Add a plugin and test for imageio

skimage/io/_plugins/imageio_plugin.ini

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+[imageio]
+description = Image reading via the ImageIO Library
+provides = imread, imsave

skimage/io/_plugins/imageio_plugin.py

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+__all__ = ['imread', 'imsave']
+
+from imageio import imread, imsave

skimage/io/tests/test_imageio.py

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+import os
+import os.path
+import numpy as np
+from numpy.testing import *
+from numpy.testing.decorators import skipif
+
+from tempfile import NamedTemporaryFile
+
+from skimage import data_dir
+from skimage.io import imread, imsave, use_plugin, reset_plugins
+import skimage.io as sio
+
+try:
+    import imageio as _imageio
+except ImportError:
+    imageio_available = False
+else:
+    imageio_available = True
+
+
+def setup():
+    if imageio_available:
+        np.random.seed(0)
+        use_plugin('imageio')
+
+
+def teardown():
+    reset_plugins()
+
+
+@skipif(not imageio_available)
+def test_imageio_flatten():
+    # a color image is flattened
+    img = imread(os.path.join(data_dir, 'color.png'), flatten=True)
+    assert img.ndim == 2
+    assert img.dtype == np.float64
+    img = imread(os.path.join(data_dir, 'camera.png'), flatten=True)
+    # check that flattening does not occur for an image that is grey already.
+    assert np.sctype2char(img.dtype) in np.typecodes['AllInteger']
+
+
+@skipif(not imageio_available)
+def test_imageio_palette():
+    img = imread(os.path.join(data_dir, 'palette_color.png'))
+    assert img.ndim == 3
+
+
+@skipif(not imageio_available)
+def test_imageio_truncated_jpg():
+    assert_raises((RuntimeError, ValueError),
+                  sio.imread,
+                  os.path.join(data_dir, 'truncated.jpg'))
+
+
+class TestSave:
+    def roundtrip(self, x, scaling=1):
+        f = NamedTemporaryFile(suffix='.png')
+        fname = f.name
+        f.close()
+        imsave(fname, x)
+        y = imread(fname)
+
+        assert_array_almost_equal((x * scaling).astype(np.int32), y)
+
+    @skipif(not imageio_available)
+    def test_imsave_roundtrip(self):
+        dtype = np.uint8
+        for shape in [(10, 10), (10, 10, 3), (10, 10, 4)]:
+            x = np.ones(shape, dtype=dtype) * np.random.rand(*shape)
+
+            if np.issubdtype(dtype, float):
+                yield self.roundtrip, x, 255
+            else:
+                x = (x * 255).astype(dtype)
+                yield self.roundtrip, x
+
+if __name__ == "__main__":
+    run_module_suite()