add cm code

Author: kaanakan

confusion_matrix.py

@@ -0,0 +1,93 @@
+import numpy as np
+
+
+def box_iou_calc(boxes1, boxes2):
+    # https://github.com/pytorch/vision/blob/master/torchvision/ops/boxes.py
+    """
+    Return intersection-over-union (Jaccard index) of boxes.
+    Both sets of boxes are expected to be in (x1, y1, x2, y2) format.
+    Arguments:
+        boxes1 (Array[N, 4])
+        boxes2 (Array[M, 4])
+    Returns:
+        iou (Array[N, M]): the NxM matrix containing the pairwise
+            IoU values for every element in boxes1 and boxes2
+
+    This implementation is taken from the above link and changed so that it only uses numpy..
+    """
+
+    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)
+    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):
+        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]), x1, y1, x2, y2, class
+        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 = np.array(all_matches)
+        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[all_matches[:, 2].argsort()[::-1]]
+
+            all_matches = all_matches[np.unique(all_matches[:, 0], return_index = True)[1]]
+
+
+        for i, label in enumerate(labels):
+            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
+            else:
+                gt_class = gt_classes[i]
+                self.matrix[(gt_class), self.num_classes] += 1
+        
+        for i, detection in enumerate(detections):
+            if all_matches.shape[0] and all_matches[all_matches[:, 1] == i].shape[0] == 1:
+                detection_class = detection_classes[i]
+                self.matrix[self.num_classes ,detection_class] += 1
+
+    def return_matrix(self):
+        return self.matrix
+
+    def print_matrix(self):
+        for i in range(self.num_classes + 1):
+            print(' '.join(map(str, self.matrix[i])))
+