# Source code for valis.feature_matcher

```
"""Functions and classes to match and filter image features
"""
import numpy as np
import cv2
import numba as nba
from sklearn import metrics
from sklearn.metrics.pairwise import pairwise_kernels
from skimage import transform
from . import warp_tools
AMBIGUOUS_METRICS = set(metrics.pairwise._VALID_METRICS).intersection(
metrics.pairwise.PAIRWISE_KERNEL_FUNCTIONS.keys())
"""set:
Metrics found in both the valid metrics ang kernel methods in
sklearn.metrics.pairwise. Issue is that metrics are distances,
while kernels are similarities. Metrics in this set are assumed
to be distaces, unless the metric_type parameter in match_descriptors
is set to "similarity". """
EPS = np.finfo(float).eps
"""float: epsilon error to avoid division by 0"""
GMS_NAME = "GMS"
"""str: If filter_method parameter in match_desc_and_kp is set to this,
Grid-based Motion Statistics will be used to remove poor matches"""
RANSAC_NAME = "RANSAC"
"""str: If filter_method parameter in match_desc_and_kp is set to this,
RANSAC will be used to remove poor matches
"""
DEFAULT_MATCH_FILTER = RANSAC_NAME
"""str: The defulat filter_method value, either RANSAC_NAME or GMS_NAME"""
DEFAULT_RANSAC = 7
"""int: Default RANSAC threshold"""
@nba.njit()
def convert_distance_to_similarity(d, n_features=64):
"""
Convert distance to similarity
Based on https://scikit-learn.org/stable/modules/metrics.html
Parameters
----------
d : float
Value to convert
n_features: int
Number of features used to calcuate distance.
Only needed when calc == 0
Returns
-------
y : float
Similarity
"""
return np.exp(-d * (1 / n_features))
@nba.njit()
def convert_similarity_to_distance(s, n_features=64):
"""Convert similarity to distance
Based on https://scikit-learn.org/stable/modules/metrics.html
Parameters
----------
s : float
Similarity to convert
n_features: int
Number of features used to calcuate similarity.
Only needed when calc == 0
Returns
-------
y : float
Distance
"""
return -np.log(s + EPS) / (1 / n_features)
def filter_matches_ransac(kp1_xy, kp2_xy, ransac_val=DEFAULT_RANSAC):
f"""Remove poor matches using RANSAC
Parameters
----------
kp1_xy : ndarray
(N, 2) array containing image 1s keypoint positions, in xy coordinates.
kp2_xy : ndarray
(N, 2) array containing image 2s keypoint positions, in xy coordinates.
ransac_val: int
RANSAC threshold, passed to cv2.findHomography as the
ransacReprojThreshold parameter. Default value is {DEFAULT_RANSAC}
Returns
-------
filtered_src_points : (N, 2) array
Inlier keypoints from kp1_xy
filtered_dst_points : (N, 2) array
Inlier keypoints from kp1_xy
good_idx : (1, N) array
Indices of inliers
"""
_, mask = cv2.findHomography(kp1_xy, kp2_xy, cv2.RANSAC, ransac_val)
good_idx = np.where(mask.reshape(-1) == 1)[0]
filtered_src_points = kp1_xy[good_idx, :]
filtered_dst_points = kp2_xy[good_idx, :]
return filtered_src_points, filtered_dst_points, good_idx
def filter_matches_gms(kp1_xy, kp2_xy, feature_d, img1_shape, img2_shape,
scaling, thresholdFactor=6.0):
"""Filter matches using GMS (Grid-based Motion Statistics) [1]
This filtering method does best when there are a large number of features,
so the ORB detector is recommended
Note that this function assumes the keypoints and distances have been
sorted such that each keypoint in kp1_xy has the same index as the
matching keypoint in kp2_xy andd corresponding feautre distance in
feature_d. For example, kp1_xy[0] should have the corresponding keypoint
at kp2_xy[0] and the corresponding feature distance at feature_d[0].
