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https://github.com/wassname/scikit-image.git
synced 2026-07-12 07:12:31 +08:00
Move FAST corner function to other corner functions
This commit is contained in:
Johannes Schönberger
+67
-67
@@ -329,6 +329,73 @@ def corner_foerstner(image, sigma=1):
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return w, q
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def corner_fast(image, n=12, threshold=0.15):
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"""Extract FAST corners for a given image.
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Parameters
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----------
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image : 2D ndarray
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Input image.
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n : int
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Minimum number of consecutive pixels out of 16 pixels on the circle
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that should all be either brighter or darker w.r.t testpixel.
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A point c on the circle is darker w.r.t test pixel p if
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`Ic < Ip - threshold` and brighter if `Ic > Ip + threshold`. Also
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stands for the n in `FAST-n` corner detector.
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threshold : float
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Threshold used in deciding whether the pixels on the circle are
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brighter, darker or similar w.r.t. the test pixel. Decrease the
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threshold when more corners are desired and vice-versa.
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Returns
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-------
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response : ndarray
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FAST corner response image.
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References
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----------
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.. [1] Edward Rosten and Tom Drummond
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"Machine Learning for high-speed corner detection",
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http://www.edwardrosten.com/work/rosten_2006_machine.pdf
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.. [2] Wikipedia, "Features from accelerated segment test",
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https://en.wikipedia.org/wiki/Features_from_accelerated_segment_test
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Examples
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--------
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>>> from skimage.feature import corner_fast, corner_peaks
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>>> square = np.zeros((12, 12))
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>>> square[3:9, 3:9] = 1
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>>> square
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array([[ 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],
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[ 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],
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[ 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],
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[ 0., 0., 0., 1., 1., 1., 1., 1., 1., 0., 0., 0.],
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[ 0., 0., 0., 1., 1., 1., 1., 1., 1., 0., 0., 0.],
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[ 0., 0., 0., 1., 1., 1., 1., 1., 1., 0., 0., 0.],
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[ 0., 0., 0., 1., 1., 1., 1., 1., 1., 0., 0., 0.],
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[ 0., 0., 0., 1., 1., 1., 1., 1., 1., 0., 0., 0.],
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[ 0., 0., 0., 1., 1., 1., 1., 1., 1., 0., 0., 0.],
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[ 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],
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[ 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],
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[ 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.]])
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>>> corner_peaks(corner_fast(square, 9), min_distance=1)
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array([[3, 3],
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[3, 8],
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[8, 3],
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[8, 8]])
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"""
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image = np.squeeze(image)
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if image.ndim != 2:
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raise ValueError("Only 2-D gray-scale images supported.")
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image = img_as_float(image)
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image = np.ascontiguousarray(image)
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response = _corner_fast(image, n, threshold)
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return response
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def corner_subpix(image, corners, window_size=11, alpha=0.99):
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"""Determine subpixel position of corners.
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@@ -545,70 +612,3 @@ def corner_peaks(image, min_distance=10, threshold_abs=0, threshold_rel=0.1,
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return np.transpose(peaks.nonzero())
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else:
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return peaks
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def corner_fast(image, n=12, threshold=0.15):
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"""Extract FAST corners for a given image.
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Parameters
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----------
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image : 2D ndarray
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Input image.
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n : int
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Minimum number of consecutive pixels out of 16 pixels on the circle
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that should all be either brighter or darker w.r.t testpixel.
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A point c on the circle is darker w.r.t test pixel p if
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`Ic < Ip - threshold` and brighter if `Ic > Ip + threshold`. Also
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stands for the n in `FAST-n` corner detector.
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threshold : float
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Threshold used in deciding whether the pixels on the circle are
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brighter, darker or similar w.r.t. the test pixel. Decrease the
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threshold when more corners are desired and vice-versa.
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Returns
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-------
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response : ndarray
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FAST corner response image.
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References
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----------
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.. [1] Edward Rosten and Tom Drummond
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"Machine Learning for high-speed corner detection",
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http://www.edwardrosten.com/work/rosten_2006_machine.pdf
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.. [2] Wikipedia, "Features from accelerated segment test",
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https://en.wikipedia.org/wiki/Features_from_accelerated_segment_test
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Examples
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--------
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>>> from skimage.feature import corner_fast, corner_peaks
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>>> square = np.zeros((12, 12))
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>>> square[3:9, 3:9] = 1
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>>> square
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array([[ 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],
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[ 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],
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[ 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],
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[ 0., 0., 0., 1., 1., 1., 1., 1., 1., 0., 0., 0.],
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[ 0., 0., 0., 1., 1., 1., 1., 1., 1., 0., 0., 0.],
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[ 0., 0., 0., 1., 1., 1., 1., 1., 1., 0., 0., 0.],
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[ 0., 0., 0., 1., 1., 1., 1., 1., 1., 0., 0., 0.],
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[ 0., 0., 0., 1., 1., 1., 1., 1., 1., 0., 0., 0.],
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[ 0., 0., 0., 1., 1., 1., 1., 1., 1., 0., 0., 0.],
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[ 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],
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[ 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],
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[ 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.]])
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>>> corner_peaks(corner_fast(square, 9), min_distance=1)
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array([[3, 3],
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[3, 8],
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[8, 3],
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[8, 8]])
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"""
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image = np.squeeze(image)
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if image.ndim != 2:
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raise ValueError("Only 2-D gray-scale images supported.")
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image = img_as_float(image)
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image = np.ascontiguousarray(image)
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response = _corner_fast(image, n, threshold)
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return response
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