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value | code stringlengths 73 34.1k | code_tokens list | docstring stringlengths 3 16k | docstring_tokens list | sha stringlengths 40 40 | url stringlengths 105 339 | partition stringclasses 1
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lessthanoptimal/BoofCV | main/boofcv-ip/src/main/java/boofcv/alg/misc/GPixelMath.java | GPixelMath.boundImage | public static <T extends ImageBase<T>> void boundImage(T input , double min , double max ) {
if( input instanceof ImageGray ) {
if (GrayU8.class == input.getClass()) {
PixelMath.boundImage((GrayU8) input, (int) min, (int) max);
} else if (GrayS8.class == input.getClass()) {
PixelMath.boundImage((GrayS8) input, (int) min, (int) max);
} else if (GrayU16.class == input.getClass()) {
PixelMath.boundImage((GrayU16) input, (int) min, (int) max);
} else if (GrayS16.class == input.getClass()) {
PixelMath.boundImage((GrayS16) input, (int) min, (int) max);
} else if (GrayS32.class == input.getClass()) {
PixelMath.boundImage((GrayS32) input, (int) min, (int) max);
} else if (GrayS64.class == input.getClass()) {
PixelMath.boundImage((GrayS64) input, (long) min, (long) max);
} else if (GrayF32.class == input.getClass()) {
PixelMath.boundImage((GrayF32) input, (float) min, (float) max);
} else if (GrayF64.class == input.getClass()) {
PixelMath.boundImage((GrayF64) input, min, max);
} else {
throw new IllegalArgumentException("Unknown image Type: " + input.getClass().getSimpleName());
}
} else if( input instanceof Planar ) {
Planar in = (Planar)input;
for (int i = 0; i < in.getNumBands(); i++) {
boundImage( in.getBand(i), min, max);
}
}
} | java | public static <T extends ImageBase<T>> void boundImage(T input , double min , double max ) {
if( input instanceof ImageGray ) {
if (GrayU8.class == input.getClass()) {
PixelMath.boundImage((GrayU8) input, (int) min, (int) max);
} else if (GrayS8.class == input.getClass()) {
PixelMath.boundImage((GrayS8) input, (int) min, (int) max);
} else if (GrayU16.class == input.getClass()) {
PixelMath.boundImage((GrayU16) input, (int) min, (int) max);
} else if (GrayS16.class == input.getClass()) {
PixelMath.boundImage((GrayS16) input, (int) min, (int) max);
} else if (GrayS32.class == input.getClass()) {
PixelMath.boundImage((GrayS32) input, (int) min, (int) max);
} else if (GrayS64.class == input.getClass()) {
PixelMath.boundImage((GrayS64) input, (long) min, (long) max);
} else if (GrayF32.class == input.getClass()) {
PixelMath.boundImage((GrayF32) input, (float) min, (float) max);
} else if (GrayF64.class == input.getClass()) {
PixelMath.boundImage((GrayF64) input, min, max);
} else {
throw new IllegalArgumentException("Unknown image Type: " + input.getClass().getSimpleName());
}
} else if( input instanceof Planar ) {
Planar in = (Planar)input;
for (int i = 0; i < in.getNumBands(); i++) {
boundImage( in.getBand(i), min, max);
}
}
} | [
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@param input Input image.
@param min minimum value. Inclusive.
@param max maximum value. Inclusive. | [
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lessthanoptimal/BoofCV | demonstrations/src/main/java/boofcv/demonstrations/feature/describe/VisualizeRegionDescriptionApp.java | VisualizeRegionDescriptionApp.updateTargetDescription | private void updateTargetDescription() {
if( targetPt != null ) {
TupleDesc feature = describe.createDescription();
describe.process(targetPt.x,targetPt.y,targetOrientation,targetRadius,feature);
tuplePanel.setDescription(feature);
} else {
tuplePanel.setDescription(null);
}
tuplePanel.repaint();
} | java | private void updateTargetDescription() {
if( targetPt != null ) {
TupleDesc feature = describe.createDescription();
describe.process(targetPt.x,targetPt.y,targetOrientation,targetRadius,feature);
tuplePanel.setDescription(feature);
} else {
tuplePanel.setDescription(null);
}
tuplePanel.repaint();
} | [
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lessthanoptimal/BoofCV | main/boofcv-geo/src/main/java/boofcv/alg/geo/MultiViewOps.java | MultiViewOps.inducedHomography13 | public static DMatrixRMaj inducedHomography13( TrifocalTensor tensor ,
Vector3D_F64 line2 ,
DMatrixRMaj output ) {
if( output == null )
output = new DMatrixRMaj(3,3);
DMatrixRMaj T = tensor.T1;
// H(:,0) = transpose(T1)*line
output.data[0] = T.data[0]*line2.x + T.data[3]*line2.y + T.data[6]*line2.z;
output.data[3] = T.data[1]*line2.x + T.data[4]*line2.y + T.data[7]*line2.z;
output.data[6] = T.data[2]*line2.x + T.data[5]*line2.y + T.data[8]*line2.z;
// H(:,1) = transpose(T2)*line
T = tensor.T2;
output.data[1] = T.data[0]*line2.x + T.data[3]*line2.y + T.data[6]*line2.z;
output.data[4] = T.data[1]*line2.x + T.data[4]*line2.y + T.data[7]*line2.z;
output.data[7] = T.data[2]*line2.x + T.data[5]*line2.y + T.data[8]*line2.z;
// H(:,2) = transpose(T3)*line
T = tensor.T3;
output.data[2] = T.data[0]*line2.x + T.data[3]*line2.y + T.data[6]*line2.z;
output.data[5] = T.data[1]*line2.x + T.data[4]*line2.y + T.data[7]*line2.z;
output.data[8] = T.data[2]*line2.x + T.data[5]*line2.y + T.data[8]*line2.z;
// Vector3D_F64 temp = new Vector3D_F64();
//
// for( int i = 0; i < 3; i++ ) {
// GeometryMath_F64.multTran(tensor.getT(i),line,temp);
// output.unsafe_set(0,i,temp.x);
// output.unsafe_set(1,i,temp.y);
// output.unsafe_set(2,i,temp.z);
// }
return output;
} | java | public static DMatrixRMaj inducedHomography13( TrifocalTensor tensor ,
Vector3D_F64 line2 ,
DMatrixRMaj output ) {
if( output == null )
output = new DMatrixRMaj(3,3);
DMatrixRMaj T = tensor.T1;
// H(:,0) = transpose(T1)*line
output.data[0] = T.data[0]*line2.x + T.data[3]*line2.y + T.data[6]*line2.z;
output.data[3] = T.data[1]*line2.x + T.data[4]*line2.y + T.data[7]*line2.z;
output.data[6] = T.data[2]*line2.x + T.data[5]*line2.y + T.data[8]*line2.z;
// H(:,1) = transpose(T2)*line
T = tensor.T2;
output.data[1] = T.data[0]*line2.x + T.data[3]*line2.y + T.data[6]*line2.z;
output.data[4] = T.data[1]*line2.x + T.data[4]*line2.y + T.data[7]*line2.z;
output.data[7] = T.data[2]*line2.x + T.data[5]*line2.y + T.data[8]*line2.z;
// H(:,2) = transpose(T3)*line
T = tensor.T3;
output.data[2] = T.data[0]*line2.x + T.data[3]*line2.y + T.data[6]*line2.z;
output.data[5] = T.data[1]*line2.x + T.data[4]*line2.y + T.data[7]*line2.z;
output.data[8] = T.data[2]*line2.x + T.data[5]*line2.y + T.data[8]*line2.z;
// Vector3D_F64 temp = new Vector3D_F64();
//
// for( int i = 0; i < 3; i++ ) {
// GeometryMath_F64.multTran(tensor.getT(i),line,temp);
// output.unsafe_set(0,i,temp.x);
// output.unsafe_set(1,i,temp.y);
// output.unsafe_set(2,i,temp.z);
// }
return output;
} | [
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p3 = H13*p1
@param tensor Input: Trifocal tensor
@param line2 Input: Line in view 2. {@link LineGeneral2D_F64 General notation}.
@param output Output: Optional storage for homography. 3x3 matrix
@return Homography from view 1 to 3 | [
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lessthanoptimal/BoofCV | main/boofcv-geo/src/main/java/boofcv/alg/geo/MultiViewOps.java | MultiViewOps.inducedHomography12 | public static DMatrixRMaj inducedHomography12( TrifocalTensor tensor ,
Vector3D_F64 line3 ,
DMatrixRMaj output ) {
if( output == null )
output = new DMatrixRMaj(3,3);
// H(:,0) = T1*line
DMatrixRMaj T = tensor.T1;
output.data[0] = T.data[0]*line3.x + T.data[1]*line3.y + T.data[2]*line3.z;
output.data[3] = T.data[3]*line3.x + T.data[4]*line3.y + T.data[5]*line3.z;
output.data[6] = T.data[6]*line3.x + T.data[7]*line3.y + T.data[8]*line3.z;
// H(:,0) = T2*line
T = tensor.T2;
output.data[1] = T.data[0]*line3.x + T.data[1]*line3.y + T.data[2]*line3.z;
output.data[4] = T.data[3]*line3.x + T.data[4]*line3.y + T.data[5]*line3.z;
output.data[7] = T.data[6]*line3.x + T.data[7]*line3.y + T.data[8]*line3.z;
// H(:,0) = T3*line
T = tensor.T3;
output.data[2] = T.data[0]*line3.x + T.data[1]*line3.y + T.data[2]*line3.z;
output.data[5] = T.data[3]*line3.x + T.data[4]*line3.y + T.data[5]*line3.z;
output.data[8] = T.data[6]*line3.x + T.data[7]*line3.y + T.data[8]*line3.z;
// Vector3D_F64 temp = new Vector3D_F64();
//
// for( int i = 0; i < 3; i++ ) {
// GeometryMath_F64.mult(tensor.getT(i), line, temp);
// output.unsafe_set(0,i,temp.x);
// output.unsafe_set(1,i,temp.y);
// output.unsafe_set(2,i,temp.z);
// }
return output;
} | java | public static DMatrixRMaj inducedHomography12( TrifocalTensor tensor ,
Vector3D_F64 line3 ,
DMatrixRMaj output ) {
if( output == null )
output = new DMatrixRMaj(3,3);
// H(:,0) = T1*line
DMatrixRMaj T = tensor.T1;
output.data[0] = T.data[0]*line3.x + T.data[1]*line3.y + T.data[2]*line3.z;
output.data[3] = T.data[3]*line3.x + T.data[4]*line3.y + T.data[5]*line3.z;
output.data[6] = T.data[6]*line3.x + T.data[7]*line3.y + T.data[8]*line3.z;
// H(:,0) = T2*line
T = tensor.T2;
output.data[1] = T.data[0]*line3.x + T.data[1]*line3.y + T.data[2]*line3.z;
output.data[4] = T.data[3]*line3.x + T.data[4]*line3.y + T.data[5]*line3.z;
output.data[7] = T.data[6]*line3.x + T.data[7]*line3.y + T.data[8]*line3.z;
// H(:,0) = T3*line
T = tensor.T3;
output.data[2] = T.data[0]*line3.x + T.data[1]*line3.y + T.data[2]*line3.z;
output.data[5] = T.data[3]*line3.x + T.data[4]*line3.y + T.data[5]*line3.z;
output.data[8] = T.data[6]*line3.x + T.data[7]*line3.y + T.data[8]*line3.z;
// Vector3D_F64 temp = new Vector3D_F64();
//
// for( int i = 0; i < 3; i++ ) {
// GeometryMath_F64.mult(tensor.getT(i), line, temp);
// output.unsafe_set(0,i,temp.x);
// output.unsafe_set(1,i,temp.y);
// output.unsafe_set(2,i,temp.z);
// }
return output;
} | [
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@param tensor Input: Trifocal tensor
@param line3 Input: Line in view 3. {@link LineGeneral2D_F64 General notation}.
