Android中的OpenCV图像比较和相似度

本文介绍了Android中的OpenCV图像比较和相似度的处理方法，对大家解决问题具有一定的参考价值，需要的朋友们下面随着小编来一起学习吧！ 问题描述 29岁程序员，3月因学历无情被辞！ 我 OpenCV 学习者。我正在尝试图像比较。我使用过 OpenCV 2.4.13.3 我有这两个图像 1.jpg 和 cam1.jpg 。I'm OpenCV learner. I was trying Image Comparison. I have used OpenCV 2.4.13.3I have these two images 1.jpg and cam1.jpg.当我在openCV中使用以下命令时When I use the following command in openCVFile sdCard = Environment.getExternalStorageDirectory();String path1, path2;path1 = sdCard.getAbsolutePath() + "/1.jpg";path2 = sdCard.getAbsolutePath() + "/cam1.jpg";FeatureDetector detector = FeatureDetector.create(FeatureDetector.ORB);DescriptorExtractor extractor = DescriptorExtractor.create(DescriptorExtractor.BRIEF);DescriptorMatcher matcher = DescriptorMatcher.create(DescriptorMatcher.BRUTEFORCE_HAMMING);Mat img1 = Highgui.imread(path1);Mat img2 = Highgui.imread(path2);Mat descriptors1 = new Mat();MatOfKeyPoint keypoints1 = new MatOfKeyPoint();detector.detect(img1, keypoints1);extractor.compute(img1, keypoints1, descriptors1);//second image// Mat img2 = Imgcodecs.imread(path2);Mat descriptors2 = new Mat();MatOfKeyPoint keypoints2 = new MatOfKeyPoint();detector.detect(img2, keypoints2);extractor.compute(img2, keypoints2, descriptors2);//matcher image descriptorsMatOfDMatch matches = new MatOfDMatch();matcher.match(descriptors1,descriptors2,matches);// Filter matches by distanceMatOfDMatch filtered = filterMatchesByDistance(matches);int total = (int) matches.size().height;int Match= (int) filtered.size().height;Log.d("LOG", "total:" + total + " Match:"+Match); 方法filterMatchesByDistance static MatOfDMatch filterMatchesByDistance(MatOfDMatch matches){ List<DMatch> matches_original = matches.toList(); List<DMatch> matches_filtered = new ArrayList<DMatch>(); int DIST_LIMIT = 30; // Check all the matches distance and if it passes add to list of filtered matches Log.d("DISTFILTER", "ORG SIZE:" + matches_original.size() + ""); for (int i = 0; i < matches_original.size(); i++) { DMatch d = matches_original.get(i); if (Math.abs(d.distance) <= DIST_LIMIT) { matches_filtered.add(d); } } Log.d("DISTFILTER", "FIL SIZE:" + matches_filtered.size() + ""); MatOfDMatch mat = new MatOfDMatch(); mat.fromList(matches_filtered); return mat;} 日志total:122 Match:30正如我们从日志匹配中看到的那样是30. 但我们可以看到两个图像都具有相同的可视元素（in）。 如何使用openCV获得match = 90 ？ 如果有人可以提供代码片段，那将会很棒。 如果使用opencv，那么我们可以找到的其他替代品是什么？As we can see from the log match is 30.But as we can see both images have same visual element (in).How can I get match=90 using openCV?It would be great if somebody can help with code snippet.If using opencv it is not possible then what are the otheralternatives we can look for?推荐答案 但我们可以看到两个图像都有相同的视觉元素（in）。 / p> But as we can see both images have same visual element (in).因此，我们应该比较不是整个图像，而是比较相同的视觉元素。如果你不比较模板和相机图像本身，你可以改善匹配值，但处理方式相同（例如转换为二进制黑/白） 模板和相机图像。例如，尝试在两个（模板和相机）图像上找到蓝色（模板徽标的背景）正方形，并比较那些正方形（感兴趣的区域）。代码可能是这样的：So, we should compare not whole images, but "same visual element" on it. You can improve Match value more if you do not compare the "template" and "camera" images themselves, but processed same way (converted to binary black/white, for example) "template" and "camera" images. For example, try to find blue (background of template logo) square on both ("template" and "camera") images and compare that squares (Region Of Interest). The code may be something like that:Bitmap bmImageTemplate = <get your template image Bitmap>;Bitmap bmTemplate = findLogo(bmImageTemplate); // process template imageBitmap bmImage = <get your camera image Bitmap>;Bitmap bmLogo = findLogo(bmImage); // process camera image same waycompareBitmaps(bmTemplate, bmLogo);其中private Bitmap findLogo(Bitmap sourceBitmap) { Bitmap roiBitmap = null; Mat sourceMat = new Mat(sourceBitmap.getWidth(), sourceBitmap.getHeight(), CvType.CV_8UC3); Utils.bitmapToMat(sourceBitmap, sourceMat); Mat roiTmp = sourceMat.clone(); final Mat hsvMat = new Mat(); sourceMat.copyTo(hsvMat); // convert mat to HSV format for Core.inRange() Imgproc.cvtColor(hsvMat, hsvMat, Imgproc.COLOR_RGB2HSV); Scalar lowerb = new Scalar(85, 50, 40); // lower color