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examples/classifier.c

@@ -44,11 +44,15 @@ void train_classifier(char *datacfg, char *cfgfile, char *weightfile, int *gpus,
     list *options = read_data_cfg(datacfg);
 
     char *backup_directory = option_find_str(options, "backup", "/backup/");
+    int tag = option_find_int_quiet(options, "tag", 0);
     char *label_list = option_find_str(options, "labels", "data/labels.list");
     char *train_list = option_find_str(options, "train", "data/train.list");
     int classes = option_find_int(options, "classes", 2);
 
-    char **labels = get_labels(label_list);
+    char **labels;
+    if(!tag){
+        labels = get_labels(label_list);
+    }
     list *plist = get_paths(train_list);
     char **paths = (char **)list_to_array(plist);
     printf("%d\n", plist->size);
@@ -76,7 +80,11 @@ void train_classifier(char *datacfg, char *cfgfile, char *weightfile, int *gpus,
     args.n = imgs;
     args.m = N;
     args.labels = labels;
-    args.type = CLASSIFICATION_DATA;
+    if (tag){
+        args.type = TAG_DATA;
+    } else {
+        args.type = CLASSIFICATION_DATA;
+    }
 
     data train;
     data buffer;
@@ -385,15 +393,13 @@ void validate_classifier_single(char *datacfg, char *filename, char *weightfile)
             }
         }
         image im = load_image_color(paths[i], 0, 0);
-        image resized = resize_min(im, net->w);
+        image crop = center_crop_image(im, net->w, net->h);
-        image crop = crop_image(resized, (resized.w - net->w)/2, (resized.h - net->h)/2, net->w, net->h);
         //show_image(im, "orig");
         //show_image(crop, "cropped");
         //cvWaitKey(0);
         float *pred = network_predict(net, crop.data);
         if(net->hierarchy) hierarchy_predictions(pred, net->outputs, net->hierarchy, 1, 1);
 
-        if(resized.data != im.data) free_image(resized);
         free_image(im);
         free_image(crop);
         top_k(pred, classes, topk, indexes);
@@ -955,6 +961,8 @@ void gun_classifier(char *datacfg, char *cfgfile, char *weightfile, int cam_inde
 void demo_classifier(char *datacfg, char *cfgfile, char *weightfile, int cam_index, const char *filename)
 {
 #ifdef OPENCV
+    char *base = basecfg(cfgfile);
+    image **alphabet = load_alphabet();
     printf("Classifier Demo\n");
     network *net = load_network(cfgfile, weightfile, 0);
     set_batch_network(net, 1);
@@ -988,8 +996,8 @@ void demo_classifier(char *datacfg, char *cfgfile, char *weightfile, int cam_ind
     int *indexes = calloc(top, sizeof(int));
 
     if(!cap) error("Couldn't connect to webcam.\n");
-    cvNamedWindow("Classifier", CV_WINDOW_NORMAL); 
+    cvNamedWindow(base, CV_WINDOW_NORMAL); 
-    cvResizeWindow("Classifier", 512, 512);
+    cvResizeWindow(base, 512, 512);
     float fps = 0;
     int i;
 
@@ -998,8 +1006,8 @@ void demo_classifier(char *datacfg, char *cfgfile, char *weightfile, int cam_ind
         gettimeofday(&tval_before, NULL);
 
         image in = get_image_from_stream(cap);
-        image in_s = resize_image(in, net->w, net->h);
+        //image in_s = resize_image(in, net->w, net->h);
-        show_image(in, "Classifier");
+        image in_s = letterbox_image(in, net->w, net->h);
 
         float *predictions = network_predict(net, in_s.data);
         if(net->hierarchy) hierarchy_predictions(predictions, net->outputs, net->hierarchy, 1, 1);
@@ -1009,11 +1017,24 @@ void demo_classifier(char *datacfg, char *cfgfile, char *weightfile, int cam_ind
         printf("\033[1;1H");
         printf("\nFPS:%.0f\n",fps);
 
