remove cython gradient implementation

menpo/feature/.gitignore

@@ -1,2 +1 @@
 windowiterator.cpp
-_gradient.cpp

menpo/feature/features.py

@@ -5,39 +5,9 @@
 scipy_gaussian_filter = None  # expensive
 
 from .base import ndfeature, winitfeature, imgfeature
-from ._gradient import gradient_cython
 from .windowiterator import WindowIterator, WindowIteratorResult
 
 
-def _np_gradient(pixels):
-    """
-    This method is used in the case of multi-channel images (not 2D images).
-    The output ordering is identical to the gradient() method, returning
-    a 2 * n_channels image with gradients in order of the first axis derivative
-    over all the channels, then the second etc. For example, in the case of
-    a 3D image with 2 channels, the ordering would be:
-        I[:, 0, 0, 0] = [A_0, B_0, A_1, B_1, A_2, B_2]
-    where A and B are the 'channel' labels (synonymous with RGB for a colour
-    image) and 0,1,2 are the axis labels (synonymous with y,x for a 2D image).
-    """
-    n_dims = pixels.ndim - 1
-    grad_per_dim_per_channel = [np.gradient(g, edge_order=1)
-                                for g in pixels]
-    # Flatten out the separate dims
-    grad_per_channel = list(itertools.chain.from_iterable(
-        grad_per_dim_per_channel))
-    # Add a channel axis for broadcasting
-    grad_per_channel = [g[None, ...] for g in grad_per_channel]
-
-    # Permute the list so it is first axis, second axis, etc
-    grad_per_channel = [grad_per_channel[i::n_dims]
-                        for i in range(n_dims)]
-    grad_per_channel = list(itertools.chain.from_iterable(grad_per_channel))
-
-    # Concatenate gradient list into an array (the new_image)
-    return np.concatenate(grad_per_channel, axis=0)
-
-
 @ndfeature
 def gradient(pixels):
     r"""
@@ -69,10 +39,24 @@ def gradient(pixels):
         all the ``y``-gradients are returned over each channel, then all
         the ``x``-gradients.
     """
-    if (pixels.ndim - 1) == 2:  # 2D Image
-        return gradient_cython(pixels)
-    else:
-        return _np_gradient(pixels)
+    if pixels.dtype == np.uint8:
+        raise TypeError("Attempting to take the gradient on a uint8 image.")
+    n_dims = pixels.ndim - 1
+    grad_per_dim_per_channel = [np.gradient(g, edge_order=1)
+                                for g in pixels]
+    # Flatten out the separate dims
+    grad_per_channel = list(itertools.chain.from_iterable(
+        grad_per_dim_per_channel))
+    # Add a channel axis for broadcasting
+    grad_per_channel = [g[None, ...] for g in grad_per_channel]
+
+    # Permute the list so it is first axis, second axis, etc
+    grad_per_channel = [grad_per_channel[i::n_dims]
+                        for i in range(n_dims)]
+    grad_per_channel = list(itertools.chain.from_iterable(grad_per_channel))
+
+    # Concatenate gradient list into an array (the new_image)
+    return np.concatenate(grad_per_channel, axis=0)
 
 
 @ndfeature

menpo/feature/test/test_gradient.py

@@ -4,7 +4,6 @@
 from menpo.image import Image
 from menpo.feature import gradient
 
-from menpo.feature.features import _np_gradient
 import menpo.io as mio
 
 
@@ -26,17 +25,6 @@ def test_gradient_float():
     assert_allclose(grad_image.pixels[1], np_grad[1])
 
 
-def test_gradient_takeo_float32():
-    dtype = np.float32
-    t = takeo.copy()
-    t.pixels = t.pixels.astype(dtype)
-    grad_image = gradient(t)
-    _check_assertions(grad_image, t.shape, t.n_channels * 2,
-                      dtype)
-    np_grad = _np_gradient(t.pixels)
-    assert_allclose(grad_image.pixels, np_grad)
-
-
 def test_gradient_double():
     dtype = np.float64
     p = example_image.astype(dtype)
@@ -49,15 +37,6 @@ def test_gradient_double():
     assert_allclose(grad_image.pixels[1], np_grad[1])
 
 
-def test_gradient_takeo_double():
-    t = takeo.copy()
-    t.pixels = t.pixels.astype(np.float64)
-    grad_image = gradient(t)
-
-    np_grad = _np_gradient(t.pixels)
-    assert_allclose(grad_image.pixels, np_grad)
-
-
 def test_gradient_uint8_exception():
     image = Image(example_image.astype(np.uint8))
     with raises(TypeError):