mirror of
https://github.com/opencv/opencv.git
synced 2024-12-17 19:08:01 +08:00
374 lines
12 KiB
C++
374 lines
12 KiB
C++
/*M///////////////////////////////////////////////////////////////////////////////////////
|
|
//
|
|
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
|
|
//
|
|
// By downloading, copying, installing or using the software you agree to this license.
|
|
// If you do not agree to this license, do not download, install,
|
|
// copy or use the software.
|
|
//
|
|
//
|
|
// License Agreement
|
|
// For Open Source Computer Vision Library
|
|
//
|
|
// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
|
|
// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
|
|
// Third party copyrights are property of their respective owners.
|
|
//
|
|
// Redistribution and use in source and binary forms, with or without modification,
|
|
// are permitted provided that the following conditions are met:
|
|
//
|
|
// * Redistribution's of source code must retain the above copyright notice,
|
|
// this list of conditions and the following disclaimer.
|
|
//
|
|
// * Redistribution's in binary form must reproduce the above copyright notice,
|
|
// this list of conditions and the following disclaimer in the documentation
|
|
// and/or other materials provided with the distribution.
|
|
//
|
|
// * The name of the copyright holders may not be used to endorse or promote products
|
|
// derived from this software without specific prior written permission.
|
|
//
|
|
// This software is provided by the copyright holders and contributors "as is" and
|
|
// any express or implied warranties, including, but not limited to, the implied
|
|
// warranties of merchantability and fitness for a particular purpose are disclaimed.
|
|
// In no event shall the Intel Corporation or contributors be liable for any direct,
|
|
// indirect, incidental, special, exemplary, or consequential damages
|
|
// (including, but not limited to, procurement of substitute goods or services;
|
|
// loss of use, data, or profits; or business interruption) however caused
|
|
// and on any theory of liability, whether in contract, strict liability,
|
|
// or tort (including negligence or otherwise) arising in any way out of
|
|
// the use of this software, even if advised of the possibility of such damage.
|
|
//
|
|
//M*/
|
|
|
|
#include "precomp.hpp"
|
|
#include <iterator>
|
|
#include <limits>
|
|
|
|
//#define DEBUG_BLOB_DETECTOR
|
|
|
|
#ifdef DEBUG_BLOB_DETECTOR
|
|
# include "opencv2/opencv_modules.hpp"
|
|
# ifdef HAVE_OPENCV_HIGHGUI
|
|
# include "opencv2/highgui.hpp"
|
|
# else
|
|
# undef DEBUG_BLOB_DETECTOR
|
|
# endif
|
|
#endif
|
|
|
|
namespace cv
|
|
{
|
|
|
|
class CV_EXPORTS_W SimpleBlobDetectorImpl : public SimpleBlobDetector
|
|
{
|
|
public:
|
|
|
|
explicit SimpleBlobDetectorImpl(const SimpleBlobDetector::Params ¶meters = SimpleBlobDetector::Params());
|
|
|
|
virtual void read( const FileNode& fn );
|
|
virtual void write( FileStorage& fs ) const;
|
|
|
|
protected:
|
|
struct CV_EXPORTS Center
|
|
{
|
|
Point2d location;
|
|
double radius;
|
|
double confidence;
|
|
};
|
|
|
|
virtual void detect( InputArray image, std::vector<KeyPoint>& keypoints, InputArray mask=noArray() );
|
|
virtual void findBlobs(InputArray image, InputArray binaryImage, std::vector<Center> ¢ers) const;
|
|
|
|
Params params;
|
|
};
|
|
|
|
/*
|
|
* SimpleBlobDetector
|
|
*/
|
|
SimpleBlobDetector::Params::Params()
|
|
{
|
|
thresholdStep = 10;
|
|
minThreshold = 50;
|
|
maxThreshold = 220;
|
|
minRepeatability = 2;
|
|
minDistBetweenBlobs = 10;
|
|
|
|
filterByColor = true;
|
|
blobColor = 0;
|
|
|
|
filterByArea = true;
|
|
minArea = 25;
|
|
maxArea = 5000;
|
|
|
|
filterByCircularity = false;
|
|
minCircularity = 0.8f;
|
|
maxCircularity = std::numeric_limits<float>::max();
|
|
|
|
filterByInertia = true;
|
|
//minInertiaRatio = 0.6;
|
|
minInertiaRatio = 0.1f;
|
|
maxInertiaRatio = std::numeric_limits<float>::max();
|
|
|
|
filterByConvexity = true;
|
|
//minConvexity = 0.8;
|
|
minConvexity = 0.95f;
|
|
maxConvexity = std::numeric_limits<float>::max();
|
|
}
|
|
|
|
void SimpleBlobDetector::Params::read(const cv::FileNode& fn )
|
|
{
|
|
thresholdStep = fn["thresholdStep"];
|
|
minThreshold = fn["minThreshold"];
|
|
maxThreshold = fn["maxThreshold"];
|
|
|
|
minRepeatability = (size_t)(int)fn["minRepeatability"];
|
|
minDistBetweenBlobs = fn["minDistBetweenBlobs"];
|
|
|
|
filterByColor = (int)fn["filterByColor"] != 0 ? true : false;
|
|
blobColor = (uchar)(int)fn["blobColor"];
