Coding style changes

yolov3.py

@@ -279,67 +279,85 @@ class BoundBox:
         self.score = -1
 
     def get_label(self):
+        """
+        Gets the label of the current object
+        """
         if self.label == -1:
             self.label = np.argmax(self.classes)
 
         return self.label
 
     def get_score(self):
+        """
+        Gets the score of the current object
+        """
         if self.score == -1:
             self.score = self.classes[self.get_label()]
 
         return self.get_score
 
-def _sigmoid(x):
+def _sigmoid(inp):
-    return 1. /(1. + np.exp(-x))
+    return 1. / (1. + np.exp(-inp))
 
 def decode_netout(netout, anchors, obj_thresh, net_h, net_w):
+    """
+    Decode output information of network.
+    """
     grid_h, grid_w = netout.shape[:2]
     nb_box = 3
     netout = netout.reshape((grid_h, grid_w, nb_box, -1))
-    nb_class = netout.shape[-1] - 5
     boxes = []
     netout[..., :2]  = _sigmoid(netout[..., :2])
     netout[..., 4:]  = _sigmoid(netout[..., 4:])
     netout[..., 5:]  = netout[..., 4][..., np.newaxis] * netout[..., 5:]
     netout[..., 5:] *= netout[..., 5:] > obj_thresh
 
-    for i in range(grid_h*grid_w):
+    for i in range(grid_h * grid_w):
         row = i / grid_w
         col = i % grid_w
-        for b in range(nb_box):
+        for j in range(nb_box):
             # 4th element is objectness score
-            objectness = netout[int(row)][int(col)][b][4]
+            objectness = netout[int(row)][int(col)][j][4]
-            if objectness.all() <= obj_thresh: continue
+
-            # first 4 elements are x, y, w, and h
+            if objectness.all() <= obj_thresh:
-            x, y, w, h = netout[int(row)][int(col)][b][:4]
+                continue
-            x = (col + x) / grid_w # center position, unit: image width
+
-            y = (row + y) / grid_h # center position, unit: image height
+            # First 4 elements are x, y, w, and h
-            w = anchors[2 * b + 0] * np.exp(w) / net_w # unit: image width
+            x, y, w, h = netout[int(row)][int(col)][j][:4]
-            h = anchors[2 * b + 1] * np.exp(h) / net_h # unit: image height
+            x = (col + x) / grid_w # Center position, unit: image width
-            # last elements are class probabilities
+            y = (row + y) / grid_h # Center position, unit: image height
-            classes = netout[int(row)][col][b][5:]
+            w = anchors[2 * j + 0] * np.exp(w) / net_w # Unit: image width
-            box = BoundBox(x-w/2, y-h/2, x+w/2, y+h/2, objectness, classes)
+            h = anchors[2 * j + 1] * np.exp(h) / net_h # Unit: image height
+            # Last elements are class probabilities
+            classes = netout[int(row)][col][j][5:]
+            box = BoundBox(x - w / 2, y - h / 2, x + w / 2, y + h / 2, objectness, classes)
             boxes.append(box)
     return boxes
 
-"""**Step 5:** strech the box to be fit to the image normal shape"""
+# Step 5
-
 def correct_yolo_boxes(boxes, image_h, image_w, net_h, net_w):
+    """
+    Strech the box to be fit to the image normal shape
+    """
     new_w, new_h = net_w, net_h
-    for i in range(len(boxes)):
+    for box in boxes:
-        x_offset, x_scale = (net_w - new_w)/2./net_w, float(new_w)/net_w
+        x_offset, x_scale = (net_w - new_w) / 2. / net_w, float(new_w) / net_w
-        y_offset, y_scale = (net_h - new_h)/2./net_h, float(new_h)/net_h
+        y_offset, y_scale = (net_h - new_h) / 2. / net_h, float(new_h) / net_h
-        boxes[i].xmin = int((boxes[i].xmin - x_offset) / x_scale * image_w)
-        boxes[i].xmax = int((boxes[i].xmax - x_offset) / x_scale * image_w)
-        boxes[i].ymin = int((boxes[i].ymin - y_offset) / y_scale * image_h)
-        boxes[i].ymax = int((boxes[i].ymax - y_offset) / y_scale * image_h)
 
-"""**Step 6:** implementing IOU"""
+        box.xmin = int((box.xmin - x_offset) / x_scale * image_w)
+        box.xmax = int((box.xmax - x_offset) / x_scale * image_w)
+        box.ymin = int((box.ymin - y_offset) / y_scale * image_h)
+        box.ymax = int((box.ymax - y_offset) / y_scale * image_h)
 
+# Step 6
 def _interval_overlap(interval_a, interval_b):
+    """
+    Implementing IOU
+
+    """
     x1, x2 = interval_a
     x3, x4 = interval_b
+
     if x3 < x1:
         if x4 < x1:
             return 0
@@ -418,14 +436,14 @@ def draw_boxes(filename, v_boxes, v_labels, v_scores):
 
 """**step 7:** declare several configuration"""
 
-# define the anchors
+# Define the anchors
-anchors = [[116,90, 156,198, 373,326], [30,61, 62,45, 59,119], [10,13, 16,30, 33,23]]
+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 the probability threshold for detected objects
-class_threshold = 0.6
+CLASS_THRESHOLD = 0.6
 
-# define the labels
+# Define the labels
-labels = ["person", "bicycle", "car", "motorbike", "aeroplane", "bus", "train", "truck",
+LABELS = ["person", "bicycle", "car", "motorbike", "aeroplane", "bus", "train", "truck",
     "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench",
     "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe",
     "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard",
@@ -482,7 +500,7 @@ def main():
         boxes = list()
         for i in range(len(yhat)):
             # decode the output of the network
-            boxes += decode_netout(yhat[i][0], anchors[i], class_threshold, input_h, input_w)
+            boxes += decode_netout(yhat[i][0], ANCHORS[i], CLASS_THRESHOLD, input_h, input_w)
 
         # correct the sizes of the bounding boxes for the shape of the image
         correct_yolo_boxes(boxes, image_h, image_w, input_h, input_w)
@@ -491,7 +509,7 @@ def main():
         do_nms(boxes, 0.5)
 
         # get the details of the detected objects
-        v_boxes, v_labels, v_scores = get_boxes(boxes, labels, class_threshold)
+        v_boxes, v_labels, v_scores = get_boxes(boxes, LABELS, CLASS_THRESHOLD)
 
         # summarize what we found
         for i in range(len(v_boxes)):