def dev_test_68(input_dir ):
# process 68 landmarks dataset with .pts files
input_path = Path(input_dir)
if not input_path.exists():
raise ValueError('input_dir not found. Please ensure it exists.')
output_path = input_path.parent / (input_path.name+'_aligned')
io.log_info(f'Output dir is % {output_path}')
if output_path.exists():
output_images_paths = pathex.get_image_paths(output_path)
if len(output_images_paths) > 0:
io.input_bool("WARNING !!! \n %s contains files! \n They will be deleted. \n Press enter to continue." % (str(output_path)), False )
for filename in output_images_paths:
Path(filename).unlink()
else:
output_path.mkdir(parents=True, exist_ok=True)
images_paths = pathex.get_image_paths(input_path)
for filepath in io.progress_bar_generator(images_paths, "Processing"):
filepath = Path(filepath)
pts_filepath = filepath.parent / (filepath.stem+'.pts')
if pts_filepath.exists():
pts = pts_filepath.read_text()
pts_lines = pts.split('\n')
lmrk_lines = None
for pts_line in pts_lines:
if pts_line == '{':
lmrk_lines = []
elif pts_line == '}':
break
else:
if lmrk_lines is not None:
lmrk_lines.append (pts_line)
if lmrk_lines is not None and len(lmrk_lines) == 68:
try:
lmrks = [ np.array ( lmrk_line.strip().split(' ') ).astype(np.float32).tolist() for lmrk_line in lmrk_lines]
except Exception as e:
print(e)
print(filepath)
continue
rect = LandmarksProcessor.get_rect_from_landmarks(lmrks)
output_filepath = output_path / (filepath.stem+'.jpg')
img = cv2_imread(filepath)
img = imagelib.normalize_channels(img, 3)
cv2_imwrite(output_filepath, img, [int(cv2.IMWRITE_JPEG_QUALITY), 95] )
DFLJPG.embed_data(output_filepath, face_type=FaceType.toString(FaceType.MARK_ONLY),
landmarks=lmrks,
source_filename=filepath.name,
source_rect=rect,
source_landmarks=lmrks
)
io.log_info("Done.")
def dev_test(input_dir):
"""
extract FaceSynthetics dataset https://github.com/microsoft/FaceSynthetics
BACKGROUND = 0
SKIN = 1
NOSE = 2
RIGHT_EYE = 3
LEFT_EYE = 4
RIGHT_BROW = 5
LEFT_BROW = 6
RIGHT_EAR = 7
LEFT_EAR = 8
MOUTH_INTERIOR = 9
TOP_LIP = 10
BOTTOM_LIP = 11
NECK = 12
HAIR = 13
BEARD = 14
CLOTHING = 15
GLASSES = 16
HEADWEAR = 17
FACEWEAR = 18
IGNORE = 255
"""
image_size = 1024
face_type = FaceType.WHOLE_FACE
input_path = Path(input_dir)
output_path = input_path.parent / f'{input_path.name}_out'
if output_path.exists():
output_images_paths = pathex.get_image_paths(output_path)
if len(output_images_paths) != 0:
io.input(f"\n WARNING !!! \n {output_path} contains files! \n They will be deleted. \n Press enter to continue.\n")
for filename in output_images_paths:
Path(filename).unlink()
output_path.mkdir(parents=True, exist_ok=True)
data = []
for filepath in io.progress_bar_generator(pathex.get_paths(input_path), "Processing"):
if filepath.suffix == '.txt':
image_filepath = filepath.parent / f'{filepath.name.split("_")[0]}.png'
if not image_filepath.exists():
print(f'{image_filepath} does not exist, skipping')
lmrks = []
for lmrk_line in filepath.read_text().split('\n'):
if len(lmrk_line) == 0:
continue
x, y = lmrk_line.split(' ')
x, y = float(x), float(y)
lmrks.append( (x,y) )
lmrks = np.array(lmrks[:68], np.float32)
rect = LandmarksProcessor.get_rect_from_landmarks(lmrks)
data += [ ExtractSubprocessor.Data(filepath=image_filepath, rects=[rect], landmarks=[ lmrks ] ) ]
if len(data) > 0:
io.log_info ("Performing 3rd pass...")
data = ExtractSubprocessor (data, 'final', image_size, 95, face_type, final_output_path=output_path, device_config=nn.DeviceConfig.CPU()).run()
for filename in io.progress_bar_generator(pathex.get_image_paths (output_path), "Processing"):
filepath = Path(filename)
dflimg = DFLJPG.load(filepath)
src_filename = dflimg.get_source_filename()
image_to_face_mat = dflimg.get_image_to_face_mat()
seg_filepath = input_path / ( Path(src_filename).stem + '_seg.png')
if not seg_filepath.exists():
raise ValueError(f'{seg_filepath} does not exist')
seg = cv2_imread(seg_filepath)
seg_inds = np.isin(seg, [1,2,3,4,5,6,9,10,11])
seg[~seg_inds] = 0
seg[seg_inds] = 1
seg = seg.astype(np.float32)
seg = cv2.warpAffine(seg, image_to_face_mat, (image_size, image_size), cv2.INTER_LANCZOS4)
dflimg.set_xseg_mask(seg)
dflimg.save()
