From 97e5e858e3762fcf8c9615a16f34091853b569b7 Mon Sep 17 00:00:00 2001
From: Heiko J Schick <heiko.schick@huawei.com>
Date: Fri, 6 Nov 2020 13:46:11 +0100
Subject: [PATCH] Renamed some variables to reduce pylint warnings

---
 yolov3.py | 68 +++++++++++++++++++++++++++++++------------------------
 1 file changed, 38 insertions(+), 30 deletions(-)

diff --git a/yolov3.py b/yolov3.py
index 8820aa7..96df0bc 100644
--- a/yolov3.py
+++ b/yolov3.py
@@ -321,15 +321,19 @@ def decode_netout(netout, anchors, obj_thresh, net_h, net_w):
             if objectness.all() <= obj_thresh:
                 continue
 
-            # First 4 elements are x, y, w, and h
-            x, y, w, h = netout[int(row)][int(col)][j][:4]
-            x = (col + x) / grid_w # Center position, unit: image width
-            y = (row + y) / grid_h # Center position, unit: image height
-            w = anchors[2 * j + 0] * np.exp(w) / net_w # Unit: image width
-            h = anchors[2 * j + 1] * np.exp(h) / net_h # Unit: image height
+            # First 4 elements to for the bounding box are x, y, w, and h
+            box_x, box_y, box_w, box_h = netout[int(row)][int(col)][j][:4]
+            box_x = (col + box_x) / grid_w # Center position, unit: image width
+            box_y = (row + box_y) / grid_h # Center position, unit: image height
+            box_w = anchors[2 * j + 0] * np.exp(box_w) / net_w # Unit: image width
+            box_h = anchors[2 * j + 1] * np.exp(box_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)
+            box = BoundBox(box_x - box_w / 2,
+                           box_y - box_h / 2,
+                           box_x + box_w / 2,
+                           box_y + box_h / 2,
+                           objectness, classes)
             boxes.append(box)
     return boxes
 
@@ -351,22 +355,24 @@ def correct_yolo_boxes(boxes, image_h, image_w, net_h, net_w):
 # Step 6
 def _interval_overlap(interval_a, interval_b):
     """
-    Implementing IOU
+    Implementing Intersection over Unit (IoU)
+    Source: https://www.pyimagesearch.com/2016/11/07/intersection-over-union-iou-for-object-detection/
+            https://medium.com/@amrokamal_47691/yolo-yolov2-and-yolov3-all-you-want-to-know-7e3e92dc4899
 
     """
-    x1, x2 = interval_a
-    x3, x4 = interval_b
+    x_1, x_2 = interval_a
+    x_3, x_4 = interval_b
 
-    if x3 < x1:
-        if x4 < x1:
+    if x_3 < x_1:
+        if x_4 < x_1:
             ret = 0
         else:
-            ret = min(x2,x4) - x1
+            ret = min(x_2, x_4) - x_1
     else:
-        if x2 < x3:
+        if x_2 < x_3:
             ret = 0
         else:
-            ret = min(x2,x4) - x3
+            ret = min(x_2, x_4) - x_3
 
     return ret
 
@@ -377,9 +383,10 @@ def bbox_iou(box1, box2):
     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
-    w1, h1 = box1.xmax - box1.xmin, box1.ymax - box1.ymin
-    w2, h2 = box2.xmax - box2.xmin, box2.ymax - box2.ymin
-    union = w1 * h1 + w2 * h2 - intersect
+    w_1, h_1 = box1.xmax - box1.xmin, box1.ymax - box1.ymin
+    w_2, h_2 = box2.xmax - box2.xmin, box2.ymax - box2.ymin
+    union = w_1 * h_1 + w_2 * h_2 - intersect
+
     return float(intersect) / union
 
 def do_nms(boxes, nms_thresh):
@@ -387,21 +394,21 @@ def do_nms(boxes, nms_thresh):
     TODO
     """
     if len(boxes) > 0:
-        nb_class = len(boxes[0].classes)
+        nb_classes = len(boxes[0].classes)
     else:
         return
-    for c in range(nb_class):
-        sorted_indices = np.argsort([-box.classes[c] for box in boxes])
+    for nb_class in range(nb_classes):
+        sorted_indices = np.argsort([-box.classes[nb_class] for box in boxes])
 
         for i, index_i in enumerate(sorted_indices):
-            if boxes[index_i].classes[c] == 0:
+            if boxes[index_i].classes[nb_class] == 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
+                    boxes[index_j].classes[nb_class] = 0
 
 def get_boxes(boxes, labels, thresh):
     """
@@ -431,20 +438,20 @@ def draw_boxes(filename, v_boxes, v_labels, v_scores):
     # Plot the image
     pyplot.imshow(data)
     # Get the context for drawing boxes
-    ax = pyplot.gca()
+    axes = pyplot.gca()
     # Plot each box
     for i, box in enumerate(v_boxes):
         # Get coordinates
-        y1, x1, y2, x2 = box.ymin, box.xmin, box.ymax, box.xmax
+        y_1, x_1, y_2, x_2 = box.ymin, box.xmin, box.ymax, box.xmax
         # Calculate width and height of the box
-        width, height = x2 - x1, y2 - y1
+        width, height = x_2 - x_1, y_2 - y_1
         # Create the shape
-        rect = Rectangle((x1, y1), width, height, fill=False, color='red', linewidth = '2')
+        rect = Rectangle((x_1, y_1), width, height, fill=False, color='red', linewidth = '2')
         # Draw the box
-        ax.add_patch(rect)
+        axes.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='white', backgroundcolor='red')
+        pyplot.text(x_1, y_1, label, color='white', backgroundcolor='red')
 
     # Show the plot
     pyplot.show()
@@ -600,7 +607,8 @@ def main():
     weight_reader.load_weights(yolov3)
 
     # Save the model to file
-    yolov3.save('yolov3.h5')
+    # yolov3.trainable = False
+    yolov3.save('yolov3')
 
     # Step 8:
     # Make Prediction