Multidimensional image processing (`scipy.ndimage`)¶

Introduction¶

Image processing and analysis are generally seen as operations on two-dimensional arrays of values. There are however a number of fields where images of higher dimensionality must be analyzed. Good examples of these are medical imaging and biological imaging. numpy is suited very well for this type of applications due its inherent multidimensional nature. The scipy.ndimage packages provides a number of general image processing and analysis functions that are designed to operate with arrays of arbitrary dimensionality. The packages currently includes functions for linear and non-linear filtering, binary morphology, B-spline interpolation, and object measurements.

Properties shared by all functions¶

All functions share some common properties. Notably, all functions allow the specification of an output array with the output argument. With this argument you can specify an array that will be changed in-place with the result with the operation. In this case the result is not returned. Usually, using the output argument is more efficient, since an existing array is used to store the result.

The type of arrays returned is dependent on the type of operation, but it is in most cases equal to the type of the input. If, however, the output argument is used, the type of the result is equal to the type of the specified output argument. If no output argument is given, it is still possible to specify what the result of the output should be. This is done by simply assigning the desired numpy type object to the output argument. For example:

>>> from scipy.ndimage import correlate
>>> correlate(np.arange(10), [1, 2.5])
array([ 0,  2,  6,  9, 13, 16, 20, 23, 27, 30])
>>> correlate(np.arange(10), [1, 2.5], output=np.float64)
array([  0. ,   2.5,   6. ,   9.5,  13. ,  16.5,  20. ,  23.5,  27. ,  30.5])

Filter functions¶

The functions described in this section all perform some type of spatial filtering of the input array: the elements in the output are some function of the values in the neighborhood of the corresponding input element. We refer to this neighborhood of elements as the filter kernel, which is often rectangular in shape but may also have an arbitrary footprint. Many of the functions described below allow you to define the footprint of the kernel, by passing a mask through the footprint parameter. For example a cross shaped kernel can be defined as follows:

>>> footprint = np.array([[0, 1, 0], [1, 1, 1], [0, 1, 0]])
>>> footprint
array([[0, 1, 0],
       [1, 1, 1],
       [0, 1, 0]])

Usually the origin of the kernel is at the center calculated by dividing the dimensions of the kernel shape by two. For instance, the origin of a one-dimensional kernel of length three is at the second element. Take for example the correlation of a one-dimensional array with a filter of length 3 consisting of ones:

>>> from scipy.ndimage import correlate1d
>>> a = [0, 0, 0, 1, 0, 0, 0]
>>> correlate1d(a, [1, 1, 1])
array([0, 0, 1, 1, 1, 0, 0])

Sometimes it is convenient to choose a different origin for the kernel. For this reason most functions support the origin parameter which gives the origin of the filter relative to its center. For example:

>>> a = [0, 0, 0, 1, 0, 0, 0]
>>> correlate1d(a, [1, 1, 1], origin = -1)
array([0, 1, 1, 1, 0, 0, 0])

The effect is a shift of the result towards the left. This feature will not be needed very often, but it may be useful especially for filters that have an even size. A good example is the calculation of backward and forward differences:

>>> a = [0, 0, 1, 1, 1, 0, 0]
>>> correlate1d(a, [-1, 1])               # backward difference
array([ 0,  0,  1,  0,  0, -1,  0])
>>> correlate1d(a, [-1, 1], origin = -1)  # forward difference
array([ 0,  1,  0,  0, -1,  0,  0])

We could also have calculated the forward difference as follows:

>>> correlate1d(a, [0, -1, 1])
array([ 0,  1,  0,  0, -1,  0,  0])

However, using the origin parameter instead of a larger kernel is more efficient. For multidimensional kernels origin can be a number, in which case the origin is assumed to be equal along all axes, or a sequence giving the origin along each axis.

Since the output elements are a function of elements in the neighborhood of the input elements, the borders of the array need to be dealt with appropriately by providing the values outside the borders. This is done by assuming that the arrays are extended beyond their boundaries according certain boundary conditions. In the functions described below, the boundary conditions can be selected using the mode parameter which must be a string with the name of the boundary condition. The following boundary conditions are currently supported:

“nearest”

Use the value at the boundary

[1 2 3]->[1 1 2 3 3]

“wrap”

Periodically replicate the array

[1 2 3]->[3 1 2 3 1]

“reflect”

Reflect the array at the boundary

[1 2 3]->[1 1 2 3 3]

“constant”

Use a constant value, default is 0.0

[1 2 3]->[0 1 2 3 0]

The “constant” mode is special since it needs an additional parameter to specify the constant value that should be used.

Note

The easiest way to implement such boundary conditions would be to copy the data to a larger array and extend the data at the borders according to the boundary conditions. For large arrays and large filter kernels, this would be very memory consuming, and the functions described below therefore use a different approach that does not require allocating large temporary buffers.

Correlation and convolution¶

The correlate1d function calculates a one-dimensional correlation along the given axis. The lines of the array along the given axis are correlated with the given weights. The weights parameter must be a one-dimensional sequences of numbers.
The function correlate implements multidimensional correlation of the input array with a given kernel.
The convolve1d function calculates a one-dimensional convolution along the given axis. The lines of the array along the given axis are convoluted with the given weights. The weights parameter must be a one-dimensional sequences of numbers.

Note

A convolution is essentially a correlation after mirroring the kernel. As a result, the origin parameter behaves differently than in the case of a correlation: the result is shifted in the opposite directions.
The function convolve implements multidimensional convolution of the input array with a given kernel.

Note

A convolution is essentially a correlation after mirroring the kernel. As a result, the origin parameter behaves differently than in the case of a correlation: the results is shifted in the opposite direction.

Smoothing filters¶

The gaussian_filter1d function implements a one-dimensional Gaussian filter. The standard-deviation of the Gaussian filter is passed through the parameter sigma. Setting order = 0 corresponds to convolution with a Gaussian kernel. An order of 1, 2, or 3 corresponds to convolution with the first, second or third derivatives of a Gaussian. Higher order derivatives are not implemented.
The gaussian_filter function implements a multidimensional Gaussian filter. The standard-deviations of the Gaussian filter along each axis are passed through the parameter sigma as a sequence or numbers. If sigma is not a sequence but a single number, the standard deviation of the filter is equal along all directions. The order of the filter can be specified separately for each axis. An order of 0 corresponds to convolution with a Gaussian kernel. An order of 1, 2, or 3 corresponds to convolution with the first, second or third derivatives of a Gaussian. Higher order derivatives are not implemented. The order parameter must be a number, to specify the same order for all axes, or a sequence of numbers to specify a different order for each axis.

Note

The multidimensional filter is implemented as a sequence of one-dimensional Gaussian filters. The intermediate arrays are stored in the same data type as the output. Therefore, for output types with a lower precision, the results may be imprecise because intermediate results may be stored with insufficient precision. This can be prevented by specifying a more precise output type.
The uniform_filter1d function calculates a one-dimensional uniform filter of the given size along the given axis.
The uniform_filter implements a multidimensional uniform filter. The sizes of the uniform filter are given for each axis as a sequence of integers by the size parameter. If size is not a sequence, but a single number, the sizes along all axis are assumed to be equal.

Note

The multidimensional 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 lower precision, the results may be imprecise because intermediate results may be stored with insufficient precision. This can be prevented by specifying a more precise output type.

