gusucode.com > vision工具箱matlab源码程序 > vision/+vision/en/GeometricTransformEstimator.m
classdef GeometricTransformEstimator< matlab.system.SFunSystem
%GeometricTransformEstimator Estimate geometric transformation from matching point pairs
% ----------------------------------------------------------------------------
% It is recommended to use the new estimateGeometricTransform function
% in place of vision.GeometricTransformEstimator.
% ----------------------------------------------------------------------------
%
% The GeometricTransformEstimator object estimates the geometric
% transformation from the matching point pairs and returns it as a
% matrix. It offers robust statistical approaches such as RANSAC or
% Least Median of Squares to compute projective, affine or nonreflective
% similarity transformations.
%
% HGEOTFORMEST = vision.GeometricTransformEstimator returns a geometric
% transform estimation System object, HGEOTFORMEST.
%
% HGEOTFORMEST = vision.GeometricTransformEstimator('PropertyName',
% PropertyValue, ...) returns a geometric transform estimation object,
% HGEOTFORMEST, with each specified property set to the specified value.
%
% Step method syntax:
%
% TFORM = step(HGEOTFORMEST, MATCHEDPOINTS1, MATCHEDPOINTS2)
% calculates the transformation matrix, TFORM, given a set of matching
% points in MATCHEDPOINTS1 and MATCHEDPOINTS2 arrays. MATCHEDPOINTS1
% and MATCHEDPOINTS2, specify the locations of matching points in two
% images. The points in the arrays are stored as a set of [x1 y1;
% x2 y2; ...; xN yN] coordinates, where N is the number of points.
% TFORM is 3-by-3 when the Transform property is 'Projective', and 3-by-2
% otherwise.
%
% [TFORM, INLIER_INDEX] = step(HGEOTFORMEST, ...) additionally outputs the
% logical vector, INLIER_INDEX, indicating which points are the inliers.
%
% System objects may be called directly like a function instead of using
% the step method. For example, y = step(obj, x) and y = obj(x) are
% equivalent.
%
% GeometricTransformEstimator methods:
%
% step - See above description for use of this method
% release - Allow property value and input characteristics changes
% clone - Create geometric transform estimation object with same
% property values
% isLocked - Locked status (logical)
%
% GeometricTransformEstimator properties:
%
% Transform - Transformation type
% ExcludeOutliers - Whether to exclude outliers from input
% points
% Method - Method to find outliers
% AlgebraicDistanceThreshold - Algebraic distance threshold for
% determining inliers
% PixelDistanceThreshold - Distance threshold for determining inliers
% in pixels
% NumRandomSamplingsMethod - How to specify number of random samplings
% NumRandomSamplings - Number of random samplings
% DesiredConfidence - Probability to find largest group of points
% MaximumRandomSamples - Maximum number of random samplings
% InlierPercentageSource - Source of inlier percentage
% InlierPercentage - Percentage of point pairs to be found to
% stop random sampling
% RefineTransformMatrix - Whether to refine transformation matrix
% TransformMatrixDataType - Data type of the transformation matrix
%
% % EXAMPLE 1: Recover a transformed image using SURF feature points
% Iin = imread('cameraman.tif'); imshow(Iin); title('Base image');
% Iout = imresize(Iin, 0.7); Iout = imrotate(Iout, 31);
% figure; imshow(Iout); title('Transformed image');
%
% % Detect and extract features from both images
% ptsIn = detectSURFFeatures(Iin);
% ptsOut = detectSURFFeatures(Iout);
% [featuresIn, validPtsIn] = extractFeatures(Iin, ptsIn);
% [featuresOut, validPtsOut] = extractFeatures(Iout, ptsOut);
%
% % Match feature vectors
% indexPairs = matchFeatures(featuresIn, featuresOut);
% % get matching points
% matchedPtsIn = validPtsIn(indexPairs(:,1));
% matchedPtsOut = validPtsOut(indexPairs(:,2));
% figure; showMatchedFeatures(Iin,Iout,matchedPtsIn,matchedPtsOut);
% title('Matched SURF points, including outliers');
%
% % Compute the transformation matrix using RANSAC
% gte = vision.GeometricTransformEstimator;
% gte.Transform = 'Nonreflective similarity';
% [tform, inlierIdx] = step(gte, matchedPtsOut.Location, ...
% matchedPtsIn.Location);
% figure; showMatchedFeatures(Iin,Iout,matchedPtsIn(inlierIdx),...
% matchedPtsOut(inlierIdx));
% title('Matching inliers'); legend('inliersIn', 'inliersOut');
%
% % Recover the original image Iin from Iout
% agt = vision.GeometricTransformer;
% Ir = step(agt, im2single(Iout), tform);
% figure; imshow(Ir); title('Recovered image');
%
% % EXAMPLE 2: Transform an image according to the specified points.
