public void zero_inliers_test()
{
// Fix the random number generator
Accord.Math.Random.Generator.Seed = 0;
double[,] data = // This is the same data used in the RANSAC sample app
{
{ 1.0, 0.79 }, { 3, 2.18 }, { 5, 5.99 }, { 7.0, 7.65 },
{ 9.0, 9.55 }, { 11, 11.89 }, { 13, 13.73 }, { 15.0, 14.77 },
{ 17.0, 18.00 }, { 1.2, 1.45 }, { 1.5, 1.18 }, { 1.8, 1.92 },
{ 2.1, 1.47 }, { 2.4, 2.41 }, { 2.7, 2.35 }, { 3.0, 3.41 },
{ 3.3, 3.78 }, { 3.6, 3.21 }, { 3.9, 4.76 }, { 4.2, 5.03 },
{ 4.5, 4.19 }, { 4.8, 3.81 }, { 5.1, 6.07 }, { 5.4, 5.74 },
{ 5.7, 6.39 }, { 6, 6.11 }, { 6.3, 6.86 }, { 6.6, 6.35 },
{ 6.9, 7.9 }, { 7.2, 8.04 }, { 7.5, 8.48 }, { 7.8, 8.07 },
{ 8.1, 8.22 }, { 8.4, 8.41 }, { 8.7, 9.4 }, { 9, 8.8 },
{ 9.3, 8.44 }, { 9.6, 9.32 }, { 9.9, 9.18 }, { 10.2, 9.86 },
{ 10.5, 10.16 }, { 10.8, 10.28 }, { 11.1, 11.07 }, { 11.4, 11.66 },
{ 11.7, 11.13 }, { 12, 11.55 }, { 12.3, 12.62 }, { 12.6, 12.27 },
{ 12.9, 12.33 }, { 13.2, 12.37 }, { 13.5, 12.75 }, { 13.8, 14.44 },
{ 14.1, 14.71 }, { 14.4, 13.72 }, { 14.7, 14.54 }, { 15, 14.67 },
{ 15.3, 16.04 }, { 15.6, 15.21 }, { 1, 3.9 }, { 2, 11.5 },
{ 3.0, 13.0 }, { 4, 0.9 }, { 5, 5.5 }, { 6, 16.2 },
{ 7.0, 0.8 }, { 8, 9.4 }, { 9, 9.5 }, { 10, 17.5 },
{ 11.0, 6.3 }, { 12, 12.6 }, { 13, 1.5 }, { 14, 1.5 },
{ 2.0, 10 }, { 3, 9 }, { 15, 2 }, { 15.5, 1.2 },
};
// First, fit simple linear regression directly for comparison reasons.
double[] x = data.GetColumn(0); // Extract the independent variable
double[] y = data.GetColumn(1); // Extract the dependent variable
// Create a simple linear regression
var regression = new SimpleLinearRegression();
Assert.AreEqual(1, regression.NumberOfInputs);
Assert.AreEqual(1, regression.NumberOfOutputs);
// Estimate a line passing through the (x, y) points
double sumOfSquaredErrors = regression.Regress(x, y);
// Now, compute the values predicted by the
// regression for the original input points
double[] commonOutput = regression.Compute(x);
// Now, fit simple linear regression using RANSAC
int maxTrials = 1000;
int minSamples = 20;
double probability = 0.950;
double errorThreshold = 1000;
int count = 0;
// Create a RANSAC algorithm to fit a simple linear regression
var ransac = new RANSAC <SimpleLinearRegression>(minSamples)
{
Probability = probability,
Threshold = errorThreshold,
MaxEvaluations = maxTrials,
// Define a fitting function
Fitting = delegate(int[] sample)
{
// Retrieve the training data
double[] inputs = x.Submatrix(sample);
double[] outputs = y.Submatrix(sample);
// Build a Simple Linear Regression model
var r = new SimpleLinearRegression();
r.Regress(inputs, outputs);
return(r);
},
// Define a check for degenerate samples
Degenerate = delegate(int[] sample)
{
// In this case, we will not be performing such checks.
return(false);
},
// Define a inlier detector function
Distances = delegate(SimpleLinearRegression r, double threshold)
{
count++;
List <int> inliers = new List <int>();
// Generate 0 inliers twice, then proceed as normal
if (count > 2)
{
for (int i = 0; i < x.Length; i++)
{
// Compute error for each point
double error = r.Compute(x[i]) - y[i];
// If the squared error is below the given threshold,
// the point is considered to be an inlier.
if (error * error < threshold)
{
inliers.Add(i);
}
}
}
return(inliers.ToArray());
}
};
// Now that the RANSAC hyperparameters have been specified, we can
// compute another regression model using the RANSAC algorithm:
int[] inlierIndices;
SimpleLinearRegression robustRegression = ransac.Compute(data.Rows(), out inlierIndices);
// Compute the output of the model fitted by RANSAC
double[] ransacOutput = robustRegression.Compute(x);
Assert.AreEqual(ransac.TrialsNeeded, 0);
Assert.AreEqual(ransac.TrialsPerformed, 3);
string a = inlierIndices.ToCSharp();
string b = ransacOutput.ToCSharp();
int[] expectedInliers = new int[] { 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75 };
double[] expectedOutput = new double[] { 4.62124895918799, 5.37525473445784, 6.12926050972769, 6.88326628499754, 7.63727206026739, 8.39127783553724, 9.14528361080709, 9.89928938607694, 10.6532951613468, 4.69664953671498, 4.80975040300545, 4.92285126929593, 5.03595213558641, 5.14905300187689, 5.26215386816736, 5.37525473445784, 5.48835560074832, 5.6014564670388, 5.71455733332927, 5.82765819961975, 5.94075906591023, 6.05385993220071, 6.16696079849118, 6.28006166478166, 6.39316253107214, 6.50626339736262, 6.61936426365309, 6.73246512994357, 6.84556599623405, 6.95866686252453, 7.071767728815, 7.18486859510548, 7.29796946139596, 7.41107032768644, 7.52417119397691, 7.63727206026739, 7.75037292655787, 7.86347379284835, 7.97657465913882, 8.0896755254293, 8.20277639171978, 8.31587725801026, 8.42897812430073, 8.54207899059121, 8.65517985688169, 8.76828072317216, 8.88138158946264, 8.99448245575312, 9.1075833220436, 9.22068418833408, 9.33378505462455, 9.44688592091503, 9.55998678720551, 9.67308765349599, 9.78618851978646, 9.89928938607694, 10.0123902523674, 10.1254911186579, 4.62124895918799, 4.99825184682292, 5.37525473445784, 5.75225762209277, 6.12926050972769, 6.50626339736262, 6.88326628499754, 7.26026917263247, 7.63727206026739, 8.01427494790232, 8.39127783553724, 8.76828072317216, 9.14528361080709, 9.52228649844202, 4.99825184682292, 5.37525473445784, 9.89928938607694, 10.0877908298944 };
Assert.IsTrue(inlierIndices.IsEqual(expectedInliers));
Assert.IsTrue(ransacOutput.IsEqual(expectedOutput, 1e-10));
}
/// <summary>
/// Creates a new RANSAC 2D circle estimator.
