From 914ab05fb64592454b3a932644c02fe1f67dea75 Mon Sep 17 00:00:00 2001 From: Ankit Agrawal Date: Thu, 5 Sep 2013 19:06:09 +0530 Subject: [PATCH] Better naming of arguments --- skimage/feature/orb.py | 24 ++++++++++++------------ 1 file changed, 12 insertions(+), 12 deletions(-) diff --git a/skimage/feature/orb.py b/skimage/feature/orb.py index d2cc5022..a92c0b2b 100644 --- a/skimage/feature/orb.py +++ b/skimage/feature/orb.py @@ -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]