Filters based on order statistics¶

The minimum_filter1d function calculates a one-dimensional minimum filter of given size along the given axis.
The maximum_filter1d function calculates a one-dimensional maximum filter of given size along the given axis.
The minimum_filter function calculates a multidimensional minimum filter. Either the sizes of a rectangular kernel or the footprint of the kernel must be provided. The size parameter, if provided, must be a sequence of sizes or a single number in which case the size of the filter is assumed to be equal along each axis. The footprint, if provided, must be an array that defines the shape of the kernel by its non-zero elements.
The maximum_filter function calculates a multidimensional maximum filter. Either the sizes of a rectangular kernel or the footprint of the kernel must be provided. The size parameter, if provided, must be a sequence of sizes or a single number in which case the size of the filter is assumed to be equal along each axis. The footprint, if provided, must be an array that defines the shape of the kernel by its non-zero elements.
The rank_filter function calculates a multidimensional rank filter. The rank may be less then zero, i.e., rank = -1 indicates the largest element. Either the sizes of a rectangular kernel or the footprint of the kernel must be provided. The size parameter, if provided, must be a sequence of sizes or a single number in which case the size of the filter is assumed to be equal along each axis. The footprint, if provided, must be an array that defines the shape of the kernel by its non-zero elements.
The percentile_filter function calculates a multidimensional percentile filter. The percentile may be less then zero, i.e., percentile = -20 equals percentile = 80. Either the sizes of a rectangular kernel or the footprint of the kernel must be provided. The size parameter, if provided, must be a sequence of sizes or a single number in which case the size of the filter is assumed to be equal along each axis. The footprint, if provided, must be an array that defines the shape of the kernel by its non-zero elements.
The median_filter function calculates a multidimensional median filter. Either the sizes of a rectangular kernel or the footprint of the kernel must be provided. The size parameter, if provided, must be a sequence of sizes or a single number in which case the size of the filter is assumed to be equal along each axis. The footprint if provided, must be an array that defines the shape of the kernel by its non-zero elements.

Derivatives¶

Derivative filters can be constructed in several ways. The function gaussian_filter1d described in Smoothing filters can be used to calculate derivatives along a given axis using the order parameter. Other derivative filters are the Prewitt and Sobel filters:

The prewitt function calculates a derivative along the given axis.
The sobel function calculates a derivative along the given axis.

The Laplace filter is calculated by the sum of the second derivatives along all axes. Thus, different Laplace filters can be constructed using different second derivative functions. Therefore we provide a general function that takes a function argument to calculate the second derivative along a given direction.

The function generic_laplace calculates a laplace filter using the function passed through derivative2 to calculate second derivatives. The function derivative2 should have the following signature

derivative2(input, axis, output, mode, cval, *extra_arguments, **extra_keywords)

It should calculate the second derivative along the dimension axis. If output is not None it should use that for the output and return None, otherwise it should return the result. mode, cval have the usual meaning.

The extra_arguments and extra_keywords arguments can be used to pass a tuple of extra arguments and a dictionary of named arguments that are passed to derivative2 at each call.

For example

>>> def d2(input, axis, output, mode, cval):
...     return correlate1d(input, [1, -2, 1], axis, output, mode, cval, 0)
...
>>> a = np.zeros((5, 5))
>>> a[2, 2] = 1
>>> from scipy.ndimage import generic_laplace
>>> generic_laplace(a, d2)
array([[ 0.,  0.,  0.,  0.,  0.],
       [ 0.,  0.,  1.,  0.,  0.],
       [ 0.,  1., -4.,  1.,  0.],
       [ 0.,  0.,  1.,  0.,  0.],
       [ 0.,  0.,  0.,  0.,  0.]])

To demonstrate the use of the extra_arguments argument we could do

>>> def d2(input, axis, output, mode, cval, weights):
...     return correlate1d(input, weights, axis, output, mode, cval, 0,)
...
>>> a = np.zeros((5, 5))
>>> a[2, 2] = 1
>>> generic_laplace(a, d2, extra_arguments = ([1, -2, 1],))
array([[ 0.,  0.,  0.,  0.,  0.],
       [ 0.,  0.,  1.,  0.,  0.],
       [ 0.,  1., -4.,  1.,  0.],
       [ 0.,  0.,  1.,  0.,  0.],
       [ 0.,  0.,  0.,  0.,  0.]])

>>> generic_laplace(a, d2, extra_keywords = {'weights': [1, -2, 1]})
array([[ 0.,  0.,  0.,  0.,  0.],
       [ 0.,  0.,  1.,  0.,  0.],
       [ 0.,  1., -4.,  1.,  0.],
       [ 0.,  0.,  1.,  0.,  0.],
       [ 0.,  0.,  0.,  0.,  0.]])

The following two functions are implemented using generic_laplace by providing appropriate functions for the second derivative function:

The function laplace calculates the Laplace using discrete differentiation for the second derivative (i.e. convolution with [1, -2, 1]).
The function gaussian_laplace calculates the Laplace filter using gaussian_filter to calculate the second derivatives. The standard-deviations of the Gaussian filter along each axis are passed through the parameter sigma as a sequence or numbers. If sigma is not a sequence but a single number, the standard deviation of the filter is equal along all directions.

The gradient magnitude is defined as the square root of the sum of the squares of the gradients in all directions. Similar to the generic Laplace function there is a generic_gradient_magnitude function that calculats the gradient magnitude of an array.

The function generic_gradient_magnitude calculates a gradient magnitude using the function passed through derivative to calculate first derivatives. The function derivative should have the following signature
```
derivative(input, axis, output, mode, cval, *extra_arguments, **extra_keywords)
```
It should calculate the derivative along the dimension axis. If output is not None it should use that for the output and return None, otherwise it should return the result. mode, cval have the usual meaning.

The extra_arguments and extra_keywords arguments can be used to pass a tuple of extra arguments and a dictionary of named arguments that are passed to derivative at each call.

For example, the sobel function fits the required signature
```
>>> a = np.zeros((5, 5))
>>> a[2, 2] = 1
>>> from scipy.ndimage import sobel, generic_gradient_magnitude
>>> generic_gradient_magnitude(a, sobel)
array([[ 0.        ,  0.        ,  0.        ,  0.        ,  0.        ],
       [ 0.        ,  1.41421356,  2.        ,  1.41421356,  0.        ],
       [ 0.        ,  2.        ,  0.        ,  2.        ,  0.        ],
       [ 0.        ,  1.41421356,  2.        ,  1.41421356,  0.        ],
       [ 0.        ,  0.        ,  0.        ,  0.        ,  0.        ]])
```
See the documentation of generic_laplace for examples of using the extra_arguments and extra_keywords arguments.

The sobel and prewitt functions fit the required signature and can therefore directly be used with generic_gradient_magnitude.

The function gaussian_gradient_magnitude calculates the gradient magnitude using gaussian_filter to calculate the first derivatives. The standard-deviations of the Gaussian filter along each axis are passed through the parameter sigma as a sequence or numbers. If sigma is not a sequence but a single number, the standard deviation of the filter is equal along all directions.

Generic filter functions¶

To implement filter functions, generic functions can be used that accept a callable object that implements the filtering operation. The iteration over the input and output arrays is handled by these generic functions, along with such details as the implementation of the boundary conditions. Only a callable object implementing a callback function that does the actual filtering work must be provided. The callback function can also be written in C and passed using a PyCapsule (see Extending scipy.ndimage in C for more information).

