mirror of
https://github.com/wassname/scikit-image.git
synced 2026-07-02 21:39:30 +08:00
tests for match_binary_descriptors; many corrections and changes
This commit is contained in:
Ankit Agrawal
committed by
Johannes Schönberger
Johannes Schönberger
parent
e9762f2673
commit
ffd144ef36
@@ -9,7 +9,7 @@ from .corner import (corner_kitchen_rosenfeld, corner_harris,
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hessian_matrix_eigvals)
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from .corner_cy import corner_moravec, corner_orientations
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from .template import match_template
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from ._brief import brief
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from ._brief import descriptor_brief
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from .match import match_binary_descriptors
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from .util import pairwise_hamming_distance
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from .censure import keypoints_censure
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@@ -29,7 +29,7 @@ __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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'descriptor_brief',
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'pairwise_hamming_distance',
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'match_binary_descriptors',
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'keypoints_censure',
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@@ -2,13 +2,13 @@ import numpy as np
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from scipy.ndimage.filters import gaussian_filter
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from .util import (_mask_border_keypoints, pairwise_hamming_distance,
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_prepare_grayscale_input_2D)
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_prepare_grayscale_input_2D)
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from ._brief_cy import _brief_loop
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def brief(image, keypoints, descriptor_size=256, mode='normal', patch_size=49,
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sample_seed=1, variance=2):
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def descriptor_brief(image, keypoints, descriptor_size=256, mode='normal',
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patch_size=49, sample_seed=1, variance=2):
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"""**Experimental function**.
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Extract BRIEF Descriptor about given keypoints for a given image.
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@@ -57,8 +57,9 @@ def brief(image, keypoints, descriptor_size=256, mode='normal', patch_size=49,
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Examples
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--------
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>> from skimage.feature import corner_peaks, corner_harris, \\
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.. pairwise_hamming_distance, brief, match_keypoints_brief
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>> import numpy as np
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>> from skimage.feature.corner import corner_peaks, corner_harris
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>> from skimage.feature import pairwise_hamming_distance, descriptor_brief, match_binary_descriptors
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>> square1 = np.zeros([8, 8], dtype=np.int32)
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>> square1[2:6, 2:6] = 1
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>> square1
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@@ -76,7 +77,7 @@ def brief(image, keypoints, descriptor_size=256, mode='normal', patch_size=49,
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[2, 5],
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[5, 2],
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[5, 5]])
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>> descriptors1, keypoints1 = brief(square1, keypoints1, patch_size=5)
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>> descriptors1, keypoints1 = descriptor_brief(square1, keypoints1, patch_size=5)
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>> keypoints1
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array([[2, 2],
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[2, 5],
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@@ -100,7 +101,7 @@ def brief(image, keypoints, descriptor_size=256, mode='normal', patch_size=49,
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[2, 6],
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[6, 2],
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[6, 6]])
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>> descriptors2, keypoints2 = brief(square2, keypoints2, patch_size=5)
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>> descriptors2, keypoints2 = descriptor_brief(square2, keypoints2, patch_size=5)
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>> keypoints2
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array([[2, 2],
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[2, 6],
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@@ -111,8 +112,11 @@ def brief(image, keypoints, descriptor_size=256, mode='normal', patch_size=49,
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[ 0.3203125, 0.03125 , 0.640625 , 0.375 ],
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[ 0.375 , 0.6328125, 0.0390625, 0.328125 ],
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[ 0.625 , 0.3671875, 0.34375 , 0.0234375]])
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>> match_keypoints_brief(keypoints1, descriptors1,
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.. keypoints2, descriptors2)
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>> matched_kpts, mask1, mask2 = match_binary_descriptors(keypoints1,
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descriptors1,
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keypoints2,
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descriptors2)
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>> matched_kpts
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array([[[ 2, 2],
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[ 2, 2]],
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@@ -165,6 +169,7 @@ def brief(image, keypoints, descriptor_size=256, mode='normal', patch_size=49,
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samples = np.random.randint(-(patch_size - 2) // 2,
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(patch_size // 2) + 1,
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(descriptor_size * 2, 2))
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samples = np.array(samples, dtype=np.int32)
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pos1, pos2 = np.split(samples, 2)
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pos1 = np.ascontiguousarray(pos1)
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@@ -73,7 +73,7 @@ def match_binary_descriptors(keypoints1, descriptors1, keypoints2,
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dtype=np.intp)
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matches[:, 0, :] = keypoints1[row_check]
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matches[:, 1, :] = matched_keypoints2[row_check]
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mask1 = np.where(row_check == True)
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mask1 = np.where(row_check == True)[0]
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mask2 = np.argmin(distance, axis=1)[row_check]
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return matches, mask1, mask2
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@@ -3,84 +3,56 @@ from numpy.testing import assert_array_equal, assert_raises
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from skimage import data
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from skimage import transform as tf
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from skimage.color import rgb2gray
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from skimage.feature import (brief, match_binary_descriptors, corner_peaks,
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from skimage.feature import (descriptor_brief, match_binary_descriptors, corner_peaks,
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corner_harris)
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def test_brief_color_image_unsupported_error():
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def test_descriptor_brief_color_image_unsupported_error():
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"""Brief descriptors can be evaluated on gray-scale images only."""
