dask_image.ndfilters package

dask_image.ndfilters.convolve(image, weights, mode='reflect', cval=0.0, origin=0)

Wrapped copy of “scipy.ndimage.filters.convolve”

Excludes the output parameter as it would not work with Dask arrays.

Original docstring:

Multidimensional convolution.

The array is convolved with the given kernel.

Parameters
  • image (array_like) – The image array.

  • weights (array_like) – Array of weights, same number of dimensions as image

  • mode (str or sequence, optional) –

    The mode parameter determines how the image array is extended when the filter overlaps a border. By passing a sequence of modes with length equal to the number of dimensions of the image array, different modes can be specified along each axis. Default value is ‘reflect’. The valid values and their behavior is as follows:

    ’reflect’ (d c b a | a b c d | d c b a)

    The image is extended by reflecting about the edge of the last pixel.

    ’constant’ (k k k k | a b c d | k k k k)

    The image is extended by filling all values beyond the edge with the same constant value, defined by the cval parameter.

    ’nearest’ (a a a a | a b c d | d d d d)

    The image is extended by replicating the last pixel.

    ’mirror’ (d c b | a b c d | c b a)

    The image is extended by reflecting about the center of the last pixel.

    ’wrap’ (a b c d | a b c d | a b c d)

    The image is extended by wrapping around to the opposite edge.

  • cval (scalar, optional) – Value to fill past edges of image if mode is ‘constant’. Default is 0.0

  • origin (int or sequence, optional) – Controls the placement of the filter on the image array’s pixels. A value of 0 (the default) centers the filter over the pixel, with positive values shifting the filter to the left, and negative ones to the right. By passing a sequence of origins with length equal to the number of dimensions of the image array, different shifts can be specified along each axis.

Returns

result – The result of convolution of image with weights.

Return type

ndarray

See also

correlate()

Correlate an image with a kernel.

Notes

Each value in result is \(C_i = \sum_j{I_{i+k-j} W_j}\), where W is the weights kernel, j is the n-D spatial index over \(W\), I is the image and k is the coordinate of the center of W, specified by origin in the image parameters.

dask_image.ndfilters.correlate(image, weights, mode='reflect', cval=0.0, origin=0)

Wrapped copy of “scipy.ndimage.filters.correlate”

Excludes the output parameter as it would not work with Dask arrays.

Original docstring:

Multi-dimensional correlation.

The array is correlated with the given kernel.

Parameters
  • image (array_like) – The image array.

  • weights (ndarray) – array of weights, same number of dimensions as image

  • mode (str or sequence, optional) –

    The mode parameter determines how the image array is extended when the filter overlaps a border. By passing a sequence of modes with length equal to the number of dimensions of the image array, different modes can be specified along each axis. Default value is ‘reflect’. The valid values and their behavior is as follows:

    ’reflect’ (d c b a | a b c d | d c b a)

    The image is extended by reflecting about the edge of the last pixel.

    ’constant’ (k k k k | a b c d | k k k k)

    The image is extended by filling all values beyond the edge with the same constant value, defined by the cval parameter.

    ’nearest’ (a a a a | a b c d | d d d d)

    The image is extended by replicating the last pixel.

    ’mirror’ (d c b | a b c d | c b a)

    The image is extended by reflecting about the center of the last pixel.

    ’wrap’ (a b c d | a b c d | a b c d)

    The image is extended by wrapping around to the opposite edge.

  • cval (scalar, optional) – Value to fill past edges of image if mode is ‘constant’. Default is 0.0.

  • origin (int or sequence, optional) – Controls the placement of the filter on the image array’s pixels. A value of 0 (the default) centers the filter over the pixel, with positive values shifting the filter to the left, and negative ones to the right. By passing a sequence of origins with length equal to the number of dimensions of the image array, different shifts can be specified along each axis.

