def retrieve_facial_image_by_open_face(full_image_path, force_continue=True):
"""Retrieve the facial image by using open face.
:param full_image_path: the path of the full image
:type full_image_path: string
:param force_continue: whether crop facial images by using bbox coordinates
:type force_continue: boolean
:return: the facial image
:rtype: numpy array
"""
try:
full_image_in_BGR = cv2.imread(full_image_path)
full_image_in_RGB = cv2.cvtColor(full_image_in_BGR, cv2.COLOR_BGR2RGB)
bounding_box = align.getLargestFaceBoundingBox(full_image_in_RGB)
facial_image_in_RGB = align.align(common.FACIAL_IMAGE_SIZE, full_image_in_RGB, bounding_box, \
landmarkIndices=openface.AlignDlib.OUTER_EYES_AND_NOSE)
# Successful case
assert facial_image_in_RGB is not None
return cv2.cvtColor(facial_image_in_RGB, cv2.COLOR_RGB2BGR)
except:
# Failure case
if force_continue:
return retrieve_facial_image_by_bbox(full_image_path)
else:
return None
def retrieve_facial_image_by_open_face(full_image_path, force_continue=True):
"""Retrieve the facial image by using open face.
:param full_image_path: the path of the full image
:type full_image_path: string
:param force_continue: whether crop facial images by using bbox coordinates
:type force_continue: boolean
:return: the facial image
:rtype: numpy array
"""
try:
full_image_in_BGR = cv2.imread(full_image_path)
full_image_in_RGB = cv2.cvtColor(full_image_in_BGR, cv2.COLOR_BGR2RGB)
bounding_box = align.getLargestFaceBoundingBox(full_image_in_RGB)
facial_image_in_RGB = align.align(common.FACIAL_IMAGE_SIZE, full_image_in_RGB, bounding_box, \
landmarkIndices=openface.AlignDlib.OUTER_EYES_AND_NOSE)
# Successful case
assert facial_image_in_RGB is not None
return cv2.cvtColor(facial_image_in_RGB, cv2.COLOR_RGB2BGR)
except:
# Failure case
if force_continue:
return retrieve_facial_image_by_bbox(full_image_path)
else:
return None
def retrieve_facial_image_by_congealingcomplex(full_image_path,
force_continue=True):
"""Retrieve the facial image by using congealingcomplex.
:param full_image_path: the path of the full image
:type full_image_path: string
:param force_continue: whether crop facial images by using bbox coordinates
:type force_continue: boolean
:return: the facial image
:rtype: numpy array
"""
def call_congealingcomplex(facial_image):
"""Call congealingcomplex to perform face frontalization.
:param facial_image: the facial image
:type facial_image: numpy array
:return: the processed facial image
:rtype: numpy array
"""
input_image_path = os.path.join("/tmp", "input_image.jpg")
output_image_path = os.path.join("/tmp", "output_image.jpg")
cv2.imwrite(input_image_path, facial_image)
input_image_info_path = os.path.join("/tmp", "input_image.txt")
output_image_info_path = os.path.join("/tmp", "output_image.txt")
with open(input_image_info_path, "w") as text_file:
text_file.write("{}\n".format(input_image_path))
with open(output_image_info_path, "w") as text_file:
text_file.write("{}\n".format(output_image_path))
subprocess.call([os.path.join(common.CONGEALINGCOMPLEX_PATH, "funnelReal"), \
input_image_info_path, \
os.path.join(common.CONGEALINGCOMPLEX_PATH, "people.train"), \
output_image_info_path])
# Read the processed facial image
processed_facial_image = cv2.imread(output_image_path)
# Omit the totally black rows and columns
gray_processed_facial_image = cv2.cvtColor(processed_facial_image,
cv2.COLOR_BGR2GRAY)
cumsum_in_row = np.cumsum(gray_processed_facial_image, axis=1)
valid_row_indexes = cumsum_in_row[:, -1] > 0
cumsum_in_column = np.cumsum(gray_processed_facial_image, axis=0)
valid_column_indexes = cumsum_in_column[-1, :] > 0
return processed_facial_image[
valid_row_indexes, :, :][:, valid_column_indexes, :]
try:
# Read the coordinates of facial image from the bbox file
bbox_file_path = full_image_path + common.BBOX_EXTENSION
y, x, w, h = common.read_from_file(bbox_file_path)
# Find the middle point of the bounding rectangle
x_middle = x + 0.5 * h
y_middle = y + 0.5 * w
# Make the bouding square a little bit larger
x_start = int(x_middle - 0.8 * h)
x_end = int(x_middle + 0.8 * h)
y_start = int(y_middle - 0.8 * w)
y_end = int(y_middle + 0.8 * w)
# Retrieve the original facial image
full_image = cv2.imread(full_image_path)
facial_image = full_image[
max(x_start, 0):min(x_end, full_image.shape[0]),
max(y_start, 0):min(y_end, full_image.shape[1]), :]
# Call congealingcomplex and resize it
facial_image = call_congealingcomplex(facial_image)
facial_image = cv2.resize(facial_image,
dsize=(common.FACIAL_IMAGE_SIZE,
common.FACIAL_IMAGE_SIZE))
# Successful case
assert facial_image is not None
return facial_image
except:
# Failure case
if force_continue:
return retrieve_facial_image_by_bbox(full_image_path)
else:
return None
def retrieve_facial_image_by_congealingcomplex(full_image_path, force_continue=True):
"""Retrieve the facial image by using congealingcomplex.
