AKAZE局所特徴のマッチング

目次

• はじめに
• Data
  • ソースコード
  • 解説
• Results
  • 見つかったマッチ

前のチュートリアル: 平面物体の検出
次のチュートリアル: AKAZE と ORB による平面トラッキング

原著者	Fedor Morozov
互換性	OpenCV >= 3.0

はじめに

このチュートリアルでは、AKAZE [12] 局所特徴を用いて2枚の画像上のキーポイントを検出しマッチングする方法を学ぶ。与えられたホモグラフィ行列を持つ画像のペアに対してキーポイントを検出し、それらをマッチングして、インライア (すなわち与えられたホモグラフィに適合するマッチ) の数を数える。

この例の拡張版はこちらにある: https://github.com/pablofdezalc/test_kaze_akaze_opencv

データ

Oxford データセット の Graffiti シーケンスの画像1と3を使用する。

ホモグラフィは 3x3 の行列で与えられる:

7.6285898e-01  -2.9922929e-01   2.2567123e+02
3.3443473e-01   1.0143901e+00  -7.6999973e+01
3.4663091e-04  -1.4364524e-05   1.0000000e+00

画像 (graf1.png, graf3.png) とホモグラフィ (H1to3p.xml) は opencv/samples/data/ にある。

ソースコード

C++

• ダウンロード可能なコード: こちら をクリック
• コードの全体像:

  #include <opencv2/features2d.hpp>
  #include <opencv2/imgproc.hpp>
  #include <opencv2/highgui.hpp>
  #include <iostream>
   
  using namespace std;
  using namespace cv;
   
  const float inlier_threshold = 2.5f; // Distance threshold to identify inliers with homography check
  const float nn_match_ratio = 0.8f; // Nearest neighbor matching ratio
   
  int main(int argc, char* argv[])
  {
   CommandLineParser parser(argc, argv,
   "{@img1 | graf1.png | input image 1}"
   "{@img2 | graf3.png | input image 2}"
   "{@homography | H1to3p.xml | homography matrix}");
   Mat img1 = imread( samples::findFile( parser.get<String>("@img1") ), IMREAD_GRAYSCALE);
   Mat img2 = imread( samples::findFile( parser.get<String>("@img2") ), IMREAD_GRAYSCALE);
   
   Mat homography;
   FileStorage fs( samples::findFile( parser.get<String>("@homography") ), FileStorage::READ);
   fs.getFirstTopLevelNode() >> homography;
   
   vector<KeyPoint> kpts1, kpts2;
   Mat desc1, desc2;
   
   Ptr<AKAZE> akaze = AKAZE::create();
   akaze->detectAndCompute(img1, noArray(), kpts1, desc1);
   akaze->detectAndCompute(img2, noArray(), kpts2, desc2);
   
   BFMatcher matcher(NORM_HAMMING);
   vector< vector<DMatch> > nn_matches;
   matcher.knnMatch(desc1, desc2, nn_matches, 2);
   
   vector<KeyPoint> matched1, matched2;
   for(size_t i = 0; i < nn_matches.size(); i++) {
   DMatch first = nn_matches[i][0];
   float dist1 = nn_matches[i][0].distance;
   float dist2 = nn_matches[i][1].distance;
   
   if(dist1 < nn_match_ratio * dist2) {
   matched1.push_back(kpts1[first.queryIdx]);
   matched2.push_back(kpts2[first.trainIdx]);
   }
   }
   
   vector<DMatch> good_matches;
   vector<KeyPoint> inliers1, inliers2;
   for(size_t i = 0; i < matched1.size(); i++) {
   Mat col = Mat::ones(3, 1, CV_64F);
   col.at<double>(0) = matched1[i].pt.x;
   col.at<double>(1) = matched1[i].pt.y;
   
   col = homography * col;
   col /= col.at<double>(2);
   double dist = sqrt( pow(col.at<double>(0) - matched2[i].pt.x, 2) +
   pow(col.at<double>(1) - matched2[i].pt.y, 2));
   
   if(dist < inlier_threshold) {
   int new_i = static_cast<int>(inliers1.size());
   inliers1.push_back(matched1[i]);
   inliers2.push_back(matched2[i]);
   good_matches.push_back(DMatch(new_i, new_i, 0));
   }
   }
   