+        int lh = in.h*.03;
+        int toph = 3*lh;
+
+        float rgb[3] = {1,1,1};
         for(i = 0; i < top; ++i){
+            printf("%d\n", toph);
             int index = indexes[i];
             printf("%.1f%%: %s\n", predictions[index]*100, names[index]);
+
+            char buff[1024];
+            sprintf(buff, "%3.1f%%: %s\n", predictions[index]*100, names[index]);
+            image label = get_label(alphabet, buff, lh);
+            draw_label(in, toph, lh, label, rgb);
+            toph += 2*lh;
+            free_image(label);
         }
 
+        show_image(in, base);
         free_image(in_s);
         free_image(in);
 

examples/detector.c

@@ -624,6 +624,177 @@ void test_detector(char *datacfg, char *cfgfile, char *weightfile, char *filenam
     }
 }
 
+void censor_detector(char *datacfg, char *cfgfile, char *weightfile, int cam_index, const char *filename, int class, float thresh, int skip)
+{
+    image **alphabet = load_alphabet();
+    char *base = basecfg(cfgfile);
+    network *net = load_network(cfgfile, weightfile, 0);
+    set_batch_network(net, 1);
+    list *options = read_data_cfg(datacfg);
+
+    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);
+    }
+
+    int top = option_find_int(options, "top", 1);
+
+    char *label_list = option_find_str(options, "labels", 0);
+    char *name_list = option_find_str(options, "names", label_list);
+    char **names = get_labels(name_list);
+
+    int *indexes = calloc(top, sizeof(int));
+
+    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];
+
+        int nboxes = num_boxes(net);
+
+        float *X = in_s.data;
+        network_predict(net, X);
+        detection *dets = get_network_boxes(net, in.w, in.h, thresh, 0, 0, 0);
+        //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, num_boxes(net));
+
+
+        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);
+        }
+    }
+}
+
+void extract_detector(char *datacfg, char *cfgfile, char *weightfile, int cam_index, const char *filename, int class, float thresh, int skip)
+{
+    image **alphabet = load_alphabet();
+    char *base = basecfg(cfgfile);
+    network *net = load_network(cfgfile, weightfile, 0);
+    set_batch_network(net, 1);
+    list *options = read_data_cfg(datacfg);
+
+    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);
+    }
+
+    int top = option_find_int(options, "top", 1);
+
+    char *label_list = option_find_str(options, "labels", 0);
+    char *name_list = option_find_str(options, "names", label_list);
+    char **names = get_labels(name_list);
+
+    int *indexes = calloc(top, sizeof(int));
+
+    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];
+
+        int nboxes = num_boxes(net);
+        show_image(in, base);
+
+        float *X = in_s.data;
+        network_predict(net, X);
+        detection *dets = get_network_boxes(net, in.w, in.h, thresh, 0, 0, 1);
+        //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, num_boxes(net));
+
+
+        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);
+        }
+    }
+}
+
 void network_detect(network *net, image im, float thresh, float hier_thresh, float nms, detection *dets)
 {
     network_predict_image(net, im);
@@ -674,12 +845,15 @@ void run_detector(int argc, char **argv)
     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], "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);
     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);

examples/regressor.c

@@ -32,6 +32,7 @@ void train_regressor(char *datacfg, char *cfgfile, char *weightfile, int *gpus,
 
     char *backup_directory = option_find_str(options, "backup", "/backup/");
     char *train_list = option_find_str(options, "train", "data/train.list");
+    int classes = option_find_int(options, "classes", 1);
 
     list *plist = get_paths(train_list);
     char **paths = (char **)list_to_array(plist);
@@ -43,9 +44,10 @@ void train_regressor(char *datacfg, char *cfgfile, char *weightfile, int *gpus,
     args.w = net->w;
     args.h = net->h;
     args.threads = 32;
+    args.classes = classes;
 
