def onClientProcessData(self, data):
filename_path = Path( data[0] )
image = cv2.imread( str(filename_path) )
if image is None:
print ( 'Failed to extract %s, reason: cv2.imread() fail.' % ( str(filename_path) ) )
else:
if self.type == 'rects':
rects = self.e.extract_from_bgr (image)
return [str(filename_path), rects]
elif self.type == 'landmarks':
rects = data[1]
landmarks = self.e.extract_from_bgr (image, rects)
return [str(filename_path), landmarks]
elif self.type == 'final':
result = []
faces = data[1]
if self.debug:
debug_output_file = '{}_{}'.format( str(Path(str(self.output_path) + '_debug') / filename_path.stem), 'debug.png')
debug_image = image.copy()
for (face_idx, face) in enumerate(faces):
output_file = '{}_{}{}'.format(str(self.output_path / filename_path.stem), str(face_idx), '.png')
rect = face[0]
image_landmarks = np.array(face[1])
if self.debug:
facelib.LandmarksProcessor.draw_rect_landmarks (debug_image, rect, image_landmarks, self.image_size, self.face_type)
if self.face_type == FaceType.MARK_ONLY:
face_image = image
face_image_landmarks = image_landmarks
else:
image_to_face_mat = facelib.LandmarksProcessor.get_transform_mat (image_landmarks, self.image_size, self.face_type)
face_image = cv2.warpAffine(image, image_to_face_mat, (self.image_size, self.image_size), cv2.INTER_LANCZOS4)
face_image_landmarks = facelib.LandmarksProcessor.transform_points (image_landmarks, image_to_face_mat)
cv2.imwrite(output_file, face_image)
DFLPNG.embed_data(output_file, face_type = FaceType.toString(self.face_type),
landmarks = face_image_landmarks.tolist(),
yaw_value = facelib.LandmarksProcessor.calc_face_yaw (face_image_landmarks),
pitch_value = facelib.LandmarksProcessor.calc_face_pitch (face_image_landmarks),
source_filename = filename_path.name,
source_rect= rect,
source_landmarks = image_landmarks.tolist()
)
result.append (output_file)
if self.debug:
cv2.imwrite(debug_output_file, debug_image )
return result
return None
def process_data(self, data):
filename_path = Path(data.filename)
filename_path_str = str(filename_path)
if self.cached_image[0] == filename_path_str:
image = self.cached_image[
1] #cached image for manual extractor
else:
image = cv2_imread(filename_path_str)
if image is None:
self.log_err(
'Failed to extract %s, reason: cv2_imread() fail.' %
(str(filename_path)))
return data
image = imagelib.normalize_channels(image, 3)
h, w, ch = image.shape
wm, hm = w % 2, h % 2
if wm + hm != 0: #fix odd image
image = image[0:h - hm, 0:w - wm, :]
self.cached_image = (filename_path_str, image)
src_dflimg = None
h, w, ch = image.shape
if h == w:
#extracting from already extracted jpg image?
if filename_path.suffix == '.png':
src_dflimg = DFLPNG.load(str(filename_path))
if filename_path.suffix == '.jpg':
src_dflimg = DFLJPG.load(str(filename_path))
if 'rects' in self.type:
if min(w, h) < 128:
self.log_err('Image is too small %s : [%d, %d]' %
(str(filename_path), w, h))
data.rects = []
else:
for rot in ([0, 90, 270, 180]):
data.rects_rotation = rot
if rot == 0:
rotated_image = image
elif rot == 90:
rotated_image = image.swapaxes(0, 1)[:, ::-1, :]
elif rot == 180:
rotated_image = image[::-1, ::-1, :]
elif rot == 270:
rotated_image = image.swapaxes(0, 1)[::-1, :, :]
rects = data.rects = self.e.extract(rotated_image,
is_bgr=True)
if len(rects) != 0:
break
return data
elif self.type == 'landmarks':
if data.rects_rotation == 0:
rotated_image = image
elif data.rects_rotation == 90:
rotated_image = image.swapaxes(0, 1)[:, ::-1, :]
elif data.rects_rotation == 180:
rotated_image = image[::-1, ::-1, :]
elif data.rects_rotation == 270:
rotated_image = image.swapaxes(0, 1)[::-1, :, :]
data.landmarks = self.e.extract(
rotated_image,
data.rects,
self.second_pass_e if
(src_dflimg is None and data.landmarks_accurate) else None,
is_bgr=True)
if data.rects_rotation != 0:
for i, (rect,
lmrks) in enumerate(zip(data.rects,
data.landmarks)):
new_rect, new_lmrks = rect, lmrks
(l, t, r, b) = rect
if data.rects_rotation == 90:
new_rect = (t, h - l, b, h - r)
if lmrks is not None:
new_lmrks = lmrks[:, ::-1].copy()
new_lmrks[:, 1] = h - new_lmrks[:, 1]
elif data.rects_rotation == 180:
if lmrks is not None:
new_rect = (w - l, h - t, w - r, h - b)
new_lmrks = lmrks.copy()
new_lmrks[:, 0] = w - new_lmrks[:, 0]
new_lmrks[:, 1] = h - new_lmrks[:, 1]
