| affine_transform(input, matrix, offset=0.0, output_shape=None, output=None, order=3, mode='constant', cval=0.0, prefilter=True) |
|
| binary_closing(input, structure=None, iterations=1, output=None, origin=0, mask=None, border_value=0, brute_force=False) |
|
| binary_dilation(input, structure=None, iterations=1, mask=None, output=None, border_value=0, origin=0, brute_force=False) |
|
| binary_erosion(input, structure=None, iterations=1, mask=None, output=None, border_value=0, origin=0, brute_force=False) |
|
| binary_fill_holes(input, structure=None, output=None, origin=0) |
|
| binary_hit_or_miss(input, structure1=None, structure2=None, output=None, origin1=0, origin2=None) |
|
| binary_opening(input, structure=None, iterations=1, output=None, origin=0, mask=None, border_value=0, brute_force=False) |
|
| binary_propagation(input, structure=None, mask=None, output=None, border_value=0, origin=0) |
|
| black_tophat(input, size=None, footprint=None, structure=None, output=None, mode='reflect', cval=0.0, origin=0) |
|
| center_of_mass(input, labels=None, index=None) |
|
| convolve(input, weights, output=None, mode='reflect', cval=0.0, origin=0) |
|
| convolve1d(input, weights, axis=-1, output=None, mode='reflect', cval=0.0, origin=0) |
Calculate a 1-D convolution along the given axis. [extrait de convolve1d.__doc__] |
| correlate(input, weights, output=None, mode='reflect', cval=0.0, origin=0) |
|
| correlate1d(input, weights, axis=-1, output=None, mode='reflect', cval=0.0, origin=0) |
Calculate a 1-D correlation along the given axis. [extrait de correlate1d.__doc__] |
| distance_transform_bf(input, metric='euclidean', sampling=None, return_distances=True, return_indices=False, distances=None, indices=None) |
|
| distance_transform_cdt(input, metric='chessboard', return_distances=True, return_indices=False, distances=None, indices=None) |
|
| distance_transform_edt(input, sampling=None, return_distances=True, return_indices=False, distances=None, indices=None) |
|
| extrema(input, labels=None, index=None) |
|
| find_objects(input, max_label=0) |
|
| fourier_ellipsoid(input, size, n=-1, axis=-1, output=None) |
|
| fourier_gaussian(input, sigma, n=-1, axis=-1, output=None) |
|
| fourier_shift(input, shift, n=-1, axis=-1, output=None) |
|
| fourier_uniform(input, size, n=-1, axis=-1, output=None) |
|
| gaussian_filter(input, sigma, order=0, output=None, mode='reflect', cval=0.0, truncate=4.0) |
Multidimensional Gaussian filter. [extrait de gaussian_filter.__doc__] |
| gaussian_filter1d(input, sigma, axis=-1, order=0, output=None, mode='reflect', cval=0.0, truncate=4.0) |
1-D Gaussian filter. [extrait de gaussian_filter1d.__doc__] |
| gaussian_gradient_magnitude(input, sigma, output=None, mode='reflect', cval=0.0, **kwargs) |
Multidimensional gradient magnitude using Gaussian derivatives. [extrait de gaussian_gradient_magnitude.__doc__] |
| gaussian_laplace(input, sigma, output=None, mode='reflect', cval=0.0, **kwargs) |
Multidimensional Laplace filter using Gaussian second derivatives. [extrait de gaussian_laplace.__doc__] |
| generate_binary_structure(rank, connectivity) |
|
| generic_filter(input, function, size=None, footprint=None, output=None, mode='reflect', cval=0.0, origin=0, extra_arguments=(), extra_keywords=None) |
Calculate a multidimensional filter using the given function. [extrait de generic_filter.__doc__] |
| generic_filter1d(input, function, filter_size, axis=-1, output=None, mode='reflect', cval=0.0, origin=0, extra_arguments=(), extra_keywords=None) |
Calculate a 1-D filter along the given axis. [extrait de generic_filter1d.__doc__] |
| generic_gradient_magnitude(input, derivative, output=None, mode='reflect', cval=0.0, extra_arguments=(), extra_keywords=None) |
Gradient magnitude using a provided gradient function. [extrait de generic_gradient_magnitude.__doc__] |
| generic_laplace(input, derivative2, output=None, mode='reflect', cval=0.0, extra_arguments=(), extra_keywords=None) |
|
| geometric_transform(input, mapping, output_shape=None, output=None, order=3, mode='constant', cval=0.0, prefilter=True, extra_arguments=(), extra_keywords={}) |
|
| grey_closing(input, size=None, footprint=None, structure=None, output=None, mode='reflect', cval=0.0, origin=0) |
|