-    args.min = net->min_crop;
+    args.min = net->min_ratio*net->w;
-    args.max = net->max_crop;
+    args.max = net->max_ratio*net->w;
     args.angle = net->angle;
     args.aspect = net->aspect;
     args.exposure = net->exposure;
@@ -160,6 +162,10 @@ void demo_regressor(char *datacfg, char *cfgfile, char *weightfile, int cam_inde
     }else{
         cap = cvCaptureFromCAM(cam_index);
     }
+    list *options = read_data_cfg(datacfg);
+    int classes = option_find_int(options, "classes", 1);
+    char *name_list = option_find_str(options, "names", 0);
+    char **names = get_labels(name_list);
 
     if(!cap) error("Couldn't connect to webcam.\n");
     cvNamedWindow("Regressor", CV_WINDOW_NORMAL); 
@@ -171,19 +177,23 @@ void demo_regressor(char *datacfg, char *cfgfile, char *weightfile, int cam_inde
         gettimeofday(&tval_before, NULL);
 
         image in = get_image_from_stream(cap);
-        image in_s = letterbox_image(in, net->w, net->h);
+        image crop = center_crop_image(in, net->w, net->h);
-        show_image(in, "Regressor");
+        grayscale_image_3c(crop);
+        show_image(crop, "Regressor");
 
-        float *predictions = network_predict(net, in_s.data);
+        float *predictions = network_predict(net, crop.data);
 
         printf("\033[2J");
         printf("\033[1;1H");
         printf("\nFPS:%.0f\n",fps);
 
-        printf("People: %f\n", predictions[0]);
+        int i;
+        for(i = 0; i < classes; ++i){
+            printf("%s: %f\n", names[i], predictions[i]);
+        }
 
-        free_image(in_s);
         free_image(in);
+        free_image(crop);
 
         cvWaitKey(10);
 

include/darknet.h

@@ -637,6 +637,8 @@ float train_networks(network **nets, int n, data d, int interval);
 void sync_nets(network **nets, int n, int interval);
 void harmless_update_network_gpu(network *net);
 #endif
+image get_label(image **characters, char *string, int size);
+void draw_label(image a, int r, int c, image label, const float *rgb);
 void save_image_png(image im, const char *name);
 void get_next_batch(data d, int n, int offset, float *X, float *y);
 void grayscale_image_3c(image im);
@@ -683,8 +685,10 @@ image load_image(char *filename, int w, int h, int c);
 image load_image_color(char *filename, int w, int h);
 image make_image(int w, int h, int c);
 image resize_image(image im, int w, int h);
+void censor_image(image im, int dx, int dy, int w, int h);
 image letterbox_image(image im, int w, int h);
 image crop_image(image im, int dx, int dy, int w, int h);
+image center_crop_image(image im, int w, int h);
 image resize_min(image im, int min);
 image resize_max(image im, int max);
 image threshold_image(image im, float thresh);

python/darknet.py

@@ -23,6 +23,15 @@ class BOX(Structure):
                 ("w", c_float),
                 ("h", c_float)]
 
+class DETECTION(Structure):
+    _fields_ = [("bbox", BOX),
+                ("classes", c_int),
+                ("prob", POINTER(c_float)),
+                ("mask", POINTER(c_float)),
+                ("objectness", c_float),
+                ("sort_class", c_int)]
+
+
 class IMAGE(Structure):
     _fields_ = [("w", c_int),
                 ("h", c_int),
@@ -53,9 +62,16 @@ make_image = lib.make_image
 make_image.argtypes = [c_int, c_int, c_int]
 make_image.restype = IMAGE
 
-make_boxes = lib.make_boxes
+get_network_boxes = lib.get_network_boxes
-make_boxes.argtypes = [c_void_p]
+get_network_boxes.argtypes = [c_void_p, c_int, c_int, c_float, c_float, POINTER(c_int), c_int]
-make_boxes.restype = POINTER(BOX)
+get_network_boxes.restype = POINTER(DETECTION)
+
+make_network_boxes = lib.make_network_boxes
+make_network_boxes.argtypes = [c_void_p]
+make_network_boxes.restype = POINTER(DETECTION)
+
+free_detections = lib.free_detections
+free_detections.argtypes = [POINTER(DETECTION), c_int]
 
 free_ptrs = lib.free_ptrs
 free_ptrs.argtypes = [POINTER(c_void_p), c_int]
@@ -64,12 +80,8 @@ num_boxes = lib.num_boxes
 num_boxes.argtypes = [c_void_p]
 num_boxes.restype = c_int
 
