter.c
Normal file
@ -0,0 +1,265 @@
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#include "darknet.h"
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#include <sys/time.h>
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#include <assert.h>
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void train_isegmenter(char *datacfg, char *cfgfile, char *weightfile, int *gpus, int ngpus, int clear, int display)
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{
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int i;
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float avg_loss = -1;
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char *base = basecfg(cfgfile);
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printf("%s\n", base);
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printf("%d\n", ngpus);
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network **nets = calloc(ngpus, sizeof(network*));
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srand(time(0));
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int seed = rand();
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for(i = 0; i < ngpus; ++i){
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srand(seed);
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#ifdef GPU
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cuda_set_device(gpus[i]);
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#endif
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nets[i] = load_network(cfgfile, weightfile, clear);
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nets[i]->learning_rate *= ngpus;
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}
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srand(time(0));
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network *net = nets[0];
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image pred = get_network_image(net);
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int div = net->w/pred.w;
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assert(pred.w * div == net->w);
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assert(pred.h * div == net->h);
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int imgs = net->batch * net->subdivisions * ngpus;
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printf("Learning Rate: %g, Momentum: %g, Decay: %g\n", net->learning_rate, net->momentum, net->decay);
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list *options = read_data_cfg(datacfg);
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char *backup_directory = option_find_str(options, "backup", "/backup/");
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char *train_list = option_find_str(options, "train", "data/train.list");
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list *plist = get_paths(train_list);
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char **paths = (char **)list_to_array(plist);
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printf("%d\n", plist->size);
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int N = plist->size;
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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.threads = 32;
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args.scale = div;
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args.num_boxes = 90;
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args.min = net->min_crop;
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args.max = net->max_crop;
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args.angle = net->angle;
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args.aspect = net->aspect;
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args.exposure = net->exposure;
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args.saturation = net->saturation;
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args.hue = net->hue;
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args.size = net->w;
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args.classes = 80;
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args.paths = paths;
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args.n = imgs;
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args.m = N;
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args.type = ISEG_DATA;
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data train;
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data buffer;
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pthread_t load_thread;
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args.d = &buffer;
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load_thread = load_data(args);
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int epoch = (*net->seen)/N;
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while(get_current_batch(net) < net->max_batches || net->max_batches == 0){
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double time = what_time_is_it_now();
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pthread_join(load_thread, 0);
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train = buffer;
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load_thread = load_data(args);
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printf("Loaded: %lf seconds\n", what_time_is_it_now()-time);
