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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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model=tf.keras.models.load_model("dental_xray_seg.h5")
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st.header("Segmentation of Teeth in Panoramic X-ray Image Using UNet")
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link='Check Out Our Github Repo ! [link](https://github.com/SerdarHelli/Segmentation-of-Teeth-in-Panoramic-X-ray-Image-Using-U-Net)'
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st.markdown(link,unsafe_allow_html=True)
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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=img[:,:,0]
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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 Image")
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image_file = st.file_uploader("Upload Images", type=["png","jpg","jpeg"])
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if image_file is not None:
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file_details = {"filename":image_file.name, "filetype":image_file.type,
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"filesize":image_file.size}
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st.write(file_details)
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img=load_image(image_file)
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st.text("Making A Prediction ....")
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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=255/2, maxval=255, type=cv2.THRESH_BINARY+cv2.THRESH_OTSU)
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cnts,hieararch=cv2.findContours(mask,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)
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output = cv2.drawContours(convert_one_channel(img), cnts, -1, (255, 0, 0) , 2)
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if output is not None :
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st.subheader("Predicted Image")
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st.image(output,width=850)
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st.text("DONE ! ....")
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