/// </summary>
///
/// <param name="threshold">Inlier threshold.</param>
/// <param name="probability">Inlier probability.</param>
///
public RansacCircle(double threshold, double probability)
{
ransac = new RANSAC <Circle>(3, threshold, probability);
ransac.Fitting = define;
ransac.Distances = distance;
ransac.Degenerate = degenerate;
}
// Creates a new RANSAC homography estimator.
public RansacHomographyEstimator(double threshold, double probability)
{
// Create a new RANSAC with the selected threshold
ransac = new RANSAC <MatrixH>(4, threshold, probability);
// Set RANSAC functions
ransac.Fitting = homography;
ransac.Distances = distance;
}
/// <summary>
/// Creates a new RANSAC homography estimator.
/// </summary>
///
/// <param name="threshold">Inlier threshold.</param>
/// <param name="probability">Inlier probability.</param>
///
public RansacFundamentalEstimator(double threshold, double probability)
{
// Create a new RANSAC with the selected threshold
ransac = new RANSAC <float[, ]>(8, threshold, probability);
// Set RANSAC functions
ransac.Fitting = fundamental;
ransac.Distances = distance;
}
/// <summary>
/// Creates a new RANSAC line estimator.
/// </summary>
///
/// <param name="threshold">Inlier threshold.</param>
/// <param name="probability">Inlier probability.</param>
///
public RansacLine(double threshold, double probability)
{
// Create a new RANSAC with the selected threshold
ransac = new RANSAC <Line>(2, threshold, probability);
// Set RANSAC functions
ransac.Fitting = defineline;
ransac.Distances = distance;
ransac.Degenerate = degenerate;
}
public void new_api_test()
{
#region doc_learn
// Fix the random number generator
Accord.Math.Random.Generator.Seed = 0;
double[,] data = // This is the same data used in the RANSAC sample app
{
{ 1.0, 0.79 }, { 3, 2.18 }, { 5, 5.99 }, { 7.0, 7.65 },
{ 9.0, 9.55 }, { 11, 11.89 }, { 13, 13.73 }, { 15.0, 14.77 },
{ 17.0, 18.00 }, { 1.2, 1.45 }, { 1.5, 1.18 }, { 1.8, 1.92 },
{ 2.1, 1.47 }, { 2.4, 2.41 }, { 2.7, 2.35 }, { 3.0, 3.41 },
{ 3.3, 3.78 }, { 3.6, 3.21 }, { 3.9, 4.76 }, { 4.2, 5.03 },
{ 4.5, 4.19 }, { 4.8, 3.81 }, { 5.1, 6.07 }, { 5.4, 5.74 },
{ 5.7, 6.39 }, { 6, 6.11 }, { 6.3, 6.86 }, { 6.6, 6.35 },
{ 6.9, 7.9 }, { 7.2, 8.04 }, { 7.5, 8.48 }, { 7.8, 8.07 },
{ 8.1, 8.22 }, { 8.4, 8.41 }, { 8.7, 9.4 }, { 9, 8.8 },
{ 9.3, 8.44 }, { 9.6, 9.32 }, { 9.9, 9.18 }, { 10.2, 9.86 },
{ 10.5, 10.16 }, { 10.8, 10.28 }, { 11.1, 11.07 }, { 11.4, 11.66 },
{ 11.7, 11.13 }, { 12, 11.55 }, { 12.3, 12.62 }, { 12.6, 12.27 },
{ 12.9, 12.33 }, { 13.2, 12.37 }, { 13.5, 12.75 }, { 13.8, 14.44 },
{ 14.1, 14.71 }, { 14.4, 13.72 }, { 14.7, 14.54 }, { 15, 14.67 },
{ 15.3, 16.04 }, { 15.6, 15.21 }, { 1, 3.9 }, { 2, 11.5 },
{ 3.0, 13.0 }, { 4, 0.9 }, { 5, 5.5 }, { 6, 16.2 },
{ 7.0, 0.8 }, { 8, 9.4 }, { 9, 9.5 }, { 10, 17.5 },
{ 11.0, 6.3 }, { 12, 12.6 }, { 13, 1.5 }, { 14, 1.5 },
{ 2.0, 10 }, { 3, 9 }, { 15, 2 }, { 15.5, 1.2 },
};
// First, fit simple linear regression directly for comparison reasons.
double[] x = data.GetColumn(0); // Extract the independent variable
double[] y = data.GetColumn(1); // Extract the dependent variable
// Use Ordinary Least Squares to learn the regression
OrdinaryLeastSquares ols = new OrdinaryLeastSquares();
// Estimate a line passing through the (x, y) points
SimpleLinearRegression regression = ols.Learn(x, y);
// Now, compute the values predicted by the
// regression for the original input points
double[] commonOutput = regression.Transform(x);
// Now, fit simple linear regression using RANSAC
int maxTrials = 1000;
int minSamples = 20;
double probability = 0.950;
double errorThreshold = 1000;
// Create a RANSAC algorithm to fit a simple linear regression
var ransac = new RANSAC <SimpleLinearRegression>(minSamples)
{
Probability = probability,
Threshold = errorThreshold,
MaxEvaluations = maxTrials,
// Define a fitting function
Fitting = (int[] sample) =>
{
// Build a Simple Linear Regression model
return(new OrdinaryLeastSquares()
.Learn(x.Get(sample), y.Get(sample)));
},
// Define a inlier detector function
Distances = (SimpleLinearRegression r, double threshold) =>
{
var inliers = new List <int>();
for (int i = 0; i < x.Length; i++)
{
// Compute error for each point
double error = r.Transform(x[i]) - y[i];
// If the square error is low enough,
if (error * error < threshold)
{
inliers.Add(i); // the point is considered an inlier.
}
}
return(inliers.ToArray());
}
};
// Now that the RANSAC hyperparameters have been specified, we can
// compute another regression model using the RANSAC algorithm:
int[] inlierIndices;
SimpleLinearRegression robustRegression = ransac.Compute(data.Rows(), out inlierIndices);
// Compute the output of the model fitted by RANSAC
double[] ransacOutput = robustRegression.Transform(x);
#endregion
Assert.AreEqual(ransac.TrialsNeeded, 0);
Assert.AreEqual(ransac.TrialsPerformed, 1);
string a = inlierIndices.ToCSharp();
string b = ransacOutput.ToCSharp();
int[] expectedInliers = new int[] { 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75 };
double[] expectedOutput = new double[] { 1.96331236445045, 3.42042856976283, 4.87754477507521, 6.33466098038758, 7.79177718569996, 9.24889339101234, 10.7060095963247, 12.1631258016371, 13.6202420069495, 2.10902398498169, 2.32759141577855, 2.5461588465754, 2.76472627737226, 2.98329370816912, 3.20186113896597, 3.42042856976283, 3.63899600055969, 3.85756343135654, 4.0761308621534, 4.29469829295026, 4.51326572374711, 4.73183315454397, 4.95040058534082, 5.16896801613768, 5.38753544693454, 5.6061028777314, 5.82467030852825, 6.04323773932511, 6.26180517012196, 6.48037260091882, 6.69894003171568, 6.91750746251253, 7.13607489330939, 7.35464232410625, 7.5732097549031, 7.79177718569996, 8.01034461649682, 8.22891204729367, 8.44747947809053, 8.66604690888738, 8.88461433968424, 9.1031817704811, 9.32174920127795, 9.54031663207481, 9.75888406287167, 9.97745149366852, 10.1960189244654, 10.4145863552622, 10.6331537860591, 10.8517212168559, 11.0702886476528, 11.2888560784497, 11.5074235092465, 11.7259909400434, 11.9445583708402, 12.1631258016371, 12.3816932324339, 12.6002606632308, 1.96331236445045, 2.69187046710664, 3.42042856976283, 4.14898667241902, 4.87754477507521, 5.6061028777314, 6.33466098038758, 7.06321908304377, 7.79177718569996, 8.52033528835615, 9.24889339101234, 9.97745149366852, 10.7060095963247, 11.4345676989809, 2.69187046710664, 3.42042856976283, 12.1631258016371, 12.5274048529652 };
Assert.IsTrue(inlierIndices.IsEqual(expectedInliers));
Assert.IsTrue(ransacOutput.IsEqual(expectedOutput, 1e-10));
}
/// <summary>
/// Creates a new RANSAC homography estimator.
