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Better naming of arguments

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This commit is contained in:
Ankit Agrawal
2013-11-29 20:44:21 +01:00
committed by Johannes Schönberger
parent 4ef1221cfd
commit 914ab05fb6
1 changed files with 12 additions and 12 deletions
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skimage/feature/orb.py
+12 -12
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@@ -11,7 +11,7 @@ from .orb_cy import _orb_loop
def keypoints_orb(image, n_keypoints=200, fast_n=9, fast_threshold=0.20,
harris_k=0.05, downscale_factor=np.sqrt(2), n_scales=5):
harris_k=0.05, downscale=np.sqrt(2), n_scales=5):
"""Compute Oriented Fast keypoints.
@@ -40,7 +40,7 @@ def keypoints_orb(image, n_keypoints=200, fast_n=9, fast_threshold=0.20,
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_factor : float
downscale : float
Downscale factor for the image pyramid.
n_scales : int
Number of scales from the bottom of the image pyramid to extract
@@ -66,7 +66,7 @@ def keypoints_orb(image, n_keypoints=200, fast_n=9, fast_threshold=0.20,
if image.ndim != 2:
raise ValueError("Only 2-D gray-scale images supported.")
pyramid = list(pyramid_gaussian(image, n_scales - 1, downscale_factor))
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],
@@ -101,8 +101,8 @@ def keypoints_orb(image, n_keypoints=200, fast_n=9, fast_threshold=0.20,
return keypoints[best_indices], orientations[best_indices], scales[best_indices]
def descriptor_orb(image, keypoints, keypoints_orientations,
keypoints_scales, downscale_factor=np.sqrt(2), n_scales=5):
def descriptor_orb(image, keypoints, orientations, scales,
downscale=np.sqrt(2), n_scales=5):
"""Compute rBRIEF descriptors of input keypoints.
Parameters
@@ -111,11 +111,11 @@ def descriptor_orb(image, keypoints, keypoints_orientations,
Input grayscale image.
keypoints : (N, 2) ndarray
Array of N input keypoint locations in the format (row, col).
keypoints_orientations : (N,) ndarray
orientations : (N,) ndarray
The orientations of the corresponding N keypoints.
keypoints_scales : (N,) ndarray
scales : (N,) ndarray
The scales of the corresponding N keypoints.
downscale_factor : float
downscale : float
Downscale factor for the image pyramid. Should be the same as that
used in `keypoints_orb`.
n_scales : int
@@ -145,7 +145,7 @@ def descriptor_orb(image, keypoints, keypoints_orientations,
image = img_as_float(image)
pyramid = list(pyramid_gaussian(image, n_scales - 1, downscale_factor))
pyramid = list(pyramid_gaussian(image, n_scales - 1, downscale))
pos0 = binary_tests[:, :2].astype(np.int32)
pos1 = binary_tests[:, 2:].astype(np.int32)
@@ -158,11 +158,11 @@ def descriptor_orb(image, keypoints, keypoints_orientations,
for k in range(n_scales):
curr_image = np.ascontiguousarray(pyramid[k])
curr_scale_mask = keypoints_scales == k
curr_scale_mask = scales == k
curr_scale_kpts = keypoints[curr_scale_mask]
curr_scale_kpts_orientation = keypoints_orientations[curr_scale_mask]
curr_scale_kpts_orientation = orientations[curr_scale_mask]
border_mask = _mask_border_keypoints(curr_image, curr_scale_kpts, 13)
border_mask = _mask_border_keypoints(curr_image, curr_scale_kpts, dist=13)
curr_scale_kpts = curr_scale_kpts[border_mask]
curr_scale_kpts_orientation = curr_scale_kpts_orientation[border_mask]