| | |
| | | #include "utils.h" |
| | | #include "parser.h" |
| | | #include "box.h" |
| | | #include "demo.h" |
| | | |
| | | #ifdef OPENCV |
| | | #include "opencv2/highgui/highgui_c.h" |
| | | #endif |
| | | |
| | | char *voc_names[] = {"aeroplane", "bicycle", "bird", "boat", "bottle", "bus", "car", "cat", "chair", "cow", "diningtable", "dog", "horse", "motorbike", "person", "pottedplant", "sheep", "sofa", "train", "tvmonitor"}; |
| | | image voc_labels[80]; |
| | | image voc_labels[20]; |
| | | |
| | | void train_yolo(char *cfgfile, char *weightfile) |
| | | { |
| | | char *train_images = "data/voc.0712.trainval"; |
| | | char *train_images = "/data/voc/train.txt"; |
| | | char *backup_directory = "/home/pjreddie/backup/"; |
| | | srand(time(0)); |
| | | data_seed = time(0); |
| | |
| | | args.d = &buffer; |
| | | args.type = REGION_DATA; |
| | | |
| | | args.angle = net.angle; |
| | | args.exposure = net.exposure; |
| | | args.saturation = net.saturation; |
| | | args.hue = net.hue; |
| | | |
| | | pthread_t load_thread = load_data_in_thread(args); |
| | | clock_t time; |
| | | //while(i*imgs < N*120){ |
| | |
| | | avg_loss = avg_loss*.9 + loss*.1; |
| | | |
| | | printf("%d: %f, %f avg, %f rate, %lf seconds, %d images\n", i, loss, avg_loss, get_current_rate(net), sec(clock()-time), i*imgs); |
| | | if(i%1000==0 || i == 600){ |
| | | if(i%1000==0 || (i < 1000 && i%100 == 0)){ |
| | | char buff[256]; |
| | | sprintf(buff, "%s/%s_%d.weights", backup_directory, base, i); |
| | | save_weights(net, buff); |
| | |
| | | save_weights(net, buff); |
| | | } |
| | | |
| | | void convert_yolo_detections(float *predictions, int classes, int num, int square, int side, int w, int h, float thresh, float **probs, box *boxes, int only_objectness) |
| | | void convert_detections(float *predictions, int classes, int num, int square, int side, int w, int h, float thresh, float **probs, box *boxes, int only_objectness) |
| | | { |
| | | int i,j,n; |
| | | //int per_cell = 5*num+classes; |
| | |
| | | srand(time(0)); |
| | | |
| | | char *base = "results/comp4_det_test_"; |
| | | list *plist = get_paths("data/voc.2007.test"); |
| | | //list *plist = get_paths("data/voc.2007.test"); |
| | | list *plist = get_paths("/home/pjreddie/data/voc/2007_test.txt"); |
| | | //list *plist = get_paths("data/voc.2012.test"); |
| | | char **paths = (char **)list_to_array(plist); |
| | | |
| | |
| | | float *predictions = network_predict(net, X); |
| | | int w = val[t].w; |
| | | int h = val[t].h; |
| | | convert_yolo_detections(predictions, classes, l.n, square, side, w, h, thresh, probs, boxes, 0); |
| | | convert_detections(predictions, classes, l.n, square, side, w, h, thresh, probs, boxes, 0); |
| | | if (nms) do_nms_sort(boxes, probs, side*side*l.n, classes, iou_thresh); |
| | | print_yolo_detections(fps, id, boxes, probs, side*side*l.n, classes, w, h); |
| | | free(id); |
| | |
| | | int i=0; |
| | | |
| | | float thresh = .001; |
| | | int nms = 0; |
| | | float iou_thresh = .5; |
| | | float nms_thresh = .5; |
| | | float nms = 0; |
| | | |
| | | int total = 0; |
| | | int correct = 0; |
| | |
| | | image sized = resize_image(orig, net.w, net.h); |
| | | char *id = basecfg(path); |
| | | float *predictions = network_predict(net, sized.data); |
| | | convert_yolo_detections(predictions, classes, l.n, square, side, 1, 1, thresh, probs, boxes, 1); |
| | | if (nms) do_nms(boxes, probs, side*side*l.n, 1, nms_thresh); |
