我想将SurfFeatureDetector限制为一组区域(蒙版)。对于测试,我只定义了一个掩码:
Mat srcImage; //RGB source image
Mat mask = Mat::zeros(srcImage.size(), srcImage.type());
Mat roi(mask, cv::Rect(10,10,100,100));
roi = Scalar(255, 255, 255);
SurfFeatureDetector detector();
std::vector<KeyPoint> keypoints;
detector.detect(srcImage, keypoints, roi); // crash
//detector.detect(srcImage, keypoints); // does not crash
当我传递“roi”作为面具时,我收到了这个错误:
OpenCV Error: Assertion failed (mask.empty() || (mask.type() == CV_8UC1 && mask.size() == image.size())) in detect, file /Users/ux/Downloads/OpenCV-iOS/OpenCV-iOS/../opencv-svn/modules/features2d/src/detectors.cpp, line 63
这有什么问题?如何正确地将掩码传递给SurfFeatureDetector的“detect”方法?
此致
答案 0 :(得分:15)
关于面具的两件事。
CV_8U
。在您的情况下,掩码是srcImage.type()类型,它是一个3通道矩阵roi
传递给探测器,但您应该通过mask
。当您对roi
进行更改时,您也正在更改mask
。以下内容应该有效
Mat srcImage; //RGB source image
Mat mask = Mat::zeros(srcImage.size(), CV_8U); // type of mask is CV_8U
// roi is a sub-image of mask specified by cv::Rect object
Mat roi(mask, cv::Rect(10,10,100,100));
// we set elements in roi region of the mask to 255
roi = Scalar(255);
SurfFeatureDetector detector();
std::vector<KeyPoint> keypoints;
detector.detect(srcImage, keypoints, mask); // passing `mask` as a parameter
答案 1 :(得分:2)
我将您的投资回报率代码添加到我正在处理的一些现有代码中,并对其进行了以下更改
cv::Mat mask = cv::Mat::zeros(frame.size(), CV_8UC1); //NOTE: using the type explicitly
cv::Mat roi(mask, cv::Rect(10,10,100,100));
roi = cv::Scalar(255, 255, 255);
//SURF feature detection
const int minHessian = 400;
cv::SurfFeatureDetector detector(minHessian);
std::vector<cv::KeyPoint> keypoints;
detector.detect(frame, keypoints, mask); //NOTE: using mask here, NOT roi
cv::Mat img_keypoints;
drawKeypoints(frame, keypoints, img_keypoints, cv::Scalar::all(-1), cv::DrawMatchesFlags::DEFAULT);
cv::imshow("input image + Keypoints", img_keypoints);
cv::waitKey(0);
如果没有更改类型和使用mask
而不是roi
作为掩码,我也会遇到运行时错误。这是有道理的,因为检测方法需要一个掩码 - 它应该与原始图像大小相同,而roi不是(它是100x100矩形)。要在视觉上看到这一点,请尝试显示蒙版和roi
cv::imshow("Mask", mask);
cv::waitKey(0);
cv::imshow("ROI", roi);
cv::waitKey(0);
类型也必须匹配;掩码应该是单通道,而您的图像类型可能是16型,它映射到CV_8UC3
,一个三通道图像
答案 2 :(得分:0)
如果您希望将其应用于不规则面具,请:
Mat& obtainIregularROI(Mat& origImag, Point2f topLeft, Point2f topRight, Point2f botLeft, Point2f botRight){
static Mat black(origImag.rows, origImag.cols, origImag.type(), cv::Scalar::all(0));
Mat mask(origImag.rows, origImag.cols, CV_8UC1, cv::Scalar(0));
vector< vector<Point> > co_ordinates;
co_ordinates.push_back(vector<Point>());
co_ordinates[0].push_back(topLeft);
co_ordinates[0].push_back(botLeft);
co_ordinates[0].push_back(botRight);
co_ordinates[0].push_back(topRight);
drawContours( mask,co_ordinates,0, Scalar(255),CV_FILLED, 8 );
// origImag.copyTo(black,mask);
//BasicAlgo::getInstance()->writeImage(black);
return mask; // returning the mask only
}
然后像往常一样,生成SIFT / SURF / ...指针
//为SIFT特征检测器创建智能指针。
Ptr<FeatureDetector> SIFT_FeatureDetector = FeatureDetector::create("SIFT");
vector<KeyPoint> SIFT_Keypoints;
vector<KeyPoint> SIFT_KeypointsRotated;
Mat maskedImg = ImageDeformationOperations::getInstance()->obtainIregularROI( rotatedImg,rotTopLeft,rotTopRight,rotBotLeft,rotBotRight);
SIFT_FeatureDetector->detect(rotatedImg, SIFT_KeypointsRotated, maskedImg);
Mat outputSIFTKeyPt;
drawKeypoints(rotatedImg, SIFT_KeypointsRotated, outputSIFTKeyPt, keypointColor, DrawMatchesFlags::DEFAULT);