Minor fix

build/darknet/x64/darknet.py

@@ -63,6 +63,9 @@ class DETECTION(Structure):
                 ("uc", POINTER(c_float)),
                 ("points", c_int)]
 
+class DETNUMPAIR(Structure):
+    _fields_ = [("num", c_int),
+                ("dets", POINTER(DETECTION))]
 
 class IMAGE(Structure):
     _fields_ = [("w", c_int),
@@ -161,6 +164,9 @@ def network_height(net):
 free_detections = lib.free_detections
 free_detections.argtypes = [POINTER(DETECTION), c_int]
 
+free_batch_detections = lib.free_batch_detections
+free_batch_detections.argtypes = [POINTER(DETNUMPAIR), c_int]
+
 free_ptrs = lib.free_ptrs
 free_ptrs.argtypes = [POINTER(c_void_p), c_int]
 
@@ -210,6 +216,11 @@ def network_height(net):
 predict_image_letterbox.argtypes = [c_void_p, IMAGE]
 predict_image_letterbox.restype = POINTER(c_float)
 
+network_predict_batch = lib.network_predict_batch
+network_predict_batch.argtypes = [c_void_p, IMAGE, c_int, c_int, c_int,
+                                   c_float, c_float, POINTER(c_int), c_int, c_int]
+network_predict_batch.restype = POINTER(DETNUMPAIR)
+
 def array_to_image(arr):
     import numpy as np
     # need to return old values to avoid python freeing memory
@@ -445,5 +456,72 @@ def performDetect(imagePath="data/dog.jpg", thresh= 0.25, configPath = "./cfg/yo
             print("Unable to show image: "+str(e))
     return detections
 
+def performBatchDetect(thresh= 0.25, configPath = "./cfg/yolov3.cfg", weightPath = "yolov3.weights", metaPath= "./cfg/coco.data", hier_thresh=.5, nms=.45, batch_size=3):
+    import cv2
+    import numpy as np
+    # NB! Image sizes should be the same
+    # You can change the images, yet, be sure that they have the same width and height
+    img_samples = ['data/person.jpg', 'data/person.jpg', 'data/person.jpg']
+    image_list = [cv2.imread(k) for k in img_samples]
+
+    net = load_net_custom(configPath.encode('utf-8'), weightPath.encode('utf-8'), 0, batch_size)
+    meta = load_meta(metaPath.encode('utf-8'))
+    pred_height, pred_width, c = image_list[0].shape
+    net_width, net_height = (network_width(net), network_height(net))
+    img_list = []
+    for custom_image_bgr in image_list:
+        custom_image = cv2.cvtColor(custom_image_bgr, cv2.COLOR_BGR2RGB)
+        custom_image = cv2.resize(
+            custom_image, (net_width, net_height), interpolation=cv2.INTER_NEAREST)
+        custom_image = custom_image.transpose(2, 0, 1)
+        img_list.append(custom_image)
+
+    arr = np.concatenate(img_list, axis=0)
+    arr = np.ascontiguousarray(arr.flat, dtype=np.float32) / 255.0
+    data = arr.ctypes.data_as(POINTER(c_float))
+    im = IMAGE(net_width, net_height, c, data)
+
+    batch_dets = network_predict_batch(net, im, batch_size, pred_width,
+                                                pred_height, thresh, hier_thresh, None, 0, 0)
+    batch_boxes = []
+    batch_scores = []
+    batch_classes = []
+    for b in range(batch_size):
+        num = batch_dets[b].num
+        dets = batch_dets[b].dets
+        if nms:
+            do_nms_obj(dets, num, meta.classes, nms)
+        boxes = []
+        scores = []
+        classes = []
+        for i in range(num):
+            det = dets[i]
+            score = -1
+            label = None
+            for c in range(det.classes):
+                p = det.prob[c]
+                if p > score:
+                    score = p
+                    label = c
+            if score > thresh:
+                box = det.bbox
+                left, top, right, bottom = map(int,(box.x - box.w / 2, box.y - box.h / 2,
+                                            box.x + box.w / 2, box.y + box.h / 2))
+                boxes.append((top, left, bottom, right))
+                scores.append(score)
+                classes.append(label)
+                boxColor = (int(255 * (1 - (score ** 2))), int(255 * (score ** 2)), 0)
+                cv2.rectangle(image_list[b], (left, top),
+                          (right, bottom), boxColor, 2)
+        cv2.imwrite(os.path.basename(img_samples[b]),image_list[b])
+
+        batch_boxes.append(boxes)
+        batch_scores.append(scores)
+        batch_classes.append(classes)
+    free_batch_detections(batch_dets, batch_size)
+    return batch_boxes, batch_scores, batch_classes    
+
 if __name__ == "__main__":
     print(performDetect())
+    #Uncomment the following line to see batch inference working 
+    #print(performBatchDetect())

