Implement assert_nD in filter and feature packages

skimage/feature/_canny.py

@@ -17,6 +17,7 @@ import scipy.ndimage as ndi
 from scipy.ndimage import (gaussian_filter,
                            generate_binary_structure, binary_erosion, label)
 from skimage import dtype_limits
+from skimage._shared.utils import assert_nD
 
 
 def smooth_with_function_and_mask(image, function, mask):
@@ -148,9 +149,7 @@ def canny(image, sigma=1., low_threshold=None, high_threshold=None, mask=None):
     # mask by one and then mask the output. We also mask out the border points
     # because who knows what lies beyond the edge of the image?
     #
-
-    if image.ndim != 2:
-        raise TypeError("The input 'image' must be a two-dimensional array.")
+    assert_nD(image)
 
     if low_threshold is None:
         low_threshold = 0.1 * dtype_limits(image)[1]

skimage/feature/_daisy.py

@@ -3,6 +3,7 @@ from scipy import sqrt, pi, arctan2, cos, sin, exp
 from scipy.ndimage import gaussian_filter
 import skimage.color
 from skimage import img_as_float, draw
+from skimage._shared.utils import assert_nD
 
 
 def daisy(img, step=4, radius=15, rings=3, histograms=8, orientations=8,
@@ -93,9 +94,7 @@ def daisy(img, step=4, radius=15, rings=3, histograms=8, orientations=8,
     .. [2] http://cvlab.epfl.ch/alumni/tola/daisy.html
     '''
 
-    # Validate image format.
-    if img.ndim != 2:
-        raise ValueError('Only grey-level images are supported.')
+    assert_nD(img, 'img')
 
     img = img_as_float(img)
 

skimage/feature/_hog.py

@@ -1,6 +1,7 @@
 import numpy as np
 from scipy import sqrt, pi, arctan2, cos, sin
 from scipy.ndimage import uniform_filter
+from skimage._shared.utils import assert_nD
 
 
 def hog(image, orientations=9, pixels_per_cell=(8, 8),
@@ -59,8 +60,7 @@ def hog(image, orientations=9, pixels_per_cell=(8, 8),
     shadowing and illumination variations.
     """
 
-    if image.ndim > 2:
-        raise ValueError("Currently only supports grey-level images")
+    assert_nD(image)
 
     if normalise:
         image = sqrt(image)
@@ -79,7 +79,7 @@ def hog(image, orientations=9, pixels_per_cell=(8, 8),
         # convert uint image to float
         # to avoid problems with subtracting unsigned numbers in np.diff()
         image = image.astype('float')
-    
+
     gx = np.empty(image.shape, dtype=np.double)
     gx[:, 0] = 0
     gx[:, -1] = 0

skimage/feature/blob.py

@@ -9,6 +9,7 @@ from skimage.util import img_as_float
 from .peak import peak_local_max
 from ._hessian_det_appx import _hessian_matrix_det
 from skimage.transform import integral_image
+from skimage._shared.utils import assert_nD
 
 
 # This basic blob detection algorithm is based on:
@@ -169,9 +170,7 @@ def blob_dog(image, min_sigma=1, max_sigma=50, sigma_ratio=1.6, threshold=2.0,
     -----
     The radius of each blob is approximately :math:`\sqrt{2}sigma`.
     """
-
-    if image.ndim != 2:
-        raise ValueError("'image' must be a grayscale ")
+    assert_nD(image)
 
     image = img_as_float(image)
 
@@ -275,8 +274,7 @@ def blob_log(image, min_sigma=1, max_sigma=50, num_sigma=10, threshold=.2,
     The radius of each blob is approximately :math:`\sqrt{2}sigma`.
     """
 
-    if image.ndim != 2:
-        raise ValueError("'image' must be a grayscale ")
+    assert_nD(image)
 
     image = img_as_float(image)
 
@@ -385,8 +383,7 @@ def blob_doh(image, min_sigma=1, max_sigma=30, num_sigma=10, threshold=0.01,
     due to the box filters used in the approximation of Hessian Determinant.
     """
 
-    if image.ndim != 2:
-        raise ValueError("'image' must be grayscale ")
+    assert_nD(image)
 
     image = img_as_float(image)
     image = integral_image(image)

skimage/feature/brief.py

@@ -5,6 +5,7 @@ from .util import (DescriptorExtractor, _mask_border_keypoints,
                    _prepare_grayscale_input_2D)
 
 from .brief_cy import _brief_loop
+from skimage._shared.utils import assert_nD
 
 
 class BRIEF(DescriptorExtractor):
@@ -137,6 +138,7 @@ class BRIEF(DescriptorExtractor):
             Keypoint coordinates as ``(row, col)``.
 