:param full_image_path: the path of the full image
:type full_image_path: string
:param force_continue: whether crop facial images by using bbox coordinates
:type force_continue: boolean
:return: the facial image
:rtype: numpy array
"""
def call_congealingcomplex(facial_image):
"""Call congealingcomplex to perform face frontalization.
:param facial_image: the facial image
:type facial_image: numpy array
:return: the processed facial image
:rtype: numpy array
"""
input_image_path = os.path.join("/tmp", "input_image.jpg")
output_image_path = os.path.join("/tmp", "output_image.jpg")
cv2.imwrite(input_image_path, facial_image)
input_image_info_path = os.path.join("/tmp", "input_image.txt")
output_image_info_path = os.path.join("/tmp", "output_image.txt")
with open(input_image_info_path, "w") as text_file:
text_file.write("{}\n".format(input_image_path))
with open(output_image_info_path, "w") as text_file:
text_file.write("{}\n".format(output_image_path))
subprocess.call([os.path.join(common.CONGEALINGCOMPLEX_PATH, "funnelReal"), \
input_image_info_path, \
os.path.join(common.CONGEALINGCOMPLEX_PATH, "people.train"), \
output_image_info_path])
# Read the processed facial image
processed_facial_image = cv2.imread(output_image_path)
# Omit the totally black rows and columns
gray_processed_facial_image = cv2.cvtColor(processed_facial_image, cv2.COLOR_BGR2GRAY)
cumsum_in_row = np.cumsum(gray_processed_facial_image, axis=1)
valid_row_indexes = cumsum_in_row[:, -1] > 0
cumsum_in_column = np.cumsum(gray_processed_facial_image, axis=0)
valid_column_indexes = cumsum_in_column[-1, :] > 0
return processed_facial_image[valid_row_indexes, :, :][:, valid_column_indexes, :]
try:
# Read the coordinates of facial image from the bbox file
bbox_file_path = full_image_path + common.BBOX_EXTENSION
y, x, w, h = common.read_from_file(bbox_file_path)
# Find the middle point of the bounding rectangle
x_middle = x + 0.5 * h
y_middle = y + 0.5 * w
# Make the bouding square a little bit larger
x_start = int(x_middle - 0.8 * h)
x_end = int(x_middle + 0.8 * h)
y_start = int(y_middle - 0.8 * w)
y_end = int(y_middle + 0.8 * w)
# Retrieve the original facial image
full_image = cv2.imread(full_image_path)
facial_image = full_image[max(x_start, 0):min(x_end, full_image.shape[0]), max(y_start, 0):min(y_end, full_image.shape[1]), :]
# Call congealingcomplex and resize it
facial_image = call_congealingcomplex(facial_image)
facial_image = cv2.resize(facial_image, dsize=(common.FACIAL_IMAGE_SIZE, common.FACIAL_IMAGE_SIZE))
# Successful case
assert facial_image is not None
return facial_image
except:
# Failure case
if force_continue:
return retrieve_facial_image_by_bbox(full_image_path)
else:
return None