Minor restructuring

yolov3.py

@@ -52,7 +52,7 @@ class WeightReader:
         for i in range(106):
             try:
                 conv_layer = model.get_layer('conv_' + str(i))
-                print("loading weights of convolution #" + str(i))
+                print("Loading weights of convolution #" + str(i))
 
                 if i not in [81, 93, 105]:
                     norm_layer = model.get_layer('bnorm_' + str(i))
@@ -76,7 +76,7 @@ class WeightReader:
                     conv_layer.set_weights([kernel])
 
             except ValueError:
-                print("no convolution #" + str(i))
+                print("No convolution #" + str(i))
 
     def reset(self):
         """
@@ -359,14 +359,16 @@ def _interval_overlap(interval_a, interval_b):
 
     if x3 < x1:
         if x4 < x1:
-            return 0
+            ret = 0
         else:
-            return min(x2,x4) - x1
+            ret = min(x2,x4) - x1
     else:
         if x2 < x3:
-            return 0
+            ret = 0
         else:
-            return min(x2,x4) - x3
+            ret = min(x2,x4) - x3
+
+    return ret
 
 def bbox_iou(box1, box2):
     """
@@ -391,9 +393,7 @@ def do_nms(boxes, nms_thresh):
     for c in range(nb_class):
         sorted_indices = np.argsort([-box.classes[c] for box in boxes])
 
-        for i in range(len(sorted_indices)):
-            index_i = sorted_indices[i]
-
+        for i, index_i in enumerate(sorted_indices):
             if boxes[index_i].classes[c] == 0:
                 continue
 
@@ -455,7 +455,7 @@ def draw_boxes(filename, v_boxes, v_labels, v_scores):
 # Define the anchors
 ANCHORS = [[116,90, 156,198, 373,326], [30,61, 62,45, 59,119], [10,13, 16,30, 33,23]]
 
-# Define the probability threshold for detected objects
+# Define thLOe probability threshold for detected objects
 CLASS_THRESHOLD = 0.6
 
 # Define the labels