Duplicate from MarkTLite/fetal-unet

Co-authored-by: Mark T <[email protected]>

.gitattributes

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README.md

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+---
+title: Fetal Unet
+emoji: 👀
+colorFrom: purple
+colorTo: purple
+sdk: gradio
+sdk_version: 3.1.4
+app_file: app.py
+pinned: false
+duplicated_from: MarkTLite/fetal-unet
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

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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()

model-best.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:2b8c26eac12162837a3398994131636564aaf20bda7728fe7ddce060d639b8c7
+size 23623240

requirements.txt

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+opencv-python==4.6.0.66
+matplotlib==3.5.2
+numpy==1.23.1
+tensorflow==2.9.1
+scikit-image==0.19.3