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Fixed database typo and removed unnecessary class identifier.

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Batuhan Berk Başoğlu 2020-10-14 10:10:37 -04:00
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import numpy as np
from ..feature.util import (FeatureDetector, DescriptorExtractor,
_mask_border_keypoints,
_prepare_grayscale_input_2D)
from ..feature import (corner_fast, corner_orientations, corner_peaks,
corner_harris)
from ..transform import pyramid_gaussian
from .._shared.utils import check_nD
from .orb_cy import _orb_loop
OFAST_MASK = np.zeros((31, 31))
OFAST_UMAX = [15, 15, 15, 15, 14, 14, 14, 13, 13, 12, 11, 10, 9, 8, 6, 3]
for i in range(-15, 16):
for j in range(-OFAST_UMAX[abs(i)], OFAST_UMAX[abs(i)] + 1):
OFAST_MASK[15 + j, 15 + i] = 1
class ORB(FeatureDetector, DescriptorExtractor):
"""Oriented FAST and rotated BRIEF feature detector and binary descriptor
extractor.
Parameters
----------
n_keypoints : int, optional
Number of keypoints to be returned. The function will return the best
`n_keypoints` according to the Harris corner response if more than
`n_keypoints` are detected. If not, then all the detected keypoints
are returned.
fast_n : int, optional
The `n` parameter in `skimage.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 test-pixel. 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
detector.
fast_threshold : float, optional
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, optional
The `k` parameter in `skimage.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, optional
Downscale factor for the image pyramid. Default value 1.2 is chosen so
that there are more dense scales which enable robust scale invariance
for a subsequent feature description.
n_scales : int, optional
Maximum number of scales from the bottom of the image pyramid to
extract the features from.
Attributes
----------
keypoints : (N, 2) array
Keypoint coordinates as ``(row, col)``.
scales : (N, ) array
Corresponding scales.
orientations : (N, ) array
Corresponding orientations in radians.
responses : (N, ) array
Corresponding Harris corner responses.
descriptors : (Q, `descriptor_size`) array of dtype bool
2D array of binary descriptors of size `descriptor_size` for Q
keypoints after filtering out border keypoints with value at an
index ``(i, j)`` either being ``True`` or ``False`` representing
the outcome of the intensity comparison for i-th keypoint on j-th
decision pixel-pair. It is ``Q == np.sum(mask)``.
References
----------
.. [1] Ethan Rublee, Vincent Rabaud, Kurt Konolige and Gary Bradski
"ORB: An efficient alternative to SIFT and SURF"
http://www.vision.cs.chubu.ac.jp/CV-R/pdf/Rublee_iccv2011.pdf
Examples
--------
>>> from skimage.feature import ORB, match_descriptors
>>> img1 = np.zeros((100, 100))
>>> img2 = np.zeros_like(img1)
>>> np.random.seed(1)
>>> square = np.random.rand(20, 20)
>>> img1[40:60, 40:60] = square
>>> img2[53:73, 53:73] = square
>>> detector_extractor1 = ORB(n_keypoints=5)
>>> detector_extractor2 = ORB(n_keypoints=5)
>>> detector_extractor1.detect_and_extract(img1)
>>> detector_extractor2.detect_and_extract(img2)
>>> matches = match_descriptors(detector_extractor1.descriptors,
... detector_extractor2.descriptors)
>>> matches
array([[0, 0],
[1, 1],
[2, 2],
[3, 3],
[4, 4]])
>>> detector_extractor1.keypoints[matches[:, 0]]
array([[42., 40.],
[47., 58.],
[44., 40.],
[59., 42.],
[45., 44.]])
>>> detector_extractor2.keypoints[matches[:, 1]]
array([[55., 53.],
[60., 71.],
[57., 53.],
[72., 55.],
[58., 57.]])
"""
def __init__(self, downscale=1.2, n_scales=8,
n_keypoints=500, fast_n=9, fast_threshold=0.08,
harris_k=0.04):
self.downscale = downscale
self.n_scales = n_scales
self.n_keypoints = n_keypoints
self.fast_n = fast_n
self.fast_threshold = fast_threshold
self.harris_k = harris_k
self.keypoints = None
self.scales = None
self.responses = None
self.orientations = None
self.descriptors = None
def _build_pyramid(self, image):
image = _prepare_grayscale_input_2D(image)
return list(pyramid_gaussian(image, self.n_scales - 1,
self.downscale, multichannel=False))
def _detect_octave(self, octave_image):
dtype = octave_image.dtype
# Extract keypoints for current octave
fast_response = corner_fast(octave_image, self.fast_n,
self.fast_threshold)
keypoints = corner_peaks(fast_response, min_distance=1,
threshold_rel=0)
if len(keypoints) == 0:
return (np.zeros((0, 2), dtype=dtype),
np.zeros((0, ), dtype=dtype),
np.zeros((0, ), dtype=dtype))
mask = _mask_border_keypoints(octave_image.shape, keypoints,
distance=16)
keypoints = keypoints[mask]
orientations = corner_orientations(octave_image, keypoints,
OFAST_MASK)
harris_response = corner_harris(octave_image, method='k',
k=self.harris_k)
responses = harris_response[keypoints[:, 0], keypoints[:, 1]]
return keypoints, orientations, responses
def detect(self, image):
"""Detect oriented FAST keypoints along with the corresponding scale.
