add multichannel deprecation warning and clarify shape handling in re…

…scale

doc/examples/plot_pyramid.py

@@ -5,7 +5,7 @@
 
 The `pyramid_gaussian` function takes an image and yields successive images
 shrunk by a constant scale factor. Image pyramids are often used, e.g., to
-implement algorithms for denoising, texture discrimination, and scale- invariant
+implement algorithms for denoising, texture discrimination, and scale-invariant
 detection.
 
 """
@@ -18,7 +18,7 @@
 
 image = data.astronaut()
 rows, cols, dim = image.shape
-pyramid = tuple(pyramid_gaussian(image, downscale=2))
+pyramid = tuple(pyramid_gaussian(image, downscale=2, multichannel=True))
 
 composite_image = np.zeros((rows, cols + cols / 2, 3), dtype=np.double)
 

doc/examples/plot_radon_transform.py

@@ -58,7 +58,7 @@
 from skimage.transform import radon, rescale
 
 image = imread(data_dir + "/phantom.png", as_grey=True)
-image = rescale(image, scale=0.4)
+image = rescale(image, scale=0.4, multichannel=False)
 
 fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(8, 4.5))
 

skimage/feature/orb.py

@@ -132,7 +132,8 @@ def __init__(self, downscale=1.2, n_scales=8,
 
     def _build_pyramid(self, image):
         image = _prepare_grayscale_input_2D(image)
-        return list(pyramid_gaussian(image, self.n_scales - 1, self.downscale))
+        return list(pyramid_gaussian(image, self.n_scales - 1,
+                                     self.downscale, multichannel=False))
 
     def _detect_octave(self, octave_image):
         # Extract keypoints for current octave

skimage/transform/_warps.py

@@ -1,3 +1,4 @@
+import warnings
 import numpy as np
 from scipy import ndimage
 
@@ -7,11 +8,16 @@
 
 
 def _multichannel_default(multichannel, ndim):
-    # utility for maintaining previous color image default behavior
-    if ndim == 3:
-        return True
+    if multichannel is not None:
+        return multichannel
     else:
-        return False
+        warnings.warn('default multichannel argument (None) is deprecated. '
+                      'Specifiy True or False explicitly.')
+        # utility for maintaining previous color image default behavior
+        if ndim == 3:
+            return True
+        else:
+            return False
 
 
 def resize(image, output_shape, order=1, mode='constant', cval=0, clip=True,
@@ -67,27 +73,31 @@ def resize(image, output_shape, order=1, mode='constant', cval=0, clip=True,
 
     """
     output_shape = np.asarray(output_shape)
-    orig_shape = image.shape
-    if len(orig_shape) < len(output_shape):
-        orig_shape = orig_shape + (1, ) * (len(output_shape) - len(orig_shape))
-    elif len(output_shape) < len(orig_shape):
-        orig_shape = orig_shape[:-1]
-    orig_shape = np.asarray(orig_shape)
-
-    dim_scales = orig_shape.astype('f8') / output_shape
-
     ndim_out = len(output_shape)
+    input_shape = image.shape
+    if ndim_out > image.ndim:
+        # append dimensions to input_shape
+        input_shape = input_shape + (1, ) * (ndim_out - image.ndim)
+    elif ndim_out == image.ndim - 1:
+        # multichannel case: append shape of last axis
+        output_shape = np.concatenate((output_shape, [image.shape[-1], ]))
+        ndim_out += 1
+    elif ndim_out < image.ndim - 1:
+        raise ValueError("len(output_shape) cannot be smaller than the image "
+                         "dimensions")
+
+    input_shape = np.asarray(input_shape, dtype=float)
+    dim_scales = input_shape / output_shape
+
     # 2-dimensional interpolation
-    if ndim_out == 2 or (ndim_out == 3 and
-                         (image.ndim == 2 or
-                          output_shape[2] == image.shape[2])):
+    if ndim_out == 2 or (ndim_out == 3 and output_shape[2] == input_shape[2]):
         rows = output_shape[0]
         cols = output_shape[1]
-        orig_rows = orig_shape[0]
-        orig_cols = orig_shape[1]
+        input_rows = input_shape[0]
+        input_cols = input_shape[1]
         if rows == 1 and cols == 1:
-            tform = AffineTransform(translation=(orig_cols / 2.0 - 0.5,
-                                                 orig_rows / 2.0 - 0.5))
+            tform = AffineTransform(translation=(input_cols / 2.0 - 0.5,
+                                                 input_rows / 2.0 - 0.5))
         else:
             # 3 control points necessary to estimate exact AffineTransform
             src_corners = np.array([[1, 1], [1, rows], [cols, rows]]) - 1
@@ -178,13 +188,12 @@ def rescale(image, scale, order=1, mode='constant', cval=0, clip=True,
     (256, 256)
 
