"""
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)