The generic_filter1d function implements a generic one-dimensional filter function, where the actual filtering operation must be supplied as a python function (or other callable object). The generic_filter1d function iterates over the lines of an array and calls function at each line. The arguments that are passed to function are one-dimensional arrays of the numpy.float64 type. The first contains the values of the current line. It is extended at the beginning end the end, according to the filter_size and origin arguments. The second array should be modified in-place to provide the output values of the line. For example consider a correlation along one dimension:
```
>>> a = np.arange(12).reshape(3,4)
>>> correlate1d(a, [1, 2, 3])
array([[ 3,  8, 14, 17],
       [27, 32, 38, 41],
       [51, 56, 62, 65]])
```
The same operation can be implemented using generic_filter1d as follows:
```
>>> def fnc(iline, oline):
...     oline[...] = iline[:-2] + 2 * iline[1:-1] + 3 * iline[2:]
...
>>> from scipy.ndimage import generic_filter1d
>>> generic_filter1d(a, fnc, 3)
array([[ 3,  8, 14, 17],
       [27, 32, 38, 41],
       [51, 56, 62, 65]])
```
Here the origin of the kernel was (by default) assumed to be in the middle of the filter of length 3. Therefore, each input line was extended by one value at the beginning and at the end, before the function was called.

Optionally extra arguments can be defined and passed to the filter function. The extra_arguments and extra_keywords arguments can be used to pass a tuple of extra arguments and/or a dictionary of named arguments that are passed to derivative at each call. For example, we can pass the parameters of our filter as an argument
```
>>> def fnc(iline, oline, a, b):
...     oline[...] = iline[:-2] + a * iline[1:-1] + b * iline[2:]
...
>>> generic_filter1d(a, fnc, 3, extra_arguments = (2, 3))
array([[ 3,  8, 14, 17],
       [27, 32, 38, 41],
       [51, 56, 62, 65]])
```
or
```
>>> generic_filter1d(a, fnc, 3, extra_keywords = {'a':2, 'b':3})
array([[ 3,  8, 14, 17],
       [27, 32, 38, 41],
       [51, 56, 62, 65]])
```
The generic_filter function implements a generic filter function, where the actual filtering operation must be supplied as a python function (or other callable object). The generic_filter function iterates over the array and calls function at each element. The argument of function is a one-dimensional array of the numpy.float64 type, that contains the values around the current element that are within the footprint of the filter. The function should return a single value that can be converted to a double precision number. For example consider a correlation:
```
>>> a = np.arange(12).reshape(3,4)
>>> correlate(a, [[1, 0], [0, 3]])
array([[ 0,  3,  7, 11],
       [12, 15, 19, 23],
       [28, 31, 35, 39]])
```
The same operation can be implemented using generic_filter as follows:
```
>>> def fnc(buffer):
...     return (buffer * np.array([1, 3])).sum()
...
>>> from scipy.ndimage import generic_filter
>>> generic_filter(a, fnc, footprint = [[1, 0], [0, 1]])
array([[ 0,  3,  7, 11],
       [12, 15, 19, 23],
       [28, 31, 35, 39]])
```
Here a kernel footprint was specified that contains only two elements. Therefore the filter function receives a buffer of length equal to two, which was multiplied with the proper weights and the result summed.

When calling generic_filter, either the sizes of a rectangular kernel or the footprint of the kernel must be provided. The size parameter, if provided, must be a sequence of sizes or a single number in which case the size of the filter is assumed to be equal along each axis. The footprint, if provided, must be an array that defines the shape of the kernel by its non-zero elements.

Optionally extra arguments can be defined and passed to the filter function. The extra_arguments and extra_keywords arguments can be used to pass a tuple of extra arguments and/or a dictionary of named arguments that are passed to derivative at each call. For example, we can pass the parameters of our filter as an argument
```
>>> def fnc(buffer, weights):
...     weights = np.asarray(weights)
...     return (buffer * weights).sum()
...
>>> generic_filter(a, fnc, footprint = [[1, 0], [0, 1]], extra_arguments = ([1, 3],))
array([[ 0,  3,  7, 11],
       [12, 15, 19, 23],
       [28, 31, 35, 39]])
```
or
```
>>> generic_filter(a, fnc, footprint = [[1, 0], [0, 1]], extra_keywords= {'weights': [1, 3]})
array([[ 0,  3,  7, 11],
       [12, 15, 19, 23],
       [28, 31, 35, 39]])
```

These functions iterate over the lines or elements starting at the last axis, i.e. the last index changes the fastest. This order of iteration is guaranteed for the case that it is important to adapt the filter depending on spatial location. Here is an example of using a class that implements the filter and keeps track of the current coordinates while iterating. It performs the same filter operation as described above for generic_filter, but additionally prints the current coordinates:

>>> a = np.arange(12).reshape(3,4)
>>>
>>> class fnc_class:
...     def __init__(self, shape):
...         # store the shape:
...         self.shape = shape
...         # initialize the coordinates:
...         self.coordinates = [0] * len(shape)
...
...     def filter(self, buffer):
...         result = (buffer * np.array([1, 3])).sum()
...         print(self.coordinates)
...         # calculate the next coordinates:
...         axes = list(range(len(self.shape)))
...         axes.reverse()
...         for jj in axes:
...             if self.coordinates[jj] < self.shape[jj] - 1:
...                 self.coordinates[jj] += 1
...                 break
...             else:
...                 self.coordinates[jj] = 0
...         return result
...
>>> fnc = fnc_class(shape = (3,4))
>>> generic_filter(a, fnc.filter, footprint = [[1, 0], [0, 1]])
[0, 0]
[0, 1]
[0, 2]
[0, 3]
[1, 0]
[1, 1]
[1, 2]
[1, 3]
[2, 0]
[2, 1]
[2, 2]
[2, 3]
array([[ 0,  3,  7, 11],
       [12, 15, 19, 23],
       [28, 31, 35, 39]])

For the generic_filter1d function the same approach works, except that this function does not iterate over the axis that is being filtered. The example for generic_filter1d then becomes this:

>>> a = np.arange(12).reshape(3,4)
>>>
>>> class fnc1d_class:
...     def __init__(self, shape, axis = -1):
...         # store the filter axis:
...         self.axis = axis
...         # store the shape:
...         self.shape = shape
...         # initialize the coordinates:
...         self.coordinates = [0] * len(shape)
...
...     def filter(self, iline, oline):
...         oline[...] = iline[:-2] + 2 * iline[1:-1] + 3 * iline[2:]
...         print(self.coordinates)
...         # calculate the next coordinates:
...         axes = list(range(len(self.shape)))
...         # skip the filter axis:
...         del axes[self.axis]
...         axes.reverse()
...         for jj in axes:
...             if self.coordinates[jj] < self.shape[jj] - 1:
...                 self.coordinates[jj] += 1
...                 break
...             else:
...                 self.coordinates[jj] = 0
...
>>> fnc = fnc1d_class(shape = (3,4))
>>> generic_filter1d(a, fnc.filter, 3)
[0, 0]
[1, 0]
[2, 0]
array([[ 3,  8, 14, 17],
       [27, 32, 38, 41],
       [51, 56, 62, 65]])

Fourier domain filters¶

The functions described in this section perform filtering operations in the Fourier domain. Thus, the input array of such a function should be compatible with an inverse Fourier transform function, such as the functions from the numpy.fft module. We therefore have to deal with arrays that may be the result of a real or a complex Fourier transform. In the case of a real Fourier transform only half of the of the symmetric complex transform is stored. Additionally, it needs to be known what the length of the axis was that was transformed by the real fft. The functions described here provide a parameter n that in the case of a real transform must be equal to the length of the real transform axis before transformation. If this parameter is less than zero, it is assumed that the input array was the result of a complex Fourier transform. The parameter axis can be used to indicate along which axis the real transform was executed.

