initial commit

samples/python/tutorial_code/core/AddingImages/adding_images.py

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+from __future__ import print_function
+
+import cv2 as cv
+
+alpha = 0.5
+
+try:
+    raw_input          # Python 2
+except NameError:
+    raw_input = input  # Python 3
+
+print(''' Simple Linear Blender
+-----------------------
+* Enter alpha [0.0-1.0]: ''')
+input_alpha = float(raw_input().strip())
+if 0 <= alpha <= 1:
+    alpha = input_alpha
+# [load]
+src1 = cv.imread(cv.samples.findFile('LinuxLogo.jpg'))
+src2 = cv.imread(cv.samples.findFile('WindowsLogo.jpg'))
+# [load]
+if src1 is None:
+    print("Error loading src1")
+    exit(-1)
+elif src2 is None:
+    print("Error loading src2")
+    exit(-1)
+# [blend_images]
+beta = (1.0 - alpha)
+dst = cv.addWeighted(src1, alpha, src2, beta, 0.0)
+# [blend_images]
+# [display]
+cv.imshow('dst', dst)
+cv.waitKey(0)
+# [display]
+cv.destroyAllWindows()

samples/python/tutorial_code/core/discrete_fourier_transform/discrete_fourier_transform.py

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+from __future__ import print_function
+import sys
+
+import cv2 as cv
+import numpy as np
+
+
+def print_help():
+    print('''
+    This program demonstrated the use of the discrete Fourier transform (DFT).
+    The dft of an image is taken and it's power spectrum is displayed.
+    Usage:
+    discrete_fourier_transform.py [image_name -- default lena.jpg]''')
+
+
+def main(argv):
+
+    print_help()
+
+    filename = argv[0] if len(argv) > 0 else 'lena.jpg'
+
+    I = cv.imread(cv.samples.findFile(filename), cv.IMREAD_GRAYSCALE)
+    if I is None:
+        print('Error opening image')
+        return -1
+    ## [expand]
+    rows, cols = I.shape
+    m = cv.getOptimalDFTSize( rows )
+    n = cv.getOptimalDFTSize( cols )
+    padded = cv.copyMakeBorder(I, 0, m - rows, 0, n - cols, cv.BORDER_CONSTANT, value=[0, 0, 0])
+    ## [expand]
+    ## [complex_and_real]
+    planes = [np.float32(padded), np.zeros(padded.shape, np.float32)]
+    complexI = cv.merge(planes)         # Add to the expanded another plane with zeros
+    ## [complex_and_real]
+    ## [dft]
+    cv.dft(complexI, complexI)         # this way the result may fit in the source matrix
+    ## [dft]
+    # compute the magnitude and switch to logarithmic scale
+    # = > log(1 + sqrt(Re(DFT(I)) ^ 2 + Im(DFT(I)) ^ 2))
+    ## [magnitude]
+    cv.split(complexI, planes)                   # planes[0] = Re(DFT(I), planes[1] = Im(DFT(I))
+    cv.magnitude(planes[0], planes[1], planes[0])# planes[0] = magnitude
+    magI = planes[0]
+    ## [magnitude]
+    ## [log]
+    matOfOnes = np.ones(magI.shape, dtype=magI.dtype)
+    cv.add(matOfOnes, magI, magI) #  switch to logarithmic scale
+    cv.log(magI, magI)
+    ## [log]
+    ## [crop_rearrange]
+    magI_rows, magI_cols = magI.shape
+    # crop the spectrum, if it has an odd number of rows or columns
+    magI = magI[0:(magI_rows & -2), 0:(magI_cols & -2)]
+    cx = int(magI_rows/2)
+    cy = int(magI_cols/2)
+
+    q0 = magI[0:cx, 0:cy]         # Top-Left - Create a ROI per quadrant
+    q1 = magI[cx:cx+cx, 0:cy]     # Top-Right
+    q2 = magI[0:cx, cy:cy+cy]     # Bottom-Left
+    q3 = magI[cx:cx+cx, cy:cy+cy] # Bottom-Right
+
+    tmp = np.copy(q0)               # swap quadrants (Top-Left with Bottom-Right)
+    magI[0:cx, 0:cy] = q3
+    magI[cx:cx + cx, cy:cy + cy] = tmp
+
+    tmp = np.copy(q1)               # swap quadrant (Top-Right with Bottom-Left)
+    magI[cx:cx + cx, 0:cy] = q2
+    magI[0:cx, cy:cy + cy] = tmp
+    ## [crop_rearrange]
+    ## [normalize]
+    cv.normalize(magI, magI, 0, 1, cv.NORM_MINMAX) # Transform the matrix with float values into a
+    ## viewable image form(float between values 0 and 1).
+    ## [normalize]
+    cv.imshow("Input Image"       , I   )    # Show the result
+    cv.imshow("spectrum magnitude", magI)
+    cv.waitKey()
+
+if __name__ == "__main__":
+    main(sys.argv[1:])

