@robot_sherrick 回答了我this question，这是他回答的后续问题。
Opencv 2.4 中的cv::SimpleBlobDetector 看起来非常令人兴奋，但我不确定我能否让它用于更详细的数据提取。
我有以下顾虑：
【问题讨论】：
所以代码应该是这样的：
cv::Mat inputImg = imread(image_file_name, CV_LOAD_IMAGE_COLOR); // Read a file
cv::SimpleBlobDetector::Params params;
params.minDistBetweenBlobs = 10.0; // minimum 10 pixels between blobs
params.filterByArea = true; // filter my blobs by area of blob
params.minArea = 20.0; // min 20 pixels squared
params.maxArea = 500.0; // max 500 pixels squared
SimpleBlobDetector myBlobDetector(params);
std::vector<cv::KeyPoint> myBlobs;
myBlobDetector.detect(inputImg, myBlobs);
如果您想在图像上突出显示这些关键点：
cv::Mat blobImg;
cv::drawKeypoints(inputImg, myBlobs, blobImg);
cv::imshow("Blobs", blobImg);
要访问关键点中的信息，您只需像这样访问每个元素：
for(std::vector<cv::KeyPoint>::iterator blobIterator = myBlobs.begin(); blobIterator != myBlobs.end(); blobIterator++){
std::cout << "size of blob is: " << blobIterator->size << std::endl;
std::cout << "point is at: " << blobIterator->pt.x << " " << blobIterator->pt.y << std::endl;
}
注意：本文未编译，可能有错别字。
【讨论】：
cv::Mat at 函数来提取和平均颜色信息。不过，重读您的问题，听起来您想filter your image 并分析结果。
这是一个允许您通过 getContours() 方法取回最后一个轮廓的版本。它们将通过索引与关键点匹配。
class BetterBlobDetector : public cv::SimpleBlobDetector
{
public:
BetterBlobDetector(const cv::SimpleBlobDetector::Params ¶meters = cv::SimpleBlobDetector::Params());
const std::vector < std::vector<cv::Point> > getContours();
protected:
virtual void detectImpl( const cv::Mat& image, std::vector<cv::KeyPoint>& keypoints, const cv::Mat& mask=cv::Mat()) const;
virtual void findBlobs(const cv::Mat &image, const cv::Mat &binaryImage,
std::vector<Center> ¢ers, std::vector < std::vector<cv::Point> >&contours) const;
};
然后是cpp
using namespace cv;
BetterBlobDetector::BetterBlobDetector(const SimpleBlobDetector::Params ¶meters)
{
}
void BetterBlobDetector::findBlobs(const cv::Mat &image, const cv::Mat &binaryImage,
vector<Center> ¢ers, std::vector < std::vector<cv::Point> >&curContours) const
{
(void)image;
centers.clear();
curContours.clear();
std::vector < std::vector<cv::Point> >contours;
Mat tmpBinaryImage = binaryImage.clone();
findContours(tmpBinaryImage, contours, CV_RETR_LIST, CV_CHAIN_APPROX_NONE);
for (size_t contourIdx = 0; contourIdx < contours.size(); contourIdx++)
{
Center center;
center.confidence = 1;
Moments moms = moments(Mat(contours[contourIdx]));
if (params.filterByArea)
{
double area = moms.m00;
if (area < params.minArea || area >= params.maxArea)
continue;
}
if (params.filterByCircularity)
{
double area = moms.m00;
double perimeter = arcLength(Mat(contours[contourIdx]), true);
double ratio = 4 * CV_PI * area / (perimeter * perimeter);
if (ratio < params.minCircularity || ratio >= params.maxCircularity)
continue;
}
if (params.filterByInertia)
{
double denominator = sqrt(pow(2 * moms.mu11, 2) + pow(moms.mu20 - moms.mu02, 2));
const double eps = 1e-2;
double ratio;
if (denominator > eps)
{
double cosmin = (moms.mu20 - moms.mu02) / denominator;
double sinmin = 2 * moms.mu11 / denominator;
double cosmax = -cosmin;
double sinmax = -sinmin;
double imin = 0.5 * (moms.mu20 + moms.mu02) - 0.5 * (moms.mu20 - moms.mu02) * cosmin - moms.mu11 * sinmin;
double imax = 0.5 * (moms.mu20 + moms.mu02) - 0.5 * (moms.mu20 - moms.mu02) * cosmax - moms.mu11 * sinmax;
ratio = imin / imax;
}
else
{
ratio = 1;
}
if (ratio < params.minInertiaRatio || ratio >= params.maxInertiaRatio)
continue;
center.confidence = ratio * ratio;
}
if (params.filterByConvexity)
{
vector < Point > hull;
convexHull(Mat(contours[contourIdx]), hull);
double area = contourArea(Mat(contours[contourIdx]));
double hullArea = contourArea(Mat(hull));
double ratio = area / hullArea;
if (ratio < params.minConvexity || ratio >= params.maxConvexity)
continue;
}
center.location = Point2d(moms.m10 / moms.m00, moms.m01 / moms.m00);
if (params.filterByColor)
{
if (binaryImage.at<uchar> (cvRound(center.location.y), cvRound(center.location.x)) != params.blobColor)
continue;
}
//compute blob radius
{
vector<double> dists;
for (size_t pointIdx = 0; pointIdx < contours[contourIdx].size(); pointIdx++)
{
Point2d pt = contours[contourIdx][pointIdx];
dists.push_back(norm(center.location - pt));
}
std::sort(dists.begin(), dists.end());
center.radius = (dists[(dists.size() - 1) / 2] + dists[dists.size() / 2]) / 2.;
}
centers.push_back(center);
curContours.push_back(contours[contourIdx]);