See also

convolve()

Convolve an image with a kernel.

dask_image.ndfilters.gaussian_filter(image, sigma, order=0, mode='reflect', cval=0.0, truncate=4.0)

Wrapped copy of “scipy.ndimage.filters.gaussian_filter”

Excludes the output parameter as it would not work with Dask arrays.

Original docstring:

Multidimensional Gaussian filter.

Parameters
  • image (array_like) – The image array.

  • sigma (scalar or sequence of scalars) – Standard deviation for Gaussian kernel. The standard deviations of the Gaussian filter are given for each axis as a sequence, or as a single number, in which case it is equal for all axes.

  • order (int or sequence of ints, optional) – The order of the filter along each axis is given as a sequence of integers, or as a single number. An order of 0 corresponds to convolution with a Gaussian kernel. A positive order corresponds to convolution with that derivative of a Gaussian.

  • mode (str or sequence, optional) –

    The mode parameter determines how the image array is extended when the filter overlaps a border. By passing a sequence of modes with length equal to the number of dimensions of the image array, different modes can be specified along each axis. Default value is ‘reflect’. The valid values and their behavior is as follows:

    ’reflect’ (d c b a | a b c d | d c b a)

    The image is extended by reflecting about the edge of the last pixel.

    ’constant’ (k k k k | a b c d | k k k k)

    The image is extended by filling all values beyond the edge with the same constant value, defined by the cval parameter.

    ’nearest’ (a a a a | a b c d | d d d d)

    The image is extended by replicating the last pixel.

    ’mirror’ (d c b | a b c d | c b a)

    The image is extended by reflecting about the center of the last pixel.

    ’wrap’ (a b c d | a b c d | a b c d)

    The image is extended by wrapping around to the opposite edge.

  • cval (scalar, optional) – Value to fill past edges of image if mode is ‘constant’. Default is 0.0.

  • truncate (float) – Truncate the filter at this many standard deviations. Default is 4.0.

Returns

gaussian_filter – Returned array of same shape as image.

Return type

ndarray

Notes

The multidimensional filter is implemented as a sequence of one-dimensional convolution filters. The intermediate arrays are stored in the same data type as the output. Therefore, for output types with a limited precision, the results may be imprecise because intermediate results may be stored with insufficient precision.

dask_image.ndfilters.gaussian_gradient_magnitude(image, sigma, mode='reflect', cval=0.0, truncate=4.0, **kwargs)

Wrapped copy of “scipy.ndimage.filters.gaussian_gradient_magnitude”

Excludes the output parameter as it would not work with Dask arrays.

Original docstring:

Multidimensional gradient magnitude using Gaussian derivatives.

Parameters
  • image (array_like) – The image array.

  • sigma (scalar or sequence of scalars) – The standard deviations of the Gaussian filter are given for each axis as a sequence, or as a single number, in which case it is equal for all axes..

  • mode (str or sequence, optional) –

    The mode parameter determines how the image array is extended when the filter overlaps a border. By passing a sequence of modes with length equal to the number of dimensions of the image array, different modes can be specified along each axis. Default value is ‘reflect’. The valid values and their behavior is as follows:

    ’reflect’ (d c b a | a b c d | d c b a)

    The image is extended by reflecting about the edge of the last pixel.

    ’constant’ (k k k k | a b c d | k k k k)

    The image is extended by filling all values beyond the edge with the same constant value, defined by the cval parameter.

    ’nearest’ (a a a a | a b c d | d d d d)

    The image is extended by replicating the last pixel.

    ’mirror’ (d c b | a b c d | c b a)

    The image is extended by reflecting about the center of the last pixel.

    ’wrap’ (a b c d | a b c d | a b c d)

    The image is extended by wrapping around to the opposite edge.

  • cval (scalar, optional) – Value to fill past edges of image if mode is ‘constant’. Default is 0.0.

  • keyword arguments will be passed to gaussian_filter() (Extra) –

Returns

gaussian_gradient_magnitude – Filtered array. Has the same shape as image.

