PictureFeature
目录
Suárez, Iago, et al. “BEBLID: Boosted efficient binary local image descriptor.” Pattern Recognition Letters 133 (2020): 366-372.
Paper: BEBLID:
Summary
- BEBLID, an efficient learned binary image descriptor;
- achieves an accuracy close to SIFT and better computational efficiency than ORB.
- BEBLID details:
- use the integral image to efficiently compute the difference between the mean gray values in a pair of square image regions.
- use the Boosted SCC algorithm to discriminatively select a set of features and combine them to produce a strong description.
Relative
- SIFT: requiring the use of a GPU to achieve real-time performance in certain contexts.
- ORB: the fastest binary approaches, use features based on the comparison of pairs of image pixels, use limited number of comparisons selected to be uncorrelated with unsupervised approach.
- DNN Relative:
code
ORB检测到强角，在不同的尺度上比较它们，并使用FAST或Harris响应来挑选最好的。它还使用局部patch的一阶矩来寻找每个角点的方向。
#pip install "opencv-contrib-python>=4.5.1"
from __future__ import print_function
import cv2 as cv
import numpy as np
import argparse
from math import sqrt
parser = argparse.ArgumentParser(description='Code from AKAZE local features matching tutorial.')
parser.add_argument('--input1', help='Path to input image 1.', default='graf1.png')
parser.add_argument('--input2', help='Path to input image 2.', default='graf3.png')
parser.add_argument('--homography', help='Path to the homography matrix.', default='H1to3p.xml')
args = parser.parse_args()
img1 = cv.imread(args.input1, cv.IMREAD_GRAYSCALE)
img2 = cv.imread(args.input2, cv.IMREAD_GRAYSCALE)
if img1 is None or img2 is None:
print('Could not open or find the images!')
exit(0)
fs = cv.FileStorage(args.homography, cv.FILE_STORAGE_READ)
homography = fs.getFirstTopLevelNode().mat()
detector = cv.ORB_create(10000) #使用orb方法
# descriptor = cv.ORB_create()
descriptor = cv.xfeatures2d.BEBLID_create(0.75) # 使用beblid 方法。
kpts1 = detector.detect(img1, None)
kpts2 = detector.detect(img2, None)
kpts1, desc1 = descriptor.compute(img1, kpts1)
kpts2, desc2 = descriptor.compute(img2, kpts2)
matcher = cv.DescriptorMatcher_create(cv.DescriptorMatcher_BRUTEFORCE_HAMMING)
nn_matches = matcher.knnMatch(desc1, desc2, 2)
matched1 = []
matched2 = []
nn_match_ratio = 0.8 # Nearest neighbor matching ratio
for m, n in nn_matches:
if m.distance < nn_match_ratio * n.distance:
matched1.append(kpts1[m.queryIdx])
matched2.append(kpts2[m.trainIdx])
inliers1 = []
inliers2 = []
good_matches = []
inlier_threshold = 2.5 # Distance threshold to identify inliers with homography check
for i, m in enumerate(matched1):
# Create the homogeneous point
col = np.ones((3, 1), dtype=np.float64)
col[0:2, 0] = m.pt
# Project from image 1 to image 2
col = np.dot(homography, col)
col /= col[2, 0]
# Calculate euclidean distance
dist = sqrt(pow(col[0, 0] - matched2[i].pt[0], 2) + \
pow(col[1, 0] - matched2[i].pt[1], 2))
if dist < inlier_threshold:
good_matches.append(cv.DMatch(len(inliers1), len(inliers2), 0))
inliers1.append(matched1[i])
inliers2.append(matched2[i])
res = np.empty((max(img1.shape[0], img2.shape[0]), img1.shape[1] + img2.shape[1], 3), dtype=np.uint8)
cv.drawMatches(img1, inliers1, img2, inliers2, good_matches, res)
cv.imwrite("matching_result.png", res)
inlier_ratio = len(inliers1) / float(len(matched1))
print('Matching Results')
print('*******************************')
print('# Keypoints 1: \t', len(kpts1))
print('# Keypoints 2: \t', len(kpts2))
print('# Matches: \t', len(matched1))
print('# Inliers: \t', len(inliers1))
print('# Inliers Ratio: \t', inlier_ratio)
cv.imshow('result', res)
cv.waitKey()