add app.py

app.py

@@ -0,0 +1,146 @@
+# Libraries
+import streamlit as st
+from tensorflow import keras
+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
+import os
+
+# Constants
+MODEL_PATH = 'model.h5'
+IMAGE_DIR = 'images'
+
+# Functions 
+def load_image(image_file):
+    img = Image.open(image_file)
+    return img
+
+def midpoint(ptA, ptB):
+    return ((ptA[0] + ptB[0]) /2 , (ptA[1] + ptB[1]) /2)
+
+def draw_dimensions(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)#original
+
+    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)
+        
+        (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))
+
+        global dimA
+        dimA = dA*0.08
+        global dimB
+        dimB = dB*0.08
+        cv2.putText(image2, "{:.1f} millimeter".format(dimA),(int(tltrX - 10), int(tltrY - 10)), cv2.FONT_HERSHEY_SIMPLEX,0.65, (0,0,0), 2)
+        cv2.putText(image2, "{:.1f} millimeter".format(dimB),(int(trbrX + 10), int(trbrY+10)), cv2.FONT_HERSHEY_SIMPLEX,0.65,(0,0,0), 2)
+
+    return image2 
+
+def segment_molar(image_file):
+
+    img=load_image(image_file)
+      
+    img = np.asarray(img)     
+    img = cv2.resize(img, (512, 256))
+    img = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
+    img = np.expand_dims(img, axis=0)
+
+    prediction = model.predict(img)
+
+    output = prediction.reshape(256,512)
+
+    return img , output
+
+def measure_molar(image_file):
+
+    img=load_image(image_file)
+      
+    img = np.asarray(img) 
+    img_prd= cv2.resize(img , (512, 256))
+    img_prd = cv2.cvtColor(img_prd, cv2.COLOR_RGB2GRAY)
+    img_prd = np.expand_dims(img_prd, axis=0)
+    prediction = model.predict(img_prd)
+    prediction=prediction*255
+    prediction = prediction.astype("uint8")
+    prediction_img=prediction.reshape(256,512)
+    img2 = np.zeros( ( np.array(prediction_img).shape[0], np.array(prediction_img).shape[1], 3 ) )
+    img2[:,:,0] = prediction_img 
+    img2[:,:,1] = prediction_img
+    img2[:,:,2] = prediction_img
+    img2 = img2.astype("uint8")
+    predicted = cv2.resize(img2 , (img.shape[1],img.shape[0]), interpolation=cv2.INTER_LANCZOS4)
+    output=draw_dimensions(img,predicted,3,2)
+
+    return output
+
+# Load model and images
+model = keras.models.load_model(MODEL_PATH)
+images = os.listdir(IMAGE_DIR)
+images = [f'{IMAGE_DIR}/image' for image in images]
+
+# UserInterface
+st.header("3rdMolar Segmentation")
+st.subheader("Select Image:")
+image_file = st.selectbox('Select Image' , images)
+
+if image_file is not None:
+
+    img , output1 = segment_molar(image_file)
+    output2 = measure_molar(image_file)
+    
+    st.image(img,width=850)
+    st.image(output1,width=850)
+    st.image(output2,width=850)
+