Return type

ndarray

dask_image.ndfilters.gaussian_laplace(image, sigma, mode='reflect', cval=0.0, truncate=4.0, **kwargs)

Wrapped copy of “scipy.ndimage.filters.gaussian_laplace”

Excludes the output parameter as it would not work with Dask arrays.

Original docstring:

Multidimensional Laplace filter using gaussian second derivatives.

Parameters
  • image (array_like) – The image array.

  • sigma (scalar or sequence of scalars) – The standard deviations of the Gaussian filter are given for each axis as a sequence, or as a single number, in which case it is equal for all axes.

  • mode (str or sequence, optional) –

    The mode parameter determines how the image array is extended when the filter overlaps a border. By passing a sequence of modes with length equal to the number of dimensions of the image array, different modes can be specified along each axis. Default value is ‘reflect’. The valid values and their behavior is as follows:

    ’reflect’ (d c b a | a b c d | d c b a)

    The image is extended by reflecting about the edge of the last pixel.

    ’constant’ (k k k k | a b c d | k k k k)

    The image is extended by filling all values beyond the edge with the same constant value, defined by the cval parameter.

    ’nearest’ (a a a a | a b c d | d d d d)

    The image is extended by replicating the last pixel.

    ’mirror’ (d c b | a b c d | c b a)

    The image is extended by reflecting about the center of the last pixel.

    ’wrap’ (a b c d | a b c d | a b c d)

    The image is extended by wrapping around to the opposite edge.

  • cval (scalar, optional) – Value to fill past edges of image if mode is ‘constant’. Default is 0.0.

  • keyword arguments will be passed to gaussian_filter() (Extra) –

dask_image.ndfilters.generic_filter(image, function, size=None, footprint=None, mode='reflect', cval=0.0, origin=0, extra_arguments=(), extra_keywords={})

Wrapped copy of “scipy.ndimage.filters.generic_filter”

Excludes the output parameter as it would not work with Dask arrays.

Original docstring:

Calculate a multi-dimensional filter using the given function.

At each element the provided function is called. The image values within the filter footprint at that element are passed to the function as a 1D array of double values.

Parameters
  • image (array_like) – The image array.

  • function ({callable, scipy.LowLevelCallable}) – Function to apply at each element.

  • size (scalar or tuple, optional) – See footprint, below. Ignored if footprint is given.

  • footprint (array, optional) – Either size or footprint must be defined. size gives the shape that is taken from the image array, at every element position, to define the image to the filter function. footprint is a boolean array that specifies (implicitly) a shape, but also which of the elements within this shape will get passed to the filter function. Thus size=(n,m) is equivalent to footprint=np.ones((n,m)). We adjust size to the number of dimensions of the image array, so that, if the image array is shape (10,10,10), and size is 2, then the actual size used is (2,2,2). When footprint is given, size is ignored.

  • mode (str or sequence, optional) –

    The mode parameter determines how the image array is extended when the filter overlaps a border. By passing a sequence of modes with length equal to the number of dimensions of the image array, different modes can be specified along each axis. Default value is ‘reflect’. The valid values and their behavior is as follows:

    ’reflect’ (d c b a | a b c d | d c b a)

    The image is extended by reflecting about the edge of the last pixel.

    ’constant’ (k k k k | a b c d | k k k k)

    The image is extended by filling all values beyond the edge with the same constant value, defined by the cval parameter.

    ’nearest’ (a a a a | a b c d | d d d d)

    The image is extended by replicating the last pixel.

    ’mirror’ (d c b | a b c d | c b a)

    The image is extended by reflecting about the center of the last pixel.

    ’wrap’ (a b c d | a b c d | a b c d)

    The image is extended by wrapping around to the opposite edge.

  • cval (scalar, optional) – Value to fill past edges of image if mode is ‘constant’. Default is 0.0.

