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import os | |
import cv2 | |
import time | |
import glob | |
import argparse | |
import scipy | |
import numpy as np | |
from PIL import Image | |
from tqdm import tqdm | |
from itertools import cycle | |
from torch.multiprocessing import Pool, Process, set_start_method | |
""" | |
brief: face alignment with FFHQ method (https://github.com/NVlabs/ffhq-dataset) | |
author: lzhbrian (https://lzhbrian.me) | |
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: | |
apt install cmake | |
conda install Pillow numpy scipy | |
pip install dlib | |
# download face landmark model from: | |
# http://dlib.net/files/shape_predictor_68_face_landmarks.dat.bz2 | |
""" | |
import numpy as np | |
from PIL import Image | |
import dlib | |
class Croper: | |
def __init__(self, path_of_lm): | |
# download model from: http://dlib.net/files/shape_predictor_68_face_landmarks.dat.bz2 | |
self.predictor = dlib.shape_predictor(path_of_lm) | |
def get_landmark(self, img_np): | |
"""get landmark with dlib | |
:return: np.array shape=(68, 2) | |
""" | |
detector = dlib.get_frontal_face_detector() | |
dets = detector(img_np, 1) | |
# print("Number of faces detected: {}".format(len(dets))) | |
# for k, d in enumerate(dets): | |
if len(dets) == 0: | |
return None | |
d = dets[0] | |
# Get the landmarks/parts for the face in box d. | |
shape = self.predictor(img_np, 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(self, img, lm, output_size=1024): | |
""" | |
:param filepath: str | |
:return: PIL Image | |
""" | |
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] # 双眼差与双嘴差相加 | |
x /= np.hypot(*x) # hypot函数计算直角三角形的斜边长,用斜边长对三角形两条直边做归一化 | |
x *= max(np.hypot(*eye_to_eye) * 2.0, np.hypot(*eye_to_mouth) * 1.8) # 双眼差和眼嘴差,选较大的作为基准尺度 | |
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]) # 定义四边形,以面部基准位置为中心上下左右平移得到四个顶点 | |
qsize = np.hypot(*x) * 2 # 定义四边形的大小(边长),为基准尺度的2倍 | |
# Shrink. | |
# 如果计算出的四边形太大了,就按比例缩小它 | |
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, 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 = Image.fromarray(np.uint8(np.clip(np.rint(img), 0, 255)), 'RGB') | |
# quad += pad[:2] | |
# Transform. | |
quad = (quad + 0.5).flatten() | |
lx = max(min(quad[0], quad[2]), 0) | |
ly = max(min(quad[1], quad[7]), 0) | |
rx = min(max(quad[4], quad[6]), img.size[0]) | |
ry = min(max(quad[3], quad[5]), img.size[0]) | |
# img = img.transform((transform_size, transform_size), Image.QUAD, (quad + 0.5).flatten(), | |
# Image.BILINEAR) | |
# if output_size < transform_size: | |
# img = img.resize((output_size, output_size), Image.ANTIALIAS) | |
# Save aligned image. | |
return crop, [lx, ly, rx, ry] | |
# def crop(self, img_np_list): | |
# for _i in range(len(img_np_list)): | |
# img_np = img_np_list[_i] | |
# lm = self.get_landmark(img_np) | |
# if lm is None: | |
# return None | |
# crop, quad = self.align_face(img=Image.fromarray(img_np), lm=lm, output_size=512) | |
# clx, cly, crx, cry = crop | |
# lx, ly, rx, ry = quad | |
# lx, ly, rx, ry = int(lx), int(ly), int(rx), int(ry) | |
# _inp = img_np_list[_i] | |
# _inp = _inp[cly:cry, clx:crx] | |
# _inp = _inp[ly:ry, lx:rx] | |
# img_np_list[_i] = _inp | |
# return img_np_list | |
def crop(self, img_np_list, still=False, xsize=512): # first frame for all video | |
img_np = img_np_list[0] | |
lm = self.get_landmark(img_np) | |
if lm is None: | |
return None | |
crop, quad = self.align_face(img=Image.fromarray(img_np), lm=lm, output_size=xsize) | |
clx, cly, crx, cry = crop | |
lx, ly, rx, ry = quad | |
lx, ly, rx, ry = int(lx), int(ly), int(rx), int(ry) | |
for _i in range(len(img_np_list)): | |
_inp = img_np_list[_i] | |
_inp = _inp[cly:cry, clx:crx] | |
# cv2.imwrite('test1.jpg', _inp) | |
if not still: | |
_inp = _inp[ly:ry, lx:rx] | |
# cv2.imwrite('test2.jpg', _inp) | |
img_np_list[_i] = _inp | |
return img_np_list, crop, quad | |
def read_video(filename, uplimit=100): | |
frames = [] | |
cap = cv2.VideoCapture(filename) | |
cnt = 0 | |
while cap.isOpened(): | |
ret, frame = cap.read() | |
