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| import streamlit as st | |
| import numpy as np | |
| import pandas as pd | |
| from PIL import Image | |
| from pathlib import Path | |
| import joblib | |
| import numpy as np | |
| import cv2 | |
| import onnxruntime as ort | |
| import imutils | |
| # import matplotlib.pyplot as plt | |
| import pandas as pd | |
| import plotly.express as px | |
| def nucleus_segmentation(): | |
| selected_box2 = st.sidebar.selectbox( | |
| 'Choose Example Input', | |
| ('Example_1.png','Example_2.png') | |
| ) | |
| st.title('Nucleus Segmentation') | |
| instructions = """ | |
| Segment Nucleii from fluorescence microscopy imagery data (C. elegans embryo) \n | |
| Either upload your own image or select from the sidebar to get a preconfigured image. | |
| The image you select or upload will be fed through the Deep Neural Network in real-time | |
| and the output will be displayed to the screen. | |
| """ | |
| st.text(instructions) | |
| file = st.file_uploader('Upload an image or choose an example') | |
| example_image = Image.open('./images/nucleus_segmentation_examples/'+selected_box2) | |
| threshold = st.sidebar.slider("Select Threshold (Applied on model output)", 0.0, 1.0, 0.1) | |
| col1, col2= st.columns(2) | |
| if file: | |
| input = Image.open(file) | |
| fig1 = px.imshow(input, binary_string=True, labels=dict(x="Input Image")) | |
| fig1.update(layout_coloraxis_showscale=False) | |
| fig1.update_layout(margin=dict(l=0, r=0, b=0, t=0)) | |
| col1.plotly_chart(fig1, use_container_width=True) | |
| else: | |
| input = example_image | |
| fig1 = px.imshow(input, binary_string=True, labels=dict(x="Input Image")) | |
| fig1.update(layout_coloraxis_showscale=False) | |
| fig1.update_layout(margin=dict(l=0, r=0, b=0, t=0)) | |
| col1.plotly_chart(fig1, use_container_width=True) | |
| pressed = st.button('Run') | |
| if pressed: | |
| st.empty() | |
| fig2 = px.imshow(onnx_segment_nucleus(np.array(input), threshold), binary_string=True, labels=dict(x="Segmentation Map")) | |
| fig2.update_layout(margin=dict(l=0, r=0, b=0, t=0)) | |
| col2.plotly_chart(fig2, use_container_width=True) | |
| def onnx_segment_nucleus(input_image, threshold): | |
| ort_session = ort.InferenceSession('onnx_models/nucleus_segmentor.onnx') | |
| img = Image.fromarray(np.uint8(input_image)) | |
| resized = img.resize((256, 256), Image.NEAREST) | |
| img_unsqueeze = expand_dims_twice(resized) | |
| onnx_outputs = ort_session.run(None, {'input': img_unsqueeze.astype('float32')}) | |
| binarized = 1.0 * (onnx_outputs[0][0][0] > threshold) | |
| resized_ret = Image.fromarray(binarized.astype(np.uint8) ).resize((708, 512), Image.NEAREST)#.convert("L") | |
| return(resized_ret) | |
| def expand_dims_twice(arr): | |
| norm=(arr-np.min(arr))/(np.max(arr)-np.min(arr)) | |
| ret = np.expand_dims(np.expand_dims(norm, axis=0), axis=0) | |
| return(ret) |