# label_dict = classifier.get_label_dict(ROOT_DIR + '/models/labels.json')
# -----------------------------------------------------------------
classifier = FaceClassifier()
# classifier.set_model_path(ROOT_DIR + '/models/model_09_02.h5')
classifier.set_model_path(ROOT_DIR + '/models/model_12_02.h5')
# classifier.set_label_path(ROOT_DIR + '/models/labels.json')
label_dict = classifier.get_label_dict(ROOT_DIR + '/models/labels.json')
face_detected = False
while (True):
# Capture frame-by-frame
ret, frame = cap.read()
# Handles the mirroring of the current frame
frame = cv2.flip(frame, 1)
if p.extract_video_face(frame) != None:
face_detected = True
faces = p.extract_video_face(frame)
for (x1, x2, y1, y2) in faces:
# print(x1, x2, y1, y2)
# x1, x2, y1, y2 = p.extract_video_face(frame)
cv2.rectangle(frame, (x1 - 8, y1 - 8), (x2 + 8, y2 + 8),
(0, 0, 255), 2)
pixels = np.asarray(frame)
face = pixels[y1:y2, x1:x2]
# resize pixels to the models size
output_image = np.array(face)
face_pixel = np.asarray(output_image)
face_pixel = cv2.resize(face_pixel, (160, 160))