Parameters
----------
image : 2D array
Input image.
"""
check_nD(image, 2)
pyramid = self._build_pyramid(image)
keypoints_list = []
orientations_list = []
scales_list = []
responses_list = []
for octave in range(len(pyramid)):
octave_image = np.ascontiguousarray(pyramid[octave])
keypoints, orientations, responses = self._detect_octave(
octave_image)
keypoints_list.append(keypoints * self.downscale ** octave)
orientations_list.append(orientations)
scales_list.append(np.full(
keypoints.shape[0], self.downscale ** octave,
dtype=octave_image.dtype))
responses_list.append(responses)
keypoints = np.vstack(keypoints_list)
orientations = np.hstack(orientations_list)
scales = np.hstack(scales_list)
responses = np.hstack(responses_list)
if keypoints.shape[0] < self.n_keypoints:
self.keypoints = keypoints
self.scales = scales
self.orientations = orientations
self.responses = responses
else:
# Choose best n_keypoints according to Harris corner response
best_indices = responses.argsort()[::-1][:self.n_keypoints]
self.keypoints = keypoints[best_indices]
self.scales = scales[best_indices]
self.orientations = orientations[best_indices]
self.responses = responses[best_indices]
def _extract_octave(self, octave_image, keypoints, orientations):
mask = _mask_border_keypoints(octave_image.shape, keypoints,
distance=20)
keypoints = np.array(keypoints[mask], dtype=np.intp, order='C',
copy=False)
orientations = np.array(orientations[mask], order='C',
copy=False)
descriptors = _orb_loop(octave_image, keypoints, orientations)
return descriptors, mask
def extract(self, image, keypoints, scales, orientations):
"""Extract rBRIEF binary descriptors for given keypoints in image.
Note that the keypoints must be extracted using the same `downscale`
and `n_scales` parameters. Additionally, if you want to extract both
keypoints and descriptors you should use the faster
`detect_and_extract`.
Parameters
----------
image : 2D array
Input image.
keypoints : (N, 2) array
Keypoint coordinates as ``(row, col)``.
scales : (N, ) array
Corresponding scales.
orientations : (N, ) array
Corresponding orientations in radians.
"""
check_nD(image, 2)
pyramid = self._build_pyramid(image)
descriptors_list = []
mask_list = []
# Determine octaves from scales
octaves = (np.log(scales) / np.log(self.downscale)).astype(np.intp)
for octave in range(len(pyramid)):
# Mask for all keypoints in current octave
octave_mask = octaves == octave
if np.sum(octave_mask) > 0:
octave_image = np.ascontiguousarray(pyramid[octave])
octave_keypoints = keypoints[octave_mask]
octave_keypoints /= self.downscale ** octave
octave_orientations = orientations[octave_mask]
descriptors, mask = self._extract_octave(octave_image,
octave_keypoints,
octave_orientations)
descriptors_list.append(descriptors)
mask_list.append(mask)
self.descriptors = np.vstack(descriptors_list).view(np.bool)
self.mask_ = np.hstack(mask_list)
def detect_and_extract(self, image):
"""Detect oriented FAST keypoints and extract rBRIEF descriptors.
Note that this is faster than first calling `detect` and then
`extract`.
Parameters
----------
image : 2D array
Input image.
"""
check_nD(image, 2)
pyramid = self._build_pyramid(image)
keypoints_list = []
responses_list = []
scales_list = []
orientations_list = []
descriptors_list = []
for octave in range(len(pyramid)):
octave_image = np.ascontiguousarray(pyramid[octave])
keypoints, orientations, responses = self._detect_octave(
octave_image)
if len(keypoints) == 0:
keypoints_list.append(keypoints)
responses_list.append(responses)
descriptors_list.append(np.zeros((0, 256), dtype=np.bool))
continue
descriptors, mask = self._extract_octave(octave_image, keypoints,
orientations)
scaled_keypoints = keypoints[mask] * self.downscale ** octave
keypoints_list.append(scaled_keypoints)
responses_list.append(responses[mask])
orientations_list.append(orientations[mask])
scales_list.append(self.downscale ** octave *
np.ones(scaled_keypoints.shape[0], dtype=np.intp))
descriptors_list.append(descriptors)
if len(scales_list) == 0:
raise RuntimeError(
"ORB found no features. Try passing in an image containing "
"greater intensity contrasts between adjacent pixels.")
keypoints = np.vstack(keypoints_list)
responses = np.hstack(responses_list)
scales = np.hstack(scales_list)
orientations = np.hstack(orientations_list)
descriptors = np.vstack(descriptors_list).view(np.bool)
if keypoints.shape[0] < self.n_keypoints:
self.keypoints = keypoints
self.scales = scales
self.orientations = orientations
self.responses = responses
self.descriptors = descriptors
else:
# Choose best n_keypoints according to Harris corner response
best_indices = responses.argsort()[::-1][:self.n_keypoints]
self.keypoints = keypoints[best_indices]
self.scales = scales[best_indices]
self.orientations = orientations[best_indices]
self.responses = responses[best_indices]
self.descriptors = descriptors[best_indices]