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| import os | |
| import bz2 | |
| import argparse | |
| from Project.aligned_image.ffhq_dataset.face_alignment import image_align | |
| from Project.aligned_image.ffhq_dataset.landmarks_detector import LandmarksDetector | |
| 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 | |
| def align(): | |
| """ | |
| 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',default='static/img_in', help='Directory with raw images for face alignment') | |
| parser.add_argument('--aligned_dir',default='static/img_aligned', 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("Project/pretrained_models/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 Exception as e: | |
| print("Exception in face alignment!") | |
| print(str(e)) | |
| except: | |
| print("Exception in landmark detection!") |