   Mat res;
   drawMatches(img1, inliers1, img2, inliers2, good_matches, res);
   imwrite("akaze_result.png", res);
   
   double inlier_ratio = inliers1.size() / (double) matched1.size();
   cout << "A-KAZE Matching Results" << endl;
   cout << "*******************************" << endl;
   cout << "# Keypoints 1: \t" << kpts1.size() << endl;
   cout << "# Keypoints 2: \t" << kpts2.size() << endl;
   cout << "# Matches: \t" << matched1.size() << endl;
   cout << "# Inliers: \t" << inliers1.size() << endl;
   cout << "# Inliers Ratio: \t" << inlier_ratio << endl;
   cout << endl;
   
   imshow("result", res);
   waitKey();
   
   return 0;
  }

Java

• ダウンロード可能なコード: こちら をクリック
• コードの全体像:

  import java.io.File;
  import java.io.IOException;
  import java.util.ArrayList;
  import java.util.List;
   
  import javax.xml.parsers.DocumentBuilder;
  import javax.xml.parsers.DocumentBuilderFactory;
  import javax.xml.parsers.ParserConfigurationException;
   
  import org.opencv.core.Core;
  import org.opencv.core.CvType;
  import org.opencv.core.DMatch;
  import org.opencv.core.KeyPoint;
  import org.opencv.core.Mat;
  import org.opencv.core.MatOfDMatch;
  import org.opencv.core.MatOfKeyPoint;
  import org.opencv.core.Scalar;
  import org.opencv.features2d.AKAZE;
  import org.opencv.features2d.DescriptorMatcher;
  import org.opencv.features2d.Features2d;
  import org.opencv.highgui.HighGui;
  import org.opencv.imgcodecs.Imgcodecs;
  import org.w3c.dom.Document;
  import org.xml.sax.SAXException;
   
  class AKAZEMatch {
   public void run(String[] args) {
   String filename1 = args.length > 2 ? args[0] : "../data/graf1.png";
   String filename2 = args.length > 2 ? args[1] : "../data/graf3.png";
   String filename3 = args.length > 2 ? args[2] : "../data/H1to3p.xml";
   Mat img1 = Imgcodecs.imread(filename1, Imgcodecs.IMREAD_GRAYSCALE);
   Mat img2 = Imgcodecs.imread(filename2, Imgcodecs.IMREAD_GRAYSCALE);
   if (img1.empty() || img2.empty()) {
   System.err.println("Cannot read images!");
   System.exit(0);
   }
   
   File file = new File(filename3);
   DocumentBuilderFactory documentBuilderFactory = DocumentBuilderFactory.newInstance();
   DocumentBuilder documentBuilder;
   Document document;
   Mat homography = new Mat(3, 3, CvType.CV_64F);
   double[] homographyData = new double[(int) (homography.total()*homography.channels())];
   try {
   documentBuilder = documentBuilderFactory.newDocumentBuilder();
   document = documentBuilder.parse(file);
   String homographyStr = document.getElementsByTagName("data").item(0).getTextContent();
   String[] splited = homographyStr.split("\\s+");
   int idx = 0;
   for (String s : splited) {
   if (!s.isEmpty()) {
   homographyData[idx] = Double.parseDouble(s);
   idx++;
   }
   }
   } catch (ParserConfigurationException e) {
   e.printStackTrace();
   System.exit(0);
   } catch (SAXException e) {
   e.printStackTrace();
   System.exit(0);
   } catch (IOException e) {
   e.printStackTrace();
   System.exit(0);
   }
   homography.put(0, 0, homographyData);
   
   AKAZE akaze = AKAZE.create();
   MatOfKeyPoint kpts1 = new MatOfKeyPoint(), kpts2 = new MatOfKeyPoint();
   Mat desc1 = new Mat(), desc2 = new Mat();
   akaze.detectAndCompute(img1, new Mat(), kpts1, desc1);
   akaze.detectAndCompute(img2, new Mat(), kpts2, desc2);
   
