美化追踪画框结果

Author: vvEverett

logs.txt

@@ -0,0 +1 @@
+INFO:root:Press "Esc", "q" or "Q" to exit.

motrackers/detectors/nanodet.py

@@ -66,15 +66,15 @@ def visualize(self, dets, meta, class_names, score_thres, wait=0):
         self.class_names = dict(zip(class_ids,class_names))
         np.random.seed(12345)
         for bb, conf, cid in zip(bboxes, confidences, class_ids):
-            bbox_colors = {key: np.random.randint(0, 255, size=(3,)).tolist() for key in self.class_names.keys()}
-            clr = [int(c) for c in bbox_colors[cid]]
-            cv2.rectangle(self.image, (bb[0], bb[1]), (bb[0] + bb[2], bb[1] + bb[3]), clr, 2)
-            label = "{}:{:.4f}".format(self.class_names[cid], conf)
-            (label_width, label_height), baseLine = cv2.getTextSize(label, cv2.FONT_HERSHEY_SIMPLEX, 0.5, 2)
-            y_label = max(bb[1], label_height)
-            cv2.rectangle(self.image, (bb[0], y_label - label_height), (bb[0] + label_width, y_label + baseLine),
-                            (255, 255, 255), cv2.FILLED)
-            cv2.putText(self.image, label, (bb[0], y_label), cv2.FONT_HERSHEY_SIMPLEX, 0.5, clr, 2)
+            # bbox_colors = {key: np.random.randint(0, 255, size=(3,)).tolist() for key in self.class_names.keys()}
+            # clr = [int(c) for c in bbox_colors[cid]]
+            cv2.rectangle(self.image, (bb[0], bb[1]), (bb[0] + bb[2], bb[1] + bb[3]), (253,230,224), 2)
+            # label = "{}:{:.4f}".format(self.class_names[cid], conf)
+            # (label_width, label_height), baseLine = cv2.getTextSize(label, cv2.FONT_HERSHEY_SIMPLEX, 0.5, 2)
+            # y_label = max(bb[1], label_height)
+            # cv2.rectangle(self.image, (bb[0], y_label - label_height), (bb[0] + label_width, y_label + baseLine),
+            #                 (255, 255, 255), cv2.FILLED)
+            # cv2.putText(self.image, label, (bb[0], y_label), cv2.FONT_HERSHEY_SIMPLEX, 0.5, clr, 2)
         bboxes = np.array(bboxes).astype('int')
         confidences = np.array(confidences)
         return bboxes , confidences , class_ids , self.image