/// </summary>
///
/// <param name="threshold">Inlier threshold.</param>
/// <param name="probability">Inlier probability.</param>
///
public RansacFundamentalEstimator(double threshold, double probability)
{
// Create a new RANSAC with the selected threshold
ransac = new RANSAC<float[,]>(8, threshold, probability);
// Set RANSAC functions
ransac.Fitting = fundamental;
ransac.Distances = distance;
}
/// <summary>
/// Creates a new RANSAC 2D circle estimator.
/// </summary>
///
/// <param name="threshold">Inlier threshold.</param>
/// <param name="probability">Inlier probability.</param>
///
public RansacCircle(double threshold, double probability)
{
ransac = new RANSAC<Circle>(3, threshold, probability);
ransac.Fitting = define;
ransac.Distances = distance;
ransac.Degenerate = degenerate;
}
public void new_api_test()
{
#region doc_learn
// Fix the random number generator
Accord.Math.Random.Generator.Seed = 0;
double[,] data = // This is the same data used in the RANSAC sample app
{
{ 1.0, 0.79 }, { 3, 2.18 }, { 5, 5.99 }, { 7.0, 7.65 },
{ 9.0, 9.55 }, { 11, 11.89 }, { 13, 13.73 }, { 15.0, 14.77 },
{ 17.0, 18.00 }, { 1.2, 1.45 }, { 1.5, 1.18 }, { 1.8, 1.92 },
{ 2.1, 1.47 }, { 2.4, 2.41 }, { 2.7, 2.35 }, { 3.0, 3.41 },
{ 3.3, 3.78 }, { 3.6, 3.21 }, { 3.9, 4.76 }, { 4.2, 5.03 },
{ 4.5, 4.19 }, { 4.8, 3.81 }, { 5.1, 6.07 }, { 5.4, 5.74 },
{ 5.7, 6.39 }, { 6, 6.11 }, { 6.3, 6.86 }, { 6.6, 6.35 },
{ 6.9, 7.9 }, { 7.2, 8.04 }, { 7.5, 8.48 }, { 7.8, 8.07 },
{ 8.1, 8.22 }, { 8.4, 8.41 }, { 8.7, 9.4 }, { 9, 8.8 },
{ 9.3, 8.44 }, { 9.6, 9.32 }, { 9.9, 9.18 }, { 10.2, 9.86 },
{ 10.5, 10.16 }, { 10.8, 10.28 }, { 11.1, 11.07 }, { 11.4, 11.66 },
{ 11.7, 11.13 }, { 12, 11.55 }, { 12.3, 12.62 }, { 12.6, 12.27 },
{ 12.9, 12.33 }, { 13.2, 12.37 }, { 13.5, 12.75 }, { 13.8, 14.44 },
{ 14.1, 14.71 }, { 14.4, 13.72 }, { 14.7, 14.54 }, { 15, 14.67 },
{ 15.3, 16.04 }, { 15.6, 15.21 }, { 1, 3.9 }, { 2, 11.5 },
{ 3.0, 13.0 }, { 4, 0.9 }, { 5, 5.5 }, { 6, 16.2 },
{ 7.0, 0.8 }, { 8, 9.4 }, { 9, 9.5 }, { 10, 17.5 },
{ 11.0, 6.3 }, { 12, 12.6 }, { 13, 1.5 }, { 14, 1.5 },
{ 2.0, 10 }, { 3, 9 }, { 15, 2 }, { 15.5, 1.2 },
};
// First, fit simple linear regression directly for comparison reasons.
double[] x = data.GetColumn(0); // Extract the independent variable
double[] y = data.GetColumn(1); // Extract the dependent variable
// Use Ordinary Least Squares to learn the regression
OrdinaryLeastSquares ols = new OrdinaryLeastSquares();
// Estimate a line passing through the (x, y) points
SimpleLinearRegression regression = ols.Learn(x, y);
// Now, compute the values predicted by the
// regression for the original input points
double[] commonOutput = regression.Transform(x);
// Now, fit simple linear regression using RANSAC
int maxTrials = 1000;
int minSamples = 20;
double probability = 0.950;
double errorThreshold = 1000;
// Create a RANSAC algorithm to fit a simple linear regression
var ransac = new RANSAC<SimpleLinearRegression>(minSamples)
{
Probability = probability,
Threshold = errorThreshold,
MaxEvaluations = maxTrials,
// Define a fitting function
Fitting = (int[] sample) =>
{
// Build a Simple Linear Regression model
return new OrdinaryLeastSquares()
.Learn(x.Get(sample), y.Get(sample));
},
// Define a inlier detector function
Distances = (SimpleLinearRegression r, double threshold) =>
{
var inliers = new List<int>();
for (int i = 0; i < x.Length; i++)
{
// Compute error for each point
double error = r.Transform(x[i]) - y[i];
// If the square error is low enough,
if (error * error < threshold)
inliers.Add(i); // the point is considered an inlier.
