dippykit.transforms module

Module of various image transform functions

This module contains an assortment of functions that perform transform operations on images in various useful manners.

dippykit.transforms.dct_2d(im: numpy.ndarray) → numpy.ndarray[source]

Computes the Discrete Cosine Transform of an image

Given an input image, this function computes its discrete cosine transform. This result is identical to the result of the dct2() Matlab function. Internally, this function is computed using scipy.fftpack.dctn(im, norm='ortho').

This function is essentially a wrapper for scipy.fftpack.dctn, so more detailed documentation may be found there.

Parameters:im (numpy.ndarray) – An image to be processed.
Return type:numpy.ndarray
Returns:The discrete cosine transform of the input image.

Note

This function wraps around functions from other packages. Reading these functions’ documentations may be useful. See the See also section for more information.

See also

scipy.fftpack.dctn
Documentation of the dctn function from Scipy

Examples:

>>> import numpy as np
>>> freq_1 = 1/4
>>> freq_2 = 1/8
>>> domain_1, domain_2 = np.meshgrid(np.arange(8), np.arange(8))
>>> domain_1
array([[0, 1, 2, 3, 4, 5, 6, 7],
       [0, 1, 2, 3, 4, 5, 6, 7],
       [0, 1, 2, 3, 4, 5, 6, 7],
       [0, 1, 2, 3, 4, 5, 6, 7],
       [0, 1, 2, 3, 4, 5, 6, 7],
       [0, 1, 2, 3, 4, 5, 6, 7],
       [0, 1, 2, 3, 4, 5, 6, 7],
       [0, 1, 2, 3, 4, 5, 6, 7]])
>>> domain_2
array([[0, 0, 0, 0, 0, 0, 0, 0],
       [1, 1, 1, 1, 1, 1, 1, 1],
       [2, 2, 2, 2, 2, 2, 2, 2],
       [3, 3, 3, 3, 3, 3, 3, 3],
       [4, 4, 4, 4, 4, 4, 4, 4],
       [5, 5, 5, 5, 5, 5, 5, 5],
       [6, 6, 6, 6, 6, 6, 6, 6],
       [7, 7, 7, 7, 7, 7, 7, 7]])
>>> im_1 = 10*np.cos(2 * np.pi * freq_1 * (domain_1 + 0.5))
>>> im_2 = 10*np.cos(2 * np.pi * freq_2 * (domain_2 + 0.5))
>>> im = im_1 + im_2
>>> np.set_printoptions(precision=2, suppress=True)
>>> im
array([[ 16.31,   2.17,   2.17,  16.31,  16.31,   2.17,   2.17,  16.31],
       [ 10.9 ,  -3.24,  -3.24,  10.9 ,  10.9 ,  -3.24,  -3.24,  10.9 ],
       [  3.24, -10.9 , -10.9 ,   3.24,   3.24, -10.9 , -10.9 ,   3.24],
       [ -2.17, -16.31, -16.31,  -2.17,  -2.17, -16.31, -16.31,  -2.17],
       [ -2.17, -16.31, -16.31,  -2.17,  -2.17, -16.31, -16.31,  -2.17],
       [  3.24, -10.9 , -10.9 ,   3.24,   3.24, -10.9 , -10.9 ,   3.24],
       [ 10.9 ,  -3.24,  -3.24,  10.9 ,  10.9 ,  -3.24,  -3.24,  10.9 ],
       [ 16.31,   2.17,   2.17,  16.31,  16.31,   2.17,   2.17,  16.31]])
>>> dct_2d(im)
array([[-0.  ,  0.  , -0.  ,  0.  , 56.57,  0.  , -0.  ,  0.  ],
       [ 0.  ,  0.  ,  0.  , -0.  , -0.  ,  0.  , -0.  , -0.  ],
       [56.57,  0.  ,  0.  ,  0.  ,  0.  ,  0.  ,  0.  ,  0.  ],
       [-0.  ,  0.  , -0.  , -0.  ,  0.  ,  0.  ,  0.  , -0.  ],
       [-0.  ,  0.  , -0.  ,  0.  , -0.  ,  0.  , -0.  ,  0.  ],
       [ 0.  ,  0.  ,  0.  , -0.  ,  0.  ,  0.  , -0.  , -0.  ],
       [-0.  ,  0.  , -0.  ,  0.  ,  0.  ,  0.  , -0.  , -0.  ],
       [ 0.  ,  0.  ,  0.  , -0.  , -0.  ,  0.  , -0.  , -0.  ]])
dippykit.transforms.idct_2d(im: numpy.ndarray) → numpy.ndarray[source]

Computes the Inverse Discrete Cosine Transform of an image

Given an input image, this function computes its inverse discrete cosine transform. This result is identical to the result of the idct2() Matlab function. Internally, this function is computed using scipy.fftpack.idctn(im, norm='ortho').

This function is essentially a wrapper for scipy.fftpack.idctn, so more detailed documentation may be found there.

Parameters:im (numpy.ndarray) – An image to be processed.
Return type:numpy.ndarray
Returns:The inverse discrete cosine transform of the input image.

Note

This function wraps around functions from other packages. Reading these functions’ documentations may be useful. See the See also section for more information.

