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