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| import os | |
| import cv2 | |
| import gradio as gr | |
| import torch | |
| from basicsr.archs.srvgg_arch import SRVGGNetCompact | |
| from gfpgan.utils import GFPGANer | |
| from realesrgan.utils import RealESRGANer | |
| os.system("pip freeze") | |
| #os.system("wget https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesr-general-x4v3.pth -P .") | |
| #os.system("wget https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.2.pth -P .") | |
| #os.system("wget https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.3.pth -P .") | |
| # 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 = '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) | |
| # Use GFPGAN for face enhancement | |
| face_enhancer_v3 = GFPGANer( | |
| model_path='GFPGANv1.3.pth', upscale=2, arch='clean', channel_multiplier=2, bg_upsampler=upsampler) | |
| face_enhancer_v2 = GFPGANer( | |
| model_path='GFPGANv1.2.pth', upscale=2, arch='clean', channel_multiplier=2, bg_upsampler=upsampler) | |
| os.makedirs('output', exist_ok=True) | |
| def inference(img, version, scale): | |
| print(img, version, scale) | |
| try: | |
| img = cv2.imread(img, cv2.IMREAD_UNCHANGED) | |
| if len(img.shape) == 3 and img.shape[2] == 4: | |
| img_mode = 'RGBA' | |
| 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) | |
| if version == 'v1.2': | |
| face_enhancer = face_enhancer_v2 | |
| else: | |
| face_enhancer = face_enhancer_v3 | |
| try: | |
| _, _, output = face_enhancer.enhance(img, has_aligned=False, only_center_face=False, paste_back=True) | |
| except RuntimeError as error: | |
| print('Error', error) | |
| else: | |
| extension = 'png' | |
| 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 | |
| gr.Interface( | |
| inference, [ | |
| gr.inputs.Image(type="filepath", label="Input"), | |
| ], [ | |
| gr.outputs.Image(type="numpy", label="Output (The whole image)"), | |
| gr.outputs.File(label="Download the output image") | |
| ], | |
| examples=[['1.jpg', 'v1.3', 2]]).launch() | |