if ret: | |
frame = cv2.resize(frame, (512, 512)) | |
frames.append(frame) | |
else: | |
break | |
cnt += 1 | |
if cnt >= uplimit: | |
break | |
cap.release() | |
assert len(frames) > 0, f'{filename}: video with no frames!' | |
return frames | |
def create_video(video_name, frames, fps=25, video_format='.mp4', resize_ratio=1): | |
# video_name = os.path.dirname(image_folder) + video_format | |
# img_list = glob.glob1(image_folder, 'frame*') | |
# img_list.sort() | |
# frame = cv2.imread(os.path.join(image_folder, img_list[0])) | |
# frame = cv2.resize(frame, (0, 0), fx=resize_ratio, fy=resize_ratio) | |
# height, width, layers = frames[0].shape | |
height, width, layers = 512, 512, 3 | |
if video_format == '.mp4': | |
fourcc = cv2.VideoWriter_fourcc(*'mp4v') | |
elif video_format == '.avi': | |
fourcc = cv2.VideoWriter_fourcc(*'XVID') | |
video = cv2.VideoWriter(video_name, fourcc, fps, (width, height)) | |
for _frame in frames: | |
_frame = cv2.resize(_frame, (height, width), interpolation=cv2.INTER_LINEAR) | |
video.write(_frame) | |
def create_images(video_name, frames): | |
height, width, layers = 512, 512, 3 | |
images_dir = video_name.split('.')[0] | |
os.makedirs(images_dir, exist_ok=True) | |
for i, _frame in enumerate(frames): | |
_frame = cv2.resize(_frame, (height, width), interpolation=cv2.INTER_LINEAR) | |
_frame_path = os.path.join(images_dir, str(i)+'.jpg') | |
cv2.imwrite(_frame_path, _frame) | |
def run(data): | |
filename, opt, device = data | |
os.environ['CUDA_VISIBLE_DEVICES'] = device | |
croper = Croper() | |
frames = read_video(filename, uplimit=opt.uplimit) | |
name = filename.split('/')[-1] # .split('.')[0] | |
name = os.path.join(opt.output_dir, name) | |
frames = croper.crop(frames) | |
if frames is None: | |
print(f'{name}: detect no face. should removed') | |
return | |
# create_video(name, frames) | |
create_images(name, frames) | |
def get_data_path(video_dir): | |
eg_video_files = ['/apdcephfs/share_1290939/quincheng/datasets/HDTF/backup_fps25/WDA_KatieHill_000.mp4'] | |
# filenames = list() | |
# VIDEO_EXTENSIONS_LOWERCASE = {'mp4'} | |
# VIDEO_EXTENSIONS = VIDEO_EXTENSIONS_LOWERCASE.union({f.upper() for f in VIDEO_EXTENSIONS_LOWERCASE}) | |
# extensions = VIDEO_EXTENSIONS | |
# for ext in extensions: | |
# filenames = sorted(glob.glob(f'{opt.input_dir}/**/*.{ext}')) | |
# print('Total number of videos:', len(filenames)) | |
return eg_video_files | |
def get_wra_data_path(video_dir): | |
if opt.option == 'video': | |
videos_path = sorted(glob.glob(f'{video_dir}/*.mp4')) | |
elif opt.option == 'image': | |
videos_path = sorted(glob.glob(f'{video_dir}/*/')) | |
else: | |
raise NotImplementedError | |
print('Example videos: ', videos_path[:2]) | |
return videos_path | |
if __name__ == '__main__': | |
set_start_method('spawn') | |
parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter) | |
parser.add_argument('--input_dir', type=str, help='the folder of the input files') | |
parser.add_argument('--output_dir', type=str, help='the folder of the output files') | |
parser.add_argument('--device_ids', type=str, default='0,1') | |
parser.add_argument('--workers', type=int, default=8) | |
parser.add_argument('--uplimit', type=int, default=500) | |
parser.add_argument('--option', type=str, default='video') | |
root = '/apdcephfs/share_1290939/quincheng/datasets/HDTF' | |
cmd = f'--input_dir {root}/backup_fps25_first20s_sync/ ' \ | |
f'--output_dir {root}/crop512_stylegan_firstframe_sync/ ' \ | |
'--device_ids 0 ' \ | |
'--workers 8 ' \ | |
'--option video ' \ | |
'--uplimit 500 ' | |
opt = parser.parse_args(cmd.split()) | |
# filenames = get_data_path(opt.input_dir) | |
filenames = get_wra_data_path(opt.input_dir) | |
os.makedirs(opt.output_dir, exist_ok=True) | |
print(f'Video numbers: {len(filenames)}') | |
pool = Pool(opt.workers) | |
args_list = cycle([opt]) | |
device_ids = opt.device_ids.split(",") | |
device_ids = cycle(device_ids) | |
for data in tqdm(pool.imap_unordered(run, zip(filenames, args_list, device_ids))): | |
None |