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Merge pull request #2159 from soupault/depr_013_filt
Remove deprecared {h/v}sobel, {h/v}prewitt, {h/v}scharr, roberts_{positive/negative} filters
This commit is contained in:
Johannes Schönberger
@@ -22,9 +22,6 @@ Version 0.14
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Version 0.13
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------------
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* Remove deprecated `None` defaults for `skimage.exposure.rescale_intensity`
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* Remove deprecated edge filters `hsobel`, `vsobel`, `hscharr`, `vscharr`,
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`hprewitt`, `vprewitt`, `roberts_positive_diagonal`,
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`roberts_negative_diagonal` in `skimage/filters/edges.py`
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* Remove supported for renamed edge mode, 'nearest' (it is now 'edge'). This
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involves removing the function _mode_deprecations from skimage._shared.utils
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as well as any uses of _mode_deprecations from restoration/_denoise.py,
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@@ -3,6 +3,9 @@ Version 0.13
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- `skimage.filter` has been removed. Use `skimage.filters` instead.
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- `skimage.filters.canny` has been removed.
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`canny` is available only from `skimage.feature` now.
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- Deprecated filters `hsobel`, `vsobel`, `hscharr`, `vscharr`, `hprewitt`,
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`vprewitt`, `roberts_positive_diagonal`, `roberts_negative_diagonal` have
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been removed from `skimage.filters.edges`.
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Version 0.12
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------------
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@@ -1,11 +1,10 @@
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from .lpi_filter import inverse, wiener, LPIFilter2D
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from ._gaussian import gaussian
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from .edges import (sobel, hsobel, vsobel, sobel_h, sobel_v,
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scharr, hscharr, vscharr, scharr_h, scharr_v,
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prewitt, hprewitt, vprewitt, prewitt_h, prewitt_v,
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roberts, roberts_positive_diagonal,
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roberts_negative_diagonal, roberts_pos_diag,
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roberts_neg_diag, laplace)
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from .edges import (sobel, sobel_h, sobel_v,
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scharr, scharr_h, scharr_v,
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prewitt, prewitt_h, prewitt_v,
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roberts, roberts_pos_diag, roberts_neg_diag,
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laplace)
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from ._rank_order import rank_order
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from ._gabor import gabor_kernel, gabor
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from .thresholding import (threshold_adaptive, threshold_otsu, threshold_yen,
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@@ -27,23 +26,15 @@ __all__ = ['inverse',
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'gaussian',
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'median',
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'sobel',
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'hsobel',
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'vsobel',
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'sobel_h',
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'sobel_v',
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'scharr',
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'hscharr',
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'vscharr',
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'scharr_h',
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'scharr_v',
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'prewitt',
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'hprewitt',
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'vprewitt',
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'prewitt_h',
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'prewitt_v',
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'roberts',
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'roberts_positive_diagonal',
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'roberts_negative_diagonal',
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'roberts_pos_diag',
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'roberts_neg_diag',
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'laplace',
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+1
-263
@@ -11,7 +11,7 @@ Original author: Lee Kamentsky
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"""
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import numpy as np
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from .. import img_as_float
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from .._shared.utils import assert_nD, deprecated
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from .._shared.utils import assert_nD
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from scipy.ndimage import convolve, binary_erosion, generate_binary_structure
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from ..restoration.uft import laplacian
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@@ -170,69 +170,6 @@ def sobel_v(image, mask=None):
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return _mask_filter_result(result, mask)
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@deprecated("skimage.filters.sobel_h")
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def hsobel(image, mask=None):
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"""Find the horizontal edges of an image using the Sobel transform.
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Parameters
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----------
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image : 2-D array
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Image to process.
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mask : 2-D array, optional
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An optional mask to limit the application to a certain area.
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Note that pixels surrounding masked regions are also masked to
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prevent masked regions from affecting the result.
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Returns
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-------
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output : 2-D array
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The absolute Sobel edge map.
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Notes
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-----
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We use the following kernel and return the absolute value of the
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result at each point::
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1 2 1
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0 0 0
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-1 -2 -1
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"""
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return np.abs(sobel_h(image, mask))
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@deprecated("skimage.filters.sobel_v")
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def vsobel(image, mask=None):
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"""Find the vertical edges of an image using the Sobel transform.
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Parameters
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----------
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image : 2-D array
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Image to process
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mask : 2-D array, optional
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An optional mask to limit the application to a certain area.
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Note that pixels surrounding masked regions are also masked to
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prevent masked regions from affecting the result.
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Returns
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-------
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output : 2-D array
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The absolute Sobel edge map.
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Notes
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-----
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We use the following kernel and return the absolute value of the
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result at each point::
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1 0 -1
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2 0 -2
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1 0 -1
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"""
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return np.abs(sobel_v(image, mask))
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def scharr(image, mask=None):
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"""Find the edge magnitude using the Scharr transform.
