Spaces:
Running
Running
| 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 | |
| from zeroscratches import EraseScratches | |
| from AinaTheme import theme | |
| os.system("pip freeze") | |
| os.system("pip freeze") | |
| # download weights | |
| 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 .") | |
| torch.hub.download_url_to_file( | |
| 'https://thumbs.dreamstime.com/b/tower-bridge-traditional-red-bus-black-white-colors-view-to-tower-bridge-london-black-white-colors-108478942.jpg', | |
| 'a1.jpg') | |
| torch.hub.download_url_to_file( | |
| 'https://media.istockphoto.com/id/523514029/photo/london-skyline-b-w.jpg?s=612x612&w=0&k=20&c=kJS1BAtfqYeUDaORupj0sBPc1hpzJhBUUqEFfRnHzZ0=', | |
| 'a2.jpg') | |
| torch.hub.download_url_to_file( | |
| 'https://i.guim.co.uk/img/media/06f614065ed82ca0e917b149a32493c791619854/0_0_3648_2789/master/3648.jpg?width=700&quality=85&auto=format&fit=max&s=05764b507c18a38590090d987c8b6202', | |
| 'a3.jpg') | |
| torch.hub.download_url_to_file( | |
| 'https://i.pinimg.com/736x/46/96/9e/46969eb94aec2437323464804d27706d--victorian-london-victorian-era.jpg', | |
| 'a4.jpg') | |
| # 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) | |
| os.makedirs('output', exist_ok=True) | |
| # def inference(img, version, scale, weight): | |
| def enhance_image(img, version, scale): | |
| # weight /= 100 | |
| print(img, version, scale) | |
| 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 < 300: | |
| img = cv2.resize(img, (w * 2, h * 2), interpolation=cv2.INTER_LANCZOS4) | |
| if version == 'M1': | |
| face_enhancer = GFPGANer( | |
| model_path='GFPGANv1.2.pth', upscale=2, arch='clean', channel_multiplier=2, bg_upsampler=upsampler) | |
| elif version == 'M2': | |
| face_enhancer = GFPGANer( | |
| model_path='GFPGANv1.3.pth', upscale=2, arch='clean', channel_multiplier=2, bg_upsampler=upsampler) | |
| elif version == 'M3': | |
| face_enhancer = GFPGANer( | |
| model_path='GFPGANv1.4.pth', upscale=2, arch='clean', channel_multiplier=2, bg_upsampler=upsampler) | |
| elif version == 'RestoreFormer': | |
| face_enhancer = GFPGANer( | |
| model_path='RestoreFormer.pth', upscale=2, arch='RestoreFormer', channel_multiplier=2, bg_upsampler=upsampler) | |
| elif version == 'CodeFormer': | |
| face_enhancer = GFPGANer( | |
| model_path='CodeFormer.pth', upscale=2, arch='CodeFormer', channel_multiplier=2, bg_upsampler=upsampler) | |
| elif version == 'RealESR-General-x4v3': | |
| face_enhancer = GFPGANer( | |
| model_path='realesr-general-x4v3.pth', upscale=2, arch='realesr-general', channel_multiplier=2, bg_upsampler=upsampler) | |
| try: | |
| # _, _, output = face_enhancer.enhance(img, has_aligned=False, only_center_face=False, paste_back=True, weight=weight) | |
| _, _, 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 | |
| # Function to remove scratches from an image | |
| def remove_scratches(img): | |
| scratch_remover = EraseScratches() | |
| img_without_scratches = scratch_remover.erase(img) | |
| return img_without_scratches | |
| import tempfile | |
| # Function for performing operations sequentially | |
| def process_image(img): | |
| try: | |
| # Create a unique temporary directory for each request | |
| temp_dir = tempfile.mkdtemp() | |
| # Generate a unique filename for the temporary file | |
| unique_filename = 'temp_image.jpg' | |
| temp_file_path = os.path.join(temp_dir, unique_filename) | |
| # Remove scratches from the input image | |
| img_without_scratches = remove_scratches(img) | |
| # Save the image without scratches to the temporary file | |
| cv2.imwrite(temp_file_path, cv2.cvtColor(img_without_scratches, cv2.COLOR_BGR2RGB)) | |
| # Enhance the image using the saved file path | |
| enhanced_img, save_path = enhance_image(temp_file_path, version='M2', scale=2) | |
| # Convert the enhanced image to RGB format | |
| enhanced_img_rgb = cv2.cvtColor(enhanced_img, cv2.COLOR_BGR2RGB) | |
| # Delete the temporary file and directory | |
| os.remove(temp_file_path) | |
| os.rmdir(temp_dir) | |
| # Return the enhanced image in RGB format and the path where it's saved | |
| return enhanced_img, save_path | |
| except Exception as e: | |
| print('Error processing image:', e) | |
| return None, None | |
| # Gradio interface | |
| title = "<span style='color: crimson;'>Aiconvert.online</span>" | |
| description = r""" | |
| """ | |
| article = r""" | |
| """ | |
| demo = gr.Interface( | |
| process_image, [ | |
| gr.Image(type="pil", label="Input"), | |
| ], [ | |
| gr.Image(type="numpy", label="Result Image"), | |
| gr.File(label="Download the output image") | |
| ], | |
| theme="gradio/monochrome", | |
| title=title, | |
| description=description, | |
| article=article) | |
| demo.queue().launch() | |