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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_allRnd_0317() | |
| 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;">ML-SIM: Reconstruction of SIM images with deep learning</h1>' | |
| description = """ | |
| This space demonstrates the ML-SIM method for reconstruction of structured illumination microscopy images. | |
| ### <a href="https://opg.optica.org/boe/viewmedia.cfm?uri=boe-12-5-2720&html=true" target='_blank' > ML-SIM: universal reconstruction of structured illumination microscopy images using transfer learning </a> | |
| _Charles N. Christensen<sup>1,2,*</sup>, Edward N. Ward<sup>1</sup>, Meng Lu<sup>1</sup>, Pietro Lio<sup>2</sup>, Clemens F. Kaminski_</br></br> | |
| <sup>1</sup>University of Cambridge, Department of Chemical Engineering and Biotechnology, Laser Analytics Group</br> | |
| <sup>2</sup>University of Cambridge, Department of Computer Science and Technology, Artificial Intelligence Group</br> | |
| - GitHub: [charlesnchr](http://github.com/charlesnchr) | |
| - Email: charles.n.chr@gmail.com | |
| - Publication: <a href='https://opg.optica.org/boe/viewmedia.cfm?uri=boe-12-5-2720&html=true' | |
| target='_blank'>Journal</a>, <a href='https://arxiv.org/abs/2003.11064' target='_blank'>Preprint</a> | |
| --- | |
| ## 🔬 To run ML-SIM | |
| Upload a TIFF image and hit submit or select one from the examples below. Note that the model here is trained for SIM stacks of 9 frames (3x3 configuration, i.e. 3 phase steps for 3 orientations). | |
| """ | |
| article = """ | |
| --- | |
| Select an example from the list above to try the model on a test image. Below you can see what the file names correspond to. | |
| The ML-SIM test images are from two different microscopes (MAI-SIM and SLM-SIM) in addition to two simulated images. | |
| Wide-field projections in pseudo-colour of these examples are shown below: | |
|  | |
| --- | |
| ### Read more | |
| - <a href='https://ML-SIM.com' target='_blank'>ML-SIM.com</a> | |
| - <a href='https://charles-christensen.com' target='_blank'>Website</a> | |
| - <a href='https://github.com/charlesnchr/ML-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 (ML-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() | |