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lessthanoptimal/BoofCV | main/boofcv-geo/src/main/java/boofcv/alg/geo/MultiViewOps.java | MultiViewOps.homographyStereo3Pts | public static DMatrixRMaj homographyStereo3Pts( DMatrixRMaj F , AssociatedPair p1, AssociatedPair p2, AssociatedPair p3) {
HomographyInducedStereo3Pts alg = new HomographyInducedStereo3Pts();
alg.setFundamental(F,null);
if( !alg.process(p1,p2,p3) )
return null;
return alg.getHomography();
} | java | public static DMatrixRMaj homographyStereo3Pts( DMatrixRMaj F , AssociatedPair p1, AssociatedPair p2, AssociatedPair p3) {
HomographyInducedStereo3Pts alg = new HomographyInducedStereo3Pts();
alg.setFundamental(F,null);
if( !alg.process(p1,p2,p3) )
return null;
return alg.getHomography();
} | [
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@param F Fundamental matrix
@param p1 Associated point observation
@param p2 Associated point observation
@param p3 Associated point observation
@return The homography from view 1 to view 2 or null if it fails | [
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lessthanoptimal/BoofCV | main/boofcv-geo/src/main/java/boofcv/alg/geo/MultiViewOps.java | MultiViewOps.homographyStereoLinePt | public static DMatrixRMaj homographyStereoLinePt( DMatrixRMaj F , PairLineNorm line, AssociatedPair point) {
HomographyInducedStereoLinePt alg = new HomographyInducedStereoLinePt();
alg.setFundamental(F,null);
alg.process(line,point);
return alg.getHomography();
} | java | public static DMatrixRMaj homographyStereoLinePt( DMatrixRMaj F , PairLineNorm line, AssociatedPair point) {
HomographyInducedStereoLinePt alg = new HomographyInducedStereoLinePt();
alg.setFundamental(F,null);
alg.process(line,point);
return alg.getHomography();
} | [
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@see HomographyInducedStereoLinePt
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@param point Point on the plane
@return The homography from view 1 to view 2 or null if it fails | [
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lessthanoptimal/BoofCV | main/boofcv-geo/src/main/java/boofcv/alg/geo/MultiViewOps.java | MultiViewOps.homographyStereo2Lines | public static DMatrixRMaj homographyStereo2Lines( DMatrixRMaj F , PairLineNorm line0, PairLineNorm line1) {
HomographyInducedStereo2Line alg = new HomographyInducedStereo2Line();
alg.setFundamental(F,null);
if( !alg.process(line0,line1) )
return null;
return alg.getHomography();
} | java | public static DMatrixRMaj homographyStereo2Lines( DMatrixRMaj F , PairLineNorm line0, PairLineNorm line1) {
HomographyInducedStereo2Line alg = new HomographyInducedStereo2Line();
alg.setFundamental(F,null);
if( !alg.process(line0,line1) )
return null;
return alg.getHomography();
} | [
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@param line1 Line on the plane
@return The homography from view 1 to view 2 or null if it fails | [
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lessthanoptimal/BoofCV | main/boofcv-geo/src/main/java/boofcv/alg/geo/MultiViewOps.java | MultiViewOps.createFundamental | public static DMatrixRMaj createFundamental(DMatrixRMaj E, CameraPinhole intrinsic ) {
DMatrixRMaj K = PerspectiveOps.pinholeToMatrix(intrinsic,(DMatrixRMaj)null);
return createFundamental(E,K);
} | java | public static DMatrixRMaj createFundamental(DMatrixRMaj E, CameraPinhole intrinsic ) {
DMatrixRMaj K = PerspectiveOps.pinholeToMatrix(intrinsic,(DMatrixRMaj)null);
return createFundamental(E,K);
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parameters.
@see #createFundamental(DMatrixRMaj, DMatrixRMaj)
@param E Essential matrix
@param intrinsic Intrinsic camera calibration
@return Fundamental matrix | [
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lessthanoptimal/BoofCV | main/boofcv-geo/src/main/java/boofcv/alg/geo/MultiViewOps.java | MultiViewOps.projectiveToMetric | public static void projectiveToMetric( DMatrixRMaj cameraMatrix , DMatrixRMaj H ,
Se3_F64 worldToView , DMatrixRMaj K )
{
DMatrixRMaj tmp = new DMatrixRMaj(3,4);
CommonOps_DDRM.mult(cameraMatrix,H,tmp);
MultiViewOps.decomposeMetricCamera(tmp,K,worldToView);
} | java | public static void projectiveToMetric( DMatrixRMaj cameraMatrix , DMatrixRMaj H ,
Se3_F64 worldToView , DMatrixRMaj K )
{
DMatrixRMaj tmp = new DMatrixRMaj(3,4);
CommonOps_DDRM.mult(cameraMatrix,H,tmp);
MultiViewOps.decomposeMetricCamera(tmp,K,worldToView);
} | [
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<pre>
P'=P*H
K,R,t = decompose(P')
</pre>
where P is the camera matrix, H is the homography, (K,R,t) are the intrinsic calibration matrix, rotation,
and translation
@see MultiViewOps#absoluteQuadraticToH
@see #decomposeMetricCamera(DMatrixRMaj, DMatrixRMaj, Se3_F64)
@param cameraMatrix (Input) camera matrix. 3x4
@param H (Input) Rectifying homography. 4x4
@param worldToView (Output) Transform from world to camera view
@param K (Output) Camera calibration matrix | [
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lessthanoptimal/BoofCV | main/boofcv-geo/src/main/java/boofcv/alg/geo/MultiViewOps.java | MultiViewOps.projectiveToMetricKnownK | public static void projectiveToMetricKnownK( DMatrixRMaj cameraMatrix ,
DMatrixRMaj H , DMatrixRMaj K,
Se3_F64 worldToView )
{
DMatrixRMaj tmp = new DMatrixRMaj(3,4);
CommonOps_DDRM.mult(cameraMatrix,H,tmp);
DMatrixRMaj K_inv = new DMatrixRMaj(3,3);
CommonOps_DDRM.invert(K,K_inv);
DMatrixRMaj P = new DMatrixRMaj(3,4);
CommonOps_DDRM.mult(K_inv,tmp,P);
CommonOps_DDRM.extract(P,0,0,worldToView.R);
worldToView.T.x = P.get(0,3);
worldToView.T.y = P.get(1,3);
worldToView.T.z = P.get(2,3);
SingularValueDecomposition_F64<DMatrixRMaj> svd = DecompositionFactory_DDRM.svd(true,true,true);
DMatrixRMaj R = worldToView.R;
if( !svd.decompose(R))
throw new RuntimeException("SVD Failed");
CommonOps_DDRM.multTransB(svd.getU(null,false),svd.getV(null,false),R);
// determinant should be +1
double det = CommonOps_DDRM.det(R);
if( det < 0 ) {
CommonOps_DDRM.scale(-1,R);
worldToView.T.scale(-1);
}
} | java | public static void projectiveToMetricKnownK( DMatrixRMaj cameraMatrix ,
DMatrixRMaj H , DMatrixRMaj K,
Se3_F64 worldToView )
{
DMatrixRMaj tmp = new DMatrixRMaj(3,4);
CommonOps_DDRM.mult(cameraMatrix,H,tmp);
DMatrixRMaj K_inv = new DMatrixRMaj(3,3);
CommonOps_DDRM.invert(K,K_inv);
DMatrixRMaj P = new DMatrixRMaj(3,4);
CommonOps_DDRM.mult(K_inv,tmp,P);
CommonOps_DDRM.extract(P,0,0,worldToView.R);
worldToView.T.x = P.get(0,3);
worldToView.T.y = P.get(1,3);
worldToView.T.z = P.get(2,3);
SingularValueDecomposition_F64<DMatrixRMaj> svd = DecompositionFactory_DDRM.svd(true,true,true);
DMatrixRMaj R = worldToView.R;
if( !svd.decompose(R))
throw new RuntimeException("SVD Failed");
CommonOps_DDRM.multTransB(svd.getU(null,false),svd.getV(null,false),R);
// determinant should be +1
double det = CommonOps_DDRM.det(R);
if( det < 0 ) {
CommonOps_DDRM.scale(-1,R);
worldToView.T.scale(-1);
}
} | [
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@param cameraMatrix (Input) camera matrix. 3x4
@param H (Input) Rectifying homography. 4x4
@param K (Input) Known calibration matrix
@param worldToView (Output) transform from world to camera view | [
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lessthanoptimal/BoofCV | main/boofcv-geo/src/main/java/boofcv/alg/geo/MultiViewOps.java | MultiViewOps.rectifyHToAbsoluteQuadratic | public static void rectifyHToAbsoluteQuadratic(DMatrixRMaj H , DMatrixRMaj Q ) {
int indexQ = 0;
for (int rowA = 0; rowA < 4; rowA++) {
for (int colB = 0; colB < 4; colB++) {
int indexA = rowA*4;
int indexB = colB*4;
double sum = 0;
for (int i = 0; i < 3; i++) {
// sum += H.get(rowA,i)*H.get(colB,i);
sum += H.data[indexA++]*H.data[indexB++];
}
// Q.set(rowA,colB,sum);
Q.data[indexQ++] = sum;
}
}
} | java | public static void rectifyHToAbsoluteQuadratic(DMatrixRMaj H , DMatrixRMaj Q ) {
int indexQ = 0;
for (int rowA = 0; rowA < 4; rowA++) {
for (int colB = 0; colB < 4; colB++) {
int indexA = rowA*4;
int indexB = colB*4;
double sum = 0;
for (int i = 0; i < 3; i++) {
// sum += H.get(rowA,i)*H.get(colB,i);
sum += H.data[indexA++]*H.data[indexB++];
}
// Q.set(rowA,colB,sum);
Q.data[indexQ++] = sum;
}
}
} | [
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<p>where I = diag(1 1 1 0)</p>
@param H (Input) 4x4 rectifying homography.
