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.beta_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)