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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 |