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align.py
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import bz2
import os
import os.path as osp
import sys
from multiprocessing import Pool
import dlib
import numpy as np
import PIL.Image
import requests
import scipy.ndimage
from tqdm import tqdm
from argparse import ArgumentParser
LANDMARKS_MODEL_URL = 'http://dlib.net/files/shape_predictor_68_face_landmarks.dat.bz2'
def image_align(src_file,
dst_file,
face_landmarks,
output_size=1024,
transform_size=4096,
enable_padding=True):
# Align function from FFHQ dataset pre-processing step
# https://github.com/NVlabs/ffhq-dataset/blob/master/download_ffhq.py
lm = np.array(face_landmarks)
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
# Load in-the-wild image.
if not os.path.isfile(src_file):
print(
'\nCannot find source image. Please run "--wilds" before "--align".'
)
return
img = PIL.Image.open(src_file)
img = img.convert('RGB')
# 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]
# Transform.
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.
img.save(dst_file, 'PNG')
class LandmarksDetector:
def __init__(self, predictor_model_path):
"""
:param predictor_model_path: path to shape_predictor_68_face_landmarks.dat file
"""
self.detector = dlib.get_frontal_face_detector(
) # cnn_face_detection_model_v1 also can be used
self.shape_predictor = dlib.shape_predictor(predictor_model_path)
def get_landmarks(self, image):
img = dlib.load_rgb_image(image)
dets = self.detector(img, 1)
for detection in dets:
face_landmarks = [
(item.x, item.y)
for item in self.shape_predictor(img, detection).parts()
]
yield face_landmarks
def unpack_bz2(src_path):
dst_path = src_path[:-4]
if os.path.exists(dst_path):
print('cached')
return dst_path
data = bz2.BZ2File(src_path).read()
with open(dst_path, 'wb') as fp:
fp.write(data)
return dst_path
def work_landmark(raw_img_path, img_name, face_landmarks):
face_img_name = '%s.png' % (os.path.splitext(img_name)[0], )
aligned_face_path = os.path.join(ALIGNED_IMAGES_DIR, face_img_name)
if os.path.exists(aligned_face_path):
return
image_align(raw_img_path,
aligned_face_path,
face_landmarks,
output_size=256)
def get_file(src, tgt):
if os.path.exists(tgt):
print('cached')
return tgt
tgt_dir = os.path.dirname(tgt)
if not os.path.exists(tgt_dir):
os.makedirs(tgt_dir)
file = requests.get(src)
open(tgt, 'wb').write(file.content)
return tgt
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 = ArgumentParser()
parser.add_argument("-i",
"--input_imgs_path",
type=str,
default="imgs",
help="input images directory path")
parser.add_argument("-o",
"--output_imgs_path",
type=str,
default="imgs_align",
help="output images directory path")
args = parser.parse_args()
# takes very long time ...
landmarks_model_path = unpack_bz2(
get_file(
'http://dlib.net/files/shape_predictor_68_face_landmarks.dat.bz2',
'temp/shape_predictor_68_face_landmarks.dat.bz2'))
# RAW_IMAGES_DIR = sys.argv[1]
# ALIGNED_IMAGES_DIR = sys.argv[2]
RAW_IMAGES_DIR = args.input_imgs_path
ALIGNED_IMAGES_DIR = args.output_imgs_path
if not osp.exists(ALIGNED_IMAGES_DIR): os.makedirs(ALIGNED_IMAGES_DIR)
files = os.listdir(RAW_IMAGES_DIR)
print(f'total img files {len(files)}')
with tqdm(total=len(files)) as progress:
def cb(*args):
# print('update')
progress.update()
def err_cb(e):
print('error:', e)
with Pool(8) as pool:
res = []
landmarks_detector = LandmarksDetector(landmarks_model_path)
for img_name in files:
raw_img_path = os.path.join(RAW_IMAGES_DIR, img_name)
# print('img_name:', img_name)
for i, face_landmarks in enumerate(
landmarks_detector.get_landmarks(raw_img_path),
start=1):
# assert i == 1, f'{i}'
# print(i, face_landmarks)
# 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=256)
work_landmark(raw_img_path, img_name, face_landmarks)
progress.update()
# job = pool.apply_async(
# work_landmark,
# (raw_img_path, img_name, face_landmarks),
# callback=cb,
# error_callback=err_cb,
# )
# res.append(job)
# pool.close()
# pool.join()
print(f"output aligned images at: {ALIGNED_IMAGES_DIR}")