一、概述
GFTT (Good Features to Track)，GFTT 是一个特征检测器。 GFTTDetector 可用于使用 Harris（以创建者命名）和 GFTT 角点检测算法检测特征。 所以，这个类实际上是两种特征检测方法合二为一，原因是GFTT实际上是Harris算法的修改版本，使用哪一种将由输入参数决定。
GFTT特征点检测器和OpenCV中其他特征点检测器有一个很大的不同之处，那就是GFTT特征点检测器只支持提取特征点，而不支持计算描述子。
二、类参考
1、函数原型
static Ptr<GFTTDetector> cv::GFTTDetector::create ( int maxCorners,
double qualityLevel,
double minDistance,
int blockSize,
int gradiantSize,
bool useHarrisDetector = false,
double k = 0.04
) 2、参数详解
| maxCorners | 检测到的最大角点数量 |
| qualityLevel | 输出角点的质量等级，取值范围是 [ 0 , 1 ]；如果某个候选点的角点响应值小于（qualityLeve * 最大角点响应值），则该点会被抛弃，相当于判定某候选点为角点的阈值； |
| minDistance | 两个角点间的最小距离，如果某两个角点间的距离小于minDistance，则会被认为是同一个角点； |
| blockSize | 计算角点响应值的邻域大小，默认值为3；如果输入图像的分辨率比较大，可以选择比较大的blockSize； |
| useHarrisDector | 布尔类型，如果为true则使用Harris角点检测；默认为false，使用shi-tomas角点检测算法； |
| k | 只在使用Harris角点检测时才生效，也就是计算角点响应值时的系数k。 |
三、OpenCV源码
1、源码路径
opencv\modules\features2d\src\gftt.cpp2、源码代码
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#include "precomp.hpp"
namespace cv
{
class GFTTDetector_Impl CV_FINAL : public GFTTDetector
{
public:
GFTTDetector_Impl( int _nfeatures, double _qualityLevel,
double _minDistance, int _blockSize, int _gradientSize,
bool _useHarrisDetector, double _k )
: nfeatures(_nfeatures), qualityLevel(_qualityLevel), minDistance(_minDistance),
blockSize(_blockSize), gradSize(_gradientSize), useHarrisDetector(_useHarrisDetector), k(_k)
{
}
void setMaxFeatures(int maxFeatures) CV_OVERRIDE { nfeatures = maxFeatures; }
int getMaxFeatures() const CV_OVERRIDE { return nfeatures; }
void setQualityLevel(double qlevel) CV_OVERRIDE { qualityLevel = qlevel; }
double getQualityLevel() const CV_OVERRIDE { return qualityLevel; }
void setMinDistance(double minDistance_) CV_OVERRIDE { minDistance = minDistance_; }
double getMinDistance() const CV_OVERRIDE { return minDistance; }
void setBlockSize(int blockSize_) CV_OVERRIDE { blockSize = blockSize_; }
int getBlockSize() const CV_OVERRIDE { return blockSize; }
//void setGradientSize(int gradientSize_) { gradSize = gradientSize_; }
//int getGradientSize() { return gradSize; }
void setHarrisDetector(bool val) CV_OVERRIDE { useHarrisDetector = val; }
bool getHarrisDetector() const CV_OVERRIDE { return useHarrisDetector; }
void setK(double k_) CV_OVERRIDE { k = k_; }
double getK() const CV_OVERRIDE { return k; }
void detect( InputArray _image, std::vector<KeyPoint>& keypoints, InputArray _mask ) CV_OVERRIDE
{
CV_INSTRUMENT_REGION();
if(_image.empty())
{
keypoints.clear();
return;
}
std::vector<Point2f> corners;
std::vector<float> cornersQuality;
if (_image.isUMat())
{
UMat ugrayImage;
if( _image.type() != CV_8U )
cvtColor( _image, ugrayImage, COLOR_BGR2GRAY );
else
ugrayImage = _image.getUMat();
goodFeaturesToTrack( ugrayImage, corners, nfeatures, qualityLevel, minDistance, _mask,
cornersQuality, blockSize, gradSize, useHarrisDetector, k );
}
else
{
Mat image = _image.getMat(), grayImage = image;
if( image.type() != CV_8U )
cvtColor( image, grayImage, COLOR_BGR2GRAY );
goodFeaturesToTrack( grayImage, corners, nfeatures, qualityLevel, minDistance, _mask,
cornersQuality, blockSize, gradSize, useHarrisDetector, k );
}
CV_Assert(corners.size() == cornersQuality.size());
keypoints.resize(corners.size());
for (size_t i = 0; i < corners.size(); i++)
keypoints[i] = KeyPoint(corners[i], (float)blockSize, -1, cornersQuality[i]);
}
int nfeatures;
double qualityLevel;
double minDistance;
int blockSize;
int gradSize;
bool useHarrisDetector;
double k;
};
Ptr<GFTTDetector> GFTTDetector::create( int _nfeatures, double _qualityLevel,
double _minDistance, int _blockSize, int _gradientSize,
bool _useHarrisDetector, double _k )
{
return makePtr<GFTTDetector_Impl>(_nfeatures, _qualityLevel,
_minDistance, _blockSize, _gradientSize, _useHarrisDetector, _k);
}
Ptr<GFTTDetector> GFTTDetector::create( int _nfeatures, double _qualityLevel,
double _minDistance, int _blockSize,
bool _useHarrisDetector, double _k )
{
return makePtr<GFTTDetector_Impl>(_nfeatures, _qualityLevel,
_minDistance, _blockSize, 3, _useHarrisDetector, _k);
}
String GFTTDetector::getDefaultName() const
{
return (Feature2D::getDefaultName() + ".GFTTDetector");
}
}
四、效果图像示例