}
return inliers.ToArray();
}
};
// Now that the RANSAC hyperparameters have been specified, we can
// compute another regression model using the RANSAC algorithm:
int[] inlierIndices;
SimpleLinearRegression robustRegression = ransac.Compute(data.Rows(), out inlierIndices);
// Compute the output of the model fitted by RANSAC
double[] ransacOutput = robustRegression.Transform(x);
#endregion
Assert.AreEqual(ransac.TrialsNeeded, 0);
Assert.AreEqual(ransac.TrialsPerformed, 1);
string a = inlierIndices.ToCSharp();
string b = ransacOutput.ToCSharp();
int[] expectedInliers = new int[] { 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75 };
double[] expectedOutput = new double[] { 1.96331236445045, 3.42042856976283, 4.87754477507521, 6.33466098038758, 7.79177718569996, 9.24889339101234, 10.7060095963247, 12.1631258016371, 13.6202420069495, 2.10902398498169, 2.32759141577855, 2.5461588465754, 2.76472627737226, 2.98329370816912, 3.20186113896597, 3.42042856976283, 3.63899600055969, 3.85756343135654, 4.0761308621534, 4.29469829295026, 4.51326572374711, 4.73183315454397, 4.95040058534082, 5.16896801613768, 5.38753544693454, 5.6061028777314, 5.82467030852825, 6.04323773932511, 6.26180517012196, 6.48037260091882, 6.69894003171568, 6.91750746251253, 7.13607489330939, 7.35464232410625, 7.5732097549031, 7.79177718569996, 8.01034461649682, 8.22891204729367, 8.44747947809053, 8.66604690888738, 8.88461433968424, 9.1031817704811, 9.32174920127795, 9.54031663207481, 9.75888406287167, 9.97745149366852, 10.1960189244654, 10.4145863552622, 10.6331537860591, 10.8517212168559, 11.0702886476528, 11.2888560784497, 11.5074235092465, 11.7259909400434, 11.9445583708402, 12.1631258016371, 12.3816932324339, 12.6002606632308, 1.96331236445045, 2.69187046710664, 3.42042856976283, 4.14898667241902, 4.87754477507521, 5.6061028777314, 6.33466098038758, 7.06321908304377, 7.79177718569996, 8.52033528835615, 9.24889339101234, 9.97745149366852, 10.7060095963247, 11.4345676989809, 2.69187046710664, 3.42042856976283, 12.1631258016371, 12.5274048529652 };
Assert.IsTrue(inlierIndices.IsEqual(expectedInliers));
Assert.IsTrue(ransacOutput.IsEqual(expectedOutput, 1e-10));
}
private void btnCompute_Click(object sender, EventArgs e)
{
DataTable dataTable = dgvAnalysisSource.DataSource as DataTable;
if (dataTable == null)
{
return;
}
// Gather the available data
double[][] data = dataTable.ToArray();
// First, fit simple linear regression directly for comparison reasons.
double[] x = data.GetColumn(0); // Extract the independent variable
double[] y = data.GetColumn(1); // Extract the dependent variable
// Create a simple linear regression
var regression = new SimpleLinearRegression();
// Estimate a line passing through the (x, y) points
double sumOfSquaredErrors = regression.Regress(x, y);
// Now, compute the values predicted by the
// regression for the original input points
double[] commonOutput = regression.Compute(x);
// Now, fit simple linear regression using RANSAC
int maxTrials = (int)numMaxTrials.Value;
int minSamples = (int)numSamples.Value;
double probability = (double)numProbability.Value;
double errorThreshold = (double)numThreshold.Value;
// Create a RANSAC algorithm to fit a simple linear regression
var ransac = new RANSAC <SimpleLinearRegression>(minSamples)
{
Probability = probability,
Threshold = errorThreshold,
MaxEvaluations = maxTrials,
// Define a fitting function
Fitting = delegate(int[] sample)
{
// Retrieve the training data
double[] inputs = x.Submatrix(sample);
double[] outputs = y.Submatrix(sample);
// Build a Simple Linear Regression model
var r = new SimpleLinearRegression();
r.Regress(inputs, outputs);
return(r);
},
// Define a check for degenerate samples
Degenerate = delegate(int[] sample)
{
// In this case, we will not be performing such checks.
return(false);
},
// Define a inlier detector function
Distances = delegate(SimpleLinearRegression r, double threshold)
{
List <int> inliers = new List <int>();
for (int i = 0; i < x.Length; i++)
{
// Compute error for each point
double error = r.Compute(x[i]) - y[i];
// If the squared error is below the given threshold,
// the point is considered to be an inlier.
if (error * error < threshold)
{
inliers.Add(i);
}
}
return(inliers.ToArray());
}
};
// Now that the RANSAC hyperparameters have been specified, we can
// compute another regression model using the RANSAC algorithm:
int[] inlierIndices;
SimpleLinearRegression robustRegression = ransac.Compute(data.Length, out inlierIndices);
if (robustRegression == null)
{
lbStatus.Text = "RANSAC failed. Please try again after adjusting its parameters.";
return; // the RANSAC algorithm did not find any inliers and no model was created
}
// Compute the output of the model fitted by RANSAC
double[] ransacOutput = robustRegression.Compute(x);
// Create scatter plot comparing the outputs from the standard
// linear regression and the RANSAC-fitted linear regression.
CreateScatterplot(graphInput, x, y, commonOutput, ransacOutput,
x.Submatrix(inlierIndices), y.Submatrix(inlierIndices));
lbStatus.Text = "Regression created! Please compare the RANSAC "
+ "regression (blue) with the simple regression (in red).";
}
/// <summary>
/// Creates a new RANSAC line estimator.
/// </summary>
///
/// <param name="threshold">Inlier threshold.</param>
/// <param name="probability">Inlier probability.</param>
///
public RansacLine(double threshold, double probability)
{
// Create a new RANSAC with the selected threshold
ransac = new RANSAC<Line>(2, threshold, probability);
// Set RANSAC functions
ransac.Fitting = defineline;
ransac.Distances = distance;
ransac.Degenerate = degenerate;
}
private void btnSampleRunAnalysis_Click(object sender, EventArgs e)
{
DataTable dataTable = dgvAnalysisSource.DataSource as DataTable;
if (dataTable == null) return;
// Gather the available data
double[][] data = dataTable.ToArray();
// First, fit simple linear regression directly for comparison reasons.
double[] x = data.GetColumn(0); // Extract the independent variable
double[] y = data.GetColumn(1); // Extract the dependent variable
// Create a simple linear regression
SimpleLinearRegression slr = new SimpleLinearRegression();
slr.Regress(x, y);
// Compute the simple linear regression output
double[] slrY = slr.Compute(x);
// Now, fit simple linear regression using RANSAC
int maxTrials = (int)numMaxTrials.Value;
int minSamples = (int)numSamples.Value;
double probability = (double)numProbability.Value;
double errorThreshold = (double)numThreshold.Value;
// Create a RANSAC algorithm to fit a simple linear regression
var ransac = new RANSAC<SimpleLinearRegression>(minSamples);
ransac.Probability = probability;
ransac.Threshold = errorThreshold;
ransac.MaxEvaluations = maxTrials;
// Set the RANSAC functions to evaluate and test the model
ransac.Fitting = // Define a fitting function
delegate(int[] sample)
{
// Retrieve the training data
double[] inputs = x.Submatrix(sample);
double[] outputs = y.Submatrix(sample);
// Build a Simple Linear Regression model
var r = new SimpleLinearRegression();
r.Regress(inputs, outputs);
return r;
};
ransac.Degenerate = // Define a check for degenerate samples
delegate(int[] sample)
{
// In this case, we will not be performing such checks.
return false;
};
ransac.Distances = // Define a inlier detector function
delegate(SimpleLinearRegression r, double threshold)
{
List<int> inliers = new List<int>();
for (int i = 0; i < x.Length; i++)
{
// Compute error for each point
double error = r.Compute(x[i]) - y[i];
// If the squared error is below the given threshold,
// the point is considered to be an inlier.
if (error * error < threshold)
inliers.Add(i);
}
return inliers.ToArray();
};
// Finally, try to fit the regression model using RANSAC
int[] idx; SimpleLinearRegression rlr = ransac.Compute(data.Length, out idx);
// Check if RANSAC was able to build a consistent model
if (rlr == null)
{
return; // RANSAC was unsucessful, just return.
