新加坡政府网站建设的特点及借鉴意义/代写

我在使用SIFT特征检测器进行实时对象匹配时遇到了一些问题。 这是我的视频解决方案。

首先，我创建了一个结构来存储匹配的关键点。该结构包含templateImage中关键点的位置，inputImage中关键点的位置和相似性度量。这里我将向量的互相关用作相似性度量。

struct MatchedPair

{

Point locationinTemplate;

Point matchedLocinImage;

float correlation;

MatchedPair(Point loc)

{

locationinTemplate=loc;

}

}

我将根据匹配的关键点的相似性选择排序方式，因此我需要一个帮助程序函数，该函数将告诉std::sort()如何比较我的MatchedPair对象。

bool comparator(MatchedPair a,MatchedPair b)

{

return a.correlation>b.correlation;

}

现在，主要代码开始。 我使用标准方法从输入图像和templateImage中检测和解密特征。计算特征后，我实现了自己的匹配功能。 这是您要寻找的答案

int main()

{

Mat templateImage = imread("template.png",IMREAD_GRAYSCALE); // read a template image

VideoCapture cap("input.mpeg");

Mat frame;

vector InputKeypts,TemplateKeypts;

SiftFeatureDetector detector;

SiftDescriptorExtractor extractor;

Mat InputDescriptor,templateDescriptor,result;

vector mpts;

Scalar s;

cap>>frame;

cvtColor(image,image,CV_BGR2GRAY);

Mat outputImage =Mat::zeros(templateImage.rows+frame.rows,templateImage.cols+frame.cols,CV_8UC1);

detector.detect(templateImage,TemplateKeypts); // detect template interesting points

extractor.compute(templateImage,TemplateKeypts,templateDescriptor);

while( true)

{

mpts.clear(); // clear for new frame

cap>>frame; // read video to frame

outputImage=Mat::zeros(templateImage.rows+frame.rows,templateImage.cols+frame.cols,CV_8UC1); // create output image

cvtColor(frame,frame,CV_BGR2GRAY);

detector.detect(frame,InputKeypts);

extractor.compute(frame,InputKeypts,InputDescriptor); // detect and descrypt frames features

/*

So far we have computed descriptors for template and current frame using traditional methods

From now onward we are going to implement our own match method

- Descriptor matrixes are by default have 128 colums to hold features of a keypoint.

- Each row in descriptor matrix represent 128 feature of a keypoint.

Match methods are using this descriptor matrixes to calculate similarity.

My approach to calculate similarity is using cross correlation of keypoints descriptor vector.Check code below to see how I achieved.

*/

// Iterate over rows of templateDesciptor ( for each keypoint extracted from // template Image) i keypoints in template,j keypoints in input

for ( int i=0;i

{

mpts.push_back(MatchedPair(TemplateKeypts[i].pt));

mpts[i].correlation =0;

for ( int j=0;j

{

matchTemplate(templateDescriptor.row(i),InputDescriptor.row(j),result,CV_TM_CCOR_NORMED);

// I have used opencvs built function to calculate correlation.I am calculating // row(i) of templateDescriptor with row(j) of inputImageDescriptor.

s=sum(result); // sum is correlation of two rows

// Here I am looking for the most similar row in input image.Storing the correlation of best match and matchLocation in input image.

if(s.val[0]>mpts[i].correlation)

{

mpts[i].correlation=s.val[0];

mpts[i].matchedLocinImage=InputKeypts[j].pt;

}

}

}

// I would like to show template,input and matching lines in one output. templateImage.copyTo(outputImage(Rect(0,0,templateImage.cols,templateImage.rows)));

frame.copyTo(outputImage(Rect(templateImage.cols,templateImage.rows,frame.cols,frame.rows)));

// Here is the matching part. I have selected 4 best matches and draw lines  // between them. You should check for correlation value again because there can // be 0 correlated match pairs.

std::sort(mpts.begin(),mpts.end(),comparator);

for( int i=0;i<4;i++)

{

if ( mpts[i].correlation>0.90)

{

// During drawing line take into account offset of locations.I have added

// template image to upper left of input image in output image.

cv::line(outputImage,mpts[i].locationinTemplate,mpts[i].matchedLocinImage+Point(templateImage.cols,templateImage.rows),Scalar::all(255));

}

}

imshow("Output",outputImage);

waitKey(33);

}

}