class VerifyUser:
def __init__(self):
self.helper = OpenCVHelper()
self.dg = DataSetGenerator(os.path.join(path, "training_face_rec"))
MAX_LABELS = len(self.dg.data_labels)
self.predictor = Predictor(self.dg, num_labels=MAX_LABELS)
def get_results(self, file_in):
if not os.path.isfile(file_in):
print("Captured file not found!")
return False
""" ------------------- Loading and process the video file ----------------- """
num_blinks = eye_p.get_num_blinks(file_in)
print "Num Blinks : %d " % num_blinks
if num_blinks <= 0:
return None, "No Eye Blink found. !"
cap = cv2.VideoCapture(file_in)
results = list()
while True:
ret, image = cap.read()
if not ret:
break
img_face, pos_face = self.helper.convert_img(image)
if img_face is not None:
cv2.rectangle(image, (pos_face[0], pos_face[2]),
(pos_face[1], pos_face[3]), (255, 0, 0), 5)
user, pred = self.predictor.classify_image(img_face)
results.append(pred)
if len(results) > 0:
r = sum(results) / float(len(results))
r = int(abs(r))
return self.dg.data_labels[r], True
return None, "No Face Found !"
while True:
ret, image = cap.read()
if ret:
cv2.imshow("Faces found", image)
# image = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY)
# image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
img_face, pos_face = helper.convert_img(image)
# img_face = helper.convert_to_greyscale(image)
if img_face is not None:
cv2.rectangle(image, (pos_face[0], pos_face[2]),
(pos_face[1], pos_face[3]), (255, 0, 0), 5)
f_img_verify = p.classify_image(img_face)
# for i in range(4):
# st_verify[i] = st_verify[i + 1]
# st_verify[4] = f_img_verify
print f_img_verify
if st_verify == [True, True, True, True, True]:
f_verify = True
elif st_verify == [False, False, False, False, False]:
f_verify = False
cv2.putText(image, f_img_verify, (50, 50),
cv2.FONT_HERSHEY_DUPLEX, 1, (0, 0, 255), 2)
cv2.imshow("Faces found", image)