void predict_regressor(char *cfgfile, char *weightfile, char *filename) { network *net = load_network(cfgfile, weightfile, 0); set_batch_network(net, 1); srand(2222222); clock_t time; char buff[256]; char *input = buff; 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); float *X = sized.data; time=clock(); float *predictions = network_predict(net, X); printf("Predicted: %f\n", predictions[0]); printf("%s: Predicted in %f seconds.\n", input, sec(clock()-time)); free_image(im); free_image(sized); if (filename) break; } free_network(net); }
float *network_predict_image(network *net, image im) { image imr = letterbox_image(im, net->w, net->h); set_batch_network(net, 1); float *p = network_predict(*net, imr.data); free_image(imr); return p; }
void *fetch_in_thread(void *ptr) { in = get_image_from_stream(cap); if(!in.data){ error("Stream closed."); } in_s = letterbox_image(in, net.w, net.h); return 0; }
void predict_classifier(char *datacfg, char *cfgfile, char *weightfile, char *filename, int top) { network net = parse_network_cfg(cfgfile); if(weightfile){ load_weights(&net, weightfile); } set_batch_network(&net, 1); srand(2222222); list *options = read_data_cfg(datacfg); char *name_list = option_find_str(options, "names", 0); if(!name_list) name_list = option_find_str(options, "labels", "data/labels.list"); if(top == 0) top = option_find_int(options, "top", 1); int i = 0; char **names = get_labels(name_list); clock_t time; int *indexes = calloc(top, sizeof(int)); char buff[256]; char *input = buff; 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 r = letterbox_image(im, net.w, net.h); //resize_network(&net, r.w, r.h); //printf("%d %d\n", r.w, r.h); float *X = r.data; time=clock(); float *predictions = network_predict(net, X); if(net.hierarchy) hierarchy_predictions(predictions, net.outputs, net.hierarchy, 1, 1); top_k(predictions, net.outputs, top, indexes); fprintf(stderr, "%s: Predicted in %f seconds.\n", input, sec(clock()-time)); for(i = 0; i < top; ++i){ int index = indexes[i]; //if(net.hierarchy) printf("%d, %s: %f, parent: %s \n",index, names[index], predictions[index], (net.hierarchy->parent[index] >= 0) ? names[net.hierarchy->parent[index]] : "Root"); //else printf("%s: %f\n",names[index], predictions[index]); printf("%5.2f%%: %s\n", predictions[index]*100, names[index]); } if(r.data != im.data) free_image(r); free_image(im); if (filename) break; } }
void demo_regressor(char *datacfg, char *cfgfile, char *weightfile, int cam_index, const char *filename) { #ifdef OPENCV printf("Regressor Demo\n"); network net = parse_network_cfg(cfgfile); if(weightfile){ load_weights(&net, weightfile); } set_batch_network(&net, 1); srand(2222222); CvCapture * cap; if(filename){ cap = cvCaptureFromFile(filename); }else{ cap = cvCaptureFromCAM(cam_index); } if(!cap) error("Couldn't connect to webcam.\n"); cvNamedWindow("Regressor", CV_WINDOW_NORMAL); cvResizeWindow("Regressor", 512, 512); float fps = 0; while(1){ struct timeval tval_before, tval_after, tval_result; gettimeofday(&tval_before, NULL); image in = get_image_from_stream(cap); image in_s = letterbox_image(in, net.w, net.h); show_image(in, "Regressor"); float *predictions = network_predict(net, in_s.data); printf("\033[2J"); printf("\033[1;1H"); printf("\nFPS:%.0f\n",fps); printf("People: %f\n", predictions[0]); free_image(in_s); free_image(in); cvWaitKey(10); gettimeofday(&tval_after, NULL); timersub(&tval_after, &tval_before, &tval_result); float curr = 1000000.f/((long int)tval_result.tv_usec); fps = .9*fps + .1*curr; } #endif }
