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app.py

@@ -1,100 +1,199 @@
-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("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)
-
-
-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 = 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:
-
-      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 ! ....")
+import streamlit as st 
+import tensorflow as tf
+from PIL import Image
+import numpy as np
+import cv2
+import matplotlib.pyplot as plt
+from imutils import perspective
+from scipy.spatial import distance as dist
+
+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"]
+
+def load_image(image_file):
+	img = Image.open(image_file)
+	img_gray = img.convert('L')
+	img_np = np.array(img_gray)
+	return img_np
+
+def convert_one_channel(img):
+    if len(img.shape)>2:
+        img= cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
+    return img
+    
+def convert_rgb(img):
+    if len(img.shape)==2:
+        img= cv2.cvtColor(img,cv2.COLOR_GRAY2RGB)  
+    return img
+
+def midpoint(ptA, ptB):
+	return ((ptA[0] + ptB[0]) * 0.5, (ptA[1] + ptB[1]) * 0.5)
+
+def CCA_Analysis(orig_image,predict_image,erode_iteration,open_iteration):
+    kernel1 =( np.ones((5,5), dtype=np.float32))
+    kernel_sharpening = np.array([[-1,-1,-1],
+                                  [-1,9,-1],
+                                 [-1,-1,-1]])
+    image = predict_image
+    image2 =orig_image
+    image=cv2.morphologyEx(image, cv2.MORPH_OPEN, kernel1,iterations=open_iteration )
+    image = cv2.filter2D(image, -1, kernel_sharpening)
+    image=cv2.erode(image,kernel1,iterations =erode_iteration)
+    image=cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
+    thresh = cv2.threshold(image, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)[1]
+    labels=cv2.connectedComponents(thresh,connectivity=8)[1]
+    a=np.unique(labels)
+    count2=0
+    for label in a:
+        if label == 0:
+            continue
+
+        # Create a mask
+        mask = np.zeros(thresh.shape, dtype="uint8")
+        mask[labels == label] = 255
+        # Find contours and determine contour area
+        cnts,hieararch = cv2.findContours(mask.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
+        cnts = cnts[0]
+        c_area = cv2.contourArea(cnts)
+        # threshhold for tooth count
+        if c_area>2000:
+            count2+=1
+
+        (x,y),radius = cv2.minEnclosingCircle(cnts)
+        rect = cv2.minAreaRect(cnts)
+        box = cv2.boxPoints(rect)
+        box = np.array(box, dtype="int")
+        box = perspective.order_points(box)
+        color1 = (list(np.random.choice(range(150), size=3)))
+        color =[int(color1[0]), int(color1[1]), int(color1[2])]
+        cv2.drawContours(image2,[box.astype("int")],0,color,2)
+        (tl,tr,br,bl)=box
+
+        (tltrX,tltrY)=midpoint(tl,tr)
+        (blbrX,blbrY)=midpoint(bl,br)
+    	# compute the midpoint between the top-left and top-right points,
+    	# followed by the midpoint between the top-righ and bottom-right
+        (tlblX,tlblY)=midpoint(tl,bl)
+        (trbrX,trbrY)=midpoint(tr,br)
+    	# draw the midpoints on the image
+        cv2.circle(image2, (int(tltrX), int(tltrY)), 5, (255, 0, 0), -1)
+        cv2.circle(image2, (int(blbrX), int(blbrY)), 5, (255, 0, 0), -1)
+        cv2.circle(image2, (int(tlblX), int(tlblY)), 5, (255, 0, 0), -1)
+        cv2.circle(image2, (int(trbrX), int(trbrY)), 5, (255, 0, 0), -1)
+        cv2.line(image2, (int(tltrX), int(tltrY)), (int(blbrX), int(blbrY)),color, 2)
+        cv2.line(image2, (int(tlblX), int(tlblY)), (int(trbrX), int(trbrY)),color, 2)
+        dA = dist.euclidean((tltrX, tltrY), (blbrX, blbrY))
+        dB = dist.euclidean((tlblX, tlblY), (trbrX, trbrY))
+
+
+
+        pixelsPerMetric=1
+        dimA = dA * pixelsPerMetric
+        dimB = dB *pixelsPerMetric
+        cv2.putText(image2, "{:.1f}pixel".format(dimA),(int(tltrX - 15), int(tltrY - 10)), cv2.FONT_HERSHEY_SIMPLEX,0.65, color, 2)
+        cv2.putText(image2, "{:.1f}pixel".format(dimB),(int(trbrX + 10), int(trbrY)), cv2.FONT_HERSHEY_SIMPLEX,0.65, color, 2)
+        cv2.putText(image2, "{:.1f}".format(label),(int(tltrX - 35), int(tltrY - 5)), cv2.FONT_HERSHEY_SIMPLEX,0.65, color, 2)
+    teeth_count=count2
+    return image2,teeth_count    
+    
+def detect_decays_static_th(images, dental_masks=None, threshhold=0.9):
+    decay_masks = []
+    for image, dental_mask in zip(images, dental_masks):
+        decay_mask = np.zeros_like(dental_mask)
+        image_masked_with_dental_mask = image * dental_mask
+        decay_mask[image_masked_with_dental_mask > threshhold*255] = 1
+        decay_masks.append(decay_mask)
+    decay_masks = np.array(decay_masks)
+    return decay_masks    
+    
+    
+st.subheader("Upload Dental Panoramic X-ray Image")
+image_file = st.file_uploader("Upload Images", type=["png","jpg","jpeg"])
+
+col1, col2, col3, col4 = st.columns(4)
+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]
+
+if image_file is not None:
+
+    image_original = Image.open(image_file) 
+    image=np.asarray(image_original)
+    image = convert_rgb(image)
+    st.subheader("Original Image")  
+    st.image(image,width=1100)
+
+    st.text("Making A Prediction ....")
+      
+    img=np.asarray(image)
+
+    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_rgb = np.expand_dims(predicted, axis=-1)
+    plt.imsave("predict.png",predicted_rgb)
+    
+    predict1 = cv2.resize(predicted, (img.shape[1], img.shape[0]), interpolation=cv2.INTER_LANCZOS4)
+    img_dc=convert_one_channel(img)
+    decay_mask = detect_decays_static_th(img_dc, predict1)
+    
+    
+    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, (0, 0, 255), 2)
+    
+    mask = np.uint8(decay_mask * 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.fillPoly(img, cnts, (255, 0, 0))
+    
+    if img is not None :      
+        st.subheader("Predicted teeth shape + caries zones")  
+        st.write(img.shape)
+        st.image(img,width=1100)
+    
+    image=np.asarray(image_original)
+    image = convert_rgb(image)
+    if image.shape[1] < 3000:
+        image = cv2.resize(image,(3100,1150),interpolation=cv2.INTER_LANCZOS4)
+    predicted=cv2.imread("predict.png")
+    predicted = cv2.resize(predicted, (image.shape[1],image.shape[0]), interpolation=cv2.INTER_LANCZOS4)
+    cca_result,teeth_count=CCA_Analysis(image,predicted,3,2)
+
+    if cca_result is not None :      
+        st.subheader("Seperate predicted teeth")  
+        st.write(cca_result.shape)
+        st.image(cca_result,width=1100)
+
+    st.text("Teeth Count = " + str(teeth_count))
+
+    st.text("DONE ! ....")
+

requirements.txt

@@ -4,4 +4,5 @@ Pillow
 scipy
 streamlit
 tensorflow
-opencv-python-headless
+opencv-python-headless
+matplotlib