}
else
{
// Compute the output of the model fitted by RANSAC
double[] rlrY = rlr.Compute(x);
// Create scatterplot comparing the outputs from the standard
// linear regression and the RANSAC-fitted linear regression.
CreateScatterplot(graphInput, x, y, slrY, rlrY, x.Submatrix(idx), y.Submatrix(idx));
}
}
/// <summary>
/// Fit line with Ransac algorithm
/// </summary>
/// <param name="xs">x components of input points</param>
/// <param name="ys">y components of input points</param>
/// <param name="errorThreshold"></param>
/// <param name="maxTrials"></param>
/// <param name="ignoreFraction"></param>
/// <param name="probability"></param>
/// <param name="xsUsed">x components of the inlier points</param>
/// <param name="ysUsed">y components of the inlier points</param>
/// <returns>
/// Fitted line with the farthest end points from inlier points
/// </returns>
public static Line RansacFitLine(double[] xs, double[] ys, double errorThreshold, int maxTrials, double ignoreFraction, double probability, out IEnumerable <double> xsUsed, out IEnumerable <double> ysUsed)
{
var xDeviation = xs.StandardDeviation();
var yDeviation = ys.StandardDeviation();
var isVertical = yDeviation > xDeviation;
if (isVertical)
{
Swap(ref xs, ref ys);
}
// Now, fit simple linear regression using RANSAC
int minSamples = (int)(xs.Length * (1 - ignoreFraction));
// Create a RANSAC algorithm to fit a simple linear regression
var ransac = new RANSAC <SimpleLinearRegression>(minSamples)
{
Probability = probability,
Threshold = errorThreshold,
MaxEvaluations = maxTrials,
// Define a fitting function
Fitting = (int[] sample) =>
{
// Build a Simple Linear Regression model
return(new OrdinaryLeastSquares()
.Learn(xs.Get(sample), ys.Get(sample)));
},
// Define a inlier detector function
Distances = (SimpleLinearRegression r, double threshold) =>
{
var inliers = new List <int>();
for (int i = 0; i < xs.Length; i++)
{
// Compute error for each point
double error = r.Transform(xs[i]) - ys[i];
// If the square error is low enough,
if (error * error < threshold)
{
inliers.Add(i); // the point is considered an inlier.
}
}
return(inliers.ToArray());
}
};
// Now that the RANSAC hyperparameters have been specified, we can
// compute another regression model using the RANSAC algorithm:
int[] inlierIndices;
SimpleLinearRegression robustRegression = ransac.Compute(xs.Length, out inlierIndices);
var weight = robustRegression.Slope;
var bias = robustRegression.Intercept;
var xStart = xs.Min();
var xEnd = xs.Max();
var yStart = xStart * weight + bias;
var yEnd = xEnd * weight + bias;
xsUsed = xs.SelectByIndices(inlierIndices);
ysUsed = ys.SelectByIndices(inlierIndices);
if (isVertical)
{
Swap(ref xsUsed, ref ysUsed);
}
return(new Line(isVertical ? yStart : xStart, isVertical ? xStart : yStart, isVertical ? yEnd : xEnd, isVertical ? xEnd : yEnd));
}
private void btnSampleRunAnalysis_Click(object sender, EventArgs e)
{
DataTable dataTable = dgvAnalysisSource.DataSource as DataTable;
if (dataTable == null)
{
return;
}
// Gather the available data
double[][] data = dataTable.ToArray();
// First, fit simple linear regression directly for comparison reasons.
double[] x = data.GetColumn(0); // Extract the independent variable
double[] y = data.GetColumn(1); // Extract the dependent variable
// Create a simple linear regression
SimpleLinearRegression slr = new SimpleLinearRegression();
slr.Regress(x, y);
// Compute the simple linear regression output
double[] slrY = slr.Compute(x);
// Now, fit simple linear regression using RANSAC
int maxTrials = (int)numMaxTrials.Value;
int minSamples = (int)numSamples.Value;
double probability = (double)numProbability.Value;
double errorThreshold = (double)numThreshold.Value;
// Create a RANSAC algorithm to fit a simple linear regression
var ransac = new RANSAC <SimpleLinearRegression>(minSamples);
ransac.Probability = probability;
ransac.Threshold = errorThreshold;
ransac.MaxEvaluations = maxTrials;
// Set the RANSAC functions to evaluate and test the model
ransac.Fitting = // Define a fitting function
delegate(int[] sample)
{
// Retrieve the training data
double[] inputs = x.Submatrix(sample);
double[] outputs = y.Submatrix(sample);
// Build a Simple Linear Regression model
var r = new SimpleLinearRegression();
r.Regress(inputs, outputs);
return(r);
};
ransac.Degenerate = // Define a check for degenerate samples
delegate(int[] sample)
{
// In this case, we will not be performing such checks.
return(false);
};
ransac.Distances = // Define a inlier detector function
delegate(SimpleLinearRegression r, double threshold)
{
List <int> inliers = new List <int>();
for (int i = 0; i < x.Length; i++)
{
// Compute error for each point
double error = r.Compute(x[i]) - y[i];
// If the squared error is below the given threshold,
// the point is considered to be an inlier.
if (error * error < threshold)
{
inliers.Add(i);
}
}
return(inliers.ToArray());
};
// Finally, try to fit the regression model using RANSAC
int[] idx; SimpleLinearRegression rlr = ransac.Compute(data.Length, out idx);
// Check if RANSAC was able to build a consistent model
if (rlr == null)
{
return; // RANSAC was unsucessful, just return.
}
else
{
// Compute the output of the model fitted by RANSAC
double[] rlrY = rlr.Compute(x);
// Create scatterplot comparing the outputs from the standard
// linear regression and the RANSAC-fitted linear regression.
