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''' ----------------------------------------
* Creation Time : Sun Aug 28 21:38:58 2022
* Last Modified : Sun Aug 28 21:41:36 2022
* Author : Charles N. Christensen
* Github : github.com/charlesnchr
----------------------------------------'''
from turtle import title
import gradio as gr
import numpy as np
from PIL import Image
import io
import base64
import skimage
from NNfunctions import *
opt = GetOptions_Swin_2702()
net = LoadModel(opt)
gr.close_all()
def predict(imagefile):
# img = np.array(skimage.io.imread(imagefile.name))
# img = np.concatenate((img,img,img),axis=2)
# img = np.transpose(img, (2,0,1))
img = skimage.io.imread(imagefile.name)
# sr,wf,out = EvaluateModel(net,opt,img,outfile)
sr, wf, sr_download = EvaluateModel(net,opt,img)
return wf, sr, sr_download
def process_example(filename):
basename = os.path.basename(filename)
basename = basename.replace('.png','.tif')
img = skimage.io.imread('TestImages/%s' % basename)
sr, wf, sr_download = EvaluateModel(net,opt,img)
return wf, sr
title = '<h1 style="text-align: center;">VSR-SIM: Spatio-temporal reconstruction method for SIM using vision transformer</h1>'
description = """
This space demonstrates the VSR-SIM method for reconstruction of structured illumination microscopy images.
_Charles N. Christensen<sup>1,2
- GitHub: [charlesnchr](http://github.com/charlesnchr)
- Email: [email protected]
- Publication: <a href='https://arxiv.org/abs/2203.00030' target='_blank'>Preprint</a>
---
## 🔬 To run VSR-SIM
Upload a TIFF image and hit submit or select one from the examples below.
"""
article = """
---
### Read more
- <a href='https://VSR-SIM.github.io' target='_blank'>VSR-SIM.github.io</a>
- <a href='https://charles-christensen.com' target='_blank'>Website</a>
- <a href='https://github.com/charlesnchr/VSR-SIM' target='_blank'>Github</a>
- <a href='https://twitter.com/charlesnchr' target='_blank'>Twitter</a>
"""
# inputs = gr.inputs.Image(label="Upload a TIFF image", type = 'pil', optional=False)
inputs = gr.inputs.File(label="Upload a TIFF image", type = 'file', optional=False)
outputs = [
gr.outputs.Image(label="INPUT (Wide-field projection)"),
gr.outputs.Image(label="OUTPUT (VSR-SIM)"),
gr.outputs.File(label="Download SR image" )
# , gr.outputs.Textbox(type="auto",label="Pet Prediction")
]
examples = glob.glob('*.tif')
interface = gr.Interface(fn=predict,
inputs=inputs,
outputs=outputs,
title = title,
description=description,
article=article,
examples=examples,
allow_flagging='never',
cache_examples=False
)
interface.launch()
# with gr.Blocks() as interface:
# gr.Markdown(title)
# gr.Markdown(description)
# with gr.Row():
# input1 = gr.inputs.File(label="Upload a TIFF image", type = 'file', optional=False)
# submit_btn = gr.Button("Reconstruct")
# with gr.Row():
# output1 = gr.outputs.Image(label="Wide-field projection")
# output2 = gr.outputs.Image(label="SIM Reconstruction")
# output3 = gr.File(label="Download SR image", visible=False)
# submit_btn.click(
# predict,
# input1,
# [output1, output2, output3]
# )
# gr.Examples(examples, input1, [output1, output2, output3])
# interface.launch()
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