mirror of
https://github.com/wassname/scikit-image.git
synced 2026-07-04 08:45:33 +08:00
Add signed edge operators and deprecate unsigned versions
This commit is contained in:
Johannes Schönberger
@@ -1,8 +1,11 @@
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from .lpi_filter import inverse, wiener, LPIFilter2D
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from ._gaussian import gaussian_filter
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from .edges import (sobel, hsobel, vsobel, scharr, hscharr, vscharr, prewitt,
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hprewitt, vprewitt, roberts, roberts_positive_diagonal,
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roberts_negative_diagonal)
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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)
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from ._rank_order import rank_order
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from ._gabor import gabor_kernel, gabor_filter
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from .thresholding import (threshold_adaptive, threshold_otsu, threshold_yen,
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@@ -34,15 +37,23 @@ __all__ = ['inverse',
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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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'denoise_tv_chambolle',
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'denoise_bilateral',
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'denoise_tv_bregman',
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+287
-25
@@ -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 skimage import img_as_float
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from skimage._shared.utils import assert_nD
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from skimage._shared.utils import assert_nD, deprecated
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from scipy.ndimage import convolve, binary_erosion, generate_binary_structure
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@@ -82,12 +82,12 @@ def sobel(image, mask=None):
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has to be further processed to perform edge detection.
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"""
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assert_nD(image, 2)
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out = np.sqrt(hsobel(image, mask)**2 + vsobel(image, mask)**2)
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out = np.sqrt(sobel_h(image, mask)**2 + sobel_v(image, mask)**2)
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out /= np.sqrt(2)
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return out
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def hsobel(image, mask=None):
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def sobel_h(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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@@ -116,19 +116,19 @@ def hsobel(image, mask=None):
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"""
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assert_nD(image, 2)
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image = img_as_float(image)
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result = np.abs(convolve(image, HSOBEL_WEIGHTS))
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result = convolve(image, HSOBEL_WEIGHTS)
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return _mask_filter_result(result, mask)
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def vsobel(image, mask=None):
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def sobel_v(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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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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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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@@ -149,10 +149,72 @@ def vsobel(image, mask=None):
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"""
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assert_nD(image, 2)
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image = img_as_float(image)
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result = np.abs(convolve(image, VSOBEL_WEIGHTS))
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result = convolve(image, VSOBEL_WEIGHTS)
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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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@@ -182,12 +244,12 @@ def scharr(image, mask=None):
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Optimization of Kernel Based Image Derivatives.
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"""
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out = np.sqrt(hscharr(image, mask)**2 + vscharr(image, mask)**2)
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out = np.sqrt(scharr_h(image, mask)**2 + scharr_v(image, mask)**2)
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out /= np.sqrt(2)
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return out
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def hscharr(image, mask=None):
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def scharr_h(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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@@ -221,11 +283,11 @@ def hscharr(image, mask=None):
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"""
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assert_nD(image, 2)
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image = img_as_float(image)
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result = np.abs(convolve(image, HSCHARR_WEIGHTS))
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result = convolve(image, HSCHARR_WEIGHTS)
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return _mask_filter_result(result, mask)
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def vscharr(image, mask=None):
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def scharr_v(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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@@ -233,7 +295,7 @@ def vscharr(image, mask=None):
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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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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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@@ -259,10 +321,82 @@ def vscharr(image, mask=None):
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"""
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assert_nD(image, 2)
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image = img_as_float(image)
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result = np.abs(convolve(image, VSCHARR_WEIGHTS))
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result = convolve(image, VSCHARR_WEIGHTS)
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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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@@ -286,12 +420,12 @@ def prewitt(image, mask=None):
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and vertical Prewitt transforms.
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"""
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assert_nD(image, 2)
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out = np.sqrt(hprewitt(image, mask)**2 + vprewitt(image, mask)**2)
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out = np.sqrt(prewitt_h(image, mask)**2 + prewitt_v(image, mask)**2)
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out /= np.sqrt(2)
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return out
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def hprewitt(image, mask=None):
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def prewitt_h(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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@@ -320,11 +454,11 @@ def hprewitt(image, mask=None):
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"""
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assert_nD(image, 2)
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image = img_as_float(image)
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result = np.abs(convolve(image, HPREWITT_WEIGHTS))
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result = convolve(image, HPREWITT_WEIGHTS)
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return _mask_filter_result(result, mask)
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def vprewitt(image, mask=None):
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def prewitt_v(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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@@ -353,10 +487,72 @@ def vprewitt(image, mask=None):
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"""
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assert_nD(image, 2)
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image = img_as_float(image)
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result = np.abs(convolve(image, VPREWITT_WEIGHTS))
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result = convolve(image, VPREWITT_WEIGHTS)
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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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@@ -375,13 +571,13 @@ def roberts(image, mask=None):
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The Roberts' Cross edge map.
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"""
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assert_nD(image, 2)
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out = np.sqrt(roberts_positive_diagonal(image, mask)**2 +
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roberts_negative_diagonal(image, mask)**2)
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out = np.sqrt(roberts_pos_diag(image, mask)**2 +
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roberts_neg_diag(image, mask)**2)
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out /= np.sqrt(2)
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return out
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def roberts_positive_diagonal(image, mask=None):
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def roberts_pos_diag(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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@@ -412,11 +608,11 @@ def roberts_positive_diagonal(image, mask=None):
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"""
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assert_nD(image, 2)
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image = img_as_float(image)
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result = np.abs(convolve(image, ROBERTS_PD_WEIGHTS))
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result = convolve(image, ROBERTS_PD_WEIGHTS)
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return _mask_filter_result(result, mask)
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def roberts_negative_diagonal(image, mask=None):
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def roberts_neg_diag(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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@@ -447,5 +643,71 @@ def roberts_negative_diagonal(image, mask=None):
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"""
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assert_nD(image, 2)
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image = img_as_float(image)
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result = np.abs(convolve(image, ROBERTS_ND_WEIGHTS))
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result = convolve(image, ROBERTS_ND_WEIGHTS)
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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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