"""

try:

return cv2.resize(image, network_size)

except Exception as ex:

"""Get images from VXG Cloud."""

try:

headers = {

'accept': 'application/json',

'Authorization': 'SI %s' % token,

}

uri = "http://web.skyvr.videoexpertsgroup.com"

end = datetime.utcnow()

start = end - timedelta(seconds=period)

origin = 'generated_from_live'

query = [

('start', start), # TODO start.isoformat()

('end', end), # TODO end.isoformat()

('origin', origin),

('order_by', '-time')

]

url = '%s/api/v2/storage/thumbnails/?%s' % (uri, urlencode(query,

safe=','))

if not os.path.exists('images'):

os.makedirs('images')

resp = requests.get(url=url, headers=headers, timeout=10).json()

return resp.get('objects', [])

except Exception:

cv2.imwrite(f"images/image-{img_id}-original.jpg", image)

conv_image = converted_image( # resize in 4 times

image, tuple(int(v / 2) for v in image.shape[-2::-1]))

for (x, y, w, h) in pedestrians:

cv2.rectangle(image, (x, y), (x+w, y+h), (0, 0, 255), 2)

cv2.putText(image, 'Human', (x, y - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.7,

(0, 0, 255), 2)

parser = argparse.ArgumentParser()

parser.add_argument('-t', '--token', help='Group token', required=True)

parser.add_argument('-p', '--period', type=int, help='Perod time',

default=3600)

parser.add_argument('-f', '--file', help='Cascade model file',

default='Samples/haarcascade_fullbody.xml')

param = parser.parse_args()

pedestrian_tracker = cv2.CascadeClassifier(param.file)

for obj in get_images(token=param.token, period=param.period):

img_resp = requests.get(obj['url'], timeout=10)

img = cv2.imdecode(

np.frombuffer(io.BytesIO(img_resp.content).read(), np.uint8),

cv2.IMREAD_COLOR

)

# resize_and_save_image(image=img, img_id=obj['id'])

# convert color image to grey image

gray_img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)

pedestrians = pedestrian_tracker.detectMultiScale(gray_img, 1.01)

print(pedestrians)

if len(pedestrians):

save_image_with_pedestrians(image=img, img_id=obj['id'],