The fourier_shift function multiplies the input array with the multidimensional Fourier transform of a shift operation for the given shift. The shift parameter is a sequences of shifts for each dimension, or a single value for all dimensions.
The fourier_gaussian function multiplies the input array with the multidimensional Fourier transform of a Gaussian filter with given standard-deviations sigma. The sigma parameter is a sequences of values for each dimension, or a single value for all dimensions.
The fourier_uniform function multiplies the input array with the multidimensional Fourier transform of a uniform filter with given sizes size. The size parameter is a sequences of values for each dimension, or a single value for all dimensions.
The fourier_ellipsoid function multiplies the input array with the multidimensional Fourier transform of a elliptically shaped filter with given sizes size. The size parameter is a sequences of values for each dimension, or a single value for all dimensions. This function is only implemented for dimensions 1, 2, and 3.

Interpolation functions¶

This section describes various interpolation functions that are based on B-spline theory. A good introduction to B-splines can be found in 1.

Spline pre-filters¶

Interpolation using splines of an order larger than 1 requires a pre-filtering step. The interpolation functions described in section Interpolation functions apply pre-filtering by calling spline_filter, but they can be instructed not to do this by setting the prefilter keyword equal to False. This is useful if more than one interpolation operation is done on the same array. In this case it is more efficient to do the pre-filtering only once and use a prefiltered array as the input of the interpolation functions. The following two functions implement the pre-filtering:

The spline_filter1d function calculates a one-dimensional spline filter along the given axis. An output array can optionally be provided. The order of the spline must be larger then 1 and less than 6.
The spline_filter function calculates a multidimensional spline filter.

Note

The multidimensional filter is implemented as a sequence of one-dimensional spline filters. The intermediate arrays are stored in the same data type as the output. Therefore, if an output with a limited precision is requested, the results may be imprecise because intermediate results may be stored with insufficient precision. This can be prevented by specifying a output type of high precision.

Interpolation functions¶

Following functions all employ spline interpolation to effect some type of geometric transformation of the input array. This requires a mapping of the output coordinates to the input coordinates, and therefore the possibility arises that input values outside the boundaries are needed. This problem is solved in the same way as described in Filter functions for the multidimensional filter functions. Therefore these functions all support a mode parameter that determines how the boundaries are handled, and a cval parameter that gives a constant value in case that the ‘constant’ mode is used.

The geometric_transform function applies an arbitrary geometric transform to the input. The given mapping function is called at each point in the output to find the corresponding coordinates in the input. mapping must be a callable object that accepts a tuple of length equal to the output array rank and returns the corresponding input coordinates as a tuple of length equal to the input array rank. The output shape and output type can optionally be provided. If not given they are equal to the input shape and type.

For example:

>>> a = np.arange(12).reshape(4,3).astype(np.float64)
>>> def shift_func(output_coordinates):
...     return (output_coordinates[0] - 0.5, output_coordinates[1] - 0.5)
...
>>> from scipy.ndimage import geometric_transform
>>> geometric_transform(a, shift_func)
array([[ 0.    ,  0.    ,  0.    ],
       [ 0.    ,  1.3625,  2.7375],
       [ 0.    ,  4.8125,  6.1875],
       [ 0.    ,  8.2625,  9.6375]])

Optionally extra arguments can be defined and passed to the filter function. The extra_arguments and extra_keywords arguments can be used to pass a tuple of extra arguments and/or a dictionary of named arguments that are passed to derivative at each call. For example, we can pass the shifts in our example as arguments

>>> def shift_func(output_coordinates, s0, s1):
...     return (output_coordinates[0] - s0, output_coordinates[1] - s1)
...
>>> geometric_transform(a, shift_func, extra_arguments = (0.5, 0.5))
array([[ 0.    ,  0.    ,  0.    ],
       [ 0.    ,  1.3625,  2.7375],
       [ 0.    ,  4.8125,  6.1875],
       [ 0.    ,  8.2625,  9.6375]])

>>> geometric_transform(a, shift_func, extra_keywords = {'s0': 0.5, 's1': 0.5})
array([[ 0.    ,  0.    ,  0.    ],
       [ 0.    ,  1.3625,  2.7375],
       [ 0.    ,  4.8125,  6.1875],
       [ 0.    ,  8.2625,  9.6375]])

Note

The mapping function can also be written in C and passed using a scipy.LowLevelCallable. See Extending scipy.ndimage in C for more information.

The function map_coordinates applies an arbitrary coordinate transformation using the given array of coordinates. The shape of the output is derived from that of the coordinate array by dropping the first axis. The parameter coordinates is used to find for each point in the output the corresponding coordinates in the input. The values of coordinates along the first axis are the coordinates in the input array at which the output value is found. (See also the numarray coordinates function.) Since the coordinates may be non- integer coordinates, the value of the input at these coordinates is determined by spline interpolation of the requested order.

Here is an example that interpolates a 2D array at (0.5, 0.5) and (1, 2):
```
>>> a = np.arange(12).reshape(4,3).astype(np.float64)
>>> a
array([[  0.,   1.,   2.],
       [  3.,   4.,   5.],
       [  6.,   7.,   8.],
       [  9.,  10.,  11.]])
>>> from scipy.ndimage import map_coordinates
>>> map_coordinates(a, [[0.5, 2], [0.5, 1]])
array([ 1.3625,  7.])
```
The affine_transform function applies an affine transformation to the input array. The given transformation matrix and offset are used to find for each point in the output the corresponding coordinates in the input. The value of the input at the calculated coordinates is determined by spline interpolation of the requested order. The transformation matrix must be two-dimensional or can also be given as a one-dimensional sequence or array. In the latter case, it is assumed that the matrix is diagonal. A more efficient interpolation algorithm is then applied that exploits the separability of the problem. The output shape and output type can optionally be provided. If not given they are equal to the input shape and type.
The shift function returns a shifted version of the input, using spline interpolation of the requested order.
The zoom function returns a rescaled version of the input, using spline interpolation of the requested order.
The rotate function returns the input array rotated in the plane defined by the two axes given by the parameter axes, using spline interpolation of the requested order. The angle must be given in degrees. If reshape is true, then the size of the output array is adapted to contain the rotated input.

Morphology¶

Binary morphology¶

The generate_binary_structure functions generates a binary structuring element for use in binary morphology operations. The rank of the structure must be provided. The size of the structure that is returned is equal to three in each direction. The value of each element is equal to one if the square of the Euclidean distance from the element to the center is less or equal to connectivity. For instance, two dimensional 4-connected and 8-connected structures are generated as follows:
```
>>> from scipy.ndimage import generate_binary_structure
>>> generate_binary_structure(2, 1)
array([[False,  True, False],
       [ True,  True,  True],
       [False,  True, False]], dtype=bool)
>>> generate_binary_structure(2, 2)
array([[ True,  True,  True],
       [ True,  True,  True],
       [ True,  True,  True]], dtype=bool)
```

Most binary morphology functions can be expressed in terms of the basic operations erosion and dilation.