Parameters
----------
kp1_xy : ndarray
(N, 2) array with image 1s keypoint positions, in xy coordinates, for
each of the N matched descriptors in desc1
kp2_xy : narray
(N, 2) array with image 2s keypoint positions, in xy coordinates, for
each of the N matched descriptors in desc2
feature_d: ndarray
Feature distances between corresponding keypoints
img1_shape: tuple
Shape of image 1 (row, col)
img2_shape: tuple
Shape of image 2 (row, col)
scaling: bool
Whether or not image scaling should be considered
thresholdFactor: float
The higher, the fewer matches
Returns
-------
filtered_src_points : (N, 2) array
Inlier keypoints from kp1_xy
filtered_dst_points : (N, 2) array
Inlier keypoints from kp1_xy
good_idx : (1, N) array
Indices of inliers
References
----------
.. [1] JiaWang Bian, Wen-Yan Lin, Yasuyuki Matsushita, Sai-Kit Yeung,
Tan Dat Nguyen, and Ming-Ming Cheng. Gms: Grid-based motion statistics for
fast, ultra-robust feature correspondence. In IEEE Conference on Computer
Vision and Pattern Recognition, 2017
"""
kp1 = cv2.KeyPoint_convert(kp1_xy.tolist())
kp2 = cv2.KeyPoint_convert(kp2_xy.tolist())
matches = [cv2.DMatch(_queryIdx=i, _trainIdx=i, _imgIdx=0, _distance=feature_d[i]) for i in range(len(kp1_xy))]
gms_matches = cv2.xfeatures2d.matchGMS(img1_shape, img2_shape, kp1, kp2, matches, withRotation=True,
withScale=scaling, thresholdFactor=thresholdFactor)
good_idx = np.array([d.queryIdx for d in gms_matches])
if len(good_idx) == 0:
filtered_src_points = []
filtered_dst_points = []
else:
filtered_src_points = kp1_xy[good_idx, :]
filtered_dst_points = kp2_xy[good_idx, :]
return np.array(filtered_src_points), np.array(filtered_dst_points), np.array(good_idx)
def filter_matches_tukey(src_xy, dst_xy):
"""Detect and remove outliers using Tukey's method
Adapted from https://towardsdatascience.com/detecting-and-treating-outliers-in-python-part-1-4ece5098b755
Parameters
----------
src_xy : ndarray
(N, 2) array containing image 1s keypoint positions, in xy coordinates.
dst_xy : ndarray
(N, 2) array containing image 2s keypoint positions, in xy coordinates.
Returns
-------
filtered_src_points : (N, 2) array
Inlier keypoints from kp1_xy
filtered_dst_points : (N, 2) array
Inlier keypoints from kp1_xy
good_idx : (1, N) array
Indices of inliers
"""
tform = transform.SimilarityTransform()
tform.estimate(src=dst_xy, dst=src_xy)
M = tform.params
warped_xy = warp_tools.warp_xy(src_xy, M)
d = warp_tools.calc_d(warped_xy, dst_xy)
q1 = np.quantile(d, 0.25)
q3 = np.quantile(d, 0.75)
iqr = q3-q1
inner_fence = 1.5*iqr
outer_fence = 3*iqr
# inner fence lower and upper end
inner_fence_le = q1-inner_fence
inner_fence_ue = q3+inner_fence
# outer fence lower and upper end
outer_fence_le = q1-outer_fence
outer_fence_ue = q3+outer_fence
outliers_prob = []
outliers_poss = []
inliers_prob = []
inliers_poss = []
for index, v in enumerate(d):
if v <= outer_fence_le or v >= outer_fence_ue:
outliers_prob.append(index)
else:
inliers_prob.append(index)
for index, v in enumerate(d):
if v <= inner_fence_le or v >= inner_fence_ue:
outliers_poss.append(index)
else:
inliers_poss.append(index)
src_xy_inlier = src_xy[inliers_prob, :]
dst_xy_inlier = dst_xy[inliers_prob, :]
return src_xy_inlier, dst_xy_inlier, inliers_prob
[docs]
def filter_matches(kp1_xy, kp2_xy, method=DEFAULT_MATCH_FILTER,
filtering_kwargs=None):
"""Use RANSAC or GMS to remove poor matches
Parameters
----------
kp1_xy : ndarray
(N, 2) array containing image 1s keypoint positions, in xy coordinates.
kp2_xy : ndarray
(N, 2) array containing image 2s keypoint positions, in xy coordinates.
method: str
`method` = "GMS" will use filter_matches_gms() to remove poor matches.
This uses the Grid-based Motion Statistics.