CreateScatterplot(graphInput, x, y, slrY, rlrY, x.Submatrix(idx), y.Submatrix(idx));
}
}
public void zero_inliers_test()
{
// Fix the random number generator
Accord.Math.Random.Generator.Seed = 0;
double[,] data = // This is the same data used in the RANSAC sample app
{
{ 1.0, 0.79 }, { 3, 2.18 }, { 5, 5.99 }, { 7.0, 7.65 },
{ 9.0, 9.55 }, { 11, 11.89 }, { 13, 13.73 }, { 15.0, 14.77 },
{ 17.0, 18.00 }, { 1.2, 1.45 }, { 1.5, 1.18 }, { 1.8, 1.92 },
{ 2.1, 1.47 }, { 2.4, 2.41 }, { 2.7, 2.35 }, { 3.0, 3.41 },
{ 3.3, 3.78 }, { 3.6, 3.21 }, { 3.9, 4.76 }, { 4.2, 5.03 },
{ 4.5, 4.19 }, { 4.8, 3.81 }, { 5.1, 6.07 }, { 5.4, 5.74 },
{ 5.7, 6.39 }, { 6, 6.11 }, { 6.3, 6.86 }, { 6.6, 6.35 },
{ 6.9, 7.9 }, { 7.2, 8.04 }, { 7.5, 8.48 }, { 7.8, 8.07 },
{ 8.1, 8.22 }, { 8.4, 8.41 }, { 8.7, 9.4 }, { 9, 8.8 },
{ 9.3, 8.44 }, { 9.6, 9.32 }, { 9.9, 9.18 }, { 10.2, 9.86 },
{ 10.5, 10.16 }, { 10.8, 10.28 }, { 11.1, 11.07 }, { 11.4, 11.66 },
{ 11.7, 11.13 }, { 12, 11.55 }, { 12.3, 12.62 }, { 12.6, 12.27 },
{ 12.9, 12.33 }, { 13.2, 12.37 }, { 13.5, 12.75 }, { 13.8, 14.44 },
{ 14.1, 14.71 }, { 14.4, 13.72 }, { 14.7, 14.54 }, { 15, 14.67 },
{ 15.3, 16.04 }, { 15.6, 15.21 }, { 1, 3.9 }, { 2, 11.5 },
{ 3.0, 13.0 }, { 4, 0.9 }, { 5, 5.5 }, { 6, 16.2 },
{ 7.0, 0.8 }, { 8, 9.4 }, { 9, 9.5 }, { 10, 17.5 },
{ 11.0, 6.3 }, { 12, 12.6 }, { 13, 1.5 }, { 14, 1.5 },
{ 2.0, 10 }, { 3, 9 }, { 15, 2 }, { 15.5, 1.2 },
};
// First, fit simple linear regression directly for comparison reasons.
double[] x = data.GetColumn(0); // Extract the independent variable
double[] y = data.GetColumn(1); // Extract the dependent variable
// Create a simple linear regression
var regression = new SimpleLinearRegression();
// Estimate a line passing through the (x, y) points
double sumOfSquaredErrors = regression.Regress(x, y);
// Now, compute the values predicted by the
// regression for the original input points
double[] commonOutput = regression.Compute(x);
// Now, fit simple linear regression using RANSAC
int maxTrials = 1000;
int minSamples = 20;
double probability = 0.950;
double errorThreshold = 1000;
int count = 0;
// Create a RANSAC algorithm to fit a simple linear regression
var ransac = new RANSAC<SimpleLinearRegression>(minSamples)
{
Probability = probability,
Threshold = errorThreshold,
MaxEvaluations = maxTrials,
// Define a fitting function
Fitting = delegate(int[] sample)
{
// Retrieve the training data
double[] inputs = x.Submatrix(sample);
double[] outputs = y.Submatrix(sample);
// Build a Simple Linear Regression model
var r = new SimpleLinearRegression();
r.Regress(inputs, outputs);
return r;
},
// Define a check for degenerate samples
Degenerate = delegate(int[] sample)
{
// In this case, we will not be performing such checks.
return false;
},
// Define a inlier detector function
Distances = delegate(SimpleLinearRegression r, double threshold)
{
count++;
List<int> inliers = new List<int>();
// Generate 0 inliers twice, then proceed as normal
if (count > 2)
{
for (int i = 0; i < x.Length; i++)
{
// Compute error for each point
double error = r.Compute(x[i]) - y[i];
// If the squared error is below the given threshold,
// the point is considered to be an inlier.
if (error * error < threshold)
inliers.Add(i);
}
}
return inliers.ToArray();
}
};
// Now that the RANSAC hyperparameters have been specified, we can
// compute another regression model using the RANSAC algorithm:
int[] inlierIndices;
SimpleLinearRegression robustRegression = ransac.Compute(data.Rows(), out inlierIndices);
// Compute the output of the model fitted by RANSAC
double[] ransacOutput = robustRegression.Compute(x);
Assert.AreEqual(ransac.TrialsNeeded, 0);
Assert.AreEqual(ransac.TrialsPerformed, 3);
string a = inlierIndices.ToCSharp();
string b = ransacOutput.ToCSharp();
int[] expectedInliers = new int[] { 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75 };
double[] expectedOutput = new double[] { 4.62124895918799, 5.37525473445784, 6.12926050972769, 6.88326628499754, 7.63727206026739, 8.39127783553724, 9.14528361080709, 9.89928938607694, 10.6532951613468, 4.69664953671498, 4.80975040300545, 4.92285126929593, 5.03595213558641, 5.14905300187689, 5.26215386816736, 5.37525473445784, 5.48835560074832, 5.6014564670388, 5.71455733332927, 5.82765819961975, 5.94075906591023, 6.05385993220071, 6.16696079849118, 6.28006166478166, 6.39316253107214, 6.50626339736262, 6.61936426365309, 6.73246512994357, 6.84556599623405, 6.95866686252453, 7.071767728815, 7.18486859510548, 7.29796946139596, 7.41107032768644, 7.52417119397691, 7.63727206026739, 7.75037292655787, 7.86347379284835, 7.97657465913882, 8.0896755254293, 8.20277639171978, 8.31587725801026, 8.42897812430073, 8.54207899059121, 8.65517985688169, 8.76828072317216, 8.88138158946264, 8.99448245575312, 9.1075833220436, 9.22068418833408, 9.33378505462455, 9.44688592091503, 9.55998678720551, 9.67308765349599, 9.78618851978646, 9.89928938607694, 10.0123902523674, 10.1254911186579, 4.62124895918799, 4.99825184682292, 5.37525473445784, 5.75225762209277, 6.12926050972769, 6.50626339736262, 6.88326628499754, 7.26026917263247, 7.63727206026739, 8.01427494790232, 8.39127783553724, 8.76828072317216, 9.14528361080709, 9.52228649844202, 4.99825184682292, 5.37525473445784, 9.89928938607694, 10.0877908298944 };
Assert.IsTrue(inlierIndices.IsEqual(expectedInliers));
Assert.IsTrue(ransacOutput.IsEqual(expectedOutput, 1e-10));
}
/// <summary>
/// Creates a new RANSAC homography estimator.
