File size: 7,507 Bytes
6c60ccc |
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 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 |
"""
brief: face alignment with FFHQ method (https://github.com/NVlabs/ffhq-dataset)
author: lzhbrian (https://lzhbrian.me)
link: https://gist.github.com/lzhbrian/bde87ab23b499dd02ba4f588258f57d5
date: 2020.1.5
note: code is heavily borrowed from
https://github.com/NVlabs/ffhq-dataset
http://dlib.net/face_landmark_detection.py.html
requirements:
conda install Pillow numpy scipy
conda install -c conda-forge dlib
# download face landmark model from:
# http://dlib.net/files/shape_predictor_68_face_landmarks.dat.bz2
"""
import os
import glob
import numpy as np
import PIL
import PIL.Image
import scipy
import scipy.ndimage
import argparse
from basicsr.utils.download_util import load_file_from_url
try:
import dlib
except ImportError:
print('Please install dlib by running:' 'conda install -c conda-forge dlib')
# download model from: http://dlib.net/files/shape_predictor_68_face_landmarks.dat.bz2
shape_predictor_url = 'https://github.com/sczhou/CodeFormer/releases/download/v0.1.0/shape_predictor_68_face_landmarks-fbdc2cb8.dat'
ckpt_path = load_file_from_url(url=shape_predictor_url,
model_dir='weights/dlib', progress=True, file_name=None)
predictor = dlib.shape_predictor('weights/dlib/shape_predictor_68_face_landmarks-fbdc2cb8.dat')
def get_landmark(filepath, only_keep_largest=True):
"""get landmark with dlib
:return: np.array shape=(68, 2)
"""
detector = dlib.get_frontal_face_detector()
img = dlib.load_rgb_image(filepath)
dets = detector(img, 1)
# Shangchen modified
print("\tNumber of faces detected: {}".format(len(dets)))
if only_keep_largest:
print('\tOnly keep the largest.')
face_areas = []
for k, d in enumerate(dets):
face_area = (d.right() - d.left()) * (d.bottom() - d.top())
face_areas.append(face_area)
largest_idx = face_areas.index(max(face_areas))
d = dets[largest_idx]
shape = predictor(img, d)
# print("Part 0: {}, Part 1: {} ...".format(
# shape.part(0), shape.part(1)))
else:
for k, d in enumerate(dets):
# print("Detection {}: Left: {} Top: {} Right: {} Bottom: {}".format(
# k, d.left(), d.top(), d.right(), d.bottom()))
# Get the landmarks/parts for the face in box d.
shape = predictor(img, d)
# print("Part 0: {}, Part 1: {} ...".format(
# shape.part(0), shape.part(1)))
t = list(shape.parts())
a = []
for tt in t:
a.append([tt.x, tt.y])
lm = np.array(a)
# lm is a shape=(68,2) np.array
return lm
def align_face(filepath, out_path):
"""
:param filepath: str
:return: PIL Image
"""
try:
lm = get_landmark(filepath)
except:
print('No landmark ...')
return
lm_chin = lm[0:17] # left-right
lm_eyebrow_left = lm[17:22] # left-right
lm_eyebrow_right = lm[22:27] # left-right
lm_nose = lm[27:31] # top-down
lm_nostrils = lm[31:36] # top-down
lm_eye_left = lm[36:42] # left-clockwise
lm_eye_right = lm[42:48] # left-clockwise
lm_mouth_outer = lm[48:60] # left-clockwise
lm_mouth_inner = lm[60:68] # left-clockwise
# Calculate auxiliary vectors.
eye_left = np.mean(lm_eye_left, axis=0)
eye_right = np.mean(lm_eye_right, axis=0)
eye_avg = (eye_left + eye_right) * 0.5
eye_to_eye = eye_right - eye_left
mouth_left = lm_mouth_outer[0]
mouth_right = lm_mouth_outer[6]
