update

tracking/dotrack/dotracks.py

@@ -59,21 +59,21 @@ class  ShoppingCart:
 
     @property    
     def incart(self):
-        img = cv2.imread(str(curpath/'cart_tempt/back_incart.png'), cv2.IMREAD_GRAYSCALE)
+        img = cv2.imread(str(parpath/'shopcart/cart_tempt/incart.png'), cv2.IMREAD_GRAYSCALE)
         ret, binary = cv2.threshold(img, 250, 255, cv2.THRESH_BINARY)
         
         return binary
         
     @property    
     def outcart(self):
-        img = cv2.imread(str(curpath/'cart_tempt/back_outcart.png'), cv2.IMREAD_GRAYSCALE)
+        img = cv2.imread(str(parpath/'shopcart/cart_tempt/outcart.png'), cv2.IMREAD_GRAYSCALE)
         ret, binary = cv2.threshold(img, 250, 255, cv2.THRESH_BINARY)
         
         return binary
     
     @property    
     def cartedge(self):
-        img = cv2.imread(str(curpath/'cart_tempt/back_cartedge.png'), cv2.IMREAD_GRAYSCALE)
+        img = cv2.imread(str(parpath/'shopcart/cart_tempt/cartedge.png'), cv2.IMREAD_GRAYSCALE)
         ret, binary = cv2.threshold(img, 250, 255, cv2.THRESH_BINARY)
         
         return binary
@@ -520,8 +520,7 @@ class doTracks:
             mergedTracks.append(cur_list)
             
         return mergedTracks
-    
-    
+
     @staticmethod
     def join_tracks(tlista, tlistb):
         """Combine two lists of stracks into a single one."""
@@ -541,6 +540,93 @@ class doTracks:
     def sub_tracks(tlista, tlistb):
         track_ids_b = {t.tid for t in tlistb}
         return [t for t in tlista if t.tid not in track_ids_b]
+
+    
+
+    def array2frame(self, bboxes):
+        frameID = np.sort(np.unique(bboxes[:, 7].astype(int)))    
+        fboxes = []
+        for fid in frameID: 
+            idx = np.where(bboxes[:, 7] == fid)[0]
+            box = bboxes[idx, :]
+            fboxes.append(box)
+        return fboxes
+
+
+    def isintrude(self):
+        '''
+        boxes: [x1, y1, x2, y2, track_id, score, cls, frame_index, box_index] 
+                0   1   2   3      4        5     6       7           8     
+        '''
+        OverlapNum = 3
+        bboxes = self.bboxes.astype(np.int64)
+        fboxes = self.array2frame(bboxes)
+        
+        incart = cv2.bitwise_not(self.incart)
+        sum_incart = np.zeros(incart.shape, dtype=np.int64)
+        for fid, boxes in enumerate(fboxes):
+            for i in range(len(boxes)):
+                x1, y1, x2, y2 = boxes[i, 0:4]   
+                sum_incart[y1:y2, x1:x2] += 1       
+        
+        sumincart = np.zeros(sum_incart.shape, dtype=np.uint8)
+        idx255 = np.where(sum_incart >= OverlapNum)
+        sumincart[idx255] = 255
+ 
+        idxnzr = np.where(sum_incart!=0)
+        base = np.zeros(sum_incart.shape, dtype=np.uint8)
+        base[idxnzr] = 255
+
+        contours_sum, _ = cv2.findContours(sumincart, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
+        contours_base, _ = cv2.findContours(base, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
+
+        have_existed, invasion = [], []
+        for k, ct_temp in enumerate(contours_base):
+            tmp1 = np.zeros(sum_incart.shape, dtype=np.uint8)
+            cv2.drawContours(tmp1, [ct_temp], -1, 255, cv2.FILLED)
+            
+            # 确定轮廓的包含关系
+            for ct_sum in contours_sum:
+                tmp2 = np.zeros(sum_incart.shape, dtype=np.uint8)
+                cv2.drawContours(tmp2, [ct_sum], -1, 255, cv2.FILLED)
+                tmp = cv2.bitwise_and(tmp1, tmp2)                
+                if np.count_nonzero(tmp) == np.count_nonzero(tmp2):
+                    have_existed.append(k)
+                    
+        inIdx = [i for i in range(len(contours_base)) if i not in have_existed]
+        invasion = np.zeros(sum_incart.shape, dtype=np.uint8)
+        
+        for i in inIdx:
+            cv2.drawContours(invasion, [contours_base[i]], -1, 255, cv2.FILLED)
+        cv2.imwrite("./result/intrude/invasion.png", invasion)
+                    
+        
+        Intrude = True if len(inIdx)>=1 else False
+        print(f"is intruded: {Intrude}")
+
+        return Intrude
+        
+        
+        
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