-make_probs = lib.make_probs
+network_predict = lib.network_predict
-make_probs.argtypes = [c_void_p]
+network_predict.argtypes = [c_void_p, POINTER(c_float)]
-make_probs.restype = POINTER(POINTER(c_float))
-
-detect = lib.network_predict
-detect.argtypes = [c_void_p, IMAGE, c_float, c_float, c_float, POINTER(BOX), POINTER(POINTER(c_float))]
 
 reset_rnn = lib.reset_rnn
 reset_rnn.argtypes = [c_void_p]
@@ -78,6 +90,12 @@ load_net = lib.load_network
 load_net.argtypes = [c_char_p, c_char_p, c_int]
 load_net.restype = c_void_p
 
+do_nms_obj = lib.do_nms_obj
+do_nms_obj.argtypes = [POINTER(DETECTION), c_int, c_int, c_float]
+
+do_nms_sort = lib.do_nms_sort
+do_nms_sort.argtypes = [POINTER(DETECTION), c_int, c_int, c_float]
+
 free_image = lib.free_image
 free_image.argtypes = [IMAGE]
 
@@ -100,9 +118,6 @@ predict_image = lib.network_predict_image
 predict_image.argtypes = [c_void_p, IMAGE]
 predict_image.restype = POINTER(c_float)
 
-network_detect = lib.network_detect
-network_detect.argtypes = [c_void_p, IMAGE, c_float, c_float, c_float, POINTER(BOX), POINTER(POINTER(c_float))]
-
 def classify(net, meta, im):
     out = predict_image(net, im)
     res = []
@@ -113,18 +128,20 @@ def classify(net, meta, im):
 
 def detect(net, meta, image, thresh=.5, hier_thresh=.5, nms=.45):
     im = load_image(image, 0, 0)
-    boxes = make_boxes(net)
-    probs = make_probs(net)
     num =   num_boxes(net)
-    network_detect(net, im, thresh, hier_thresh, nms, boxes, probs)
+    predict_image(net, im)
+    dets = get_network_boxes(net, im.w, im.h, thresh, hier_thresh, None, 0)
+    if (nms): do_nms_obj(dets, num, meta.classes, nms);
+
     res = []
     for j in range(num):
         for i in range(meta.classes):
-            if probs[j][i] > 0:
+            if dets[j].prob[i] > 0:
-                res.append((meta.names[i], probs[j][i], (boxes[j].x, boxes[j].y, boxes[j].w, boxes[j].h)))
+                b = dets[j].bbox
+                res.append((meta.names[i], dets[j].prob[i], (b.x, b.y, b.w, b.h)))
     res = sorted(res, key=lambda x: -x[1])
     free_image(im)
-    free_ptrs(cast(probs, POINTER(c_void_p)), num)
+    free_detections(dets, num)
     return res
     
 if __name__ == "__main__":

src/data.c

@@ -548,19 +548,22 @@ void fill_hierarchy(float *truth, int k, tree *hierarchy)
     }
 }
 
-matrix load_regression_labels_paths(char **paths, int n)
+matrix load_regression_labels_paths(char **paths, int n, int k)
 {
-    matrix y = make_matrix(n, 1);
+    matrix y = make_matrix(n, k);
-    int i;
+    int i,j;
     for(i = 0; i < n; ++i){
         char labelpath[4096];
-        find_replace(paths[i], "images", "targets", labelpath);
+        find_replace(paths[i], "images", "labels", labelpath);
-        find_replace(labelpath, "JPEGImages", "targets", labelpath);
+        find_replace(labelpath, "JPEGImages", "labels", labelpath);
         find_replace(labelpath, ".jpg", ".txt", labelpath);
+        find_replace(labelpath, ".JPG", ".txt", labelpath);
         find_replace(labelpath, ".png", ".txt", labelpath);
 