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time = what_time_is_it_now();
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float loss = 0;
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#ifdef GPU
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if(ngpus == 1){
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loss = train_network(net, train);
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} else {
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loss = train_networks(nets, ngpus, train, 4);
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}
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#else
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loss = train_network(net, train);
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#endif
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if(display){
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image tr = float_to_image(net->w/div, net->h/div, 80, train.y.vals[net->batch*(net->subdivisions-1)]);
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image im = float_to_image(net->w, net->h, net->c, train.X.vals[net->batch*(net->subdivisions-1)]);
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pred.c = 80;
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image mask = mask_to_rgb(tr);
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image prmask = mask_to_rgb(pred);
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show_image(im, "input", 1);
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show_image(prmask, "pred", 1);
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show_image(mask, "truth", 100);
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free_image(mask);
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free_image(prmask);
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}
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if(avg_loss == -1) avg_loss = loss;
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avg_loss = avg_loss*.9 + loss*.1;
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printf("%ld, %.3f: %f, %f avg, %f rate, %lf seconds, %ld images\n", get_current_batch(net), (float)(*net->seen)/N, loss, avg_loss, get_current_rate(net), what_time_is_it_now()-time, *net->seen);
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free_data(train);
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if(*net->seen/N > epoch){
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epoch = *net->seen/N;
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char buff[256];
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sprintf(buff, "%s/%s_%d.weights",backup_directory,base, epoch);
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save_weights(net, buff);
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}
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if(get_current_batch(net)%100 == 0){
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char buff[256];
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sprintf(buff, "%s/%s.backup",backup_directory,base);
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save_weights(net, buff);
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}
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}
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char buff[256];
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sprintf(buff, "%s/%s.weights", backup_directory, base);
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save_weights(net, buff);
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free_network(net);
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free_ptrs((void**)paths, plist->size);
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free_list(plist);
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free(base);
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}
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void predict_isegmenter(char *datafile, char *cfg, char *weights, char *filename)
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{
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network *net = load_network(cfg, weights, 0);
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set_batch_network(net, 1);
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srand(2222222);
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clock_t time;
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char buff[256];
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char *input = buff;
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while(1){
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if(filename){
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strncpy(input, filename, 256);
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}else{
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printf("Enter Image Path: ");
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fflush(stdout);
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input = fgets(input, 256, stdin);
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if(!input) return;
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strtok(input, "\n");
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}
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image im = load_image_color(input, 0, 0);
