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| import os | |
| os.system('wget https://huggingface.co/akhaliq/RetinaFace-R50/resolve/main/RetinaFace-R50.pth -O weights/RetinaFace-R50.pth') | |
| os.system('wget https://huggingface.co/akhaliq/GPEN-BFR-512/resolve/main/GPEN-BFR-512.pth -O weights/GPEN-BFR-512.pth') | |
| os.system('wget https://huggingface.co/akhaliq/realesrnet_x2/resolve/main/realesrnet_x2.pth -O weights/realesrnet_x2.pth') | |
| os.system('wget https://huggingface.co/akhaliq/ParseNet-latest/resolve/main/ParseNet-latest.pth -O weights/ParseNet-latest.pth') | |
| import gradio as gr | |
| ''' | |
| @paper: GAN Prior Embedded Network for Blind Face Restoration in the Wild (CVPR2021) | |
| @author: yangxy ([email protected]) | |
| ''' | |
| 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 | |
| import torch | |
| torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/e/ec/Mona_Lisa%2C_by_Leonardo_da_Vinci%2C_from_C2RMF_retouched.jpg/800px-Mona_Lisa%2C_by_Leonardo_da_Vinci%2C_from_C2RMF_retouched.jpg', 'mona.jpg') | |
| torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/5/50/Albert_Einstein_%28Nobel%29.png', 'einstein.png') | |
| 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 | |
| model = "GPEN-BFR-512" | |
| key = None | |
| size = 512 | |
| channel_multiplier = 2 | |
| narrow = 1 | |
| use_sr = False | |
| use_cuda = False | |
| sr_model = 'realesrnet_x2' | |
| faceenhancer = FaceEnhancement(size=size, model=model, use_sr=use_sr, sr_model=sr_model, channel_multiplier=channel_multiplier, narrow=narrow, key=key, device='cpu') | |
| def inference(file): | |
| im = cv2.imread(file, cv2.IMREAD_COLOR) | |
| img, orig_faces, enhanced_faces = faceenhancer.process(im) | |
| return enhanced_faces[0][:,:,::-1] | |
| title = "GPEN" | |
| description = "Gradio demo for GAN Prior Embedded Network for Blind Face Restoration in the Wild. To use it, simply upload your image, or click one of the examples to load them. Read more at the links below." | |
| article = "<p style='text-align: center'><a href='https://arxiv.org/abs/2105.06070' target='_blank'>GAN Prior Embedded Network for Blind Face Restoration in the Wild</a> | <a href='https://github.com/yangxy/GPEN' target='_blank'>Github Repo</a></p><center><img src='https://visitor-badge.glitch.me/badge?page_id=akhaliq_GPEN' alt='visitor badge'></center>" | |
| gr.Interface( | |
| inference, | |
| [gr.inputs.Image(type="filepath", label="Input")], | |
| gr.outputs.Image(type="numpy", label="Output"), | |
| title=title, | |
| description=description, | |
| article=article, | |
| examples=[ | |
| ['sample.png'] | |
| ], | |
| enable_queue=True | |
| ).launch() |