| | | convert_detections(predictions, classes, l.n, square, side, 1, 1, thresh, probs, boxes, 1); |
| | | if (nms) do_nms(boxes, probs, side*side*l.n, 1, nms); |
| | | |
| | | char *labelpath = find_replace(path, "images", "labels"); |
| | | labelpath = find_replace(labelpath, "JPEGImages", "labels"); |
| | |
| | | time=clock(); |
| | | float *predictions = network_predict(net, X); |
| | | printf("%s: Predicted in %f seconds.\n", input, sec(clock()-time)); |
| | | convert_yolo_detections(predictions, l.classes, l.n, l.sqrt, l.side, 1, 1, thresh, probs, boxes, 0); |
| | | convert_detections(predictions, l.classes, l.n, l.sqrt, l.side, 1, 1, thresh, probs, boxes, 0); |
| | | if (nms) do_nms_sort(boxes, probs, l.side*l.side*l.n, l.classes, nms); |
| | | //draw_detections(im, l.side*l.side*l.n, thresh, boxes, probs, voc_names, voc_labels, 20); |
| | | draw_detections(im, l.side*l.side*l.n, thresh, boxes, probs, voc_names, voc_labels, 20); |
| | | save_image(im, "predictions"); |
| | | show_image(im, "predictions"); |
| | | |
| | | show_image(sized, "resized"); |
| | |
| | | } |
| | | } |
| | | |
| | | /* |
| | | #ifdef OPENCV |
| | | image ipl_to_image(IplImage* src); |
| | | #include "opencv2/highgui/highgui_c.h" |
| | | #include "opencv2/imgproc/imgproc_c.h" |
| | | |
| | | void demo_swag(char *cfgfile, char *weightfile, float thresh) |
| | | { |
| | | network net = parse_network_cfg(cfgfile); |
| | | if(weightfile){ |
| | | load_weights(&net, weightfile); |
| | | } |
| | | detection_layer layer = net.layers[net.n-1]; |
| | | CvCapture *capture = cvCaptureFromCAM(-1); |
| | | set_batch_network(&net, 1); |
| | | srand(2222222); |
| | | while(1){ |
| | | IplImage* frame = cvQueryFrame(capture); |
| | | image im = ipl_to_image(frame); |
| | | cvReleaseImage(&frame); |
| | | rgbgr_image(im); |
| | | |
| | | image sized = resize_image(im, net.w, net.h); |
| | | float *X = sized.data; |
| | | float *predictions = network_predict(net, X); |
| | | draw_swag(im, predictions, layer.side, layer.n, "predictions", thresh); |
| | | free_image(im); |
| | | free_image(sized); |
| | | cvWaitKey(10); |
| | | } |
| | | } |
| | | #else |
| | | void demo_swag(char *cfgfile, char *weightfile, float thresh){} |
| | | #endif |
| | | */ |
| | | |
| | | void demo_yolo(char *cfgfile, char *weightfile, float thresh, int cam_index); |
| | | #ifndef GPU |
| | | void demo_yolo(char *cfgfile, char *weightfile, float thresh, int cam_index) |
| | | { |
| | | fprintf(stderr, "Darknet must be compiled with CUDA for YOLO demo.\n"); |
| | | } |
| | | #endif |
| | | |
| | | void run_yolo(int argc, char **argv) |
| | | { |
| | | int i; |
| | |
| | | |
| | | float thresh = find_float_arg(argc, argv, "-thresh", .2); |
| | | int cam_index = find_int_arg(argc, argv, "-c", 0); |
| | | int frame_skip = find_int_arg(argc, argv, "-s", 0); |
| | | if(argc < 4){ |
| | | fprintf(stderr, "usage: %s %s [train/test/valid] [cfg] [weights (optional)]\n", argv[0], argv[1]); |
| | | return; |
| | |
| | | else if(0==strcmp(argv[2], "train")) train_yolo(cfg, weights); |
| | | else if(0==strcmp(argv[2], "valid")) validate_yolo(cfg, weights); |
| | | else if(0==strcmp(argv[2], "recall")) validate_yolo_recall(cfg, weights); |
| | | else if(0==strcmp(argv[2], "demo")) demo_yolo(cfg, weights, thresh, cam_index); |
| | | else if(0==strcmp(argv[2], "demo")) demo(cfg, weights, thresh, cam_index, filename, voc_names, voc_labels, 20, frame_skip); |
| | | } |