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Better wrapping of lines; making ofast_mask a global constant

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This commit is contained in:
Ankit Agrawal
2013-09-19 19:31:11 +05:30
committed by Johannes Schönberger
parent 260753974a
commit 1fac309125
1 changed files with 28 additions and 24 deletions
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skimage/feature/orb.py
+28 -24
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@@ -9,8 +9,17 @@ from skimage.transform import pyramid_gaussian
from .orb_cy import _orb_loop
OFAST_MASK = np.array([[0, 0, 1, 1, 1, 0, 0],
[0, 1, 1, 1, 1, 1, 0],
[1, 1, 1, 1, 1, 1, 1],
[1, 1, 1, 1, 1, 1, 1],
[1, 1, 1, 1, 1, 1, 1],
[0, 1, 1, 1, 1, 1, 0],
[0, 0, 1, 1, 1, 0, 0]], dtype=np.uint8)
def keypoints_orb(image, n_keypoints=200, fast_n=9, fast_threshold=0.20,
harris_k=0.05, downscale=np.sqrt(2), n_scales=5):
harris_k=0.05, downscale=np.sqrt(2), n_scales=3):
"""Detect Oriented Fast keypoints.
@@ -20,24 +29,24 @@ def keypoints_orb(image, n_keypoints=200, fast_n=9, fast_threshold=0.20,
Input grayscale image.
n_keypoints : int
Number of keypoints to be returned from this function. The function
will return best `n_keypoints` if more than n_keypoints are detected
will return best ``n_keypoints`` if more than n_keypoints are detected
based on the values of other parameters. If not, then all the detected
keypoints are returned.
fast_n : int
The `n` parameter in `feature.corner_fast`. Minimum number of
The ``n`` parameter in ``feature.corner_fast``. Minimum number of
consecutive pixels out of 16 pixels on the circle that should all be
either brighter or darker w.r.t testpixel. A point c on the circle is
darker w.r.t test pixel p if `Ic < Ip - threshold` and brighter if
`Ic > Ip + threshold`. Also stands for the n in `FAST-n` corner
darker w.r.t test pixel p if ``Ic < Ip - threshold`` and brighter if
``Ic > Ip + threshold``. Also stands for the n in ``FAST-n`` corner
detector.
fast_threshold : float
The `threshold` parameter in `feature.corner_fast`. Threshold used to
The ``threshold`` parameter in ``feature.corner_fast``. Threshold used to
decide whether the pixels on the circle are brighter, darker or
similar w.r.t. the test pixel. Decrease the threshold when more
corners are desired and vice-versa.
harris_k : float
The `k` parameter in `feature.corner_harris`. Sensitivity factor to
separate corners from edges, typically in range `[0, 0.2]`. Small
The ``k`` parameter in ``feature.corner_harris``. Sensitivity factor to
separate corners from edges, typically in range ``[0, 0.2]``. Small
values of k result in detection of sharp corners.
downscale : float
Downscale factor for the image pyramid.
@@ -80,22 +89,17 @@ def keypoints_orb(image, n_keypoints=200, fast_n=9, fast_threshold=0.20,
>>> orientations
array([-2.35619449, -0.78539816, 2.35619449, 0.78539816, 0.78539816,
2.35619449, -0.78539816, -2.35619449])
>>> np.rad2deg(orientations)
array([-135., -45., 135., 45., 45., 135., -45., -135.])
>>> scales
array([0, 0, 0, 0, 1, 1, 1, 1])
"""
image = _prepare_grayscale_input_2D(image)
pyramid = list(pyramid_gaussian(image, n_scales - 1, downscale))
ofast_mask = np.array([[0, 0, 1, 1, 1, 0, 0],
[0, 1, 1, 1, 1, 1, 0],
[1, 1, 1, 1, 1, 1, 1],
[1, 1, 1, 1, 1, 1, 1],
[1, 1, 1, 1, 1, 1, 1],
[0, 1, 1, 1, 1, 1, 0],
[0, 0, 1, 1, 1, 0, 0]], dtype=np.uint8)
keypoints_list = []
orientations_list = []
scales_list = []
@@ -121,8 +125,8 @@ def keypoints_orb(image, n_keypoints=200, fast_n=9, fast_threshold=0.20,
return keypoints, orientations, scales
else:
best_indices = harris_measure.argsort()[::-1][:n_keypoints]
return keypoints[best_indices], orientations[best_indices], \
scales[best_indices]
return (keypoints[best_indices], orientations[best_indices],
scales[best_indices])
def descriptor_orb(image, keypoints, orientations, scales,
@@ -141,7 +145,7 @@ def descriptor_orb(image, keypoints, orientations, scales,
The scales of the corresponding N keypoints.
downscale : float
Downscale factor for the image pyramid. Should be the same as that
used in `keypoints_orb`.
used in ``keypoints_orb``.
n_scales : int
Number of scales from the bottom of the image pyramid to extract
the features from.
@@ -168,13 +172,13 @@ def descriptor_orb(image, keypoints, orientations, scales,
>>> from skimage.feature import keypoints_orb, descriptor_orb
>>> square = np.zeros((50, 50))
>>> square[20:30, 20:30] = 1
>>> keypoints, orientations, scales = keypoints_orb(square, n_keypoints=8, \
n_scales=2)
>>> keypoints, orientations, scales = keypoints_orb(square, n_keypoints=8,
... n_scales=2)
>>> keypoints.shape
(8, 2)
>>> descriptors, filtered_keypoints = descriptor_orb(square, keypoints, \
orientations, scales, \
n_scales=2)
>>> descriptors, filtered_keypoints = descriptor_orb(square, keypoints,
... orientations, scales,
... n_scales=2)
>>> filtered_keypoints.shape
(8, 2)
>>> descriptors.shape