本文主要是介绍【教程】Autojs使用OpenCV进行SIFT/BRISK等算法进行图像匹配，希望对大家解决编程问题提供一定的参考价值，需要的开发者们随着小编来一起学习吧！
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此代码可以替代内置的images.findImage函数使用，但可能会误匹配，如果是对匹配结果要求比较高的，还是得谨慎使用。
runtime.images.initOpenCvIfNeeded();
importClass(java.util.ArrayList);
importClass(java.util.List);
importClass(java.util.LinkedList);
importClass(org.opencv.imgproc.Imgproc);
importClass(org.opencv.imgcodecs.Imgcodecs);
importClass(org.opencv.core.Core);
importClass(org.opencv.core.Mat);
importClass(org.opencv.core.MatOfDMatch);
importClass(org.opencv.core.MatOfKeyPoint);
importClass(org.opencv.core.MatOfRect);
importClass(org.opencv.core.Size);
importClass(org.opencv.features2d.DescriptorMatcher);
importClass(org.opencv.features2d.Features2d);
importClass(org.opencv.features2d.SIFT);
importClass(org.opencv.features2d.ORB);
importClass(org.opencv.features2d.BRISK);
importClass(org.opencv.features2d.AKAZE);
importClass(org.opencv.features2d.BFMatcher);
importClass(org.opencv.core.MatOfPoint2f);
importClass(org.opencv.calib3d.Calib3d);
importClass(org.opencv.core.CvType);
importClass(org.opencv.core.Point);
importClass(org.opencv.core.Scalar);
importClass(org.opencv.core.MatOfByte);/** 用法示例：* var image1 = captureScreen();* var image2 = images.read('xxxx');* match(image1, image2);*/function match(img1, img2, method) {console.time("匹配耗时");// 指定特征点算法SIFTvar match_alg = null;if(method == 'sift') {match_alg = SIFT.create();}else if(method == 'orb') {match_alg = ORB.create();}else if(method == 'brisk') {match_alg = BRISK.create();}else {match_alg = AKAZE.create();}var bigTrainImage = Imgcodecs.imdecode(new MatOfByte(images.toBytes(img1)), Imgcodecs.IMREAD_UNCHANGED);var smallTrainImage = Imgcodecs.imdecode(new MatOfByte(images.toBytes(img2)), Imgcodecs.IMREAD_UNCHANGED);// 转灰度图// console.log("转灰度图");var big_trainImage_gray = new Mat(bigTrainImage.rows(), bigTrainImage.cols(), CvType.CV_8UC1);var small_trainImage_gray = new Mat(smallTrainImage.rows(), smallTrainImage.cols(), CvType.CV_8UC1);Imgproc.cvtColor(bigTrainImage, big_trainImage_gray, Imgproc.COLOR_BGR2GRAY);Imgproc.cvtColor(smallTrainImage, small_trainImage_gray, Imgproc.COLOR_BGR2GRAY);// 获取图片的特征点// console.log("detect");var big_keyPoints = new MatOfKeyPoint();var small_keyPoints = new MatOfKeyPoint();match_alg.detect(bigTrainImage, big_keyPoints);match_alg.detect(smallTrainImage, small_keyPoints);// 提取图片的特征点// console.log("compute");var big_trainDescription = new Mat(big_keyPoints.rows(), 128, CvType.CV_32FC1);var small_trainDescription = new Mat(small_keyPoints.rows(), 128, CvType.CV_32FC1);match_alg.compute(big_trainImage_gray, big_keyPoints, big_trainDescription);match_alg.compute(small_trainImage_gray, small_keyPoints, small_trainDescription);// console.log("matcher.train");var matcher = new BFMatcher();matcher.clear();var train_desc_collection = new ArrayList();train_desc_collection.add(big_trainDescription);// vector<Mat>train_desc_collection(1, trainDescription);matcher.add(train_desc_collection);matcher.train();// console.log("knnMatch");var matches = new ArrayList();matcher.knnMatch(small_trainDescription, matches, 2);//对匹配结果进行筛选，依据distance进行筛选// console.log("对匹配结果进行筛选");var goodMatches = new ArrayList();var nndrRatio = 0.8;var len = matches.size();for (var i = 0; i < len; i++) {var matchObj = matches.get(i);var dmatcharray = matchObj.toArray();var m1 = dmatcharray[0];var m2 = dmatcharray[1];if (m1.distance <= m2.distance * nndrRatio) {goodMatches.add(m1);}}var matchesPointCount = goodMatches.size();//当匹配后的特征点大于等于 4 个，则认为模板图在原图中，该值可以自行调整if (matchesPointCount >= 4) {log("模板图在原图匹配成功！");var templateKeyPoints = small_keyPoints;var originalKeyPoints = big_keyPoints;var templateKeyPointList = templateKeyPoints.toList();var originalKeyPointList = originalKeyPoints.toList();var objectPoints = new LinkedList();var scenePoints = new LinkedList();var goodMatchesList = goodMatches;var len = goodMatches.size();for (var i = 0; i < len; i++) {var goodMatch = goodMatches.get(i);objectPoints.addLast(templateKeyPointList.get(goodMatch.queryIdx).pt);scenePoints.addLast(originalKeyPointList.get(goodMatch.trainIdx).pt);}var objMatOfPoint2f = new MatOfPoint2f();objMatOfPoint2f.fromList(objectPoints);var scnMatOfPoint2f = new MatOfPoint2f();scnMatOfPoint2f.fromList(scenePoints);//使用 findHomography 寻找匹配上的关键点的变换var homography = Calib3d.findHomography(objMatOfPoint2f, scnMatOfPoint2f, Calib3d.RANSAC, 3);/*** 透视变换(Perspective Transformation)是将图片投影到一个新的视平面(Viewing Plane)，也称作投影映射(Projective Mapping)。*/var templateCorners = new Mat(4, 1, CvType.CV_32FC2);var templateTransformResult = new Mat(4, 1, CvType.CV_32FC2);var templateImage = smallTrainImage;var doubleArr = util.java.array("double", 2);doubleArr[0] = 0;doubleArr[1] = 0;templateCorners.put(0, 0, doubleArr);doubleArr[0] = templateImage.cols();doubleArr[1] = 0;templateCorners.put(1, 0, doubleArr);doubleArr[0] = templateImage.cols();doubleArr[1] = templateImage.rows();templateCorners.put(2, 0, doubleArr);doubleArr[0] = 0;doubleArr[1] = templateImage.rows();templateCorners.put(3, 0, doubleArr);//使用 perspectiveTransform 将模板图进行透视变以矫正图象得到标准图片Core.perspectiveTransform(templateCorners, templateTransformResult, homography);//矩形四个顶点var pointA = templateTransformResult.get(0, 0);var pointB = templateTransformResult.get(1, 0);var pointC = templateTransformResult.get(2, 0);var pointD = templateTransformResult.get(3, 0);var y0 = Math.round(pointA[1])>0?Math.round(pointA[1]):0;var y1 = Math.round(pointC[1])>0?Math.round(pointC[1]):0;var x0 = Math.round(pointD[0])>0?Math.round(pointD[0]):0;var x1 = Math.round(pointB[0])>0?Math.round(pointB[0]):0;console.timeEnd("匹配耗时");return {x: x0, y: y0};} else {console.timeEnd("匹配耗时");log("模板图不在原图中！");return null;}
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