From 5479c5dd8581b021fb230e37855e0ffd98e2d397 Mon Sep 17 00:00:00 2001
From: Aymeric Chaumont <aymeric.chaumont@student-cs.fr>
Date: Wed, 20 Jul 2022 11:01:46 +0200
Subject: [PATCH] cleanify cameras inventory, allow for multiple cams per
restaurant, cleanify rtsp capture, fix websockets in video-capture
---
.gitignore | 3 +-
backend/cameras.py.template | 28 +++++++++
backend/video_capture.py | 112 ++++++++++++++++++------------------
3 files changed, 86 insertions(+), 57 deletions(-)
create mode 100644 backend/cameras.py.template
diff --git a/.gitignore b/.gitignore
index de88a2b..a17152a 100644
--- a/.gitignore
+++ b/.gitignore
@@ -3,4 +3,5 @@ node_modules/
build/
env/
.env
-__pycache__
\ No newline at end of file
+__pycache__
+cameras.py
\ No newline at end of file
diff --git a/backend/cameras.py.template b/backend/cameras.py.template
new file mode 100644
index 0000000..c44cdae
--- /dev/null
+++ b/backend/cameras.py.template
@@ -0,0 +1,28 @@
+restaurants = [
+ {
+ "restaurant": "local",
+ "a_factor": 30,
+ "b_factor": 120,
+ "cameras":
+ [
+ {
+ "IP": "",
+ "user": "",
+ "password": "",
+ "stream": "stream1",
+ "mask_points":
+ [
+ [
+ [70, 370],
+ [420, 720],
+ [1280, 720],
+ [1280, 250],
+ [930, 215],
+ [450, 550],
+ [130, 350]
+ ]
+ ]
+ }
+ ]
+ }
+]
diff --git a/backend/video_capture.py b/backend/video_capture.py
index 9806146..c83c95d 100644
--- a/backend/video_capture.py
+++ b/backend/video_capture.py
@@ -3,75 +3,75 @@ from datetime import datetime, timedelta
import numpy as np
import keras
from utils.preprocessing import fix_singular_shape, norm_by_imagenet
-from dotenv import load_dotenv
import json
-import os
+import time
+from cameras import restaurants
from db import models
from db.database import SessionLocal
from routers.websocket import manager
+
-
-def handle_cameras():
+async def handle_cameras():
model = keras.models.load_model('assets', compile=False)
db = SessionLocal()
- load_dotenv()
- camera_number = int(os.getenv('CAM_NUMBER'))
- cameras = []
- for i in range(camera_number):
- camera = {}
- camera["place"] = os.getenv(f'CAM_{i}_PLACE')
- camera["IP"] = os.getenv(f'CAM_{i}_IP')
- camera["user"] = os.getenv(f'CAM_{i}_USER')
- camera["password"] = os.getenv(f'CAM_{i}_PASSWORD')
- camera["stream"] = os.getenv(f'CAM_{i}_STREAM')
- camera["a_factor"] = int(os.getenv(f'CAM_{i}_A_FACTOR'))
- camera["b_factor"] = int(os.getenv(f'CAM_{i}_B_FACTOR'))
- camera["framegap"] = int(os.getenv(f'CAM_{i}_FRAMEGAP'))
- camera["count"] = 0
- camera["cap"] = cv2.VideoCapture(
- f'rtsp://{camera["user"]}:{camera["password"]}@{camera["IP"]}/{camera["stream"]}')
- mask_length = int(os.getenv(f'CAM_{i}_POINTS_NB'))
- mask_points = []
- for j in range(mask_length):
- point = os.getenv(f'CAM_{i}_POINT_{j}')
- mask_points.append(list(map(int, point.split(','))))
- mask = np.zeros((720, 1280, 3), dtype=np.float32)
- cv2.fillPoly(mask, [np.array(mask_points)], (255, 255, 255))
- camera["mask"] = mask
- cameras.append(camera)
+ for restaurant in restaurants:
+ for camera in restaurant["cameras"]:
+ mask = np.zeros((720, 1280, 3), dtype=np.float32)
+ cv2.fillPoly(mask, np.array(camera["mask_points"]), (255, 255, 255))
+ camera["mask"] = mask
while True:
- for camera in cameras:
- if camera['cap'].isOpened():
- ret, frame = camera['cap'].read()
- if ret and camera['count'] % camera['framegap'] == 0:
- current_time = datetime.now()
- masked_img = cv2.bitwise_and(
- frame.astype(np.float32), camera["mask"])
- treated_img = fix_singular_shape(masked_img, 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)
+
+ 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:
+
+ is_open = db.query(
+ models.OpeningHours).filter(
+ models.OpeningHours.place == restaurant["restaurant"],
+ models.OpeningHours.day == weekday,
+ models.OpeningHours.open_time <= current_time,
+ models.OpeningHours.close_time >= current_time).first() is not None
+
+ if is_open:
+ count_prediction = 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():
+ _, frame = cap.read()
+ masked_img = cv2.bitwise_and(
+ frame.astype(np.float32), camera["mask"])
+ treated_img = fix_singular_shape(masked_img, 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)
+ else:
+ cams_working = False
+ cap.release()
+
+ if cams_working:
waiting_time = timedelta(
- seconds=camera['b_factor'] +
- int(count_prediction) *
- camera['a_factor'])
+ seconds=restaurant['b_factor'] +
+ int(count_prediction *
+ restaurant['a_factor']))
db_record = models.Records(
- place=camera['place'],
- date=current_time,
+ place=restaurant['restaurant'],
+ date=current_date,
density=int(count_prediction),
waiting_time=waiting_time)
db.add(db_record)
db.commit()
- manager.broadcast(json.dumps({"type": "data"}))
- camera['count'] += 1
- else:
- camera["cap"] = cv2.VideoCapture(
- f"rtsp://{camera['user']}:{camera['password']}@{camera['IP']}/{camera['stream']}")
- print("tentative de reconnexion")
+ await manager.broadcast(json.dumps({"type": "data"}))
+ print("owarida")
+ time.sleep(60 - time.time() + start_timestamp)
--
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