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| import gradio as gr | |
| def greet(name): | |
| return "Hello " + name | |
| title = "A Machine Learning Strategy for Automatic Phenotyping of High Risk Pregnancies" | |
| description = """ | |
| The bot was trained to segment, measure and make informed prediction of high risk pregnancy based off of what fetal skull Head circumference (HC) can imply! | |
| """ | |
| # <img src="https://huggingface.co/spaces/course-demos/Rick_and_Morty_QA/resolve/main/rick.png" width=200px> | |
| article = "Check out [the github repository](https://github.com/MarkTLite) that this website and model are based off of." | |
| import cv2, math | |
| import matplotlib.pyplot as plt | |
| import numpy as np | |
| from tensorflow.keras.utils import normalize | |
| from tensorflow.keras.models import load_model | |
| from skimage import measure | |
| def predict(input_img): | |
| input_img = input_img.reshape((256,256,1)) | |
| test_normalized_image = normalize(input_img, axis=1) | |
| # load model | |
| model = load_model('model-best.h5',compile=False) | |
| model.compile(optimizer='adam', loss = "binary_crossentropy") | |
| test_img = test_normalized_image | |
| orig_img = input_img | |
| test_img_norm=test_img[:,:,0] | |
| test_img_input=np.expand_dims(test_img_norm, 0) | |
| # Predict and threshold for values above 0.08 probability | |
| prediction = (model.predict(test_img_input) > 0.08).astype(np.uint8) | |
| prediction = prediction[0] | |
| label_image = measure.label(prediction, connectivity=orig_img.ndim) | |
| fig, ax = plt.subplots() | |
| ax.imshow(label_image[:,:,0], cmap=plt.cm.gray) | |
| regions = measure.regionprops(label_image[:,:,0]) | |
| prev_hc, hc = 0,0 | |
| for props in regions: | |
| y0, x0 = props.centroid | |
| orientation = props.orientation | |
| x1 = x0 + math.cos(orientation) * 0.5 * props.minor_axis_length | |
| y1 = y0 - math.sin(orientation) * 0.5 * props.minor_axis_length | |
| x2 = x0 - math.sin(orientation) * 0.5 * props.major_axis_length | |
| y2 = y0 - math.cos(orientation) * 0.5 * props.major_axis_length | |
| minor_distance = ((x0 - x1)**2 + (y0 - y1)**2)**0.5 | |
| print(minor_distance*2) | |
| major_distance = ((x0 - x2)**2 + (y0 - y2)**2)**0.5 | |
| print(major_distance*2) | |
| prev_hc = 1.62*(minor_distance+major_distance) | |
| if(prev_hc>hc): | |
| hc = prev_hc | |
| print("HC = ",hc, " mm") | |
| ax.plot((x0, x1), (y0, y1), '-r', linewidth=2.5) | |
| ax.plot((x0, x2), (y0, y2), '-r', linewidth=2.5) | |
| ax.plot(x0, y0, '.g', markersize=15) | |
| plt.show() | |
| # Overlap prediction on original image | |
| drawn_img = cv2.cvtColor(orig_img, cv2.COLOR_GRAY2BGR) | |
| contours, hierarchy = cv2.findContours(prediction,cv2.RETR_TREE,cv2.CHAIN_APPROX_NONE) | |
| cv2.drawContours(drawn_img, contours, -1, (255,0,0), 2) | |
| return drawn_img, "Head Circumference = " + str(hc) + " mm" | |
| examples = [ | |
| ['image.png'] | |
| ] | |
| gr.Interface(predict,gr.Image(shape=(256, 256), image_mode='L'), [gr.outputs.Image(type='plot'),'text'], | |
| description=description, article=article, title=title, examples=examples, analytics_enabled=False).launch() |