|
|
|
|
filterByArea = (int)fn["filterByArea"] != 0 ? true : false;
|
|
minArea = fn["minArea"];
|
|
maxArea = fn["maxArea"];
|
|
|
|
filterByCircularity = (int)fn["filterByCircularity"] != 0 ? true : false;
|
|
minCircularity = fn["minCircularity"];
|
|
maxCircularity = fn["maxCircularity"];
|
|
|
|
filterByInertia = (int)fn["filterByInertia"] != 0 ? true : false;
|
|
minInertiaRatio = fn["minInertiaRatio"];
|
|
maxInertiaRatio = fn["maxInertiaRatio"];
|
|
|
|
filterByConvexity = (int)fn["filterByConvexity"] != 0 ? true : false;
|
|
minConvexity = fn["minConvexity"];
|
|
maxConvexity = fn["maxConvexity"];
|
|
}
|
|
|
|
void SimpleBlobDetector::Params::write(cv::FileStorage& fs) const
|
|
{
|
|
fs << "thresholdStep" << thresholdStep;
|
|
fs << "minThreshold" << minThreshold;
|
|
fs << "maxThreshold" << maxThreshold;
|
|
|
|
fs << "minRepeatability" << (int)minRepeatability;
|
|
fs << "minDistBetweenBlobs" << minDistBetweenBlobs;
|
|
|
|
fs << "filterByColor" << (int)filterByColor;
|
|
fs << "blobColor" << (int)blobColor;
|
|
|
|
fs << "filterByArea" << (int)filterByArea;
|
|
fs << "minArea" << minArea;
|
|
fs << "maxArea" << maxArea;
|
|
|
|
fs << "filterByCircularity" << (int)filterByCircularity;
|
|
fs << "minCircularity" << minCircularity;
|
|
fs << "maxCircularity" << maxCircularity;
|
|
|
|
fs << "filterByInertia" << (int)filterByInertia;
|
|
fs << "minInertiaRatio" << minInertiaRatio;
|
|
fs << "maxInertiaRatio" << maxInertiaRatio;
|
|
|
|
fs << "filterByConvexity" << (int)filterByConvexity;
|
|
fs << "minConvexity" << minConvexity;
|
|
fs << "maxConvexity" << maxConvexity;
|
|
}
|
|
|
|
SimpleBlobDetectorImpl::SimpleBlobDetectorImpl(const SimpleBlobDetector::Params ¶meters) :
|
|
params(parameters)
|
|
{
|
|
}
|
|
|
|
void SimpleBlobDetectorImpl::read( const cv::FileNode& fn )
|
|
{
|
|
params.read(fn);
|
|
}
|
|
|
|
void SimpleBlobDetectorImpl::write( cv::FileStorage& fs ) const
|
|
{
|
|
params.write(fs);
|
|
}
|
|
|
|
void SimpleBlobDetectorImpl::findBlobs(InputArray _image, InputArray _binaryImage, std::vector<Center> ¢ers) const
|
|
{
|
|
Mat image = _image.getMat(), binaryImage = _binaryImage.getMat();
|
|
(void)image;
|
|
centers.clear();
|
|
|
|
std::vector < std::vector<Point> > contours;
|
|
Mat tmpBinaryImage = binaryImage.clone();
|
|
findContours(tmpBinaryImage, contours, RETR_LIST, CHAIN_APPROX_NONE);
|
|
|
|
#ifdef DEBUG_BLOB_DETECTOR
|
|
// Mat keypointsImage;
|
|
// cvtColor( binaryImage, keypointsImage, CV_GRAY2RGB );
|
|
//
|
|
// Mat contoursImage;
|
|
// cvtColor( binaryImage, contoursImage, CV_GRAY2RGB );
|
|
// drawContours( contoursImage, contours, -1, Scalar(0,255,0) );
|
|
// imshow("contours", contoursImage );
|
|
#endif
|
|
|
|
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 = std::sqrt(std::pow(2 * moms.mu11, 2) + std::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)
|
|
{
|
|
std::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;
|
|
}
|
|
|
|
if(moms.m00 == 0.0)
|
|
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
|
|
{
|
|
std::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);
|
|
|
|
|
|
#ifdef DEBUG_BLOB_DETECTOR
|
|
// circle( keypointsImage, center.location, 1, Scalar(0,0,255), 1 );
|
|
#endif
|
|
}
|
|
#ifdef DEBUG_BLOB_DETECTOR
|
|
// imshow("bk", keypointsImage );
|
|
// waitKey();
|
|
#endif
|
|
}
|
|
|
|
void SimpleBlobDetectorImpl::detect(InputArray image, std::vector<cv::KeyPoint>& keypoints, InputArray)
|
|
{
|
|
//TODO: support mask
|
|
keypoints.clear();
|
|
Mat grayscaleImage;
|
|
if (image.channels() == 3)
|
|
cvtColor(image, grayscaleImage, COLOR_BGR2GRAY);
|
|
else
|
|
grayscaleImage = image.getMat();
|
|
|
|
std::vector < std::vector<Center> > centers;
|
|
for (double thresh = params.minThreshold; thresh < params.maxThreshold; thresh += params.thresholdStep)
|
|
{
|
|
Mat binarizedImage;
|
|
threshold(grayscaleImage, binarizedImage, thresh, 255, THRESH_BINARY);
|
|
|
|
std::vector < Center > curCenters;
|
|
findBlobs(grayscaleImage, binarizedImage, curCenters);
|
|
std::vector < std::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(std::vector<Center> (1, curCenters[i]));
|
|
}
|
|
std::copy(newCenters.begin(), newCenters.end(), std::back_inserter(centers));
|
|
}
|
|
|
|
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) * 2.0f);
|
|
keypoints.push_back(kpt);
|
|
}
|
|
}
|
|
|
|
Ptr<SimpleBlobDetector> SimpleBlobDetector::create(const SimpleBlobDetector::Params& params)
|
|
{
|
|
return makePtr<SimpleBlobDetectorImpl>(params);
|
|
}
|
|
|
|
}
|