From 135dab0b35acd32e185691ae7fb260c92e2c7280 Mon Sep 17 00:00:00 2001
From: Aymeric Chaumont <aymeric.chaumont@student-cs.fr>
Date: Fri, 15 Jul 2022 11:35:36 +0200
Subject: [PATCH] allow for multiple cameras
---
backend/video-capture.py | 58 +++++++++++++++++++++++++---------------
1 file changed, 36 insertions(+), 22 deletions(-)
diff --git a/backend/video-capture.py b/backend/video-capture.py
index 530f4e6..610adad 100644
--- a/backend/video-capture.py
+++ b/backend/video-capture.py
@@ -10,27 +10,41 @@ from db.database import SessionLocal
db = SessionLocal()
model = keras.models.load_model('assets')
-cap = cv2.VideoCapture("rtsp://viarezocam:superponey@10.148.38.9/stream1")
-count = 0
frame_gap = 450
-while(cap.isOpened()):
- ret, frame = cap.read()
- if ret and count % frame_gap == 0:
- current_time = datetime.now()
- treated_img = fix_singular_shape(frame, 16)
- input_image = np.expand_dims(np.squeeze(norm_by_imagenet(np.array([treated_img]))), axis=0)
- pred_map = np.squeeze(model.predict(input_image))
- count_prediction = np.sum(pred_map)
- waiting_time = timedelta(seconds=120 + count_prediction * 30)
- record = {"place": "local",
- "date": current_time,
- "density": count_prediction,
- "waiting_time": waiting_time}
- db_record = models.Records(**record)
- db.add(db_record)
- db.commit()
- db.refresh(db_record)
- count += 1
-cap.release()
-cv2.destroyAllWindows()
+cameras = [{
+ "place": "local",
+ "IP": "10.148.38.9",
+ "user": "viarezocam",
+ "password": "superponey",
+ "stream": "stream1",
+ "framegap": 900, # 60 * camera frequency
+ "count": 0, # mandatory
+ "cap": None
+}]
+
+for camera in cameras:
+ camera.cap = cv2.VideoCapture(f"rtsp://{camera.user}:{camera.password}@{camera.ip}/{camera.stream}")
+
+while True:
+ for camera in cameras:
+ if camera.cap.isOpened():
+ ret, frame = camera.cap.read()
+ if ret and camera.count % camera.frame_gap == 0:
+ current_time = datetime.now()
+ treated_img = fix_singular_shape(frame, 16)
+ input_image = np.expand_dims(np.squeeze(norm_by_imagenet(np.array([treated_img]))), axis=0)
+ pred_map = np.squeeze(model.predict(input_image))
+ count_prediction = np.sum(pred_map)
+ waiting_time = timedelta(seconds=120 + count_prediction * 30)
+ record = {"place": camera.place,
+ "date": current_time,
+ "density": count_prediction,
+ "waiting_time": waiting_time}
+ db_record = models.Records(**record)
+ db.add(db_record)
+ db.commit()
+ db.refresh(db_record)
+ camera.count += 1
+ else:
+ camera.cap.release()
--
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