Coding style changes

yolov3.py

@@ -17,7 +17,6 @@ from matplotlib.patches import Rectangle
 
 # Step 1:
 # Define WeightReader class
-
 class WeightReader:
     """
     WeightReader class is used to parse the "yolov3.weights" file and load the model weights into
@@ -85,7 +84,7 @@ class WeightReader:
         """
         self.offset = 0
 
-# Step 2:
+# Step 2
 def _conv_block(input_layer, convs, skip=True):
     """
     Function to create convolutional layer.
@@ -370,6 +369,9 @@ def _interval_overlap(interval_a, interval_b):
             return min(x2,x4) - x3
 
 def bbox_iou(box1, box2):
+    """
+    TODO
+    """
     intersect_w = _interval_overlap([box1.xmin, box1.xmax], [box2.xmin, box2.xmax])
     intersect_h = _interval_overlap([box1.ymin, box1.ymax], [box2.ymin, box2.ymax])
     intersect = intersect_w * intersect_h
@@ -379,6 +381,9 @@ def bbox_iou(box1, box2):
     return float(intersect) / union
 
 def do_nms(boxes, nms_thresh):
+    """
+    TODO
+    """
     if len(boxes) > 0:
         nb_class = len(boxes[0].classes)
     else:
@@ -387,54 +392,63 @@ def do_nms(boxes, nms_thresh):
         sorted_indices = np.argsort([-box.classes[c] for box in boxes])
         for i in range(len(sorted_indices)):
             index_i = sorted_indices[i]
-            if boxes[index_i].classes[c] == 0: continue
+
+            if boxes[index_i].classes[c] == 0:
+                continue
+
             for j in range(i+1, len(sorted_indices)):
                 index_j = sorted_indices[j]
+
                 if bbox_iou(boxes[index_i], boxes[index_j]) >= nms_thresh:
                     boxes[index_j].classes[c] = 0
 
-# get all of the results above a threshold
 def get_boxes(boxes, labels, thresh):
+    """
+    Get all of the results above a threshold
+    """
     v_boxes, v_labels, v_scores = list(), list(), list()
-    # enumerate all boxes
+
+    # Enumerate all boxes
     for box in boxes:
-        # enumerate all possible labels
+        # Enumerate all possible labels
-        for i in range(len(labels)):
+        for i, label in enumerate(labels):
-            # check if the threshold for this label is high enough
+            # Check if the threshold for this label is high enough
             if box.classes[i] > thresh:
                 v_boxes.append(box)
-                v_labels.append(labels[i])
+                v_labels.append(label)
                 v_scores.append(box.classes[i]*100)
-                # don't break, many labels may trigger for one box
+                # Don't break, many labels may trigger for one box
+
     return v_boxes, v_labels, v_scores
 
-# draw all results
 def draw_boxes(filename, v_boxes, v_labels, v_scores):
-
+    """
-    # load the image
+    Draw all results
+    """
+    # Load the image
     data = pyplot.imread(filename)
-    # plot the image
+    # Plot the image
     pyplot.imshow(data)
-    # get the context for drawing boxes
+    # Get the context for drawing boxes
     ax = pyplot.gca()
-    # plot each box
+    # Plot each box
-    for i in range(len(v_boxes)):
+    for i, box in enumerate(v_boxes):
-        box = v_boxes[i]
+        # Get coordinates
-        # get coordinates
         y1, x1, y2, x2 = box.ymin, box.xmin, box.ymax, box.xmax
-        # calculate width and height of the box
+        # Calculate width and height of the box
         width, height = x2 - x1, y2 - y1
-        # create the shape
+        # Create the shape
         rect = Rectangle((x1, y1), width, height, fill=False, color='red', linewidth = '2')
-        # draw the box
+        # Draw the box
         ax.add_patch(rect)
-        # draw text and score in top left corner
+        # Draw text and score in top left corner
         label = "%s (%.3f)" % (v_labels[i], v_scores[i])
         pyplot.text(x1, y1, label, color='red')
-    # show the plot
+    # Show the plot
     pyplot.show()
 
-"""**step 7:** declare several configuration"""
+# Step 7:
+# Dclare several configurationd
 
 # Define the anchors
 ANCHORS = [[116,90, 156,198, 373,326], [30,61, 62,45, 59,119], [10,13, 16,30, 33,23]]
@@ -482,25 +496,22 @@ def main():
     # Step 8:
     # Make Prediction
     for photo_filename in glob.glob("images/test/dog/*"):
-
+        # Define the expected input shape for the model
-        # for fn in upload.keys():
-        #  photo_filename = '/content/' + fn
-        # photo_filename = 'test.jpg'
-
-        # define the expected input shape for the model
         input_w, input_h = 416, 416
 
         image, image_w, image_h = load_image_pixels(photo_filename, (input_w, input_h))
 
-        # make prediction
+        # Make prediction
-        yhat = yolov3.predict(image)
+        netouts = yolov3.predict(image)
-        # summarize the shape of the list of arrays
+
-        print([a.shape for a in yhat])
+        # Summarize the shape of the list of arrays
+        print([a.shape for a in netouts])
 
         boxes = list()
-        for i in range(len(yhat)):
+
-            # decode the output of the network
+        for i, netout in enumerate(netouts):
-            boxes += decode_netout(yhat[i][0], ANCHORS[i], CLASS_THRESHOLD, input_h, input_w)
+            # Decode the output of the network
+            boxes += decode_netout(netout[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)
@@ -518,7 +529,5 @@ def main():
         # draw what we found
         draw_boxes(photo_filename, v_boxes, v_labels, v_scores)
 
-        print([a.shape for a in yhat])
-
 if __name__ == "__main__":
     main()