% input = checkerboard;
% [h, w] = size(input);
% inImageCorners = [1 1; w 1; w h; 1 h];
% outImageCorners = [4 21; 21 121; 79 51; 26 6];
% hgte1 = vision.GeometricTransformEstimator('ExcludeOutliers', false);
% tform = step(hgte1, inImageCorners, outImageCorners);
%
% % Use tform to transform the image
% hgt = vision.GeometricTransformer;
% output = step(hgt, input, tform);
% figure; imshow(input); title('Original image');
% figure; imshow(output); title('Transformed image');
%
% See also estimateGeometricTransform, imwarp, extractFeatures.
% Copyright 2008-2016 The MathWorks, Inc.
methods
function out=GeometricTransformEstimator
%GeometricTransformEstimator Estimate geometric transformation from matching point pairs
% ----------------------------------------------------------------------------
% It is recommended to use the new estimateGeometricTransform function
% in place of vision.GeometricTransformEstimator.
% ----------------------------------------------------------------------------
%
% The GeometricTransformEstimator object estimates the geometric
% transformation from the matching point pairs and returns it as a
% matrix. It offers robust statistical approaches such as RANSAC or
% Least Median of Squares to compute projective, affine or nonreflective
% similarity transformations.
%
% HGEOTFORMEST = vision.GeometricTransformEstimator returns a geometric
% transform estimation System object, HGEOTFORMEST.
%
% HGEOTFORMEST = vision.GeometricTransformEstimator('PropertyName',
% PropertyValue, ...) returns a geometric transform estimation object,
% HGEOTFORMEST, with each specified property set to the specified value.
%
% Step method syntax:
%
% TFORM = step(HGEOTFORMEST, MATCHEDPOINTS1, MATCHEDPOINTS2)
% calculates the transformation matrix, TFORM, given a set of matching
% points in MATCHEDPOINTS1 and MATCHEDPOINTS2 arrays. MATCHEDPOINTS1
% and MATCHEDPOINTS2, specify the locations of matching points in two
% images. The points in the arrays are stored as a set of [x1 y1;
% x2 y2; ...; xN yN] coordinates, where N is the number of points.
% TFORM is 3-by-3 when the Transform property is 'Projective', and 3-by-2
% otherwise.
%
% [TFORM, INLIER_INDEX] = step(HGEOTFORMEST, ...) additionally outputs the
% logical vector, INLIER_INDEX, indicating which points are the inliers.
%
% System objects may be called directly like a function instead of using
% the step method. For example, y = step(obj, x) and y = obj(x) are
% equivalent.
%
% GeometricTransformEstimator methods:
%
% step - See above description for use of this method
% release - Allow property value and input characteristics changes
% clone - Create geometric transform estimation object with same
% property values
% isLocked - Locked status (logical)
%
% GeometricTransformEstimator properties:
%
% Transform - Transformation type
% ExcludeOutliers - Whether to exclude outliers from input
% points
% Method - Method to find outliers
% AlgebraicDistanceThreshold - Algebraic distance threshold for
% determining inliers
% PixelDistanceThreshold - Distance threshold for determining inliers
% in pixels
% NumRandomSamplingsMethod - How to specify number of random samplings
% NumRandomSamplings - Number of random samplings
% DesiredConfidence - Probability to find largest group of points
% MaximumRandomSamples - Maximum number of random samplings
% InlierPercentageSource - Source of inlier percentage
% InlierPercentage - Percentage of point pairs to be found to
% stop random sampling
% RefineTransformMatrix - Whether to refine transformation matrix
% TransformMatrixDataType - Data type of the transformation matrix
%
% % EXAMPLE 1: Recover a transformed image using SURF feature points
% Iin = imread('cameraman.tif'); imshow(Iin); title('Base image');
% Iout = imresize(Iin, 0.7); Iout = imrotate(Iout, 31);
% figure; imshow(Iout); title('Transformed image');
%
% % Detect and extract features from both images
% ptsIn = detectSURFFeatures(Iin);
% ptsOut = detectSURFFeatures(Iout);
% [featuresIn, validPtsIn] = extractFeatures(Iin, ptsIn);
% [featuresOut, validPtsOut] = extractFeatures(Iout, ptsOut);
%
% % Match feature vectors
% indexPairs = matchFeatures(featuresIn, featuresOut);
% % get matching points
% matchedPtsIn = validPtsIn(indexPairs(:,1));
% matchedPtsOut = validPtsOut(indexPairs(:,2));
% figure; showMatchedFeatures(Iin,Iout,matchedPtsIn,matchedPtsOut);
% title('Matched SURF points, including outliers');
%
% % Compute the transformation matrix using RANSAC
% gte = vision.GeometricTransformEstimator;
% gte.Transform = 'Nonreflective similarity';
% [tform, inlierIdx] = step(gte, matchedPtsOut.Location, ...
% matchedPtsIn.Location);
% figure; showMatchedFeatures(Iin,Iout,matchedPtsIn(inlierIdx),...
% matchedPtsOut(inlierIdx));
% title('Matching inliers'); legend('inliersIn', 'inliersOut');
%
% % Recover the original image Iin from Iout
% agt = vision.GeometricTransformer;
% Ir = step(agt, im2single(Iout), tform);
% figure; imshow(Ir); title('Recovered image');
%
% % EXAMPLE 2: Transform an image according to the specified points.
