cv::segmentation::IntelligentScissorsMB Class Reference

Intelligent Scissors image segmentation. More...

#include <opencv2/imgproc/segmentation.hpp>

Public Member Functions
	IntelligentScissorsMB ()
IntelligentScissorsMB &	applyImage (InputArray image)
	Specify input image and extract image features. More...
IntelligentScissorsMB &	applyImageFeatures (InputArray non_edge, InputArray gradient_direction, InputArray gradient_magnitude, InputArray image=noArray())
	Specify custom features of input image. More...
void	buildMap (const Point &sourcePt)
	Prepares a map of optimal paths for the given source point on the image. More...
void	getContour (const Point &targetPt, OutputArray contour, bool backward=false) const
	Extracts optimal contour for the given target point on the image. More...
IntelligentScissorsMB &	setEdgeFeatureCannyParameters (double threshold1, double threshold2, int apertureSize=3, bool L2gradient=false)
	Switch edge feature extractor to use Canny edge detector. More...
IntelligentScissorsMB &	setEdgeFeatureZeroCrossingParameters (float gradient_magnitude_min_value=0.0f)
	Switch to "Laplacian Zero-Crossing" edge feature extractor and specify its parameters. More...
IntelligentScissorsMB &	setGradientMagnitudeMaxLimit (float gradient_magnitude_threshold_max=0.0f)
	Specify gradient magnitude max value threshold. More...
IntelligentScissorsMB &	setWeights (float weight_non_edge, float weight_gradient_direction, float weight_gradient_magnitude)
	Specify weights of feature functions. More...

Detailed Description

Intelligent Scissors image segmentation.

This class is used to find the path (contour) between two points which can be used for image segmentation.

Usage example:

 segmentation::IntelligentScissorsMB tool;
 tool.setEdgeFeatureCannyParameters(16, 100) // using Canny() as edge feature extractor
 .setGradientMagnitudeMaxLimit(200);

 // calculate image features
 tool.applyImage(image);

 // calculate map for specified source point
 Point source_point(200, 100);
 tool.buildMap(source_point);

 // fast fetching of contours
 // for specified target point and the pre-calculated map (stored internally)
 Point target_point(400, 300);
 std::vector<Point> pts;
 tool.getContour(target_point, pts);

Reference: "Intelligent Scissors for Image Composition" algorithm designed by Eric N. Mortensen and William A. Barrett, Brigham Young University [182]

Constructor & Destructor Documentation

IntelligentScissorsMB()

cv::segmentation::IntelligentScissorsMB::IntelligentScissorsMB

(

)

Member Function Documentation

applyImage()

IntelligentScissorsMB& cv::segmentation::IntelligentScissorsMB::applyImage

(

InputArray

image

)

Python:
	cv.segmentation.IntelligentScissorsMB.applyImage(	image	) ->	retval

Specify input image and extract image features.

Parameters

image

input image. Type is CV_8UC1 / CV_8UC3

applyImageFeatures()

IntelligentScissorsMB& cv::segmentation::IntelligentScissorsMB::applyImageFeatures	(	InputArray	non_edge,
		InputArray	gradient_direction,
		InputArray	gradient_magnitude,
		InputArray	image = noArray()
	)

Python:
	cv.segmentation.IntelligentScissorsMB.applyImageFeatures(	non_edge, gradient_direction, gradient_magnitude[, image]	) ->	retval

Specify custom features of input image.

Customized advanced variant of applyImage() call.

Parameters

non_edge	Specify cost of non-edge pixels. Type is CV_8UC1. Expected values are {0, 1}.
gradient_direction	Specify gradient direction feature. Type is CV_32FC2. Values are expected to be normalized: x^2 + y^2 == 1
gradient_magnitude	Specify cost of gradient magnitude function: Type is CV_32FC1. Values should be in range [0, 1].
image	Optional parameter. Must be specified if subset of features is specified (non-specified features are calculated internally)

buildMap()

void cv::segmentation::IntelligentScissorsMB::buildMap

(

const Point &

sourcePt

)

Python:
	cv.segmentation.IntelligentScissorsMB.buildMap(	sourcePt	) ->	None

Prepares a map of optimal paths for the given source point on the image.

