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‎README.md

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# cscs
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Download ID encoder weight ms1mv3_arcface_r100_fp16_backbone.pth from:
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https://onedrive.live.com/?id=4A83B6B633B029CC!5577&resid=4A83B6B633B029CC!5577&authkey=!AFZjr283nwZHqbA&cid=4a83b6b633b029cc
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and should be placed in ./model/arcface/
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Before swapping, use facealign.sh to align the face images.
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After alignment, inference_adapter.sh is utilized to swapping

‎align/.ipynb_checkpoints/align_face-checkpoint.py

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# Code adapted from https://gist.github.com/lzhbrian/bde87ab23b499dd02ba4f588258f57d5
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"""
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brief: face alignment with FFHQ method (https://github.com/NVlabs/ffhq-dataset)
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author: lzhbrian (https://lzhbrian.me)
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date: 2020.1.5
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note: code is heavily borrowed from
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https://github.com/NVlabs/ffhq-dataset
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http://dlib.net/face_landmark_detection.py.html
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requirements:
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apt install cmake
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conda install Pillow numpy scipy
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pip install dlib
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# download face landmark models from:
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# http://dlib.net/files/shape_predictor_68_face_landmarks.dat.bz2
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"""
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import glob
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import os
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import argparse
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import numpy as np
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import PIL
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import PIL.Image
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import scipy
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import scipy.ndimage
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import dlib # pip install dlib if not found
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from tqdm import tqdm
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# download models from: http://dlib.net/files/shape_predictor_68_face_landmarks.dat.bz2
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predictor = dlib.shape_predictor('./shape_predictor_68_face_landmarks.dat')
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def get_landmark(filepath):
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"""get landmark with dlib
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:return: np.array shape=(68, 2)
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"""
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detector = dlib.get_frontal_face_detector()
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img = dlib.load_rgb_image(filepath)
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dets = detector(img, 1)
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if len(dets) == 0:
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print(' * No face detected.')
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raise Exception
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# exit()
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# my editing here
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if len(dets) > 1:
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print(' * WARNING: {} faces detected in the image. Only preserve the largest face.'.format(len(dets)))
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img_ws = [d.right() - d.left() for d in dets]
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largest_idx = np.argmax(img_ws)
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dets = [dets[largest_idx]]
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assert len(dets) == 1
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shape = predictor(img, dets[0])
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t = list(shape.parts())
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a = []
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for tt in t:
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a.append([tt.x, tt.y])
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lm = np.array(a)
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return lm
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def align_face(filepath):
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"""
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:param filepath: str
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:return: PIL Image
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"""
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lm = get_landmark(filepath)
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lm_chin = lm[0: 17] # left-right
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lm_eyebrow_left = lm[17: 22] # left-right
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lm_eyebrow_right = lm[22: 27] # left-right
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lm_nose = lm[27: 31] # top-down
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lm_nostrils = lm[31: 36] # top-down
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lm_eye_left = lm[36: 42] # left-clockwise
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lm_eye_right = lm[42: 48] # left-clockwise
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lm_mouth_outer = lm[48: 60] # left-clockwise
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lm_mouth_inner = lm[60: 68] # left-clockwise
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# Calculate auxiliary vectors.
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eye_left = np.mean(lm_eye_left, axis=0)
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eye_right = np.mean(lm_eye_right, axis=0)
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eye_avg = (eye_left + eye_right) * 0.5
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eye_to_eye = eye_right - eye_left
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mouth_left = lm_mouth_outer[0]
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mouth_right = lm_mouth_outer[6]
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mouth_avg = (mouth_left + mouth_right) * 0.5
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eye_to_mouth = mouth_avg - eye_avg
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# Choose oriented crop rectangle.
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x = eye_to_eye - np.flipud(eye_to_mouth) * [-1, 1]
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x /= np.hypot(*x)
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x *= max(np.hypot(*eye_to_eye) * 2.0, np.hypot(*eye_to_mouth) * 1.8)
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y = np.flipud(x) * [-1, 1]
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c = eye_avg + eye_to_mouth * 0.1
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quad = np.stack([c - x - y, c - x + y, c + x + y, c + x - y])
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qsize = np.hypot(*x) * 2
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# read image
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img = PIL.Image.open(filepath)
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output_size = 1024
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transform_size = 4096
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enable_padding = True
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# Shrink.
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shrink = int(np.floor(qsize / output_size * 0.5))
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if shrink > 1:
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rsize = (int(np.rint(float(img.size[0]) / shrink)), int(np.rint(float(img.size[1]) / shrink)))
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img = img.resize(rsize, PIL.Image.ANTIALIAS)
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quad /= shrink
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qsize /= shrink
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# Crop.
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# crop: x0, y0, x1, y1
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border = max(int(np.rint(qsize * 0.1)), 3)
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crop = (int(np.floor(min(quad[:, 0]))), int(np.floor(min(quad[:, 1]))), int(np.ceil(max(quad[:, 0]))),
