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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 onnx_segment_membrane(input_image, threshold): | |
| ort_session = ort.InferenceSession('onnx_models/membrane_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((356, 256), Image.NEAREST)#.convert("L") | |
| centroid_img = generate_centroid_image(np.array(onnx_outputs[0][0][0])) *255 | |
| resized_centroid_img = Image.fromarray(centroid_img.astype(np.uint8)).resize((356, 256), Image.NEAREST) | |
| return(resized_ret, resized_centroid_img) | |
| def generate_centroid_image(thresh): | |
| thresh = cv2.blur(thresh, (5,5)) | |
| thresh = thresh.astype(np.uint8) | |
| centroid_image = np.zeros(thresh.shape) | |
| cnts = cv2.findContours(thresh, cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_SIMPLE) | |
| cnts = imutils.grab_contours(cnts) | |
| centroids = [] | |
| for c in cnts: | |
| try: | |
| # compute the center of the contour | |
| M = cv2.moments(c) | |
| cX = int(M["m10"] / M["m00"]) | |
| cY = int(M["m01"] / M["m00"]) | |
| # draw the contour and center of the shape on the image | |
| # cv2.drawContours(centroid_image, [c], -1, (255, 255, 255), 2) | |
| cv2.circle(centroid_image, (cX, cY), 2, (1, 1, 1), -1) | |
| centroids.append((cX, cY)) | |
| except: | |
| pass | |
| return(centroid_image) | |
| 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) | |
| def cell_membrane_segmentation(): | |
| selected_box2 = st.sidebar.selectbox( | |
| 'Choose Example Input', | |
| ('Example_1.png','Example_2.png') | |
| ) | |
| st.title('Cell Membrane Segmentation') | |
| instructions = """ | |
| Segment Cell Membrane from C. elegans embryo imaging data \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/cell_membrane_segmentation_examples/'+selected_box2) | |
| threshold = st.sidebar.slider("Select Threshold (Applied on model output)", 0.0, 1.0, 0.1) | |
| col1, col2, col3 = st.columns(3) | |
| 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() | |
| model_output = onnx_segment_membrane(np.array(input), threshold) | |
| fig2 = px.imshow(model_output[0], 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) | |
| fig3 = px.imshow(model_output[1], binary_string=True, labels=dict(x="Centroid Map")) | |
| fig3.update_layout(margin=dict(l=0, r=0, b=0, t=0)) | |
| col3.plotly_chart(fig3, use_container_width=True) |