关键点的描述符KeyPoint对象

corners：包含大量本地信息的像素块，并能够在另一张图中被快速识别

keypoints：作为 corners 的扩展，它将像素块的信息进行编码从而使得更易辨识，至少在原则上唯一

descriptors：它是对 keypoints 进一步处理的结果。通常它具有更低的维度，从而使得图像块能够在另一幅不同的图像中被更快地识别

KeyPoints对象

为了描述关键点，Opencv 关键点的类定义如下：

class cv::KeyPoint {
public:
	cv::Point2f pt; // coordinates of the keypoint
	float size; // diameter of the meaningful keypoint neighborhood
	float angle; // computed orientation of the keypoint (-1 if none)
	float response; // response for which the keypoints was selected
	int octave; // octave (pyramid layer) keypoint was extracted from
	int class_id; // object id, can be used to cluster keypoints by object
	cv::KeyPoint(
		cv::Point2f _pt,
		float _size,
		float _angle = -1,
		float _response = 0,
		int _octave = 0,
		int _class_id = -1
	);
	cv::KeyPoint(
		float x,
		float y,
		float _size,
		float _angle = -1,
		float _response = 0,
		int _octave = 0,
		int _class_id = -1
	);
	...
};

参数说明：

pt：关键点的位置
size：关键点的范围
angle：关键点角度
response：能够给某个关键点更强烈响应的检测器，有时能够被理解为特性实际存在的概率
octave：标示了关键点被找到的层级，总是希望在相同的层级找到对应的关键点
class_id：标示关键点来自于哪一个目标
为了查找并计算描述符，Opencv 定义了如下抽象类:

class cv::Feature2D : public cv::Algorithm {
public:
	virtual void detect(
		cv::InputArray image, // Image on which to detect
		vector< cv::KeyPoint >& keypoints, // Array of found keypoints
		cv::InputArray mask = cv::noArray()
	) const;
	virtual void detect(
		cv::InputArrayOfArrays images, // Images on which to detect
		vector<vector< cv::KeyPoint > >& keypoints, // keypoints for each image
		cv::InputArrayOfArrays masks = cv::noArray()
	) const;
	virtual void compute(
		cv::InputArray image, // Image where keypoints are located
		std::vector<cv::KeyPoint>& keypoints, // input/output vector of keypoints
		cv::OutputArray descriptors); // computed descriptors, M x N matrix,
									  // where M is the number of keypoints
									  // and N is the descriptor size
	virtual void compute(
		cv::InputArrayOfArrays image, // Images where keypoints are located
		std::vector<std::vector<cv::KeyPoint> >& keypoints, //I/O vec of keypnts
		cv::OutputArrayOfArrays descriptors); // computed descriptors,
											  // vector of (Mi x N) matrices, where
											  // Mi is the number of keypoints in
											  // the i-th image and N is the
											  // descriptor size
	virtual void detectAndCompute(
		cv::InputArray image, // Image on which to detect
		cv::InputArray mask, // Optional region of interest mask
		std::vector<cv::KeyPoint>& keypoints, // found or provided keypoints
		cv::OutputArray descriptors, // computed descriptors
		bool useProvidedKeypoints = false); // if true,
											// the provided keypoints are used,
											// otherwise they are detected
	virtual int descriptorSize() const; // size of each descriptor in elements
	virtual int descriptorType() const; // type of descriptor elements
	virtual int defaultNorm() const; // the recommended norm to be used
									 // for comparing descriptors.
									 // Usually, it's NORM_HAMMING for
									 // binary descriptors and NORM_L2
									 // for all others.
	virtual void read(const cv::FileNode&);
	virtual void write(cv::FileStorage&) const;
	...
};

函数说明：

detect：用于计算 Keypoint
compute：用于计算 descriptor
detectAndCompute：不同的关键点检测算法对于同一幅图像常常得到不同的结果，而且在算法计算过程中需要一种特殊的图像表示，其计算量很大，如果分开进行此步骤将重复进行两次，因此如果需要得到描述符，通常建议直接使detectAndCompute 函数
descriptorSize：返回描述符向量的长度
descriptorType：描述符元素的类型
defaultNorm：描述符的归一化方法，指定了如何比较两个描述符，比如对于01描述符，可以使用 NORM_HAMMING；而对于 SIFT 和 SURF，则可以使用 NORM_L2 或者 NORM_L1。
一个实际的实现可以只实现其中的某个或几个方法：

cv::Feature2D::detect()：FAST（只寻找FAST关键点，因为FAST只能寻找关键点）
cv::Feature2D::compute()：FREAK（对已知的关键点计算FREAK描述符，FREAK只能用于描述符）
cv::Feature2D::detectAndCompute()：SIFT，SURF，ORB，BRISK。（4种方法既能够寻找关键点也可以生成对应的描述符）算法中将隐式调用检测和计算方法

来源：CSDN

作者：Wake_up.

链接：https://blog.csdn.net/qq_40851561/article/details/104216303