         FILE *file = fopen(labelpath, "r");
-        fscanf(file, "%f", &(y.vals[i][0]));
+        for(j = 0; j < k; ++j){
+            fscanf(file, "%f", &(y.vals[i][j]));
+        }
         fclose(file);
     }
     return y;
@@ -583,18 +586,14 @@ matrix load_tags_paths(char **paths, int n, int k)
 {
     matrix y = make_matrix(n, k);
     int i;
-    int count = 0;
+    //int count = 0;
     for(i = 0; i < n; ++i){
         char label[4096];
-        find_replace(paths[i], "imgs", "labels", label);
+        find_replace(paths[i], "images", "labels", label);
-        find_replace(label, "_iconl.jpeg", ".txt", label);
+        find_replace(label, ".jpg", ".txt", label);
         FILE *file = fopen(label, "r");
-        if(!file){
+        if (!file) continue;
-            find_replace(label, "labels", "labels2", label);
+        //++count;
-            file = fopen(label, "r");
-            if(!file) continue;
-        }
-        ++count;
         int tag;
         while(fscanf(file, "%d", &tag) == 1){
             if(tag < k){
@@ -603,7 +602,7 @@ matrix load_tags_paths(char **paths, int n, int k)
         }
         fclose(file);
     }
-    printf("%d/%d\n", count, n);
+    //printf("%d/%d\n", count, n);
     return y;
 }
 
@@ -1010,7 +1009,7 @@ void *load_thread(void *ptr)
     if (a.type == OLD_CLASSIFICATION_DATA){
         *a.d = load_data_old(a.paths, a.n, a.m, a.labels, a.classes, a.w, a.h);
     } else if (a.type == REGRESSION_DATA){
-        *a.d = load_data_regression(a.paths, a.n, a.m, a.min, a.max, a.size, a.angle, a.aspect, a.hue, a.saturation, a.exposure);
+        *a.d = load_data_regression(a.paths, a.n, a.m, a.classes, a.min, a.max, a.size, a.angle, a.aspect, a.hue, a.saturation, a.exposure);
     } else if (a.type == CLASSIFICATION_DATA){
         *a.d = load_data_augment(a.paths, a.n, a.m, a.labels, a.classes, a.hierarchy, a.min, a.max, a.size, a.angle, a.aspect, a.hue, a.saturation, a.exposure, a.center);
     } else if (a.type == SUPER_DATA){
@@ -1166,13 +1165,13 @@ data load_data_super(char **paths, int n, int m, int w, int h, int scale)
     return d;
 }
 
-data load_data_regression(char **paths, int n, int m, int min, int max, int size, float angle, float aspect, float hue, float saturation, float exposure)
+data load_data_regression(char **paths, int n, int m, int k, int min, int max, int size, float angle, float aspect, float hue, float saturation, float exposure)
 {
     if(m) paths = get_random_paths(paths, n, m);
     data d = {0};
     d.shallow = 0;
     d.X = load_image_augment_paths(paths, n, min, max, size, angle, aspect, hue, saturation, exposure, 0);
-    d.y = load_regression_labels_paths(paths, n);
+    d.y = load_regression_labels_paths(paths, n, k);
     if(m) free(paths);
     return d;
 }

src/data.h

@@ -29,7 +29,7 @@ data load_data_tag(char **paths, int n, int m, int k, int min, int max, int size
 matrix load_image_augment_paths(char **paths, int n, int min, int max, int size, float angle, float aspect, float hue, float saturation, float exposure, int center);
 data load_data_super(char **paths, int n, int m, int w, int h, int scale);
 data load_data_augment(char **paths, int n, int m, char **labels, int k, tree *hierarchy, int min, int max, int size, float angle, float aspect, float hue, float saturation, float exposure, int center);
-data load_data_regression(char **paths, int n, int m, int min, int max, int size, float angle, float aspect, float hue, float saturation, float exposure);
+data load_data_regression(char **paths, int n, int m, int classes, int min, int max, int size, float angle, float aspect, float hue, float saturation, float exposure);
 data load_go(char *filename);
 