Note

applyImage() / applyImageFeatures() must be called before this call

Parameters

sourcePt

The source point used to find the paths

getContour()

void cv::segmentation::IntelligentScissorsMB::getContour	(	const Point &	targetPt,
		OutputArray	contour,
		bool	backward = false
	)		const

Python:
	cv.segmentation.IntelligentScissorsMB.getContour(	targetPt[, contour[, backward]]	) ->	contour

Extracts optimal contour for the given target point on the image.

Note

buildMap() must be called before this call

Parameters

	targetPt	The target point
[out]	contour	The list of pixels which contains optimal path between the source and the target points of the image. Type is CV_32SC2 (compatible with std::vector<Point>)
	backward	Flag to indicate reverse order of retrived pixels (use "true" value to fetch points from the target to the source point)

setEdgeFeatureCannyParameters()

IntelligentScissorsMB& cv::segmentation::IntelligentScissorsMB::setEdgeFeatureCannyParameters	(	double	threshold1,
		double	threshold2,
		int	apertureSize = 3,
		bool	L2gradient = false
	)

Python:
	cv.segmentation.IntelligentScissorsMB.setEdgeFeatureCannyParameters(	threshold1, threshold2[, apertureSize[, L2gradient]]	) ->	retval

Switch edge feature extractor to use Canny edge detector.

Note

"Laplacian Zero-Crossing" feature extractor is used by default (following to original article)

See also

Canny

setEdgeFeatureZeroCrossingParameters()

IntelligentScissorsMB& cv::segmentation::IntelligentScissorsMB::setEdgeFeatureZeroCrossingParameters

(

float

gradient_magnitude_min_value = 0.0f

)

Python:
	cv.segmentation.IntelligentScissorsMB.setEdgeFeatureZeroCrossingParameters(	[, gradient_magnitude_min_value]	) ->	retval

Switch to "Laplacian Zero-Crossing" edge feature extractor and specify its parameters.

This feature extractor is used by default according to article.

Implementation has additional filtering for regions with low-amplitude noise. This filtering is enabled through parameter of minimal gradient amplitude (use some small value 4, 8, 16).

Note

Current implementation of this feature extractor is based on processing of grayscale images (color image is converted to grayscale image first).

Canny edge detector is a bit slower, but provides better results (especially on color images): use setEdgeFeatureCannyParameters().

Parameters

gradient_magnitude_min_value

Minimal gradient magnitude value for edge pixels (default: 0, check is disabled)

setGradientMagnitudeMaxLimit()

IntelligentScissorsMB& cv::segmentation::IntelligentScissorsMB::setGradientMagnitudeMaxLimit

(

float

gradient_magnitude_threshold_max = 0.0f

)

Python:
	cv.segmentation.IntelligentScissorsMB.setGradientMagnitudeMaxLimit(	[, gradient_magnitude_threshold_max]	) ->	retval

Specify gradient magnitude max value threshold.

Zero limit value is used to disable gradient magnitude thresholding (default behavior, as described in original article). Otherwize pixels with gradient magnitude >= threshold have zero cost.

Note

Thresholding should be used for images with irregular regions (to avoid stuck on parameters from high-contract areas, like embedded logos).

Parameters

gradient_magnitude_threshold_max

Specify gradient magnitude max value threshold (default: 0, disabled)

setWeights()

IntelligentScissorsMB& cv::segmentation::IntelligentScissorsMB::setWeights	(	float	weight_non_edge,
		float	weight_gradient_direction,
		float	weight_gradient_magnitude
	)

Python:
	cv.segmentation.IntelligentScissorsMB.setWeights(	weight_non_edge, weight_gradient_direction, weight_gradient_magnitude	) ->	retval

Specify weights of feature functions.

Consider keeping weights normalized (sum of weights equals to 1.0) Discrete dynamic programming (DP) goal is minimization of costs between pixels.

Parameters

weight_non_edge	Specify cost of non-edge pixels (default: 0.43f)
weight_gradient_direction	Specify cost of gradient direction function (default: 0.43f)
weight_gradient_magnitude	Specify cost of gradient magnitude function (default: 0.14f)

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The documentation for this class was generated from the following file:

• opencv2/imgproc/segmentation.hpp