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 = "Aiconvert.online" 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()