allow checkouts detection

backend/cameras.py.template

@@ -21,6 +21,21 @@ restaurants = [
                                 [450, 550],
                                 [130, 350]
                             ]
+                        ],
+                    "caisses":
+                        [
+                            {
+                                "x1": 380,
+                                "x2": 435,
+                                "y1": 740,
+                                "y2": 780
+                            },
+                            {
+                                "x1": 300,
+                                "x2": 350,
+                                "y1": 830,
+                                "y2": 880
+                            }
                         ]
                 }
             ]

backend/video_capture.py

@@ -25,7 +25,6 @@ async def handle_cameras():
 
         start_timestamp = time.time()
         current_date = datetime.fromtimestamp(start_timestamp)
-        print(current_date)
         weekday, current_time = current_date.weekday(), current_date.time()
 
         for restaurant in restaurants:
@@ -39,11 +38,12 @@ async def handle_cameras():
 
             if is_open:
                 count_prediction = 0
+                open_checkouts = 0
                 cams_working = True
 
                 for camera in restaurant["cameras"]:
                     cap = cv2.VideoCapture(f'rtsp://{camera["user"]}:{camera["password"]}@{camera["IP"]}/{camera["stream"]}')
-                    if cap.isOpened():
+                    if cams_working and cap.isOpened():
                         _, frame = cap.read()
                         masked_img = cv2.bitwise_and(
                             frame.astype(np.float32), camera["mask"])
@@ -54,8 +54,13 @@ async def handle_cameras():
                                     np.array(
                                         [treated_img]))),
                             axis=0)
-                        pred_map = np.squeeze(model.predict(input_image))
+                        pre_pred=time.time()
+                        pred_map = np.squeeze(model.predict(input_image, verbose=0))
+                        print(time.time()-pre_pred)
                         count_prediction += np.sum(pred_map)
+                        for caisse in camera["caisses"]:
+                            if np.sum(pred_map[caisse["x1"]//2:caisse["x2"]//2, caisse["y1"]//2:caisse["y2"]//2]) > 0.5:
+                                open_checkouts += 1
                     else:
                         cams_working = False
                     cap.release()
@@ -64,7 +69,7 @@ async def handle_cameras():
                     waiting_time = timedelta(
                         seconds=restaurant['b_factor'] +
                         int(count_prediction *
-                        restaurant['a_factor']))
+                        restaurant['a_factor'] / max(open_checkouts, 1)))
                     db_record = models.Records(
                         place=restaurant['restaurant'],
                         date=current_date,
@@ -73,5 +78,4 @@ async def handle_cameras():
                     db.add(db_record)
                     db.commit()
                     await manager.broadcast(json.dumps({"type": "data"}))
-        print("owarida")
         time.sleep(60 - time.time() + start_timestamp)