From cedf49bbd447f2563f8bea90936fc410975af34f Mon Sep 17 00:00:00 2001
From: Aymeric Chaumont <aymeric.chaumont@student-cs.fr>
Date: Wed, 20 Jul 2022 12:01:32 +0200
Subject: [PATCH] linting
---
backend/video_capture.py | 10 +++++-----
1 file changed, 5 insertions(+), 5 deletions(-)
diff --git a/backend/video_capture.py b/backend/video_capture.py
index 6de64c6..a0d537c 100644
--- a/backend/video_capture.py
+++ b/backend/video_capture.py
@@ -10,7 +10,7 @@ from cameras import restaurants
from db import models
from db.database import SessionLocal
from routers.websocket import manager
-
+
async def handle_cameras():
model = keras.models.load_model('assets', compile=False)
@@ -54,12 +54,12 @@ async def handle_cameras():
np.array(
[treated_img]))),
axis=0)
- pre_pred=time.time()
+ pre_pred = time.time()
pred_map = np.squeeze(model.predict(input_image, verbose=0))
- print(time.time()-pre_pred)
+ 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:
+ 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
@@ -69,7 +69,7 @@ async def handle_cameras():
waiting_time = timedelta(
seconds=restaurant['b_factor'] +
int(count_prediction *
- restaurant['a_factor'] / max(open_checkouts, 1)))
+ restaurant['a_factor'] / max(open_checkouts, 1)))
db_record = models.Records(
place=restaurant['restaurant'],
date=current_date,
--
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