mirror of
https://github.com/wassname/scikit-image.git
synced 2026-06-29 05:17:50 +08:00
François Boulogne
432 lines
11 KiB
Python
432 lines
11 KiB
Python
"""
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Sobel and Prewitt filters originally part of CellProfiler, code licensed under
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both GPL and BSD licenses.
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Website: http://www.cellprofiler.org
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Copyright (c) 2003-2009 Massachusetts Institute of Technology
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Copyright (c) 2009-2011 Broad Institute
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All rights reserved.
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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 scipy.ndimage import convolve, binary_erosion, generate_binary_structure
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EROSION_SELEM = generate_binary_structure(2, 2)
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HSOBEL_WEIGHTS = np.array([[ 1, 2, 1],
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[ 0, 0, 0],
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[-1,-2,-1]]) / 4.0
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VSOBEL_WEIGHTS = HSOBEL_WEIGHTS.T
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HSCHARR_WEIGHTS = np.array([[ 3, 10, 3],
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[ 0, 0, 0],
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[-3, -10, -3]]) / 16.0
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VSCHARR_WEIGHTS = HSCHARR_WEIGHTS.T
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HPREWITT_WEIGHTS = np.array([[ 1, 1, 1],
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[ 0, 0, 0],
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[-1,-1,-1]]) / 3.0
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VPREWITT_WEIGHTS = HPREWITT_WEIGHTS.T
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ROBERTS_PD_WEIGHTS = np.array([[1, 0],
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[0, -1]], dtype=np.double)
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ROBERTS_ND_WEIGHTS = np.array([[0, 1],
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[-1, 0]], dtype=np.double)
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def _mask_filter_result(result, mask):
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"""Return result after masking.
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Input masks are eroded so that mask areas in the original image don't
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affect values in the result.
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"""
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if mask is None:
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result[0, :] = 0
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result[-1, :] = 0
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result[:, 0] = 0
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result[:, -1] = 0
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return result
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else:
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mask = binary_erosion(mask, EROSION_SELEM, border_value=0)
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return result * mask
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def sobel(image, mask=None):
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"""Find the edge magnitude 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 Sobel edge map.
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Notes
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-----
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Take the square root of the sum of the squares of the horizontal and
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vertical Sobels to get a magnitude that's somewhat insensitive to
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direction.
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Note that ``scipy.ndimage.sobel`` returns a directional Sobel which
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has to be further processed to perform edge detection.
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"""
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return np.sqrt(hsobel(image, mask)**2 + vsobel(image, mask)**2)
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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 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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image = img_as_float(image)
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result = np.abs(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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"""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 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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image = img_as_float(image)
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result = np.abs(convolve(image, VSOBEL_WEIGHTS))
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return _mask_filter_result(result, 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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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 Scharr edge map.
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Notes
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-----
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Take the square root of the sum of the squares of the horizontal and
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vertical Scharrs to get a magnitude that's somewhat insensitive to
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direction.
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References
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----------
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.. [1] D. Kroon, 2009, Short Paper University Twente, Numerical Optimization
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of Kernel Based Image Derivatives.
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"""
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return np.sqrt(hscharr(image, mask)**2 + vscharr(image, mask)**2)
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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 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 Optimization
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of Kernel Based Image Derivatives.
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"""
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image = img_as_float(image)
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result = np.abs(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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"""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 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 Optimization
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of Kernel Based Image Derivatives.
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"""
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image = img_as_float(image)
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result = np.abs(convolve(image, VSCHARR_WEIGHTS))
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return _mask_filter_result(result, 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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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 Prewitt edge map.
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Notes
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-----
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Return the square root of the sum of squares of the horizontal
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and vertical Prewitt transforms.
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"""
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return np.sqrt(hprewitt(image, mask)**2 + vprewitt(image, mask)**2)
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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 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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image = img_as_float(image)
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result = np.abs(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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"""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 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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image = img_as_float(image)
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result = np.abs(convolve(image, VPREWITT_WEIGHTS))
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return _mask_filter_result(result, 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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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 Roberts' Cross edge map.
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"""
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return np.sqrt(roberts_positive_diagonal(image, mask)**2 +
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roberts_negative_diagonal(image, mask)**2)
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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 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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image = img_as_float(image)
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result = np.abs(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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"""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 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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image = img_as_float(image)
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result = np.abs(convolve(image, ROBERTS_ND_WEIGHTS))
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return _mask_filter_result(result, mask)
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