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| import os | |
| import sys | |
| from torchvision.transforms import functional | |
| sys.modules["torchvision.transforms.functional_tensor"] = functional | |
| from basicsr.archs.srvgg_arch import SRVGGNetCompact | |
| from gfpgan.utils import GFPGANer | |
| from realesrgan.utils import RealESRGANer | |
| import torch | |
| import cv2 | |
| import gradio as gr | |
| # Baixar Modelos Necessários | |
| if not os.path.exists('realesr-general-x4v3.pth'): | |
| os.system("wget https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesr-general-x4v3.pth -P .") | |
| if not os.path.exists('GFPGANv1.2.pth'): | |
| os.system("wget https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.2.pth -P .") | |
| if not os.path.exists('GFPGANv1.3.pth'): | |
| os.system("wget https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.3.pth -P .") | |
| if not os.path.exists('GFPGANv1.4.pth'): | |
| os.system("wget https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.4.pth -P .") | |
| if not os.path.exists('RestoreFormer.pth'): | |
| os.system("wget https://github.com/TencentARC/GFPGAN/releases/download/v1.3.4/RestoreFormer.pth -P .") | |
| model = SRVGGNetCompact(num_in_ch=3, num_out_ch=3, num_feat=64, num_conv=32, upscale=4, act_type='prelu') | |
| model_path = '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) | |
| # Função de Upscaling e Restauração de Imagem | |
| def upscaler(img, version, scale): | |
| try: | |
| 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: | |
| img_mode = None | |
| img = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR) | |
| else: | |
| img_mode = None | |
| h, w = img.shape[0:2] | |
| if h < 300: | |
| img = cv2.resize(img, (w * 2, h * 2), interpolation=cv2.INTER_LANCZOS4) | |
| face_enhancer = GFPGANer( | |
| model_path=f'{version}.pth', | |
| upscale=2, | |
| arch='RestoreFormer' if version == 'RestoreFormer' else 'clean', | |
| 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('Erro', 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('Erro de escala.', error) | |
| output = cv2.cvtColor(output, cv2.COLOR_BGR2RGB) | |
| return output | |
| except Exception as error: | |
| print('Exceção global', error) | |
| return None, None | |
| if __name__ == "__main__": | |
| demo = gr.Interface( | |
| upscaler, [ | |
| gr.Image(type="filepath", label="Entrada"), | |
| gr.Radio(['GFPGANv1.2', 'GFPGANv1.3', 'GFPGANv1.4', 'RestoreFormer'], type="value", label='Versão'), | |
| gr.Number(label="Fator de Redimensionamento"), | |
| ], [ | |
| gr.Image(type="numpy", label="Saída"), | |
| ], | |
| allow_flagging="never" | |
| ) | |
| demo.queue() | |
| demo.launch() | |