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([])