#include "qColorimetricSegmenter.h"
#include "HSV.h"
#include "HSVDialog.h"
#include "KmeansDlg.h"
#include "QuantiDialog.h"
#include "RgbDialog.h"
#include "ScalarDialog.h"
#include <CVLog.h>
#include <ecvPointCloud.h>
#include <DistanceComputationTools.h>
#include <algorithm>
#include <map>
#include <QMainWindow>

Include dependency graph for qColorimetricSegmenter.cpp:

Typedefs
typedef QSharedPointer< cloudViewer::ReferenceCloud >	_Region

typedef std::vector< _Region >	_RegionSet

typedef std::vector< _RegionSet >	SetOfRegionSet

typedef std::map< size_t, std::vector< unsigned > >	ClusterMap

Functions
static void	ShowDurationNow (const std::chrono::high_resolution_clock::time_point &startTime)

static bool	Inside (ColorCompType lower, ColorCompType value, ColorCompType upper)

static bool	KNNRegions (ccPointCloud *basePointCloud, const _RegionSet &regions, const _Region &region, unsigned k, _RegionSet &neighbourRegions, unsigned thresholdDistance)
	KNNRegions Determines the neighboring regions of a region. More...

static double	ColorimetricalDifference (ecvColor::Rgb c1, ecvColor::Rgb c2)
	colorimetricalDifference Compute colorimetrical difference between two RGB color values. More...

static ecvColor::Rgb	ComputeAverageColor (const ccPointCloud &cloud, cloudViewer::ReferenceCloud *subset)

double	ColorimetricalDifference (const ccPointCloud &basePointCloud, cloudViewer::ReferenceCloud c1, cloudViewer::ReferenceCloud c2)
	colorimetricalDifference compute mean colorimetrical difference between two reference clouds. The points in both clouds must be represented in RGB value. More...

static int	FindRegion (const std::vector< _RegionSet > &container, cloudViewer::ReferenceCloud *region)
	findRegion Find a given region in a vector of reference clouds. More...

static bool	GetKeyCluster (const ccPointCloud &cloud, size_t clusterPerDim, ClusterMap &clusterMap)

static ecvColor::Rgb	ComputeAverageColor (const ccPointCloud &cloud, const std::vector< unsigned > &bucket)

static int	ColorDistance (const ecvColor::Rgb &c1, const ecvColor::Rgb &c2)

static ccPointCloud *	ComputeKmeansClustering (ccPointCloud *theCloud, unsigned K, int maxIterationCount)

Variables
static const unsigned	TNN = 1

static const double	TPP = 2.0

static const double	TD = 2.0

static const double	TRR = 2.0

static const unsigned	Min = 2

Typedef Documentation

◆ _Region

typedef QSharedPointer<cloudViewer::ReferenceCloud> _Region

Definition at line 384 of file qColorimetricSegmenter.cpp.

◆ _RegionSet

typedef std::vector<_Region> _RegionSet

Definition at line 385 of file qColorimetricSegmenter.cpp.

◆ ClusterMap

typedef std::map<size_t, std::vector<unsigned> > ClusterMap

Generate nxnxn clusters of points according to their color value (RGB)

Parameters

cloud	: the cloud which we work with
clusterPerDim	: coefficient uses to split each RGB component Returns a map of nxnxn keys, for each key a vector of the points index in the partition

Definition at line 1029 of file qColorimetricSegmenter.cpp.

◆ SetOfRegionSet

typedef std::vector<_RegionSet> SetOfRegionSet

Definition at line 386 of file qColorimetricSegmenter.cpp.

Function Documentation

◆ ColorDistance()

static int ColorDistance	(	const ecvColor::Rgb &	c1,
		const ecvColor::Rgb &	c2
	)

static

Compute the distance between two colors /!\ the formula can be modified, here it is simple to be as quick as possible

Definition at line 1099 of file qColorimetricSegmenter.cpp.

References ecvColor::RgbTpl< Type >::b, ecvColor::RgbTpl< Type >::g, and ecvColor::RgbTpl< Type >::r.

Referenced by ComputeKmeansClustering().

◆ ColorimetricalDifference() [1/2]

double ColorimetricalDifference	(	const ccPointCloud &	basePointCloud,
		cloudViewer::ReferenceCloud *	c1,
		cloudViewer::ReferenceCloud *	c2
	)

colorimetricalDifference compute mean colorimetrical difference between two reference clouds. The points in both clouds must be represented in RGB value.

Parameters

basePointCloud	The base cloud on which the reference clouds are based.
c1	The first reference cloud.
c2	The second reference cloud.

Returns: Colorimetrical difference.

Definition at line 537 of file qColorimetricSegmenter.cpp.

