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https://github.com/wassname/scikit-image.git
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Better docstring. Better test coverage.
This commit is contained in:
François Orieux
@@ -29,6 +29,8 @@ measure
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Measurement of image properties, e.g., similarity and contours.
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morphology
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Morphological operations, e.g. opening or skeletonization.
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restoration
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Deconvolution algorithms.
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segmentation
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Splitting an image into self-similar regions.
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transform
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@@ -52,7 +52,7 @@ def wiener(image, psf, balance, reg=None, is_real=True):
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The impulse response (input image's space) or the transfer
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function (Fourier space). Both are accepted. The transfer
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function is recognize as being complex
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(`np.iscomplexobj(psf)`).
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(``np.iscomplexobj(psf)``).
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balance : float
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The regularisation parameter value that tune the balance
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between the data and the prior information.
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@@ -60,12 +60,12 @@ def wiener(image, psf, balance, reg=None, is_real=True):
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The regularisation operator. The Laplacian by default. It can
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be an impulse response or a transfer function, as for the psf.
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is_real : boolean, optional
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True by default. Specify if `psf` and `reg` are provided with
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hermitian hypothesis, that is only half of the frequency plane
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is provided (due to the redundancy of Fourier transform of real
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signal). It's apply only if `psf` and/or `reg` are provided as
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transfer function. For the hermitian property see `uft`
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module or `np.fft.rfftn`.
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True by default. Specify if ``psf`` and ``reg`` are provided
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with hermitian hypothesis, that is only half of the frequency
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plane is provided (due to the redundancy of Fourier transform
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of real signal). It's apply only if ``psf`` and/or ``reg`` are
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provided as transfer function. For the hermitian property see
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``uft`` module or ``np.fft.rfftn``.
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Returns
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-------
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@@ -154,8 +154,8 @@ def unsupervised_wiener(image, psf, reg=None, user_params=None, is_real=True):
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Return the deconvolution with a Wiener-Hunt approach, where the
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hyperparameters are automatically estimated. The algorithm is a
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stochastic iterative process (Gibbs sampler) described in ref
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belo]. See also `wiener` function.
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stochastic iterative process (Gibbs sampler) described in
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reference below. See also ``wiener`` function.
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Parameters
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----------
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@@ -165,7 +165,7 @@ def unsupervised_wiener(image, psf, reg=None, user_params=None, is_real=True):
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The impulse response (input image's space) or the transfer
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function (Fourier space). Both are accepted. The transfer
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function is recognize as being complex
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(`np.iscomplexobj(psf)`).
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(``np.iscomplexobj(psf)``).
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reg : ndarray, optional
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The regularisation operator. The Laplacian by default. It can
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be an impulse response or a transfer function, as for the psf.
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@@ -177,12 +177,12 @@ def unsupervised_wiener(image, psf, reg=None, user_params=None, is_real=True):
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x_postmean : (M, N) ndarray
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The deconvolved image (the posterior mean).
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chains : dict
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The keys 'noise' and 'prior' contain the chain list of noise and
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prior precision respectively.
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The keys ``noise`` and ``prior`` contain the chain list of
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noise and prior precision respectively.
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Other parameters
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----------------
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The keys of `user_params` are:
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The keys of ``user_params`` are:
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threshold : float
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The stopping criterion: the norm of the difference between to
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@@ -194,7 +194,7 @@ def unsupervised_wiener(image, psf, reg=None, user_params=None, is_real=True):
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min_iter : int
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The minimum number of iterations. 30 by default.
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max_iter : int
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The maximum number of iterations if `threshold` is not
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The maximum number of iterations if ``threshold`` is not
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satisfied. 150 by default.
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callback : callable (None by default)
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A user provided callable to which is passed, if the function
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@@ -338,7 +338,8 @@ def richardson_lucy(image, psf, iterations=50):
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psf : ndarray
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The point spread function
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iterations : int
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Number of iterations. This parameter play to role of regularisation.
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Number of iterations. This parameter play to role of
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regularisation.
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Returns
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-------
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Binary file not shown.
+23
-24
@@ -31,7 +31,7 @@ equal to
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.. math:: \frac{1}{\sqrt{n}} \sum_i x_i
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or the Fourier tranform have the same energy than the original image
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(see `image_quad_norm` function). The transform is applied from the
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(see ``image_quad_norm`` function). The transform is applied from the
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last axes for performance reason (c order array). You may use directly
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the numpy.fft module for more sophisticated purpose.
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@@ -62,13 +62,13 @@ def ufftn(inarray, dim=None):
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inarray : ndarray
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The array to transform.
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dim : int, optional
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The `dim` last axis along wich to compute the transform. All
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The ``dim`` last axis along wich to compute the transform. All
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axes by default.
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Returns
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-------
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outarray : ndarray (same shape than inarray)
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The unitary N-D Fourier transform of `inarray`.
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The unitary N-D Fourier transform of ``inarray``.
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Examples
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--------
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@@ -93,13 +93,13 @@ def uifftn(inarray, dim=None):
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inarray : ndarray
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The array to transform.
