形狀距離與匹配

詳細說明

類
類	cv::AffineTransformer
	OpenCV仿射變換演算法的包裝類。 : 更多...
類	cv::ChiHistogramCostExtractor
	基於Chi的成本提取。 : 更多...
類	cv::EMDHistogramCostExtractor
	基於EMD的成本提取。 : 更多...
類	cv::EMDL1HistogramCostExtractor
	基於EMD-L1的成本提取。 : 更多...
類	cv::HausdorffDistanceExtractor
	簡單地測量由輪廓定義的形狀之間的 Hausdorff 距離。 更多...
類	cv::HistogramCostExtractor
	直方圖成本演算法的抽象基類。 更多...
類	cv::NormHistogramCostExtractor
	基於範數的成本提取。 : 更多...
類	cv::ShapeContextDistanceExtractor
	形狀上下文描述符和匹配演算法的實現。 更多...
類	cv::ShapeDistanceExtractor
	形狀距離演算法的抽象基類。 更多...
類	cv::ShapeTransformer
	形狀變換演算法的抽象基類。 更多...
類	cv::ThinPlateSplineShapeTransformer
	變換的定義。 更多...

函式
Ptr< AffineTransformer >	cv::createAffineTransformer (bool fullAffine)
Ptr< HistogramCostExtractor >	cv::createChiHistogramCostExtractor (int nDummies=25, float defaultCost=0.2f)
Ptr< HistogramCostExtractor >	cv::createEMDHistogramCostExtractor (int flag=DIST_L2, int nDummies=25, float defaultCost=0.2f)
Ptr< HistogramCostExtractor >	cv::createEMDL1HistogramCostExtractor (int nDummies=25, float defaultCost=0.2f)
Ptr< HausdorffDistanceExtractor >	cv::createHausdorffDistanceExtractor (int distanceFlag=cv::NORM_L2, float rankProp=0.6f)
Ptr< HistogramCostExtractor >	cv::createNormHistogramCostExtractor (int flag=DIST_L2, int nDummies=25, float defaultCost=0.2f)
Ptr< ShapeContextDistanceExtractor >	cv::createShapeContextDistanceExtractor (int nAngularBins=12, int nRadialBins=4, float innerRadius=0.2f, float outerRadius=2, int iterations=3, const Ptr< HistogramCostExtractor > &comparer=createChiHistogramCostExtractor(), const Ptr< ShapeTransformer > &transformer=createThinPlateSplineShapeTransformer())
Ptr< ThinPlateSplineShapeTransformer >	cv::createThinPlateSplineShapeTransformer (double regularizationParameter=0)
float	cv::EMDL1 (InputArray signature1, InputArray signature2)
	計算兩個加權點配置之間的“最小工作”距離，基於論文“EMD-L1：一種高效魯棒的直方圖描述符比較演算法”，作者：Haibin Ling 和 Kazunori Okuda；以及“Earth Mover's Distance是Mallows Distance：來自統計學的一些見解”，作者：Elizaveta Levina 和 Peter Bickel。

函式文件 (Function Documentation)

createAffineTransformer()

Ptr< AffineTransformer > cv::createAffineTransformer

(

bool

fullAffine

)

Python
	cv.createAffineTransformer(	fullAffine	) ->	retval

#include <opencv2/shape/shape_transformer.hpp>

完整建構函式

createChiHistogramCostExtractor()

Ptr< HistogramCostExtractor > cv::createChiHistogramCostExtractor	(	int	nDummies = 25,
		float	defaultCost = 0.2f )

Python
	cv.createChiHistogramCostExtractor(	[, nDummies[, defaultCost]]	) ->	retval

#include <opencv2/shape/hist_cost.hpp>

createEMDHistogramCostExtractor()

Ptr< HistogramCostExtractor > cv::createEMDHistogramCostExtractor	(	int	flag = DIST_L2,
		int	nDummies = 25,
		float	defaultCost = 0.2f )

Python
	cv.createEMDHistogramCostExtractor(	[, flag[, nDummies[, defaultCost]]]	) ->	retval

#include <opencv2/shape/hist_cost.hpp>

createEMDL1HistogramCostExtractor()

Ptr< HistogramCostExtractor > cv::createEMDL1HistogramCostExtractor	(	int	nDummies = 25,
		float	defaultCost = 0.2f )

Python
	cv.createEMDL1HistogramCostExtractor(	[, nDummies[, defaultCost]]	) ->	retval

#include <opencv2/shape/hist_cost.hpp>

createHausdorffDistanceExtractor()

Ptr< HausdorffDistanceExtractor > cv::createHausdorffDistanceExtractor	(	int	distanceFlag = cv::NORM_L2,
		float	rankProp = 0.6f )

Python
	cv.createHausdorffDistanceExtractor(	[, distanceFlag[, rankProp]]	) ->	retval

#include <opencv2/shape/shape_distance.hpp>

createNormHistogramCostExtractor()

Ptr< HistogramCostExtractor > cv::createNormHistogramCostExtractor	(	int	flag = DIST_L2,
		int	nDummies = 25,
		float	defaultCost = 0.2f )

Python
	cv.createNormHistogramCostExtractor(	[, flag[, nDummies[, defaultCost]]]	) ->	retval

#include <opencv2/shape/hist_cost.hpp>

createShapeContextDistanceExtractor()

Ptr< ShapeContextDistanceExtractor > cv::createShapeContextDistanceExtractor	(	int	nAngularBins = 12,
		int	nRadialBins = 4,
		float	innerRadius = 0.2f,
		float	outerRadius = 2,
		int	iterations = 3,
		const Ptr< HistogramCostExtractor > &	comparer = createChiHistogramCostExtractor(),
		const Ptr< ShapeTransformer > &	transformer = createThinPlateSplineShapeTransformer() )

Python
	cv.createShapeContextDistanceExtractor(	[, nAngularBins[, nRadialBins[, innerRadius[, outerRadius[, iterations[, comparer[, transformer]]]]]]]	) ->	retval

#include <opencv2/shape/shape_distance.hpp>

示例

modules/shape/samples/shape_example.cpp.

createThinPlateSplineShapeTransformer()

Ptr< ThinPlateSplineShapeTransformer > cv::createThinPlateSplineShapeTransformer

(

double

regularizationParameter = 0

)

Python
	cv.createThinPlateSplineShapeTransformer(	[, regularizationParameter]	) ->	retval

#include <opencv2/shape/shape_transformer.hpp>

完整建構函式

EMDL1()

float cv::EMDL1	(	InputArray	signature1,
		InputArray	signature2 )

#include <opencv2/shape/emdL1.hpp>

計算兩個加權點配置之間的“最小工作”距離，基於論文“EMD-L1：一種高效魯棒的直方圖描述符比較演算法”，作者：Haibin Ling 和 Kazunori Okuda；以及“Earth Mover's Distance是Mallows Distance：來自統計學的一些見解”，作者：Elizaveta Levina 和 Peter Bickel。

引數

signature1	第一個簽名，單列浮點矩陣。每一行是每個 bin 的直方圖值。
signature2	第二個簽名，格式和大小與 signature1 相同。