border for BLUE Scalar upperb = new Scalar(135, 255, 255); // upper color border for BLUE Core.inRange(hsvMat, lowerb, upperb, roiTmp); // select only blue pixels // find contours List<MatOfPoint> contours = new ArrayList<>(); List<Rect> squares = new ArrayList<>(); Imgproc.findContours(roiTmp, contours, new Mat(), Imgproc.RETR_LIST, Imgproc.CHAIN_APPROX_SIMPLE); // find appropriate bounding rectangles for (MatOfPoint contour : contours) { MatOfPoint2f areaPoints = new MatOfPoint2f(contour.toArray()); RotatedRect boundingRect = Imgproc.minAreaRect(areaPoints); double rectangleArea = boundingRect.size.area(); // test min ROI area in pixels if (rectangleArea > 400) { Point rotated_rect_points[] = new Point[4]; boundingRect.points(rotated_rect_points); Rect rect = Imgproc.boundingRect(new MatOfPoint(rotated_rect_points)); double aspectRatio = rect.width > rect.height ? (double) rect.height / (double) rect.width : (double) rect.width / (double) rect.height; if (aspectRatio >= 0.9) { squares.add(rect); } } } Mat logoMat = extractSquareMat(roiTmp, getBiggestSquare(squares)); roiBitmap = Bitmap.createBitmap(logoMat.cols(), logoMat.rows(), Bitmap.Config.ARGB_8888); Utils.matToBitmap(logoMat, roiBitmap); return roiBitmap;}方法 extractSquareMat（）从整个图像中提取感兴趣区域（徽标）method extractSquareMat() just extract Region of Interest (logo) from whole imagepublic static Mat extractSquareMat(Mat sourceMat, Rect rect) { Mat squareMat = null; int padding = 50; if (rect != null) { Rect truncatedRect = new Rect((int) rect.tl().x + padding, (int) rect.tl().y + padding, rect.width - 2 * padding, rect.height - 2 * padding); squareMat = new Mat(sourceMat, truncatedRect); } return squareMat ;}和 compareBitmaps（）只为你的代码包装：private void compareBitmaps(Bitmap bitmap1, Bitmap bitmap2) { Mat mat1 = new Mat(bitmap1.getWidth(), bitmap1.getHeight(), CvType.CV_8UC3); Utils.bitmapToMat(bitmap1, mat1); Mat mat2 = new Mat(bitmap2.getWidth(), bitmap2.getHeight(), CvType.CV_8UC3); Utils.bitmapToMat(bitmap2, mat2); compareMats(mat1, mat2);}您的代码方法：private void compareMats(Mat img1, Mat img2) { FeatureDetector detector = FeatureDetector.create(FeatureDetector.ORB); DescriptorExtractor extractor = DescriptorExtractor.create(DescriptorExtractor.BRIEF); DescriptorMatcher matcher = DescriptorMatcher.create(DescriptorMatcher.BRUTEFORCE_HAMMING); Mat descriptors1 = new Mat(); MatOfKeyPoint keypoints1 = new MatOfKeyPoint(); detector.detect(img1, keypoints1); extractor.compute(img1, keypoints1, descriptors1); //second image // Mat img2 = Imgcodecs.imread(path2); Mat descriptors2 = new Mat(); MatOfKeyPoint keypoints2 = new MatOfKeyPoint(); detector.detect(img2, keypoints2); extractor.compute(img2, keypoints2, descriptors2); //matcher image descriptors MatOfDMatch matches = new MatOfDMatch(); matcher.match(descriptors1,descriptors2,matches); // Filter matches by distance MatOfDMatch filtered = filterMatchesByDistance(matches); int total = (int) matches.size().height; int Match= (int) filtered.size().height; Log.d("LOG", "total:" + total + " Match:" + Match);}static MatOfDMatch filterMatchesByDistance(MatOfDMatch matches){ List<DMatch> matches_original = matches.toList(); List<DMatch> matches_filtered = new ArrayList<DMatch>(); int DIST_LIMIT = 30; // Check all the matches distance and if it passes add to list of filtered matches Log.d("DISTFILTER", "ORG SIZE:" + matches_original.size() + ""); for (int i = 0; i < matches_original.size(); i++) { DMatch d = matches_original.get(i); if (Math.abs(d.distance) <= DIST_LIMIT) { matches_filtered.add(d); } } Log.d("DISTFILTER", "FIL SIZE:" + matches_filtered.size() + ""); MatOfDMatch mat = new MatOfDMatch(); mat.fromList(matches_filtered); return mat;}从问题结果中保存的调整大小（缩放为50％）的结果是：As result for resized (scaled for 50%) images saved from your question result is:D/DISTFILTER: ORG SIZE:237D/DISTFILTER: FIL SIZE:230D/LOG: total:237 Match:230 NB！这是一个快速而肮脏的例子，仅用于演示给定模板的方法。NB! This is a quick and dirty example just to demonstrate approach only for given template. 这篇关于Android中的OpenCV图像比较和相似度的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持！ 上岸，阿里云！