`method` = "RANSAC" will use RANSAC to remove poor matches
filtering_kwargs: dict
Extra arguments passed to filtering function
If `method` == "GMS", these need to include: img1_shape, img2_shape,
scaling, thresholdFactor. See filter_matches_gms for details
If `method` == "RANSAC", this can be None, since the ransac value is
a class attribute
Returns
-------
filtered_src_points : ndarray
(M, 2) ndarray of inlier keypoints from kp1_xy
filtered_dst_points : (N, 2) array
(M, 2) ndarray of inlier keypoints from kp2_xy
good_idx : ndarray
(M, 1) array containing ndices of inliers
"""
all_matching_args = filtering_kwargs.copy()
all_matching_args.update({"kp1_xy": kp1_xy, "kp2_xy": kp2_xy})
if method.upper() == GMS_NAME:
filter_fxn = filter_matches_gms
else:
filter_fxn = filter_matches_ransac
filtered_src_points, filtered_dst_points, good_idx = filter_fxn(**all_matching_args)
# Do additional filtering to remove other outliers that may have been missed by RANSAC
filtered_src_points, filtered_dst_points, good_idx = filter_matches_tukey(filtered_src_points, filtered_dst_points)
return filtered_src_points, filtered_dst_points, good_idx
def match_descriptors(descriptors1, descriptors2, metric=None,
metric_type=None, p=2, max_distance=np.inf,
cross_check=True, max_ratio=1.0, metric_kwargs=None):
"""Brute-force matching of descriptors
For each descriptor in the first set this matcher finds the closest
descriptor in the second set (and vice-versa in the case of enabled
cross-checking).
Parameters
----------
descriptors1 : ndarray
(M, P) array of descriptors of size P about M keypoints in image 1.
descriptors2 : ndarray
(N, P) array of descriptors of size P about N keypoints in image 2.
metric : string or callable
Distance metrics used in spatial.distance.cdist() or sklearn.metrics.pairwise()
Alterntively, can also use similarity metrics in sklearn.metrics.pairwise.PAIRWISE_KERNEL_FUNCTIONS.
By default the L2-norm is used for all descriptors of dtype float or
double and the Hamming distance is used for binary descriptors automatically.
p : int, optional
The p-norm to apply for ``metric='minkowski'``.
max_distance : float, optional
Maximum allowed distance between descriptors of two keypoints
in separate images to be regarded as a match.
cross_check : bool, optional
If True, the matched keypoints are returned after cross checking i.e. a
matched pair (keypoint1, keypoint2) is returned if keypoint2 is the
best match for keypoint1 in second image and keypoint1 is the best
match for keypoint2 in first image.
max_ratio : float, optional
Maximum ratio of distances between first and second closest descriptor
in the second set of descriptors. This threshold is useful to filter
ambiguous matches between the two descriptor sets. The choice of this
value depends on the statistics of the chosen descriptor, e.g.,
for SIFT descriptors a value of 0.8 is usually chosen, see
D.G. Lowe, "Distinctive Image Features from Scale-Invariant Keypoints",
International Journal of Computer Vision, 2004.
metric_kwargs : dict
Optionl keyword arguments to be passed into pairwise_distances() or pairwise_kernels()
from the sklearn.metrics.pairwise module
Returns
-------
matches : (Q, 2) array
Indices of corresponding matches in first and second set of
descriptors, where ``matches[:, 0]`` denote the indices in the first
and ``matches[:, 1]`` the indices in the second set of descriptors.
distances : (Q, 1) array
Distance values between each pair of matched descriptor
metric_name : str or function
Name metric used to calculate distances or similarity
NOTE
----
Modified from scikit-image to use scikit-learn's distance and kernal methods.
"""
if descriptors1.shape[1] != descriptors2.shape[1]:
raise ValueError("Descriptor length must equal.")
