""" 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)