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from base64 import b64encode
from io import BytesIO
from pathlib import Path
import numpy as np
from basicsr.archs.rrdbnet_arch import RRDBNet
from PIL import Image
from realesrgan import RealESRGANer
class EndpointHandler:
def __init__(self, path=""):
model = RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=23, num_grow_ch=32, scale=4)
self.upsampler = RealESRGANer(
scale=4,
model_path=str(Path(path) / "RealESRGAN_x4plus.pth"),
model=model,
tile=0,
tile_pad=10,
pre_pad=0,
half=True,
)
def __call__(self, data):
"""
Args:
data (:obj:):
includes the input data and the parameters for the inference.
Return:
A :obj:`dict`:. base64 encoded image
"""
image = data.pop("inputs", data)
# image = Image.open(BytesIO(image)).convert("RGB")
image = np.array(image)
image = image[:, :, ::-1] # RGB -> BGR
image, _ = self.upsampler.enhance(image, outscale=4)
image = image[:, :, ::-1] # BGR -> RGB
image = Image.fromarray(image)
# encode image as base 64
buffered = BytesIO()
image.save(buffered, format="PNG")
img_str = b64encode(buffered.getvalue())
# postprocess the prediction
return {"image": img_str.decode()} |