if metric is None:
if np.issubdtype(descriptors1.dtype, np.bool_):
metric = 'hamming'
else:
metric = 'euclidean'
if metric_kwargs is None:
metric_kwargs = {}
if metric == 'minkowski':
metric_kwargs['p'] = p
if metric in AMBIGUOUS_METRICS:
print("metric", metric, "could be a distance in pairwise_distances() or similarity in pairwise_kernels().",
"Please set metric_type. Otherwise, metric is assumed to be a distance")
if callable(metric) or metric in metrics.pairwise._VALID_METRICS:
distances = metrics.pairwise_distances(descriptors1, descriptors2, metric=metric, **metric_kwargs)
if callable(metric) and metric_type is None:
print(Warning("Metric passed as a function or class, but the metric type not provided",
"Assuming the metric function returns a distance. If a similarity is actually returned",
"set metric_type = 'similiarity'. If metric is a distance, set metric_type = 'distance'"
"to avoid this message"))
metric_type = "distance"
if metric_type == "similarity":
distances = convert_similarity_to_distance(distances, n_features=descriptors1.shape[1])
if metric in metrics.pairwise.PAIRWISE_KERNEL_FUNCTIONS:
similarities = pairwise_kernels(descriptors1, descriptors2, metric=metric, **metric_kwargs)
distances = convert_similarity_to_distance(similarities, n_features=descriptors1.shape[1])
if callable(metric):
metric_name = metric.__name__
else:
metric_name = metric
indices1 = np.arange(descriptors1.shape[0])
indices2 = np.argmin(distances, axis=1)
if cross_check:
matches1 = np.argmin(distances, axis=0)
mask = indices1 == matches1[indices2]
indices1 = indices1[mask]
indices2 = indices2[mask]
if max_distance < np.inf:
mask = distances[indices1, indices2] < max_distance
indices1 = indices1[mask]
indices2 = indices2[mask]
if max_ratio < 1.0:
best_distances = distances[indices1, indices2]
distances[indices1, indices2] = np.inf
second_best_indices2 = np.argmin(distances[indices1], axis=1)
second_best_distances = distances[indices1, second_best_indices2]
second_best_distances[second_best_distances == 0] \
= np.finfo(np.double).eps
ratio = best_distances / second_best_distances
mask = ratio < max_ratio
indices1 = indices1[mask]
indices2 = indices2[mask]
return np.column_stack((indices1, indices2)), best_distances[indices1, indices2], metric, metric_type
else:
return np.column_stack((indices1, indices2)), distances[indices1, indices2], metric_name, metric_type
[docs]
def match_desc_and_kp(desc1, kp1_xy, desc2, kp2_xy, metric=None,
metric_type=None, metric_kwargs=None, max_ratio=1.0,
filter_method=DEFAULT_MATCH_FILTER,
filtering_kwargs=None):
"""Match the descriptors of image 1 with those of image 2 and remove outliers.
Metric can be a string to use a distance in scipy.distnce.cdist(),
or a custom distance function
Parameters
----------
desc1 : ndarray
(N, P) array of image 1's descriptions for N keypoints,
which each keypoint having P features
kp1_xy : ndarray
(N, 2) array containing image 1's keypoint positions (xy)
desc2 : ndarray
(M, P) array of image 2's descriptions for M keypoints,
which each keypoint having P features
kp2_xy : (M, 2) array
(M, 2) array containing image 2's keypoint positions (xy)
metric: string, or callable
Metric to calculate distance between each pair of features
in desc1 and desc2. Can be a string to use as distance in
spatial.distance.cdist, or a custom distance function
metric_kwargs : dict
Optionl keyword arguments to be passed into pairwise_distances()
or pairwise_kernels() from the sklearn.metrics.pairwise module
max_ratio : float, optional
Maximum ratio of distances between first and second closest descriptor
in the second set of descriptors. This threshold is useful to filter
ambiguous matches between the two descriptor sets. The choice of this
value depends on the statistics of the chosen descriptor, e.g.,
for SIFT descriptors a value of 0.8 is usually chosen, see
D.G. Lowe, "Distinctive Image Features from Scale-Invariant Keypoints",
International Journal of Computer Vision, 2004.
filter_method: str
"GMS" will use uses the Grid-based Motion Statistics
"RANSAC" will use RANSAC
filtering_kwargs: dict
Dictionary containing extra arguments for the filtering method.
kp1_xy, kp2_xy, feature_d are calculated here, and don't need to
be in filtering_kwargs. If filter_method == "GMS", then the
required arguments are: img1_shape, img2_shape, scaling,
thresholdFactor. See filter_matches_gms for details.
If filter_method == "RANSAC", then the required
arguments are: ransac_val. See filter_matches_ransac for details.
