Coding style changes

yolov3.py

@@ -390,6 +390,7 @@ def do_nms(boxes, nms_thresh):
         return
     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]
 
@@ -416,7 +417,7 @@ def get_boxes(boxes, labels, thresh):
             if box.classes[i] > thresh:
                 v_boxes.append(box)
                 v_labels.append(label)
-                v_scores.append(box.classes[i]*100)
+                v_scores.append(box.classes[i] * 100)
                 # Don't break, many labels may trigger for one box
 
     return v_boxes, v_labels, v_scores
@@ -443,7 +444,8 @@ def draw_boxes(filename, v_boxes, v_labels, v_scores):
         ax.add_patch(rect)
         # Draw text and score in top left corner
         label = "%s (%.3f)" % (v_labels[i], v_scores[i])
-        pyplot.text(x1, y1, label, color='red')
+        pyplot.text(x1, y1, label, color='white', backgroundcolor='red')
+
     # Show the plot
     pyplot.show()
 
@@ -457,17 +459,86 @@ ANCHORS = [[116,90, 156,198, 373,326], [30,61, 62,45, 59,119], [10,13, 16,30, 33
 CLASS_THRESHOLD = 0.6
 
 # Define the labels
-LABELS = ["person", "bicycle", "car", "motorbike", "aeroplane", "bus", "train", "truck",
+LABELS = ["person",             # 0
-    "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench",
+          "bicycle",
-    "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe",
+          "car",
-    "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard",
+          "motorbike",
-    "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard",
+          "aeroplane",
-    "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana",
+          "bus",                # 5
-    "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake",
+          "train",
-    "chair", "sofa", "pottedplant", "bed", "diningtable", "toilet", "tvmonitor", "laptop", "mouse",
+          "truck",
-    "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator",
+          "boat",
-    "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"]
+          "traffic light",
-
+          "fire hydrant",       # 10
+          "stop sign",
+          "parking meter",
+          "bench",
+          "bird",
+          "cat",                # 15
+          "dog",
+          "horse",
+          "sheep",
+          "cow",
+          "elephant",           # 20
+          "bear",
+          "zebra",
+          "giraffe",
+          "backpack",
+          "umbrella",           # 25
+          "handbag",
+          "tie",
+          "suitcase",
+          "frisbee",
+          "skis",               # 30
+          "snowboard",
+          "sports ball",
+          "kite",
+          "baseball bat",
+          "baseball glove",     # 35
+          "skateboard",
+          "surfboard",
+          "tennis racket",
+          "bottle",
+          "wine glass",         # 40
+          "cup",
+          "fork",
+          "knife",
+          "spoon",
+          "bowl",               # 45
+          "banana",
+          "apple",
+          "sandwich",
+          "orange",
+          "broccoli",           # 50
+          "carrot",
+          "hot dog",
+          "pizza",
+          "donut",
+          "cake",               # 55
+          "chair",
+          "sofa",
+          "pottedplant",
+          "bed",
+          "diningtable",        # 60
+          "toilet",
+          "tvmonitor",
+          "laptop",
+          "mouse",
+          "remote",             # 65
+          "keyboard",
+          "cell phone",
+          "microwave",
+          "oven",
+          "toaster",            # 70
+          "sink",
+          "refrigerator",
+          "book",
+          "clock",
+          "vase",               # 75
+          "scissors",
+          "teddy bear",
+          "hair drier",
+          "toothbrush"]
 
 def main():
     """
@@ -513,20 +584,20 @@ def main():
             # 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 the sizes of the bounding boxes for the shape of the image
         correct_yolo_boxes(boxes, image_h, image_w, input_h, input_w)
 
-        # suppress non-maximal boxes
+        # Suppress non-maximal boxes
         do_nms(boxes, 0.5)
 
-        # get the details of the detected objects
+        # Get the details of the detected objects
         v_boxes, v_labels, v_scores = get_boxes(boxes, LABELS, CLASS_THRESHOLD)
 
-        # summarize what we found
+        # Summarize what we found
         for i in range(len(v_boxes)):
             print(v_labels[i], v_scores[i])
 
-        # draw what we found
+        # Draw what we found
         draw_boxes(photo_filename, v_boxes, v_labels, v_scores)
 
 if __name__ == "__main__":