  • origin (int or sequence, optional) – Controls the placement of the filter on the image array’s pixels. A value of 0 (the default) centers the filter over the pixel, with positive values shifting the filter to the left, and negative ones to the right. By passing a sequence of origins with length equal to the number of dimensions of the image array, different shifts can be specified along each axis.

  • extra_arguments (sequence, optional) – Sequence of extra positional arguments to pass to passed function.

  • extra_keywords (dict, optional) – dict of extra keyword arguments to pass to passed function.

Notes

This function also accepts low-level callback functions with one of the following signatures and wrapped in scipy.LowLevelCallable:

int callback(double *buffer, npy_intp filter_size,
             double *return_value, void *user_data)
int callback(double *buffer, intptr_t filter_size,
             double *return_value, void *user_data)

The calling function iterates over the elements of the image and output arrays, calling the callback function at each element. The elements within the footprint of the filter at the current element are passed through the buffer parameter, and the number of elements within the footprint through filter_size. The calculated value is returned in return_value. user_data is the data pointer provided to scipy.LowLevelCallable as-is.

The callback function must return an integer error status that is zero if something went wrong and one otherwise. If an error occurs, you should normally set the python error status with an informative message before returning, otherwise a default error message is set by the calling function.

In addition, some other low-level function pointer specifications are accepted, but these are for backward compatibility only and should not be used in new code.

dask_image.ndfilters.laplace(image, mode='reflect', cval=0.0)

Wrapped copy of “scipy.ndimage.filters.laplace”

Excludes the output parameter as it would not work with Dask arrays.

Original docstring:

N-dimensional Laplace filter based on approximate second derivatives.

Parameters
  • image (array_like) – The image array.

  • mode (str or sequence, optional) –

    The mode parameter determines how the image array is extended when the filter overlaps a border. By passing a sequence of modes with length equal to the number of dimensions of the image array, different modes can be specified along each axis. Default value is ‘reflect’. The valid values and their behavior is as follows:

    ’reflect’ (d c b a | a b c d | d c b a)

    The image is extended by reflecting about the edge of the last pixel.

    ’constant’ (k k k k | a b c d | k k k k)

    The image is extended by filling all values beyond the edge with the same constant value, defined by the cval parameter.

    ’nearest’ (a a a a | a b c d | d d d d)

    The image is extended by replicating the last pixel.

    ’mirror’ (d c b | a b c d | c b a)

    The image is extended by reflecting about the center of the last pixel.

    ’wrap’ (a b c d | a b c d | a b c d)

    The image is extended by wrapping around to the opposite edge.

  • cval (scalar, optional) – Value to fill past edges of image if mode is ‘constant’. Default is 0.0.

dask_image.ndfilters.maximum_filter(image, size=None, footprint=None, mode='reflect', cval=0.0, origin=0)

Wrapped copy of “scipy.ndimage.filters.maximum_filter”

Excludes the output parameter as it would not work with Dask arrays.

Original docstring:

Calculate a multi-dimensional maximum filter.

Parameters
  • image (array_like) – The image array.

  • size (scalar or tuple, optional) – See footprint, below. Ignored if footprint is given.

  • footprint (array, optional) – Either size or footprint must be defined. size gives the shape that is taken from the image array, at every element position, to define the image to the filter function. footprint is a boolean array that specifies (implicitly) a shape, but also which of the elements within this shape will get passed to the filter function. Thus size=(n,m) is equivalent to footprint=np.ones((n,m)). We adjust size to the number of dimensions of the image array, so that, if the image array is shape (10,10,10), and size is 2, then the actual size used is (2,2,2). When footprint is given, size is ignored.

  • mode (str or sequence, optional) –

    The mode parameter determines how the image array is extended when the filter overlaps a border. By passing a sequence of modes with length equal to the number of dimensions of the image array, different modes can be specified along each axis. Default value is ‘reflect’. The valid values and their behavior is as follows:

    ’reflect’ (d c b a | a b c d | d c b a)

    The image is extended by reflecting about the edge of the last pixel.

