File size: 2,861 Bytes
db534ca |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 |
import os
import sys
import bz2
import argparse
from keras.utils import get_file
from ffhq_dataset.face_alignment import image_align
from ffhq_dataset.landmarks_detector import LandmarksDetector
import multiprocessing
def unpack_bz2(src_path):
data = bz2.BZ2File(src_path).read()
dst_path = src_path[:-4]
with open(dst_path, 'wb') as fp:
fp.write(data)
return dst_path
if __name__ == "__main__":
"""
Extracts and aligns all faces from images using DLib and a function from original FFHQ dataset preparation step
python align_images.py /raw_images /aligned_images
"""
parser = argparse.ArgumentParser(description='Align faces from input images', formatter_class=argparse.ArgumentDefaultsHelpFormatter)
parser.add_argument('raw_dir', help='Directory with raw images for face alignment')
parser.add_argument('aligned_dir', help='Directory for storing aligned images')
parser.add_argument('--output_size', default=1024, help='The dimension of images for input to the model', type=int)
parser.add_argument('--x_scale', default=1, help='Scaling factor for x dimension', type=float)
parser.add_argument('--y_scale', default=1, help='Scaling factor for y dimension', type=float)
parser.add_argument('--em_scale', default=0.1, help='Scaling factor for eye-mouth distance', type=float)
parser.add_argument('--use_alpha', default=False, help='Add an alpha channel for masking', type=bool)
args, other_args = parser.parse_known_args()
landmarks_model_path = unpack_bz2("shape_predictor_68_face_landmarks.dat.bz2")
RAW_IMAGES_DIR = args.raw_dir
ALIGNED_IMAGES_DIR = args.aligned_dir
landmarks_detector = LandmarksDetector(landmarks_model_path)
for img_name in os.listdir(RAW_IMAGES_DIR):
print('Aligning %s ...' % img_name)
try:
raw_img_path = os.path.join(RAW_IMAGES_DIR, img_name)
fn = face_img_name = '%s_%02d.png' % (os.path.splitext(img_name)[0], 1)
if os.path.isfile(fn):
continue
print('Getting landmarks...')
for i, face_landmarks in enumerate(landmarks_detector.get_landmarks(raw_img_path), start=1):
try:
print('Starting face alignment...')
face_img_name = '%s_%02d.png' % (os.path.splitext(img_name)[0], i)
aligned_face_path = os.path.join(ALIGNED_IMAGES_DIR, face_img_name)
image_align(raw_img_path, aligned_face_path, face_landmarks, output_size=args.output_size, x_scale=args.x_scale, y_scale=args.y_scale, em_scale=args.em_scale, alpha=args.use_alpha)
print('Wrote result %s' % aligned_face_path)
except:
print("Exception in face alignment!")
except:
print("Exception in landmark detection!")
|