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@@ -354,78 +291,6 @@ def scharr_v(image, mask=None):
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return _mask_filter_result(result, mask)
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@deprecated("skimage.filters.scharr_h")
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def hscharr(image, mask=None):
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"""Find the horizontal edges of an image using the Scharr transform.
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Parameters
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----------
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image : 2-D array
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Image to process.
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mask : 2-D array, optional
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An optional mask to limit the application to a certain area.
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Note that pixels surrounding masked regions are also masked to
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prevent masked regions from affecting the result.
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Returns
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-------
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output : 2-D array
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The absolute Scharr edge map.
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Notes
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-----
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We use the following kernel and return the absolute value of the
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result at each point::
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3 10 3
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0 0 0
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-3 -10 -3
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References
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----------
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.. [1] D. Kroon, 2009, Short Paper University Twente, Numerical
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Optimization of Kernel Based Image Derivatives.
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"""
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return np.abs(scharr_h(image, mask))
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@deprecated("skimage.filters.scharr_v")
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def vscharr(image, mask=None):
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"""Find the vertical edges of an image using the Scharr transform.
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Parameters
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----------
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image : 2-D array
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Image to process
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mask : 2-D array, optional
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An optional mask to limit the application to a certain area.
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Note that pixels surrounding masked regions are also masked to
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prevent masked regions from affecting the result.
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Returns
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-------
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output : 2-D array
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The absolute Scharr edge map.
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Notes
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-----
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We use the following kernel and return the absolute value of the
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result at each point::
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3 0 -3
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10 0 -10
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3 0 -3
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References
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----------
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.. [1] D. Kroon, 2009, Short Paper University Twente, Numerical
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Optimization of Kernel Based Image Derivatives.
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"""
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return np.abs(scharr_v(image, mask))
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def prewitt(image, mask=None):
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"""Find the edge magnitude using the Prewitt transform.
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@@ -534,68 +399,6 @@ def prewitt_v(image, mask=None):
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return _mask_filter_result(result, mask)
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@deprecated("skimage.filters.prewitt_h")
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def hprewitt(image, mask=None):
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"""Find the horizontal edges of an image using the Prewitt transform.
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Parameters
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----------
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image : 2-D array
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Image to process.
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mask : 2-D array, optional
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An optional mask to limit the application to a certain area.
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Note that pixels surrounding masked regions are also masked to
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prevent masked regions from affecting the result.
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Returns
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-------
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output : 2-D array
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The absolute Prewitt edge map.
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Notes
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-----
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We use the following kernel and return the absolute value of the
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result at each point::
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1 1 1
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0 0 0
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-1 -1 -1
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"""
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return np.abs(prewitt_h(image, mask))
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@deprecated("skimage.filters.prewitt_v")
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def vprewitt(image, mask=None):
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"""Find the vertical edges of an image using the Prewitt transform.
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Parameters
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----------
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image : 2-D array
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Image to process.
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mask : 2-D array, optional
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An optional mask to limit the application to a certain area.
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Note that pixels surrounding masked regions are also masked to
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prevent masked regions from affecting the result.
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Returns
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-------
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output : 2-D array
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The absolute Prewitt edge map.
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Notes
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-----
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We use the following kernel and return the absolute value of the
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result at each point::
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1 0 -1
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1 0 -1
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1 0 -1
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"""
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return np.abs(prewitt_v(image, mask))
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def roberts(image, mask=None):
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"""Find the edge magnitude using Roberts' cross operator.
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@@ -700,71 +503,6 @@ def roberts_neg_diag(image, mask=None):
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return _mask_filter_result(result, mask)
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@deprecated("skimage.filters.roberts_pos_diag")
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def roberts_positive_diagonal(image, mask=None):
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"""Find the cross edges of an image using Roberts' cross operator.
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The kernel is applied to the input image to produce separate measurements
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of the gradient component one orientation.
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Parameters
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----------
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image : 2-D array
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Image to process.
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mask : 2-D array, optional
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An optional mask to limit the application to a certain area.
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Note that pixels surrounding masked regions are also masked to
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prevent masked regions from affecting the result.
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Returns
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-------
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output : 2-D array
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The absolute Robert's edge map.
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Notes
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-----
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We use the following kernel and return the absolute value of the
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result at each point::
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1 0
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0 -1
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"""
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return np.abs(roberts_pos_diag(image, mask))
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@deprecated("skimage.filters.roberts_neg_diag")
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def roberts_negative_diagonal(image, mask=None):
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"""Find the cross edges of an image using the Roberts' Cross operator.
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The kernel is applied to the input image to produce separate measurements
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of the gradient component one orientation.
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Parameters
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----------
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image : 2-D array
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Image to process.