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image sized = letterbox_image(im, net->w, net->h);
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float *X = sized.data;
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time=clock();
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float *predictions = network_predict(net, X);
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image pred = get_network_image(net);
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image prmask = mask_to_rgb(pred);
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printf("Predicted: %f\n", predictions[0]);
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printf("%s: Predicted in %f seconds.\n", input, sec(clock()-time));
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show_image(sized, "orig", 1);
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show_image(prmask, "pred", 0);
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free_image(im);
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free_image(sized);
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free_image(prmask);
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if (filename) break;
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}
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}
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void demo_isegmenter(char *datacfg, char *cfg, char *weights, int cam_index, const char *filename)
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{
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#ifdef OPENCV
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printf("Classifier Demo\n");
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network *net = load_network(cfg, weights, 0);
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set_batch_network(net, 1);
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srand(2222222);
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CvCapture * cap;
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if(filename){
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cap = cvCaptureFromFile(filename);
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}else{
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cap = cvCaptureFromCAM(cam_index);
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}
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if(!cap) error("Couldn't connect to webcam.\n");
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cvNamedWindow("Segmenter", CV_WINDOW_NORMAL);
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cvResizeWindow("Segmenter", 512, 512);
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float fps = 0;
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while(1){
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struct timeval tval_before, tval_after, tval_result;
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gettimeofday(&tval_before, NULL);
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image in = get_image_from_stream(cap);
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image in_s = letterbox_image(in, net->w, net->h);
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network_predict(net, in_s.data);
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printf("\033[2J");
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printf("\033[1;1H");
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printf("\nFPS:%.0f\n",fps);
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image pred = get_network_image(net);
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image prmask = mask_to_rgb(pred);
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show_image(prmask, "Segmenter", 10);
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free_image(in_s);
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free_image(in);
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free_image(prmask);
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|
||||
gettimeofday(&tval_after, NULL);
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timersub(&tval_after, &tval_before, &tval_result);
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float curr = 1000000.f/((long int)tval_result.tv_usec);
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fps = .9*fps + .1*curr;
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}
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#endif
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||||
}
|
||||
|
||||
|
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void run_isegmenter(int argc, char **argv)
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{
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if(argc < 4){
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fprintf(stderr, "usage: %s %s [train/test/valid] [cfg] [weights (optional)]\n", argv[0], argv[1]);
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return;
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}
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|
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char *gpu_list = find_char_arg(argc, argv, "-gpus", 0);
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int *gpus = 0;
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int gpu = 0;
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int ngpus = 0;
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if(gpu_list){