    ’constant’ (k k k k | a b c d | k k k k)

    The image is extended by filling all values beyond the edge with the same constant value, defined by the cval parameter.

    ’nearest’ (a a a a | a b c d | d d d d)

    The image is extended by replicating the last pixel.

    ’mirror’ (d c b | a b c d | c b a)

    The image is extended by reflecting about the center of the last pixel.

    ’wrap’ (a b c d | a b c d | a b c d)

    The image is extended by wrapping around to the opposite edge.

  • cval (scalar, optional) – Value to fill past edges of image if mode is ‘constant’. Default is 0.0.

  • origin (int or sequence, optional) – Controls the placement of the filter on the image array’s pixels. A value of 0 (the default) centers the filter over the pixel, with positive values shifting the filter to the left, and negative ones to the right. By passing a sequence of origins with length equal to the number of dimensions of the image array, different shifts can be specified along each axis.

Returns

maximum_filter – Filtered array. Has the same shape as image.

Return type

ndarray

dask_image.ndfilters.median_filter(image, size=None, footprint=None, mode='reflect', cval=0.0, origin=0)

Wrapped copy of “scipy.ndimage.filters.median_filter”

Excludes the output parameter as it would not work with Dask arrays.

Original docstring:

Calculate a multidimensional median filter.

Parameters
  • image (array_like) – The image array.

  • size (scalar or tuple, optional) – See footprint, below. Ignored if footprint is given.

  • footprint (array, optional) – Either size or footprint must be defined. size gives the shape that is taken from the image array, at every element position, to define the image to the filter function. footprint is a boolean array that specifies (implicitly) a shape, but also which of the elements within this shape will get passed to the filter function. Thus size=(n,m) is equivalent to footprint=np.ones((n,m)). We adjust size to the number of dimensions of the image array, so that, if the image array is shape (10,10,10), and size is 2, then the actual size used is (2,2,2). When footprint is given, size is ignored.

  • mode (str or sequence, optional) –

    The mode parameter determines how the image array is extended when the filter overlaps a border. By passing a sequence of modes with length equal to the number of dimensions of the image array, different modes can be specified along each axis. Default value is ‘reflect’. The valid values and their behavior is as follows:

    ’reflect’ (d c b a | a b c d | d c b a)

    The image is extended by reflecting about the edge of the last pixel.

    ’constant’ (k k k k | a b c d | k k k k)

    The image is extended by filling all values beyond the edge with the same constant value, defined by the cval parameter.

    ’nearest’ (a a a a | a b c d | d d d d)

    The image is extended by replicating the last pixel.

    ’mirror’ (d c b | a b c d | c b a)

    The image is extended by reflecting about the center of the last pixel.

    ’wrap’ (a b c d | a b c d | a b c d)

    The image is extended by wrapping around to the opposite edge.

  • cval (scalar, optional) – Value to fill past edges of image if mode is ‘constant’. Default is 0.0.

  • origin (int or sequence, optional) – Controls the placement of the filter on the image array’s pixels. A value of 0 (the default) centers the filter over the pixel, with positive values shifting the filter to the left, and negative ones to the right. By passing a sequence of origins with length equal to the number of dimensions of the image array, different shifts can be specified along each axis.

Returns

median_filter – Filtered array. Has the same shape as image.

Return type

ndarray

dask_image.ndfilters.minimum_filter(image, size=None, footprint=None, mode='reflect', cval=0.0, origin=0)

Wrapped copy of “scipy.ndimage.filters.minimum_filter”

Excludes the output parameter as it would not work with Dask arrays.

Original docstring:

Calculate a multi-dimensional minimum filter.

Parameters
  • image (array_like) – The image array.

  • size (scalar or tuple, optional) – See footprint, below. Ignored if footprint is given.