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img = np.zeros((20, 20, 3))
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keypoints = [[7, 5], [11, 13]]
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assert_raises(ValueError, brief, img, keypoints)
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assert_raises(ValueError, descriptor_brief, img, keypoints)
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def test_matching_brief_descriptors_lena_translation():
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"""Test matched keypoints between lena image and its translated version."""
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def test_descriptor_brief_normal_mode():
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"""Verify the computed BRIEF descriptors with expected for normal mode."""
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img = data.lena()
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img = rgb2gray(img)
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img.shape
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tform = tf.SimilarityTransform(scale=1, rotation=0, translation=(15, 20))
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translated_img = tf.warp(img, tform)
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keypoints = corner_peaks(corner_harris(img), min_distance=5)
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descriptors, keypoints = descriptor_brief(img, keypoints[:8],
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descriptor_size=8)
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keypoints1 = corner_peaks(corner_harris(img), min_distance=5)
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descriptors1, keypoints1 = brief(img, keypoints1, descriptor_size=512)
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expected = np.array([[ True, False, True, False, True, True, False, False],
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[False, False, False, False, True, False, False, False],
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[ True, True, True, True, True, True, True, True],
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[ True, False, True, True, False, True, False, True],
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[False, True, True, True, True, True, True, True],
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[ True, False, False, False, False, True, False, True],
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[False, True, True, True, False, False, True, False],
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[False, False, False, False, True, False, False, False]], dtype=bool)
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keypoints2 = corner_peaks(corner_harris(translated_img), min_distance=5)
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descriptors2, keypoints2 = brief(translated_img, keypoints2,
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descriptor_size=512)
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matched_keypoints, m1, m2 = match_binary_descriptors(keypoints1,
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descriptors1,
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keypoints2,
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descriptors2,
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threshold=0.10)
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assert_array_equal(matched_keypoints[:, 0, :], matched_keypoints[:, 1, :] +
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[20, 15])
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assert_array_equal(descriptors, expected)
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def test_matching_brief_descriptors_lena_rotation():
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"""Verify matched keypoints result between lena image and its rotated
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version with the expected keypoint pairs."""
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def test_descriptor_brief_uniform_mode():
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"""Verify the computed BRIEF descriptors with expected for uniform mode."""
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img = data.lena()
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img = rgb2gray(img)
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img.shape
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tform = tf.SimilarityTransform(scale=1, rotation=0.10, translation=(0, 0))
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rotated_img = tf.warp(img, tform)
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keypoints = corner_peaks(corner_harris(img), min_distance=5)
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descriptors, keypoints = descriptor_brief(img, keypoints[:8],
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descriptor_size=8,
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mode='uniform')
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keypoints1 = corner_peaks(corner_harris(img), min_distance=5)
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descriptors1, keypoints1 = brief(img, keypoints1, descriptor_size=512)
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expected = np.array([[ True, False, True, False, False, True, False, False],
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[False, True, False, False, True, True, True, True],
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[ True, False, False, False, False, False, False, False],
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[False, True, True, False, False, False, True, False],
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[False, False, False, False, False, False, True, False],
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[False, True, False, False, True, False, False, False],
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[False, False, True, True, False, False, True, True],
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[ True, True, False, False, False, False, False, False]], dtype=bool)
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keypoints2 = corner_peaks(corner_harris(rotated_img), min_distance=5)
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descriptors2, keypoints2 = brief(rotated_img, keypoints2,
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descriptor_size=512)
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matched_keypoints, m1, m2 = match_binary_descriptors(keypoints1,
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descriptors1,
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keypoints2,
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descriptors2,
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threshold=0.07)
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expected = np.array([[[263, 272],
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[234, 298]],
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[[271, 120],
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[258, 146]],
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[[323, 164],
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[305, 195]],
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[[414, 70],
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[405, 111]],
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[[435, 181],
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[415, 223]],
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[[454, 176],
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[435, 221]]])
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print matched_keypoints
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assert_array_equal(matched_keypoints, expected)
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assert_array_equal(descriptors, expected)
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if __name__ == '__main__':
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@@ -0,0 +1,173 @@
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import numpy as np
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from numpy.testing import assert_array_equal, assert_raises
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from skimage import data
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from skimage import transform as tf
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from skimage.color import rgb2gray
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from skimage.feature import (descriptor_brief, match_binary_descriptors,
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corner_peaks, corner_harris)
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def test_match_binary_descriptors_unequal_descriptor_keypoints_error():
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"""Number of descriptors should be equal to the number of keypoints."""
