--- a
+++ b/Finalcode.py
@@ -0,0 +1,98 @@
+import cv2
+import argparse
+import numpy as np
+import matplotlib.pyplot as plt
+%matplotlib inline
+
+s = r'C:\Users\Arnab Sinha\Documents\GitHub\Kidney-Stone-Detection-IP\images'
+image_no = '\image1.jpg'
+s = s + image_no
+
+img = cv2.imread(s,0)
+
+def build_filters():
+    #returns a list of kernels in several orientations
+    filters = []
+    ksize = 31
+    for theta in np.arange(0, np.pi, np.pi / 32):
+        params = {'ksize': (ksize, ksize), 'sigma': 0.0225, 'theta': theta, 'lambd': 15.0,
+                  'gamma': 0.01, 'psi': 0, 'ktype': cv2.CV_32F}
+        
+        kern = cv2.getGaborKernel(**params)
+        kern /= 1.5*kern.sum()
+        filters.append((kern, params))
+    return filters
+
+
+def process(img, filters):
+    #returns the img filtered by the filter list
+    accum = np.zeros_like(img)
+    for kern, params in filters:
+        fimg = cv2.filter2D(img, cv2.CV_8UC3, kern)
+        np.maximum(accum, fimg, accum)
+    return accum
+
+def Histeq(img):
+    equ = cv2.equalizeHist(img)
+    return equ
+
+def GaborFilter(img):
+    filters = build_filters()
+    p = process(img, filters)
+    return p
+
+def Laplacian(img,par):  
+    lap = cv2.Laplacian(img,cv2.CV_64F)
+    sharp = img - par*lap
+    sharp = np.uint8(cv2.normalize(sharp, None, 0 , 255, cv2.NORM_MINMAX))
+    return sharp
+
+def Watershed(img):
+    ret, thresh = cv2.threshold(img,0,255,cv2.THRESH_BINARY+cv2.THRESH_OTSU)
+
+    # noise removal
+    kernel = np.ones((3,3),np.uint8)
+    opening = cv2.morphologyEx(thresh,cv2.MORPH_OPEN,kernel, iterations = 2)
+
+    # sure background area
+    sure_bg = cv2.dilate(opening,kernel,iterations=3)
+
+    # Finding sure foreground area
+    dist_transform = cv2.distanceTransform(opening,cv2.DIST_L2,5)
+    ret, sure_fg = cv2.threshold(dist_transform,0.23*dist_transform.max(),255,0)
+
+    # Finding unknown region
+    sure_fg = np.uint8(sure_fg)
+    unknown = cv2.subtract(sure_bg,sure_fg)
+    
+    # Marker labelling
+    ret, markers = cv2.connectedComponents(sure_fg)
+
+    # Add one to all labels so that sure background is not 0, but 1
+    markers = markers+1
+
+    # Now, mark the region of unknown with zero
+    markers[unknown==255] = 0
+
+    img2 = cv2.imread(s,1)
+    img2 = cv2.medianBlur(img2,5)
+    markers = cv2.watershed(img2,markers)
+    img2[markers == -1] = [255,0,0]
+
+    return img2
+
+if image_no=='\image1.jpg':
+    img3 = Laplacian(img,0.239)
+    
+elif image_no=='\image2.jpg':
+    img3 = GaborFilter(img)
+    img3 = Histeq(img3)
+
+elif image_no=='\image4.jpg':
+    img3 = GaborFilter(img)
+
+img3 = Watershed(img)
+
+plt.imshow(img3,'gray')
+plt.title('Marked')
+plt.xticks([]),plt.yticks([])