Add Java and Python code for the following tutorials:

- Changing the contrast and brightness of an image!
      - Operations with images

samples/python/tutorial_code/core/mat_operations/mat_operations.py

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+from __future__ import division
+import cv2 as cv
+import numpy as np
+
+# Snippet code for Operations with images tutorial (not intended to be run)
+
+def load():
+    # Input/Output
+    filename = 'img.jpg'
+    ## [Load an image from a file]
+    img = cv.imread(filename)
+    ## [Load an image from a file]
+
+    ## [Load an image from a file in grayscale]
+    img = cv.imread(filename, cv.IMREAD_GRAYSCALE)
+    ## [Load an image from a file in grayscale]
+
+    ## [Save image]
+    cv.imwrite(filename, img)
+    ## [Save image]
+
+def access_pixel():
+    # Accessing pixel intensity values
+    img = np.empty((4,4,3), np.uint8)
+    y = 0
+    x = 0
+    ## [Pixel access 1]
+    intensity = img[y,x]
+    ## [Pixel access 1]
+
+    ## [Pixel access 3]
+    blue = img[y,x,0]
+    green = img[y,x,1]
+    red = img[y,x,2]
+    ## [Pixel access 3]
+
+    ## [Pixel access 5]
+    img[y,x] = 128
+    ## [Pixel access 5]
+
+def reference_counting():
+    # Memory management and reference counting
+    ## [Reference counting 2]
+    img = cv.imread('image.jpg')
+    img1 = np.copy(img)
+    ## [Reference counting 2]
+
+    ## [Reference counting 3]
+    img = cv.imread('image.jpg')
+    sobelx = cv.Sobel(img, cv.CV_32F, 1, 0);
+    ## [Reference counting 3]
+
+def primitive_operations():
+    img = np.empty((4,4,3), np.uint8)
+    ## [Set image to black]
+    img[:] = 0
+    ## [Set image to black]
+
+    ## [Select ROI]
+    smallImg = img[10:110,10:110]
+    ## [Select ROI]
+
+    ## [BGR to Gray]
+    img = cv.imread('image.jpg')
+    grey = cv.cvtColor(img, cv.COLOR_BGR2GRAY)
+    ## [BGR to Gray]
+
+    src = np.ones((4,4), np.uint8)
+    ## [Convert to CV_32F]
+    dst = src.astype(np.float32)
+    ## [Convert to CV_32F]
+
+def visualize_images():
+    ## [imshow 1]
+    img = cv.imread('image.jpg')
+    cv.namedWindow('image', cv.WINDOW_AUTOSIZE)
+    cv.imshow('image', img)
+    cv.waitKey()
+    ## [imshow 1]
+
+    ## [imshow 2]
+    img = cv.imread('image.jpg')
+    grey = cv.cvtColor(img, cv.COLOR_BGR2GRAY)
+    sobelx = cv.Sobel(grey, cv.CV_32F, 1, 0)
+    # find minimum and maximum intensities
+    minVal = np.amin(sobelx)
+    maxVal = np.amax(sobelx)
+    draw = cv.convertScaleAbs(sobelx, alpha=255.0/(maxVal - minVal), beta=-minVal * 255.0/(maxVal - minVal))
+    cv.namedWindow('image', cv.WINDOW_AUTOSIZE)
+    cv.imshow('image', draw)
+    cv.waitKey()
+    ## [imshow 2]

samples/python/tutorial_code/imgProc/changing_contrast_brightness_image/BasicLinearTransforms.py