  • footprint (array, optional) – Either size or footprint must be defined. size gives the shape that is taken from the image array, at every element position, to define the image to the filter function. footprint is a boolean array that specifies (implicitly) a shape, but also which of the elements within this shape will get passed to the filter function. Thus size=(n,m) is equivalent to footprint=np.ones((n,m)). We adjust size to the number of dimensions of the image array, so that, if the image array is shape (10,10,10), and size is 2, then the actual size used is (2,2,2). When footprint is given, size is ignored.

  • mode (str or sequence, optional) –

    The mode parameter determines how the image array is extended when the filter overlaps a border. By passing a sequence of modes with length equal to the number of dimensions of the image array, different modes can be specified along each axis. Default value is ‘reflect’. The valid values and their behavior is as follows:

    ’reflect’ (d c b a | a b c d | d c b a)

    The image is extended by reflecting about the edge of the last pixel.

    ’constant’ (k k k k | a b c d | k k k k)

    The image is extended by filling all values beyond the edge with the same constant value, defined by the cval parameter.

    ’nearest’ (a a a a | a b c d | d d d d)

    The image is extended by replicating the last pixel.

    ’mirror’ (d c b | a b c d | c b a)

    The image is extended by reflecting about the center of the last pixel.

    ’wrap’ (a b c d | a b c d | a b c d)

    The image is extended by wrapping around to the opposite edge.

  • cval (scalar, optional) – Value to fill past edges of image if mode is ‘constant’. Default is 0.0.

  • origin (int or sequence, optional) – Controls the placement of the filter on the image array’s pixels. A value of 0 (the default) centers the filter over the pixel, with positive values shifting the filter to the left, and negative ones to the right. By passing a sequence of origins with length equal to the number of dimensions of the image array, different shifts can be specified along each axis.

Returns

minimum_filter – Filtered array. Has the same shape as image.

Return type

ndarray

dask_image.ndfilters.percentile_filter(image, percentile, size=None, footprint=None, mode='reflect', cval=0.0, origin=0)

Wrapped copy of “scipy.ndimage.filters.percentile_filter”

Excludes the output parameter as it would not work with Dask arrays.

Original docstring:

Calculate a multi-dimensional percentile filter.

Parameters
  • image (array_like) – The image array.

  • percentile (scalar) – The percentile parameter may be less then zero, i.e., percentile = -20 equals percentile = 80

  • size (scalar or tuple, optional) – See footprint, below. Ignored if footprint is given.

  • footprint (array, optional) – Either size or footprint must be defined. size gives the shape that is taken from the image array, at every element position, to define the image to the filter function. footprint is a boolean array that specifies (implicitly) a shape, but also which of the elements within this shape will get passed to the filter function. Thus size=(n,m) is equivalent to footprint=np.ones((n,m)). We adjust size to the number of dimensions of the image array, so that, if the image array is shape (10,10,10), and size is 2, then the actual size used is (2,2,2). When footprint is given, size is ignored.

  • mode (str or sequence, optional) –

    The mode parameter determines how the image array is extended when the filter overlaps a border. By passing a sequence of modes with length equal to the number of dimensions of the image array, different modes can be specified along each axis. Default value is ‘reflect’. The valid values and their behavior is as follows:

    ’reflect’ (d c b a | a b c d | d c b a)

    The image is extended by reflecting about the edge of the last pixel.

    ’constant’ (k k k k | a b c d | k k k k)

    The image is extended by filling all values beyond the edge with the same constant value, defined by the cval parameter.

    ’nearest’ (a a a a | a b c d | d d d d)

    The image is extended by replicating the last pixel.

    ’mirror’ (d c b | a b c d | c b a)

    The image is extended by reflecting about the center of the last pixel.

    ’wrap’ (a b c d | a b c d | a b c d)

    The image is extended by wrapping around to the opposite edge.

  • cval (scalar, optional) – Value to fill past edges of image if mode is ‘constant’. Default is 0.0.