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int(np.ceil(max(quad[:, 1]))))
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crop = (max(crop[0] - border, 0), max(crop[1] - border, 0), min(crop[2] + border, img.size[0]),
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min(crop[3] + border, img.size[1]))
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if crop[2] - crop[0] < img.size[0] or crop[3] - crop[1] < img.size[1]:
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img = img.crop(crop)
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quad -= crop[0:2]
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# Pad.
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pad = (int(np.floor(min(quad[:, 0]))), int(np.floor(min(quad[:, 1]))), int(np.ceil(max(quad[:, 0]))),
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int(np.ceil(max(quad[:, 1]))))
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pad = (max(-pad[0] + border, 0), max(-pad[1] + border, 0), max(pad[2] - img.size[0] + border, 0),
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max(pad[3] - img.size[1] + border, 0))
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if enable_padding and max(pad) > border - 4:
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pad = np.maximum(pad, int(np.rint(qsize * 0.3)))
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img = np.pad(np.float32(img), ((pad[1], pad[3]), (pad[0], pad[2]), (0, 0)), 'reflect')
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h, w, _ = img.shape
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y, x, _ = np.ogrid[:h, :w, :1]
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mask = np.maximum(1.0 - np.minimum(np.float32(x) / pad[0], np.float32(w - 1 - x) / pad[2]),
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1.0 - np.minimum(np.float32(y) / pad[1], np.float32(h - 1 - y) / pad[3]))
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blur = qsize * 0.02
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img += (scipy.ndimage.gaussian_filter(img, [blur, blur, 0]) - img) * np.clip(mask * 3.0 + 1.0, 0.0, 1.0)
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img += (np.median(img, axis=(0, 1)) - img) * np.clip(mask, 0.0, 1.0)
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img = PIL.Image.fromarray(np.uint8(np.clip(np.rint(img), 0, 255)), 'RGB')
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quad += pad[:2]
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# Transform.
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# print(quad)
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# print(quad+.5)
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# print((quad+.5).flatten())
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img = img.transform((transform_size, transform_size), PIL.Image.QUAD, (quad + 0.5).flatten(), PIL.Image.BILINEAR)
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img.save('transform.jpg')
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if output_size < transform_size:
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img = img.resize((output_size, output_size), PIL.Image.ANTIALIAS)
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# Save aligned image.
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return crop, quad+.5, img
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def main():
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parser = argparse.ArgumentParser(description="Align face images to FFHQ format")
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parser.add_argument("--input", type=str, help="path of the input image")
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parser.add_argument("-o", "--output", type=str, default=None, help="output path of the aligned face")
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parser.add_argument("--save_np", action='store_true')
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parser.add_argument('--resize', type=int, default=0)
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# parser.add_argument("--checkpoint", type=str, default='./shape_predictor_68_face_landmarks.dat',)
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vgg_face2=False
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args = parser.parse_args()
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# origin_images = glob.glob(os.path.join(args.input, "**/*.JPG"))
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if vgg_face2:
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origin_images = glob.glob(os.path.join(args.input, "**/*.jpg"))
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else:
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origin_images = glob.glob(os.path.join(args.input, "*.jpg")) + glob.glob(os.path.join(args.input, "*.JPG")) + glob.glob(os.path.join(args.input, "*.png"))
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print(f"image nums: {len(origin_images)}")
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for path in tqdm(origin_images):
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# img = align_face(args.input)
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try:
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crop, quad, img = align_face(path)
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# save_path = path.replace(args.input, args.output)
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if vgg_face2:
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# print(args.output)
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# print(path[len(args.input):])
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save_path = os.path.join(args.output, path[len(args.input)+1:]) # need absolute path
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# print(save_path)
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save_dir = save_path[:-len(os.path.split(save_path)[-1])]
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# print(save_dir)
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# break
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os.makedirs(save_dir, exist_ok=True)
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else:
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save_path = os.path.join(args.output, os.path.basename(path))
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os.makedirs(os.path.split(save_path)[0], exist_ok=True)
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# print(' * Saving the aligned image to {}'.format(save_path))
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if args.resize>0:
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img.resize((args.resize, args.resize))
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img.save(save_path)
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if args.save_np:
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np.save(save_path[:-4], crop)
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np.save(save_path[:-4]+'_quad', quad)
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except Exception as e:
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print(e)
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print(path)
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def test_one_image():
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parser = argparse.ArgumentParser(description="Align face images to FFHQ format")
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parser.add_argument("--input", type=str, help="path of the input image")
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parser.add_argument("-o", "--output", type=str, default=None, help="output path of the aligned face")
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# parser.add_argument("--checkpoint", type=str, default='./shape_predictor_68_face_landmarks.dat',)
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args = parser.parse_args()
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# origin_images = glob.glob(os.path.join(args.input, "**/*.JPG"))
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# origin_images = glob.glob(os.path.join(args.input, "*.jpg")) + glob.glob(os.path.join(args.input, "*.JPG")) + glob.glob(os.path.join(args.input, "*.png"))
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# print(f"image nums: {len(origin_images)}")
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# for path in tqdm(origin_images):
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# # img = align_face(args.input)
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# img = align_face(path)
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# save_path = path.replace(args.input, args.output)
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# os.makedirs(os.path.split(save_path)[0], exist_ok=True)
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# # print(' * Saving the aligned image to {}'.format(save_path))
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# img.save(save_path)
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align_face(args.input)
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if __name__ == '__main__':
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main()

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