References ColorimetricalDifference(), and ComputeAverageColor().

◆ ColorimetricalDifference() [2/2]

static double ColorimetricalDifference	(	ecvColor::Rgb	c1,
		ecvColor::Rgb	c2
	)

static

colorimetricalDifference Compute colorimetrical difference between two RGB color values.

Parameters

c1	First color value.
c2	Second color value.

Returns: Colorimetrical difference.

Definition at line 480 of file qColorimetricSegmenter.cpp.

References ecvColor::RgbTpl< Type >::b, ecvColor::RgbTpl< Type >::g, and ecvColor::RgbTpl< Type >::r.

Referenced by ColorimetricalDifference().

◆ ComputeAverageColor() [1/2]

static ecvColor::Rgb ComputeAverageColor	(	const ccPointCloud &	cloud,
		cloudViewer::ReferenceCloud *	subset
	)

static

Compute the average color (RGB)

Parameters

cloud	: cloud which contains the points
subset	: subset of points Returns average color (RGB)

Definition at line 493 of file qColorimetricSegmenter.cpp.

References ecvColor::RgbTpl< Type >::b, count, ecvColor::RgbTpl< Type >::g, ccPointCloud::getPointColor(), cloudViewer::ReferenceCloud::getPointGlobalIndex(), ecvColor::MAX, min(), ecvColor::RgbTpl< Type >::r, cloudViewer::ReferenceCloud::size(), and ecvColor::white().

Referenced by ColorimetricalDifference(), and ComputeKmeansClustering().

◆ ComputeAverageColor() [2/2]

static ecvColor::Rgb ComputeAverageColor	(	const ccPointCloud &	cloud,
		const std::vector< unsigned > &	bucket
	)

static

Compute the average color (RGB)

Parameters

cloud	: cloud which contains the points
bucket	: vector of indexes of points Returns average color (RGB)

Definition at line 1066 of file qColorimetricSegmenter.cpp.

References ecvColor::RgbTpl< Type >::b, count, ecvColor::RgbTpl< Type >::g, ccPointCloud::getPointColor(), ecvColor::MAX, min(), ecvColor::RgbTpl< Type >::r, and ecvColor::white().

◆ ComputeKmeansClustering()

static ccPointCloud* ComputeKmeansClustering	(	ccPointCloud *	theCloud,
		unsigned	K,
		int	maxIterationCount
	)

static

K-means algorithm

Parameters

k	: k clusters
it	: limit of iterations before returns a result Returns a cloud quantified

Definition at line 1193 of file qColorimetricSegmenter.cpp.

References abs(), cloudViewer::utility::ceil(), ccPointCloud::cloneThis(), color, ColorDistance(), ComputeAverageColor(), ccPointCloud::getPointColor(), CVLog::Print(), ccPointCloud::resize(), ccObject::setName(), ccPointCloud::setPointColor(), cloudViewer::PointCloudTpl< T >::size(), and CVLog::Warning().

◆ FindRegion()

static int FindRegion	(	const std::vector< _RegionSet > &	container,
		cloudViewer::ReferenceCloud *	region
	)

static

findRegion Find a given region in a vector of reference clouds.

Parameters

container	Container containing all the regions.
region	Region to search for in the vector.

Returns: The index of the region if found, -1 in the other case.

Definition at line 635 of file qColorimetricSegmenter.cpp.

◆ GetKeyCluster()

static bool GetKeyCluster	(	const ccPointCloud &	cloud,
		size_t	clusterPerDim,
		ClusterMap &	clusterMap
	)

static

Definition at line 1030 of file qColorimetricSegmenter.cpp.

References ecvColor::RgbTpl< Type >::b, ecvColor::RgbTpl< Type >::g, ccPointCloud::getPointColor(), ecvColor::MAX, ecvColor::RgbTpl< Type >::r, and cloudViewer::PointCloudTpl< T >::size().

◆ Inside()

static bool Inside	(	ColorCompType	lower,
		ColorCompType	value,
		ColorCompType	upper
	)

inlinestatic

Definition at line 43 of file qColorimetricSegmenter.cpp.

◆ KNNRegions()

static bool KNNRegions	(	ccPointCloud *	basePointCloud,
		const _RegionSet &	regions,
		const _Region &	region,
		unsigned	k,
		_RegionSet &	neighbourRegions,
		unsigned	thresholdDistance
	)

static

KNNRegions Determines the neighboring regions of a region.

Parameters

basePointCloud	The base cloud containing the points.
regions	The list containing all the regions in the base cloud point.
region	The region to compare with others.
k	Max number of neighbours to find.
neighbours	The resulting nearest regions.
thresholdDistance	The maximum distance to search for neighbors.