See also

scipy.fftpack.idctn
Documentation of the dctn function from Scipy

Examples:

>>> import numpy as np
>>> im_DCT = np.zeros((8, 8))
>>> im_DCT[0, 2] = 1
>>> np.set_printoptions(precision=2, suppress=True)
>>> idct_2d(im_DCT)
array([[ 0.16,  0.07, -0.07, -0.16, -0.16, -0.07,  0.07,  0.16],
       [ 0.16,  0.07, -0.07, -0.16, -0.16, -0.07,  0.07,  0.16],
       [ 0.16,  0.07, -0.07, -0.16, -0.16, -0.07,  0.07,  0.16],
       [ 0.16,  0.07, -0.07, -0.16, -0.16, -0.07,  0.07,  0.16],
       [ 0.16,  0.07, -0.07, -0.16, -0.16, -0.07,  0.07,  0.16],
       [ 0.16,  0.07, -0.07, -0.16, -0.16, -0.07,  0.07,  0.16],
       [ 0.16,  0.07, -0.07, -0.16, -0.16, -0.07,  0.07,  0.16],
       [ 0.16,  0.07, -0.07, -0.16, -0.16, -0.07,  0.07,  0.16]])
dippykit.transforms.edge_detect(im: numpy.ndarray, mode: str = 'sobel', as_bool: bool = False, **kwargs) → numpy.ndarray[source]

Performs an edge detection operation on an image

Given an input image and an edge detection mode, this function applies an edge detection operation to the image and returns the result. Optionally, this result can be returned as a binary/boolean image.

This function is essentially a wrapper for several external functions, so more detailed documentation may be found there.

Parameters:
  • im (numpy.ndarray) – The input image.
  • mode (str) – The edge detection mode/algorithm to be used. Acceptable values are: ‘sobel’, ‘sobel_h’, ‘sobel_v’, ‘canny’, ‘scharr’, ‘prewitt’, ‘roberts’, and ‘laplace’.
  • as_bool (bool) – (default=False) If set to True, this function will return the result as a binary/boolean image. This binary image is built by taking the original edge image and substituting any values below threshold as False, and all other values as True. The value of threshold is defined as the 95th percentile of values in the original edge image.
  • kwargs – The keyword arguments to be passed to the edge detecting functions. If mode=’canny’, and there is no keyword argument ‘sigma’, then this function will set the ‘sigma’ keyword argument to the square root of 2 by default.
Return type:

numpy.ndarray
Returns:

The edge image.

Note

This function wraps around functions from other packages. Reading these functions’ documentations may be useful. See the See also section for more information.

See also

skimage.feature.canny
Documentation of the canny function from Scikit Image
skimage.filters.sobel
Documentation of the sobel function from Scikit Image
skimage.filters.sobel_h
Documentation of the sobel_h function from Scikit Image
skimage.filters.sobel_v
Documentation of the sobel_v function from Scikit Image
skimage.filters.scharr
Documentation of the scharr function from Scikit Image
skimage.filters.prewitt
Documentation of the prewitt function from Scikit Image
skimage.filters.roberts
Documentation of the roberts function from Scikit Image
skimage.filters.laplace
Documentation of the laplace function from Scikit Image

Examples:

>>> import numpy as np
>>> im = np.array(array([[0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. ],
...                      [0. , 0. , 0. , 0.5, 0.5, 0. , 0. , 0. ],
...                      [0. , 0. , 1. , 1. , 1. , 1. , 0. , 0. ],
...                      [0. , 0.5, 1. , 1. , 1. , 1. , 0. , 0. ],
...                      [0. , 0.5, 1. , 1. , 1. , 1. , 0. , 0. ],
...                      [0. , 0. , 1. , 1. , 1. , 1. , 0. , 0. ],
...                      [0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. ],
...                      [0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. ]])
>>> np.round(edge_detect(im), 2)
array([[0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.  ],
       [0.  , 0.25, 0.64, 0.73, 0.73, 0.64, 0.25, 0.  ],
       [0.  , 0.64, 0.75, 0.45, 0.45, 0.76, 0.56, 0.  ],
       [0.  , 0.73, 0.45, 0.  , 0.  , 0.71, 0.71, 0.  ],
       [0.  , 0.73, 0.45, 0.  , 0.  , 0.71, 0.71, 0.  ],
       [0.  , 0.64, 0.76, 0.71, 0.71, 0.75, 0.56, 0.  ],
       [0.  , 0.25, 0.56, 0.71, 0.71, 0.56, 0.25, 0.  ],
       [0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.  , 0.  ]])
>>> edge_detect(im, as_bool=True)
array([[False, False, False, False, False, False, False, False],
       [False, False, False, False, False, False, False, False],
       [False, False,  True, False, False,  True, False, False],
       [False, False, False, False, False, False, False, False],
       [False, False, False, False, False, False, False, False],
       [False, False,  True, False, False,  True, False, False],
       [False, False, False, False, False, False, False, False],
       [False, False, False, False, False, False, False, False]])
>>> np.round(dip.edge_detect(im, 'sobel_h'), 2)
array([[ 0.  ,  0.  ,  0.  ,  0.  ,  0.  ,  0.  ,  0.  ,  0.  ],
       [ 0.  ,  0.25,  0.75,  1.  ,  1.  ,  0.75,  0.25,  0.  ],
       [ 0.  ,  0.5 ,  0.75,  0.62,  0.62,  0.62,  0.25,  0.  ],
       [ 0.  ,  0.25,  0.12,  0.  ,  0.  ,  0.  ,  0.  ,  0.  ],
       [ 0.  , -0.25, -0.12,  0.  ,  0.  ,  0.  ,  0.  ,  0.  ],
       [ 0.  , -0.5 , -0.88, -1.  , -1.  , -0.75, -0.25,  0.  ],
       [ 0.  , -0.25, -0.75, -1.  , -1.  , -0.75, -0.25,  0.  ],
       [ 0.  ,  0.  ,  0.  ,  0.  ,  0.  ,  0.  ,  0.  ,  0.  ]])