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mask : 2-D array, optional
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An optional mask to limit the application to a certain area.
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Note that pixels surrounding masked regions are also masked to
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prevent masked regions from affecting the result.
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Returns
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-------
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output : 2-D array
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The absolute Robert's edge map.
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Notes
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-----
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We use the following kernel and return the absolute value of the
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result at each point::
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0 1
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-1 0
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"""
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return np.abs(roberts_neg_diag(image, mask))
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def laplace(image, ksize=3, mask=None):
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"""Find the edges of an image using the Laplace operator.
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@@ -67,13 +67,13 @@ def test_sobel_vertical():
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assert (np.all(result[np.abs(j) > 1] == 0))
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def test_hsobel_zeros():
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def test_sobel_h_zeros():
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"""Horizontal sobel on an array of all zeros."""
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result = filters.sobel_h(np.zeros((10, 10)), np.ones((10, 10), bool))
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assert (np.all(result == 0))
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def test_hsobel_mask():
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def test_sobel_h_mask():
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"""Horizontal Sobel on a masked array should be zero."""
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np.random.seed(0)
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result = filters.sobel_h(np.random.uniform(size=(10, 10)),
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@@ -81,7 +81,7 @@ def test_hsobel_mask():
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assert (np.all(result == 0))
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def test_hsobel_horizontal():
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def test_sobel_h_horizontal():
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"""Horizontal Sobel on an edge should be a horizontal line."""
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i, j = np.mgrid[-5:6, -5:6]
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image = (i >= 0).astype(float)
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@@ -92,7 +92,7 @@ def test_hsobel_horizontal():
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assert (np.all(result[np.abs(i) > 1] == 0))
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def test_hsobel_vertical():
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def test_sobel_h_vertical():
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"""Horizontal Sobel on a vertical edge should be zero."""
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i, j = np.mgrid[-5:6, -5:6]
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image = (j >= 0).astype(float) * np.sqrt(2)
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@@ -100,13 +100,13 @@ def test_hsobel_vertical():
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assert_allclose(result, 0, atol=1e-10)
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def test_vsobel_zeros():
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def test_sobel_v_zeros():
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"""Vertical sobel on an array of all zeros."""
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result = filters.sobel_v(np.zeros((10, 10)), np.ones((10, 10), bool))
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assert_allclose(result, 0)
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def test_vsobel_mask():
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def test_sobel_v_mask():
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"""Vertical Sobel on a masked array should be zero."""
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np.random.seed(0)
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result = filters.sobel_v(np.random.uniform(size=(10, 10)),
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@@ -114,7 +114,7 @@ def test_vsobel_mask():
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assert_allclose(result, 0)
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def test_vsobel_vertical():
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def test_sobel_v_vertical():
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"""Vertical Sobel on an edge should be a vertical line."""
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i, j = np.mgrid[-5:6, -5:6]
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image = (j >= 0).astype(float)
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@@ -125,7 +125,7 @@ def test_vsobel_vertical():
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assert (np.all(result[np.abs(j) > 1] == 0))
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def test_vsobel_horizontal():
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def test_sobel_v_horizontal():
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"""vertical Sobel on a horizontal edge should be zero."""
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i, j = np.mgrid[-5:6, -5:6]
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image = (i >= 0).astype(float)
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@@ -168,13 +168,13 @@ def test_scharr_vertical():
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assert (np.all(result[np.abs(j) > 1] == 0))
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def test_hscharr_zeros():
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def test_scharr_h_zeros():
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"""Horizontal Scharr on an array of all zeros."""
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result = filters.scharr_h(np.zeros((10, 10)), np.ones((10, 10), bool))
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assert_allclose(result, 0)
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def test_hscharr_mask():
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def test_scharr_h_mask():
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"""Horizontal Scharr on a masked array should be zero."""
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np.random.seed(0)
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result = filters.scharr_h(np.random.uniform(size=(10, 10)),
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@@ -182,7 +182,7 @@ def test_hscharr_mask():
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assert_allclose(result, 0)
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def test_hscharr_horizontal():
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def test_scharr_h_horizontal():
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"""Horizontal Scharr on an edge should be a horizontal line."""
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i, j = np.mgrid[-5:6, -5:6]
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image = (i >= 0).astype(float)
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@@ -193,7 +193,7 @@ def test_hscharr_horizontal():
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assert (np.all(result[np.abs(i) > 1] == 0))
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def test_hscharr_vertical():
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def test_scharr_h_vertical():
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"""Horizontal Scharr on a vertical edge should be zero."""
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i, j = np.mgrid[-5:6, -5:6]
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image = (j >= 0).astype(float)
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@@ -201,13 +201,13 @@ def test_hscharr_vertical():
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assert_allclose(result, 0)
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def test_vscharr_zeros():
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def test_scharr_v_zeros():
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"""Vertical Scharr on an array of all zeros."""
