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import streamlit as st |
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import tensorflow as tf |
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from PIL import Image |
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import numpy as np |
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import cv2 |
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from huggingface_hub import from_pretrained_keras |
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try: |
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model=from_pretrained_keras("SerdarHelli/Segmentation-of-Teeth-in-Panoramic-X-ray-Image-Using-U-Net") |
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except: |
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model=tf.keras.models.load_model("dental_xray_seg.h5") |
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pass |
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st.title('Denta.tn') |
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st.header("Dentavoon X-ray") |
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examples=["107.png","108.png","109.png"] |
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def load_image(image_file): |
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img = Image.open(image_file) |
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return img |
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def convert_one_channel(img): |
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if len(img.shape)>2: |
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img= cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) |
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return img |
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else: |
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return img |
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def convert_rgb(img): |
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if len(img.shape)==2: |
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img= cv2.cvtColor(img,cv2.COLOR_GRAY2RGB) |
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return img |
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else: |
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return img |
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st.subheader("Upload Dental Panoramic X-ray Image") |
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image_file = st.file_uploader("Upload Images", type=["png","jpg","jpeg"]) |
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col1, col2, col3 = st.columns(3) |
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with col1: |
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ex=load_image(examples[0]) |
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st.image(ex,width=200) |
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if st.button('Example 1'): |
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image_file=examples[0] |
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with col2: |
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ex1=load_image(examples[1]) |
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st.image(ex1,width=200) |
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if st.button('Example 2'): |
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image_file=examples[1] |
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with col3: |
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ex2=load_image(examples[2]) |
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st.image(ex2,width=200) |
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if st.button('Example 3'): |
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image_file=examples[2] |
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if image_file is not None: |
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img=load_image(image_file) |
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st.text("Predecting ....") |
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st.image(img,width=850) |
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img=np.asarray(img) |
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img_cv=convert_one_channel(img) |
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img_cv=cv2.resize(img_cv,(512,512), interpolation=cv2.INTER_LANCZOS4) |
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img_cv=np.float32(img_cv/255) |
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img_cv=np.reshape(img_cv,(1,512,512,1)) |
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prediction=model.predict(img_cv) |
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predicted=prediction[0] |
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predicted = cv2.resize(predicted, (img.shape[1],img.shape[0]), interpolation=cv2.INTER_LANCZOS4) |
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mask=np.uint8(predicted*255) |
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_, mask = cv2.threshold(mask, thresh=0, maxval=255, type=cv2.THRESH_BINARY+cv2.THRESH_OTSU) |
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kernel =( np.ones((5,5), dtype=np.float32)) |
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mask=cv2.morphologyEx(mask, cv2.MORPH_OPEN, kernel,iterations=1 ) |
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mask=cv2.morphologyEx(mask, cv2.MORPH_CLOSE, kernel,iterations=1 ) |
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cnts,hieararch=cv2.findContours(mask,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE) |
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output = cv2.drawContours(convert_rgb(img), cnts, -1, (255, 0, 0) , 3) |
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if output is not None : |
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st.subheader("Predicted Image") |
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st.write(output.shape) |
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st.image(output,width=850) |
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st.text("Output ....") |
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