bugfix & refactor

Author: Berkay Ugur Senocak

confusion_matrix.py

@@ -19,66 +19,62 @@ def box_iou_calc(boxes1, boxes2):
     def box_area(box):
         # box = 4xn
         return (box[2] - box[0]) * (box[3] - box[1])
-    
 
     area1 = box_area(boxes1.T)
     area2 = box_area(boxes2.T)
 
     lt = np.maximum(boxes1[:, None, :2], boxes2[:, :2])  # [N,M,2]
     rb = np.minimum(boxes1[:, None, 2:], boxes2[:, 2:])  # [N,M,2]
 
-    inter = np.prod(np.clip(rb - lt, a_min = 0, a_max = None), 2)
+    inter = np.prod(np.clip(rb - lt, a_min=0, a_max=None), 2)
     return inter / (area1[:, None] + area2 - inter)  # iou = inter / (area1 + area2 - inter)
 
 
 class ConfusionMatrix:
-    def __init__(self, num_classes, CONF_THRESHOLD = 0.3, IOU_THRESHOLD = 0.5):
+    def __init__(self, num_classes, CONF_THRESHOLD=0.3, IOU_THRESHOLD=0.5):
         self.matrix = np.zeros((num_classes + 1, num_classes + 1))
         self.num_classes = num_classes
         self.CONF_THRESHOLD = CONF_THRESHOLD
         self.IOU_THRESHOLD = IOU_THRESHOLD
-    
+
     def process_batch(self, detections, labels):
-        '''
+        """
         Return intersection-over-union (Jaccard index) of boxes.
         Both sets of boxes are expected to be in (x1, y1, x2, y2) format.
         Arguments:
             detections (Array[N, 6]), x1, y1, x2, y2, conf, class
             labels (Array[M, 5]), class, x1, y1, x2, y2
         Returns:
             None, updates confusion matrix accordingly
-        '''
+        """
         detections = detections[detections[:, 4] > self.CONF_THRESHOLD]
         gt_classes = labels[:, 0].astype(np.int16)
         detection_classes = detections[:, 5].astype(np.int16)
 
         all_ious = box_iou_calc(labels[:, 1:], detections[:, :4])
         want_idx = np.where(all_ious > self.IOU_THRESHOLD)
 
-        all_matches = []
-        for i in range(want_idx[0].shape[0]):
-            all_matches.append([want_idx[0][i], want_idx[1][i], all_ious[want_idx[0][i], want_idx[1][i]]])
-        
+        all_matches = [[want_idx[0][i], want_idx[1][i], all_ious[want_idx[0][i], want_idx[1][i]]]
+                       for i in range(want_idx[0].shape[0])]
+
         all_matches = np.array(all_matches)
-        if all_matches.shape[0] > 0: # if there is match
+        if all_matches.shape[0] > 0:  # if there is match
             all_matches = all_matches[all_matches[:, 2].argsort()[::-1]]
 
-            all_matches = all_matches[np.unique(all_matches[:, 1], return_index = True)[1]]
+            all_matches = all_matches[np.unique(all_matches[:, 1], return_index=True)[1]]
 
             all_matches = all_matches[all_matches[:, 2].argsort()[::-1]]
 
-            all_matches = all_matches[np.unique(all_matches[:, 0], return_index = True)[1]]
-
+            all_matches = all_matches[np.unique(all_matches[:, 0], return_index=True)[1]]
 
         for i, label in enumerate(labels):
+            gt_class = gt_classes[i]
             if all_matches.shape[0] > 0 and all_matches[all_matches[:, 0] == i].shape[0] == 1:
-                gt_class = gt_classes[i]
                 detection_class = detection_classes[int(all_matches[all_matches[:, 0] == i, 1][0])]
-                self.matrix[(gt_class), detection_class] += 1
+                self.matrix[detection_class, gt_class] += 1
             else:
-                gt_class = gt_classes[i]
-                self.matrix[self.num_classes, (gt_class)] += 1
-        
+                self.matrix[self.num_classes, gt_class] += 1
+
         for i, detection in enumerate(detections):
             if all_matches.shape[0] and all_matches[all_matches[:, 1] == i].shape[0] == 0:
                 detection_class = detection_classes[i]
@@ -90,4 +86,3 @@ def return_matrix(self):
     def print_matrix(self):
         for i in range(self.num_classes + 1):
             print(' '.join(map(str, self.matrix[i])))
-        

requirements.txt

@@ -0,0 +1 @@
+numpy