   DescriptorMatcher matcher = DescriptorMatcher.create(DescriptorMatcher.BRUTEFORCE_HAMMING);
   List<MatOfDMatch> knnMatches = new ArrayList<>();
   matcher.knnMatch(desc1, desc2, knnMatches, 2);
   
   float ratioThreshold = 0.8f; // Nearest neighbor matching ratio
   List<KeyPoint> listOfMatched1 = new ArrayList<>();
   List<KeyPoint> listOfMatched2 = new ArrayList<>();
   List<KeyPoint> listOfKeypoints1 = kpts1.toList();
   List<KeyPoint> listOfKeypoints2 = kpts2.toList();
   for (int i = 0; i < knnMatches.size(); i++) {
   DMatch[] matches = knnMatches.get(i).toArray();
   float dist1 = matches[0].distance;
   float dist2 = matches[1].distance;
   if (dist1 < ratioThreshold * dist2) {
   listOfMatched1.add(listOfKeypoints1.get(matches[0].queryIdx));
   listOfMatched2.add(listOfKeypoints2.get(matches[0].trainIdx));
   }
   }
   
   double inlierThreshold = 2.5; // Distance threshold to identify inliers with homography check
   List<KeyPoint> listOfInliers1 = new ArrayList<>();
   List<KeyPoint> listOfInliers2 = new ArrayList<>();
   List<DMatch> listOfGoodMatches = new ArrayList<>();
   for (int i = 0; i < listOfMatched1.size(); i++) {
   Mat col = new Mat(3, 1, CvType.CV_64F);
   double[] colData = new double[(int) (col.total() * col.channels())];
   colData[0] = listOfMatched1.get(i).pt.x;
   colData[1] = listOfMatched1.get(i).pt.y;
   colData[2] = 1.0;
   col.put(0, 0, colData);
   
   Mat colRes = new Mat();
   Core.gemm(homography, col, 1.0, new Mat(), 0.0, colRes);
   colRes.get(0, 0, colData);
   Core.multiply(colRes, new Scalar(1.0 / colData[2]), col);
   col.get(0, 0, colData);
   
   double dist = Math.sqrt(Math.pow(colData[0] - listOfMatched2.get(i).pt.x, 2) +
   Math.pow(colData[1] - listOfMatched2.get(i).pt.y, 2));
   
   if (dist < inlierThreshold) {
   listOfGoodMatches.add(new DMatch(listOfInliers1.size(), listOfInliers2.size(), 0));
   listOfInliers1.add(listOfMatched1.get(i));
   listOfInliers2.add(listOfMatched2.get(i));
   }
   }
   
   Mat res = new Mat();
   MatOfKeyPoint inliers1 = new MatOfKeyPoint(listOfInliers1.toArray(new KeyPoint[listOfInliers1.size()]));
   MatOfKeyPoint inliers2 = new MatOfKeyPoint(listOfInliers2.toArray(new KeyPoint[listOfInliers2.size()]));
   MatOfDMatch goodMatches = new MatOfDMatch(listOfGoodMatches.toArray(new DMatch[listOfGoodMatches.size()]));
   Features2d.drawMatches(img1, inliers1, img2, inliers2, goodMatches, res);
   Imgcodecs.imwrite("akaze_result.png", res);
   
   double inlierRatio = listOfInliers1.size() / (double) listOfMatched1.size();
   System.out.println("A-KAZE Matching Results");
   System.out.println("*******************************");
   System.out.println("# Keypoints 1: \t" + listOfKeypoints1.size());
   System.out.println("# Keypoints 2: \t" + listOfKeypoints2.size());
   System.out.println("# Matches: \t" + listOfMatched1.size());
   System.out.println("# Inliers: \t" + listOfInliers1.size());
   System.out.println("# Inliers Ratio: \t" + inlierRatio);
   
   HighGui.imshow("result", res);
   HighGui.waitKey();
   
   System.exit(0);
   }
  }
   
  public class AKAZEMatchDemo {
   public static void main(String[] args) {
   // Load the native OpenCV library
   System.loadLibrary(Core.NATIVE_LIBRARY_NAME);
   
   new AKAZEMatch().run(args);
   }
  }

Python

• ダウンロード可能なコード: こちら をクリック
• コードの全体像:

  from __future__ import print_function
  import cv2 as cv
  import numpy as np
  import argparse
  from math import sqrt
   
   
  parser = argparse.ArgumentParser(description='Code for 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(cv.samples.findFile(args.input1), cv.IMREAD_GRAYSCALE)
  img2 = cv.imread(cv.samples.findFile(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(cv.samples.findFile(args.homography), cv.FILE_STORAGE_READ)
  homography = fs.getFirstTopLevelNode().mat()
   