samples/python/tutorial_code/core/file_input_output/file_input_output.py

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+from __future__ import print_function
+
+import numpy as np
+import cv2 as cv
+import sys
+
+def help(filename):
+    print (
+        '''
+        {0} shows the usage of the OpenCV serialization functionality. \n\n
+        usage:\n
+            python3 {0} outputfile.yml.gz\n\n
+        The output file may be either in XML, YAML or JSON. You can even compress it\n
+        by specifying this in its extension like xml.gz yaml.gz etc... With\n
+        FileStorage you can serialize objects in OpenCV.\n\n
+        For example: - create a class and have it serialized\n
+                     - use it to read and write matrices.\n
+        '''.format(filename)
+    )
+
+class MyData:
+    A = 97
+    X = np.pi
+    name = 'mydata1234'
+
+    def __repr__(self):
+        s = '{ name = ' + self.name + ', X = ' + str(self.X)
+        s = s + ', A = ' +  str(self.A) + '}'
+        return s
+
+    ## [inside]
+    def write(self, fs):
+        fs.write('MyData','{')
+        fs.write('A', self.A)
+        fs.write('X', self.X)
+        fs.write('name', self.name)
+        fs.write('MyData','}')
+
+    def read(self, node):
+        if (not node.empty()):
+            self.A = int(node.getNode('A').real())
+            self.X = node.getNode('X').real()
+            self.name = node.getNode('name').string()
+        else:
+            self.A = self.X = 0
+            self.name = ''
+    ## [inside]
+
+def main(argv):
+    if len(argv) != 2:
+        help(argv[0])
+        exit(1)
+
+    # write
+    ## [iomati]
+    R = np.eye(3,3)
+    T = np.zeros((3,1))
+    ## [iomati]
+    ## [customIOi]
+    m = MyData()
+    ## [customIOi]
+
+    filename = argv[1]
+
+    ## [open]
+    s = cv.FileStorage(filename, cv.FileStorage_WRITE)
+    # or:
+    # s = cv.FileStorage()
+    # s.open(filename, cv.FileStorage_WRITE)
+    ## [open]
+
+    ## [writeNum]
+    s.write('iterationNr', 100)
+    ## [writeNum]
+
+    ## [writeStr]
+    s.write('strings', '[')
+    s.write('image1.jpg','Awesomeness')
+    s.write('../data/baboon.jpg',']')
+    ## [writeStr]
+
+    ## [writeMap]
+    s.write ('Mapping', '{')
+    s.write ('One', 1)
+    s.write ('Two', 2)
+    s.write ('Mapping', '}')
+    ## [writeMap]
+
+    ## [iomatw]
+    s.write ('R_MAT', R)
+    s.write ('T_MAT', T)
+    ## [iomatw]
+
+    ## [customIOw]
+    m.write(s)
+    ## [customIOw]
+    ## [close]
+    s.release()
+    ## [close]
+    print ('Write Done.')
+
+    # read
+    print ('\nReading: ')
+    s = cv.FileStorage()
+    s.open(filename, cv.FileStorage_READ)
+
+    ## [readNum]
+    n = s.getNode('iterationNr')
+    itNr = int(n.real())
+    ## [readNum]
+    print (itNr)
+
+    if (not s.isOpened()):
+        print ('Failed to open ', filename, file=sys.stderr)
+        help(argv[0])
+        exit(1)
+
+    ## [readStr]
+    n = s.getNode('strings')
+    if (not n.isSeq()):
+        print ('strings is not a sequence! FAIL', file=sys.stderr)
+        exit(1)
+
+    for i in range(n.size()):
+        print (n.at(i).string())
+    ## [readStr]
+
+    ## [readMap]
+    n = s.getNode('Mapping')
+    print ('Two',int(n.getNode('Two').real()),'; ')
+    print ('One',int(n.getNode('One').real()),'\n')
+    ## [readMap]
+
+    ## [iomat]
+    R = s.getNode('R_MAT').mat()
+    T = s.getNode('T_MAT').mat()
+    ## [iomat]
+    ## [customIO]
+    m.read(s.getNode('MyData'))
+    ## [customIO]
+
+    print ('\nR =',R)
+    print ('T =',T,'\n')
+    print ('MyData =','\n',m,'\n')
+
+    ## [nonexist]
+    print ('Attempt to read NonExisting (should initialize the data structure',
+            'with its default).')
+    m.read(s.getNode('NonExisting'))
+    print ('\nNonExisting =','\n',m)
+    ## [nonexist]
+
+    print ('\nTip: Open up',filename,'with a text editor to see the serialized data.')
+
+if __name__ == '__main__':
+    main(sys.argv)