  • origin (int or sequence, optional) – Controls the placement of the filter on the image array’s pixels. A value of 0 (the default) centers the filter over the pixel, with positive values shifting the filter to the left, and negative ones to the right. By passing a sequence of origins with length equal to the number of dimensions of the image array, different shifts can be specified along each axis.

Returns

percentile_filter – Filtered array. Has the same shape as image.

Return type

ndarray

dask_image.ndfilters.prewitt(image, axis=-1, mode='reflect', cval=0.0)

Wrapped copy of “scipy.ndimage.filters.prewitt”

Excludes the output parameter as it would not work with Dask arrays.

Original docstring:

Calculate a Prewitt filter.

Parameters
  • image (array_like) – The image array.

  • axis (int, optional) – The axis of image along which to calculate. Default is -1.

  • mode (str or sequence, optional) –

    The mode parameter determines how the image array is extended when the filter overlaps a border. By passing a sequence of modes with length equal to the number of dimensions of the image array, different modes can be specified along each axis. Default value is ‘reflect’. The valid values and their behavior is as follows:

    ’reflect’ (d c b a | a b c d | d c b a)

    The image is extended by reflecting about the edge of the last pixel.

    ’constant’ (k k k k | a b c d | k k k k)

    The image is extended by filling all values beyond the edge with the same constant value, defined by the cval parameter.

    ’nearest’ (a a a a | a b c d | d d d d)

    The image is extended by replicating the last pixel.

    ’mirror’ (d c b | a b c d | c b a)

    The image is extended by reflecting about the center of the last pixel.

    ’wrap’ (a b c d | a b c d | a b c d)

    The image is extended by wrapping around to the opposite edge.

  • cval (scalar, optional) – Value to fill past edges of image if mode is ‘constant’. Default is 0.0.

dask_image.ndfilters.rank_filter(image, rank, size=None, footprint=None, mode='reflect', cval=0.0, origin=0)

Wrapped copy of “scipy.ndimage.filters.rank_filter”

Excludes the output parameter as it would not work with Dask arrays.

Original docstring:

Calculate a multi-dimensional rank filter.

Parameters
  • image (array_like) – The image array.

  • rank (int) – The rank parameter may be less then zero, i.e., rank = -1 indicates the largest element.

  • size (scalar or tuple, optional) – See footprint, below. Ignored if footprint is given.

  • footprint (array, optional) – Either size or footprint must be defined. size gives the shape that is taken from the image array, at every element position, to define the image to the filter function. footprint is a boolean array that specifies (implicitly) a shape, but also which of the elements within this shape will get passed to the filter function. Thus size=(n,m) is equivalent to footprint=np.ones((n,m)). We adjust size to the number of dimensions of the image array, so that, if the image array is shape (10,10,10), and size is 2, then the actual size used is (2,2,2). When footprint is given, size is ignored.

  • mode (str or sequence, optional) –

    The mode parameter determines how the image array is extended when the filter overlaps a border. By passing a sequence of modes with length equal to the number of dimensions of the image array, different modes can be specified along each axis. Default value is ‘reflect’. The valid values and their behavior is as follows:

    ’reflect’ (d c b a | a b c d | d c b a)

    The image is extended by reflecting about the edge of the last pixel.

    ’constant’ (k k k k | a b c d | k k k k)

    The image is extended by filling all values beyond the edge with the same constant value, defined by the cval parameter.

    ’nearest’ (a a a a | a b c d | d d d d)

    The image is extended by replicating the last pixel.

    ’mirror’ (d c b | a b c d | c b a)

    The image is extended by reflecting about the center of the last pixel.

    ’wrap’ (a b c d | a b c d | a b c d)

    The image is extended by wrapping around to the opposite edge.

  • cval (scalar, optional) – Value to fill past edges of image if mode is ‘constant’. Default is 0.0.

