import os import cv2 import gradio as gr import torch from basicsr.archs.srvgg_arch import SRVGGNetCompact from gfpgan.utils import GFPGANer from huggingface_hub import snapshot_download, hf_hub_download from realesrgan.utils import RealESRGANer import examples REALESRGAN_REPO_ID = 'leonelhs/realesrgan' GFPGAN_REPO_ID = 'leonelhs/gfpgan' os.system("pip freeze") examples.download() # background enhancer with RealESRGAN model = SRVGGNetCompact(num_in_ch=3, num_out_ch=3, num_feat=64, num_conv=32, upscale=4, act_type='prelu') model_path = hf_hub_download(repo_id=REALESRGAN_REPO_ID, filename='realesr-general-x4v3.pth') half = True if torch.cuda.is_available() else False upsampler = RealESRGANer(scale=4, model_path=model_path, model=model, tile=0, tile_pad=10, pre_pad=0, half=half) os.makedirs('output', exist_ok=True) # def inference(img, version, scale, weight): def predict(img, version, scale): # weight /= 100 print(img, version, scale) if scale > 4: scale = 4 # avoid too large scale value try: extension = os.path.splitext(os.path.basename(str(img)))[1] img = cv2.imread(img, cv2.IMREAD_UNCHANGED) if len(img.shape) == 3 and img.shape[2] == 4: img_mode = 'RGBA' elif len(img.shape) == 2: # for gray inputs img_mode = None img = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR) else: img_mode = None h, w = img.shape[0:2] if h > 3500 or w > 3500: print('too large size') return None, None if h < 300: img = cv2.resize(img, (w * 2, h * 2), interpolation=cv2.INTER_LANCZOS4) face_enhancer = None snapshot_folder = snapshot_download(repo_id=GFPGAN_REPO_ID) if version == 'v1.2': path = os.path.join(snapshot_folder, 'GFPGANv1.2.pth') face_enhancer = GFPGANer( model_path=path, upscale=2, arch='clean', channel_multiplier=2, bg_upsampler=upsampler) elif version == 'v1.3': path = os.path.join(snapshot_folder, 'GFPGANv1.3.pth') face_enhancer = GFPGANer( model_path=path, upscale=2, arch='clean', channel_multiplier=2, bg_upsampler=upsampler) elif version == 'v1.4': path = os.path.join(snapshot_folder, 'GFPGANv1.4.pth') face_enhancer = GFPGANer( model_path=path, upscale=2, arch='clean', channel_multiplier=2, bg_upsampler=upsampler) elif version == 'RestoreFormer': path = os.path.join(snapshot_folder, 'RestoreFormer.pth') face_enhancer = GFPGANer( model_path=path, upscale=2, arch='RestoreFormer', channel_multiplier=2, bg_upsampler=upsampler) try: _, _, output = face_enhancer.enhance(img, has_aligned=False, only_center_face=False, paste_back=True) except RuntimeError as error: print('Error', error) try: if scale != 2: interpolation = cv2.INTER_AREA if scale < 2 else cv2.INTER_LANCZOS4 h, w = img.shape[0:2] output = cv2.resize(output, (int(w * scale / 2), int(h * scale / 2)), interpolation=interpolation) except Exception as error: print('wrong scale input.', error) if img_mode == 'RGBA': # RGBA images should be saved in png format extension = 'png' else: extension = 'jpg' save_path = f'output/out.{extension}' cv2.imwrite(save_path, output) output = cv2.cvtColor(output, cv2.COLOR_BGR2RGB) return output, save_path except Exception as error: print('global exception', error) return None, None title = "GFPGAN: Practical Face Restoration Algorithm" description = r"""Gradio demo for GFPGAN: Towards Real-World Blind Face Restoration with Generative Facial Prior.
It can be used to restore your **old photos** or improve **AI-generated faces**.
To use it, simply upload your image.
If GFPGAN is helpful, please help to ⭐ the Github Repo and recommend it to your friends 😊 """ article = r""" [![download](https://img.shields.io/github/downloads/TencentARC/GFPGAN/total.svg)](https://github.com/TencentARC/GFPGAN/releases) [![GitHub Stars](https://img.shields.io/github/stars/TencentARC/GFPGAN?style=social)](https://github.com/TencentARC/GFPGAN) [![arXiv](https://img.shields.io/badge/arXiv-Paper-.svg)](https://arxiv.org/abs/2101.04061) If you have any question, please email 📧 `xintao.wang@outlook.com` or `xintaowang@tencent.com`.
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""" demo = gr.Interface( predict, [ gr.Image(type="filepath", label="Input"), gr.Radio(['v1.2', 'v1.3', 'v1.4', 'RestoreFormer'], type="value", value='v1.4', label='version'), gr.Number(label="Rescaling factor", value=2), ], [ gr.Image(type="numpy", label="Output (The whole image)"), gr.File(label="Download the output image") ], title=title, description=description, article=article, examples=[['AI-generate.jpg', 'v1.4', 2], ['lincoln.jpg', 'v1.4', 2], ['Blake_Lively.jpg', 'v1.4', 2], ['10045.png', 'v1.4', 2]]) demo.queue().launch()