@param Q (Output) Absolute quadratic. Typically found in auto calibration. Not modified. | [
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] | f01c0243da0ec086285ee722183804d5923bc3ac | https://github.com/lessthanoptimal/BoofCV/blob/f01c0243da0ec086285ee722183804d5923bc3ac/main/boofcv-geo/src/main/java/boofcv/alg/geo/MultiViewOps.java#L1489-L1506 | train |
lessthanoptimal/BoofCV | main/boofcv-geo/src/main/java/boofcv/alg/geo/MultiViewOps.java | MultiViewOps.intrinsicFromAbsoluteQuadratic | public static void intrinsicFromAbsoluteQuadratic( DMatrixRMaj Q , DMatrixRMaj P , CameraPinhole intrinsic )
{
DMatrixRMaj tmp = new DMatrixRMaj(3,4);
DMatrixRMaj tmp2 = new DMatrixRMaj(3,3);
CommonOps_DDRM.mult(P,Q,tmp);
CommonOps_DDRM.multTransB(tmp,P,tmp2);
decomposeDiac(tmp2,intrinsic);
} | java | public static void intrinsicFromAbsoluteQuadratic( DMatrixRMaj Q , DMatrixRMaj P , CameraPinhole intrinsic )
{
DMatrixRMaj tmp = new DMatrixRMaj(3,4);
DMatrixRMaj tmp2 = new DMatrixRMaj(3,3);
CommonOps_DDRM.mult(P,Q,tmp);
CommonOps_DDRM.multTransB(tmp,P,tmp2);
decomposeDiac(tmp2,intrinsic);
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@param Q (Input) Dual absolute qudratic
@param P (Input) Camera matrix for a view
@param intrinsic (Output) found intrinsic parameters | [
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lessthanoptimal/BoofCV | main/boofcv-geo/src/main/java/boofcv/alg/geo/MultiViewOps.java | MultiViewOps.split2 | public static Tuple2<List<Point2D_F64>,List<Point2D_F64>> split2( List<AssociatedPair> input )
{
List<Point2D_F64> list1 = new ArrayList<>();
List<Point2D_F64> list2 = new ArrayList<>();
for (int i = 0; i < input.size(); i++) {
list1.add( input.get(i).p1 );
list2.add( input.get(i).p2 );
}
return new Tuple2<>(list1,list2);
} | java | public static Tuple2<List<Point2D_F64>,List<Point2D_F64>> split2( List<AssociatedPair> input )
{
List<Point2D_F64> list1 = new ArrayList<>();
List<Point2D_F64> list2 = new ArrayList<>();
for (int i = 0; i < input.size(); i++) {
list1.add( input.get(i).p1 );
list2.add( input.get(i).p2 );
}
return new Tuple2<>(list1,list2);
} | [
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@param input List of associated features
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lessthanoptimal/BoofCV | main/boofcv-geo/src/main/java/boofcv/alg/geo/MultiViewOps.java | MultiViewOps.split3 | public static Tuple3<List<Point2D_F64>,List<Point2D_F64>,List<Point2D_F64>> split3(List<AssociatedTriple> input )
{
List<Point2D_F64> list1 = new ArrayList<>();
List<Point2D_F64> list2 = new ArrayList<>();
List<Point2D_F64> list3 = new ArrayList<>();
for (int i = 0; i < input.size(); i++) {
list1.add( input.get(i).p1 );
list2.add( input.get(i).p2 );
list3.add( input.get(i).p3 );
}
return new Tuple3<>(list1,list2,list3);
} | java | public static Tuple3<List<Point2D_F64>,List<Point2D_F64>,List<Point2D_F64>> split3(List<AssociatedTriple> input )
{
List<Point2D_F64> list1 = new ArrayList<>();
List<Point2D_F64> list2 = new ArrayList<>();
List<Point2D_F64> list3 = new ArrayList<>();
for (int i = 0; i < input.size(); i++) {
list1.add( input.get(i).p1 );
list2.add( input.get(i).p2 );
list3.add( input.get(i).p3 );
}
return new Tuple3<>(list1,list2,list3);
} | [
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lessthanoptimal/BoofCV | main/boofcv-ip/src/main/java/boofcv/alg/denoise/wavelet/SubbandShrink.java | SubbandShrink.performShrinkage | protected void performShrinkage( I transform , int numLevels ) {
// step through each layer in the pyramid.
for( int i = 0; i < numLevels; i++ ) {
int w = transform.width;
int h = transform.height;
int ww = w/2;
int hh = h/2;
Number threshold;
I subband;
// HL
subband = transform.subimage(ww,0,w,hh, null);
threshold = computeThreshold(subband);
rule.process(subband,threshold);
// System.out.print("HL = "+threshold);
// LH
subband = transform.subimage(0,hh,ww,h, null);
threshold = computeThreshold(subband);
rule.process(subband,threshold);
// System.out.print(" LH = "+threshold);
// HH
subband = transform.subimage(ww,hh,w,h, null);
threshold = computeThreshold(subband);
rule.process(subband,threshold);
// System.out.println(" HH = "+threshold);
transform = transform.subimage(0,0,ww,hh, null);
}
} | java | protected void performShrinkage( I transform , int numLevels ) {
// step through each layer in the pyramid.
for( int i = 0; i < numLevels; i++ ) {
int w = transform.width;
int h = transform.height;
int ww = w/2;
int hh = h/2;
Number threshold;
I subband;
// HL
subband = transform.subimage(ww,0,w,hh, null);
threshold = computeThreshold(subband);
rule.process(subband,threshold);
// System.out.print("HL = "+threshold);
// LH
subband = transform.subimage(0,hh,ww,h, null);
threshold = computeThreshold(subband);
rule.process(subband,threshold);
// System.out.print(" LH = "+threshold);
// HH
subband = transform.subimage(ww,hh,w,h, null);
threshold = computeThreshold(subband);
rule.process(subband,threshold);
// System.out.println(" HH = "+threshold);
transform = transform.subimage(0,0,ww,hh, null);
}
} | [
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@param transform The image being transformed.
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lessthanoptimal/BoofCV | main/boofcv-ip/src/main/java/boofcv/alg/denoise/wavelet/DenoiseVisuShrink_F32.java | DenoiseVisuShrink_F32.denoise | @Override
public void denoise(GrayF32 transform , int numLevels ) {
int scale = UtilWavelet.computeScale(numLevels);
final int h = transform.height;
final int w = transform.width;
// width and height of scaling image
final int innerWidth = w/scale;
final int innerHeight = h/scale;
GrayF32 subbandHH = transform.subimage(w/2,h/2,w,h, null);
float sigma = UtilDenoiseWavelet.estimateNoiseStdDev(subbandHH,null);
float threshold = (float) UtilDenoiseWavelet.universalThreshold(subbandHH,sigma);
// apply same threshold to all wavelet coefficients
rule.process(transform.subimage(innerWidth,0,w,h, null),threshold);
rule.process(transform.subimage(0,innerHeight,innerWidth,h, null),threshold);
} | java | @Override
public void denoise(GrayF32 transform , int numLevels ) {
int scale = UtilWavelet.computeScale(numLevels);
final int h = transform.height;
final int w = transform.width;
// width and height of scaling image
final int innerWidth = w/scale;
final int innerHeight = h/scale;
GrayF32 subbandHH = transform.subimage(w/2,h/2,w,h, null);
float sigma = UtilDenoiseWavelet.estimateNoiseStdDev(subbandHH,null);
float threshold = (float) UtilDenoiseWavelet.universalThreshold(subbandHH,sigma);
// apply same threshold to all wavelet coefficients
rule.process(transform.subimage(innerWidth,0,w,h, null),threshold);
rule.process(transform.subimage(0,innerHeight,innerWidth,h, null),threshold);
} | [
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@param transform Mult-level wavelet transform. Modified.
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lessthanoptimal/BoofCV | main/boofcv-ip/src/main/java/boofcv/factory/transform/wavelet/FactoryWaveletCoiflet.java | FactoryWaveletCoiflet.generate_F32 | public static WaveletDescription<WlCoef_F32> generate_F32( int I ) {
if( I != 6 ) {
throw new IllegalArgumentException("Only 6 is currently supported");
}
WlCoef_F32 coef = new WlCoef_F32();
coef.offsetScaling = -2;
coef.offsetWavelet = -2;
coef.scaling = new float[6];
coef.wavelet = new float[6];
double sqrt7 = Math.sqrt(7);
double div = 16.0*Math.sqrt(2);
coef.scaling[0] = (float)((1.0-sqrt7)/div);
coef.scaling[1] = (float)((5.0+sqrt7)/div);
coef.scaling[2] = (float)((14.0+2.0*sqrt7)/div);
coef.scaling[3] = (float)((14.0-2.0*sqrt7)/div);
coef.scaling[4] = (float)((1.0-sqrt7)/div);
coef.scaling[5] = (float)((-3.0+sqrt7)/div);
coef.wavelet[0] = coef.scaling[5];
coef.wavelet[1] = -coef.scaling[4];
coef.wavelet[2] = coef.scaling[3];
coef.wavelet[3] = -coef.scaling[2];
coef.wavelet[4] = coef.scaling[1];
coef.wavelet[5] = -coef.scaling[0];
WlBorderCoefStandard<WlCoef_F32> inverse = new WlBorderCoefStandard<>(coef);
return new WaveletDescription<>(new BorderIndex1D_Wrap(), coef, inverse);
} | java | public static WaveletDescription<WlCoef_F32> generate_F32( int I ) {
if( I != 6 ) {
throw new IllegalArgumentException("Only 6 is currently supported");
}
WlCoef_F32 coef = new WlCoef_F32();
coef.offsetScaling = -2;
coef.offsetWavelet = -2;
coef.scaling = new float[6];
coef.wavelet = new float[6];
double sqrt7 = Math.sqrt(7);
double div = 16.0*Math.sqrt(2);
coef.scaling[0] = (float)((1.0-sqrt7)/div);
coef.scaling[1] = (float)((5.0+sqrt7)/div);
coef.scaling[2] = (float)((14.0+2.0*sqrt7)/div);
coef.scaling[3] = (float)((14.0-2.0*sqrt7)/div);
coef.scaling[4] = (float)((1.0-sqrt7)/div);
coef.scaling[5] = (float)((-3.0+sqrt7)/div);
coef.wavelet[0] = coef.scaling[5];
coef.wavelet[1] = -coef.scaling[4];
coef.wavelet[2] = coef.scaling[3];
coef.wavelet[3] = -coef.scaling[2];
coef.wavelet[4] = coef.scaling[1];
coef.wavelet[5] = -coef.scaling[0];
WlBorderCoefStandard<WlCoef_F32> inverse = new WlBorderCoefStandard<>(coef);
return new WaveletDescription<>(new BorderIndex1D_Wrap(), coef, inverse);
} | [
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lessthanoptimal/BoofCV | main/boofcv-feature/src/main/java/boofcv/alg/shapes/ShapeFittingOps.java | ShapeFittingOps.fitEllipse_F64 | public static FitData<EllipseRotated_F64> fitEllipse_F64( List<Point2D_F64> points, int iterations ,
boolean computeError ,
FitData<EllipseRotated_F64> outputStorage ) {
if( outputStorage == null ) {
outputStorage = new FitData<>(new EllipseRotated_F64());
}
// Compute the optimal algebraic error
FitEllipseAlgebraic_F64 algebraic = new FitEllipseAlgebraic_F64();
if( !algebraic.process(points)) {
// could be a line or some other weird case. Create a crude estimate instead
FitData<Circle2D_F64> circleData = averageCircle_F64(points,null,null);
Circle2D_F64 circle = circleData.shape;
outputStorage.shape.set(circle.center.x,circle.center.y,circle.radius,circle.radius,0);
} else {
UtilEllipse_F64.convert(algebraic.getEllipse(),outputStorage.shape);
}
// Improve the solution from algebraic into Euclidean
if( iterations > 0 ) {
RefineEllipseEuclideanLeastSquares_F64 leastSquares = new RefineEllipseEuclideanLeastSquares_F64();
leastSquares.setMaxIterations(iterations);
leastSquares.refine(outputStorage.shape,points);
outputStorage.shape.set( leastSquares.getFound() );
}
// compute the average Euclidean error if the user requests it
if( computeError ) {
ClosestPointEllipseAngle_F64 closestPoint = new ClosestPointEllipseAngle_F64(1e-8,100);
closestPoint.setEllipse(outputStorage.shape);
double total = 0;
for( Point2D_F64 p : points ) {
closestPoint.process(p);
total += p.distance(closestPoint.getClosest());
}
outputStorage.error = total/points.size();
} else {
outputStorage.error = 0;
}
return outputStorage;
} | java | public static FitData<EllipseRotated_F64> fitEllipse_F64( List<Point2D_F64> points, int iterations ,
boolean computeError ,
FitData<EllipseRotated_F64> outputStorage ) {
if( outputStorage == null ) {
outputStorage = new FitData<>(new EllipseRotated_F64());
}
// Compute the optimal algebraic error
FitEllipseAlgebraic_F64 algebraic = new FitEllipseAlgebraic_F64();
if( !algebraic.process(points)) {
// could be a line or some other weird case. Create a crude estimate instead
FitData<Circle2D_F64> circleData = averageCircle_F64(points,null,null);
Circle2D_F64 circle = circleData.shape;
outputStorage.shape.set(circle.center.x,circle.center.y,circle.radius,circle.radius,0);
} else {
UtilEllipse_F64.convert(algebraic.getEllipse(),outputStorage.shape);
}
// Improve the solution from algebraic into Euclidean
if( iterations > 0 ) {
RefineEllipseEuclideanLeastSquares_F64 leastSquares = new RefineEllipseEuclideanLeastSquares_F64();
leastSquares.setMaxIterations(iterations);
leastSquares.refine(outputStorage.shape,points);
outputStorage.shape.set( leastSquares.getFound() );
}
// compute the average Euclidean error if the user requests it
if( computeError ) {
ClosestPointEllipseAngle_F64 closestPoint = new ClosestPointEllipseAngle_F64(1e-8,100);
closestPoint.setEllipse(outputStorage.shape);
double total = 0;
for( Point2D_F64 p : points ) {
closestPoint.process(p);
total += p.distance(closestPoint.getClosest());
}
outputStorage.error = total/points.size();
} else {
outputStorage.error = 0;
}
return outputStorage;
} | [
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... | Computes the best fit ellipse based on minimizing Euclidean distance. An estimate is initially provided
using algebraic algorithm which is then refined using non-linear optimization. The amount of non-linear
optimization can be controlled using 'iterations' parameter. Will work with partial and complete contours
of objects.