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dim : int, optional
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The `dim` last axis along wich to compute the transform. All
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The ``dim`` last axis along wich to compute the transform. All
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axes by default.
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Returns
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-------
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outarray : ndarray (same shape than inarray)
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The unitary inverse N-D Fourier transform of `inarray`.
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The unitary inverse N-D Fourier transform of ``inarray``.
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Examples
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--------
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@@ -127,17 +127,17 @@ def urfftn(inarray, dim=None):
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inarray : ndarray
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The array to transform.
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dim : int, optional
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The `dim` last axis along wich to compute the transform. All
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The ``dim`` last axis along wich to compute the transform. All
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axes by default.
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Returns
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-------
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outarray : ndarray (the last dim as N / 2 + 1 lenght)
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The unitary N-D real Fourier transform of `inarray`.
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The unitary N-D real Fourier transform of ``inarray``.
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Notes
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-----
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The `urfft` functions assume an input array of real
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The ``urfft`` functions assume an input array of real
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values. Consequently, the output have an Hermitian property and
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redondant values are not computed and returned.
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@@ -167,21 +167,21 @@ def uirfftn(inarray, dim=None, shape=None):
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inarray : ndarray
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The array to transform.
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dim : int, optional
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The `dim` last axis along wich to compute the transform. All
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The ``dim`` last axis along wich to compute the transform. All
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axes by default.
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shape : tuple of int
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The shape of the output. The shape of `rfft` is ambiguous in
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The shape of the output. The shape of ``rfft`` is ambiguous in
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case of odd shape. In this case, the parameter must be
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used. see `np.fft.irfftn`.
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used. see ``np.fft.irfftn``.
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Returns
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-------
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outarray : ndarray
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The unitary N-D inverse real Fourier transform of `inarray`.
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The unitary N-D inverse real Fourier transform of ``inarray``.
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Notes
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-----
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The `uirfft` function assume that output array is of real
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The ``uirfft`` function assume that output array is of real
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values. Consequently, the input is assumed of having an Hermitian
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property and redondant values are implicit.
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@@ -213,7 +213,7 @@ def ufft2(inarray):
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Returns
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-------
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outarray : ndarray (same shape than inarray)
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The unitary 2-D Fourier transform of `inarray`.
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The unitary 2-D Fourier transform of ``inarray``.
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See Also
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--------
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@@ -244,7 +244,7 @@ def uifft2(inarray):
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Returns
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-------
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outarray : ndarray (same shape than inarray)
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The unitary 2-D inverse Fourier transform of `inarray`.
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The unitary 2-D inverse Fourier transform of ``inarray``.
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See Also
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--------
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@@ -277,7 +277,7 @@ def urfft2(inarray):
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Returns
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-------
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outarray : ndarray (the last dim as (N - 1) *2 lenght)
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The unitary 2-D real Fourier transform of `inarray`.
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The unitary 2-D real Fourier transform of ``inarray``.
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See Also
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--------
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@@ -310,7 +310,7 @@ def uirfft2(inarray, shape=None):
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Returns
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-------
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outarray : ndarray (the last dim as (N - 1) *2 lenght)
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The unitary 2-D inverse real Fourier transform of `inarray`.
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The unitary 2-D inverse real Fourier transform of ``inarray``.
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See Also
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--------
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@@ -331,8 +331,7 @@ def uirfft2(inarray, shape=None):
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def image_quad_norm(inarray):
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"""Return quadratic norm of images in Fourier space
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This function detect if the image suppose the hermitian
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property.
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This function detect if the image suppose the hermitian property.
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Parameters
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----------
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@@ -342,7 +341,7 @@ def image_quad_norm(inarray):
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Returns
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-------
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norm : float
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The quadratic norm of `inarray`.
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The quadratic norm of ``inarray``.
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Examples
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--------
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@@ -376,7 +375,7 @@ def ir2tf(imp_resp, shape, dim=None, is_real=True):
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A tuple of integer corresponding to the target shape of the
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tranfert function.
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dim : int, optional
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The `dim` last axis along wich to compute the transform. All
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The ``dim`` last axis along wich to compute the transform. All
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axes by default.
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is_real : boolean (optionnal, default True)
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If True, imp_resp is supposed real and the hermissian property
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@@ -385,7 +384,7 @@ def ir2tf(imp_resp, shape, dim=None, is_real=True):
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Returns
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-------
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y : complex ndarray
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The tranfert function of shape `shape`.
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The tranfert function of shape ``shape``.
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See Also
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--------
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@@ -404,8 +403,8 @@ def ir2tf(imp_resp, shape, dim=None, is_real=True):
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-----
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The input array can be composed of multiple dimentionnal IR with
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an arbitraru number of IR. The individual IR must be accesed
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through first axes. The last `dim` axes of space definition. The
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`dim` parameter must be specified to compute the transform only
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through first axes. The last ``dim`` axes of space definition. The
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``dim`` parameter must be specified to compute the transform only
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along these last axes.
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"""
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if not dim:
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