The binary_erosion function implements binary erosion of arrays of arbitrary rank with the given structuring element. The origin parameter controls the placement of the structuring element as described in Filter functions. If no structuring element is provided, an element with connectivity equal to one is generated using generate_binary_structure. The border_value parameter gives the value of the array outside boundaries. The erosion is repeated iterations times. If iterations is less than one, the erosion is repeated until the result does not change anymore. If a mask array is given, only those elements with a true value at the corresponding mask element are modified at each iteration.
The binary_dilation function implements binary dilation of arrays of arbitrary rank with the given structuring element. The origin parameter controls the placement of the structuring element as described in Filter functions. If no structuring element is provided, an element with connectivity equal to one is generated using generate_binary_structure. The border_value parameter gives the value of the array outside boundaries. The dilation is repeated iterations times. If iterations is less than one, the dilation is repeated until the result does not change anymore. If a mask array is given, only those elements with a true value at the corresponding mask element are modified at each iteration.

Here is an example of using binary_dilation to find all elements that touch the border, by repeatedly dilating an empty array from the border using the data array as the mask:

>>> struct = np.array([[0, 1, 0], [1, 1, 1], [0, 1, 0]])
>>> a = np.array([[1,0,0,0,0], [1,1,0,1,0], [0,0,1,1,0], [0,0,0,0,0]])
>>> a
array([[1, 0, 0, 0, 0],
       [1, 1, 0, 1, 0],
       [0, 0, 1, 1, 0],
       [0, 0, 0, 0, 0]])
>>> from scipy.ndimage import binary_dilation
>>> binary_dilation(np.zeros(a.shape), struct, -1, a, border_value=1)
array([[ True, False, False, False, False],
       [ True,  True, False, False, False],
       [False, False, False, False, False],
       [False, False, False, False, False]], dtype=bool)

The binary_erosion and binary_dilation functions both have an iterations parameter which allows the erosion or dilation to be repeated a number of times. Repeating an erosion or a dilation with a given structure n times is equivalent to an erosion or a dilation with a structure that is n-1 times dilated with itself. A function is provided that allows the calculation of a structure that is dilated a number of times with itself:

The iterate_structure function returns a structure by dilation of the input structure iteration - 1 times with itself.

For instance:

>>> struct = generate_binary_structure(2, 1)
>>> struct
array([[False,  True, False],
       [ True,  True,  True],
       [False,  True, False]], dtype=bool)
>>> from scipy.ndimage import iterate_structure
>>> iterate_structure(struct, 2)
array([[False, False,  True, False, False],
       [False,  True,  True,  True, False],
       [ True,  True,  True,  True,  True],
       [False,  True,  True,  True, False],
       [False, False,  True, False, False]], dtype=bool)

If the origin of the original structure is equal to 0, then it is
also equal to 0 for the iterated structure. If not, the origin
must also be adapted if the equivalent of the *iterations*
erosions or dilations must be achieved with the iterated
structure. The adapted origin is simply obtained by multiplying
with the number of iterations. For convenience the
:func:`iterate_structure` also returns the adapted origin if the
*origin* parameter is not ``None``:

.. code:: python

   >>> iterate_structure(struct, 2, -1)
   (array([[False, False,  True, False, False],
           [False,  True,  True,  True, False],
           [ True,  True,  True,  True,  True],
           [False,  True,  True,  True, False],
           [False, False,  True, False, False]], dtype=bool), [-2, -2])

Other morphology operations can be defined in terms of erosion and d dilation. The following functions provide a few of these operations for convenience:

The binary_opening function implements binary opening of arrays of arbitrary rank with the given structuring element. Binary opening is equivalent to a binary erosion followed by a binary dilation with the same structuring element. The origin parameter controls the placement of the structuring element as described in Filter functions. If no structuring element is provided, an element with connectivity equal to one is generated using generate_binary_structure. The iterations parameter gives the number of erosions that is performed followed by the same number of dilations.
The binary_closing function implements binary closing of arrays of arbitrary rank with the given structuring element. Binary closing is equivalent to a binary dilation followed by a binary erosion with the same structuring element. The origin parameter controls the placement of the structuring element as described in Filter functions. If no structuring element is provided, an element with connectivity equal to one is generated using generate_binary_structure. The iterations parameter gives the number of dilations that is performed followed by the same number of erosions.
The binary_fill_holes function is used to close holes in objects in a binary image, where the structure defines the connectivity of the holes. The origin parameter controls the placement of the structuring element as described in Filter functions. If no structuring element is provided, an element with connectivity equal to one is generated using generate_binary_structure.
The binary_hit_or_miss function implements a binary hit-or-miss transform of arrays of arbitrary rank with the given structuring elements. The hit-or-miss transform is calculated by erosion of the input with the first structure, erosion of the logical not of the input with the second structure, followed by the logical and of these two erosions. The origin parameters control the placement of the structuring elements as described in Filter functions. If origin2 equals None it is set equal to the origin1 parameter. If the first structuring element is not provided, a structuring element with connectivity equal to one is generated using generate_binary_structure, if structure2 is not provided, it is set equal to the logical not of structure1.

Grey-scale morphology¶

Grey-scale morphology operations are the equivalents of binary morphology operations that operate on arrays with arbitrary values. Below we describe the grey-scale equivalents of erosion, dilation, opening and closing. These operations are implemented in a similar fashion as the filters described in Filter functions, and we refer to this section for the description of filter kernels and footprints, and the handling of array borders. The grey-scale morphology operations optionally take a structure parameter that gives the values of the structuring element. If this parameter is not given the structuring element is assumed to be flat with a value equal to zero. The shape of the structure can optionally be defined by the footprint parameter. If this parameter is not given, the structure is assumed to be rectangular, with sizes equal to the dimensions of the structure array, or by the size parameter if structure is not given. The size parameter is only used if both structure and footprint are not given, in which case the structuring element is assumed to be rectangular and flat with the dimensions given by size. The size parameter, if provided, must be a sequence of sizes or a single number in which case the size of the filter is assumed to be equal along each axis. The footprint parameter, if provided, must be an array that defines the shape of the kernel by its non-zero elements.

Similar to binary erosion and dilation there are operations for grey-scale erosion and dilation:

The grey_erosion function calculates a multidimensional grey- scale erosion.
The grey_dilation function calculates a multidimensional grey-scale dilation.

Grey-scale opening and closing operations can be defined similar to their binary counterparts:

The grey_opening function implements grey-scale opening of arrays of arbitrary rank. Grey-scale opening is equivalent to a grey-scale erosion followed by a grey-scale dilation.
The grey_closing function implements grey-scale closing of arrays of arbitrary rank. Grey-scale opening is equivalent to a grey-scale dilation followed by a grey-scale erosion.
The morphological_gradient function implements a grey-scale morphological gradient of arrays of arbitrary rank. The grey-scale morphological gradient is equal to the difference of a grey-scale dilation and a grey-scale erosion.
The morphological_laplace function implements a grey-scale morphological laplace of arrays of arbitrary rank. The grey-scale morphological laplace is equal to the sum of a grey-scale dilation and a grey-scale erosion minus twice the input.
The white_tophat function implements a white top-hat filter of arrays of arbitrary rank. The white top-hat is equal to the difference of the input and a grey-scale opening.
The black_tophat function implements a black top-hat filter of arrays of arbitrary rank. The black top-hat is equal to the difference of a grey-scale closing and the input.

Distance transforms¶

Distance transforms are used to calculate the minimum distance from each element of an object to the background. The following functions implement distance transforms for three different distance metrics: Euclidean, City Block, and Chessboard distances.

The function distance_transform_cdt uses a chamfer type algorithm to calculate the distance transform of the input, by replacing each object element (defined by values larger than zero) with the shortest distance to the background (all non-object elements). The structure determines the type of chamfering that is done. If the structure is equal to ‘cityblock’ a structure is generated using generate_binary_structure with a squared distance equal to 1. If the structure is equal to ‘chessboard’, a structure is generated using generate_binary_structure with a squared distance equal to the rank of the array. These choices correspond to the common interpretations of the cityblock and the chessboard distance metrics in two dimensions.

