detector->detect ( img_2,keypoints_2 ); //-- 第二步:根据角点位置计算 BRIEF 描述子 descriptor->compute ( img_1, keypoints_1, descriptors_1 ); descriptor->compute ( img_2, keypoints_2, descriptors_2 ); Mat outimg1; drawKe
void cv::drawKeypoints( InputArray image, const std::vector< KeyPoint > & keypoints, InputOutputArray outImage, const Scalar & color = Scalar::all(-1), int flags = DrawMatchesFlags::DEFAULT ) //Python: outImage = cv.drawKeypoints(image, keypoints, outImage[, color[, flags]]) 1. ...
int minHessian = 400; SurfFeatureDetector detector( minHessian ); std::vector<KeyPoint> keypoints_1, keypoints_2; detector.detect( img_1, keypoints_1 ); detector.detect( img_2, keypoints_2 ); //-- Draw keypoints Mat img_keypoints_1; Mat img_keypoints_2; drawKeypoints( img_1, ke...
CV_OUT std::vector<KeyPoint>& keypoints,//检测到的关键点,(在图像集中关键点是在图像中检测到的一组关键点) InputArray mask=noArray() //指定在哪里寻找关键点的掩码(必须是在感兴趣区域中具有非零值的8位整数矩阵) ); 函数drawKeypoints()的参数说明: void drawKeypoints( InputArray image, //源图像...
问在python opencv中使用模块会出现错误：' drawKeypoints‘对象没有’drawKeypoints‘属性ENSIFT （尺度...
y; keyPoints.push_back(keyPoint); } drawKeypoints(img, keyPoints, img); imshow("img", img); waitKey(0); exit(0); return 0; } 运行结果: 三、亚像素级别角点位置优化 通过上面的角点检测算法,虽然能在原图像中检测出角点的位置,但仔细观察函数可以发现检测出的角点坐标都为整数值,这是因为...
drawKeypoints(src,keypoints,kp_image,Scalar(0,0,255),DrawMatchesFlags::DRAW_RICH_KEYPOINTS); imshow("keypoints",kp_image); 总结: 图像的BLOB特征提取与分析,除了使用SimpleBlobDetector类之外还可以通过findContours与几何矩Moments计算相结合来实现。后者更加考察...
要绘制这些特征,我们再次使用cv::drawKeypoints OpenCV 函数,但这一次使用另一个遮罩,因为我们还想显示与每个特征相关的比例因子: // Draw the keypoints with scale and orientation information cv::drawKeypoints(image, // original image keypoints, // vector of keypoints featureImage, // the resulting ...
drawKeypoints(srcImage,detectKeyPoint,keyPointImage2,Scalar(0,0,255),DrawMatchesFlags::DEFAULT); imshow("src image",srcImage); imshow("keyPoint image1",keyPointImage1); imshow("keyPoint image2",keyPointImage2); imwrite("F:\\opencv\\OpenCVImage\\FeatureDetectSrc1SimpleBlobDetectorKeyPointImage...
img=cv.drawKeypoints(img2,kp2,img2)cv.imshow('SIFT key points',img)cv.imwrite('sift_keypoint.jpg',img)cv.waitKey() 运行之后可以看到绘制的特征点详情如下图所示: 特征匹配 Brute Force匹配和FLANN匹配是Opencv二维特征点匹配常见的两种办法,分别对应BFMatcher(BruteForceMatcher)和FlannBasedMatcher ...