Metadata-Version: 1.1
Name: pystackreg
Version: 0.2.5
Summary: Image registration tool (python implementation of the ImageJ/FIJI Plugin TurboReg/StackReg)
Home-page: https://github.com/glichtner/pystackreg
Author: Gregor Lichtner (python/C++ port);TurboReg Author: Philippe Thévenaz, Biomedical Imaging Group,Swiss Federal Institute of Technology Lausanne
Author-email: UNKNOWN
License: UNKNOWN
Description: pyStackReg
        ==========
        
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        Summary
        -------
        Python/C++ port of the ImageJ extension TurboReg/StackReg written by Philippe Thevenaz/EPFL.
        
        A python extension for the automatic alignment of a source image or a stack (movie) to a target image/reference frame.
        
        Description
        -----------
        pyStackReg is used to align (register) one or more images to a common reference image, as is required usually in time-resolved fluorescence or wide-field microscopy. It is directly ported from the source code of the ImageJ plugin ``TurboReg`` and provides additionally the functionality of the ImageJ plugin ``StackReg``, both of which were written by Philippe Thevenaz/EPFL (available at http://bigwww.epfl.ch/thevenaz/turboreg/).
        
        pyStackReg provides the following five types of distortion:
        
        - translation
        - rigid body (translation + rotation)
        - scaled rotation (translation + rotation + scaling)
        - affine (translation + rotation + scaling + shearing)
        - bilinear (non-linear transformation; does not preserve straight lines)
        
        pyStackReg supports the full functionality of StackReg plus some additional options, e.g., using different reference images and having access to the actual transformation matrices (please see the examples below).
        
        Please note: The bilinear transformation cannot be propagated, as a combination of bilinear transformations does not generally result in a bilinear transformation. Therefore, stack registration/transform functions won't work with bilinear transformation when using "previous" image as reference image. You can either use another reference ("first" or "mean" for first or mean image, respectively), or try to register/transform each image of the stack separately to its respective previous image (and use the already transformed previous image as reference for the next image).
        
        
        Installation
        ------------
        The package is available on conda forge and on PyPi.
        
        - Install using **conda**
        
        .. code-block:: python
        
            conda install pystackreg -c conda-forge
        
        - Install using **pip**
        
        .. code-block:: python
        
            pip install pystackreg
        
        
        Documentation
        -------------
        The documentation can be found on readthedocs:
        
        https://pystackreg.readthedocs.io/
        
        Tutorial
        --------
        * A tutorial notebook can be found in the `examples/notebooks` folder
          or statically here: https://pystackreg.readthedocs.io/en/latest/tutorial.html
        
        Usage
        -----
        The following example opens two different files and registers them using all different possible transformations
        
        .. code-block:: python
        
            from pystackreg import StackReg
            from skimage import io
        
            #load reference and "moved" image
            ref = io.imread('some_original_image.tif')
            mov = io.imread('some_changed_image.tif')
        
            #Translational transformation
            sr = StackReg(StackReg.TRANSLATION)
            out_tra = sr.register_transform(ref, mov)
        
            #Rigid Body transformation
            sr = StackReg(StackReg.RIGID_BODY)
            out_rot = sr.register_transform(ref, mov)
        
            #Scaled Rotation transformation
            sr = StackReg(StackReg.SCALED_ROTATION)
            out_sca = sr.register_transform(ref, mov)
        
            #Affine transformation
            sr = StackReg(StackReg.AFFINE)
            out_aff = sr.register_transform(ref, mov)
        
            #Bilinear transformation
            sr = StackReg(StackReg.BILINEAR)
            out_bil = sr.register_transform(ref, mov)
        
        
        The next example shows how to separate registration from transformation (e.g., to register in one color channel and then use that information to transform another color channel):
        
        
        .. code-block:: python
        
            from pystackreg import StackReg
            from skimage import io
        
            img0 = io.imread('some_multiframe_image.tif')
            img1 = io.imread('another_multiframe_image.tif')
            # img0.shape: frames x width x height (3D)
        
            sr = StackReg(StackReg.RIGID_BODY)
        
            # register 2nd image to 1st
            sr.register(img0[0, :, :], img0[1,:,:])
        
            # use the transformation from the above registration to register another frame
            out = sr.transform(img1[1,:,:])
        
        The next examples shows how to register and transform a whole stack:
        
        .. code-block:: python
        
            from pystackreg import StackReg
            from skimage import io
        
            img0 = io.imread('some_multiframe_image.tif') # 3 dimensions : frames x width x height
        
            sr = StackReg(StackReg.RIGID_BODY)
        
            # register each frame to the previous (already registered) one
            # this is what the original StackReg ImageJ plugin uses
            out_previous = sr.register_transform_stack(img0, reference='previous')
        
            # register to first image
            out_first = sr.register_transform_stack(img0, reference='first')
        
            # register to mean image
            out_mean = sr.register_transform_stack(img0, reference='mean')
        
            # register to mean of first 10 images
            out_first10 = sr.register_transform_stack(img0, reference='first', n_frames=10)
        
