mirror of
https://github.com/wassname/scikit-image.git
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Ankit Agrawal
51 lines
1.6 KiB
Python
51 lines
1.6 KiB
Python
import numpy as np
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from scipy.ndimage.filters import maximum_filter
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from fast_cy import _corner_fast
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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 : integer
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Number of consecutive pixels out of 16 pixels on the circle that
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should be brighter or darker with respect to test pixel above the
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`threshold` so as to classify the test pixel as a FAST corner. 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 to the test pixel. Decrease the threshold
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when more corners are desired and vice-versa.
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Returns
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-------
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corners : (N, 2) ndarray
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Location i.e. (row, col) of extracted FAST corners.
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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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"""
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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 = np.ascontiguousarray(image, dtype=np.double)
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corner_response = _corner_fast(image, n, threshold)
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# Non-maximal Suppression
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corner_zero_mask = corner_response != 0
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maximas = (maximum_filter(corner_response, (3, 3)) == corner_response) & corner_zero_mask
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x, y = np.where(maximas == True)
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corners = np.squeeze(np.dstack((x, y)))
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return corners
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