         """
+        assert_nD(image)
 
         np.random.seed(self.sample_seed)
 

skimage/feature/censure.py

@@ -9,7 +9,7 @@ from skimage.morphology import octagon, star
 from skimage.feature.util import _mask_border_keypoints
 
 from skimage.feature.censure_cy import _censure_dob_loop
-
+from skimage._shared.utils import assert_nD
 
 # The paper(Reference [1]) mentions the sizes of the Octagon shaped filter
 # kernel for the first seven scales only. The sizes of the later scales
@@ -231,6 +231,8 @@ class CENSURE(FeatureDetector):
         # (4) Finally, we remove the border keypoints and return the keypoints
         # along with its corresponding scale.
 
+        assert_nD(image)
+
         num_scales = self.max_scale - self.min_scale
 
         image = np.ascontiguousarray(_prepare_grayscale_input_2D(image))

skimage/feature/orb.py

@@ -7,6 +7,7 @@ from skimage.feature.util import (FeatureDetector, DescriptorExtractor,
 from skimage.feature import (corner_fast, corner_orientations, corner_peaks,
                              corner_harris)
 from skimage.transform import pyramid_gaussian
+from skimage._shared.utils import assert_nD
 
 from .orb_cy import _orb_loop
 
@@ -166,6 +167,7 @@ class ORB(FeatureDetector, DescriptorExtractor):
             Input image.
 
         """
+        assert_nD(image)
 
         pyramid = self._build_pyramid(image)
 
@@ -237,6 +239,7 @@ class ORB(FeatureDetector, DescriptorExtractor):
             Corresponding orientations in radians.
 
         """
+        assert_nD(image)
 
         pyramid = self._build_pyramid(image)
 
@@ -282,6 +285,7 @@ class ORB(FeatureDetector, DescriptorExtractor):
             Input image.
 
         """
+        assert_nD(image)
 
         pyramid = self._build_pyramid(image)
 

skimage/feature/texture.py

@@ -3,7 +3,7 @@ Methods to characterize image textures.
 """
 
 import numpy as np
-
+from skimage._shared.utils import assert_nD
 from ._texture import _glcm_loop, _local_binary_pattern
 
 
@@ -279,6 +279,7 @@ def local_binary_pattern(image, P, R, method='default'):
            http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.214.6851,
            2004.
     """
+    assert_nD(image)
 
     methods = {
         'default': ord('D'),

skimage/feature/util.py

@@ -1,6 +1,7 @@
 import numpy as np
 
 from skimage.util import img_as_float
+from skimage._shared.utils import assert_nD
 
 
 class FeatureDetector(object):
@@ -124,9 +125,7 @@ def plot_matches(ax, image1, image2, keypoints1, keypoints2, matches,
 
 def _prepare_grayscale_input_2D(image):
     image = np.squeeze(image)
-    if image.ndim != 2:
-        raise ValueError("Only 2-D gray-scale images supported.")
-
+    assert_nD(image)
     return img_as_float(image)
 
 

skimage/filter/lpi_filter.py

@@ -5,6 +5,7 @@
 
 import numpy as np
 from scipy.fftpack import ifftshift
+from skimage._shared.utils import assert_nD
 
 eps = np.finfo(float).eps
 
@@ -118,6 +119,7 @@ class LPIFilter2D(object):
         data : (M,N) ndarray
 
         """
+        assert_nD(data, 'data')
         F, G = self._prepare(data)
         out = np.dual.ifftn(F * G)
         out = np.abs(_centre(out, data.shape))
@@ -155,6 +157,7 @@ def forward(data, impulse_response=None, filter_params={},
     >>> filtered = forward(data.coins(), filt_func)
 
     """
+    assert_nD(data, 'data')
     if predefined_filter is None:
         predefined_filter = LPIFilter2D(impulse_response, **filter_params)
     return predefined_filter(data)
@@ -184,6 +187,7 @@ def inverse(data, impulse_response=None, filter_params={}, max_gain=2,
         images, construct the LPIFilter2D and specify it here.
 
     """
+    assert_nD(data, 'data')
     if predefined_filter is None:
         filt = LPIFilter2D(impulse_response, **filter_params)
     else:
@@ -222,6 +226,8 @@ def wiener(data, impulse_response=None, filter_params={}, K=0.25,
         images, construct the LPIFilter2D and specify it here.
 
     """
+    assert_nD(data, 'data')
+    assert_nD(K, 'K')
     if predefined_filter is None:
         filt = LPIFilter2D(impulse_response, **filter_params)
     else:

skimage/filter/thresholding.py

@@ -6,6 +6,7 @@ __all__ = ['threshold_adaptive',
 import numpy as np
 import scipy.ndimage
 from skimage.exposure import histogram
+from skimage._shared.utils import assert_nD
 
 
 def threshold_adaptive(image, block_size, method='gaussian', offset=0,
@@ -65,6 +66,7 @@ def threshold_adaptive(image, block_size, method='gaussian', offset=0,
     >>> func = lambda arr: arr.mean()
     >>> binary_image2 = threshold_adaptive(image, 15, 'generic', param=func)
     """
+    assert_nD(image)
     thresh_image = np.zeros(image.shape, 'double')
     if method == 'generic':
         scipy.ndimage.generic_filter(image, param, block_size,