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''' |
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@paper: GAN Prior Embedded Network for Blind Face Restoration in the Wild (CVPR2021) |
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@author: yangxy (yangtao9009@gmail.com) |
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''' |
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import os |
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import cv2 |
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import glob |
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import time |
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import numpy as np |
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from PIL import Image |
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import __init_paths |
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from retinaface.retinaface_detection import RetinaFaceDetection |
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from face_model.face_gan import FaceGAN |
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from sr_model.real_esrnet import RealESRNet |
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from align_faces import warp_and_crop_face, get_reference_facial_points |
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class FaceEnhancement(object): |
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def __init__(self, base_dir='./', size=512, out_size=None, model=None, channel_multiplier=2, narrow=1, key=None, device='cpu', u=False): |
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self.facedetector = RetinaFaceDetection(base_dir, device) |
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self.facegan = FaceGAN(base_dir, size, out_size, model, channel_multiplier, narrow, key, device=device) |
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self.srmodel = RealESRNet(base_dir, 'realesrnet', 2, 0, device=device) |
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self.use_sr = u |
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self.size = size |
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self.out_size = size if out_size==None else out_size |
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self.threshold = 0.9 |
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self.mask = np.zeros((512, 512), np.float32) |
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cv2.rectangle(self.mask, (26, 26), (486, 486), (1, 1, 1), -1, cv2.LINE_AA) |
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self.mask = cv2.GaussianBlur(self.mask, (101, 101), 11) |
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self.mask = cv2.GaussianBlur(self.mask, (101, 101), 11) |
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self.kernel = np.array(( |
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[0.0625, 0.125, 0.0625], |
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[0.125, 0.25, 0.125], |
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[0.0625, 0.125, 0.0625]), dtype="float32") |
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default_square = True |
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inner_padding_factor = 0.25 |
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outer_padding = (0, 0) |
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self.reference_5pts = get_reference_facial_points( |
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(self.size, self.size), inner_padding_factor, outer_padding, default_square) |
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def mask_postprocess(self, mask, thres=20): |
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mask[:thres, :] = 0; mask[-thres:, :] = 0 |
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mask[:, :thres] = 0; mask[:, -thres:] = 0 |
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mask = cv2.GaussianBlur(mask, (101, 101), 11) |
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mask = cv2.GaussianBlur(mask, (101, 101), 11) |
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return mask.astype(np.float32) |
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def process(self, img, aligned=False): |
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orig_faces, enhanced_faces = [], [] |
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if aligned: |
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ef = self.facegan.process(img) |
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orig_faces.append(img) |
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enhanced_faces.append(ef) |
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if self.use_sr: |
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ef = self.srmodel.process(ef) |
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return ef, orig_faces, enhanced_faces |
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if self.use_sr: |
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img_sr = self.srmodel.process(img) |
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if img_sr is not None: |
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img = cv2.resize(img, img_sr.shape[:2][::-1]) |
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facebs, landms = self.facedetector.detect(img) |
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height, width = img.shape[:2] |
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full_mask = np.zeros((height, width), dtype=np.float32) |
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full_img = np.zeros(img.shape, dtype=np.uint8) |
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for i, (faceb, facial5points) in enumerate(zip(facebs, landms)): |
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if faceb[4]<self.threshold: continue |
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fh, fw = (faceb[3]-faceb[1]), (faceb[2]-faceb[0]) |
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facial5points = np.reshape(facial5points, (2, 5)) |
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of, tfm_inv = warp_and_crop_face(img, facial5points, reference_pts=self.reference_5pts, crop_size=(self.size, self.size)) |
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ef = self.facegan.process(of) |
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orig_faces.append(of) |
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enhanced_faces.append(ef) |
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tmp_mask = self.mask |
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tmp_mask = cv2.resize(tmp_mask, (self.size, self.size)) |
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tmp_mask = cv2.warpAffine(tmp_mask, tfm_inv, (width, height), flags=3) |
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if min(fh, fw)<100: |
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ef = cv2.filter2D(ef, -1, self.kernel) |
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if self.size!=self.out_size: |
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ef = cv2.resize(ef, (self.size, self.size)) |
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tmp_img = cv2.warpAffine(ef, tfm_inv, (width, height), flags=3) |
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mask = tmp_mask - full_mask |
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full_mask[np.where(mask>0)] = tmp_mask[np.where(mask>0)] |
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full_img[np.where(mask>0)] = tmp_img[np.where(mask>0)] |
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full_mask = full_mask[:, :, np.newaxis] |
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if self.use_sr and img_sr is not None: |
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img = cv2.convertScaleAbs(img_sr*(1-full_mask) + full_img*full_mask) |
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else: |
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img = cv2.convertScaleAbs(img*(1-full_mask) + full_img*full_mask) |
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return img, orig_faces, enhanced_faces |
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