在两幅图像之间绘制图像 – 图像识别

我试图显示两个图像(一个是从我的相机和另一个从数据库中捕获的)之间匹配的关键点,

任何人都可以帮我在我的代码中写出DrawMatches函数,以显示两个图像之间的匹配线。

这是我的代码:

public final class ImageDetectionFilter{ // Flag draw target Image corner. private boolean flagDraw ; // The reference image (this detector's target). private final Mat mReferenceImage; // Features of the reference image. private final MatOfKeyPoint mReferenceKeypoints = new MatOfKeyPoint(); // Descriptors of the reference image's features. private final Mat mReferenceDescriptors = new Mat(); // The corner coordinates of the reference image, in pixels. // CvType defines the color depth, number of channels, and // channel layout in the image. Here, each point is represented // by two 32-bit floats. private final Mat mReferenceCorners = new Mat(4, 1, CvType.CV_32FC2); // Features of the scene (the current frame). private final MatOfKeyPoint mSceneKeypoints = new MatOfKeyPoint(); // Descriptors of the scene's features. private final Mat mSceneDescriptors = new Mat(); // Tentative corner coordinates detected in the scene, in // pixels. private final Mat mCandidateSceneCorners = new Mat(4, 1, CvType.CV_32FC2); // Good corner coordinates detected in the scene, in pixels. private final Mat mSceneCorners = new Mat(4, 1, CvType.CV_32FC2); // The good detected corner coordinates, in pixels, as integers. private final MatOfPoint mIntSceneCorners = new MatOfPoint(); // A grayscale version of the scene. private final Mat mGraySrc = new Mat(); // Tentative matches of scene features and reference features. private final MatOfDMatch mMatches = new MatOfDMatch(); // A feature detector, which finds features in images. private final FeatureDetector mFeatureDetector = FeatureDetector.create(FeatureDetector.ORB); // A descriptor extractor, which creates descriptors of // features. private final DescriptorExtractor mDescriptorExtractor = DescriptorExtractor.create(DescriptorExtractor.ORB); // A descriptor matcher, which matches features based on their // descriptors. private final DescriptorMatcher mDescriptorMatcher = DescriptorMatcher .create(DescriptorMatcher.BRUTEFORCE_HAMMINGLUT); // The color of the outline drawn around the detected image. private final Scalar mLineColor = new Scalar(0, 255, 0); public ImageDetectionFilter(final Context context, final int referenceImageResourceID) throws IOException { // Load the reference image from the app's resources. // It is loaded in BGR (blue, green, red) format. mReferenceImage = Utils.loadResource(context, referenceImageResourceID, Imgcodecs.CV_LOAD_IMAGE_COLOR); // Create grayscale and RGBA versions of the reference image. final Mat referenceImageGray = new Mat(); Imgproc.cvtColor(mReferenceImage, referenceImageGray, Imgproc.COLOR_BGR2GRAY); Imgproc.cvtColor(mReferenceImage, mReferenceImage, Imgproc.COLOR_BGR2RGBA); // Store the reference image's corner coordinates, in pixels. mReferenceCorners.put(0, 0, new double[] { 0.0, 0.0 }); mReferenceCorners.put(1, 0, new double[] { referenceImageGray.cols(),0.0 }); mReferenceCorners.put(2, 0, new double[] { referenceImageGray.cols(), referenceImageGray.rows() }); mReferenceCorners.put(3, 0, new double[] { 0.0, referenceImageGray.rows() }); // Detect the reference features and compute their // descriptors. mFeatureDetector.detect(referenceImageGray, mReferenceKeypoints); mDescriptorExtractor.compute(referenceImageGray, mReferenceKeypoints,mReferenceDescriptors); } public void apply(Mat src, Mat dst) { // Convert the scene to grayscale. Imgproc.cvtColor(src, mGraySrc, Imgproc.COLOR_RGBA2GRAY); // Detect the same features, compute their descriptors, // and match the scene descriptors to reference descriptors. mFeatureDetector.detect(mGraySrc, mSceneKeypoints); mDescriptorExtractor.compute(mGraySrc, mSceneKeypoints, mSceneDescriptors); mDescriptorMatcher.match(mSceneDescriptors, mReferenceDescriptors,mMatches); findSceneCorners(); // If the corners have been found, draw an outline around the // target image. // Else, draw a thumbnail of the target image. draw(src, dst); } private void findSceneCorners() { flagDraw = false; final List<DMatch> matchesList = mMatches.toList(); if (matchesList.size() < 4) { // There are too few matches to find the homography. return; } final List<KeyPoint> referenceKeypointsList = mReferenceKeypoints.toList(); final List<KeyPoint> sceneKeypointsList = mSceneKeypoints.toList(); // Calculate the max and min distances between keypoints. double maxDist = 0.0; double minDist = Double.MAX_VALUE; for (final DMatch match : matchesList) { final double dist = match.distance; if (dist < minDist) { minDist = dist; } if (dist > maxDist) { maxDist = dist; } } // The thresholds for minDist are chosen subjectively // based on testing. The unit is not related to pixel // distances; it is related to the number of failed tests // for similarity between the matched descriptors. if (minDist > 50.0) { // The target is completely lost. // Discard any previously found corners. mSceneCorners.create(0, 0, mSceneCorners.type()); return; } else if (minDist > 25.0) { // The target is lost but maybe it is still close. // Keep any previously found corners. return; } // Identify "good" keypoints and on match distance. final ArrayList<Point> goodReferencePointsList = new ArrayList<Point>(); final ArrayList<Point> goodScenePointsList = new ArrayList<Point>(); final double maxGoodMatchDist = 1.75 * minDist; for (final DMatch match : matchesList) { if (match.distance < maxGoodMatchDist) { goodReferencePointsList.add( referenceKeypointsList.get(match.trainIdx).pt); goodScenePointsList .add(sceneKeypointsList.get(match.queryIdx).pt); } } if (goodReferencePointsList.size() < 4 || goodScenePointsList.size() < 4) { // There are too few good points to find the homography. return; } // There are enough good points to find the homography. // (Otherwise, the method would have already returned.) // Convert the matched points to MatOfPoint2f format, as // required by the Calib3d.findHomography function. final MatOfPoint2f goodReferencePoints = new MatOfPoint2f(); goodReferencePoints.fromList(goodReferencePointsList); final MatOfPoint2f goodScenePoints = new MatOfPoint2f(); goodScenePoints.fromList(goodScenePointsList); // Find the homography. final Mat homography = Calib3d.findHomography( goodReferencePoints,goodScenePoints); // Use the homography to project the reference corner // coordinates into scene coordinates. Core.perspectiveTransform(mReferenceCorners, mCandidateSceneCorners,homography); // Convert the scene corners to integer format, as required // by the Imgproc.isContourConvex function. mCandidateSceneCorners.convertTo(mIntSceneCorners, CvType.CV_32S); // Check whether the corners form a convex polygon. If not, // (that is, if the corners form a concave polygon), the // detection result is invalid because no real perspective can // make the corners of a rectangular image look like a concave // polygon! if (Imgproc.isContourConvex(mIntSceneCorners)) { // The corners form a convex polygon, so record them as // valid scene corners. mCandidateSceneCorners.copyTo(mSceneCorners); flagDraw = true; } } protected void draw(final Mat src, final Mat dst) { if (dst != src) { src.copyTo(dst); } // Outline the found target in green. Imgproc.line(dst, new Point(mSceneCorners.get(0, 0)), new Point( mSceneCorners.get(1, 0)), mLineColor, 4); Imgproc.line(dst, new Point(mSceneCorners.get(1, 0)), new Point( mSceneCorners.get(2, 0)), mLineColor, 4); Imgproc.line(dst, new Point(mSceneCorners.get(2, 0)), new Point( mSceneCorners.get(3, 0)), mLineColor, 4); Imgproc.line(dst, new Point(mSceneCorners.get(3, 0)), new Point( mSceneCorners.get(0, 0)), mLineColor, 4); } public boolean getFlagDraw(){ return flagDraw; } } 

