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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 | |
| from NNfunctions import * | |
| opt = GetOptions_allRnd_0317() | |
| net = LoadModel(opt) | |
| def predict(image): | |
| img = np.array(image) | |
| img = np.concatenate((img,img,img),axis=2) | |
| img = np.transpose(img, (2,0,1)) | |
| # sr,wf,out = EvaluateModel(net,opt,img,outfile) | |
| sr_img = EvaluateModel(net,opt,img) | |
| return sr_img | |
| title = '<h1 style="text-align: center;">ML-SIM: Reconstruction of SIM images with deep learning</h1>' | |
| description = """ | |
| ## About | |
| This space demonstrates the use of a semantic segmentation model to segment pets and classify them | |
| according to the pixels. | |
| ## 🚀 To run | |
| Upload a pet image and hit submit or select one from the given examples | |
| """ | |
| inputs = gr.inputs.Image(label="Upload a TIFF image", type = 'pil', optional=False) | |
| outputs = [ | |
| gr.outputs.Image(label="SIM Reconstruction") | |
| # , gr.outputs.Textbox(type="auto",label="Pet Prediction") | |
| ] | |
| examples = [ | |
| "./examples/dogcat.jpeg", | |
| ] | |
| interface = gr.Interface(fn=predict, | |
| inputs=inputs, | |
| outputs=outputs, | |
| title = title, | |
| description=description, | |
| examples=examples | |
| ) | |
| interface.launch() | |