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import os
os.system('mim install mmcv')
import numpy as np
import models
import gradio as gr
import torch
from torchvision import transforms
from torchvision.transforms import InterpolationMode
device = 'cuda:0' if torch.cuda.is_available() else 'cpu'
def construct_sample(img, mean=0.5, std=0.5):
img = transforms.ToTensor()(img)
img = transforms.Resize((48, 48), InterpolationMode.BICUBIC)(img)
img = transforms.Normalize(mean, std)(img)
return img
def build_model(cp):
model_spec = torch.load(cp, map_location='cpu')['model']
print(model_spec['args'])
model = models.make(model_spec, load_sd=True).to(device)
return model
# Function for building extraction
def sr_func(img, cp, scale):
if cp == 'UC':
checkpoint = 'pretrain/UC_FunSR_RDN.pth'
elif cp == 'AID':
checkpoint = 'pretrain/AID_FunSR_RDN.pth'
else:
raise NotImplementedError
sample = construct_sample(img)
print('Use: ', device)
model = build_model(checkpoint)
model.eval()
sample = sample.to(device)
sample = sample.unsqueeze(0)
ori_size = torch.tensor(sample.shape[2:]) # BCHW
target_size = ori_size * scale
target_size = target_size.long()
lr_target_size_img = torch.nn.functional.interpolate(sample, scale_factor=scale, mode='nearest')
with torch.no_grad():
pred = model(sample, target_size.tolist())
if isinstance(pred, list):
pred = pred[-1]
pred = pred * 0.5 + 0.5
pred *= 255
pred = pred[0].detach().cpu()
lr_target_size_img = lr_target_size_img * 0.5 + 0.5
lr_target_size_img = 255 * lr_target_size_img[0].detach().cpu()
lr_target_size_img = torch.clamp(lr_target_size_img, 0, 255).permute(1,2,0).numpy().astype(np.uint8)
pred = torch.clamp(pred, 0, 255).permute(1,2,0).numpy().astype(np.uint8)
line = np.ones((pred.shape[0], 5, 3), dtype=np.uint8) * 255
pred = np.concatenate((lr_target_size_img, line, pred), axis=1)
return pred
title = "FunSR"
description = "Gradio demo for continuous remote sensing image super-resolution. Upload image from UCMerced or AID Dataset or click any one of the examples, " \
"Then change the upscaling magnification, and click \"Submit\" and wait for the super-resolved result. \n" \
"Paper: Continuous Remote Sensing Image Super-Resolution based on Context Interaction in Implicit Function Space"
article = "<p style='text-align: center'><a href='https://kyanchen.github.io/FunSR/' target='_blank'>FunSR Project " \
"Page</a></p> "
default_scale = 2.0
examples = [
['examples/AID_school_161_LR.png', 'AID', default_scale],
['examples/AID_bridge_19_LR.png', 'AID', default_scale],
['examples/AID_parking_60_LR.png', 'AID', default_scale],
['examples/AID_commercial_32_LR.png', 'AID', default_scale],
['examples/UC_airplane95_LR.png', 'UC', default_scale],
['examples/UC_freeway35_LR.png', 'UC', default_scale],
['examples/UC_storagetanks54_LR.png', 'UC', default_scale],
['examples/UC_airplane00_LR.png', 'UC', default_scale],
]
with gr.Blocks() as demo:
image_input = gr.Image(type='pil', label='Input Img')
# with gr.Row().style(equal_height=True):
# image_LR_output = gr.outputs.Image(label='LR Img', type='numpy')
image_output = gr.Image(label='SR Result', type='numpy')
with gr.Row():
checkpoint = gr.Radio(['UC', 'AID'], label='Checkpoint')
scale = gr.Slider(1, 10, value=4.0, step=0.1, label='scale')
io = gr.Interface(fn=sr_func,
inputs=[image_input,
checkpoint,
scale
],
outputs=[
# image_LR_output,
image_output
],
title=title,
description=description,
article=article,
allow_flagging='auto',
examples=examples,
cache_examples=True,
)
io.launch()
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