opencv/apps/opencv_stitching_tool/opencv_stitching/feature_matcher.py

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import math
import cv2 as cv
import numpy as np
class FeatureMatcher:
MATCHER_CHOICES = ('homography', 'affine')
DEFAULT_MATCHER = 'homography'
DEFAULT_RANGE_WIDTH = -1
def __init__(self,
matcher_type=DEFAULT_MATCHER,
range_width=DEFAULT_RANGE_WIDTH,
**kwargs):
if matcher_type == "affine":
"""https://docs.opencv.org/master/d3/dda/classcv_1_1detail_1_1AffineBestOf2NearestMatcher.html""" # noqa
self.matcher = cv.detail_AffineBestOf2NearestMatcher(**kwargs)
elif range_width == -1:
"""https://docs.opencv.org/master/d4/d26/classcv_1_1detail_1_1BestOf2NearestMatcher.html""" # noqa
self.matcher = cv.detail.BestOf2NearestMatcher_create(**kwargs)
else:
"""https://docs.opencv.org/master/d8/d72/classcv_1_1detail_1_1BestOf2NearestRangeMatcher.html""" # noqa
self.matcher = cv.detail.BestOf2NearestRangeMatcher_create(
range_width, **kwargs
)
def match_features(self, features, *args, **kwargs):
pairwise_matches = self.matcher.apply2(features, *args, **kwargs)
self.matcher.collectGarbage()
return pairwise_matches
@staticmethod
def draw_matches_matrix(imgs, features, matches, conf_thresh=1,
inliers=False, **kwargs):
matches_matrix = FeatureMatcher.get_matches_matrix(matches)
for idx1, idx2 in FeatureMatcher.get_all_img_combinations(len(imgs)):
match = matches_matrix[idx1, idx2]
if match.confidence < conf_thresh:
continue
if inliers:
kwargs['matchesMask'] = match.getInliers()
yield idx1, idx2, FeatureMatcher.draw_matches(
imgs[idx1], features[idx1],
imgs[idx2], features[idx2],
match,
**kwargs
)
@staticmethod
def draw_matches(img1, features1, img2, features2, match1to2, **kwargs):
kwargs.setdefault('flags', cv.DrawMatchesFlags_NOT_DRAW_SINGLE_POINTS)
keypoints1 = features1.getKeypoints()
keypoints2 = features2.getKeypoints()
matches = match1to2.getMatches()
return cv.drawMatches(
img1, keypoints1, img2, keypoints2, matches, None, **kwargs
)
@staticmethod
def get_matches_matrix(pairwise_matches):
return FeatureMatcher.array_in_sqare_matrix(pairwise_matches)
@staticmethod
def get_confidence_matrix(pairwise_matches):
matches_matrix = FeatureMatcher.get_matches_matrix(pairwise_matches)
match_confs = [[m.confidence for m in row] for row in matches_matrix]
match_conf_matrix = np.array(match_confs)
return match_conf_matrix
@staticmethod
def array_in_sqare_matrix(array):
matrix_dimension = int(math.sqrt(len(array)))
rows = []
for i in range(0, len(array), matrix_dimension):
rows.append(array[i:i+matrix_dimension])
return np.array(rows)
def get_all_img_combinations(number_imgs):
ii, jj = np.triu_indices(number_imgs, k=1)
for i, j in zip(ii, jj):
yield i, j
@staticmethod
def get_match_conf(match_conf, feature_detector_type):
if match_conf is None:
match_conf = \
FeatureMatcher.get_default_match_conf(feature_detector_type)
return match_conf
@staticmethod
def get_default_match_conf(feature_detector_type):
if feature_detector_type == 'orb':
return 0.3
return 0.65