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import streamlit as st
import tensorflow as tf
from PIL import Image
import numpy as np
import cv2
import matplotlib.pyplot as plt
model=tf.keras.models.load_model("dental_xray_seg.h5")
st.header("Segmentation of Teeth in Panoramic X-ray Image")
examples=["teeth_01.png","teeth_02.png","teeth_03.png","teeth_04.png","teeth_05.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 Panoramic X-ray Image Image")
image_file = st.file_uploader("Upload Images", type=["png","jpg","jpeg"])
col1, col2, col3, col4, col5 = st.columns(5)
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]
with col4:
ex2=load_image(examples[3])
st.image(ex2,width=200)
if st.button('Example 4'):
image_file=examples[3]
with col5:
ex2=load_image(examples[4])
st.image(ex2,width=200)
if st.button('Example 5'):
image_file=examples[4]
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 ! ....")
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))
predict_img=model.predict(img_cv)
# predict=predict_img[1,:,:,0]
plt.imsave("/content/predict.png",predict_img)
## Plotting - Пример результата
img = cv2.imread(image_file)
predict1 = cv2.resize(predict, (img.shape[1], img.shape[0]), interpolation=cv2.INTER_LANCZOS4)
mask = np.uint8(predict1 * 255)
_, mask = cv2.threshold(mask, thresh=255/2, maxval=255, type=cv2.THRESH_BINARY)
cnts, hierarchy = cv2.findContours(mask, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
img = cv2.drawContours(img, cnts, -1, (255, 0, 0), 2)
cv2_imshow(img)
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