import _pickle as cPickle
import cv2
# construct the argument parser and parse the argument
ap.add_argument("-c",
"--conf",
required=True,
help="path to configuration file")
ap.add_argument("-i",
"--image",
required=True,
help="path to the image being classified")
args = vars(ap.parse_args())
# load the configuration file
conf = Conf(args["conf"])
#load the classifier
model = cPickle.loads(open(conf["classifier_path"]).read())
hog = HOG(orientations=conf["orientations"],
pixelsPerCell=tuple(conf["pixels_per_cell"]),
cellsPerBlock=tuple(conf["cells_per_block"]),
normalize=conf["normalize"])
od = ObjectDetector(model, hog)
# loads the image and convert it to grayscale
image = cv2.imread(args["image"])
image = imutils.resize(image, width=min(260, image.shape[1]))
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
#detect the objects
import cv2
import find_face
import use_model
from tensorflow.keras.models import load_model
from pyimagesearch.notifications.twilionotifier import TwilioNotifier
from pyimagesearch.utils.conf import Conf
import time
conf = Conf('config/config.json')
tn = TwilioNotifier(conf)
model = load_model('mask_model.h5')
capture = cv2.VideoCapture(0)
while True:
find = False
ret, frame = capture.read()
cv2.imshow("VideoFrame", frame)
find = find_face.img_processing('./sv_img/face.jpg', frame)
if cv2.waitKey(1) > 0:
break
if not find:
continue
pred = use_model.predict_mask(model)
if pred == 0:
start_time = time.time()
while True:
ret, frame = capture.read()