Upload app.py

app.py

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