   
   
  akaze = cv.AKAZE_create()
  kpts1, desc1 = akaze.detectAndCompute(img1, None)
  kpts2, desc2 = akaze.detectAndCompute(img2, None)
   
   
   
  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):
   col = np.ones((3,1), dtype=np.float64)
   col[0:2,0] = m.pt
   
   col = np.dot(homography, col)
   col /= col[2,0]
   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("akaze_result.png", res)
   
  inlier_ratio = len(inliers1) / float(len(matched1))
  print('A-KAZE 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()
   

解説

• 画像とホモグラフィの読み込み

C++

 CommandLineParser parser(argc, argv,
 "{@img1 | graf1.png | input image 1}"
 "{@img2 | graf3.png | input image 2}"
 "{@homography | H1to3p.xml | homography matrix}");
 Mat img1 = imread( samples::findFile( parser.get<String>("@img1") ), IMREAD_GRAYSCALE);
 Mat img2 = imread( samples::findFile( parser.get<String>("@img2") ), IMREAD_GRAYSCALE);
 
 Mat homography;
 FileStorage fs( samples::findFile( parser.get<String>("@homography") ), FileStorage::READ);
    fs.getFirstTopLevelNode() >> homography;

Java

 String filename1 = args.length > 2 ? args[0] : "../data/graf1.png";
 String filename2 = args.length > 2 ? args[1] : "../data/graf3.png";
 String filename3 = args.length > 2 ? args[2] : "../data/H1to3p.xml";
        Mat img1 = Imgcodecs.imread(filename1, Imgcodecs.IMREAD_GRAYSCALE);
        Mat img2 = Imgcodecs.imread(filename2, Imgcodecs.IMREAD_GRAYSCALE);
 if (img1.empty() || img2.empty()) {
            System.err.println("Cannot read images!");
            System.exit(0);
        }
 
        File file = new File(filename3);
        DocumentBuilderFactory documentBuilderFactory = DocumentBuilderFactory.newInstance();
        DocumentBuilder documentBuilder;
        Document document;
        Mat homography = new Mat(3, 3, CvType.CV_64F);
 double[] homographyData = new double[(int) (homography.total()*homography.channels())];
 try {
            documentBuilder = documentBuilderFactory.newDocumentBuilder();
            document = documentBuilder.parse(file);
 String homographyStr = document.getElementsByTagName("data").item(0).getTextContent();
 String[] splited = homographyStr.split("\\s+");
 int idx = 0;
 for (String s : splited) {
 if (!s.isEmpty()) {
                    homographyData[idx] = Double.parseDouble(s);
                    idx++;
                }
            }
        } catch (ParserConfigurationException e) {
            e.printStackTrace();
            System.exit(0);
        } catch (SAXException e) {
            e.printStackTrace();
            System.exit(0);
        } catch (IOException e) {
            e.printStackTrace();
            System.exit(0);
        }
        homography.put(0, 0, homographyData);

Python

parser = argparse.ArgumentParser(description='Code for 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(cv.samples.findFile(args.input1), cv.IMREAD_GRAYSCALE)
img2 = cv.imread(cv.samples.findFile(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(cv.samples.findFile(args.homography), cv.FILE_STORAGE_READ)
homography = fs.getFirstTopLevelNode().mat()

ここではグレースケール画像を読み込んでいる。ホモグラフィは FileStorage で作成した xml に保存されている。

• AKAZE を用いてキーポイントを検出し記述子を計算する

C++

    vector<KeyPoint> kpts1, kpts2;
 Mat desc1, desc2;
 
 Ptr<AKAZE> akaze = AKAZE::create();
    akaze->detectAndCompute(img1, noArray(), kpts1, desc1);
    akaze->detectAndCompute(img2, noArray(), kpts2, desc2);

