OpenCV:如何使用掩码参数进行特征点检测(SURF)

I want to limit a SurfFeatureDetector to a set of regions (mask). For a test I define only a single mask:

我想把SurfFeatureDetector限制在一组区域(面罩)。对于一个测试，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

When I pass the "roi" as the mask I get this error:

当我通过“roi”作为蒙版时，我得到了这个错误:

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

What is wrong with this? How can I correctly pass a mask to the SurfFeatureDetector's "detect" method?

这有什么问题?如何正确地将口罩传递给SurfFeatureDetector的“检测”方法?

Regards,

问候,

3 个解决方案

#1

Two things about the mask.

面具有两点。

the mask should be a 1-channel matrix of 8-bit unsigned chars, which translates to opencv type CV_8U. In your case the mask is of type srcImage.type(), which is a 3-channel matrix
掩码应该是一个8位无符号字符的1通道矩阵，转换为opencv类型CV_8U。在您的例子中，掩码的类型是srci .type()，它是一个3通道矩阵
you are passing roi to the detector but you should be passing mask. When you are making changes to roi, you are also changing mask.
你将roi传递给检测器，但你应该通过掩码。当您对roi进行更改时，您也在更改蒙版。

the following should work

下面的工作

Mat srcImage; //RGB source image
Mat mask = Mat::zeros(srcImage.size(), CV_8U);  // type of mask is CV_8U
Mat roi(mask, cv::Rect(10,10,100,100));
roi = Scalar(255, 255, 255);
SurfFeatureDetector detector();
std::vector<KeyPoint> keypoints;
detector.detect(srcImage, keypoints, mask);     // passing `mask` as a parameter

#2

I tacked your ROI code onto some existing code I was working on, with the following changes it worked for me

我将您的ROI代码附加到我正在处理的一些现有代码上，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);

Without the changes to the type and the use of mask instead of roi as your mask, I'd get a runtime error as well. This makes sense, as the detect method wants a mask -- it should be the same size as the original image, and roi isn't (it's a 100x100 rectangle). To see this visually, try displaying the mask and the roi

如果不改变类型和使用掩码，而不是使用roi作为您的掩码，我也会得到一个运行时错误。这是有意义的，因为检测方法需要一个蒙版——它应该与原始图像的大小相同，而roi不是(它是一个100x100的矩形)。要直观地看到这一点，请尝试显示蒙版和roi

cv::imshow("Mask", mask);
cv::waitKey(0);

cv::imshow("ROI", roi);
cv::waitKey(0);

The type has to match also; the mask should be single channel, while your image type is likely of type 16, which maps to CV_8UC3, a triple channel image

类型也必须匹配;该掩码应该是单一通道，而您的图像类型可能是16型，它映射到CV_8UC3，这是一个三重通道图像。

#3

If you are looking to apply the same for irregular mask then:

如果你想在不定期口罩上使用同样的方法，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
    }

Then as usual, generate SIFT/SURF/... pointer

然后像往常一样生成SIFT/SURF/…指针

// Create smart pointer for SIFT feature detector.

//为SIFT feature detector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);

#1