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from upcunet_v3 import RealWaifuUpScaler
import gradio as gr
import time
import logging
import os
from PIL import ImageOps
import numpy as np
import math
def greet(input_img, input_model_name, input_tile_mode):
if input_img.size[0] * input_img.size[1] > 256 * 256:
y = int(math.sqrt(256*256/input_img.size[0]*input_img.size[1]))
x = int(input_img.size[0]/input_img.size[1]*y)
input_img = ImageOps.fit(input_img, (x, y))
input_img = np.array(input_img)
if input_model_name not in model_cache:
t1 = time.time()
upscaler = RealWaifuUpScaler(input_model_name[2], ModelPath + input_model_name, half=False, device="cpu")
t2 = time.time()
logger.info(f'load model time, {t2 - t1}')
model_cache[input_model_name] = upscaler
else:
upscaler = model_cache[input_model_name]
logger.info(f'load model from cache')
start = time.time()
result = upscaler(input_img, tile_mode=input_tile_mode)
end = time.time()
logger.info(f'input_model_name, {input_model_name}')
logger.info(f'input_tile_mode, {input_tile_mode}')
logger.info(f'input shape, {input_img.shape}')
logger.info(f'output shape, {result.shape}')
logger.info(f'speed time, {end - start}')
return result
if __name__ == '__main__':
logging.basicConfig(level=logging.INFO, format="[%(asctime)s] [%(process)d] [%(levelname)s] %(message)s")
logger = logging.getLogger()
ModelPath = "weights_v3/"
model_cache = {}
input_model_name = gr.inputs.Dropdown(os.listdir(ModelPath), default="up2x-latest-no-denoise.pth", label='选择model')
input_tile_mode = gr.inputs.Dropdown([0, 1, 2, 3, 4], default=2, label='选择tile_mode')
input_img = gr.inputs.Image(label='image', type='pil')
inputs = [input_img, input_model_name, input_tile_mode]
outputs = "image"
iface = gr.Interface(fn=greet,
inputs=inputs,
outputs=outputs,
allow_screenshot=False,
allow_flagging='never',
examples=[['test-img.jpg', "up2x-latest-no-denoise.pth", 2]],
article='[https://github.com/bilibili/ailab/tree/main/Real-CUGAN](https://github.com/bilibili/ailab/tree/main/Real-CUGAN)<br>'
'感谢b站开源的项目，图片过大会导致内存不足，所有我将图片裁剪小，想体验大图片的效果请自行前往上面的链接。<br>'
'The large image will lead to memory limit exceeded. So I crop and resize image. '
'If you want to experience the large image, please go to the link above.')
iface.launch()
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