Java

        AKAZE akaze = AKAZE.create();
        MatOfKeyPoint kpts1 = new MatOfKeyPoint(), kpts2 = new MatOfKeyPoint();
        Mat desc1 = new Mat(), desc2 = new Mat();
        akaze.detectAndCompute(img1, new Mat(), kpts1, desc1);
        akaze.detectAndCompute(img2, new Mat(), kpts2, desc2);

Python

akaze = cv.AKAZE_create()
kpts1, desc1 = akaze.detectAndCompute(img1, None)
kpts2, desc2 = akaze.detectAndCompute(img2, None)

AKAZE を作成し、AKAZE のキーポイントと記述子を検出・計算する。mask 引数は不要なので、noArray() を使用する。

• 総当たりマッチャーを用いて 2-nn マッチを見つける

C++

 BFMatcher matcher(NORM_HAMMING);
    vector< vector<DMatch> > nn_matches;
    matcher.knnMatch(desc1, desc2, nn_matches, 2);

Java

        DescriptorMatcher matcher = DescriptorMatcher.create(DescriptorMatcher.BRUTEFORCE_HAMMING);
        List<MatOfDMatch> knnMatches = new ArrayList<>();
        matcher.knnMatch(desc1, desc2, knnMatches, 2);

Python

matcher = cv.DescriptorMatcher_create(cv.DescriptorMatcher_BRUTEFORCE_HAMMING)
nn_matches = matcher.knnMatch(desc1, desc2, 2)

AKAZE はデフォルトでバイナリ記述子を使用するため、ハミング距離を使用する。

• 2-nn マッチと比率基準を用いて正しいキーポイントマッチを見つける
  C++

   vector<KeyPoint> matched1, matched2;
   for(size_t i = 0; i < nn_matches.size(); i++) {
   DMatch first = nn_matches[i][0];
   float dist1 = nn_matches[i][0].distance;
   float dist2 = nn_matches[i][1].distance;
   
   if(dist1 < nn_match_ratio * dist2) {
   matched1.push_back(kpts1[first.queryIdx]);
   matched2.push_back(kpts2[first.trainIdx]);
   }
   }

Java

 float ratioThreshold = 0.8f; // Nearest neighbor matching ratio
        List<KeyPoint> listOfMatched1 = new ArrayList<>();
        List<KeyPoint> listOfMatched2 = new ArrayList<>();
        List<KeyPoint> listOfKeypoints1 = kpts1.toList();
        List<KeyPoint> listOfKeypoints2 = kpts2.toList();
 for (int i = 0; i < knnMatches.size(); i++) {
            DMatch[] matches = knnMatches.get(i).toArray();
 float dist1 = matches[0].distance;
 float dist2 = matches[1].distance;
 if (dist1 < ratioThreshold * dist2) {
                listOfMatched1.add(listOfKeypoints1.get(matches[0].queryIdx));
                listOfMatched2.add(listOfKeypoints2.get(matches[0].trainIdx));
            }
        }

Python

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])

最も近いマッチの距離が2番目に近いマッチの距離より十分に小さければ、そのマッチは正しい (マッチが曖昧でない)。

• マッチがホモグラフィモデルに適合するか確認する

C++

    vector<DMatch> good_matches;
    vector<KeyPoint> inliers1, inliers2;
 for(size_t i = 0; i < matched1.size(); i++) {
 Mat col = Mat::ones(3, 1, CV_64F);
        col.at<double>(0) = matched1[i].pt.x;
        col.at<double>(1) = matched1[i].pt.y;
 
        col = homography * col;
        col /= col.at<double>(2);
 double dist = sqrt( pow(col.at<double>(0) - matched2[i].pt.x, 2) +
                            pow(col.at<double>(1) - matched2[i].pt.y, 2));
 
 if(dist < inlier_threshold) {
 int new_i = static_cast<int>(inliers1.size());
            inliers1.push_back(matched1[i]);
            inliers2.push_back(matched2[i]);
            good_matches.push_back(DMatch(new_i, new_i, 0));
        }
    }

Java

 double inlierThreshold = 2.5; // Distance threshold to identify inliers with homography check
        List<KeyPoint> listOfInliers1 = new ArrayList<>();
        List<KeyPoint> listOfInliers2 = new ArrayList<>();
        List<DMatch> listOfGoodMatches = new ArrayList<>();
 for (int i = 0; i < listOfMatched1.size(); i++) {
            Mat col = new Mat(3, 1, CvType.CV_64F);
 double[] colData = new double[(int) (col.total() * col.channels())];
            colData[0] = listOfMatched1.get(i).pt.x;
            colData[1] = listOfMatched1.get(i).pt.y;
            colData[2] = 1.0;
            col.put(0, 0, colData);
 