 

src/image.c

@@ -131,6 +131,7 @@ image tile_images(image a, image b, int dx)
 
 image get_label(image **characters, char *string, int size)
 {
+    size = size/10;
     if(size > 7) size = 7;
     image label = make_empty_image(0,0,0);
     while(*string){
@@ -291,7 +292,7 @@ void draw_detections(image im, detection *dets, int num, float thresh, char **na
 
             draw_box_width(im, left, top, right, bot, width, red, green, blue);
             if (alphabet) {
-                image label = get_label(alphabet, labelstr, (im.h*.03)/10);
+                image label = get_label(alphabet, labelstr, (im.h*.03));
                 draw_label(im, top + width, left, label, rgb);
                 free_image(label);
             }
@@ -395,6 +396,35 @@ void ghost_image(image source, image dest, int dx, int dy)
     }
 }
 
+void blocky_image(image im, int s)
+{
+    int i,j,k;
+    for(k = 0; k < im.c; ++k){
+        for(j = 0; j < im.h; ++j){
+            for(i = 0; i < im.w; ++i){
+                im.data[i + im.w*(j + im.h*k)] = im.data[i/s*s + im.w*(j/s*s + im.h*k)];
+            }
+        }
+    }
+}
+
+void censor_image(image im, int dx, int dy, int w, int h)
+{
+    int i,j,k;
+    int s = 32;
+    if(dx < 0) dx = 0;
+    if(dy < 0) dy = 0;
+
+    for(k = 0; k < im.c; ++k){
+        for(j = dy; j < dy + h && j < im.h; ++j){
+            for(i = dx; i < dx + w && i < im.w; ++i){
+                im.data[i + im.w*(j + im.h*k)] = im.data[i/s*s + im.w*(j/s*s + im.h*k)];
+                //im.data[i + j*im.w + k*im.w*im.h] = 0;
+            }
+        }
+    }
+}
+
 void embed_image(image source, image dest, int dx, int dy)
 {
     int x,y,k;

src/image.h

@@ -22,12 +22,10 @@ void show_image_cv(image p, const char *name, IplImage *disp);
 float get_color(int c, int x, int max);
 void draw_box(image a, int x1, int y1, int x2, int y2, float r, float g, float b);
 void draw_bbox(image a, box bbox, int w, float r, float g, float b);
-void draw_label(image a, int r, int c, image label, const float *rgb);
 void write_label(image a, int r, int c, image *characters, char *string, float *rgb);
 image image_distance(image a, image b);
 void scale_image(image m, float s);
 image rotate_crop_image(image im, float rad, float s, int w, int h, float dx, float dy, float aspect);
-image center_crop_image(image im, int w, int h);
 image random_crop_image(image im, int w, int h);
 image random_augment_image(image im, float angle, float aspect, int low, int high, int w, int h);
 augment_args random_augment_args(image im, float angle, float aspect, int low, int high, int w, int h);

src/region_layer.c

@@ -582,7 +582,7 @@ void backward_region_layer_gpu(const layer l, network net)
                 gradient_array_gpu(l.output_gpu + index, (l.coords - 4)*l.w*l.h, LOGISTIC, l.delta_gpu + index);
             }
             index = entry_index(l, b, n*l.w*l.h, l.coords);
-            if(!l.background) gradient_array_gpu(l.output_gpu + index,   l.w*l.h, LOGISTIC, l.delta_gpu + index);
+            //if(!l.background) gradient_array_gpu(l.output_gpu + index,   l.w*l.h, LOGISTIC, l.delta_gpu + index);
         }
     }
     axpy_gpu(l.batch*l.inputs, 1, l.delta_gpu, 1, net.delta_gpu, 1);