% input = checkerboard;
% [h, w] = size(input);
% inImageCorners = [1 1; w 1; w h; 1 h];
% outImageCorners = [4 21; 21 121; 79 51; 26 6];
% hgte1 = vision.GeometricTransformEstimator('ExcludeOutliers', false);
% tform = step(hgte1, inImageCorners, outImageCorners);
%
% % Use tform to transform the image
% hgt = vision.GeometricTransformer;
% output = step(hgt, input, tform);
% figure; imshow(input); title('Original image');
% figure; imshow(output); title('Transformed image');
%
% See also estimateGeometricTransform, imwarp, extractFeatures.
end
function isInactivePropertyImpl(in) %#ok<MANU>
end
function setPortDataTypeConnections(in) %#ok<MANU>
end
end
methods (Abstract)
end
properties
%AlgebraicDistanceThreshold Algebraic distance threshold for
% determining inliers
% Specify a scalar threshold value for determining inliers as a
% positive scalar value. The threshold controls the upper limit used
% to find the algebraic distance in the RANSAC Method. This property
% is applicable when the Transform property is 'Projective' and the
% Method property is 'Random Sample Consensus (RANSAC)'. The default
% value of this property is 2.5. This property is tunable.
AlgebraicDistanceThreshold;
%DesiredConfidence Probability to find largest group of points
% Specify the probability to find the largest group of points that
% can be mapped by a transformation matrix as a percentage. This
% property is applicable when the NumRandomSamplingsMethod property
% is 'Desired confidence'. The default value of this property is 99.
% This property is tunable.
DesiredConfidence;
%ExcludeOutliers Whether to exclude outliers from input points
% Set this property to true to find and exclude outliers from the
% input points and use only the inlier points to calculate the
% transformation matrix. When this property is false, all input
% points are used to calculate the transformation matrix. The default
% value of this property is true.
ExcludeOutliers;
%InlierOutputPort Enables output of the inlier point pairs
% Set this property to true to output the inlier point pairs that
% were used to calculate the transformation matrix. This property is
% applicable when the ExcludeOutliers property is true. The default
% value of this property is true.
InlierOutputPort;
%InlierPercentage Percentage of point pairs to be found to stop random
% sampling
% Specify the percentage of point pairs that need to be determined as
% inliers to stop random sampling. This property is applicable when
% the InlierPercentageSource property is 'Property'. The
% default value of this property is 75. This property is tunable.
InlierPercentage;
%InlierPercentageSource Source of inlier percentage
% Indicate how to specify the threshold to stop random sampling when
% a percentage of input point pairs have been found as inliers. This
% property can be set to one of [{'Auto'} | 'Property']. If set to 'Auto'
% then inlier threshold is disabled. This property is applicable when
% the Method property is 'Random Sample Consensus (RANSAC)'.
InlierPercentageSource;
%MaximumRandomSamples Maximum number of random samplings
% Specify the maximum number of random samplings as a positive
% integer value. This property is applicable when the
% NumRandomSamplingsMethod property is 'Desired confidence'. The
% default value of this property is 1000. This property is tunable.
MaximumRandomSamples;
%Method Method to find outliers
% Specify the Method to find outliers as one of [{'Random Sample
% Consensus (RANSAC)'} | 'Least Median of Squares'].
Method;
%NumRandomSamplings Number of random samplings
% Specify the number of random samplings for the Method to perform
% as a positive integer value. This property is applicable when the
% NumRandomSamplingsMethod property is 'Specified value'. The default
% value of this property is 500. This property is tunable.
NumRandomSamplings;
%NumRandomSamplingsMethod How to specify number of random samplings
% Indicate how to specify number of random samplings as one of
% [{'Specified value'} | 'Desired confidence']. Set this property to
% 'Desired confidence' to specify the number of random samplings as a
% percentage and a maximum number. This property is applicable when
% the ExcludeOutliers property is true and the Method property is
% 'Random Sample Consensus (RANSAC)'.
NumRandomSamplingsMethod;
%PixelDistanceThreshold Distance threshold for determining inliers in
% pixels
% Specify the upper limit distance a point can differ from the
% projection location of its associating point as a positive scalar
% value. This property is applicable when the Transform property is
% 'Nonreflective similarity' or 'Affine', and the Method property is
% 'Random Sample Consensus (RANSAC)'. The default value of this
% property is 2.5. This property is tunable.
PixelDistanceThreshold;
%RefineTransformMatrix Whether to refine transformation matrix
% Set this property to true to perform additional iterative
% refinement on the transformation matrix. This property is
% applicable when the ExcludeOutliers property is true. The default
% value of this property is false.
RefineTransformMatrix;
%Transform Transformation type
% Specify transformation type as one of ['Nonreflective similarity' |
% {'Affine'} | 'Projective'].
Transform;
%TransformMatrixDataType Data type of the transformation matrix
% Specify transformation matrix data type as one of [{'single'} |
% 'double'] when the input points are built-in integers. This
% property is not used when the data type of points is single or
% double.
TransformMatrixDataType;
end
end