     """
-    if multichannel is None:
-        multichannel = _multichannel_default(multichannel, image.ndim)
+    multichannel = _multichannel_default(multichannel, image.ndim)
     scale = np.atleast_1d(scale)
     if len(scale) > 1 and len(scale) != image.ndim:
         raise ValueError("must supply a single scale or one value per axis.")
     orig_shape = np.asarray(image.shape)
-    output_shape = np.round(scale * orig_shape).astype('i8')
+    output_shape = np.round(scale * orig_shape)
     if multichannel:  # don't scale channel dimension
         output_shape[-1] = orig_shape[-1]
 

skimage/transform/pyramids.py

@@ -8,8 +8,7 @@
 
 def _smooth(image, sigma, mode, cval, multichannel=None):
     """Return image with each channel smoothed by the Gaussian filter."""
-    if multichannel is None:
-        multichannel = _multichannel_default(multichannel, image.ndim)
+    multichannel = _multichannel_default(multichannel, image.ndim)
     smoothed = np.empty(image.shape, dtype=np.double)
 
     # apply Gaussian filter to all channels independently
@@ -52,6 +51,7 @@ def pyramid_reduce(image, downscale=2, sigma=None, order=1,
         cval is the value when mode is equal to 'constant'.
     cval : float, optional
         Value to fill past edges of input if mode is 'constant'.
+    multichannel : bool, optional
         If True, last axis will not be rescaled.  The default is True for 3D
         (2D+color) inputs, False otherwise.
 
@@ -65,8 +65,7 @@ def pyramid_reduce(image, downscale=2, sigma=None, order=1,
     .. [1] http://web.mit.edu/persci/people/adelson/pub_pdfs/pyramid83.pdf
 
     """
-    if multichannel is None:
-        multichannel = _multichannel_default(multichannel, image.ndim)
+    multichannel = _multichannel_default(multichannel, image.ndim)
     _check_factor(downscale)
 
     image = img_as_float(image)
@@ -121,8 +120,7 @@ def pyramid_expand(image, upscale=2, sigma=None, order=1,
     .. [1] http://web.mit.edu/persci/people/adelson/pub_pdfs/pyramid83.pdf
 
     """
-    if multichannel is None:
-        multichannel = _multichannel_default(multichannel, image.ndim)
+    multichannel = _multichannel_default(multichannel, image.ndim)
     _check_factor(upscale)
 
     image = img_as_float(image)
@@ -270,8 +268,7 @@ def pyramid_laplacian(image, max_layer=-1, downscale=2, sigma=None, order=1,
     .. [2] http://sepwww.stanford.edu/~morgan/texturematch/paper_html/node3.html
 
     """
-    if multichannel is None:
-        multichannel = _multichannel_default(multichannel, image.ndim)
+    multichannel = _multichannel_default(multichannel, image.ndim)
     _check_factor(downscale)
 
     # cast to float for consistent data type in pyramid

skimage/transform/tests/test_pyramids.py

@@ -2,18 +2,21 @@
 import numpy as np
 from skimage import data
 from skimage.transform import pyramids
+from skimage._shared._warnings import expected_warnings
 
 
 image = data.astronaut()
 image_gray = image[..., 0]
 
 
+@expected_warnings(['default multichannel'])
 def test_pyramid_reduce_rgb():
     rows, cols, dim = image.shape
     out = pyramids.pyramid_reduce(image, downscale=2)
     assert_array_equal(out.shape, (rows / 2, cols / 2, dim))
 
 
+@expected_warnings(['default multichannel'])
 def test_pyramid_reduce_gray():
     rows, cols = image_gray.shape
     out = pyramids.pyramid_reduce(image_gray, downscale=2)
@@ -29,12 +32,14 @@ def test_pyramid_reduce_nd():
         assert_array_equal(out.shape, expected_shape)
 
 
+@expected_warnings(['default multichannel'])
 def test_pyramid_expand_rgb():
     rows, cols, dim = image.shape
     out = pyramids.pyramid_expand(image, upscale=2)
     assert_array_equal(out.shape, (rows * 2, cols * 2, dim))
 
 
+@expected_warnings(['default multichannel'])
 def test_pyramid_expand_gray():
     rows, cols = image_gray.shape
     out = pyramids.pyramid_expand(image_gray, upscale=2)
@@ -50,6 +55,7 @@ def test_pyramid_expand_nd():
         assert_array_equal(out.shape, expected_shape)
 
 
+@expected_warnings(['default multichannel'])
 def test_build_gaussian_pyramid_rgb():
     rows, cols, dim = image.shape
     pyramid = pyramids.pyramid_gaussian(image, downscale=2)
@@ -58,6 +64,7 @@ def test_build_gaussian_pyramid_rgb():
         assert_array_equal(out.shape, layer_shape)
 
 
+@expected_warnings(['default multichannel'])
 def test_build_gaussian_pyramid_gray():
     rows, cols = image_gray.shape
     pyramid = pyramids.pyramid_gaussian(image_gray, downscale=2)
@@ -77,6 +84,7 @@ def test_build_gaussian_pyramid_nd():
             assert_array_equal(out.shape, layer_shape)
 
