import streamlit as st 

import tensorflow as tf
from PIL import Image
import numpy as np
import cv2

model=tf.keras.models.load_model("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("Upload Dental Panoramic X-ray Image 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 ! ....")