Merge pull request #1113 from jucoste/move_canny

Move canny from filter to feature

TODO.txt

@@ -3,6 +3,8 @@ Remember to list any API changes below in `doc/source/api_changes.txt`.
 Version 0.13
 ------------
 * Remove deprecated `None` defaults for `skimage.exposure.rescale_intensity`
+* Remove deprecated `skimage.filter.canny` import in filter/__init__.py file (canny is now in `skimage.feature.canny`).
+    * Don't forget to complete api_changes.txt. (`GitHub discuss <https://github.com/scikit-image/scikit-image/pull/1113>`__ )
 
 Version 0.12
 ------------

doc/examples/applications/plot_coins_segmentation.py

@@ -57,7 +57,7 @@ segmentation. To do this, we first get the edges of features using the Canny
 edge-detector.
 """
 
-from skimage.filter import canny
+from skimage.feature import canny
 edges = canny(coins/255.)
 
 fig, ax = plt.subplots(figsize=(4, 3))

doc/examples/plot_canny.py

@@ -19,7 +19,7 @@ import numpy as np
 import matplotlib.pyplot as plt
 from scipy import ndimage
 
-from skimage import filter
+from skimage import feature
 
 
 # Generate noisy image of a square
@@ -31,8 +31,8 @@ im = ndimage.gaussian_filter(im, 4)
 im += 0.2 * np.random.random(im.shape)
 
 # Compute the Canny filter for two values of sigma
-edges1 = filter.canny(im)
-edges2 = filter.canny(im, sigma=3)
+edges1 = feature.canny(im)
+edges2 = feature.canny(im, sigma=3)
 
 # display results
 fig, (ax1, ax2, ax3) = plt.subplots(nrows=1, ncols=3, figsize=(8, 3))

doc/examples/plot_circular_elliptical_hough_transform.py

@@ -37,16 +37,16 @@ Its size is extended by two times the larger radius.
 import numpy as np
 import matplotlib.pyplot as plt
 
-from skimage import data, filter, color
+from skimage import data, color
 from skimage.transform import hough_circle
-from skimage.feature import peak_local_max
+from skimage.feature import peak_local_max, canny
 from skimage.draw import circle_perimeter
 from skimage.util import img_as_ubyte
 
 
 # Load picture and detect edges
 image = img_as_ubyte(data.coins()[0:95, 70:370])
-edges = filter.canny(image, sigma=3, low_threshold=10, high_threshold=50)
+edges = canny(image, sigma=3, low_threshold=10, high_threshold=50)
 
 fig, ax = plt.subplots(ncols=1, nrows=1, figsize=(5, 2))
 
@@ -106,14 +106,15 @@ References
 
 import matplotlib.pyplot as plt
 
-from skimage import data, filter, color
+from skimage import data, color
+from skimage.feature import canny
 from skimage.transform import hough_ellipse
 from skimage.draw import ellipse_perimeter
 
 # Load picture, convert to grayscale and detect edges
 image_rgb = data.coffee()[0:220, 160:420]
 image_gray = color.rgb2gray(image_rgb)
-edges = filter.canny(image_gray, sigma=2.0,
+edges = canny(image_gray, sigma=2.0,
                      low_threshold=0.55, high_threshold=0.8)
 
 # Perform a Hough Transform

doc/examples/plot_line_hough_transform.py

@@ -58,7 +58,7 @@ References
 
 from skimage.transform import (hough_line, hough_line_peaks,
                                probabilistic_hough_line)
-from skimage.filter import canny
+from skimage.feature import canny
 from skimage import data
 
 import numpy as np

doc/source/user_guide/tutorial_segmentation.txt

@@ -38,11 +38,11 @@ Edge-based segmentation
 
 Let us first try to detect edges that enclose the coins. For edge
 detection, we use the `Canny detector 
-<http://en.wikipedia.org/wiki/Canny_edge_detector>`_ of ``skimage.filter.canny``
+<http://en.wikipedia.org/wiki/Canny_edge_detector>`_ of ``skimage.feature.canny``
 
 ::
 
-    >>> from skimage.filter import canny
+    >>> from skimage.feature import canny
     >>> edges = canny(coins/255.)
 
 As the background is very smooth, almost all edges are found at the

skimage/feature/__init__.py

@@ -1,3 +1,4 @@
+from ._canny import canny
 from ._daisy import daisy
 from ._hog import hog
 from .texture import greycomatrix, greycoprops, local_binary_pattern
@@ -17,7 +18,8 @@ from .util import plot_matches
 from .blob import blob_dog, blob_log, blob_doh
 
 
-__all__ = ['daisy',
+__all__ = ['canny'
+           'daisy',
            'hog',
            'greycomatrix',
            'greycoprops',

skimage/feature/tests/test_canny.py

@@ -1,7 +1,7 @@
 import unittest
 import numpy as np
 from scipy.ndimage import binary_dilation, binary_erosion
-import skimage.filter as F
+import skimage.feature as F
 
 
 class TestCanny(unittest.TestCase):

skimage/filter/__init__.py

@@ -1,6 +1,5 @@
 from .lpi_filter import inverse, wiener, LPIFilter2D
 from ._gaussian import gaussian_filter
-from ._canny import canny
 from .edges import (sobel, hsobel, vsobel, scharr, hscharr, vscharr, prewitt,
                     hprewitt, vprewitt, roberts, roberts_positive_diagonal,
                     roberts_negative_diagonal)
@@ -10,7 +9,6 @@ from .thresholding import (threshold_adaptive, threshold_otsu, threshold_yen,
                            threshold_isodata)
 from . import rank
 
-
 from skimage._shared.utils import deprecated
 from skimage import restoration
 denoise_bilateral = deprecated('skimage.restoration.denoise_bilateral')\
@@ -20,6 +18,13 @@ denoise_tv_bregman = deprecated('skimage.restoration.denoise_tv_bregman')\
 denoise_tv_chambolle = deprecated('skimage.restoration.denoise_tv_chambolle')\
                         (restoration.denoise_tv_chambolle)
 
+# Backward compatibility v<0.11
+@deprecated
+def canny(*args, **kwargs):
+    # Hack to avoid circular import
+    from skimage.feature._canny import canny as canny_
+    return canny_(*args, **kwargs)
+
 
 __all__ = ['inverse',
            'wiener',

skimage/viewer/plugins/canny.py

@@ -1,7 +1,7 @@
 import numpy as np
 
 import skimage
-from skimage.filter import canny
+from skimage.feature import canny
 from .overlayplugin import OverlayPlugin
 from ..widgets import Slider, ComboBox