import os os.system("wget https://public-vigen-video.oss-cn-shanghai.aliyuncs.com/robin/models/RetinaFace-R50.pth -P ./weights/") os.system("wget https://public-vigen-video.oss-cn-shanghai.aliyuncs.com/robin/models/GPEN-BFR-512.pth -P ./weights/") os.system("wget https://public-vigen-video.oss-cn-shanghai.aliyuncs.com/robin/models/ParseNet-latest.pth -P ./weights/") os.system("wget https://public-vigen-video.oss-cn-shanghai.aliyuncs.com/robin/models/rrdb_realesrnet_psnr.pth -P ./weights/") import gradio as gr torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/a/ab/Abraham_Lincoln_O-77_matte_collodion_print.jpg/1024px-Abraham_Lincoln_O-77_matte_collodion_print.jpg', 'lincoln.jpg') torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/5/50/Albert_Einstein_%28Nobel%29.png', 'einstein.png') ''' @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]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 = 'rrdb_realesrnet_psnr' faceenhancer = FaceEnhancement(size=size, model=model, use_sr=use_sr, sr_model=sr_model, channel_multiplier=channel_multiplier, narrow=narrow, key=key, device='cuda' if args.use_cuda else 'cpu') def inference(file): im = cv2.imread(file, cv2.IMREAD_COLOR) img, orig_faces, enhanced_faces = faceenhancer.process(im) return enhanced_faces[0] title = "GFP-GAN" description = "Gradio demo for GFP-GAN: Towards Real-World Blind Face Restoration with Generative Facial Prior. To use it, simply upload your image, or click one of the examples to load them. Read more at the links below. Please click submit only once" article = "

Towards Real-World Blind Face Restoration with Generative Facial Prior | Github Repo

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" gr.Interface( inference, [gr.inputs.Image(type="filepath", label="Input")], gr.outputs.Image(type="numpy", label="Output"), title=title, description=description, article=article, examples=[ ['lincoln.jpg'], ['einstein.png'] ], enable_queue=True ).launch(debug=True)