Returns
-------
match_info12 : MatchInfo
Contains information regarding the matches between image 1 and
image 2. These results haven't undergone filtering, so
contain many poor matches.
filtered_match_info12 : MatchInfo
Contains information regarding the matches between image 1 and
image 2. These results have undergone filtering, and so
contain good matches
match_info21 : MatchInfo
Contains information regarding the matches between image 2 and
image 1. These results haven't undergone filtering, so contain
many poor matches.
filtered_match_info21 : MatchInfo
Contains information regarding the matches between image 2 and
image 1. These results have undergone filtering, and so contain
good matches
"""
if metric_kwargs is None:
metric_kwargs = {}
if filter_method.upper() == GMS_NAME:
# GMS is supposed to perform best with a large number of features #
cross_check = False
else:
cross_check = True
matches, match_distances, metric_name, metric_type = \
match_descriptors(desc1, desc2, metric=metric,
metric_type=metric_type,
metric_kwargs=metric_kwargs,
max_ratio=max_ratio,
cross_check=cross_check)
desc1_match_idx = matches[:, 0]
matched_kp1_xy = kp1_xy[desc1_match_idx, :]
matched_desc1 = desc1[desc1_match_idx, :]
desc2_match_idx = matches[:, 1]
matched_kp2_xy = kp2_xy[desc2_match_idx, :]
matched_desc2 = desc2[desc2_match_idx, :]
mean_unfiltered_distance = np.mean(match_distances)
mean_unfiltered_similarity = np.mean(convert_distance_to_similarity(match_distances, n_features=desc1.shape[1]))
match_info12 = MatchInfo(matched_kp1_xy=matched_kp1_xy, matched_desc1=matched_desc1,
matches12=desc1_match_idx, matched_kp2_xy=matched_kp2_xy,
matched_desc2=matched_desc2, matches21=desc2_match_idx,
match_distances=match_distances, distance=mean_unfiltered_distance,
similarity=mean_unfiltered_similarity, metric_name=metric_name,
metric_type=metric_type)
match_info21 = MatchInfo(matched_kp1_xy=matched_kp2_xy, matched_desc1=matched_desc2,
matches12=desc2_match_idx, matched_kp2_xy=matched_kp1_xy,
matched_desc2=matched_desc1, matches21=desc1_match_idx,
match_distances=match_distances, distance=mean_unfiltered_distance,
similarity=mean_unfiltered_similarity, metric_name=metric_name,
metric_type=metric_type)
# Filter matches #
all_filtering_kwargs = {"kp1_xy": matched_kp1_xy, "kp2_xy": matched_kp2_xy}
if filtering_kwargs is None:
if filter_method != RANSAC_NAME:
print(Warning(f"filtering_kwargs not provided for {filter_method} match filtering. Will use RANSAC instead"))
filter_method = RANSAC_NAME
all_filtering_kwargs.update({"ransac_val": DEFAULT_RANSAC})
else:
all_filtering_kwargs.update({"ransac_val": DEFAULT_RANSAC})
else:
all_filtering_kwargs.update(filtering_kwargs)
if filter_method == GMS_NAME:
# At this point, filtering_kwargs needs to include:
# img1_shape, img2_shape, scaling, and thresholdFactor.
# Already added kp1_xy, kp2_xy. Now adding feature_d to
# the argument dictionary
all_filtering_kwargs.update({"feature_d": match_distances})
filtered_matched_kp1_xy, filtered_matched_kp2_xy, good_matches_idx = \
filter_matches(matched_kp1_xy, matched_kp2_xy, filter_method, all_filtering_kwargs)
if len(good_matches_idx) > 0:
filterd_match_distances = match_distances[good_matches_idx]
filterd_matched_desc1 = matched_desc1[good_matches_idx, :]
filterd_matched_desc2 = matched_desc2[good_matches_idx, :]
good_matches12 = desc1_match_idx[good_matches_idx]
good_matches21 = desc2_match_idx[good_matches_idx]
mean_filtered_distance = np.mean(filterd_match_distances)
mean_filtered_similarity = \
np.mean(convert_distance_to_similarity(filterd_match_distances,
n_features=desc1.shape[1]))
else:
filterd_match_distances = []
filterd_matched_desc1 = []
filterd_matched_desc2 = []
good_matches12 = []
good_matches21 = []
mean_filtered_distance = np.inf
mean_filtered_similarity = 0
# Record filtered matches
filtered_match_info12 = MatchInfo(matched_kp1_xy=filtered_matched_kp1_xy, matched_desc1=filterd_matched_desc1,
matches12=good_matches12, matched_kp2_xy=filtered_matched_kp2_xy,
matched_desc2=filterd_matched_desc2, matches21=good_matches21,
match_distances=filterd_match_distances, distance=mean_filtered_distance,
similarity=mean_filtered_similarity, metric_name=metric_name,
metric_type=metric_type)
filtered_match_info21 = MatchInfo(matched_kp1_xy=filtered_matched_kp2_xy, matched_desc1=filterd_matched_desc2,
matches12=good_matches21, matched_kp2_xy=filtered_matched_kp1_xy,