elif data.rects_rotation == 270:
new_rect = (w - b, l, w - t, r)
if lmrks is not None:
new_lmrks = lmrks[:, ::-1].copy()
new_lmrks[:, 0] = w - new_lmrks[:, 0]
data.rects[i], data.landmarks[i] = new_rect, new_lmrks
return data
elif self.type == 'final':
data.final_output_files = []
rects = data.rects
landmarks = data.landmarks
if self.debug_dir is not None:
debug_output_file = str(
Path(self.debug_dir) / (filename_path.stem + '.jpg'))
debug_image = image.copy()
if src_dflimg is not None and len(rects) != 1:
# if re-extracting from dflimg and more than 1 or zero faces detected:
# don't process and just copy it
print("src_dflimg is not None and len(rects) != 1",
str(filename_path))
output_file = str(self.final_output_path /
filename_path.name)
if str(filename_path) != str(output_file):
shutil.copy(str(filename_path), str(output_file))
data.final_output_files.append(output_file)
else:
face_idx = 0
for rect, image_landmarks in zip(rects, landmarks):
if src_dflimg is not None and face_idx > 1:
#cannot extract more than 1 face from dflimg
break
if image_landmarks is None:
continue
rect = np.array(rect)
rect_area = mathlib.polygon_area(
np.array(rect[[0, 2, 2, 0]]),
np.array(rect[[1, 1, 3, 3]]))
# `self.image_size` is the output size for the entire process,
# we don't want to overwrite it
face_image_size = self.image_size
if face_image_size == 0 and self.face_type != FaceType.MARK_ONLY:
face_image_size = LandmarksProcessor.calc_image_size_for_unscaled(
image_landmarks, self.face_type)
# if landmarks are bad, could get HUGE scale...
# ...so set a max size, same as input image biggest
# dimension
face_image_size = min(face_image_size, max(w, h))
if self.face_type == FaceType.MARK_ONLY:
image_to_face_mat = None
face_image = image
face_image_landmarks = image_landmarks
else:
image_to_face_mat = LandmarksProcessor.get_transform_mat(
image_landmarks, face_image_size,
self.face_type)
face_image = cv2.warpAffine(
image, image_to_face_mat,
(face_image_size, face_image_size),
cv2.INTER_LANCZOS4)
face_image_landmarks = LandmarksProcessor.transform_points(
image_landmarks, image_to_face_mat)
landmarks_bbox = LandmarksProcessor.transform_points(
[(0, 0), (0, face_image_size - 1),
(face_image_size - 1, face_image_size - 1),
(face_image_size - 1, 0)], image_to_face_mat,
True)
landmarks_area = mathlib.polygon_area(
landmarks_bbox[:, 0], landmarks_bbox[:, 1])
# get rid of faces which umeyama-landmark-area > 4*detector-rect-area
if self.face_type not in (
FaceType.HEAD, FaceType.HEAD_NO_ALIGN
) and landmarks_area > 4 * rect_area:
continue
if self.debug_dir is not None:
LandmarksProcessor.draw_rect_landmarks(
debug_image,
rect,
image_landmarks,
face_image_size,
self.face_type,
transparent_mask=True)
if filename_path.suffix == '.jpg':
#if extracting from dflimg and jpg copy it in order not to lose quality
output_file = '{}_{}{}'.format(
str(self.final_output_path /
filename_path.stem), str(face_idx), '.jpg')
if str(filename_path) != str(output_file):
shutil.copy(str(filename_path),
str(output_file))
cv2_imwrite(output_file, face_image,
[int(cv2.IMWRITE_JPEG_QUALITY), 95])
DFLJPG.embed_data(
output_file,
face_type=FaceType.toString(self.face_type),
landmarks=face_image_landmarks.tolist(),
source_filename=filename_path.name,
source_rect=rect,
source_landmarks=image_landmarks.tolist(),
image_to_face_mat=image_to_face_mat,
pitch_yaw_roll=data.pitch_yaw_roll)
else:
output_file = '{}_{}{}'.format(
str(self.final_output_path /
filename_path.stem), str(face_idx), '.png')
cv2_imwrite(output_file, face_image,
[int(cv2.IMWRITE_PNG_COMPRESSION), 3])
DFLPNG.embed_data(
output_file,
face_type=FaceType.toString(self.face_type),
landmarks=face_image_landmarks.tolist(),
source_filename=filename_path.name,
source_rect=rect,
source_landmarks=image_landmarks.tolist(),
image_to_face_mat=image_to_face_mat,
pitch_yaw_roll=data.pitch_yaw_roll)
data.final_output_files.append(output_file)
face_idx += 1
data.faces_detected = face_idx
if self.debug_dir is not None:
cv2_imwrite(debug_output_file, debug_image,
[int(cv2.IMWRITE_JPEG_QUALITY), 50])
return data
elif self.type == 'fanseg':
if src_dflimg is not None:
fanseg_mask = self.e.extract(image / 255.0)
src_dflimg.embed_and_set(
filename_path_str,
fanseg_mask=fanseg_mask,
#fanseg_mask_ver=FANSegmentator.VERSION,
)
