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| from fastapi import FastAPI, File, UploadFile, Form, Request | |
| from fastapi.responses import HTMLResponse, FileResponse | |
| from fastapi.staticfiles import StaticFiles | |
| from fastapi.templating import Jinja2Templates | |
| import cv2 | |
| import os | |
| import torch | |
| from basicsr.archs.srvgg_arch import SRVGGNetCompact | |
| from gfpgan.utils import GFPGANer | |
| from realesrgan.utils import RealESRGANer | |
| app = FastAPI() | |
| 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 inference(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 == 'v1.2': | |
| face_enhancer = GFPGANer( | |
| model_path='GFPGANv1.2.pth', upscale=2, arch='clean', channel_multiplier=2, bg_upsampler=upsampler) | |
| elif version == 'v1.3': | |
| face_enhancer = GFPGANer( | |
| model_path='GFPGANv1.3.pth', upscale=2, arch='clean', channel_multiplier=2, bg_upsampler=upsampler) | |
| elif version == 'v1.4': | |
| 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 | |
| async def upload_image(file: UploadFile = File(...), version: str = Form(...), scale: int = Form(...)): | |
| try: | |
| # Save the uploaded file | |
| with open(f"uploaded_image{os.path.splitext(file.filename)[1]}", "wb") as buffer: | |
| shutil.copyfileobj(file.file, buffer) | |
| # Perform image enhancement | |
| enhanced_image, save_path = inference(f"uploaded_image{os.path.splitext(file.filename)[1]}", version, scale) | |
| # Return the enhanced image | |
| if enhanced_image is not None: | |
| return FileResponse(path=save_path, media_type="image/jpeg") | |
| else: | |
| return {"error": "Failed to enhance the image."} | |
| except Exception as e: | |
| return {"error": str(e)} | |
| app.mount("/", StaticFiles(directory="static", html=True), name="static") | |
| def index() -> FileResponse: | |
| return FileResponse(path="/app/static/index.html", media_type="text/html") | |