In addition to the distance transform, the feature transform can be calculated. In this case the index of the closest background element is returned along the first axis of the result. The return_distances, and return_indices flags can be used to indicate if the distance transform, the feature transform, or both must be returned.

The distances and indices arguments can be used to give optional output arrays that must be of the correct size and type (both numpy.int32). The basics of the algorithm used to implement this function is described in 2.
The function distance_transform_edt calculates the exact euclidean distance transform of the input, by replacing each object element (defined by values larger than zero) with the shortest euclidean distance to the background (all non-object elements).

In addition to the distance transform, the feature transform can be calculated. In this case the index of the closest background element is returned along the first axis of the result. The return_distances, and return_indices flags can be used to indicate if the distance transform, the feature transform, or both must be returned.

Optionally the sampling along each axis can be given by the sampling parameter which should be a sequence of length equal to the input rank, or a single number in which the sampling is assumed to be equal along all axes.

The distances and indices arguments can be used to give optional output arrays that must be of the correct size and type (numpy.float64 and numpy.int32).The algorithm used to implement this function is described in 3.
The function distance_transform_bf uses a brute-force algorithm to calculate the distance transform of the input, by replacing each object element (defined by values larger than zero) with the shortest distance to the background (all non-object elements). The metric must be one of “euclidean”, “cityblock”, or “chessboard”.

In addition to the distance transform, the feature transform can be calculated. In this case the index of the closest background element is returned along the first axis of the result. The return_distances, and return_indices flags can be used to indicate if the distance transform, the feature transform, or both must be returned.

Optionally the sampling along each axis can be given by the sampling parameter which should be a sequence of length equal to the input rank, or a single number in which the sampling is assumed to be equal along all axes. This parameter is only used in the case of the euclidean distance transform.

The distances and indices arguments can be used to give optional output arrays that must be of the correct size and type (numpy.float64 and numpy.int32).

Note

This function uses a slow brute-force algorithm, the function distance_transform_cdt can be used to more efficiently calculate cityblock and chessboard distance transforms. The function distance_transform_edt can be used to more efficiently calculate the exact euclidean distance transform.

Segmentation and labeling¶

Segmentation is the process of separating objects of interest from the background. The most simple approach is probably intensity thresholding, which is easily done with numpy functions:

>>> a = np.array([[1,2,2,1,1,0],
...               [0,2,3,1,2,0],
...               [1,1,1,3,3,2],
...               [1,1,1,1,2,1]])
>>> np.where(a > 1, 1, 0)
array([[0, 1, 1, 0, 0, 0],
       [0, 1, 1, 0, 1, 0],
       [0, 0, 0, 1, 1, 1],
       [0, 0, 0, 0, 1, 0]])

The result is a binary image, in which the individual objects still need to be identified and labeled. The function label generates an array where each object is assigned a unique number:

The label function generates an array where the objects in the input are labeled with an integer index. It returns a tuple consisting of the array of object labels and the number of objects found, unless the output parameter is given, in which case only the number of objects is returned. The connectivity of the objects is defined by a structuring element. For instance, in two dimensions using a four-connected structuring element gives:
```
>>> a = np.array([[0,1,1,0,0,0],[0,1,1,0,1,0],[0,0,0,1,1,1],[0,0,0,0,1,0]])
>>> s = [[0, 1, 0], [1,1,1], [0,1,0]]
>>> from scipy.ndimage import label
>>> label(a, s)
(array([[0, 1, 1, 0, 0, 0],
        [0, 1, 1, 0, 2, 0],
        [0, 0, 0, 2, 2, 2],
        [0, 0, 0, 0, 2, 0]]), 2)
```
These two objects are not connected because there is no way in which we can place the structuring element such that it overlaps with both objects. However, an 8-connected structuring element results in only a single object:
```
>>> a = np.array([[0,1,1,0,0,0],[0,1,1,0,1,0],[0,0,0,1,1,1],[0,0,0,0,1,0]])
>>> s = [[1,1,1], [1,1,1], [1,1,1]]
>>> label(a, s)[0]
array([[0, 1, 1, 0, 0, 0],
       [0, 1, 1, 0, 1, 0],
       [0, 0, 0, 1, 1, 1],
       [0, 0, 0, 0, 1, 0]])
```
If no structuring element is provided, one is generated by calling generate_binary_structure (see Binary morphology) using a connectivity of one (which in 2D is the 4-connected structure of the first example). The input can be of any type, any value not equal to zero is taken to be part of an object. This is useful if you need to ‘re-label’ an array of object indices, for instance after removing unwanted objects. Just apply the label function again to the index array. For instance:
```
>>> l, n = label([1, 0, 1, 0, 1])
>>> l
array([1, 0, 2, 0, 3])
>>> l = np.where(l != 2, l, 0)
>>> l
array([1, 0, 0, 0, 3])
>>> label(l)[0]
array([1, 0, 0, 0, 2])
```
Note

The structuring element used by label is assumed to be symmetric.

There is a large number of other approaches for segmentation, for instance from an estimation of the borders of the objects that can be obtained for instance by derivative filters. One such an approach is watershed segmentation. The function watershed_ift generates an array where each object is assigned a unique label, from an array that localizes the object borders, generated for instance by a gradient magnitude filter. It uses an array containing initial markers for the objects:

The watershed_ift function applies a watershed from markers algorithm, using an Iterative Forest Transform, as described in 4.

The inputs of this function are the array to which the transform is applied, and an array of markers that designate the objects by a unique label, where any non-zero value is a marker. For instance:

>>> input = np.array([[0, 0, 0, 0, 0, 0, 0],
...                   [0, 1, 1, 1, 1, 1, 0],
...                   [0, 1, 0, 0, 0, 1, 0],
...                   [0, 1, 0, 0, 0, 1, 0],
...                   [0, 1, 0, 0, 0, 1, 0],
...                   [0, 1, 1, 1, 1, 1, 0],
...                   [0, 0, 0, 0, 0, 0, 0]], np.uint8)
>>> markers = np.array([[1, 0, 0, 0, 0, 0, 0],
...                     [0, 0, 0, 0, 0, 0, 0],
...                     [0, 0, 0, 0, 0, 0, 0],
...                     [0, 0, 0, 2, 0, 0, 0],
...                     [0, 0, 0, 0, 0, 0, 0],
...                     [0, 0, 0, 0, 0, 0, 0],
...                     [0, 0, 0, 0, 0, 0, 0]], np.int8)
>>> from scipy.ndimage import watershed_ift
>>> watershed_ift(input, markers)
array([[1, 1, 1, 1, 1, 1, 1],
       [1, 1, 2, 2, 2, 1, 1],
       [1, 2, 2, 2, 2, 2, 1],
       [1, 2, 2, 2, 2, 2, 1],
       [1, 2, 2, 2, 2, 2, 1],
       [1, 1, 2, 2, 2, 1, 1],
       [1, 1, 1, 1, 1, 1, 1]], dtype=int8)

Here two markers were used to designate an object (marker = 2) and the background (marker = 1). The order in which these are processed is arbitrary: moving the marker for the background to the lower right corner of the array yields a different result:

