| | |
| | | |
| | | char *voc_names[] = {"aeroplane", "bicycle", "bird", "boat", "bottle", "bus", "car", "cat", "chair", "cow", "diningtable", "dog", "horse", "motorbike", "person", "pottedplant", "sheep", "sofa", "train", "tvmonitor"}; |
| | | |
| | | void draw_swag(image im, float *predictions, int side, int num, char *label, float thresh) |
| | | void draw_swag(image im, int num, float thresh, box *boxes, float **probs, char *label) |
| | | { |
| | | int classes = 20; |
| | | int i,n; |
| | | int i; |
| | | |
| | | for(i = 0; i < side*side; ++i){ |
| | | int row = i / side; |
| | | int col = i % side; |
| | | for(n = 0; n < num; ++n){ |
| | | int p_index = side*side*classes + i*num + n; |
| | | int box_index = side*side*(classes + num) + (i*num + n)*4; |
| | | int class_index = i*classes; |
| | | float scale = predictions[p_index]; |
| | | int class = max_index(predictions+class_index, classes); |
| | | float prob = scale * predictions[class_index + class]; |
| | | if(prob > thresh){ |
| | | int width = sqrt(prob)*5 + 1; |
| | | printf("%f %s\n", prob, voc_names[class]); |
| | | float red = get_color(0,class,classes); |
| | | float green = get_color(1,class,classes); |
| | | float blue = get_color(2,class,classes); |
| | | box b = float_to_box(predictions+box_index); |
| | | b.x = (b.x + col)/side; |
| | | b.y = (b.y + row)/side; |
| | | b.w = b.w*b.w; |
| | | b.h = b.h*b.h; |
| | | for(i = 0; i < num; ++i){ |
| | | int class = max_index(probs[i], classes); |
| | | float prob = probs[i][class]; |
| | | if(prob > thresh){ |
| | | int width = pow(prob, 1./3.)*10 + 1; |
| | | printf("%f %s\n", prob, voc_names[class]); |
| | | float red = get_color(0,class,classes); |
| | | float green = get_color(1,class,classes); |
| | | float blue = get_color(2,class,classes); |
| | | //red = green = blue = 0; |
| | | box b = boxes[i]; |
| | | |
| | | int left = (b.x-b.w/2)*im.w; |
| | | int right = (b.x+b.w/2)*im.w; |
| | | int top = (b.y-b.h/2)*im.h; |
| | | int bot = (b.y+b.h/2)*im.h; |
| | | draw_box_width(im, left, top, right, bot, width, red, green, blue); |
| | | } |
| | | int left = (b.x-b.w/2.)*im.w; |
| | | int right = (b.x+b.w/2.)*im.w; |
| | | int top = (b.y-b.h/2.)*im.h; |
| | | int bot = (b.y+b.h/2.)*im.h; |
| | | draw_box_width(im, left, top, right, bot, width, red, green, blue); |
| | | } |
| | | } |
| | | show_image(im, label); |
| | |
| | | |
| | | void train_swag(char *cfgfile, char *weightfile) |
| | | { |
| | | //char *train_images = "/home/pjreddie/data/voc/person_detection/2010_person.txt"; |
| | | //char *train_images = "/home/pjreddie/data/people-art/train.txt"; |
| | | //char *train_images = "/home/pjreddie/data/voc/test/2012_trainval.txt"; |
| | | char *train_images = "/home/pjreddie/data/voc/test/train.txt"; |
| | | //char *train_images = "/home/pjreddie/data/voc/test/train_all.txt"; |
| | | //char *train_images = "/home/pjreddie/data/voc/test/2007_trainval.txt"; |
| | | char *backup_directory = "/home/pjreddie/backup/"; |
| | | srand(time(0)); |
| | | data_seed = time(0); |
| | |
| | | if (avg_loss < 0) avg_loss = loss; |
| | | avg_loss = avg_loss*.9 + loss*.1; |
| | | |
| | | printf("%d: %f, %f avg, %lf seconds, %d images\n", i, loss, avg_loss, sec(clock()-time), i*imgs); |
| | | 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){ |
| | | char buff[256]; |
| | | sprintf(buff, "%s/%s_%d.weights", backup_directory, base, i); |
