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