mouth_avg = (mouth_left + mouth_right) * 0.5
eye_to_mouth = mouth_avg - eye_avg
# Choose oriented crop rectangle.
x = eye_to_eye - np.flipud(eye_to_mouth) * [-1, 1]
x /= np.hypot(*x)
x *= max(np.hypot(*eye_to_eye) * 2.0, np.hypot(*eye_to_mouth) * 1.8)
y = np.flipud(x) * [-1, 1]
c = eye_avg + eye_to_mouth * 0.1
quad = np.stack([c - x - y, c - x + y, c + x + y, c + x - y])
qsize = np.hypot(*x) * 2
# read image
img = PIL.Image.open(filepath)
output_size = 512
transform_size = 4096
enable_padding = False
# Shrink.
shrink = int(np.floor(qsize / output_size * 0.5))
if shrink > 1:
rsize = (int(np.rint(float(img.size[0]) / shrink)),
int(np.rint(float(img.size[1]) / shrink)))
img = img.resize(rsize, PIL.Image.ANTIALIAS)
quad /= shrink
qsize /= shrink
# Crop.
border = max(int(np.rint(qsize * 0.1)), 3)
crop = (int(np.floor(min(quad[:, 0]))), int(np.floor(min(quad[:, 1]))),
int(np.ceil(max(quad[:, 0]))), int(np.ceil(max(quad[:, 1]))))
crop = (max(crop[0] - border, 0), max(crop[1] - border, 0),
min(crop[2] + border,
img.size[0]), min(crop[3] + border, img.size[1]))
if crop[2] - crop[0] < img.size[0] or crop[3] - crop[1] < img.size[1]:
img = img.crop(crop)
quad -= crop[0:2]
# Pad.
pad = (int(np.floor(min(quad[:, 0]))), int(np.floor(min(quad[:, 1]))),
int(np.ceil(max(quad[:, 0]))), int(np.ceil(max(quad[:, 1]))))
pad = (max(-pad[0] + border,
0), max(-pad[1] + border,
0), max(pad[2] - img.size[0] + border,
0), max(pad[3] - img.size[1] + border, 0))
if enable_padding and max(pad) > border - 4:
pad = np.maximum(pad, int(np.rint(qsize * 0.3)))
img = np.pad(
np.float32(img), ((pad[1], pad[3]), (pad[0], pad[2]), (0, 0)),
'reflect')
h, w, _ = img.shape
y, x, _ = np.ogrid[:h, :w, :1]
mask = np.maximum(
1.0 -
np.minimum(np.float32(x) / pad[0],
np.float32(w - 1 - x) / pad[2]), 1.0 -
np.minimum(np.float32(y) / pad[1],
np.float32(h - 1 - y) / pad[3]))
blur = qsize * 0.02
img += (scipy.ndimage.gaussian_filter(img, [blur, blur, 0]) -
img) * np.clip(mask * 3.0 + 1.0, 0.0, 1.0)
img += (np.median(img, axis=(0, 1)) - img) * np.clip(mask, 0.0, 1.0)
img = PIL.Image.fromarray(
np.uint8(np.clip(np.rint(img), 0, 255)), 'RGB')
quad += pad[:2]
img = img.transform((transform_size, transform_size), PIL.Image.QUAD,
(quad + 0.5).flatten(), PIL.Image.BILINEAR)
if output_size < transform_size:
img = img.resize((output_size, output_size), PIL.Image.ANTIALIAS)
# Save aligned image.
# print('saveing: ', out_path)
img.save(out_path)
return img, np.max(quad[:, 0]) - np.min(quad[:, 0])
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('-i', '--in_dir', type=str, default='./inputs/whole_imgs')
parser.add_argument('-o', '--out_dir', type=str, default='./inputs/cropped_faces')
args = parser.parse_args()
if args.out_dir.endswith('/'): # solve when path ends with /
args.out_dir = args.out_dir[:-1]
dir_name = os.path.abspath(args.out_dir)
os.makedirs(dir_name, exist_ok=True)
img_list = sorted(glob.glob(os.path.join(args.in_dir, '*.[jpJP][pnPN]*[gG]')))
test_img_num = len(img_list)
for i, in_path in enumerate(img_list):
img_name = os.path.basename(in_path)
print(f'[{i+1}/{test_img_num}] Processing: {img_name}')
out_path = os.path.join(args.out_dir, in_path.split("/")[-1])
out_path = out_path.replace('.jpg', '.png')
size_ = align_face(in_path, out_path) |