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from base64 import b64encode, b64decode |
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from io import BytesIO |
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from pathlib import Path |
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import numpy as np |
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from basicsr.archs.rrdbnet_arch import RRDBNet |
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from PIL import Image |
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from realesrgan import RealESRGANer |
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class EndpointHandler: |
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def __init__(self, path=""): |
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model = RRDBNet(num_in_ch=3, num_out_ch=3) |
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self.upsampler = RealESRGANer( |
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scale=4, |
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model_path=str(Path(path) / "RealESRGAN_x4plus.pth"), |
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model=model, |
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tile=0, |
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pre_pad=0, |
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half=True, |
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) |
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def __call__(self, data): |
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""" |
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Args: |
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data (:obj:): |
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includes the input data and the parameters for the inference. |
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Return: |
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A :obj:`dict`:. base64 encoded image |
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""" |
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image = data.pop("inputs", data) |
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if isinstance(image, str): |
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image = Image.open(BytesIO(b64decode(image))) |
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elif isinstance(image, bytes): |
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image = Image.open(BytesIO(image)) |
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image = np.array(image) |
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image = image[:, :, ::-1] |
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image, _ = self.upsampler.enhance(image, outscale=4) |
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image = image[:, :, ::-1] |
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image = Image.fromarray(image) |
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buffered = BytesIO() |
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image.save(buffered, format="PNG") |
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img_bytes = b64encode(buffered.getvalue()) |
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img_str = img_bytes.decode() |
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return {"image": img_str} |