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+from __future__ import print_function
+from builtins import input
+import cv2 as cv
+import numpy as np
+import argparse
+
+# Read image given by user
+## [basic-linear-transform-load]
+parser = argparse.ArgumentParser(description='Code for Changing the contrast and brightness of an image! tutorial.')
+parser.add_argument('--input', help='Path to input image.', default='../data/lena.jpg')
+args = parser.parse_args()
+
+image = cv.imread(args.input)
+if image is None:
+    print('Could not open or find the image: ', args.input)
+    exit(0)
+## [basic-linear-transform-load]
+
+## [basic-linear-transform-output]
+new_image = np.zeros(image.shape, image.dtype)
+## [basic-linear-transform-output]
+
+## [basic-linear-transform-parameters]
+alpha = 1.0 # Simple contrast control
+beta = 0    # Simple brightness control
+
+# Initialize values
+print(' Basic Linear Transforms ')
+print('-------------------------')
+try:
+    alpha = float(input('* Enter the alpha value [1.0-3.0]: '))
+    beta = int(input('* Enter the beta value [0-100]: '))
+except ValueError:
+    print('Error, not a number')
+## [basic-linear-transform-parameters]
+
+# Do the operation new_image(i,j) = alpha*image(i,j) + beta
+# Instead of these 'for' loops we could have used simply:
+# new_image = cv.convertScaleAbs(image, alpha=alpha, beta=beta)
+# but we wanted to show you how to access the pixels :)
+## [basic-linear-transform-operation]
+for y in range(image.shape[0]):
+    for x in range(image.shape[1]):
+        for c in range(image.shape[2]):
+            new_image[y,x,c] = np.clip(alpha*image[y,x,c] + beta, 0, 255)
+## [basic-linear-transform-operation]
+
+## [basic-linear-transform-display]
+# Show stuff
+cv.imshow('Original Image', image)
+cv.imshow('New Image', new_image)
+
+# Wait until user press some key
+cv.waitKey()
+## [basic-linear-transform-display]

samples/python/tutorial_code/imgProc/changing_contrast_brightness_image/changing_contrast_brightness_image.py

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+from __future__ import print_function
+from __future__ import division
+import cv2 as cv
+import numpy as np
+import argparse
+
+alpha = 1.0
+alpha_max = 500
+beta = 0
+beta_max = 200
+gamma = 1.0
+gamma_max = 200
+
+def basicLinearTransform():
+    res = cv.convertScaleAbs(img_original, alpha=alpha, beta=beta)
+    img_corrected = cv.hconcat([img_original, res])
+    cv.imshow("Brightness and contrast adjustments", img_corrected)
+
+def gammaCorrection():
+    ## [changing-contrast-brightness-gamma-correction]
+    lookUpTable = np.empty((1,256), np.uint8)
+    for i in range(256):
+        lookUpTable[0,i] = np.clip(pow(i / 255.0, gamma) * 255.0, 0, 255)
+
+    res = cv.LUT(img_original, lookUpTable)
+    ## [changing-contrast-brightness-gamma-correction]
+
+    img_gamma_corrected = cv.hconcat([img_original, res]);
+    cv.imshow("Gamma correction", img_gamma_corrected);
+
+def on_linear_transform_alpha_trackbar(val):
+    global alpha
+    alpha = val / 100
+    basicLinearTransform()
+
+def on_linear_transform_beta_trackbar(val):
+    global beta
+    beta = val - 100
+    basicLinearTransform()
+
+def on_gamma_correction_trackbar(val):
+    global gamma
+    gamma = val / 100
+    gammaCorrection()
+
+parser = argparse.ArgumentParser(description='Code for Changing the contrast and brightness of an image! tutorial.')
+parser.add_argument('--input', help='Path to input image.', default='../data/lena.jpg')
+args = parser.parse_args()
+
+img_original = cv.imread(args.input)
+if img_original is None:
+    print('Could not open or find the image: ', args.input)
+    exit(0)
+
+img_corrected = np.empty((img_original.shape[0], img_original.shape[1]*2, img_original.shape[2]), img_original.dtype)
+img_gamma_corrected = np.empty((img_original.shape[0], img_original.shape[1]*2, img_original.shape[2]), img_original.dtype)
+
+img_corrected = cv.hconcat([img_original, img_original])
+img_gamma_corrected = cv.hconcat([img_original, img_original])
+
+cv.namedWindow('Brightness and contrast adjustments')
+cv.namedWindow('Gamma correction')
+
+alpha_init = int(alpha *100)
+cv.createTrackbar('Alpha gain (contrast)', 'Brightness and contrast adjustments', alpha_init, alpha_max, on_linear_transform_alpha_trackbar)
+beta_init = beta + 100
+cv.createTrackbar('Beta bias (brightness)', 'Brightness and contrast adjustments', beta_init, beta_max, on_linear_transform_beta_trackbar)
+gamma_init = int(gamma * 100)
+cv.createTrackbar('Gamma correction', 'Gamma correction', gamma_init, gamma_max, on_gamma_correction_trackbar)
+
+on_linear_transform_alpha_trackbar(alpha_init)
+on_gamma_correction_trackbar(gamma_init)
+
+cv.waitKey()