| | |
| | | srand(time(0)); |
| | | |
| | | char *base = "results/comp4_det_test_"; |
| | | //base = "/home/pjreddie/comp4_det_test_"; |
| | | //list *plist = get_paths("/home/pjreddie/data/people-art/test.txt"); |
| | | //list *plist = get_paths("/home/pjreddie/data/cubist/test.txt"); |
| | | list *plist = get_paths("/home/pjreddie/data/voc/test/2007_test.txt"); |
| | | char **paths = (char **)list_to_array(plist); |
| | | |
| | |
| | | int nms = 1; |
| | | float iou_thresh = .5; |
| | | |
| | | int nthreads = 8; |
| | | int nthreads = 2; |
| | | image *val = calloc(nthreads, sizeof(image)); |
| | | image *val_resized = calloc(nthreads, sizeof(image)); |
| | | image *buf = calloc(nthreads, sizeof(image)); |
| | |
| | | int w = val[t].w; |
| | | int h = val[t].h; |
| | | convert_swag_detections(predictions, classes, l.n, square, side, w, h, thresh, probs, boxes, 0); |
| | | if (nms) do_nms(boxes, probs, side*side*l.n, classes, iou_thresh); |
| | | if (nms) do_nms_sort(boxes, probs, side*side*l.n, classes, iou_thresh); |
| | | print_swag_detections(fps, id, boxes, probs, side*side*l.n, classes, w, h); |
| | | free(id); |
| | | free_image(val[t]); |
| | |
| | | image sized = resize_image(orig, net.w, net.h); |
| | | char *id = basecfg(path); |
| | | float *predictions = network_predict(net, sized.data); |
| | | int w = orig.w; |
| | | int h = orig.h; |
| | | convert_swag_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); |
| | | |
| | |
| | | if(weightfile){ |
| | | load_weights(&net, weightfile); |
| | | } |
| | | region_layer layer = net.layers[net.n-1]; |
| | | region_layer l = net.layers[net.n-1]; |
| | | set_batch_network(&net, 1); |
| | | srand(2222222); |
| | | clock_t time; |
| | | char buff[256]; |
| | | char *input = buff; |
| | | int j; |
| | | float nms=.5; |
| | | box *boxes = calloc(l.side*l.side*l.n, sizeof(box)); |
| | | float **probs = calloc(l.side*l.side*l.n, sizeof(float *)); |
| | | for(j = 0; j < l.side*l.side*l.n; ++j) probs[j] = calloc(l.classes, sizeof(float *)); |
| | | while(1){ |
| | | if(filename){ |
| | | strncpy(input, filename, 256); |
| | |
| | | time=clock(); |
| | | float *predictions = network_predict(net, X); |
| | | printf("%s: Predicted in %f seconds.\n", input, sec(clock()-time)); |
| | | draw_swag(im, predictions, layer.side, layer.n, "predictions", thresh); |
| | | convert_swag_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_swag(im, l.side*l.side*l.n, thresh, boxes, probs, "predictions"); |
| | | |
| | | show_image(sized, "resized"); |
| | | free_image(im); |
| | | free_image(sized); |
| | |
| | | } |
| | | } |
| | | |
| | | |
| | | /* |
| | | #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); |
| | | } |
| | | region_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_swag(char *cfgfile, char *weightfile, float thresh); |
| | | #ifndef GPU |
| | | void demo_swag(char *cfgfile, char *weightfile, float thresh){} |
| | | #endif |
| | | |
| | | void run_swag(int argc, char **argv) |
| | | { |
| | | float thresh = find_float_arg(argc, argv, "-thresh", .2); |
| | |
| | | else if(0==strcmp(argv[2], "train")) train_swag(cfg, weights); |
| | | else if(0==strcmp(argv[2], "valid")) validate_swag(cfg, weights); |
| | | else if(0==strcmp(argv[2], "recall")) validate_swag_recall(cfg, weights); |
| | | else if(0==strcmp(argv[2], "demo")) demo_swag(cfg, weights, thresh); |
| | | } |