<p>NOTE: To improve speed, make calls directly to classes in Georegression. Look at the code for details.</p>
@param points (Input) Set of unordered points. Not modified.
@param iterations Number of iterations used to refine the fit. If set to zero then an algebraic solution
is returned.
@param computeError If true it will compute the average Euclidean distance error
@param outputStorage (Output/Optional) Storage for the ellipse. Can be null.
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lessthanoptimal/BoofCV | main/boofcv-feature/src/main/java/boofcv/alg/shapes/ShapeFittingOps.java | ShapeFittingOps.convert_I32_F64 | public static List<Point2D_F64> convert_I32_F64(List<Point2D_I32> points) {
return convert_I32_F64(points,null).toList();
} | java | public static List<Point2D_F64> convert_I32_F64(List<Point2D_I32> points) {
return convert_I32_F64(points,null).toList();
} | [
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@param points Original points
@return Converted points | [
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] | f01c0243da0ec086285ee722183804d5923bc3ac | https://github.com/lessthanoptimal/BoofCV/blob/f01c0243da0ec086285ee722183804d5923bc3ac/main/boofcv-feature/src/main/java/boofcv/alg/shapes/ShapeFittingOps.java#L174-L176 | train |
lessthanoptimal/BoofCV | main/boofcv-feature/src/main/java/boofcv/alg/shapes/ShapeFittingOps.java | ShapeFittingOps.averageCircle_I32 | public static FitData<Circle2D_F64> averageCircle_I32(List<Point2D_I32> points, GrowQueue_F64 optional,
FitData<Circle2D_F64> outputStorage) {
if( outputStorage == null ) {
outputStorage = new FitData<>(new Circle2D_F64());
}
if( optional == null ) {
optional = new GrowQueue_F64();
}
Circle2D_F64 circle = outputStorage.shape;
int N = points.size();
// find center of the circle by computing the mean x and y
int sumX=0,sumY=0;
for( int i = 0; i < N; i++ ) {
Point2D_I32 p = points.get(i);
sumX += p.x;
sumY += p.y;
}
optional.reset();
double centerX = circle.center.x = sumX/(double)N;
double centerY = circle.center.y = sumY/(double)N;
double meanR = 0;
for( int i = 0; i < N; i++ ) {
Point2D_I32 p = points.get(i);
double dx = p.x-centerX;
double dy = p.y-centerY;
double r = Math.sqrt(dx*dx + dy*dy);
optional.push(r);
meanR += r;
}
meanR /= N;
circle.radius = meanR;
// compute radius variance
double variance = 0;
for( int i = 0; i < N; i++ ) {
double diff = optional.get(i)-meanR;
variance += diff*diff;
}
outputStorage.error = variance/N;
return outputStorage;
} | java | public static FitData<Circle2D_F64> averageCircle_I32(List<Point2D_I32> points, GrowQueue_F64 optional,
FitData<Circle2D_F64> outputStorage) {
if( outputStorage == null ) {
outputStorage = new FitData<>(new Circle2D_F64());
}
if( optional == null ) {
optional = new GrowQueue_F64();
}
Circle2D_F64 circle = outputStorage.shape;
int N = points.size();
// find center of the circle by computing the mean x and y
int sumX=0,sumY=0;
for( int i = 0; i < N; i++ ) {
Point2D_I32 p = points.get(i);
sumX += p.x;
sumY += p.y;
}
optional.reset();
double centerX = circle.center.x = sumX/(double)N;
double centerY = circle.center.y = sumY/(double)N;
double meanR = 0;
for( int i = 0; i < N; i++ ) {
Point2D_I32 p = points.get(i);
double dx = p.x-centerX;
double dy = p.y-centerY;
double r = Math.sqrt(dx*dx + dy*dy);
optional.push(r);
meanR += r;
}
meanR /= N;
circle.radius = meanR;
// compute radius variance
double variance = 0;
for( int i = 0; i < N; i++ ) {
double diff = optional.get(i)-meanR;
variance += diff*diff;
}
outputStorage.error = variance/N;
return outputStorage;
} | [
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"==",... | Computes a circle which has it's center at the mean position of the provided points and radius is equal to the
average distance of each point from the center. While fast to compute the provided circle is not a best
fit circle by any reasonable metric, except for special cases.
@param points (Input) Set of unordered points. Not modified.
@param optional (Optional) Used internally to store the distance of each point from the center. Can be null.
@param outputStorage (Output/Optional) Storage for results. If null then a new circle instance will be returned.
@return The found circle fit. | [
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"... | f01c0243da0ec086285ee722183804d5923bc3ac | https://github.com/lessthanoptimal/BoofCV/blob/f01c0243da0ec086285ee722183804d5923bc3ac/main/boofcv-feature/src/main/java/boofcv/alg/shapes/ShapeFittingOps.java#L218-L265 | train |
lessthanoptimal/BoofCV | main/boofcv-sfm/src/main/java/boofcv/alg/sfm/structure2/ProjectiveInitializeAllCommon.java | ProjectiveInitializeAllCommon.fixate | public void fixate() {
ransac = FactoryMultiViewRobust.trifocalRansac(configTriRansac,configError,configRansac);
sba = FactoryMultiView.bundleSparseProjective(configSBA);
} | java | public void fixate() {
ransac = FactoryMultiViewRobust.trifocalRansac(configTriRansac,configError,configRansac);
sba = FactoryMultiView.bundleSparseProjective(configSBA);
} | [
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lessthanoptimal/BoofCV | main/boofcv-sfm/src/main/java/boofcv/alg/sfm/structure2/ProjectiveInitializeAllCommon.java | ProjectiveInitializeAllCommon.selectInitialTriplet | boolean selectInitialTriplet( View seed , GrowQueue_I32 motions , int selected[] ) {
double bestScore = 0;
for (int i = 0; i < motions.size; i++) {
View viewB = seed.connections.get(i).other(seed);
for (int j = i+1; j < motions.size; j++) {
View viewC = seed.connections.get(j).other(seed);
double s = scoreTripleView(seed,viewB,viewC);
if( s > bestScore ) {
bestScore = s;
selected[0] = i;
selected[1] = j;
}
}
}
return bestScore != 0;
} | java | boolean selectInitialTriplet( View seed , GrowQueue_I32 motions , int selected[] ) {
double bestScore = 0;
for (int i = 0; i < motions.size; i++) {
View viewB = seed.connections.get(i).other(seed);
for (int j = i+1; j < motions.size; j++) {
View viewC = seed.connections.get(j).other(seed);
double s = scoreTripleView(seed,viewB,viewC);
if( s > bestScore ) {
bestScore = s;
selected[0] = i;
selected[1] = j;
}
}
}
return bestScore != 0;
} | [
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lessthanoptimal/BoofCV | main/boofcv-sfm/src/main/java/boofcv/alg/sfm/structure2/ProjectiveInitializeAllCommon.java | ProjectiveInitializeAllCommon.triangulateFeatures | private void triangulateFeatures(List<AssociatedTriple> inliers,
DMatrixRMaj P1, DMatrixRMaj P2, DMatrixRMaj P3) {
List<DMatrixRMaj> cameraMatrices = new ArrayList<>();
cameraMatrices.add(P1);
cameraMatrices.add(P2);
cameraMatrices.add(P3);
// need elements to be non-empty so that it can use set(). probably over optimization
List<Point2D_F64> triangObs = new ArrayList<>();
triangObs.add(null);
triangObs.add(null);
triangObs.add(null);
Point4D_F64 X = new Point4D_F64();
for (int i = 0; i < inliers.size(); i++) {
AssociatedTriple t = inliers.get(i);
triangObs.set(0,t.p1);
triangObs.set(1,t.p2);
triangObs.set(2,t.p3);
// triangulation can fail if all 3 views have the same pixel value. This has been observed in
// simulated 3D scenes
if( triangulator.triangulate(triangObs,cameraMatrices,X)) {
structure.points[i].set(X.x,X.y,X.z,X.w);
} else {
throw new RuntimeException("Failed to triangulate a point in the inlier set?! Handle if this is common");
}
}
} | java | private void triangulateFeatures(List<AssociatedTriple> inliers,
DMatrixRMaj P1, DMatrixRMaj P2, DMatrixRMaj P3) {
List<DMatrixRMaj> cameraMatrices = new ArrayList<>();
cameraMatrices.add(P1);
cameraMatrices.add(P2);
cameraMatrices.add(P3);
// need elements to be non-empty so that it can use set(). probably over optimization
List<Point2D_F64> triangObs = new ArrayList<>();
triangObs.add(null);
triangObs.add(null);
triangObs.add(null);
Point4D_F64 X = new Point4D_F64();
for (int i = 0; i < inliers.size(); i++) {
AssociatedTriple t = inliers.get(i);
triangObs.set(0,t.p1);
triangObs.set(1,t.p2);
triangObs.set(2,t.p3);
// triangulation can fail if all 3 views have the same pixel value. This has been observed in
// simulated 3D scenes
if( triangulator.triangulate(triangObs,cameraMatrices,X)) {
structure.points[i].set(X.x,X.y,X.z,X.w);
} else {
throw new RuntimeException("Failed to triangulate a point in the inlier set?! Handle if this is common");
}
}
} | [
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lessthanoptimal/BoofCV | main/boofcv-sfm/src/main/java/boofcv/alg/sfm/structure2/ProjectiveInitializeAllCommon.java | ProjectiveInitializeAllCommon.initializeProjective3 | private void initializeProjective3(FastQueue<AssociatedTriple> associated ,
FastQueue<AssociatedTripleIndex> associatedIdx ,
int totalViews,
View viewA , View viewB , View viewC ,
int idxViewB , int idxViewC ) {
ransac.process(associated.toList());
List<AssociatedTriple> inliers = ransac.getMatchSet();
TrifocalTensor model = ransac.getModelParameters();
if( verbose != null )
verbose.println("Remaining after RANSAC "+inliers.size()+" / "+associated.size());
// projective camera matrices for each view
DMatrixRMaj P1 = CommonOps_DDRM.identity(3,4);
DMatrixRMaj P2 = new DMatrixRMaj(3,4);
DMatrixRMaj P3 = new DMatrixRMaj(3,4);
MultiViewOps.extractCameraMatrices(model,P2,P3);
// Initialize the 3D scene structure, stored in a format understood by bundle adjustment
structure.initialize(totalViews,inliers.size());
// specify the found projective camera matrices
db.lookupShape(viewA.id,shape);
// The first view is assumed to be the coordinate system's origin and is identity by definition
structure.setView(0,true, P1,shape.width,shape.height);
db.lookupShape(viewB.id,shape);
structure.setView(idxViewB,false,P2,shape.width,shape.height);
db.lookupShape(viewC.id,shape);
structure.setView(idxViewC,false,P3,shape.width,shape.height);
// triangulate homogenous coordinates for each point in the inlier set
triangulateFeatures(inliers, P1, P2, P3);
// Update the list of common features by pruning features not in the inlier set
seedToStructure.resize(viewA.totalFeatures);
seedToStructure.fill(-1); // -1 indicates no match
inlierToSeed.resize(inliers.size());
for (int i = 0; i < inliers.size(); i++) {
int inputIdx = ransac.getInputIndex(i);
// table to go from inlier list into seed feature index
inlierToSeed.data[i] = matchesTripleIdx.get(inputIdx).a;
// seed feature index into the ouptut structure index
seedToStructure.data[inlierToSeed.data[i]] = i;
}
} | java | private void initializeProjective3(FastQueue<AssociatedTriple> associated ,
FastQueue<AssociatedTripleIndex> associatedIdx ,
int totalViews,
View viewA , View viewB , View viewC ,
int idxViewB , int idxViewC ) {
ransac.process(associated.toList());
List<AssociatedTriple> inliers = ransac.getMatchSet();
TrifocalTensor model = ransac.getModelParameters();
if( verbose != null )
verbose.println("Remaining after RANSAC "+inliers.size()+" / "+associated.size());