matched_desc2=filterd_matched_desc1, matches21=good_matches12,
match_distances=filterd_match_distances, distance=mean_filtered_distance,
similarity=mean_filtered_similarity, metric_name=metric_name,
metric_type=metric_type)
return match_info12, filtered_match_info12, match_info21, filtered_match_info21
[docs]
class MatchInfo(object):
"""Class that stores information related to matches. One per pair of images
All attributes are all set as parameters during initialization
"""
[docs]
def __init__(self,
matched_kp1_xy, matched_desc1, matches12,
matched_kp2_xy, matched_desc2, matches21,
match_distances, distance, similarity,
metric_name, metric_type,
img1_name=None, img2_name=None):
"""Stores information about matches and features
Parameters
----------
matched_kp1_xy : ndarray
(Q, 2) array of image 1 keypoint xy coordinates after filtering
matched_desc1 : ndarray
(Q, P) array of matched descriptors for image 1, each of which has P features
matches12 : ndarray
(1, Q) array of indices of featiures in image 1 that matched those in image 2
matched_kp2_xy : ndarray
(Q, 2) array containing Q matched image 2 keypoint xy coordinates after filtering
matched_desc2 : ndarray
(Q, P) containing Q matched descriptors for image 2, each of which has P features
matches21 : ndarray
(1, Q) containing indices of featiures in image 2 that matched those in image 1
match_distances : ndarray
Distances between each of the Q pairs of matched descriptors
n_matches : int
Number of good matches (i.e. the number of inlier keypoints)
distance : float
Mean distance of features
similarity : float
Mean similarity of features
metric_name : str
Name of metric
metric_type : str
"distsnce" or "similarity"
img1_name : str
Name of the image that kp1 and desc1 belong to
img2_name : str
Name of the image that kp2 and desc2 belong to
"""
self.matched_kp1_xy = matched_kp1_xy
self.matched_desc1 = matched_desc1
self.matches12 = matches12
self.matched_kp2_xy = matched_kp2_xy
self.matched_desc2 = matched_desc2
self.matches21 = matches21
self.match_distances = match_distances
self.n_matches = len(match_distances)
self.distance = distance
self.similarity = similarity
self.metric_name = metric_name
self.metric_type = metric_type
self.img1_name = img1_name
self.img2_name = img2_name
def set_names(self, img1_name, img2_name):
self.img1_name = img1_name
self.img2_name = img2_name
[docs]
class Matcher(object):
"""Class that matchs the descriptors of image 1 with those of image 2
Outliers removed using RANSAC or GMS
Attributes
----------
metric: str, or callable
Metric to calculate distance between each pair of features in
desc1 and desc2. Can be a string to use as distance in
spatial.distance.cdist, or a custom distance function
metric_name: str
Name metric used. Will be the same as metric if metric is string.
If metric is function, this will be the name of the function.
metric_type: str, or callable
String describing what the custom metric function returns, e.g.
'similarity' or 'distance'. If None, and metric is a function it
is assumed to be a distance, but there will be a warning that this
variable should be provided to either define that it is a
similarity, or to avoid the warning by having
metric_type='distance' In the case of similarity, the number of
features will be used to convert distances
ransac : int
The residual threshold to determine if a match is an inlier.
Only used if filter_method == {RANSAC_NAME}. Default is "RANSAC"
gms_threshold : int
Used when filter_method is "GMS".
The higher, the fewer matches.
scaling: bool
Whether or not image scaling should be considered when
filter_method is "GMS"
metric_kwargs : dict
Keyword arguments passed into the metric when calling
spatial.distance.cdist
match_filter_method: str
"GMS" will use filter_matches_gms() to remove poor matches.
This uses the Grid-based Motion Statistics (GMS) or RANSAC.
"""
[docs]
def __init__(self, metric=None, metric_type=None, metric_kwargs=None,
match_filter_method=DEFAULT_MATCH_FILTER, ransac_thresh=DEFAULT_RANSAC,
gms_threshold=15, scaling=False):
"""
Parameters
----------
metric: str, or callable
Metric to calculate distance between each pair of features in
desc1 and desc2. Can be a string to use as distance in
spatial.distance.cdist, or a custom distance function
metric_type: str, or callable
String describing what the custom metric function returns, e.g.