  • origin (int or sequence, optional) – Controls the placement of the filter on the image array’s pixels. A value of 0 (the default) centers the filter over the pixel, with positive values shifting the filter to the left, and negative ones to the right. By passing a sequence of origins with length equal to the number of dimensions of the image array, different shifts can be specified along each axis.

Returns

rank_filter – Filtered array. Has the same shape as image.

Return type

ndarray

dask_image.ndfilters.sobel(image, axis=-1, mode='reflect', cval=0.0)

Wrapped copy of “scipy.ndimage.filters.sobel”

Excludes the output parameter as it would not work with Dask arrays.

Original docstring:

Calculate a Sobel filter.

Parameters
  • image (array_like) – The image array.

  • axis (int, optional) – The axis of image along which to calculate. Default is -1.

  • mode (str or sequence, optional) –

    The mode parameter determines how the image array is extended when the filter overlaps a border. By passing a sequence of modes with length equal to the number of dimensions of the image array, different modes can be specified along each axis. Default value is ‘reflect’. The valid values and their behavior is as follows:

    ’reflect’ (d c b a | a b c d | d c b a)

    The image is extended by reflecting about the edge of the last pixel.

    ’constant’ (k k k k | a b c d | k k k k)

    The image is extended by filling all values beyond the edge with the same constant value, defined by the cval parameter.

    ’nearest’ (a a a a | a b c d | d d d d)

    The image is extended by replicating the last pixel.

    ’mirror’ (d c b | a b c d | c b a)

    The image is extended by reflecting about the center of the last pixel.

    ’wrap’ (a b c d | a b c d | a b c d)

    The image is extended by wrapping around to the opposite edge.

  • cval (scalar, optional) – Value to fill past edges of image if mode is ‘constant’. Default is 0.0.

dask_image.ndfilters.uniform_filter(image, size=3, mode='reflect', cval=0.0, origin=0)

Wrapped copy of “scipy.ndimage.filters.uniform_filter”

Excludes the output parameter as it would not work with Dask arrays.

Original docstring:

Multi-dimensional uniform filter.

Parameters
  • image (array_like) – The image array.

  • size (int or sequence of ints, optional) – The sizes of the uniform filter are given for each axis as a sequence, or as a single number, in which case the size is equal for all axes.

  • mode (str or sequence, optional) –

    The mode parameter determines how the image array is extended when the filter overlaps a border. By passing a sequence of modes with length equal to the number of dimensions of the image array, different modes can be specified along each axis. Default value is ‘reflect’. The valid values and their behavior is as follows:

    ’reflect’ (d c b a | a b c d | d c b a)

    The image is extended by reflecting about the edge of the last pixel.

    ’constant’ (k k k k | a b c d | k k k k)

    The image is extended by filling all values beyond the edge with the same constant value, defined by the cval parameter.

    ’nearest’ (a a a a | a b c d | d d d d)

    The image is extended by replicating the last pixel.

    ’mirror’ (d c b | a b c d | c b a)

    The image is extended by reflecting about the center of the last pixel.

    ’wrap’ (a b c d | a b c d | a b c d)

    The image is extended by wrapping around to the opposite edge.

  • cval (scalar, optional) – Value to fill past edges of image if mode is ‘constant’. Default is 0.0.

  • origin (int or sequence, optional) – Controls the placement of the filter on the image array’s pixels. A value of 0 (the default) centers the filter over the pixel, with positive values shifting the filter to the left, and negative ones to the right. By passing a sequence of origins with length equal to the number of dimensions of the image array, different shifts can be specified along each axis.

Returns

uniform_filter – Filtered array. Has the same shape as image.

Return type

ndarray

Notes

The multi-dimensional filter is implemented as a sequence of one-dimensional uniform filters. The intermediate arrays are stored in the same data type as the output. Therefore, for output types with a limited precision, the results may be imprecise because intermediate results may be stored with insufficient precision.