// projective camera matrices for each view
DMatrixRMaj P1 = CommonOps_DDRM.identity(3,4);
DMatrixRMaj P2 = new DMatrixRMaj(3,4);
DMatrixRMaj P3 = new DMatrixRMaj(3,4);
MultiViewOps.extractCameraMatrices(model,P2,P3);
// Initialize the 3D scene structure, stored in a format understood by bundle adjustment
structure.initialize(totalViews,inliers.size());
// specify the found projective camera matrices
db.lookupShape(viewA.id,shape);
// The first view is assumed to be the coordinate system's origin and is identity by definition
structure.setView(0,true, P1,shape.width,shape.height);
db.lookupShape(viewB.id,shape);
structure.setView(idxViewB,false,P2,shape.width,shape.height);
db.lookupShape(viewC.id,shape);
structure.setView(idxViewC,false,P3,shape.width,shape.height);
// triangulate homogenous coordinates for each point in the inlier set
triangulateFeatures(inliers, P1, P2, P3);
// Update the list of common features by pruning features not in the inlier set
seedToStructure.resize(viewA.totalFeatures);
seedToStructure.fill(-1); // -1 indicates no match
inlierToSeed.resize(inliers.size());
for (int i = 0; i < inliers.size(); i++) {
int inputIdx = ransac.getInputIndex(i);
// table to go from inlier list into seed feature index
inlierToSeed.data[i] = matchesTripleIdx.get(inputIdx).a;
// seed feature index into the ouptut structure index
seedToStructure.data[inlierToSeed.data[i]] = i;
}
} | [
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3) Triangulate location of 3D homogenous points
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lessthanoptimal/BoofCV | main/boofcv-sfm/src/main/java/boofcv/alg/sfm/structure2/ProjectiveInitializeAllCommon.java | ProjectiveInitializeAllCommon.findRemainingCameraMatrices | boolean findRemainingCameraMatrices(LookupSimilarImages db, View seed, GrowQueue_I32 motions) {
points3D.reset(); // points in 3D
for (int i = 0; i < structure.points.length; i++) {
structure.points[i].get(points3D.grow());
}
// contains associated pairs of pixel observations
// save a call to db by using the previously loaded points
assocPixel.reset();
for (int i = 0; i < inlierToSeed.size; i++) {
// inliers from triple RANSAC
// each of these inliers was declared a feature in the world reference frame
assocPixel.grow().p1.set(matchesTriple.get(i).p1);
}
DMatrixRMaj cameraMatrix = new DMatrixRMaj(3,4);
for (int motionIdx = 0; motionIdx < motions.size; motionIdx++) {
// skip views already in the scene's structure
if( motionIdx == selectedTriple[0] || motionIdx == selectedTriple[1])
continue;
int connectionIdx = motions.get(motionIdx);
Motion edge = seed.connections.get(connectionIdx);
View viewI = edge.other(seed);
// Lookup pixel locations of features in the connected view
db.lookupPixelFeats(viewI.id,featsB);
if ( !computeCameraMatrix(seed, edge,featsB,cameraMatrix) ) {
if( verbose != null ) {
verbose.println("Pose estimator failed! motionIdx="+motionIdx);
}
return false;
}
db.lookupShape(edge.other(seed).id,shape);
structure.setView(motionIdx,false,cameraMatrix,shape.width,shape.height);
}
return true;
} | java | boolean findRemainingCameraMatrices(LookupSimilarImages db, View seed, GrowQueue_I32 motions) {
points3D.reset(); // points in 3D
for (int i = 0; i < structure.points.length; i++) {
structure.points[i].get(points3D.grow());
}
// contains associated pairs of pixel observations
// save a call to db by using the previously loaded points
assocPixel.reset();
for (int i = 0; i < inlierToSeed.size; i++) {
// inliers from triple RANSAC
// each of these inliers was declared a feature in the world reference frame
assocPixel.grow().p1.set(matchesTriple.get(i).p1);
}
DMatrixRMaj cameraMatrix = new DMatrixRMaj(3,4);
for (int motionIdx = 0; motionIdx < motions.size; motionIdx++) {
// skip views already in the scene's structure
if( motionIdx == selectedTriple[0] || motionIdx == selectedTriple[1])
continue;
int connectionIdx = motions.get(motionIdx);
Motion edge = seed.connections.get(connectionIdx);
View viewI = edge.other(seed);
// Lookup pixel locations of features in the connected view
db.lookupPixelFeats(viewI.id,featsB);
if ( !computeCameraMatrix(seed, edge,featsB,cameraMatrix) ) {
if( verbose != null ) {
verbose.println("Pose estimator failed! motionIdx="+motionIdx);
}
return false;
}
db.lookupShape(edge.other(seed).id,shape);
structure.setView(motionIdx,false,cameraMatrix,shape.width,shape.height);
}
return true;
} | [
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@return true if successful or false if not | [
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lessthanoptimal/BoofCV | main/boofcv-sfm/src/main/java/boofcv/alg/sfm/structure2/ProjectiveInitializeAllCommon.java | ProjectiveInitializeAllCommon.computeCameraMatrix | private boolean computeCameraMatrix(View seed, Motion edge, FastQueue<Point2D_F64> featsB, DMatrixRMaj cameraMatrix ) {
boolean seedSrc = edge.src == seed;
int matched = 0;
for (int i = 0; i < edge.inliers.size; i++) {
// need to go from i to index of detected features in view 'seed' to index index of feature in
// the reconstruction
AssociatedIndex a = edge.inliers.get(i);
int featId = seedToStructure.data[seedSrc ? a.src : a.dst];
if( featId == -1 )
continue;
assocPixel.get(featId).p2.set( featsB.get(seedSrc?a.dst:a.src) );
matched++;
}
// All views should have matches for all features, simple sanity check
if( matched != assocPixel.size)
throw new RuntimeException("BUG! Didn't find all features in the view");
// Estimate the camera matrix given homogenous pixel observations
if( poseEstimator.processHomogenous(assocPixel.toList(),points3D.toList()) ) {
cameraMatrix.set(poseEstimator.getProjective());
return true;
} else {
return false;
}
} | java | private boolean computeCameraMatrix(View seed, Motion edge, FastQueue<Point2D_F64> featsB, DMatrixRMaj cameraMatrix ) {
boolean seedSrc = edge.src == seed;
int matched = 0;
for (int i = 0; i < edge.inliers.size; i++) {
// need to go from i to index of detected features in view 'seed' to index index of feature in
// the reconstruction
AssociatedIndex a = edge.inliers.get(i);
int featId = seedToStructure.data[seedSrc ? a.src : a.dst];
if( featId == -1 )
continue;
assocPixel.get(featId).p2.set( featsB.get(seedSrc?a.dst:a.src) );
matched++;
}
// All views should have matches for all features, simple sanity check
if( matched != assocPixel.size)
throw new RuntimeException("BUG! Didn't find all features in the view");
// Estimate the camera matrix given homogenous pixel observations
if( poseEstimator.processHomogenous(assocPixel.toList(),points3D.toList()) ) {
cameraMatrix.set(poseEstimator.getProjective());
return true;
} else {
return false;
}
} | [
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@param seed This will be the source view. It's observations have already been added to assocPixel
@param edge The edge which connects them
@param featsB The dst view
@param cameraMatrix (Output) resulting camera matrix
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lessthanoptimal/BoofCV | main/boofcv-sfm/src/main/java/boofcv/alg/sfm/structure2/ProjectiveInitializeAllCommon.java | ProjectiveInitializeAllCommon.createObservationsForBundleAdjustment | private SceneObservations createObservationsForBundleAdjustment(LookupSimilarImages db, View seed, GrowQueue_I32 motions) {
// seed view + the motions
SceneObservations observations = new SceneObservations(motions.size+1);
// Observations for the seed view are a special case
SceneObservations.View obsView = observations.getView(0);
for (int i = 0; i < inlierToSeed.size; i++) {
int id = inlierToSeed.data[i];
Point2D_F64 o = featsA.get(id); // featsA is never modified after initially loaded
id = seedToStructure.data[id];
obsView.add(id,(float)o.x,(float)o.y);
}
// Now add observations for edges connected to the seed
for (int i = 0; i < motions.size(); i++) {
obsView = observations.getView(i+1);
Motion m = seed.connections.get(motions.get(i));
View v = m.other(seed);
boolean seedIsSrc = m.src == seed;
db.lookupPixelFeats(v.id,featsB);
for (int j = 0; j < m.inliers.size; j++) {
AssociatedIndex a = m.inliers.get(j);
int id = seedToStructure.data[seedIsSrc?a.src:a.dst];
if( id < 0 )
continue;
Point2D_F64 o = featsB.get(seedIsSrc?a.dst:a.src);
obsView.add(id,(float)o.x,(float)o.y);
}
}
return observations;
} | java | private SceneObservations createObservationsForBundleAdjustment(LookupSimilarImages db, View seed, GrowQueue_I32 motions) {
// seed view + the motions
SceneObservations observations = new SceneObservations(motions.size+1);
// Observations for the seed view are a special case
SceneObservations.View obsView = observations.getView(0);
for (int i = 0; i < inlierToSeed.size; i++) {
int id = inlierToSeed.data[i];
Point2D_F64 o = featsA.get(id); // featsA is never modified after initially loaded
id = seedToStructure.data[id];
obsView.add(id,(float)o.x,(float)o.y);
}
// Now add observations for edges connected to the seed
for (int i = 0; i < motions.size(); i++) {
obsView = observations.getView(i+1);
Motion m = seed.connections.get(motions.get(i));
View v = m.other(seed);
boolean seedIsSrc = m.src == seed;
db.lookupPixelFeats(v.id,featsB);
for (int j = 0; j < m.inliers.size; j++) {
AssociatedIndex a = m.inliers.get(j);
int id = seedToStructure.data[seedIsSrc?a.src:a.dst];
if( id < 0 )
continue;
Point2D_F64 o = featsB.get(seedIsSrc?a.dst:a.src);
obsView.add(id,(float)o.x,(float)o.y);
}
}
return observations;
} | [
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@param seed The first view which all other views are connected to
@param motions Which edges in seed
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lessthanoptimal/BoofCV | main/boofcv-sfm/src/main/java/boofcv/alg/sfm/structure2/ProjectiveInitializeAllCommon.java | ProjectiveInitializeAllCommon.refineWithBundleAdjustment | private boolean refineWithBundleAdjustment(SceneObservations observations) {
if( scaleSBA ) {
scaler.applyScale(structure,observations);
}
sba.setVerbose(verbose,verboseLevel);
sba.setParameters(structure,observations);
sba.configure(converge.ftol,converge.gtol,converge.maxIterations);
if( !sba.optimize(structure) ) {
return false;
}
if( scaleSBA ) {
// only undo scaling on camera matrices since observations are discarded
for (int i = 0; i < structure.views.length; i++) {
DMatrixRMaj P = structure.views[i].worldToView;
scaler.pixelScaling.get(i).remove(P,P);
}
scaler.undoScale(structure,observations);
}
return true;
} | java | private boolean refineWithBundleAdjustment(SceneObservations observations) {
if( scaleSBA ) {
scaler.applyScale(structure,observations);
}
sba.setVerbose(verbose,verboseLevel);
sba.setParameters(structure,observations);
sba.configure(converge.ftol,converge.gtol,converge.maxIterations);
if( !sba.optimize(structure) ) {
return false;
}
if( scaleSBA ) {