darknet.py

@@ -522,6 +522,6 @@ def performBatchDetect(thresh= 0.25, configPath = "./cfg/yolov3.cfg", weightPath
     return batch_boxes, batch_scores, batch_classes    
 
 if __name__ == "__main__":
-    #print(performDetect())
+    print(performDetect())
     #Uncomment the following line to see batch inference working 
-    print(performBatchDetect())
+    #print(performBatchDetect())

include/darknet.h

@@ -831,10 +831,10 @@ typedef struct detection{
 } detection;
 
 // network.c -batch inference
-typedef struct detNumPair {
+typedef struct det_num_pair {
     int num;
     detection *dets;
-} detNumPair, *pdetNumPair;
+} det_num_pair, *pdet_num_pair;
 
 // matrix.h
 typedef struct matrix {
@@ -945,7 +945,9 @@ LIB_API void diounms_sort(detection *dets, int total, int classes, float thresh,
 LIB_API float *network_predict(network net, float *input);
 LIB_API float *network_predict_ptr(network *net, float *input);
 LIB_API detection *get_network_boxes(network *net, int w, int h, float thresh, float hier, int *map, int relative, int *num, int letter);
+LIB_API det_num_pair* network_predict_batch(network *net, image im, int batch_size, int w, int h, float thresh, float hier, int *map, int relative, int letter);
 LIB_API void free_detections(detection *dets, int n);
+LIB_API void free_batch_detections(det_num_pair *det_num_pairs, int n);
 LIB_API void fuse_conv_batchnorm(network net);
 LIB_API void calculate_binary_weights(network net);
 LIB_API char *detection_to_json(detection *dets, int nboxes, int classes, char **names, long long int frame_id, char *filename);

src/demo.c

@@ -322,12 +322,12 @@ void demo(char *cfgfile, char *weightfile, float thresh, float hier_thresh, int
             }
 
             while (custom_atomic_load_int(&run_detect_in_thread)) {
-                if(avg_fps > 200) this_thread_yield();
+                if(avg_fps > 180) this_thread_yield();
                 else this_thread_sleep_for(thread_wait_ms);   // custom_join(detect_thread, 0);
             }
             if (!benchmark) {
                 while (custom_atomic_load_int(&run_fetch_in_thread)) {
-                    if (avg_fps > 200) this_thread_yield();
+                    if (avg_fps > 180) this_thread_yield();
                     else this_thread_sleep_for(thread_wait_ms);   // custom_join(fetch_thread, 0);
                 }
                 free_image(det_s);

src/network.c

@@ -895,12 +895,12 @@ void free_detections(detection *dets, int n)
     free(dets);
 }
 
-void free_batch_detections(detNumPair *detNumPairs, int n)
+void free_batch_detections(det_num_pair *det_num_pairs, int n)
 {
     int  i;
     for(i=0; i<n; ++i)
-        free_detections(detNumPairs[i].dets, detNumPairs[i].num);
-    free(detNumPairs);
+        free_detections(det_num_pairs[i].dets, det_num_pairs[i].num);
+    free(det_num_pairs);
 }
 
 // JSON format:
@@ -978,14 +978,14 @@ float *network_predict_image(network *net, image im)
     return p;
 }
 
-detNumPair* network_predict_batch(network *net, image im, int batch_size, int w, int h, float thresh, float hier, int *map, int relative, int letter)
+det_num_pair* network_predict_batch(network *net, image im, int batch_size, int w, int h, float thresh, float hier, int *map, int relative, int letter)
 {
     network_predict(*net, im.data);
-    detNumPair *pdets = (struct detNumPair *)calloc(batch_size, sizeof(detNumPair));
+    det_num_pair *pdets = (struct det_num_pair *)calloc(batch_size, sizeof(det_num_pair));
     int num;
     for(int batch=0; batch<batch_size; batch++){
         detection *dets = make_network_boxes_batch(net, thresh, &num, batch);
-        fill_network_boxes_batch(net, w, h, thresh, hier, map, relative, dets, letter, batch);    
+        fill_network_boxes_batch(net, w, h, thresh, hier, map, relative, dets, letter, batch);
         pdets[batch].num = num;
         pdets[batch].dets = dets;
     }