JT: added PyFITS plugin, tests and data

scikits/image/io/_plugins/fits_plugin.ini

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+[fits]
+description = FITS image reading via PyFITS
+provides = imread, imread_collection
+

scikits/image/io/_plugins/fits_plugin.py

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+__all__ = ['imread', 'imread_collection']
+
+import numpy as np
+import scikits.image.io as io
+
+try:
+    import pyfits
+except ImportError:
+    raise ImportError("PyFITS could not be found. Please refer to\n"
+        "http://www.stsci.edu/resources/software_hardware/pyfits\n"
+        "for further instructions.")
+
+
+def imread(fname, as_grey=True, dtype=None):
+    """Load an image from a FITS file.
+
+    Parameters
+    ----------
+    fname : string
+        Image file name, e.g. ``test.fits``.
+    as_grey : bool
+        For FITS images, this is ignored (treated as True).
+    dtype : dtype, optional
+        For FITS, this argument is ignored because Stefan is planning on
+        removing the dtype argument from imread anyway.
+
+    Returns
+    -------
+    img_array : ndarray
+        Unlike other plugins where the different colour bands/channels are
+        stored in the third dimension, here the native dimensionality defined
+        in the FITS file is used.
+
+        Currently if no image is found in the file, None will be returned
+
+    Notes
+    -----
+
+    Currently FITS imread() always returns the first image extension when
+    given a Multi-Extension FITS file; use imread_collection() (which does
+    lazy loading) to get all the extensions at once.
+
+    """
+
+    hdulist = pyfits.open(fname)
+
+    # Iterate over FITS image extensions, ignoring any other extension types
+    # such as binary tables, and get the first image data array:
+    img_array = None
+    for hdu in hdulist:
+        if isinstance(hdu, pyfits.ImageHDU) or \
+           isinstance(hdu, pyfits.PrimaryHDU):
+           if hdu.data is not None:
+               img_array = hdu.data
+               break
+    hdulist.close()
+
+    return img_array
+
+
+def imread_collection(load_pattern, conserve_memory=True):
+    """Load a collection of images from one or more FITS files
+
+       Parameters
+       ----------
+       load_pattern : str or list
+           List of extensions to load. Filename globbing is currently
+           unsupported.
+       converve_memory : bool
+           If True, never keep more than one in memory at a specific
+           time. Otherwise, images will be cached once they are loaded.
+
+       Returns
+       -------
+
+       ic : ImageCollection
+           Collection of images.
+
+    """
+
+    intype = type(load_pattern)
+    if intype is not list and intype is not str:
+        raise TypeError("Input must be a file or list of files")
+
+    # Ensure we have a list, otherwise we'll end up iterating over the string:
+    if intype is not list:
+        load_pattern = [load_pattern]
+
+    # Generate a list of filename/extension pairs by opening the list of
+    # files and finding the image extensions in each one:
+    ext_list = []
+    for filename in load_pattern:
+        hdulist = pyfits.open(filename)
+        for n, hdu in zip(range(len(hdulist)), hdulist):
+            if isinstance(hdu, pyfits.ImageHDU) or \
+               isinstance(hdu, pyfits.PrimaryHDU):
+                # Ignore (primary) header units with no data (use '.size'
+                # rather than '.data' to avoid actually loading the image):
+                if hdu.size() > 0:
+                    ext_list.append((filename, n))
+        hdulist.close()
+
+    return io.ImageCollection(ext_list, load_func=FITSFactory,
+                              conserve_memory=conserve_memory)
+
+
+    imread(fname, as_grey=as_grey, dtype=dtype,
+                       plugin=plugin, **plugin_args)
+
+
+def FITSFactory(image_ext):
+    """Load an image extension from a FITS file and return a NumPy array
+    
+    Parameters
+    ----------
+
+       image_ext : tuple
+           FITS extension to load, in the format (filename, ext_num).
+           The (extname, extver) format is unsupported, since this function
+           is not called directly by the user and imread_collection() does
+           the work of figuring out which extensions need loading.
+
+    """
+
+    # Expect a length-2 tuple with a filename as the first element:
+    if not isinstance(image_ext, tuple):
+        raise TypeError("Expected a tuple")
+
+    if len(image_ext) != 2:
+        raise ValueError("Expected a tuple of length 2")
+
+    filename = image_ext[0]
+    extnum = image_ext[1]
+
+    if type(filename) is not str or type(extnum) is not int:
+        raise ValueError("Expected a (filename, extension) tuple")
+
+    hdulist = pyfits.open(filename)
+        
+    data = hdulist[extnum].data
+
+    hdulist.close()
+
+    if data is None:
+        raise RuntimeError("Extension %d of %s has no data" %
+                  (extnum, filename))
+
+    return data
+

scikits/image/io/tests/test_fits.py

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+import os.path
+import numpy as np
+import scikits.image.io as io
+from scikits.image import data_dir
+import scikits.image.io._plugins.fits_plugin as fplug
+
+
+import_success = True
+
+try:
+    import pyfits
+except ImportError:
+    import_success = False
+
+
+def test_fits_plugin_import():
+    # Make sure we get an import exception if PyFITS isn't there
+    # (not sure how useful this is, but it ensures there isn't some other
+    # error when trying to load the plugin)
+    try:
+        io.use_plugin('fits')
+    except ImportError:
+        assert import_success == False
+    else:
+        assert import_success == True
+
+def test_imread_MEF():
+    if import_success:
+        io.use_plugin('fits')
+        testfile = os.path.join(data_dir, 'multi.fits')
+        img = io.imread(testfile)
+        assert np.all(img==pyfits.getdata(testfile, 1))
+
+def test_imread_simple():
+    if import_success:
+        io.use_plugin('fits')
+        testfile = os.path.join(data_dir, 'simple.fits')
+        img = io.imread(testfile)
+        assert np.all(img==pyfits.getdata(testfile, 0))
+
+def test_imread_collection_single_MEF():
+    if import_success:
+        io.use_plugin('fits')
+        testfile = os.path.join(data_dir, 'multi.fits')
+        ic1 = io.imread_collection(testfile)
+        ic2 = io.ImageCollection([(testfile, 1), (testfile, 2), (testfile, 3)],
+                  load_func=fplug.FITSFactory)
+        assert _same_ImageCollection(ic1, ic2)
+
+def test_imread_collection_MEF_and_simple():
+    if import_success:
+        io.use_plugin('fits')
+        testfile1 = os.path.join(data_dir, 'multi.fits')
+        testfile2 = os.path.join(data_dir, 'simple.fits')
+        ic1 = io.imread_collection([testfile1, testfile2])
+        ic2 = io.ImageCollection([(testfile1, 1), (testfile1, 2),
+                                  (testfile1, 3), (testfile2, 0)],
+                                 load_func=fplug.FITSFactory)
+        assert _same_ImageCollection(ic1, ic2)
+
+def _same_ImageCollection(collection1, collection2):
+    """Ancillary function to compare two ImageCollection objects, checking
+       that their constituent arrays are equal.
+    """
+    if len(collection1) != len(collection2):
+        return False
+    for ext1, ext2 in zip(collection1, collection2):
+        if not np.all(ext1 == ext2):
+            return False
+    return True
+