// only undo scaling on camera matrices since observations are discarded
for (int i = 0; i < structure.views.length; i++) {
DMatrixRMaj P = structure.views[i].worldToView;
scaler.pixelScaling.get(i).remove(P,P);
}
scaler.undoScale(structure,observations);
}
return true;
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lessthanoptimal/BoofCV | main/boofcv-ip/src/main/java/boofcv/core/encoding/ConvertNV21.java | ConvertNV21.nv21ToBoof | public static void nv21ToBoof(byte[] data, int width, int height, ImageBase output) {
if( output instanceof Planar) {
Planar ms = (Planar) output;
if (ms.getBandType() == GrayU8.class) {
ConvertNV21.nv21TPlanarRgb_U8(data, width, height, ms);
} else if (ms.getBandType() == GrayF32.class) {
ConvertNV21.nv21ToPlanarRgb_F32(data, width, height , ms);
} else {
throw new IllegalArgumentException("Unsupported output band format");
}
} else if( output instanceof ImageGray) {
if (output.getClass() == GrayU8.class) {
nv21ToGray(data, width, height, (GrayU8) output);
} else if (output.getClass() == GrayF32.class) {
nv21ToGray(data, width, height, (GrayF32) output);
} else {
throw new IllegalArgumentException("Unsupported output type");
}
} else if( output instanceof ImageInterleaved ) {
if( output.getClass() == InterleavedU8.class ) {
ConvertNV21.nv21ToInterleaved(data, width, height, (InterleavedU8) output);
} else if( output.getClass() == InterleavedF32.class ) {
ConvertNV21.nv21ToInterleaved(data, width, height, (InterleavedF32) output);
} else {
throw new IllegalArgumentException("Unsupported output type");
}
} else {
throw new IllegalArgumentException("Boofcv image type not yet supported");
}
} | java | public static void nv21ToBoof(byte[] data, int width, int height, ImageBase output) {
if( output instanceof Planar) {
Planar ms = (Planar) output;
if (ms.getBandType() == GrayU8.class) {
ConvertNV21.nv21TPlanarRgb_U8(data, width, height, ms);
} else if (ms.getBandType() == GrayF32.class) {
ConvertNV21.nv21ToPlanarRgb_F32(data, width, height , ms);
} else {
throw new IllegalArgumentException("Unsupported output band format");
}
} else if( output instanceof ImageGray) {
if (output.getClass() == GrayU8.class) {
nv21ToGray(data, width, height, (GrayU8) output);
} else if (output.getClass() == GrayF32.class) {
nv21ToGray(data, width, height, (GrayF32) output);
} else {
throw new IllegalArgumentException("Unsupported output type");
}
} else if( output instanceof ImageInterleaved ) {
if( output.getClass() == InterleavedU8.class ) {
ConvertNV21.nv21ToInterleaved(data, width, height, (InterleavedU8) output);
} else if( output.getClass() == InterleavedF32.class ) {
ConvertNV21.nv21ToInterleaved(data, width, height, (InterleavedF32) output);
} else {
throw new IllegalArgumentException("Unsupported output type");
}
} else {
throw new IllegalArgumentException("Boofcv image type not yet supported");
}
} | [
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@param data (input) NV21 byte array
@param width (input) image width
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@param output (output) BoofCV image | [
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lessthanoptimal/BoofCV | main/boofcv-ip/src/main/java/boofcv/core/encoding/ConvertNV21.java | ConvertNV21.nv21ToGray | public static <T extends ImageGray<T>>
T nv21ToGray( byte[] data , int width , int height ,
T output , Class<T> outputType ) {
if( outputType == GrayU8.class ) {
return (T)nv21ToGray(data,width,height,(GrayU8)output);
} else if( outputType == GrayF32.class ) {
return (T)nv21ToGray(data,width,height,(GrayF32)output);
} else {
throw new IllegalArgumentException("Unsupported BoofCV Image Type "+outputType.getSimpleName());
}
} | java | public static <T extends ImageGray<T>>
T nv21ToGray( byte[] data , int width , int height ,
T output , Class<T> outputType ) {
if( outputType == GrayU8.class ) {
return (T)nv21ToGray(data,width,height,(GrayU8)output);
} else if( outputType == GrayF32.class ) {
return (T)nv21ToGray(data,width,height,(GrayF32)output);
} else {
throw new IllegalArgumentException("Unsupported BoofCV Image Type "+outputType.getSimpleName());
}
} | [
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@param data Input: NV21 image data
@param width Input: NV21 image width
@param height Input: NV21 image height
@param output Output: Optional storage for output image. Can be null.
@param outputType Output: Type of output image
@param <T> Output image type
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lessthanoptimal/BoofCV | main/boofcv-ip/src/main/java/boofcv/core/encoding/ConvertNV21.java | ConvertNV21.nv21ToGray | public static GrayU8 nv21ToGray(byte[] data , int width , int height , GrayU8 output ) {
if( output != null ) {
output.reshape(width,height);
} else {
output = new GrayU8(width,height);
}
if(BoofConcurrency.USE_CONCURRENT ) {
ImplConvertNV21_MT.nv21ToGray(data, output);
} else {
ImplConvertNV21.nv21ToGray(data, output);
}
return output;
} | java | public static GrayU8 nv21ToGray(byte[] data , int width , int height , GrayU8 output ) {
if( output != null ) {
output.reshape(width,height);
} else {
output = new GrayU8(width,height);
}
if(BoofConcurrency.USE_CONCURRENT ) {
ImplConvertNV21_MT.nv21ToGray(data, output);
} else {
ImplConvertNV21.nv21ToGray(data, output);
}
return output;
} | [
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lessthanoptimal/BoofCV | main/boofcv-recognition/src/main/java/boofcv/alg/fiducial/square/FiducialSquareGenerator.java | FiducialSquareGenerator.generate | public void generate( long value , int gridWidth ) {
renderer.init();
drawBorder();
double whiteBorder = whiteBorderDoc /markerWidth;
double X0 = whiteBorder+blackBorder;
double Y0 = whiteBorder+blackBorder;
double bw = (1.0-2*X0)/gridWidth;
// Draw the black corner used to ID the orientation
square(X0,1.0-whiteBorder-blackBorder-bw,bw);
final int bitCount = gridWidth*gridWidth - 4;
for (int j = 0; j < bitCount; j++) {
if( (value & (1L<<j)) != 0 ) {
box(bw,j,gridWidth);
}
}
// int s2 = (int)Math.round(ret.width*borderFraction);
// int s5 = s2+square*(gridWidth-1);
//
// int N = gridWidth*gridWidth-4;
// for (int i = 0; i < N; i++) {
// if( (value& (1<<i)) != 0 )
// continue;
//
// int where = index(i, gridWidth);
// int x = where%gridWidth;
// int y = gridWidth-1-(where/gridWidth);
//
// x = s2 + square*x;
// y = s2 + square*y;
//
// ImageMiscOps.fillRectangle(ret,0xFF,x,y,square,square);
// }
// ImageMiscOps.fillRectangle(ret,0xFF,s2,s2,square,square);
// ImageMiscOps.fillRectangle(ret,0xFF,s5,s5,square,square);
// ImageMiscOps.fillRectangle(ret,0xFF,s5,s2,square,square);
} | java | public void generate( long value , int gridWidth ) {
renderer.init();
drawBorder();
double whiteBorder = whiteBorderDoc /markerWidth;
double X0 = whiteBorder+blackBorder;
double Y0 = whiteBorder+blackBorder;
double bw = (1.0-2*X0)/gridWidth;
// Draw the black corner used to ID the orientation
square(X0,1.0-whiteBorder-blackBorder-bw,bw);
final int bitCount = gridWidth*gridWidth - 4;
for (int j = 0; j < bitCount; j++) {
if( (value & (1L<<j)) != 0 ) {
box(bw,j,gridWidth);
}
}
// int s2 = (int)Math.round(ret.width*borderFraction);
// int s5 = s2+square*(gridWidth-1);
//
// int N = gridWidth*gridWidth-4;
// for (int i = 0; i < N; i++) {
// if( (value& (1<<i)) != 0 )
// continue;
//
// int where = index(i, gridWidth);
// int x = where%gridWidth;
// int y = gridWidth-1-(where/gridWidth);
//
// x = s2 + square*x;
// y = s2 + square*y;
//
// ImageMiscOps.fillRectangle(ret,0xFF,x,y,square,square);
// }
// ImageMiscOps.fillRectangle(ret,0xFF,s2,s2,square,square);
// ImageMiscOps.fillRectangle(ret,0xFF,s5,s5,square,square);
// ImageMiscOps.fillRectangle(ret,0xFF,s5,s2,square,square);
} | [
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@param value Value encoded in the fiducial
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lessthanoptimal/BoofCV | main/boofcv-sfm/src/main/java/boofcv/alg/sfm/overhead/CameraPlaneProjection.java | CameraPlaneProjection.setConfiguration | public void setConfiguration( Se3_F64 planeToCamera ,
CameraPinholeBrown intrinsic )
{
this.planeToCamera = planeToCamera;
normToPixel = LensDistortionFactory.narrow(intrinsic).distort_F64(false, true);
pixelToNorm = LensDistortionFactory.narrow(intrinsic).undistort_F64(true, false);
planeToCamera.invert(cameraToPlane);
} | java | public void setConfiguration( Se3_F64 planeToCamera ,
CameraPinholeBrown intrinsic )
{
this.planeToCamera = planeToCamera;
normToPixel = LensDistortionFactory.narrow(intrinsic).distort_F64(false, true);
pixelToNorm = LensDistortionFactory.narrow(intrinsic).undistort_F64(true, false);
planeToCamera.invert(cameraToPlane);
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@param planeToCamera Transform from the plane to the camera
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lessthanoptimal/BoofCV | main/boofcv-sfm/src/main/java/boofcv/alg/sfm/overhead/CameraPlaneProjection.java | CameraPlaneProjection.setIntrinsic | public void setIntrinsic(CameraPinholeBrown intrinsic )
{
normToPixel = LensDistortionFactory.narrow(intrinsic).distort_F64(false, true);
pixelToNorm = LensDistortionFactory.narrow(intrinsic).undistort_F64(true, false);
} | java | public void setIntrinsic(CameraPinholeBrown intrinsic )
{
normToPixel = LensDistortionFactory.narrow(intrinsic).distort_F64(false, true);
pixelToNorm = LensDistortionFactory.narrow(intrinsic).undistort_F64(true, false);
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lessthanoptimal/BoofCV | main/boofcv-sfm/src/main/java/boofcv/alg/sfm/overhead/CameraPlaneProjection.java | CameraPlaneProjection.setPlaneToCamera | public void setPlaneToCamera(Se3_F64 planeToCamera, boolean computeInverse ) {
this.planeToCamera = planeToCamera;
if( computeInverse )
planeToCamera.invert(cameraToPlane);
} | java | public void setPlaneToCamera(Se3_F64 planeToCamera, boolean computeInverse ) {
this.planeToCamera = planeToCamera;
if( computeInverse )
planeToCamera.invert(cameraToPlane);
} | [
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lessthanoptimal/BoofCV | main/boofcv-sfm/src/main/java/boofcv/alg/sfm/overhead/CameraPlaneProjection.java | CameraPlaneProjection.planeToPixel | public boolean planeToPixel( double pointX , double pointY , Point2D_F64 pixel ) {
// convert it into a 3D coordinate and transform into camera reference frame
plain3D.set(-pointY, 0, pointX);
SePointOps_F64.transform(planeToCamera, plain3D, camera3D);
// if it's behind the camera it can't be seen
if( camera3D.z <= 0 )
return false;
// normalized image coordinates and convert into pixels
double normX = camera3D.x / camera3D.z;
double normY = camera3D.y / camera3D.z;
normToPixel.compute(normX,normY,pixel);
return true;
} | java | public boolean planeToPixel( double pointX , double pointY , Point2D_F64 pixel ) {