'similarity' or 'distance'. If None, and metric is a function it
is assumed to be a distance, but there will be a warning that this
variable should be provided to either define that it is a
similarity, or to avoid the warning by having
metric_type='distance' In the case of similarity, the number of
features will be used to convert distances
metric_kwargs : dict
Keyword arguments passed into the metric when calling
spatial.distance.cdist
filter_method: str
"GMS" will use filter_matches_gms() to remove poor matches.
This uses the Grid-based Motion Statistics (GMS) or RANSAC.
ransac_val : int
The residual threshold to determine if a match is an inlier.
Only used if filter_method is "RANSAC".
gms_threshold : int
Used when filter_method is "GMS".
The higher, the fewer matches.
scaling: bool
Whether or not image scaling should be considered when
filter_method is "GMS".
"""
self.metric = metric
if metric is not None:
if isinstance(metric, str):
self.metric_name = metric
elif callable(metric):
self.metric_name = metric.__name__
else:
self.metric_name = None
self.metric_type = metric_type
self.ransac = ransac_thresh
self.gms_threshold = gms_threshold
self.scaling = scaling
self.metric_kwargs = metric_kwargs
self.match_filter_method = match_filter_method
[docs]
def match_images(self, desc1, kp1_xy, desc2, kp2_xy,
additional_filtering_kwargs=None):
"""Match the descriptors of image 1 with those of image 2,
Outliers removed using match_filter_method. Metric can be a string
to use a distance in scipy.distnce.cdist(), or a custom distance
function. Sets atttributes for Matcher object
Parameters
----------
desc1 : (N, P) array
Image 1s 2D array containinng N keypoints, each of which
has P features
kp1_xy : (N, 2) array
Image 1s keypoint positions, in xy coordinates, for each of the
N descriptors in desc1
desc2 : (M, P) array
Image 2s 2D array containinng M keypoints, each of which has
P features
kp2_xy : (M, 2) array
Image 1s keypoint positions, in xy coordinates, for each of
the M descriptors in desc2
additional_filtering_kwargs: dict, optional
Extra arguments passed to filtering function
If self.match_filter_method == "GMS", these need to
include: img1_shape, img2_shape. See filter_matches_gms for details
If If self.match_filter_method == "RANSAC", this can be None,
since the ransac value is class attribute
Returns
-------
match_info12 : MatchInfo
Contains information regarding the matches between image 1
and image 2. These results haven't undergone filtering,
so contain many poor matches.
filtered_match_info12 : MatchInfo
Contains information regarding the matches between image 1
and image 2. These results have undergone
filtering, and so contain good matches
match_info21 : MatchInfo
Contains information regarding the matches between image 2
and image 1. These results haven't undergone filtering, so
contain many poor matches.
filtered_match_info21 : MatchInfo
Contains information regarding the matches between image 2
and image 1.
"""
if self.match_filter_method == GMS_NAME:
if additional_filtering_kwargs is not None:
# At this point arguments need to include: img1_shape, img2_shape #
filtering_kwargs = additional_filtering_kwargs.copy()
filtering_kwargs.update({"scaling": self.scaling,
"thresholdFactor": self.gms_threshold})
else:
print(Warning(f"Selected {self.match_filter_method},\
but did not provide argument\
additional_filtering_kwargs.\
Defaulting to RANSAC"))
self.match_filter_method = RANSAC_NAME
filtering_kwargs = {"ransac_val": self.ransac}
elif self.match_filter_method == RANSAC_NAME:
filtering_kwargs = {"ransac_val": self.ransac}
else:
print(Warning(f"Dont know {self.match_filter_method}.\
Defaulting to RANSAC"))
self.match_filter_method = RANSAC_NAME
filtering_kwargs = {"ransac_val": self.ransac}
match_info12, filtered_match_info12, match_info21, filtered_match_info21 = \
match_desc_and_kp(desc1, kp1_xy, desc2, kp2_xy,
metric=self.metric, metric_type=self.metric_type,
metric_kwargs=self.metric_kwargs,
filter_method=self.match_filter_method,
filtering_kwargs=filtering_kwargs)
if self.metric_name is None:
self.metric_name = match_info12.metric_name
return match_info12, filtered_match_info12, match_info21, filtered_match_info21
```