/// </summary>
///
/// <param name="threshold">Inlier threshold.</param>
/// <param name="probability">Inlier probability.</param>
///
public RansacHomographyEstimator(double threshold, double probability)
{
// Create a new RANSAC with the selected threshold
ransac = new RANSAC<MatrixH>(4, threshold, probability);
// Set RANSAC functions
ransac.Fitting = homography;
ransac.Degenerate = degenerate;
ransac.Distances = distance;
}
private void btnCompute_Click(object sender, EventArgs e)
{
DataTable dataTable = dgvAnalysisSource.DataSource as DataTable;
if (dataTable == null)
return;
// Gather the available data
double[][] data = dataTable.ToArray();
// First, fit simple linear regression directly for comparison reasons.
double[] x = data.GetColumn(0); // Extract the independent variable
double[] y = data.GetColumn(1); // Extract the dependent variable
// Create a simple linear regression
var regression = new SimpleLinearRegression();
// Estimate a line passing through the (x, y) points
double sumOfSquaredErrors = regression.Regress(x, y);
// Now, compute the values predicted by the
// regression for the original input points
double[] commonOutput = regression.Compute(x);
// Now, fit simple linear regression using RANSAC
int maxTrials = (int)numMaxTrials.Value;
int minSamples = (int)numSamples.Value;
double probability = (double)numProbability.Value;
double errorThreshold = (double)numThreshold.Value;
// Create a RANSAC algorithm to fit a simple linear regression
var ransac = new RANSAC<SimpleLinearRegression>(minSamples)
{
Probability = probability,
Threshold = errorThreshold,
MaxEvaluations = maxTrials,
// Define a fitting function
Fitting = delegate(int[] sample)
{
// Retrieve the training data
double[] inputs = x.Submatrix(sample);
double[] outputs = y.Submatrix(sample);
// Build a Simple Linear Regression model
var r = new SimpleLinearRegression();
r.Regress(inputs, outputs);
return r;
},
// Define a check for degenerate samples
Degenerate = delegate(int[] sample)
{
// In this case, we will not be performing such checks.
return false;
},
// Define a inlier detector function
Distances = delegate(SimpleLinearRegression r, double threshold)
{
List<int> inliers = new List<int>();
for (int i = 0; i < x.Length; i++)
{
// Compute error for each point
double error = r.Compute(x[i]) - y[i];
// If the squared error is below the given threshold,
// the point is considered to be an inlier.
if (error * error < threshold)
inliers.Add(i);
}
return inliers.ToArray();
}
};
// Now that the RANSAC hyperparameters have been specified, we can
// compute another regression model using the RANSAC algorithm:
int[] inlierIndices;
SimpleLinearRegression robustRegression = ransac.Compute(data.Length, out inlierIndices);
if (robustRegression == null)
{
lbStatus.Text = "RANSAC failed. Please try again after adjusting its parameters.";
return; // the RANSAC algorithm did not find any inliers and no model was created
}
// Compute the output of the model fitted by RANSAC
double[] ransacOutput = robustRegression.Compute(x);
// Create scatter plot comparing the outputs from the standard
// linear regression and the RANSAC-fitted linear regression.
CreateScatterplot(graphInput, x, y, commonOutput, ransacOutput,
x.Submatrix(inlierIndices), y.Submatrix(inlierIndices));
lbStatus.Text = "Regression created! Please compare the RANSAC "
+ "regression (blue) with the simple regression (in red).";
}
public void TestMatricesFromNoisedPoints()
{
List <Image <Arthmetic, double> > ptsReal = new List <Image <Arthmetic, double> >();
List <PointF> pts1 = new List <PointF>();
List <PointF> pts2 = new List <PointF>();
List <PointF> pts3 = new List <PointF>();
List <PointF> pts1Ref = new List <PointF>();
List <PointF> pts2Ref = new List <PointF>();
List <PointF> pts3Ref = new List <PointF>();
Random rand = new Random(1003);
double stddev = 3;
int pointsCount = 100;
for (int i = 0; i < pointsCount; ++i)
{
var real = new Image <Arthmetic, double>(new double[, , ] {
{ { rand.Next(100, 200) } },
{ { rand.Next(100, 200) } },
{ { rand.Next(50, 100) } },
{ { 1 } },
});
ptsReal.Add(real);
var i1 = P1.Multiply(real).ToPointF();
pts1Ref.Add(i1);
i1 = new PointF(i1.X + Noise(stddev, rand), i1.Y + Noise(stddev, rand));
pts1.Add(i1);
var i2 = P2.Multiply(real).ToPointF();
pts2Ref.Add(i2);
i2 = new PointF(i2.X + Noise(stddev, rand), i2.Y + Noise(stddev, rand));
pts2.Add(i2);
var i3 = P3.Multiply(real).ToPointF();
pts3Ref.Add(i3);
i3 = new PointF(i3.X + Noise(stddev, rand), i3.Y + Noise(stddev, rand));
pts3.Add(i3);
}
double rangeLx = pts1.Max((x) => x.X) - pts1.Min((x) => x.X);
double rangeLy = pts1.Max((x) => x.Y) - pts1.Min((x) => x.Y);
double rangeRx = pts2.Max((x) => x.X) - pts2.Min((x) => x.X);
double rangeRy = pts2.Max((x) => x.Y) - pts2.Min((x) => x.Y);
var pts1_n = new List <PointF>(pts1);
var pts2_n = new List <PointF>(pts2);
var pts3_n = new List <PointF>(pts3);
FindTransformation.NormalizePoints2d(pts1_n, out Image <Arthmetic, double> N1);
FindTransformation.NormalizePoints2d(pts2_n, out Image <Arthmetic, double> N2);
FindTransformation.NormalizePoints2d(pts3_n, out Image <Arthmetic, double> N3);
var F = ComputeMatrix.F(new VectorOfPointF(pts1_n.ToArray()), new VectorOfPointF(pts2_n.ToArray()));
var F23 = ComputeMatrix.F(new VectorOfPointF(pts2_n.ToArray()), new VectorOfPointF(pts3_n.ToArray()));
// F is normalized - lets denormalize it
F = N2.T().Multiply(F).Multiply(N1);
F23 = N3.T().Multiply(F23).Multiply(N2);
var E = ComputeMatrix.E(F, K);
var E23 = ComputeMatrix.E(F23, K);
var svd = new Svd(E);
var svd23 = new Svd(E23);
FindTransformation.DecomposeToRTAndTriangulate(pts1, pts2, K, E, out var RR, out var TT, out Image <Arthmetic, double> estReal_);
FindTransformation.DecomposeToRTAndTriangulate(pts2, pts3, K, E23, out var RR23, out var TT23, out Image <Arthmetic, double> estReal23_);
var rr0 = RotationConverter.MatrixToEulerXYZ(R12);
var rr1 = RotationConverter.MatrixToEulerXYZ(RR);
double idealScale = T23.Norm / T12.Norm;
double idealScale2 = T12.Norm / T23.Norm;
var tt0 = T12.Mul(1 / T12.Norm);
var tt1 = TT.Mul(1 / TT.Norm).Mul(T12[0, 0] * TT[0, 0] < 0 ? -1 : 1);
var tt1_23 = TT23.Mul(1 / TT23.Norm).Mul(T23[0, 0] * TT23[0, 0] < 0 ? -1 : 1);
FindTransformation.TriangulateChieral(pts1, pts2, K, RR, tt1, out var estReal12);