            # calculate a moving average of 10 images, then register the moving average to the mean of
            # the first 10 images and transform the original image (not the moving average)
            out_moving10 = sr.register_transform_stack(img0, reference='first', n_frames=10, moving_average = 10)
        
        The next example shows how to separate registration from transformation for a stack (e.g., to register in one color channel and then use that information to transform another color channel):
        
        .. code-block:: python
        
            from pystackreg import StackReg
            from skimage import io
        
            img0 = io.imread('some_multiframe_image.tif') # 3 dimensions : frames x width x height
            img1 = io.imread('another_multiframe_image.tif') # same shape as img0
        
            # both stacks must have the same shape
            assert img0.shape == img1.shape
        
            sr = StackReg(StackReg.RIGID_BODY)
        
            # register each frame to the previous (already registered) one
            # this is what the original StackReg ImageJ plugin uses
            tmats = sr.register_stack(img0, reference='previous')
            out = sr.transform_stack(img1)
        
            # tmats contains the transformation matrices -> they can be saved
            # and loaded at another time
            import numpy as np
            np.save('transformation_matrices.npy', tmats)
        
            tmats_loaded = np.load('transformation_matrices.npy')
        
            # make sure you use the correct transformation here!
            sr = StackReg(StackReg.RIGID_BODY)
        
            # transform stack using the tmats loaded from file
            sr.transform_stack(img1, tmats=tmats_loaded)
        
            # with the transformation matrices at hand you can also
            # use the transformation algorithms from other packages:
            from skimage import transform as tf
        
            out = np.zeros(img0.shape).astype(np.float)
        
            for i in range(tmats.shape[0]):
                tform = tf.AffineTransform(matrix=tmats[i, :, :])
                out[i, :, :] = tf.warp(img1[i, :, :], tform)
        
        
        Author information
        -------------------
        This is a port of the original Java code by Philippe Thevenaz to C++ with a Python wrapper around it. All credit goes to the original author:
        ::
        
            /*====================================================================
            | Philippe Thevenaz
            | EPFL/STI/IMT/LIB/BM.4.137
            | Station 17
            | CH-1015 Lausanne VD
            | Switzerland
            |
            | phone (CET): +41(21)693.51.61
            | fax: +41(21)693.37.01
            | RFC-822: philippe.thevenaz@epfl.ch
            | X-400: /C=ch/A=400net/P=switch/O=epfl/S=thevenaz/G=philippe/
            | URL: http://bigwww.epfl.ch/
            \===================================================================*/
        
            /*====================================================================
            | This work is based on the following paper:
            |
            | P. Thevenaz, U.E. Ruttimann, M. Unser
            | A Pyramid Approach to Subpixel Registration Based on Intensity
            | IEEE Transactions on Image Processing
            | vol. 7, no. 1, pp. 27-41, January 1998.
            |
            | This paper is available on-line at
            | http://bigwww.epfl.ch/publications/thevenaz9801.html
            |
            | Other relevant on-line publications are available at
            | http://bigwww.epfl.ch/publications/
            \===================================================================*/
        
        License
        -------
        
        ::
        
            You are free to use this software for commercial and non-commercial
            purposes. However, we expect you to include a citation or acknowledgement
            whenever you present or publish research results that are based
            on this software. You are free to modify this software or derive
            works from it, but you are only allowed to distribute it under the
            same terms as this license specifies. Additionally, you must include
            a reference to the research paper above in all software and works
            derived from this software.
        
        
        #########
        Changelog
        #########
        
        0.2.5
        =====
        
        fixed
        -----
        - Compilation in environment without numpy
        
        0.2.3
        =====
        
        added
        -----
        - Added example data and tutorial notebook
        - Added unit tests
        - Additional documentation
        - Detection of time series axis in stacks - will raise a warning if supplied axis in
          stack registration does not correspond to the detected axis
        
        changed
        -------
        - `progress_callback` function now gets called with the iteration number, not
          iteration index (iteration number = iteration index + 1)
        
        fixed
        -----
        - Fixed exception when using a different axis than 0 for registering stacks
        
        0.2.2
        =====
        
        changed
        -------
        - License changed to allow distribution on python package repositories
        
        0.2.1
        =====
        
        added
        -----
        - progress callback function can be supplied to `register_stack()` and `register_transform_stack()` functions via the `progress_callback` that is then called after every iteration (i.e. after each image registration)
        
        changed
        -------
        - progress bar output not shown by default, has to be enabled by using the `verbose=True` parameter in the `register_stack()` and `register_transform_stack()` functions
        
        0.2.0
        =====
        
        added
        -----
        - bilinear transformation
        
Platform: UNKNOWN
Classifier: Development Status :: 5 - Production/Stable
Classifier: Intended Audience :: Science/Research
Classifier: License :: Free To Use But Restricted
Classifier: Operating System :: Microsoft :: Windows
Classifier: Operating System :: Unix
Classifier: Operating System :: POSIX
Classifier: Programming Language :: C++
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 2.7
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Topic :: Scientific/Engineering :: Image Processing
Classifier: Topic :: Utilities