>>> markers = np.array([[0, 0, 0, 0, 0, 0, 0],
...                     [0, 0, 0, 0, 0, 0, 0],
...                     [0, 0, 0, 0, 0, 0, 0],
...                     [0, 0, 0, 2, 0, 0, 0],
...                     [0, 0, 0, 0, 0, 0, 0],
...                     [0, 0, 0, 0, 0, 0, 0],
...                     [0, 0, 0, 0, 0, 0, 1]], np.int8)
>>> watershed_ift(input, markers)
array([[1, 1, 1, 1, 1, 1, 1],
       [1, 1, 1, 1, 1, 1, 1],
       [1, 1, 2, 2, 2, 1, 1],
       [1, 1, 2, 2, 2, 1, 1],
       [1, 1, 2, 2, 2, 1, 1],
       [1, 1, 1, 1, 1, 1, 1],
       [1, 1, 1, 1, 1, 1, 1]], dtype=int8)

The result is that the object (marker = 2) is smaller because the second marker was processed earlier. This may not be the desired effect if the first marker was supposed to designate a background object. Therefore watershed_ift treats markers with a negative value explicitly as background markers and processes them after the normal markers. For instance, replacing the first marker by a negative marker gives a result similar to the first example:

>>> markers = np.array([[0, 0, 0, 0, 0, 0, 0],
...                     [0, 0, 0, 0, 0, 0, 0],
...                     [0, 0, 0, 0, 0, 0, 0],
...                     [0, 0, 0, 2, 0, 0, 0],
...                     [0, 0, 0, 0, 0, 0, 0],
...                     [0, 0, 0, 0, 0, 0, 0],
...                     [0, 0, 0, 0, 0, 0, -1]], np.int8)
>>> watershed_ift(input, markers)
array([[-1, -1, -1, -1, -1, -1, -1],
       [-1, -1,  2,  2,  2, -1, -1],
       [-1,  2,  2,  2,  2,  2, -1],
       [-1,  2,  2,  2,  2,  2, -1],
       [-1,  2,  2,  2,  2,  2, -1],
       [-1, -1,  2,  2,  2, -1, -1],
       [-1, -1, -1, -1, -1, -1, -1]], dtype=int8)

The connectivity of the objects is defined by a structuring element. If no structuring element is provided, one is generated by calling generate_binary_structure (see Binary morphology) using a connectivity of one (which in 2D is a 4-connected structure.) For example, using an 8-connected structure with the last example yields a different object:

>>> watershed_ift(input, markers,
...               structure = [[1,1,1], [1,1,1], [1,1,1]])
array([[-1, -1, -1, -1, -1, -1, -1],
       [-1,  2,  2,  2,  2,  2, -1],
       [-1,  2,  2,  2,  2,  2, -1],
       [-1,  2,  2,  2,  2,  2, -1],
       [-1,  2,  2,  2,  2,  2, -1],
       [-1,  2,  2,  2,  2,  2, -1],
       [-1, -1, -1, -1, -1, -1, -1]], dtype=int8)

Note

The implementation of watershed_ift limits the data types of the input to numpy.uint8 and numpy.uint16.

Object measurements¶

Given an array of labeled objects, the properties of the individual objects can be measured. The find_objects function can be used to generate a list of slices that for each object, give the smallest sub-array that fully contains the object:

The find_objects function finds all objects in a labeled array and returns a list of slices that correspond to the smallest regions in the array that contains the object.

For instance:

>>> a = np.array([[0,1,1,0,0,0],[0,1,1,0,1,0],[0,0,0,1,1,1],[0,0,0,0,1,0]])
>>> l, n = label(a)
>>> from scipy.ndimage import find_objects
>>> f = find_objects(l)
>>> a[f[0]]
array([[1, 1],
       [1, 1]])
>>> a[f[1]]
array([[0, 1, 0],
       [1, 1, 1],
       [0, 1, 0]])

The function find_objects returns slices for all objects, unless the max_label parameter is larger then zero, in which case only the first max_label objects are returned. If an index is missing in the label array, None is return instead of a slice. For example:

>>> from scipy.ndimage import find_objects
>>> find_objects([1, 0, 3, 4], max_label = 3)
[(slice(0, 1, None),), None, (slice(2, 3, None),)]

The list of slices generated by find_objects is useful to find the position and dimensions of the objects in the array, but can also be used to perform measurements on the individual objects. Say we want to find the sum of the intensities of an object in image:

>>> image = np.arange(4 * 6).reshape(4, 6)
>>> mask = np.array([[0,1,1,0,0,0],[0,1,1,0,1,0],[0,0,0,1,1,1],[0,0,0,0,1,0]])
>>> labels = label(mask)[0]
>>> slices = find_objects(labels)

Then we can calculate the sum of the elements in the second object:

>>> np.where(labels[slices[1]] == 2, image[slices[1]], 0).sum()
80

That is however not particularly efficient, and may also be more complicated for other types of measurements. Therefore a few measurements functions are defined that accept the array of object labels and the index of the object to be measured. For instance calculating the sum of the intensities can be done by:

>>> from scipy.ndimage import sum as ndi_sum
>>> ndi_sum(image, labels, 2)
80

For large arrays and small objects it is more efficient to call the measurement functions after slicing the array:

>>> ndi_sum(image[slices[1]], labels[slices[1]], 2)
80

Alternatively, we can do the measurements for a number of labels with a single function call, returning a list of results. For instance, to measure the sum of the values of the background and the second object in our example we give a list of labels:

>>> ndi_sum(image, labels, [0, 2])
array([178.0, 80.0])

The measurement functions described below all support the index parameter to indicate which object(s) should be measured. The default value of index is None. This indicates that all elements where the label is larger than zero should be treated as a single object and measured. Thus, in this case the labels array is treated as a mask defined by the elements that are larger than zero. If index is a number or a sequence of numbers it gives the labels of the objects that are measured. If index is a sequence, a list of the results is returned. Functions that return more than one result, return their result as a tuple if index is a single number, or as a tuple of lists, if index is a sequence.

The sum function calculates the sum of the elements of the object with label(s) given by index, using the labels array for the object labels. If index is None, all elements with a non-zero label value are treated as a single object. If label is None, all elements of input are used in the calculation.
The mean function calculates the mean of the elements of the object with label(s) given by index, using the labels array for the object labels. If index is None, all elements with a non-zero label value are treated as a single object. If label is None, all elements of input are used in the calculation.
The variance function calculates the variance of the elements of the object with label(s) given by index, using the labels array for the object labels. If index is None, all elements with a non-zero label value are treated as a single object. If label is None, all elements of input are used in the calculation.
The standard_deviation function calculates the standard deviation of the elements of the object with label(s) given by index, using the labels array for the object labels. If index is None, all elements with a non-zero label value are treated as a single object. If label is None, all elements of input are used in the calculation.
The minimum function calculates the minimum of the elements of the object with label(s) given by index, using the labels array for the object labels. If index is None, all elements with a non-zero label value are treated as a single object. If label is None, all elements of input are used in the calculation.
The maximum function calculates the maximum of the elements of the object with label(s) given by index, using the labels array for the object labels. If index is None, all elements with a non-zero label value are treated as a single object. If label is None, all elements of input are used in the calculation.
The minimum_position function calculates the position of the minimum of the elements of the object with label(s) given by index, using the labels array for the object labels. If index is None, all elements with a non-zero label value are treated as a single object. If label is None, all elements of input are used in the calculation.
The maximum_position function calculates the position of the maximum of the elements of the object with label(s) given by index, using the labels array for the object labels. If index is None, all elements with a non-zero label value are treated as a single object. If label is None, all elements of input are used in the calculation.
The extrema function calculates the minimum, the maximum, and their positions, of the elements of the object with label(s) given by index, using the labels array for the object labels. If index is None, all elements with a non-zero label value are treated as a single object. If label is None, all elements of input are used in the calculation. The result is a tuple giving the minimum, the maximum, the position of the minimum and the position of the maximum. The result is the same as a tuple formed by the results of the functions minimum, maximum, minimum_position, and maximum_position that are described above.
The center_of_mass function calculates the center of mass of the of the object with label(s) given by index, using the labels array for the object labels. If index is None, all elements with a non-zero label value are treated as a single object. If label is None, all elements of input are used in the calculation.
The histogram function calculates a histogram of the of the object with label(s) given by index, using the labels array for the object labels. If index is None, all elements with a non-zero label value are treated as a single object. If label is None, all elements of input are used in the calculation. Histograms are defined by their minimum (min), maximum (max) and the number of bins (bins). They are returned as one-dimensional arrays of type numpy.int32.

