Metadata-Version: 2.1
Name: elasticdeform
Version: 0.3.1
Summary: Elastic deformations for N-D images.
Home-page: https://github.com/gvtulder/elasticdeform
Author: Gijs van Tulder
Author-email: gvtulder@gmail.com
License: BSD
Description: Elastic deformations for N-dimensional images (Python, SciPy, NumPy)
        ====================================================================
        
        This library implements elastic grid-based deformations for N-dimensional images.
        
        The elastic deformation approach is described in
        *   Ronneberger, Fischer, and Brox, "U-Net: Convolutional Networks for Biomedical
            Image Segmentation" (https://arxiv.org/abs/1505.04597)
        *   Çiçek et al., "3D U-Net: Learning Dense Volumetric
            Segmentation from Sparse Annotation" (https://arxiv.org/abs/1606.06650)
        
        The procedure generates a coarse displacement grid with a random displacement
        for each grid point. This grid is then interpolated to compute a displacement for
        each pixel in the input image. The input image is then deformed using the
        displacement vectors and a spline interpolation.
        
        
        Installation
        ------------
        
        ```
        pip install elasticdeform
        or
        pip install git+https://github.com/gvtulder/elasticdeform
        ```
        
        This library requires Python 3 and NumPy development headers.
        
        
        Examples
        --------
        
        This basic example deforms an image with a random 3 x 3 deformation grid:
        ```python
        import numpy, imageio, elasticdeform
        X = numpy.zeros((200, 300))
        X[::10, ::10] = 1
        
        # apply deformation with a random 3 x 3 grid
        X_deformed = elasticdeform.deform_random_grid(X, sigma=25, points=3)
        
        imageio.imsave('test_X.png', X)
        imageio.imsave('test_X_deformed.png', X_deformed)
        ```
        
        ### Multiple inputs
        
        If you have multiple images, e.g., an image and a segmentation image, you can
        deform both simultaneously by providing a list of inputs. You can specify
        a different spline order for each input.
        ```python
        # apply deformation to inputs X and Y
        [X_deformed, Y_deformed] = elasticdeform.deform_random_grid([X, Y])
        
        # apply deformation to inputs X and Y,
        # with a different interpolation for each input
        [X_deformed, Y_deformed] = elasticdeform.deform_random_grid([X, Y], order=[3, 0])
        ```
        
        ### Multi-channel images
        
        By default, a deformation will be applied to every dimension of the input. If you
        have multi-channel images, you can use the `axis` parameter to specify which axes
        should be deformed. The same deformation will be applied for each channel.
        
        For example, to deform an RGB image across the first two dimensions, run:
        ```python
        X_deformed = elasticdeform.deform_random_grid(X, axis=(0, 1))
        ```
        
        When deforming multiple inputs, you can provide a tuple of axes for each input:
        ```python
        X = numpy.random.rand(3, 200, 300)
        Y = numpy.random.rand(200, 300)
        [X_deformed, Y_deformed] = elasticdeform.deform_random_grid([X, Y], axis=[(1, 2), (0, 1)])
        ```
        
        ### Cropping
        
        If you intend to crop a small subpatch from the deformed image, you can provide
        the crop dimensions to the deform function. It will then compute only the cropped
        output pixels, while still computing the deformation grid based on the full image
        dimensions. This saves computation time.
        ```python
        X = numpy.random.rand(200, 300)
        
        # define a crop region
        crop = (slice(50, 150), slice(0, 100))
        
        # generate a deformation grid
        displacement = numpy.random.randn(2, 3, 3) * 25
        
        # deform full image
        X_deformed = elasticdeform.deform_grid(X, displacement)
        # compute only the cropped region
        X_deformed_crop = elasticdeform.deform_grid(X, displacement, crop=crop)
        
        # the deformation is the same
        numpy.testing.assert_equal(X_deformed[crop], X_deformed_crop)
        ```
        
        License information
        -------------------
        
        This library was written by Gijs van Tulder (https://vantulder.net/) at the
        Biomedical Imaging Group Rotterdam, Erasmus MC, Rotterdam, the Netherlands
        (https://www.bigr.nl/).
        
        It is inspired by a similar, Python-based implementation by Florian Calvet
        (https://github.com/fcalvet/image_tools). This C-based implementation gives
        the same results but is faster.
        
        This C implementation includes a modified version of the `NI_GeometricTransform`
        from SciPy's ndimage library. (https://github.com/scipy/scipy/blob/28636fbc3f16d562eab7b823546276111f6da98a/scipy/ndimage/src/ni_interpolation.c#L242)
        
        This code is made available under the BSD license. See ``LICENSE.txt`` for details.
        
        https://github.com/gvtulder/elasticdeform
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 2
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: BSD License
Description-Content-Type: text/markdown
