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Update main.py
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main.py
CHANGED
@@ -10,87 +10,124 @@ from gfpgan.utils import GFPGANer
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from realesrgan.utils import RealESRGANer
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app = FastAPI()
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try:
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if version == 'v1.2':
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face_enhancer = GFPGANer(
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elif version == 'v1.3':
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face_enhancer = GFPGANer(
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elif version == 'v1.4':
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face_enhancer = GFPGANer(
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elif version == 'RealESR-General-x4v3':
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_, _, output = face_enhancer.enhance(
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if scale != 2:
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interpolation = cv2.INTER_AREA if scale < 2 else cv2.INTER_LANCZOS4
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h, w =
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output = cv2.resize(output, (int(w * scale / 2), int(h * scale / 2)), interpolation=interpolation)
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return output_path
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else:
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# Download weights
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download_weights()
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# Initialize model
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model, half = initialize_models()
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@app.post("/
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async def
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try:
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else:
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return {"error": "Failed to
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except Exception as e:
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return {"error":
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app.mount("/", StaticFiles(directory="static", html=True), name="static")
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from realesrgan.utils import RealESRGANer
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app = FastAPI()
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os.system("pip freeze")
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# download weights
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if not os.path.exists('realesr-general-x4v3.pth'):
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os.system("wget https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesr-general-x4v3.pth -P .")
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if not os.path.exists('GFPGANv1.2.pth'):
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os.system("wget https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.2.pth -P .")
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if not os.path.exists('GFPGANv1.3.pth'):
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os.system("wget https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.3.pth -P .")
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if not os.path.exists('GFPGANv1.4.pth'):
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os.system("wget https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.4.pth -P .")
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torch.hub.download_url_to_file(
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'https://thumbs.dreamstime.com/b/tower-bridge-traditional-red-bus-black-white-colors-view-to-tower-bridge-london-black-white-colors-108478942.jpg',
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'a1.jpg')
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torch.hub.download_url_to_file(
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'https://media.istockphoto.com/id/523514029/photo/london-skyline-b-w.jpg?s=612x612&w=0&k=20&c=kJS1BAtfqYeUDaORupj0sBPc1hpzJhBUUqEFfRnHzZ0=',
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'a2.jpg')
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torch.hub.download_url_to_file(
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'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',
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'a3.jpg')
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torch.hub.download_url_to_file(
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'https://i.pinimg.com/736x/46/96/9e/46969eb94aec2437323464804d27706d--victorian-london-victorian-era.jpg',
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'a4.jpg')
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# background enhancer with RealESRGAN
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model = SRVGGNetCompact(num_in_ch=3, num_out_ch=3, num_feat=64, num_conv=32, upscale=4, act_type='prelu')
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model_path = 'realesr-general-x4v3.pth'
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half = True if torch.cuda.is_available() else False
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upsampler = RealESRGANer(scale=4, model_path=model_path, model=model, tile=0, tile_pad=10, pre_pad=0, half=half)
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os.makedirs('output', exist_ok=True)
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# def inference(img, version, scale, weight):
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def inference(img, version, scale):
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# weight /= 100
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print(img, version, scale)
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try:
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extension = os.path.splitext(os.path.basename(str(img)))[1]
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img = cv2.imread(img, cv2.IMREAD_UNCHANGED)
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if len(img.shape) == 3 and img.shape[2] == 4:
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img_mode = 'RGBA'
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elif len(img.shape) == 2: # for gray inputs
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img_mode = None
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img = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR)
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else:
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img_mode = None
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h, w = img.shape[0:2]
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if h < 300:
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img = cv2.resize(img, (w * 2, h * 2), interpolation=cv2.INTER_LANCZOS4)
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if version == 'v1.2':
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face_enhancer = GFPGANer(
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model_path='GFPGANv1.2.pth', upscale=2, arch='clean', channel_multiplier=2, bg_upsampler=upsampler)
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elif version == 'v1.3':
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face_enhancer = GFPGANer(
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model_path='GFPGANv1.3.pth', upscale=2, arch='clean', channel_multiplier=2, bg_upsampler=upsampler)
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elif version == 'v1.4':
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face_enhancer = GFPGANer(
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model_path='GFPGANv1.4.pth', upscale=2, arch='clean', channel_multiplier=2, bg_upsampler=upsampler)
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elif version == 'RestoreFormer':
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face_enhancer = GFPGANer(
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model_path='RestoreFormer.pth', upscale=2, arch='RestoreFormer', channel_multiplier=2, bg_upsampler=upsampler)
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elif version == 'CodeFormer':
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face_enhancer = GFPGANer(
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model_path='CodeFormer.pth', upscale=2, arch='CodeFormer', channel_multiplier=2, bg_upsampler=upsampler)
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elif version == 'RealESR-General-x4v3':
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face_enhancer = GFPGANer(
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model_path='realesr-general-x4v3.pth', upscale=2, arch='realesr-general', channel_multiplier=2, bg_upsampler=upsampler)
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try:
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# _, _, output = face_enhancer.enhance(img, has_aligned=False, only_center_face=False, paste_back=True, weight=weight)
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_, _, output = face_enhancer.enhance(img, has_aligned=False, only_center_face=False, paste_back=True)
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except RuntimeError as error:
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print('Error', error)
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try:
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if scale != 2:
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interpolation = cv2.INTER_AREA if scale < 2 else cv2.INTER_LANCZOS4
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h, w = img.shape[0:2]
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output = cv2.resize(output, (int(w * scale / 2), int(h * scale / 2)), interpolation=interpolation)
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except Exception as error:
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print('wrong scale input.', error)
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if img_mode == 'RGBA': # RGBA images should be saved in png format
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extension = 'png'
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else:
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extension = 'jpg'
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save_path = f'output/out.{extension}'
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cv2.imwrite(save_path, output)
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output = cv2.cvtColor(output, cv2.COLOR_BGR2RGB)
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return output, save_path
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except Exception as error:
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print('global exception', error)
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return None, None
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@app.post("/upload/")
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async def upload_image(file: UploadFile = File(...), version: str = Form(...), scale: int = Form(...)):
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try:
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# Save the uploaded file
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with open(f"uploaded_image{os.path.splitext(file.filename)[1]}", "wb") as buffer:
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shutil.copyfileobj(file.file, buffer)
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# Perform image enhancement
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enhanced_image, save_path = inference(f"uploaded_image{os.path.splitext(file.filename)[1]}", version, scale)
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# Return the enhanced image
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if enhanced_image is not None:
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return FileResponse(path=save_path, media_type="image/jpeg")
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else:
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return {"error": "Failed to enhance the image."}
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except Exception as e:
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return {"error": str(e)}
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app.mount("/", StaticFiles(directory="static", html=True), name="static")
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