def extract_eyes(cv2_image):
"""Returns a list of images that contain the eyes extracted from the original image.
First result is the left eye, second result is the right eye."""
global _face_detector, _face_predictor
if _detectors_are_initialised() == False:
_initialize_detectors()
gray_image = Utils.convert_to_gray_image(cv2_image)
rects = _face_detector(gray_image, 0)
if len(rects) > 0:
shape = _face_predictor(gray_image, rects[0])
shape = face_utils.shape_to_np(shape)
eyes = []
for eye in ["left_eye", "right_eye"]:
# get the points for the contour
(eye_start, eye_end) = face_utils.FACIAL_LANDMARKS_IDXS[eye]
contour = shape[eye_start:eye_end]
# get the upper left point, lower right point for this eye
start = [
min(contour, key=lambda x: x[0])[0],
min(contour, key=lambda x: x[1])[1]
]
end = [
max(contour, key=lambda x: x[0])[0],
max(contour, key=lambda x: x[1])[1]
]
# extract the current eye
eyes.append(cv2_image[start[1]:end[1], start[0]:end[0]])
return eyes
return None
def extract_face(cv2_image):
"""Returns the face part extracted from the image"""
global _face_detector
if _detectors_are_initialised() == False:
_initialize_detectors()
gray_image = Utils.convert_to_gray_image(cv2_image)
rects = _face_detector(gray_image, 0)
if len(rects) > 0:
# only for the first face found
(x, y, w, h) = face_utils.rect_to_bb(rects[0])
return cv2_image[y:y + h, x:x + w]
return None
def extract_eye_strips(X):
"""This extracts eye strips from the images.
Returns a `np.array`."""
for i in range(0, len(X)):
X[i] = face_detector.extract_eye_strip(X[i])
# in case no face was detected
if X[i] is None:
continue
# resize this image
X[i] = resize_cv2_image(X[i], fixed_dim=(
Config.EYE_STRIP_WIDTH, Config.EYE_STRIP_HEIGHT))
if X[i] is None:
continue
# also convert to grayscale, for now
X[i] = convert_to_gray_image(X[i])
# normalise
X[i] = np.true_divide(X[i], 255)
return np.array(X)
def extract_faces(X):
"""
Does all the processing related to face extraction.
Return a `np.array`."""
for i in range(0, len(X)):
X[i] = face_detector.extract_face(X[i])
# in case no face was detected
if X[i] is None:
continue
# resize this image
X[i] = resize_cv2_image(X[i], fixed_dim=(
Config.FACE_WIDTH, Config.FACE_HEIGHT))
if X[i] is None:
continue
# also convert to grayscale, for now
X[i] = convert_to_gray_image(X[i])
# normalise
X[i] = np.true_divide(X[i], 255)
return np.array(X)
def get_img_info(cv2_image):
"""Returns a dictionary with necessary info about the image:
eye strip, if mouth is opened, if eyes are opened."""
global _face_detector, _face_predictor
res = {
"image": cv2_image,
"mouth_is_opened": (None, 0),
"eyes_are_opened": ((None, None), (0, 0)),
}
if _detectors_are_initialised() == False:
_initialize_detectors()
gray_image = Utils.convert_to_gray_image(cv2_image)
rects = _face_detector(gray_image, 0)
if len(rects) > 0:
shape = _face_predictor(gray_image, rects[0])
shape = face_utils.shape_to_np(shape)
res["mouth_is_opened"] = _is_mouth_opened(shape)
res["eyes_are_opened"] = _are_eyes_opened(shape)
return res
def extract_eye_strip(cv2_image):
"""Returns a horizontal image containing the two eyes extracted from the image"""
global _face_detector, _face_predictor
if _detectors_are_initialised() == False:
_initialize_detectors()
gray_image = Utils.convert_to_gray_image(cv2_image)
rects = _face_detector(gray_image, 0)
if len(rects) > 0:
# only for the first face found
shape = _face_predictor(gray_image, rects[0])
shape = face_utils.shape_to_np(shape)
(left_eye_start,
left_eye_end) = face_utils.FACIAL_LANDMARKS_IDXS["left_eye"]
(right_eye_start,
right_eye_end) = face_utils.FACIAL_LANDMARKS_IDXS["right_eye"]
# get the contour
start, end = min(left_eye_start,
right_eye_start), max(left_eye_end, right_eye_end)
strip = shape[start:end]
# get the upper left point, lower right point
start = [
min(strip, key=lambda x: x[0])[0],
min(strip, key=lambda x: x[1])[1]
]
end = [
max(strip, key=lambda x: x[0])[0],
max(strip, key=lambda x: x[1])[1]
]
# go a little outside the bounding box, to capture more details
distance = (end[0] - start[0], end[1] - start[1])
# 20 percent more details on the X axis, 60% more details on the Y axis
percents = [20, 60]
for i in range(0, 2):
start[i] -= int(percents[i] / 100 * distance[i])
end[i] += int(percents[i] / 100 * distance[i])
return cv2_image[start[1]:end[1], start[0]:end[0]]
return None
def extract_eyes_for_heatmap(cv2_image):
global _face_detector, _face_predictor
if _detectors_are_initialised() == False:
_initialize_detectors()
gray_image = Utils.convert_to_gray_image(cv2_image)
rects = _face_detector(gray_image, 0)
if len(rects) > 0:
shape = _face_predictor(gray_image, rects[0])
shape = face_utils.shape_to_np(shape)
eyes = []
for eye in ["left_eye", "right_eye"]:
# get the points for the contour
(eye_start, eye_end) = face_utils.FACIAL_LANDMARKS_IDXS[eye]
# increase a little bit the size of the eye
contour = shape[eye_start:eye_end]
# get the upper left point, lower right point for this eye
start = [
min(contour, key=lambda x: x[0])[0],
min(contour, key=lambda x: x[1])[1]
]
end = [
max(contour, key=lambda x: x[0])[0],
max(contour, key=lambda x: x[1])[1]
]
# increase a little bit the size of the eye
distance = (end[0] - start[0], end[1] - start[1])
percents = [40, 40]
for i in range(0, 2):
start[i] -= int(percents[i] / 100 * distance[i])
end[i] += int(percents[i] / 100 * distance[i])
# extract the current eye
eyes.append(cv2_image[start[1]:end[1], start[0]:end[0]])
return eyes
return None