import cv2

import cv2 as cv # I use this import to make the calls easier.

import numpy as np

from matplotlib import pyplot as plt

import mahotas

import mahotas as mh #typical import

import argparse

image = cv2.imread(image)

image = cv2.imread(image, 0)

image = cv2.imread(image, cv2.IMREAD_GRAYSCALE) #both commands do the same thing

(h, w, c) = image.shape #h: Height, w: Width, c: Channels

size = image.size #Number of pixels

imgtype = image.dtype #Image type

max = image.max() #Maximum value

min = image.min() #Minimum value

cv2.namedWindow(window_name, property) #creates a window with specific properties:

#cv2.WINDOW_NORMAL :User can resize the window (no constrain)

#cv2.WINDOW_AUTOSIZE: Window size is automatically adjusted

#cv2.WINDOW_OPENGL: Window created with OPENGL support

cv2.imshow(window_name, image) #display image, automatic window properties

cv2.destroyWindow(window_name) #Closes specified window

cv2.destroyAllWindows() #Close all opencv windows

cv2.waitKey() #Display the windows and wait for user keypress

key = cv.waitKey(0) &0xFF #Display the windows and store keypress

img = cv2.imread(image, 0)

plt.imshow(img, cmap = 'gray', interpolation = 'bicubic')

plt.xticks([]), plt.yticks([]) # to hide tick values on X and Y axis

plt.show()

gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)

blurred = cv2.GaussianBlur(image, (5, 5), 0) #image: source image

#(5, 5): Kernel size, must be positive and odd

blurred = cv2.blur(image, (5,5)) #image: Source image

#(5,5): Kernel size, must be positive and odd

blurred = cv2.medianBlur(image, 5) #image: Source image

#5: Kernel size, most be positive and odd

blurred = mahotas.gaussian_filter(image, 8) #imageö Source image

#8: standard deviation for Gaussian kernel (in pixel units)

(T, thresh) = cv2.threshold(src, C, 255, cv2.THRESH_BINARY) #src: source image

#C: index (as explained above)

#255: max value to assign if value greater than C

#Method: cv2.THRESH_BINARY

# cv2.THRESH_BINARY_INV

#Returns: T: max value

# thresh: Thresholded image

(T, thresh) = cv2.threshold(blurred, 155, 255, cv2.THRESH_BINARY)

(T, thresh) = cv2.threshold(blurred, 155, 255, cv2.THRESH_BINARY_INV)

image = cv.resize(image, None, fx = 0.5, fy = 0.5, interpolation = cv.INTER_AREA)

"""

result = cv.addWeighted(image, alpha, np.zeros(image.shape, image.dtype), beta, gamma)