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kp1 = np.array([[40, 50],
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[60, 40],
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[30, 70]])
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des1 = np.array([[True, True, False, True],
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[False, True, False, True]])
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kp2 = np.array([[60, 50],
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[50, 80]])
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des2 = np.array([[True, False, False, True],
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[False, True, True, True]])
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assert_raises(ValueError, match_binary_descriptors, kp1, des1, kp2, des2)
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def test_match_binary_descriptors_unequal_descriptor_sizes_error():
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"""Sizes of descriptors of keypoints to be matched should be equal."""
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kp1 = np.array([[40, 50],
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[60, 40]])
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des1 = np.array([[True, True, False, True],
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[False, True, False, True]])
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kp2 = np.array([[60, 50],
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[50, 80]])
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des2 = np.array([[True, False, False, True, False],
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[False, True, True, True, False]])
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assert_raises(ValueError, match_binary_descriptors, kp1, des1, kp2, des2)
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def test_match_binary_descriptors_lena_rotation_crosscheck_false():
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"""Verify matched keypoints and their corresponding masks results between
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lena image and its rotated version with the expected keypoint pairs with
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cross_check disabled."""
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img = data.lena()
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img = rgb2gray(img)
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tform = tf.SimilarityTransform(scale=1, rotation=0.15, translation=(0, 0))
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rotated_img = tf.warp(img, tform)
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keypoints1 = corner_peaks(corner_harris(img), min_distance=5)
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descriptors1, keypoints1 = descriptor_brief(img, keypoints1, descriptor_size=512)
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keypoints2 = corner_peaks(corner_harris(rotated_img), min_distance=5)
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descriptors2, keypoints2 = descriptor_brief(rotated_img, keypoints2,
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descriptor_size=512)
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matched_keypoints, m1, m2 = match_binary_descriptors(keypoints1,
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descriptors1,
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keypoints2,
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descriptors2,
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threshold=0.13,
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cross_check=False)
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expected_mask1 = np.array([11, 12, 16, 20, 24, 26, 27, 29, 35, 39, 40, 42, 45])
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expected_mask2 = np.array([ 1, 3, 0, 4, 6, 7, 8, 9, 10, 10, 11, 12, 13])
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expected = np.array([[[245, 141],
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[221, 176]],
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[[247, 130],
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[225, 165]],
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[[263, 272],
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[219, 309]],
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[[271, 120],
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[250, 159]],
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[[311, 174],
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[282, 218]],
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[[323, 164],
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[294, 210]],
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[[327, 147],
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[301, 195]],
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[[377, 157],
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[349, 211]],
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[[414, 70],
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[399, 131]],
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[[425, 67],
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[399, 131]],
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[[435, 181],
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[403, 244]],
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[[454, 176],
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[423, 242]],
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[[467, 166],
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[437, 234]]])
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assert_array_equal(matched_keypoints, expected)
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assert_array_equal(m1, expected_mask1)
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assert_array_equal(m2, expected_mask2)
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def test_match_binary_descriptors_lena_rotation_crosscheck_true():
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"""Verify matched keypoints and their corresponding masks results between
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lena image and its rotated version with the expected keypoint pairs with
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cross_check enabled."""
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img = data.lena()
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img = rgb2gray(img)
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tform = tf.SimilarityTransform(scale=1, rotation=0.15, translation=(0, 0))
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rotated_img = tf.warp(img, tform)
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keypoints1 = corner_peaks(corner_harris(img), min_distance=5)
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descriptors1, keypoints1 = descriptor_brief(img, keypoints1, descriptor_size=512)
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keypoints2 = corner_peaks(corner_harris(rotated_img), min_distance=5)
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descriptors2, keypoints2 = descriptor_brief(rotated_img, keypoints2,
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descriptor_size=512)
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matched_keypoints, m1, m2 = match_binary_descriptors(keypoints1,
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descriptors1,
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keypoints2,
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descriptors2,
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threshold=0.13)
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expected = np.array([[[245, 141],
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[221, 176]],
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[[247, 130],
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[225, 165]],
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[[263, 272],
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[219, 309]],
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[[271, 120],
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[250, 159]],
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[[311, 174],
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[282, 218]],
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[[323, 164],
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[294, 210]],
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[[327, 147],
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[301, 195]],
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[[377, 157],
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[349, 211]],
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[[414, 70],
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[399, 131]],
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[[435, 181],
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[403, 244]],
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[[454, 176],
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[423, 242]],
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[[467, 166],
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[437, 234]]])
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expected_mask1 = np.array([11, 12, 16, 20, 24, 26, 27, 29, 35, 40, 42, 45])
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expected_mask2 = np.array([ 1, 3, 0, 4, 6, 7, 8, 9, 10, 11, 12, 13])
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assert_array_equal(matched_keypoints, expected)
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assert_array_equal(m1, expected_mask1)
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assert_array_equal(m2, expected_mask2)
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if __name__ == '__main__':
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from numpy import testing
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testing.run_module_suite()
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@@ -47,3 +47,9 @@ def pairwise_hamming_distance(array1, array2):
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"""
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distance = (array1[:, None] != array2[None]).mean(axis=2)
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return distance
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def _create_keypoint_recarray(row, col, octave=None, orientation=None,
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response=None):
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kpt_recarray = np.recarray()
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