Extending `scipy.ndimage` in C¶

A few functions in scipy.ndimage take a callback argument. This can be either a python function or a scipy.LowLevelCallable containing a pointer to a C function. Using a C function will generally be more efficient since it avoids the overhead of calling a python function on many elements of an array. To use a C function you must write a C extension that contains the callback function and a Python function that returns a scipy.LowLevelCallable containing a pointer to the callback.

An example of a function that supports callbacks is geometric_transform, which accepts a callback function that defines a mapping from all output coordinates to corresponding coordinates in the input array. Consider the following python example which uses geometric_transform to implement a shift function.

from scipy import ndimage

def transform(output_coordinates, shift):
    input_coordinates = output_coordinates[0] - shift, output_coordinates[1] - shift
    return input_coordinates

im = np.arange(12).reshape(4, 3).astype(np.float64)
shift = 0.5
print(ndimage.geometric_transform(im, transform, extra_arguments=(shift,)))

We can also implement the callback function with the following C code.

/* example.c */

#include <Python.h>
#include <numpy/npy_common.h>

static int
_transform(npy_intp *output_coordinates, double *input_coordinates,
           int output_rank, int input_rank, void *user_data)
{
    npy_intp i;
    double shift = *(double *)user_data;

    for (i = 0; i < input_rank; i++) {
        input_coordinates[i] = output_coordinates[i] - shift;
    }
    return 1;
}

static char *transform_signature = "int (npy_intp *, double *, int, int, void *)";

static PyObject *
py_get_transform(PyObject *obj, PyObject *args)
{
    if (!PyArg_ParseTuple(args, "")) return NULL;
    return PyCapsule_New(_transform, transform_signature, NULL);
}

static PyMethodDef ExampleMethods[] = {
    {"get_transform", (PyCFunction)py_get_transform, METH_VARARGS, ""},
    {NULL, NULL, 0, NULL}
};

/* Initialize the module */
#if PY_VERSION_HEX >= 0x03000000
static struct PyModuleDef example = {
    PyModuleDef_HEAD_INIT,
    "example",
    NULL,
    -1,
    ExampleMethods,
    NULL,
    NULL,
    NULL,
    NULL
};

PyMODINIT_FUNC
PyInit_example(void)
{
    return PyModule_Create(&example);
}
#else
PyMODINIT_FUNC
initexample(void)
{
    Py_InitModule("example", ExampleMethods);
}
#endif

More information on writing Python extension modules can be found here. If the C code is in the file example.c, then it can be compiled with the following setup.py,

from distutils.core import setup, Extension
import numpy

shift = Extension('example',
                  ['example.c'],
                  include_dirs=[numpy.get_include()]
)

setup(name='example',
      ext_modules=[shift]
)

and now running the script

import ctypes
import numpy as np
from scipy import ndimage, LowLevelCallable

from example import get_transform

shift = 0.5

user_data = ctypes.c_double(shift)
ptr = ctypes.cast(ctypes.pointer(user_data), ctypes.c_void_p)
callback = LowLevelCallable(get_transform(), ptr)
im = np.arange(12).reshape(4, 3).astype(np.float64)
print(ndimage.geometric_transform(im, callback))

produces the same result as the original python script.

In the C version _transform is the callback function and the parameters output_coordinates and input_coordinates play the same role as they do in the python version while output_rank and input_rank provide the equivalents of len(output_coordinates) and len(input_coordinates). The variable shift is passed through user_data instead of extra_arguments. Finally, the C callback function returns an integer status which is one upon success and zero otherwise.

The function py_transform wraps the callback function in a PyCapsule. The main steps are:

Initialize a PyCapsule. The first argument is a pointer to the callback function.
The second argument is the function signature which must match exactly the one expected by ndimage.
Above, we used scipy.LowLevelCallable to specify user_data that we generated with ctypes.

A different approach would be to supply the data in the capsule context, that can be set by PyCapsule_SetContext and omit specifying user_data in scipy.LowLevelCallable. However, in this approach we would need to deal with allocation/freeing of the data — freeing the data after the capsule is destroyed can be done by specifying a non-NULL callback function in the third argument of PyCapsule_New.

C callback functions for ndimage all follow this scheme. The next section lists the ndimage functions that accept a C callback function and gives the prototype of the function.

References¶

1: M. Unser, “Splines: A Perfect Fit for Signal and Image Processing,” IEEE Signal Processing Magazine, vol. 16, no. 6, pp. 22-38, November 1999.
2: G. Borgefors, “Distance transformations in arbitrary dimensions.”, Computer Vision, Graphics, and Image Processing, 27:321-345, 1984.
3: C. R. Maurer, Jr., R. Qi, and V. Raghavan, “A linear time algorithm for computing exact euclidean distance transforms of binary images in arbitrary dimensions. IEEE Trans. PAMI 25, 265-270, 2003.
4: P. Felkel, R. Wegenkittl, and M. Bruckschwaiger, “Implementation and Complexity of the Watershed-from-Markers Algorithm Computed as a Minimal Cost Forest.”, Eurographics 2001, pp. C:26-35.

Multidimensional image processing (`scipy.ndimage`)¶

Introduction¶

Properties shared by all functions¶

Filter functions¶

Correlation and convolution¶

Smoothing filters¶

Filters based on order statistics¶

Derivatives¶

Generic filter functions¶

Fourier domain filters¶

Interpolation functions¶

Spline pre-filters¶

Interpolation functions¶

Morphology¶

Binary morphology¶

Grey-scale morphology¶

Distance transforms¶

Segmentation and labeling¶

Object measurements¶

Extending `scipy.ndimage` in C¶

References¶

Table of Contents

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“nearest”	Use the value at the boundary	[1 2 3]->[1 1 2 3 3]
“wrap”	Periodically replicate the array	[1 2 3]->[3 1 2 3 1]
“reflect”	Reflect the array at the boundary	[1 2 3]->[1 1 2 3 3]
“constant”	Use a constant value, default is 0.0	[1 2 3]->[0 1 2 3 0]

Multidimensional image processing (scipy.ndimage)¶

Introduction¶

Properties shared by all functions¶

Filter functions¶

Correlation and convolution¶

Smoothing filters¶

Filters based on order statistics¶

Derivatives¶

Generic filter functions¶

Fourier domain filters¶

Interpolation functions¶

Spline pre-filters¶

Interpolation functions¶

Morphology¶

Binary morphology¶

Grey-scale morphology¶

Distance transforms¶

Segmentation and labeling¶

Object measurements¶

Extending scipy.ndimage in C¶

References¶

Table of Contents

Previous topic

Next topic

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Multidimensional image processing (`scipy.ndimage`)¶

Extending `scipy.ndimage` in C¶