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| ''' | |
| @paper: GAN Prior Embedded Network for Blind Face Restoration in the Wild (CVPR2021) | |
| @author: yangxy (yangtao9009@gmail.com) | |
| ''' | |
| import os | |
| import cv2 | |
| import glob | |
| import time | |
| import argparse | |
| import numpy as np | |
| from PIL import Image | |
| import __init_paths | |
| from face_detect.retinaface_detection import RetinaFaceDetection | |
| from face_parse.face_parsing import FaceParse | |
| from face_model.face_gan import FaceGAN | |
| from sr_model.real_esrnet import RealESRNet | |
| from align_faces import warp_and_crop_face, get_reference_facial_points | |
| class FaceEnhancement(object): | |
| def __init__(self, base_dir='./', size=512, model=None, use_sr=True, sr_model=None, channel_multiplier=2, narrow=1, key=None, device='cuda'): | |
| self.facedetector = RetinaFaceDetection(base_dir, device) | |
| self.facegan = FaceGAN(base_dir, size, model, channel_multiplier, narrow, key, device=device) | |
| self.srmodel = RealESRNet(base_dir, sr_model, device=device) | |
| self.faceparser = FaceParse(base_dir, device=device) | |
| self.use_sr = use_sr | |
| self.size = size | |
| self.threshold = 0.9 | |
| # the mask for pasting restored faces back | |
| self.mask = np.zeros((512, 512), np.float32) | |
| cv2.rectangle(self.mask, (26, 26), (486, 486), (1, 1, 1), -1, cv2.LINE_AA) | |
| self.mask = cv2.GaussianBlur(self.mask, (101, 101), 11) | |
| self.mask = cv2.GaussianBlur(self.mask, (101, 101), 11) | |
| self.kernel = np.array(( | |
| [0.0625, 0.125, 0.0625], | |
| [0.125, 0.25, 0.125], | |
| [0.0625, 0.125, 0.0625]), dtype="float32") | |
| # get the reference 5 landmarks position in the crop settings | |
| default_square = True | |
| inner_padding_factor = 0.25 | |
| outer_padding = (0, 0) | |
| self.reference_5pts = get_reference_facial_points( | |
| (self.size, self.size), inner_padding_factor, outer_padding, default_square) | |
| def mask_postprocess(self, mask, thres=20): | |
| mask[:thres, :] = 0; mask[-thres:, :] = 0 | |
| mask[:, :thres] = 0; mask[:, -thres:] = 0 | |
| mask = cv2.GaussianBlur(mask, (101, 101), 11) | |
| mask = cv2.GaussianBlur(mask, (101, 101), 11) | |
| return mask.astype(np.float32) | |
| def process(self, img): | |
| if self.use_sr: | |
| img_sr = self.srmodel.process(img) | |
| if img_sr is not None: | |
| img = cv2.resize(img, img_sr.shape[:2][::-1]) | |
| facebs, landms = self.facedetector.detect(img) | |
| orig_faces, enhanced_faces = [], [] | |
| height, width = img.shape[:2] | |
| full_mask = np.zeros((height, width), dtype=np.float32) | |
| full_img = np.zeros(img.shape, dtype=np.uint8) | |
| for i, (faceb, facial5points) in enumerate(zip(facebs, landms)): | |
| if faceb[4]<self.threshold: continue | |
| fh, fw = (faceb[3]-faceb[1]), (faceb[2]-faceb[0]) | |
| facial5points = np.reshape(facial5points, (2, 5)) | |
| of, tfm_inv = warp_and_crop_face(img, facial5points, reference_pts=self.reference_5pts, crop_size=(self.size, self.size)) | |
| # enhance the face | |
| ef = self.facegan.process(of) | |
| orig_faces.append(of) | |
| enhanced_faces.append(ef) | |
| #tmp_mask = self.mask | |
| tmp_mask = self.mask_postprocess(self.faceparser.process(ef)[0]/255.) | |