| grey_dilation(input, size=None, footprint=None, structure=None, output=None, mode='reflect', cval=0.0, origin=0) |
|
| grey_erosion(input, size=None, footprint=None, structure=None, output=None, mode='reflect', cval=0.0, origin=0) |
|
| grey_opening(input, size=None, footprint=None, structure=None, output=None, mode='reflect', cval=0.0, origin=0) |
|
| histogram(input, min, max, bins, labels=None, index=None) |
|
| iterate_structure(structure, iterations, origin=None) |
|
| label(input, structure=None, output=None) |
|
| labeled_comprehension(input, labels, index, func, out_dtype, default, pass_positions=False) |
|
| laplace(input, output=None, mode='reflect', cval=0.0) |
N-D Laplace filter based on approximate second derivatives. [extrait de laplace.__doc__] |
| map_coordinates(input, coordinates, output=None, order=3, mode='constant', cval=0.0, prefilter=True) |
|
| maximum(input, labels=None, index=None) |
|
| maximum_filter(input, size=None, footprint=None, output=None, mode='reflect', cval=0.0, origin=0) |
Calculate a multidimensional maximum filter. [extrait de maximum_filter.__doc__] |
| maximum_filter1d(input, size, axis=-1, output=None, mode='reflect', cval=0.0, origin=0) |
Calculate a 1-D maximum filter along the given axis. [extrait de maximum_filter1d.__doc__] |
| maximum_position(input, labels=None, index=None) |
|
| mean(input, labels=None, index=None) |
|
| median(input, labels=None, index=None) |
|
| median_filter(input, size=None, footprint=None, output=None, mode='reflect', cval=0.0, origin=0) |
|
| minimum(input, labels=None, index=None) |
|
| minimum_filter(input, size=None, footprint=None, output=None, mode='reflect', cval=0.0, origin=0) |
Calculate a multidimensional minimum filter. [extrait de minimum_filter.__doc__] |
| minimum_filter1d(input, size, axis=-1, output=None, mode='reflect', cval=0.0, origin=0) |
Calculate a 1-D minimum filter along the given axis. [extrait de minimum_filter1d.__doc__] |
| minimum_position(input, labels=None, index=None) |
|
| morphological_gradient(input, size=None, footprint=None, structure=None, output=None, mode='reflect', cval=0.0, origin=0) |
|
| morphological_laplace(input, size=None, footprint=None, structure=None, output=None, mode='reflect', cval=0.0, origin=0) |
|
| percentile_filter(input, percentile, size=None, footprint=None, output=None, mode='reflect', cval=0.0, origin=0) |
Calculate a multidimensional percentile filter. [extrait de percentile_filter.__doc__] |
| prewitt(input, axis=-1, output=None, mode='reflect', cval=0.0) |
Calculate a Prewitt filter. [extrait de prewitt.__doc__] |
| rank_filter(input, rank, size=None, footprint=None, output=None, mode='reflect', cval=0.0, origin=0) |
Calculate a multidimensional rank filter. [extrait de rank_filter.__doc__] |
| rotate(input, angle, axes=(1, 0), reshape=True, output=None, order=3, mode='constant', cval=0.0, prefilter=True) |
|
| shift(input, shift, output=None, order=3, mode='constant', cval=0.0, prefilter=True) |
|
| sobel(input, axis=-1, output=None, mode='reflect', cval=0.0) |
Calculate a Sobel filter. [extrait de sobel.__doc__] |
| spline_filter(input, order=3, output=<class 'numpy.float64'>, mode='mirror') |
|
| spline_filter1d(input, order=3, axis=-1, output=<class 'numpy.float64'>, mode='mirror') |
|
| standard_deviation(input, labels=None, index=None) |
|
| sum(input, labels=None, index=None) |
|
| sum_labels(input, labels=None, index=None) |
|
| test(label='fast', verbose=1, extra_argv=None, doctests=False, coverage=False, tests=None, parallel=None) |
|
| uniform_filter(input, size=3, output=None, mode='reflect', cval=0.0, origin=0) |
Multidimensional uniform filter. [extrait de uniform_filter.__doc__] |
| uniform_filter1d(input, size, axis=-1, output=None, mode='reflect', cval=0.0, origin=0) |
Calculate a 1-D uniform filter along the given axis. [extrait de uniform_filter1d.__doc__] |
| variance(input, labels=None, index=None) |
|
| watershed_ift(input, markers, structure=None, output=None) |
|
| white_tophat(input, size=None, footprint=None, structure=None, output=None, mode='reflect', cval=0.0, origin=0) |
|
| zoom(input, zoom, output=None, order=3, mode='constant', cval=0.0, prefilter=True, *, grid_mode=False) |
|
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