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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 numpy as np | |
| from PIL import Image | |
| import __init_paths | |
| from retinaface.retinaface_detection import RetinaFaceDetection | |
| from face_model.face_gan import FaceGAN | |
| from align_faces import warp_and_crop_face, get_reference_facial_points | |
| from google.colab.patches import cv2_imshow | |
| class FaceEnhancement(object): | |
| def __init__(self, base_dir='./', size=512, out_size=None, model=None, channel_multiplier=2, narrow=1, key=None, device='cuda'): | |
| self.facedetector = RetinaFaceDetection(base_dir, device) | |
| self.facegan = FaceGAN(base_dir, size, out_size, model, channel_multiplier, narrow, key, device=device) | |
| self.size = size | |
| self.out_size = size if out_size==None else out_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, aligned=False): | |
| orig_faces, enhanced_faces = [], [] | |
| if aligned: | |
| ef = self.facegan.process(img) | |
| orig_faces.append(img) | |
| enhanced_faces.append(ef) | |
| facebs, landms = self.facedetector.detect(img) | |
| 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 = cv2.resize(tmp_mask, (self.size, self.size)) | |
| 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) | |
| if self.size!=self.out_size: | |
| ef = cv2.resize(ef, (self.size, self.size)) | |
| 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] | |
| img = cv2.convertScaleAbs(img*(1-full_mask) + full_img*full_mask) | |
| if self.use_sr and img_sr == None: | |
| img = cv2.convertScaleAbs(img*(1-full_mask) + full_img*full_mask) | |
| else: | |
| img = cv2.convertScaleAbs(img_sr*(1-full_mask) + full_img*full_mask) | |
| return img, orig_faces, enhanced_faces | |