Update app.py

app.py

@@ -1,5 +1,3 @@
-
-
 import streamlit as st 
 
 import tensorflow as tf
@@ -11,7 +9,7 @@ model=tf.keras.models.load_model("dental_xray_seg.h5")
 
 st.header("Segmentation of Teeth in Panoramic X-ray Image Using UNet")
 
-
+examples=["107.png","108.png","109.png"]
 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)
 
@@ -28,13 +26,42 @@ def convert_one_channel(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 Panoramic X-ray Image Image")
 image_file = st.file_uploader("Upload Images", type=["png","jpg","jpeg"])
-      
+
+
+col1, col2, col3 = st.columns(3)
+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]
+
+
+with col3:
+    ex2=load_image(examples[2])
+    st.image(ex2,width=200)
+    if st.button('Example 3'):
+        image_file=examples[2]
+    
+    
 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 ....")
@@ -51,14 +78,17 @@ if image_file is not None:
       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)
+      _, 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_one_channel(img), cnts, -1, (255, 0, 0) , 2)
+      output = cv2.drawContours(convert_rgb(img), cnts, -1, (255, 0, 0) , 3)
+
 
       if output is not None :      
-          st.subheader("Predicted Image")      
+          st.subheader("Predicted Image")  
+          st.write(output.shape)
           st.image(output,width=850)
 
       st.text("DONE ! ....")
-
-