 
+@expected_warnings(['default multichannel'])
 def test_build_laplacian_pyramid_rgb():
     rows, cols, dim = image.shape
     pyramid = pyramids.pyramid_laplacian(image, downscale=2)

skimage/transform/tests/test_radon_transform.py

@@ -11,8 +11,8 @@
 
 
 PHANTOM = imread(os.path.join(data_dir, "phantom.png"),
-                   as_grey=True)[::2, ::2]
-PHANTOM = rescale(PHANTOM, 0.5, order=1)
+                 as_grey=True)[::2, ::2]
+PHANTOM = rescale(PHANTOM, 0.5, order=1, multichannel=False)
 
 
 def _debug_plot(original, result, sinogram=None):

skimage/transform/tests/test_warps.py

@@ -75,6 +75,7 @@ def test_warp_nd():
         assert_almost_equal(outx, refx)
 
 
+@expected_warnings(['default multichannel'])
 def test_warp_clip():
     x = np.zeros((5, 5), dtype=np.double)
     x[2, 2] = 1
@@ -130,6 +131,7 @@ def test_rotate_center():
     assert_almost_equal(x0, x)
 
 
+@expected_warnings(['default multichannel'])
 def test_rescale():
     # same scale factor
     x = np.zeros((5, 5), dtype=np.double)
@@ -156,9 +158,6 @@ def test_rescale_multichannel():
     # 2D
     scaled = rescale(x, 2, order=0, multichannel=False)
     assert_equal(scaled.shape, (16, 6))
-    # multichannel defaults to False
-    scaled = rescale(x, 2, order=0)
-    assert_equal(scaled.shape, (16, 6))
 
     # 2D + channels
     x = np.zeros((8, 8, 3), dtype=np.double)
@@ -177,6 +176,21 @@ def test_rescale_multichannel():
     assert_equal(scaled.shape, (16, 16, 16, 6))
 
 
+@expected_warnings(['default multichannel'])
+def test_rescale_multichannel_defaults():
+    # Tests to ensure multichannel=None matches the previous default behaviour
+
+    # 2D: multichannel should default to False
+    x = np.zeros((8, 3), dtype=np.double)
+    scaled = rescale(x, 2, order=0)
+    assert_equal(scaled.shape, (16, 6))
+
+    # 3D: multichannel should default to True
+    x = np.zeros((8, 8, 3), dtype=np.double)
+    scaled = rescale(x, 2, order=0,)
+    assert_equal(scaled.shape, (16, 16, 3))
+
+
 def test_resize2d():
     x = np.zeros((5, 5), dtype=np.double)
     x[1, 1] = 1
@@ -269,7 +283,7 @@ def test_warp_identity():
     assert np.allclose(img, warp(img, AffineTransform(rotation=0)))
     assert not np.allclose(img, warp(img, AffineTransform(rotation=0.1)))
     rgb_img = np.transpose(np.asarray([img, np.zeros_like(img), img]),
-                            (1, 2, 0))
+                           (1, 2, 0))
     warped_rgb_img = warp(rgb_img, AffineTransform(rotation=0.1))
     assert np.allclose(rgb_img, warp(rgb_img, AffineTransform(rotation=0)))
     assert not np.allclose(rgb_img, warped_rgb_img)
@@ -288,14 +302,14 @@ def test_warp_coords_example():
 def test_downscale_local_mean():
     image1 = np.arange(4 * 6).reshape(4, 6)
     out1 = downscale_local_mean(image1, (2, 3))
-    expected1 = np.array([[  4.,   7.],
-                          [ 16.,  19.]])
+    expected1 = np.array([[ 4.,   7.],
+                          [16.,  19.]])
     assert_equal(expected1, out1)
 
     image2 = np.arange(5 * 8).reshape(5, 8)
     out2 = downscale_local_mean(image2, (4, 5))
-    expected2 = np.array([[ 14. ,  10.8],
-                          [  8.5,   5.7]])
+    expected2 = np.array([[14. ,  10.8],
+                          [ 8.5,   5.7]])
     assert_equal(expected2, out2)
 
 
@@ -320,6 +334,7 @@ def test_slow_warp_nonint_oshape():
     warp(image, lambda xy: xy, output_shape=(13.0001, 19.9999))
 
 
+@expected_warnings(['default multichannel'])
 def test_keep_range():
     image = np.linspace(0, 2, 25).reshape(5, 5)
 
@@ -337,6 +352,5 @@ def test_keep_range():
     assert out.max() == 2 / 255.0
 
 
-
 if __name__ == "__main__":
     run_module_suite()

skimage/viewer/tests/test_viewer.py

@@ -44,7 +44,7 @@ def make_key_event(key):
 def test_collection_viewer():
 
     img = data.astronaut()
-    img_collection = tuple(pyramid_gaussian(img))
+    img_collection = tuple(pyramid_gaussian(img, multichannel=True))
 
     view = CollectionViewer(img_collection)
     make_key_event(48)