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result = filters.scharr_v(np.zeros((10, 10)), np.ones((10, 10), bool))
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assert_allclose(result, 0)
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def test_vscharr_mask():
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def test_scharr_v_mask():
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"""Vertical Scharr on a masked array should be zero."""
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np.random.seed(0)
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result = filters.scharr_v(np.random.uniform(size=(10, 10)),
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@@ -215,7 +215,7 @@ def test_vscharr_mask():
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assert_allclose(result, 0)
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def test_vscharr_vertical():
|
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def test_scharr_v_vertical():
|
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"""Vertical Scharr on an edge should be a vertical line."""
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i, j = np.mgrid[-5:6, -5:6]
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image = (j >= 0).astype(float)
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@@ -226,7 +226,7 @@ def test_vscharr_vertical():
|
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assert (np.all(result[np.abs(j) > 1] == 0))
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|
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|
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def test_vscharr_horizontal():
|
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def test_scharr_v_horizontal():
|
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"""vertical Scharr on a horizontal edge should be zero."""
|
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i, j = np.mgrid[-5:6, -5:6]
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image = (i >= 0).astype(float)
|
||||
@@ -269,13 +269,13 @@ def test_prewitt_vertical():
|
||||
assert_allclose(result[np.abs(j) > 1], 0, atol=1e-10)
|
||||
|
||||
|
||||
def test_hprewitt_zeros():
|
||||
def test_prewitt_h_zeros():
|
||||
"""Horizontal prewitt on an array of all zeros."""
|
||||
result = filters.prewitt_h(np.zeros((10, 10)), np.ones((10, 10), bool))
|
||||
assert_allclose(result, 0)
|
||||
|
||||
|
||||
def test_hprewitt_mask():
|
||||
def test_prewitt_h_mask():
|
||||
"""Horizontal prewitt on a masked array should be zero."""
|
||||
np.random.seed(0)
|
||||
result = filters.prewitt_h(np.random.uniform(size=(10, 10)),
|
||||
@@ -283,7 +283,7 @@ def test_hprewitt_mask():
|
||||
assert_allclose(result, 0)
|
||||
|
||||
|
||||
def test_hprewitt_horizontal():
|
||||
def test_prewitt_h_horizontal():
|
||||
"""Horizontal prewitt on an edge should be a horizontal line."""
|
||||
i, j = np.mgrid[-5:6, -5:6]
|
||||
image = (i >= 0).astype(float)
|
||||
@@ -294,7 +294,7 @@ def test_hprewitt_horizontal():
|
||||
assert_allclose(result[np.abs(i) > 1], 0, atol=1e-10)
|
||||
|
||||
|
||||
def test_hprewitt_vertical():
|
||||
def test_prewitt_h_vertical():
|
||||
"""Horizontal prewitt on a vertical edge should be zero."""
|
||||
i, j = np.mgrid[-5:6, -5:6]
|
||||
image = (j >= 0).astype(float)
|
||||
@@ -302,13 +302,13 @@ def test_hprewitt_vertical():
|
||||
assert_allclose(result, 0, atol=1e-10)
|
||||
|
||||
|
||||
def test_vprewitt_zeros():
|
||||
def test_prewitt_v_zeros():
|
||||
"""Vertical prewitt on an array of all zeros."""
|
||||
result = filters.prewitt_v(np.zeros((10, 10)), np.ones((10, 10), bool))
|
||||
assert_allclose(result, 0)
|
||||
|
||||
|
||||
def test_vprewitt_mask():
|
||||
def test_prewitt_v_mask():
|
||||
"""Vertical prewitt on a masked array should be zero."""
|
||||
np.random.seed(0)
|
||||
result = filters.prewitt_v(np.random.uniform(size=(10, 10)),
|
||||
@@ -316,7 +316,7 @@ def test_vprewitt_mask():
|
||||
assert_allclose(result, 0)
|
||||
|
||||
|
||||
def test_vprewitt_vertical():
|
||||
def test_prewitt_v_vertical():
|
||||
"""Vertical prewitt on an edge should be a vertical line."""
|
||||
i, j = np.mgrid[-5:6, -5:6]
|
||||
image = (j >= 0).astype(float)
|
||||
@@ -327,7 +327,7 @@ def test_vprewitt_vertical():
|
||||
assert_allclose(result[np.abs(j) > 1], 0, atol=1e-10)
|
||||
|
||||
|
||||
def test_vprewitt_horizontal():
|
||||
def test_prewitt_v_horizontal():
|
||||
"""Vertical prewitt on a horizontal edge should be zero."""
|
||||
i, j = np.mgrid[-5:6, -5:6]
|
||||
image = (i >= 0).astype(float)
|
||||
|
||||
Reference in New Issue
Block a user