Solutions Collecting From Web of "在两幅图像之间绘制图像 – 图像识别"

我不坚定的Java,不知道这将是有益的,但我张贴了一个例子,我如何设法实现这个python使用openCV。 也许这会帮助你作为指导。

(这个例子是从这个网站改编的, 这个网站有更多的解释可能会引起人们的兴趣)

在这个例子中,我find了一组六个卡通动物中的一个卡通动物的旋转版本。

基本上,你想调用cv2.drawMatches()与你的训练和查询图像中的关键点,并掩盖不良匹配。 我的代码的相关部分是在最底部。

你的例子不是一个简单的代码示例,我没有完成所有的工作,但似乎你已经有了你的关键点,应该准备好了吗?

在这里输入图像说明

在这里输入图像说明

在这里输入图像说明

 import numpy as np import cv2 from matplotlib import pyplot as plt MIN_MATCH_COUNT = 4 img1 = cv2.imread('d:/one_animal_rotated.jpg',0) # queryImage img2 = cv2.imread('d:/many_animals.jpg',0) # trainImage # Initiate SIFT detector sift = cv2.xfeatures2d.SIFT_create(0,3,0) # find the keypoints and descriptors with SIFT kp1, des1 = sift.detectAndCompute(img1,None) kp2, des2 = sift.detectAndCompute(img2,None) #find matches using FLANN FLANN_INDEX_KDTREE = 0 index_params = dict(algorithm = FLANN_INDEX_KDTREE, trees = 5) search_params = dict(checks = 50) flann = cv2.FlannBasedMatcher(index_params, search_params) matches = flann.knnMatch(des1,des2,k=2) #apply ratio test to find best matches (values from 0.7-1 made sense here) good = [] for m,n in matches: if m.distance < 1*n.distance: good.append(m) #find homography to transform the edges of the query image and draw them on the train image #This is also used to mask all keypoints that aren't inside this box further below. src_pts = np.float32([ kp1[m.queryIdx].pt for m in good]).reshape(-1,1,2) dst_pts = np.float32([ kp2[m.trainIdx].pt for m in good]).reshape(-1,1,2) M, mask = cv2.findHomography(src_pts, dst_pts, cv2.RANSAC,5.0) matchesMask = mask.ravel().tolist() h,w = img1.shape pts = np.float32([ [0,0],[0,h-1],[w-1,h-1],[w-1,0] ]).reshape(-1,1,2) dst = cv2.perspectiveTransform(pts,M) img2 = cv2.polylines(img2,[np.int32(dst)],True,255,3, cv2.LINE_AA) #draw the good matched key points draw_params = dict(matchColor = (0,255,0), # draw matches in green color singlePointColor = None, matchesMask = matchesMask, # draw only inliers flags = 2) img3 = cv2.drawMatches(img1,kp1,img2,kp2,good,None,**draw_params) plt.figure() plt.imshow(img3, 'gray'),plt.show()