// convert it into a 3D coordinate and transform into camera reference frame
plain3D.set(-pointY, 0, pointX);
SePointOps_F64.transform(planeToCamera, plain3D, camera3D);
// if it's behind the camera it can't be seen
if( camera3D.z <= 0 )
return false;
// normalized image coordinates and convert into pixels
double normX = camera3D.x / camera3D.z;
double normY = camera3D.y / camera3D.z;
normToPixel.compute(normX,normY,pixel);
return true;
} | [
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@param pixel (output) Pixel in the image
@return true if the point is in front of the camera. False if not. | [
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lessthanoptimal/BoofCV | main/boofcv-sfm/src/main/java/boofcv/alg/sfm/overhead/CameraPlaneProjection.java | CameraPlaneProjection.planeToNormalized | public boolean planeToNormalized( double pointX , double pointY , Point2D_F64 normalized ) {
// convert it into a 3D coordinate and transform into camera reference frame
plain3D.set(-pointY, 0, pointX);
SePointOps_F64.transform(planeToCamera, plain3D, camera3D);
// if it's behind the camera it can't be seen
if( camera3D.z <= 0 )
return false;
// normalized image coordinates and convert into pixels
normalized.x = camera3D.x / camera3D.z;
normalized.y = camera3D.y / camera3D.z;
return true;
} | java | public boolean planeToNormalized( double pointX , double pointY , Point2D_F64 normalized ) {
// convert it into a 3D coordinate and transform into camera reference frame
plain3D.set(-pointY, 0, pointX);
SePointOps_F64.transform(planeToCamera, plain3D, camera3D);
// if it's behind the camera it can't be seen
if( camera3D.z <= 0 )
return false;
// normalized image coordinates and convert into pixels
normalized.x = camera3D.x / camera3D.z;
normalized.y = camera3D.y / camera3D.z;
return true;
} | [
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@param pointX (input) Point on the plane, x-axis
@param pointY (input) Point on the plane, y-axis
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@return true if the point is in front of the camera. False if not. | [
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lessthanoptimal/BoofCV | main/boofcv-recognition/src/main/java/boofcv/alg/fiducial/calib/chess/ChessboardCornerGraph.java | ChessboardCornerGraph.convert | public void convert( FeatureGraph2D graph ) {
graph.nodes.resize(corners.size);
graph.reset();
for (int i = 0; i < corners.size; i++) {
Node c = corners.get(i);
FeatureGraph2D.Node n = graph.nodes.grow();
n.reset();
n.set(c.x,c.y);
n.index = c.index;
}
for (int i = 0; i < corners.size; i++) {
Node c = corners.get(i);
for (int j = 0; j < 4; j++) {
if( c.edges[j] == null )
continue;
graph.connect(c.index,c.edges[j].index);
}
}
} | java | public void convert( FeatureGraph2D graph ) {
graph.nodes.resize(corners.size);
graph.reset();
for (int i = 0; i < corners.size; i++) {
Node c = corners.get(i);
FeatureGraph2D.Node n = graph.nodes.grow();
n.reset();
n.set(c.x,c.y);
n.index = c.index;
}
for (int i = 0; i < corners.size; i++) {
Node c = corners.get(i);
for (int j = 0; j < 4; j++) {
if( c.edges[j] == null )
continue;
graph.connect(c.index,c.edges[j].index);
}
}
} | [
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lessthanoptimal/BoofCV | main/boofcv-sfm/src/main/java/boofcv/alg/sfm/d2/ImageMotionPointTrackerKey.java | ImageMotionPointTrackerKey.process | public boolean process( I frame ) {
keyFrame = false;
// update the feature tracker
tracker.process(frame);
totalFramesProcessed++;
List<PointTrack> tracks = tracker.getActiveTracks(null);
if( tracks.size() == 0 )
return false;
List<AssociatedPair> pairs = new ArrayList<>();
for( PointTrack t : tracks ) {
pairs.add((AssociatedPair)t.getCookie());
}
// fit the motion model to the feature tracks
if( !modelMatcher.process((List)pairs) ) {
return false;
}
if( modelRefiner != null ) {
if( !modelRefiner.fitModel(modelMatcher.getMatchSet(),modelMatcher.getModelParameters(),keyToCurr) )
return false;
} else {
keyToCurr.set(modelMatcher.getModelParameters());
}
// mark that the track is in the inlier set
for( AssociatedPair p : modelMatcher.getMatchSet() ) {
((AssociatedPairTrack)p).lastUsed = totalFramesProcessed;
}
// prune tracks which aren't being used
pruneUnusedTracks();
// Update the motion
worldToKey.concat(keyToCurr, worldToCurr);
return true;
} | java | public boolean process( I frame ) {
keyFrame = false;
// update the feature tracker
tracker.process(frame);
totalFramesProcessed++;
List<PointTrack> tracks = tracker.getActiveTracks(null);
if( tracks.size() == 0 )
return false;
List<AssociatedPair> pairs = new ArrayList<>();
for( PointTrack t : tracks ) {
pairs.add((AssociatedPair)t.getCookie());
}
// fit the motion model to the feature tracks
if( !modelMatcher.process((List)pairs) ) {
return false;
}
if( modelRefiner != null ) {
if( !modelRefiner.fitModel(modelMatcher.getMatchSet(),modelMatcher.getModelParameters(),keyToCurr) )
return false;
} else {
keyToCurr.set(modelMatcher.getModelParameters());
}
// mark that the track is in the inlier set
for( AssociatedPair p : modelMatcher.getMatchSet() ) {
((AssociatedPairTrack)p).lastUsed = totalFramesProcessed;
}
// prune tracks which aren't being used
pruneUnusedTracks();
// Update the motion
worldToKey.concat(keyToCurr, worldToCurr);
return true;
} | [
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lessthanoptimal/BoofCV | main/boofcv-sfm/src/main/java/boofcv/alg/sfm/d2/ImageMotionPointTrackerKey.java | ImageMotionPointTrackerKey.changeKeyFrame | public void changeKeyFrame() {
// drop all inactive tracks since their location is unknown in the current frame
List<PointTrack> inactive = tracker.getInactiveTracks(null);
for( PointTrack l : inactive ) {
tracker.dropTrack(l);
}
// set the keyframe for active tracks as their current location
List<PointTrack> active = tracker.getActiveTracks(null);
for( PointTrack l : active ) {
AssociatedPairTrack p = l.getCookie();
p.p1.set(l);
p.lastUsed = totalFramesProcessed;
}
tracker.spawnTracks();
List<PointTrack> spawned = tracker.getNewTracks(null);
for( PointTrack l : spawned ) {
AssociatedPairTrack p = l.getCookie();
if( p == null ) {
l.cookie = p = new AssociatedPairTrack();
// little bit of trickery here. Save the reference so that the point
// in the current frame is updated for free as PointTrack is
p.p2 = l;
}
p.p1.set(l);
p.lastUsed = totalFramesProcessed;
}
worldToKey.set(worldToCurr);
keyToCurr.reset();
keyFrame = true;
} | java | public void changeKeyFrame() {
// drop all inactive tracks since their location is unknown in the current frame
List<PointTrack> inactive = tracker.getInactiveTracks(null);
for( PointTrack l : inactive ) {
tracker.dropTrack(l);
}
// set the keyframe for active tracks as their current location
List<PointTrack> active = tracker.getActiveTracks(null);
for( PointTrack l : active ) {
AssociatedPairTrack p = l.getCookie();
p.p1.set(l);
p.lastUsed = totalFramesProcessed;
}
tracker.spawnTracks();
List<PointTrack> spawned = tracker.getNewTracks(null);
for( PointTrack l : spawned ) {
AssociatedPairTrack p = l.getCookie();
if( p == null ) {
l.cookie = p = new AssociatedPairTrack();
// little bit of trickery here. Save the reference so that the point
// in the current frame is updated for free as PointTrack is
p.p2 = l;
}
p.p1.set(l);
p.lastUsed = totalFramesProcessed;
}
worldToKey.set(worldToCurr);
keyToCurr.reset();
keyFrame = true;
} | [
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lessthanoptimal/BoofCV | main/boofcv-geo/src/main/java/boofcv/alg/cloud/PointCloudUtils.java | PointCloudUtils.autoScale | public static double autoScale( List<Point3D_F64> cloud , double target ) {
Point3D_F64 mean = new Point3D_F64();
Point3D_F64 stdev = new Point3D_F64();
statistics(cloud, mean, stdev);
double scale = target/(Math.max(Math.max(stdev.x,stdev.y),stdev.z));
int N = cloud.size();
for (int i = 0; i < N ; i++) {
cloud.get(i).scale(scale);
}
return scale;
} | java | public static double autoScale( List<Point3D_F64> cloud , double target ) {
Point3D_F64 mean = new Point3D_F64();
Point3D_F64 stdev = new Point3D_F64();
statistics(cloud, mean, stdev);
double scale = target/(Math.max(Math.max(stdev.x,stdev.y),stdev.z));
int N = cloud.size();
for (int i = 0; i < N ; i++) {
cloud.get(i).scale(scale);
}
return scale;
} | [
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@param cloud The point cloud
@param target The desired standard deviation of the cloud. Try 100
@return The selected scale factor | [
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lessthanoptimal/BoofCV | main/boofcv-geo/src/main/java/boofcv/alg/cloud/PointCloudUtils.java | PointCloudUtils.statistics | public static void statistics( List<Point3D_F64> cloud , Point3D_F64 mean , Point3D_F64 stdev ) {
final int N = cloud.size();
for (int i = 0; i < N; i++) {
Point3D_F64 p = cloud.get(i);
mean.x += p.x / N;
mean.y += p.y / N;
mean.z += p.z / N;
}
for (int i = 0; i < N; i++) {
Point3D_F64 p = cloud.get(i);
double dx = p.x-mean.x;
double dy = p.y-mean.y;
double dz = p.z-mean.z;
stdev.x += dx*dx/N;
stdev.y += dy*dy/N;
stdev.z += dz*dz/N;
}
stdev.x = Math.sqrt(stdev.x);
stdev.y = Math.sqrt(stdev.y);
stdev.z = Math.sqrt(stdev.z);
} | java | public static void statistics( List<Point3D_F64> cloud , Point3D_F64 mean , Point3D_F64 stdev ) {
final int N = cloud.size();
for (int i = 0; i < N; i++) {
Point3D_F64 p = cloud.get(i);
mean.x += p.x / N;
mean.y += p.y / N;
mean.z += p.z / N;
}
for (int i = 0; i < N; i++) {
Point3D_F64 p = cloud.get(i);
double dx = p.x-mean.x;
double dy = p.y-mean.y;
double dz = p.z-mean.z;
stdev.x += dx*dx/N;
stdev.y += dy*dy/N;
stdev.z += dz*dz/N;
}
stdev.x = Math.sqrt(stdev.x);
stdev.y = Math.sqrt(stdev.y);
stdev.z = Math.sqrt(stdev.z);
} | [
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@param mean (Output) mean of each axis
@param stdev (Output) standard deviation of each axis | [
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lessthanoptimal/BoofCV | main/boofcv-geo/src/main/java/boofcv/alg/cloud/PointCloudUtils.java | PointCloudUtils.prune | public static void prune(List<Point3D_F64> cloud , int minNeighbors , double radius ) {
if( minNeighbors < 0 )
throw new IllegalArgumentException("minNeighbors must be >= 0");
NearestNeighbor<Point3D_F64> nn = FactoryNearestNeighbor.kdtree(new KdTreePoint3D_F64() );
NearestNeighbor.Search<Point3D_F64> search = nn.createSearch();
nn.setPoints(cloud,false);
FastQueue<NnData<Point3D_F64>> results = new FastQueue(NnData.class,true);
// It will always find itself
minNeighbors += 1;
// distance is Euclidean squared
radius *= radius;
for( int i = cloud.size()-1; i >= 0; i-- ) {
search.findNearest(cloud.get(i),radius,minNeighbors,results);
if( results.size < minNeighbors ) {
cloud.remove(i);
}
}
} | java | public static void prune(List<Point3D_F64> cloud , int minNeighbors , double radius ) {
if( minNeighbors < 0 )