            Mat colRes = new Mat();
            Core.gemm(homography, col, 1.0, new Mat(), 0.0, colRes);
            colRes.get(0, 0, colData);
            Core.multiply(colRes, new Scalar(1.0 / colData[2]), col);
            col.get(0, 0, colData);
 
 double dist = Math.sqrt(Math.pow(colData[0] - listOfMatched2.get(i).pt.x, 2) +
                    Math.pow(colData[1] - listOfMatched2.get(i).pt.y, 2));
 
 if (dist < inlierThreshold) {
                listOfGoodMatches.add(new DMatch(listOfInliers1.size(), listOfInliers2.size(), 0));
                listOfInliers1.add(listOfMatched1.get(i));
                listOfInliers2.add(listOfMatched2.get(i));
            }
        }

Python

inliers1 = []
inliers2 = []
good_matches = []
inlier_threshold = 2.5 # Distance threshold to identify inliers with homography check
for i, m in enumerate(matched1):
    col = np.ones((3,1), dtype=np.float64)
    col[0:2,0] = m.pt
 
    col = np.dot(homography, col)
    col /= col[2,0]
    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])

1つ目のキーポイントの投影から2つ目のキーポイントまでの距離がしきい値より小さければ、ホモグラフィモデルに適合する。

描画関数で必要となるため、インライア用に新しいマッチの集合を作成する。

• 結果の出力

C++

 Mat res;
 drawMatches(img1, inliers1, img2, inliers2, good_matches, res);
 imwrite("akaze_result.png", res);
 
 double inlier_ratio = inliers1.size() / (double) matched1.size();
    cout << "A-KAZE Matching Results" << endl;
    cout << "*******************************" << endl;
    cout << "# Keypoints 1:                        \t" << kpts1.size() << endl;
    cout << "# Keypoints 2:                        \t" << kpts2.size() << endl;
    cout << "# Matches:                            \t" << matched1.size() << endl;
    cout << "# Inliers:                            \t" << inliers1.size() << endl;
    cout << "# Inliers Ratio:                      \t" << inlier_ratio << endl;
    cout << endl;
 
 imshow("result", res);
 waitKey();

Java

        Mat res = new Mat();
        MatOfKeyPoint inliers1 = new MatOfKeyPoint(listOfInliers1.toArray(new KeyPoint[listOfInliers1.size()]));
        MatOfKeyPoint inliers2 = new MatOfKeyPoint(listOfInliers2.toArray(new KeyPoint[listOfInliers2.size()]));
        MatOfDMatch goodMatches = new MatOfDMatch(listOfGoodMatches.toArray(new DMatch[listOfGoodMatches.size()]));
        Features2d.drawMatches(img1, inliers1, img2, inliers2, goodMatches, res);
        Imgcodecs.imwrite("akaze_result.png", res);
 
 double inlierRatio = listOfInliers1.size() / (double) listOfMatched1.size();
        System.out.println("A-KAZE Matching Results");
        System.out.println("*******************************");
        System.out.println("# Keypoints 1:                        \t" + listOfKeypoints1.size());
        System.out.println("# Keypoints 2:                        \t" + listOfKeypoints2.size());
        System.out.println("# Matches:                            \t" + listOfMatched1.size());
        System.out.println("# Inliers:                            \t" + listOfInliers1.size());
        System.out.println("# Inliers Ratio:                      \t" + inlierRatio);
 
        HighGui.imshow("result", res);
        HighGui.waitKey();

Python

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("akaze_result.png", res)
 
inlier_ratio = len(inliers1) / float(len(matched1))
print('A-KAZE 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()

ここでは結果の画像を保存し、いくつかの統計情報を出力する。

結果

見つかったマッチ

使用している OpenCV のバージョンによっては、次と整合する結果が得られるはずである:

Keypoints 1:   2943
Keypoints 2:   3511
Matches:       447
Inliers:       308
Inlier Ratio: 0.689038