mirror of
https://github.com/wassname/scikit-image.git
synced 2026-07-02 03:00:05 +08:00
Clearing docs and making feature.util
This commit is contained in:
Ankit Agrawal
@@ -7,7 +7,7 @@ from .corner import (corner_kitchen_rosenfeld, corner_harris, corner_shi_tomasi,
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from .corner_cy import corner_moravec
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from .template import match_template
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from ._brief import brief
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from .util import hamming_distance
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__all__ = ['daisy',
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'hog',
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@@ -23,4 +23,5 @@ __all__ = ['daisy',
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'corner_peaks',
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'corner_moravec',
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'match_template',
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'brief']
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'brief',
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'hamming_distance']
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+19
-57
@@ -1,33 +1,20 @@
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import numpy as np
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from scipy.ndimage.filters import gaussian_filter
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from scipy.spatial.distance import hamming
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from ..color import rgb2gray
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from ..util import img_as_float
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from .util import _remove_border_keypoints
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from ._brief_cy import _brief_loop
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def _remove_border_keypoints(image, keypoints, dist):
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width = image.shape[0]
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height = image.shape[1]
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keypoints = keypoints[(dist < keypoints[:, 0])
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& (keypoints[:, 0] < width - dist)
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& (dist < keypoints[:, 1])
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& (keypoints[:, 1] < height - dist)]
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return keypoints
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def brief(image, keypoints, descriptor_size=256, mode='normal', patch_size=49,
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sample_seed=1):
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sample_seed=1, variance=2, return_keypoints=False):
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"""Extract BRIEF Descriptor about given keypoints for a given image.
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Parameters
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----------
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image : ndarray
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image : 2D ndarray
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Input image.
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keypoints : (P, 2) ndarray
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Array of keypoint locations.
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@@ -42,13 +29,20 @@ def brief(image, keypoints, descriptor_size=256, mode='normal', patch_size=49,
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the keypoints. Default is 49.
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sample_seed : int
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Seed for sampling the decision pixel-pairs. Default is 1.
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return_keypoints : bool
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If True, return the Q keypoints (after filtering out the border
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keypoints) about which the descriptors are extracted. Default is False.
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Returns
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-------
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descriptor : ndarray with dtype bool
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2D ndarray of dimensions (no_of_keypoints, descriptor_size) with value
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at an index (i, j) either being True or False representing the outcome
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descriptors : (Q, descriptor_size) ndarray of dtype bool
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2D ndarray of binary descriptors of size descriptor_size about Q
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keypoints after filtering out border keypoints with value at an index
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(i, j) either being True or False representing the outcome
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of Intensity comparison about ith keypoint on jth decision pixel-pair.
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keypoints : (Q, 2) ndarray
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Keypoints after removing out those that are near border.
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Returned only if return_keypoints is True.
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References
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----------
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@@ -66,9 +60,9 @@ def brief(image, keypoints, descriptor_size=256, mode='normal', patch_size=49,
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image = img_as_float(image)
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# Gaussian Low pass filtering with variance 2 to alleviate noise
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# Gaussian Low pass filtering to alleviate noise
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# sensitivity
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image = gaussian_filter(image, 2)
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image = gaussian_filter(image, variance)
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image = np.ascontiguousarray(image)
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@@ -105,39 +99,7 @@ def brief(image, keypoints, descriptor_size=256, mode='normal', patch_size=49,
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_brief_loop(image, descriptors.view(np.uint8), keypoints, pos1, pos2)
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return descriptors
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def hamming_distance(descriptor_1, descriptor_2):
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"""A dissimilarity measure used for matching keypoints in different images
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using binary feature descriptors like BRIEF etc.
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Parameters
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----------
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descriptor_1 : ndarray with dtype bool
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Binary feature descriptor for keypoints in the first image.
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2D ndarray of dimensions (no_of_keypoints_in_image_1, descriptor_size)
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with value at an index (i, j) either being True or False representing
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the outcome of Intensity comparison about ith keypoint on jth decision
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pixel-pair.
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descriptor_2 : ndarray with dtype bool
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Binary feature descriptor for keypoints in the second image.
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2D ndarray of dimensions (no_of_keypoints_in_image_2, descriptor_size)
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with value at an index (i, j) either being True or False representing
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the outcome of Intensity comparison about ith keypoint on jth decision
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pixel-pair.
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Returns
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-------
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distance : ndarray
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2D ndarray of dimensions (no_of_rows_in_descripto_1, no_of_rows_in_descripto_2)
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with value at an index (i, j) between the range [0, 1] representing the
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extent of dissimilarity between ith keypoint of in first image and jth
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keypoint in second image.
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"""
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distance = np.zeros((len(descriptor_1), len(descriptor_2)), dtype=float)
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for i in range(len(descriptor_1)):
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for j in range(len(descriptor_2)):
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distance[i, j] = hamming(descriptor_1[i][:], descriptor_2[j][:])
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return distance
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if return_keypoints:
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return descriptors, keypoints
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else:
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return descriptors
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@@ -0,0 +1,50 @@
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import numpy as np
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from scipy.spatial.distance import hamming
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def _remove_border_keypoints(image, keypoints, dist):
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"""Removes keypoints that are within dist pixels from the image border.
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"""
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width = image.shape[0]
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height = image.shape[1]
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keypoints = keypoints[(dist < keypoints[:, 0])
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& (keypoints[:, 0] < width - dist)
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& (dist < keypoints[:, 1])
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& (keypoints[:, 1] < height - dist)]
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return keypoints
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def hamming_distance(descriptors1, descriptors2):
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"""A dissimilarity measure used for matching keypoints in different images
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using binary feature descriptors like BRIEF etc.
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Parameters
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----------
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descriptors1 : (P1, D) array of dtype bool
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Binary feature descriptors for keypoints in the first image.
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2D ndarray with a binary descriptors of size D about P1 keypoints
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with value at an index (i, j) either being True or False representing
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the outcome of Intensity comparison about ith keypoint on jth decision
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pixel-pair.
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descriptors2 : (P2, D) array of dtype bool
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Binary feature descriptors for keypoints in the second image.
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2D ndarray with a binary descriptors of size D about P2 keypoints
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with value at an index (i, j) either being True or False representing
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the outcome of Intensity comparison about ith keypoint on jth decision
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pixel-pair.
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Returns
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-------
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distance : (P1, P2) array of dtype float
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2D ndarray with value at an index (i, j) in the range [0, 1]
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representing the extent of dissimilarity between ith keypoint of in
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first image and jth keypoint in second image.
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"""
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distance = np.zeros((descriptors1.shape[0], descriptors2.shape[0]), dtype=float)
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for i in range(descriptors1.shape[0]):
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for j in range(descriptors2.shape[0]):
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distance[i, j] = hamming(descriptors1[i, :], descriptors2[j, :])
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return distance
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