FindTransformation.TriangulateChieral(pts2, pts3, K, RR23, tt1_23, out var estReal23);
RansacScaleEstimation ransacModel = new RansacScaleEstimation(estReal12, estReal23, RR, ComputeMatrix.Center(tt1, RR));
int sampleSize = (int)(0.1 * pointsCount);
int minGoodPoints = (int)(0.2 * pointsCount);
int maxIterations = 1000;
double meanRefPointSize = ScaleBy3dPointsMatch.GetMeanSize(estReal12);
double threshold = meanRefPointSize * meanRefPointSize * 0.1;
var result = RANSAC.ProcessMostInliers(ransacModel, maxIterations, sampleSize, minGoodPoints, threshold, 1.0);
double scale = (double)result.BestModel;
var estRealRef23To12 = ScaleBy3dPointsMatch.TransfromBack3dPoints(RR, tt1, estReal23, scale);
double simpleScaleMean = 0.0;
for (int i = 0; i < pointsCount; ++i)
{
var p12 = new Image <Arthmetic, double>(new double[, , ] {
{ { estReal12[1, i] } },
{ { estReal12[2, i] } },
{ { estReal12[3, i] } },
});
var p23 = new Image <Arthmetic, double>(new double[, , ] {
{ { estReal23[0, i] } },
{ { estReal23[1, i] } },
{ { estReal23[2, i] } },
});
double n1 = p12.Norm;
double n2 = p23.Norm;
double simpleScale = n2 / n1;
simpleScaleMean += simpleScale;
}
simpleScaleMean /= pointsCount;
double simpleScaleMean2 = 1 / simpleScaleMean;
// TODO: compute below only on inliers
Image <Arthmetic, double> inliersOnly12 = new Image <Arthmetic, double>(result.Inliers.Count, 4);
Image <Arthmetic, double> inliersOnly12Ref = new Image <Arthmetic, double>(result.Inliers.Count, 4);
Image <Arthmetic, double> inliersOnly23 = new Image <Arthmetic, double>(result.Inliers.Count, 4);
for (int i = 0; i < result.Inliers.Count; ++i)
{
int k = result.Inliers[i];
for (int j = 0; j < 4; ++j)
{
inliersOnly12[j, i] = estReal12[j, k];
inliersOnly12Ref[j, i] = ptsReal[k][j, 0];
inliersOnly23[j, i] = estRealRef23To12[j, k];
}
}
var ptsRealM = Utils.Matrixify(ptsReal);
Errors.TraingulationError(ptsRealM, estReal12, out double mean1x, out double median1x, out List <double> errors1x);
Errors.TraingulationError(ptsRealM, estRealRef23To12, out double mean1z, out double median1z, out List <double> errors1z);
Errors.TraingulationError(inliersOnly12Ref, inliersOnly12, out double mean_in1, out double median_in1, out List <double> errors_in1);
Errors.TraingulationError(inliersOnly12Ref, inliersOnly23, out double mean_in2, out double median_in2, out List <double> errors_in2);
Errors.TraingulationError(inliersOnly12, inliersOnly23, out double mean_in3, out double median_in3, out List <double> errors_in3);
var ptsReal23M = ForwardProject3dPoints(ptsRealM, R12, C2);
Errors.TraingulationError(ptsReal23M, estReal23, out double mean1h, out double median1h, out List <double> errors1h);
var estC2 = ComputeMatrix.Center(tt1, RR);
var ptsEst23M = ForwardProject3dPoints(estReal12, RR, estC2);
Errors.TraingulationError(ptsEst23M, estReal23, out double mean1t, out double median1t, out List <double> errors1t);
int dummy = 0;
//Errors.TraingulationError(ptsReal, estReal, out double mean1, out double median1, out List<double> errors1);
//Errors.ReprojectionError(estReal, pts2, K, RR, tt1, out double mean_r1a, out double median_r1a, out List<double> _1);
//Errors.ReprojectionError(estReal, pts2Ref, K, RR, tt1, out double mean_r1b, out double median_r1b, out List<double> _2);
//Errors.ReprojectionError(estReal, pts2Ref, K, R12, tt0, out double mean_r1c, out double median_r1c, out List<double> _3);
//Errors.ReprojectionError(Errors.Matrixify(ptsReal), pts2Ref, K, RR, tt1, out double mean_r1e, out double median_r1e, out List<double> _5);
var H1 = FindTransformation.EstimateHomography(pts1, pts2, K);
var H2 = FindTransformation.EstimateHomography(pts1Ref, pts2Ref, K);
var hrr1 = RotationConverter.MatrixToEulerXYZ(H1);
var hrr2 = RotationConverter.MatrixToEulerXYZ(H2);
//var zeroT = new Image<Arthmetic, double>(1, 3);
//var H3 = RotationConverter.EulerXYZToMatrix(hrr1);
//var hrr3 = RotationConverter.MatrixToEulerXYZ(H1);
//var svdH = new Svd(H1);
bool isRotation = FindTransformation.IsPureRotation(H1);
int dummy2 = 0;
//Errors.ReprojectionError2d(pts1Ref, pts2Ref, K, H2, out double mean_h2, out double median_h2, out var err_h2);
//Errors.ReprojectionError2d(pts1, pts2, K, H1, out double mean_h1, out double median_h1, out var err_h1);
//Errors.ReprojectionError2d(pts1, pts2, K, H3, out double mean_h3, out double median_h3, out var err_h3);
//Errors.ReprojectionError2dWithT(pts1, pts2, K, H1, zeroT, out double scale1, out double mean_h1a, out double median_h1a, out var err_h1a);
//Errors.ReprojectionError2dWithT(pts1, pts2, K, H3, zeroT, out double scale1x, out double mean_h1ax, out double median_h1ax, out var err_h1ax);
//// Errors.ReprojectionError2dWithT(pts1, pts2, K, R12, tt0, out double scale2, out double mean_h1b, out double median_h1b, out var err_h1b);
//Errors.ReprojectionError2dWithT(pts1, pts2, K, RR, tt1, out double scale3, out double mean_h1c, out double median_h1c, out var err_h1c);
//Errors.ReprojectionError2dWithT(pts1, pts2, K, R12, tt1, out double scale5, out double mean_h1c1, out double median_h1c1, out var err_h1c1);
//Errors.ReprojectionError2dWithT(pts1Ref, pts2Ref, K, R12, tt0, out double scale6, out double mean_h1c2, out double median_h1c2, out var err_h1c2);
//Errors.ReprojectionError2dWithT(pts1, pts2, K, H1, tt1, out double scale4, out double mean_h1d, out double median_h1d, out var err_h1d);
//var KK = EstimateCameraFromImagePair.K(F, 600, 500);
//var EE = ComputeMatrix.E(F, KK);
//var svd2 = new Svd(EE);
//FindTransformation.DecomposeToRTAndTriangulate(pts1, pts2, KK, EE, out var RR2, out var TT2, out Image<Arthmetic, double> estReal2);
//var tt2 = TT2.Mul(1 / TT2.Norm);
//var rr2 = RotationConverter.MatrixToEulerXYZ(RR2);
//Errors.TraingulationError(ptsReal, estReal2, out double mean2, out double median2, out List<double> errors2);
//Errors.ReprojectionError(estReal2, pts2, KK, RR2, tt2, out double mean_r2a, out double median_r2a, out List<double> _1x);
//Errors.ReprojectionError(estReal2, pts2Ref, KK, RR2, tt2, out double mean_r2b, out double median_r2b, out List<double> _2x);
//Errors.ReprojectionError(estReal2, pts2Ref, KK, R12, tt0, out double mean_r2c, out double median_r2c, out List<double> _3x);
//Errors.ReprojectionError(Errors.Matrixify(ptsReal), pts2Ref, KK, RR2, tt2, out double mean_r2e, out double median_r2e, out List<double> _5x);
}
