diff --git a/.gitignore b/.gitignore
index de88a2b3adcdd604db49c8ff544b4427e4f935e9..a17152a7e7ce9d3f563aa4472d25aa817a7d29ef 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/README.md b/README.md
index 6be0232a3f8b924143ae4d7b56a5d0aef8aae9c7..c8107378dada63bf4e092718fbe13b49905a3a2f 100644
--- a/README.md
+++ b/README.md
@@ -46,9 +46,6 @@ Navigate to [http://localhost:3000](http://localhost:3000)
# TODO
## Coté algo
-- Faire tournez le script de comptage que pendant les créneaux d'ouvertures du RU associé
-- Mettre en place le script pour compter les caisses ouvertes
-- Mettre en place le couplage des caméras (implique de pouvoir définir les masques proprement) dans le script de comptage de personnes
- Accéder à d'autre infos telle que l'API des cours sur demande à la DISI pour intégrer ça aux prédictions (ex: cours en promo complète juste implique plus d'attente)
## Coté dev
diff --git a/backend/cameras.py.template b/backend/cameras.py.template
new file mode 100644
index 0000000000000000000000000000000000000000..5d197e572134b2e9d63518bcbcd5a227ee42bde0
--- /dev/null
+++ b/backend/cameras.py.template
@@ -0,0 +1,43 @@
+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]
+ ]
+ ],
+ "caisses":
+ [
+ {
+ "x1": 380,
+ "x2": 435,
+ "y1": 740,
+ "y2": 780
+ },
+ {
+ "x1": 300,
+ "x2": 350,
+ "y1": 830,
+ "y2": 880
+ }
+ ]
+ }
+ ]
+ }
+]
diff --git a/backend/db/crud.py b/backend/db/crud.py
index df872ffc41042fd3a602dcdb7dcd8fccb57b2130..a856d45a090291fc14ce11865323dec349e15d31 100644
--- a/backend/db/crud.py
+++ b/backend/db/crud.py
@@ -231,7 +231,7 @@ def get_news(place: str, db: Session):
next_timetable = None
for time_slot in opening_hours:
if current_date.time() < time_slot.open_time:
- next_timetable=time_slot.open_time
+ next_timetable = time_slot.open_time
break
if not next_timetable:
closure = db.query(
diff --git a/backend/main.py b/backend/main.py
index d6c986ac7a837ba1f558c9d386bfb257ded706f8..ba7a7273e7fbcd7de70ec586c1bd11c248187224 100644
--- a/backend/main.py
+++ b/backend/main.py
@@ -2,6 +2,7 @@ from fastapi import FastAPI
from fastapi.middleware.cors import CORSMiddleware
from dotenv import load_dotenv
from threading import Thread
+from asyncio import run
import os
from db import database, models
@@ -30,7 +31,7 @@ app.add_middleware(
async def on_startup():
# Database creation
models.Base.metadata.create_all(bind=database.engine)
- t = Thread(target=handle_cameras)
+ t = Thread(target=run, args=(handle_cameras(),))
t.start()
diff --git a/backend/video_capture.py b/backend/video_capture.py
index 9806146f1aad3258b8e71d350cf91b4c6093d04f..08cf8533470937c824551fdf2c7e28441e7858e8 100644
--- a/backend/video_capture.py
+++ b/backend/video_capture.py
@@ -3,75 +3,76 @@ 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)
+
+ current_date = datetime.now()
+ 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
+ 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 cams_working and 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, verbose=0))
+ 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()
+
+ if cams_working and open_checkouts:
waiting_time = timedelta(
- seconds=camera['b_factor'] +
- int(count_prediction) *
- camera['a_factor'])
+ seconds=restaurant['b_factor'] +
+ int(count_prediction *
+ restaurant['a_factor'] / open_checkouts))
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"}))
+ time.sleep(max(0, 60 - (datetime.now() - current_date).total_seconds()))