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printf("%s\n", gpu_list);
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int len = strlen(gpu_list);
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ngpus = 1;
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int i;
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for(i = 0; i < len; ++i){
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if (gpu_list[i] == ',') ++ngpus;
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}
|
||||
gpus = calloc(ngpus, sizeof(int));
|
||||
for(i = 0; i < ngpus; ++i){
|
||||
gpus[i] = atoi(gpu_list);
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||||
gpu_list = strchr(gpu_list, ',')+1;
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||||
}
|
||||
} else {
|
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gpu = gpu_index;
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gpus = &gpu;
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ngpus = 1;
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}
|
||||
|
||||
int cam_index = find_int_arg(argc, argv, "-c", 0);
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||||
int clear = find_arg(argc, argv, "-clear");
|
||||
int display = find_arg(argc, argv, "-display");
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char *data = argv[3];
|
||||
char *cfg = argv[4];
|
||||
char *weights = (argc > 5) ? argv[5] : 0;
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||||
char *filename = (argc > 6) ? argv[6]: 0;
|
||||
if(0==strcmp(argv[2], "test")) predict_isegmenter(data, cfg, weights, filename);
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else if(0==strcmp(argv[2], "train")) train_isegmenter(data, cfg, weights, gpus, ngpus, clear, display);
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||||
else if(0==strcmp(argv[2], "demo")) demo_isegmenter(data, cfg, weights, cam_index, filename);
|
||||
}
|
||||
|
||||
|
||||
@ -460,13 +460,9 @@ void inter_dcgan(char *cfgfile, char *weightfile)
|
||||
printf("%s: Predicted in %f seconds.\n", input, sec(clock()-time));
|
||||
//char buff[256];
|
||||
sprintf(buff, "out%05d", c);
|
||||
show_image(out, "out");
|
||||
save_image(out, "out");
|
||||
save_image(out, buff);
|
||||
#ifdef OPENCV
|
||||
//cvWaitKey(0);
|
||||
#endif
|
||||
|
||||
show_image(out, "out", 0);
|
||||
}
|
||||
}
|
||||
|
||||
@ -499,11 +495,8 @@ void test_dcgan(char *cfgfile, char *weightfile)
|
||||
//yuv_to_rgb(out);
|
||||
normalize_image(out);
|
||||
printf("%s: Predicted in %f seconds.\n", input, sec(clock()-time));
|
||||
show_image(out, "out");
|
||||
save_image(out, "out");
|
||||
#ifdef OPENCV
|
||||
cvWaitKey(0);
|
||||
#endif
|
||||
show_image(out, "out", 0);
|
||||
|
||||
free_image(im);
|
||||
}
|
||||
@ -639,11 +632,10 @@ void train_prog(char *cfg, char *weight, char *acfg, char *aweight, int clear, i
|
||||
if(display){
|
||||
image im = float_to_image(anet->w, anet->h, anet->c, gen.X.vals[0]);
|
||||
image im2 = float_to_image(anet->w, anet->h, anet->c, train.X.vals[0]);
|
||||
show_image(im, "gen");
|
||||
show_image(im2, "train");
|
||||
show_image(im, "gen", 1);
|
||||
show_image(im2, "train", 1);
|
||||
save_image(im, "gen");
|
||||
save_image(im2, "train");
|
||||
cvWaitKey(1);
|
||||
}
|
||||
#endif
|
||||
|
||||
@ -826,11 +818,10 @@ void train_dcgan(char *cfg, char *weight, char *acfg, char *aweight, int clear,
|
||||
if(display){
|
||||
image im = float_to_image(anet->w, anet->h, anet->c, gen.X.vals[0]);
|
||||
image im2 = float_to_image(anet->w, anet->h, anet->c, train.X.vals[0]);
|
||||
show_image(im, "gen");
|
||||
show_image(im2, "train");
|
||||
show_image(im, "gen", 1);
|
||||
show_image(im2, "train", 1);
|
||||
save_image(im, "gen");
|
||||
save_image(im2, "train");
|
||||
cvWaitKey(1);
|
||||
}
|
||||
#endif
|
||||
|
||||
@ -1010,9 +1001,8 @@ void train_colorizer(char *cfg, char *weight, char *acfg, char *aweight, int cle
|
||||
if(display){
|
||||
image im = float_to_image(anet->w, anet->h, anet->c, gray.X.vals[0]);
|
||||
image im2 = float_to_image(anet->w, anet->h, anet->c, train.X.vals[0]);
|
||||
show_image(im, "gen");
|
||||
show_image(im2, "train");
|
||||
cvWaitKey(1);
|
||||
show_image(im, "gen", 1);
|
||||
show_image(im2, "train", 1);
|
||||
}
|
||||
#endif
|
||||
free_data(merge);
|
||||
@ -1342,12 +1332,9 @@ void test_lsd(char *cfg, char *weights, char *filename, int gray)
|
||||
//yuv_to_rgb(out);
|
||||
constrain_image(out);
|
||||
printf("%s: Predicted in %f seconds.\n", input, sec(clock()-time));
|
||||
show_image(out, "out");
|
||||
show_image(crop, "crop");
|
||||
save_image(out, "out");
|
||||
#ifdef OPENCV
|
||||
cvWaitKey(0);
|
||||
#endif
|
||||
show_image(out, "out", 1);
|
||||
show_image(crop, "crop", 0);
|
||||
|
||||
free_image(im);
|
||||
free_image(resized);
|
||||
|
||||
@ -376,10 +376,7 @@ void run_nightmare(int argc, char **argv)
|
||||
if(reconstruct){
|
||||
reconstruct_picture(net, features, im, update, rate, momentum, lambda, smooth_size, 1);
|
||||
//if ((n+1)%30 == 0) rate *= .5;
|
||||
show_image(im, "reconstruction");
|
||||
#ifdef OPENCV
|
||||
cvWaitKey(10);
|
||||
#endif
|
||||
show_image(im, "reconstruction", 10);
|
||||
}else{
|
||||
int layer = max_layer + rand()%range - range/2;
|
||||
int octave = rand()%octaves;
|
||||
@ -400,8 +397,7 @@ void run_nightmare(int argc, char **argv)