void demo_segmenter(char *datacfg, char *cfg, char *weights, int cam_index, const char *filename) { #ifdef OPENCV printf("Classifier Demo\n"); network *net = load_network(cfg, weights, 0); set_batch_network(net, 1); srand(2222222); void * cap = open_video_stream(filename, cam_index, 0,0,0); if(!cap) error("Couldn't connect to webcam.\n"); float fps = 0; while(1){ struct timeval tval_before, tval_after, tval_result; gettimeofday(&tval_before, NULL); image in = get_image_from_stream(cap); image in_s = letterbox_image(in, net->w, net->h); network_predict(net, in_s.data); printf("\033[2J"); printf("\033[1;1H"); printf("\nFPS:%.0f\n",fps); image pred = get_network_image(net); image prmask = mask_to_rgb(pred); show_image(prmask, "Segmenter", 10); free_image(in_s); free_image(in); free_image(prmask); gettimeofday(&tval_after, NULL); timersub(&tval_after, &tval_before, &tval_result); float curr = 1000000.f/((long int)tval_result.tv_usec); fps = .9*fps + .1*curr; } free_network(net); #endif }
void demo(char *cfgfile, char *weightfile, float thresh, int cam_index, const char *filename, char **names, int classes, int delay, char *prefix, int avg_frames, float hier, int w, int h, int frames, int fullscreen) { demo_delay = delay; demo_frame = avg_frames; predictions = calloc(demo_frame, sizeof(float*)); image **alphabet = load_alphabet(); demo_names = names; demo_alphabet = alphabet; demo_classes = classes; demo_thresh = thresh; demo_hier = hier; printf("Demo\n"); net = parse_network_cfg(cfgfile); if(weightfile){ load_weights(&net, weightfile); } set_batch_network(&net, 1); pthread_t detect_thread; pthread_t fetch_thread; srand(2222222); if(filename){ printf("video file: %s\n", 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(frames){ cvSetCaptureProperty(cap, CV_CAP_PROP_FPS, frames); } } if(!cap) error("Couldn't connect to webcam.\n"); layer l = net.layers[net.n-1]; demo_detections = l.n*l.w*l.h; int j; avg = (float *) calloc(l.outputs, sizeof(float)); last_avg = (float *) calloc(l.outputs, sizeof(float)); last_avg2 = (float *) calloc(l.outputs, sizeof(float)); for(j = 0; j < demo_frame; ++j) predictions[j] = (float *) calloc(l.outputs, sizeof(float)); boxes = (box *)calloc(l.w*l.h*l.n, sizeof(box)); probs = (float **)calloc(l.w*l.h*l.n, sizeof(float *)); for(j = 0; j < l.w*l.h*l.n; ++j) probs[j] = (float *)calloc(l.classes+1, sizeof(float)); buff[0] = get_image_from_stream(cap); buff[1] = copy_image(buff[0]); buff[2] = copy_image(buff[0]); buff_letter[0] = letterbox_image(buff[0], net.w, net.h); buff_letter[1] = letterbox_image(buff[0], net.w, net.h); buff_letter[2] = letterbox_image(buff[0], net.w, net.h); ipl = cvCreateImage(cvSize(buff[0].w,buff[0].h), IPL_DEPTH_8U, buff[0].c); int count = 0; if(!prefix){ cvNamedWindow("Demo", CV_WINDOW_NORMAL); if(fullscreen){ cvSetWindowProperty("Demo", CV_WND_PROP_FULLSCREEN, CV_WINDOW_FULLSCREEN); } else { cvMoveWindow("Demo", 0, 0); cvResizeWindow("Demo", 1352, 1013); } } demo_time = get_wall_time(); while(!demo_done){ buff_index = (buff_index + 1) %3; if(pthread_create(&fetch_thread, 0, fetch_in_thread, 0)) error("Thread creation failed"); if(pthread_create(&detect_thread, 0, detect_in_thread, 0)) error("Thread creation failed"); if(!prefix){ if(count % (demo_delay+1) == 0){ fps = 1./(get_wall_time() - demo_time); demo_time = get_wall_time(); float *swap = last_avg; last_avg = last_avg2; last_avg2 = swap; memcpy(last_avg, avg, l.outputs*sizeof(float)); } display_in_thread(0); }else{ char name[256]; sprintf(name, "%s_%08d", prefix, count); save_image(buff[(buff_index + 1)%3], name); } pthread_join(fetch_thread, 0); pthread_join(detect_thread, 0); ++count; } }