samples/python/tutorial_code/core/mat_mask_operations/mat_mask_operations.py

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+from __future__ import print_function
+import sys
+import time
+
+import numpy as np
+import cv2 as cv
+
+## [basic_method]
+def is_grayscale(my_image):
+    return len(my_image.shape) < 3
+
+
+def saturated(sum_value):
+    if sum_value > 255:
+        sum_value = 255
+    if sum_value < 0:
+        sum_value = 0
+
+    return sum_value
+
+
+def sharpen(my_image):
+    if is_grayscale(my_image):
+        height, width = my_image.shape
+    else:
+        my_image = cv.cvtColor(my_image, cv.CV_8U)
+        height, width, n_channels = my_image.shape
+
+    result = np.zeros(my_image.shape, my_image.dtype)
+    ## [basic_method_loop]
+    for j in range(1, height - 1):
+        for i in range(1, width - 1):
+            if is_grayscale(my_image):
+                sum_value = 5 * my_image[j, i] - my_image[j + 1, i] - my_image[j - 1, i] \
+                            - my_image[j, i + 1] - my_image[j, i - 1]
+                result[j, i] = saturated(sum_value)
+            else:
+                for k in range(0, n_channels):
+                    sum_value = 5 * my_image[j, i, k] - my_image[j + 1, i, k]  \
+                                - my_image[j - 1, i, k] - my_image[j, i + 1, k]\
+                                - my_image[j, i - 1, k]
+                    result[j, i, k] = saturated(sum_value)
+    ## [basic_method_loop]
+    return result
+## [basic_method]
+
+def main(argv):
+    filename = 'lena.jpg'
+
+    img_codec = cv.IMREAD_COLOR
+    if argv:
+        filename = sys.argv[1]
+        if len(argv) >= 2 and sys.argv[2] == "G":
+            img_codec = cv.IMREAD_GRAYSCALE
+
+    src = cv.imread(cv.samples.findFile(filename), img_codec)
+
+    if src is None:
+        print("Can't open image [" + filename + "]")
+        print("Usage:")
+        print("mat_mask_operations.py [image_path -- default lena.jpg] [G -- grayscale]")
+        return -1
+
+    cv.namedWindow("Input", cv.WINDOW_AUTOSIZE)
+    cv.namedWindow("Output", cv.WINDOW_AUTOSIZE)
+
+    cv.imshow("Input", src)
+    t = round(time.time())
+
+    dst0 = sharpen(src)
+
+    t = (time.time() - t)
+    print("Hand written function time passed in seconds: %s" % t)
+
+    cv.imshow("Output", dst0)
+    cv.waitKey()
+
+    t = time.time()
+    ## [kern]
+    kernel = np.array([[0, -1, 0],
+                       [-1, 5, -1],
+                       [0, -1, 0]], np.float32)  # kernel should be floating point type
+    ## [kern]
+    ## [filter2D]
+    dst1 = cv.filter2D(src, -1, kernel)
+    # ddepth = -1, means destination image has depth same as input image
+    ## [filter2D]
+
+    t = (time.time() - t)
+    print("Built-in filter2D time passed in seconds:     %s" % t)
+
+    cv.imshow("Output", dst1)
+
+    cv.waitKey(0)
+    cv.destroyAllWindows()
+    return 0
+
+
+if __name__ == "__main__":
+    main(sys.argv[1:])

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]