SciPy – 50 – elaborazione di immagini multidimensionali – 2

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Correlazione e convoluzione
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.

Filtri di appianamento (smoothing)
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.

Filtri basati sulle statistiche degli ordini
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.

:mrgreen:

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