|
||||
}
|
||||
printf("%d %s\n", e, buff);
|
||||
save_image(im, buff);
|
||||
//show_image(im, buff);
|
||||
//cvWaitKey(0);
|
||||
//show_image(im, buff, 0);
|
||||
|
||||
if(rotate){
|
||||
image rot = rotate_image(im, rotate);
|
||||
|
||||
@ -179,7 +179,6 @@ void demo_regressor(char *datacfg, char *cfgfile, char *weightfile, int cam_inde
|
||||
image in = get_image_from_stream(cap);
|
||||
image crop = center_crop_image(in, net->w, net->h);
|
||||
grayscale_image_3c(crop);
|
||||
show_image(crop, "Regressor");
|
||||
|
||||
float *predictions = network_predict(net, crop.data);
|
||||
|
||||
@ -192,11 +191,10 @@ void demo_regressor(char *datacfg, char *cfgfile, char *weightfile, int cam_inde
|
||||
printf("%s: %f\n", names[i], predictions[i]);
|
||||
}
|
||||
|
||||
show_image(crop, "Regressor", 10);
|
||||
free_image(in);
|
||||
free_image(crop);
|
||||
|
||||
cvWaitKey(10);
|
||||
|
||||
gettimeofday(&tval_after, NULL);
|
||||
timersub(&tval_after, &tval_before, &tval_result);
|
||||
float curr = 1000000.f/((long int)tval_result.tv_usec);
|
||||
|
||||
@ -42,7 +42,6 @@ void train_segmenter(char *datacfg, char *cfgfile, char *weightfile, int *gpus,
|
||||
char **paths = (char **)list_to_array(plist);
|
||||
printf("%d\n", plist->size);
|
||||
int N = plist->size;
|
||||
clock_t time;
|
||||
|
||||
load_args args = {0};
|
||||
args.w = net->w;
|
||||
@ -73,14 +72,14 @@ void train_segmenter(char *datacfg, char *cfgfile, char *weightfile, int *gpus,
|
||||
|
||||
int epoch = (*net->seen)/N;
|
||||
while(get_current_batch(net) < net->max_batches || net->max_batches == 0){
|
||||
time=clock();
|
||||
double time = what_time_is_it_now();
|
||||
|
||||
pthread_join(load_thread, 0);
|
||||
train = buffer;
|
||||
load_thread = load_data(args);
|
||||
|
||||
printf("Loaded: %lf seconds\n", sec(clock()-time));
|
||||
time=clock();
|
||||
printf("Loaded: %lf seconds\n", what_time_is_it_now()-time);
|
||||
time = what_time_is_it_now();
|
||||
|
||||
float loss = 0;
|
||||
#ifdef GPU
|
||||
@ -97,18 +96,15 @@ void train_segmenter(char *datacfg, char *cfgfile, char *weightfile, int *gpus,
|
||||
image im = float_to_image(net->w, net->h, net->c, train.X.vals[net->batch*(net->subdivisions-1)]);
|
||||
image mask = mask_to_rgb(tr);
|
||||
image prmask = mask_to_rgb(pred);
|
||||
show_image(im, "input");
|
||||
show_image(prmask, "pred");
|
||||
show_image(mask, "truth");
|
||||
#ifdef OPENCV
|
||||
cvWaitKey(100);
|
||||
#endif
|
||||
show_image(im, "input", 1);
|
||||
show_image(prmask, "pred", 1);
|
||||
show_image(mask, "truth", 100);
|
||||
free_image(mask);
|
||||
free_image(prmask);
|
||||
}
|
||||
if(avg_loss == -1) avg_loss = loss;
|
||||
avg_loss = avg_loss*.9 + loss*.1;
|
||||
printf("%ld, %.3f: %f, %f avg, %f rate, %lf seconds, %ld images\n", get_current_batch(net), (float)(*net->seen)/N, loss, avg_loss, get_current_rate(net), sec(clock()-time), *net->seen);
|
||||
printf("%ld, %.3f: %f, %f avg, %f rate, %lf seconds, %ld images\n", get_current_batch(net), (float)(*net->seen)/N, loss, avg_loss, get_current_rate(net), what_time_is_it_now()-time, *net->seen);
|
||||
free_data(train);
|
||||
if(*net->seen/N > epoch){
|
||||
epoch = *net->seen/N;
|
||||
@ -159,13 +155,10 @@ void predict_segmenter(char *datafile, char *cfg, char *weights, char *filename)
|
||||
float *predictions = network_predict(net, X);
|
||||
image pred = get_network_image(net);
|
||||
image prmask = mask_to_rgb(pred);
|
||||
show_image(sized, "orig");
|
||||
show_image(prmask, "pred");
|
||||
#ifdef OPENCV
|
||||
cvWaitKey(0);
|
||||
#endif
|
||||
printf("Predicted: %f\n", predictions[0]);
|
||||
printf("%s: Predicted in %f seconds.\n", input, sec(clock()-time));
|
||||
show_image(sized, "orig", 1);
|
||||
show_image(prmask, "pred", 0);
|
||||
free_image(im);
|
||||
free_image(sized);
|
||||
free_image(prmask);
|
||||
@ -210,14 +203,12 @@ void demo_segmenter(char *datacfg, char *cfg, char *weights, int cam_index, cons
|
||||
|
||||
image pred = get_network_image(net);
|
||||
image prmask = mask_to_rgb(pred);
|
||||
show_image(prmask, "Segmenter");
|
||||
show_image(prmask, "Segmenter", 10);
|
||||
|
||||
free_image(in_s);
|
||||
free_image(in);
|
||||
free_image(prmask);
|
||||
|
||||
cvWaitKey(10);
|
||||
|
||||
gettimeofday(&tval_after, NULL);
|
||||
timersub(&tval_after, &tval_before, &tval_result);
|
||||
float curr = 1000000.f/((long int)tval_result.tv_usec);
|
||||
|
||||
@ -93,7 +93,7 @@ void test_super(char *cfgfile, char *weightfile, char *filename)
|
||||
image out = get_network_image(net);
|
||||
printf("%s: Predicted in %f seconds.\n", input, sec(clock()-time));
|
||||
save_image(out, "out");
|
||||
show_image(out, "out");
|
||||
show_image(out, "out", 0);
|
||||
|
||||
free_image(im);
|
||||
if (filename) break;
|
||||
|
||||
@ -296,14 +296,10 @@ void test_yolo(char *cfgfile, char *weightfile, char *filename, float thresh)
|
||||
|
||||
draw_detections(im, dets, l.side*l.side*l.n, thresh, voc_names, alphabet, 20);
|
||||
save_image(im, "predictions");
|
||||
show_image(im, "predictions");
|
||||
show_image(im, "predictions", 0);
|
||||
free_detections(dets, nboxes);
|
||||
free_image(im);
|
||||
free_image(sized);
|
||||
#ifdef OPENCV
|
||||
cvWaitKey(0);
|
||||
cvDestroyAllWindows();
|
||||
#endif
|
||||
if (filename) break;
|
||||
}
|
||||
}
|
||||
|
||||
Reference in New Issue
Block a user