def _pad_sample(self, image, landmarks):
image = pad_image(image, (self.size, self.size))[0]
padded_landmarks = []
for l in landmarks:
l = pad_image(l, (self.size, self.size))[0]
padded_landmarks.append(l)
landmarks = np.array(padded_landmarks)
return image, landmarks
def __call__(self, sample):
"""
:param image: numpy image
:param annotations: (x1, x2, y1, y2, label) unscaled
"""
if isinstance(sample, dict):
image = sample['image']
image = pad_image(image, (max(image.shape), max(image.shape)))[0]
sample['image'] = image
else:
sample = pad_image(sample,
(max(sample.shape), max(sample.shape)))[0]
return sample
def pad_heatmaps(heatmaps, size):
padded_landmarks = []
for l in heatmaps:
l = pad_image(l, size)[0]
padded_landmarks.append(l)
heatmaps = np.array(padded_landmarks)
return heatmaps
def process_image(self, image):
image = resize_image_keep_ratio(
image, (self.config.image_size, self.config.image_size))
image = pad_image(image,
(self.config.image_size, self.config.image_size))
image = image / 255.0
return image
def _pad_sample(self, image, landmarks):
image = pad_image(image, (self.size, self.size))[0]
heatmaps = pad_heatmaps(landmarks, (self.size, self.size))
return image, heatmaps
def process_image(image, target_size):
image = image_processing.resize_image(image, target_size[0])
image = image_processing.pad_image(image, target_size)[0]
image = image_processing.normalize_image(image)
image = image.transpose([2, 0, 1])
return image
while True:
# Grab a single frame of video
ret, frame = video_capture.read()
# Only process every other frame of video to save time
# if process_this_frame:
hits = face_identifier.identify(frame)
cache = unite_dicts(cache, hits)
# Display the results
for name, faces in cache.items():
if len(faces) == 5:
locs, frames = zip(*faces)
frames = np.array([pad_image(resize_image(f, 128), (128, 128))[0] for f in frames], dtype='uint8')
result = liveness_detector.predict(frames)
color = (0, 255, 0) if result[0] > 0.95 else (0, 0, 255)
top, right, bottom, left = locs[-1]
# Draw a box around the face
cv2.rectangle(frame, (left, top), (right, bottom), color, 2)
# Draw a label with a name below the face
cv2.rectangle(frame, (left, bottom - 35), (right, bottom), color, cv2.FILLED)
font = cv2.FONT_HERSHEY_DUPLEX
cv2.putText(frame, name, (left + 6, bottom - 6), font, 1.0, (255, 255, 255), 1)
cache[name].pop(0)
# Display the resulting image
cv2.imshow('Video', frame)