}
static std::vector < std::vector<cv::Point> > _contours;
const std::vector < std::vector<cv::Point> > BetterBlobDetector::getContours() {
return _contours;
}
void BetterBlobDetector::detectImpl(const cv::Mat& image, std::vector<cv::KeyPoint>& keypoints, const cv::Mat&) const
{
//TODO: support mask
_contours.clear();
keypoints.clear();
Mat grayscaleImage;
if (image.channels() == 3)
cvtColor(image, grayscaleImage, CV_BGR2GRAY);
else
grayscaleImage = image;
vector < vector<Center> > centers;
vector < vector<cv::Point> >contours;
for (double thresh = params.minThreshold; thresh < params.maxThreshold; thresh += params.thresholdStep)
{
Mat binarizedImage;
threshold(grayscaleImage, binarizedImage, thresh, 255, THRESH_BINARY);
vector < Center > curCenters;
vector < vector<cv::Point> >curContours, newContours;
findBlobs(grayscaleImage, binarizedImage, curCenters, curContours);
vector < vector<Center> > newCenters;
for (size_t i = 0; i < curCenters.size(); i++)
{
bool isNew = true;
for (size_t j = 0; j < centers.size(); j++)
{
double dist = norm(centers[j][ centers[j].size() / 2 ].location - curCenters[i].location);
isNew = dist >= params.minDistBetweenBlobs && dist >= centers[j][ centers[j].size() / 2 ].radius && dist >= curCenters[i].radius;
if (!isNew)
{
centers[j].push_back(curCenters[i]);
size_t k = centers[j].size() - 1;
while( k > 0 && centers[j][k].radius < centers[j][k-1].radius )
{
centers[j][k] = centers[j][k-1];
k--;
}
centers[j][k] = curCenters[i];
break;
}
}
if (isNew)
{
newCenters.push_back(vector<Center> (1, curCenters[i]));
newContours.push_back(curContours[i]);
//centers.push_back(vector<Center> (1, curCenters[i]));
}
}
std::copy(newCenters.begin(), newCenters.end(), std::back_inserter(centers));
std::copy(newContours.begin(), newContours.end(), std::back_inserter(contours));
}
for (size_t i = 0; i < centers.size(); i++)
{
if (centers[i].size() < params.minRepeatability)
continue;
Point2d sumPoint(0, 0);
double normalizer = 0;
for (size_t j = 0; j < centers[i].size(); j++)
{
sumPoint += centers[i][j].confidence * centers[i][j].location;
normalizer += centers[i][j].confidence;
}
sumPoint *= (1. / normalizer);
KeyPoint kpt(sumPoint, (float)(centers[i][centers[i].size() / 2].radius));
keypoints.push_back(kpt);
_contours.push_back(contours[i]);
}
}
【讨论】：
//Access SimpleBlobDetector datas for video
#include "opencv2/imgproc/imgproc.hpp" //
#include "opencv2/highgui/highgui.hpp"
#include <iostream>
#include <math.h>
#include <vector>
#include <fstream>
#include <string>
#include <sstream>
#include <algorithm>
#include "opencv2/objdetect/objdetect.hpp"
#include "opencv2/features2d/features2d.hpp"
using namespace cv;
using namespace std;
int main(int argc, char *argv[])
{
const char* fileName ="C:/Users/DAGLI/Desktop/videos/new/m3.avi";
VideoCapture cap(fileName); //
if(!cap.isOpened()) //
{
cout << "Couldn't open Video " << fileName << "\n";
return -1;
}
for(;;) // videonun frameleri icin sonsuz dongu
{
Mat frame,labelImg;
cap >> frame;
if(frame.empty()) break;
//imshow("main",frame);
Mat frame_gray;
cvtColor(frame,frame_gray,CV_RGB2GRAY);
//////////////////////////////////////////////////////////////////////////
// convert binary_image
Mat binaryx;
threshold(frame_gray,binaryx,120,255,CV_THRESH_BINARY);
Mat src, gray, thresh, binary;
Mat out;
vector<KeyPoint> keyPoints;
SimpleBlobDetector::Params params;
params.minThreshold = 120;
params.maxThreshold = 255;
params.thresholdStep = 100;
params.minArea = 20;
params.minConvexity = 0.3;
params.minInertiaRatio = 0.01;
params.maxArea = 1000;
params.maxConvexity = 10;
params.filterByColor = false;
params.filterByCircularity = false;
src = binaryx.clone();
SimpleBlobDetector blobDetector( params );
blobDetector.create("SimpleBlob");
blobDetector.detect( src, keyPoints );
drawKeypoints( src, keyPoints, out, CV_RGB(255,0,0), DrawMatchesFlags::DEFAULT);
cv::Mat blobImg;
cv::drawKeypoints(frame, keyPoints, blobImg);
cv::imshow("Blobs", blobImg);
for(int i=0; i<keyPoints.size(); i++){
//circle(out, keyPoints[i].pt, 20, cvScalar(255,0,0), 10);
//cout<<keyPoints[i].response<<endl;
//cout<<keyPoints[i].angle<<endl;
//cout<<keyPoints[i].size()<<endl;
cout<<keyPoints[i].pt.x<<endl;
cout<<keyPoints[i].pt.y<<endl;
}
imshow( "out", out );
if ((cvWaitKey(40)&0xff)==27) break; // esc 'ye basilinca break
}
system("pause");
}
【讨论】：