| tmp_mask = cv2.resize(tmp_mask, ef.shape[:2]) | |
| tmp_mask = cv2.warpAffine(tmp_mask, tfm_inv, (width, height), flags=3) | |
| if min(fh, fw)<100: # gaussian filter for small faces | |
| ef = cv2.filter2D(ef, -1, self.kernel) | |
| tmp_img = cv2.warpAffine(ef, tfm_inv, (width, height), flags=3) | |
| mask = tmp_mask - full_mask | |
| full_mask[np.where(mask>0)] = tmp_mask[np.where(mask>0)] | |
| full_img[np.where(mask>0)] = tmp_img[np.where(mask>0)] | |
| full_mask = full_mask[:, :, np.newaxis] | |
| if self.use_sr and img_sr is not None: | |
| img = cv2.convertScaleAbs(img_sr*(1-full_mask) + full_img*full_mask) | |
| else: | |
| img = cv2.convertScaleAbs(img*(1-full_mask) + full_img*full_mask) | |
| return img, orig_faces, enhanced_faces | |
| if __name__=='__main__': | |
| parser = argparse.ArgumentParser() | |
| parser.add_argument('--model', type=str, default='GPEN-BFR-512', help='GPEN model') | |
| parser.add_argument('--key', type=str, default=None, help='key of GPEN model') | |
| parser.add_argument('--size', type=int, default=512, help='resolution of GPEN') | |
| parser.add_argument('--channel_multiplier', type=int, default=2, help='channel multiplier of GPEN') | |
| parser.add_argument('--narrow', type=float, default=1, help='channel narrow scale') | |
| parser.add_argument('--use_sr', action='store_true', help='use sr or not') | |
| parser.add_argument('--use_cuda', action='store_true', help='use cuda or not') | |
| parser.add_argument('--sr_model', type=str, default='rrdb_realesrnet_psnr', help='SR model') | |
| parser.add_argument('--sr_scale', type=int, default=2, help='SR scale') | |
| parser.add_argument('--indir', type=str, default='examples/imgs', help='input folder') | |
| parser.add_argument('--outdir', type=str, default='results/outs-BFR', help='output folder') | |
| args = parser.parse_args() | |
| #model = {'name':'GPEN-BFR-512', 'size':512, 'channel_multiplier':2, 'narrow':1} | |
| #model = {'name':'GPEN-BFR-256', 'size':256, 'channel_multiplier':1, 'narrow':0.5} | |
| os.makedirs(args.outdir, exist_ok=True) | |
| faceenhancer = FaceEnhancement(size=args.size, model=args.model, use_sr=args.use_sr, sr_model=args.sr_model, channel_multiplier=args.channel_multiplier, narrow=args.narrow, key=args.key, device='cuda' if args.use_cuda else 'cpu') | |
| files = sorted(glob.glob(os.path.join(args.indir, '*.*g'))) | |
| for n, file in enumerate(files[:]): | |
| filename = os.path.basename(file) | |
| im = cv2.imread(file, cv2.IMREAD_COLOR) # BGR | |
| if not isinstance(im, np.ndarray): print(filename, 'error'); continue | |
| #im = cv2.resize(im, (0,0), fx=2, fy=2) # optional | |
| img, orig_faces, enhanced_faces = faceenhancer.process(im) | |
| im = cv2.resize(im, img.shape[:2][::-1]) | |
| cv2.imwrite(os.path.join(args.outdir, '.'.join(filename.split('.')[:-1])+'_COMP.jpg'), np.hstack((im, img))) | |
| cv2.imwrite(os.path.join(args.outdir, '.'.join(filename.split('.')[:-1])+'_GPEN.jpg'), img) | |
| for m, (ef, of) in enumerate(zip(enhanced_faces, orig_faces)): | |
| of = cv2.resize(of, ef.shape[:2]) | |
| cv2.imwrite(os.path.join(args.outdir, '.'.join(filename.split('.')[:-1])+'_face%02d'%m+'.jpg'), np.hstack((of, ef))) | |
| if n%10==0: print(n, filename) | |