throw new IllegalArgumentException("minNeighbors must be >= 0");
NearestNeighbor<Point3D_F64> nn = FactoryNearestNeighbor.kdtree(new KdTreePoint3D_F64() );
NearestNeighbor.Search<Point3D_F64> search = nn.createSearch();
nn.setPoints(cloud,false);
FastQueue<NnData<Point3D_F64>> results = new FastQueue(NnData.class,true);
// It will always find itself
minNeighbors += 1;
// distance is Euclidean squared
radius *= radius;
for( int i = cloud.size()-1; i >= 0; i-- ) {
search.findNearest(cloud.get(i),radius,minNeighbors,results);
if( results.size < minNeighbors ) {
cloud.remove(i);
}
}
} | [
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@param cloud Point cloud
@param minNeighbors Minimum number of neighbors for it to not be pruned
@param radius search distance for neighbors | [
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lessthanoptimal/BoofCV | main/boofcv-geo/src/main/java/boofcv/alg/geo/LowLevelMultiViewOps.java | LowLevelMultiViewOps.computeNormalizationLL | public static void computeNormalizationLL(List<List<Point2D_F64>> points, NormalizationPoint2D normalize )
{
double meanX = 0;
double meanY = 0;
int count = 0;
for (int i = 0; i < points.size(); i++) {
List<Point2D_F64> l = points.get(i);
for (int j = 0; j < l.size(); j++) {
Point2D_F64 p = l.get(j);
meanX += p.x;
meanY += p.y;
}
count += l.size();
}
meanX /= count;
meanY /= count;
double stdX = 0;
double stdY = 0;
for (int i = 0; i < points.size(); i++) {
List<Point2D_F64> l = points.get(i);
for (int j = 0; j < l.size(); j++) {
Point2D_F64 p = l.get(j);
double dx = p.x - meanX;
double dy = p.y - meanY;
stdX += dx*dx;
stdY += dy*dy;
}
}
normalize.meanX = meanX;
normalize.meanY = meanY;
normalize.stdX = Math.sqrt(stdX/count);
normalize.stdY = Math.sqrt(stdY/count);
} | java | public static void computeNormalizationLL(List<List<Point2D_F64>> points, NormalizationPoint2D normalize )
{
double meanX = 0;
double meanY = 0;
int count = 0;
for (int i = 0; i < points.size(); i++) {
List<Point2D_F64> l = points.get(i);
for (int j = 0; j < l.size(); j++) {
Point2D_F64 p = l.get(j);
meanX += p.x;
meanY += p.y;
}
count += l.size();
}
meanX /= count;
meanY /= count;
double stdX = 0;
double stdY = 0;
for (int i = 0; i < points.size(); i++) {
List<Point2D_F64> l = points.get(i);
for (int j = 0; j < l.size(); j++) {
Point2D_F64 p = l.get(j);
double dx = p.x - meanX;
double dy = p.y - meanY;
stdX += dx*dx;
stdY += dy*dy;
}
}
normalize.meanX = meanX;
normalize.meanY = meanY;
normalize.stdX = Math.sqrt(stdX/count);
normalize.stdY = Math.sqrt(stdY/count);
} | [
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@param points Input: List of observed points. Not modified.
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lessthanoptimal/BoofCV | main/autocode/src/main/java/boofcv/AutocodeConcurrentApp.java | AutocodeConcurrentApp.convertFile | public static void convertFile( File original ) throws IOException {
File outputFile = determineClassName(original);
String classNameOld = className(original);
String classNameNew = className(outputFile);
// Read the file and split it up into lines
List<String> inputLines = FileUtils.readLines(original,"UTF-8");
List<String> outputLines = new ArrayList<>();
List<Macro> macros = new ArrayList<>();
// parse each line by line looking for instructions
boolean foundClassDef = false;
for (int i = 0; i < inputLines.size(); i++) {
String line = inputLines.get(i);
int where = line.indexOf(prefix);
if( where < 0 ) {
if( !foundClassDef && line.contains("class "+classNameOld)) {
foundClassDef = true;
line = line.replaceFirst("class "+classNameOld,"class "+classNameNew);
} else {
line = line.replace(classNameOld+"(",classNameNew+"(");
}
outputLines.add(line);
continue;
}
String type = readType(line,where+prefix.length());
String whitespaces = line.substring(0,where);
int frontLength =where+prefix.length()+type.length();
String message = line.length()>frontLength ? line.substring(frontLength+1) : "";
switch(type) {
case "CLASS_NAME":continue; // ignore. already processed
case "INLINE":
outputLines.add(whitespaces+message);
break;
case "ABOVE":
// remove the previous line
outputLines.remove(outputLines.size()-1);
outputLines.add(whitespaces+message);
break;
case "BELOW":
outputLines.add(whitespaces+message);
i += 1; // skip next line
break;
case "REMOVE_ABOVE":
outputLines.remove(outputLines.size()-1);
break;
case "REMOVE_BELOW":
i += 1; // skip next line
break;
case "MACRO":
throw new RuntimeException("MACRO not handled yet");
default:
throw new RuntimeException("Unknown: "+type);
}
}
PrintStream out = new PrintStream(outputFile);
for (int i = 0; i < outputLines.size(); i++) {
out.println(outputLines.get(i));
}
out.close();
createTestIfNotThere(outputFile);
} | java | public static void convertFile( File original ) throws IOException {
File outputFile = determineClassName(original);
String classNameOld = className(original);
String classNameNew = className(outputFile);
// Read the file and split it up into lines
List<String> inputLines = FileUtils.readLines(original,"UTF-8");
List<String> outputLines = new ArrayList<>();
List<Macro> macros = new ArrayList<>();
// parse each line by line looking for instructions
boolean foundClassDef = false;
for (int i = 0; i < inputLines.size(); i++) {
String line = inputLines.get(i);
int where = line.indexOf(prefix);
if( where < 0 ) {
if( !foundClassDef && line.contains("class "+classNameOld)) {
foundClassDef = true;
line = line.replaceFirst("class "+classNameOld,"class "+classNameNew);
} else {
line = line.replace(classNameOld+"(",classNameNew+"(");
}
outputLines.add(line);
continue;
}
String type = readType(line,where+prefix.length());
String whitespaces = line.substring(0,where);
int frontLength =where+prefix.length()+type.length();
String message = line.length()>frontLength ? line.substring(frontLength+1) : "";
switch(type) {
case "CLASS_NAME":continue; // ignore. already processed
case "INLINE":
outputLines.add(whitespaces+message);
break;
case "ABOVE":
// remove the previous line
outputLines.remove(outputLines.size()-1);
outputLines.add(whitespaces+message);
break;
case "BELOW":
outputLines.add(whitespaces+message);
i += 1; // skip next line
break;
case "REMOVE_ABOVE":
outputLines.remove(outputLines.size()-1);
break;
case "REMOVE_BELOW":
i += 1; // skip next line
break;
case "MACRO":
throw new RuntimeException("MACRO not handled yet");
default:
throw new RuntimeException("Unknown: "+type);
}
}
PrintStream out = new PrintStream(outputFile);
for (int i = 0; i < outputLines.size(); i++) {
out.println(outputLines.get(i));
}
out.close();
createTestIfNotThere(outputFile);
} | [
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lessthanoptimal/BoofCV | main/autocode/src/main/java/boofcv/AutocodeConcurrentApp.java | AutocodeConcurrentApp.determineClassName | private static File determineClassName( File original ) throws IOException {
String text = FileUtils.readFileToString(original, "UTF-8");
if(!text.contains("//CONCURRENT"))
throw new IOException("Not a concurrent file");
String pattern = "//CONCURRENT_CLASS_NAME ";
int where = text.indexOf(pattern);
if( where < 0 ) {
String name = className(original);
return new File(original.getParent(),name+"_MT.java");
}
String name = readUntilEndOfLine(text,where+pattern.length());
return new File(original.getParent(),name+".java");
} | java | private static File determineClassName( File original ) throws IOException {
String text = FileUtils.readFileToString(original, "UTF-8");
if(!text.contains("//CONCURRENT"))
throw new IOException("Not a concurrent file");
String pattern = "//CONCURRENT_CLASS_NAME ";
int where = text.indexOf(pattern);
if( where < 0 ) {
String name = className(original);
return new File(original.getParent(),name+"_MT.java");
}
String name = readUntilEndOfLine(text,where+pattern.length());
return new File(original.getParent(),name+".java");
} | [
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@param original Input file
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lessthanoptimal/BoofCV | main/boofcv-types/src/main/java/boofcv/struct/pyramid/ImagePyramidBase.java | ImagePyramidBase.initialize | @Override
public void initialize(int width, int height) {
// see if it has already been initialized
if( bottomWidth == width && bottomHeight == height )
return;
this.bottomWidth = width;
this.bottomHeight = height;
layers = imageType.createArray(getNumLayers());
double scaleFactor = getScale(0);
if (scaleFactor == 1) {
if (!saveOriginalReference) {
layers[0] = imageType.createImage(bottomWidth, bottomHeight);
}
} else {
layers[0] = imageType.createImage((int)Math.ceil(bottomWidth / scaleFactor), (int)Math.ceil(bottomHeight / scaleFactor));
}
for (int i = 1; i < layers.length; i++) {
scaleFactor = getScale(i);
layers[i] = imageType.createImage((int)Math.ceil(bottomWidth / scaleFactor), (int)Math.ceil(bottomHeight / scaleFactor));
}
} | java | @Override
public void initialize(int width, int height) {
// see if it has already been initialized
if( bottomWidth == width && bottomHeight == height )
return;
this.bottomWidth = width;
this.bottomHeight = height;
layers = imageType.createArray(getNumLayers());
double scaleFactor = getScale(0);
if (scaleFactor == 1) {
if (!saveOriginalReference) {
layers[0] = imageType.createImage(bottomWidth, bottomHeight);
}
} else {
layers[0] = imageType.createImage((int)Math.ceil(bottomWidth / scaleFactor), (int)Math.ceil(bottomHeight / scaleFactor));
}
for (int i = 1; i < layers.length; i++) {
scaleFactor = getScale(i);
layers[i] = imageType.createImage((int)Math.ceil(bottomWidth / scaleFactor), (int)Math.ceil(bottomHeight / scaleFactor));
}
} | [
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@param width Image width
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lessthanoptimal/BoofCV | main/boofcv-types/src/main/java/boofcv/struct/pyramid/ImagePyramidBase.java | ImagePyramidBase.checkScales | protected void checkScales() {
if( getScale(0) < 0 ) {
throw new IllegalArgumentException("The first layer must be more than zero.");
}
double prevScale = 0;
for( int i = 0; i < getNumLayers(); i++ ) {
double s = getScale(i);
if( s < prevScale )
throw new IllegalArgumentException("Higher layers must be the same size or larger than previous layers.");
prevScale = s;
}
} | java | protected void checkScales() {
if( getScale(0) < 0 ) {
throw new IllegalArgumentException("The first layer must be more than zero.");
}
double prevScale = 0;
for( int i = 0; i < getNumLayers(); i++ ) {
double s = getScale(i);
if( s < prevScale )
throw new IllegalArgumentException("Higher layers must be the same size or larger than previous layers.");
prevScale = s;
}
} | [
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lessthanoptimal/BoofCV | main/boofcv-recognition/src/main/java/boofcv/alg/fiducial/calib/circle/EllipseClustersIntoRegularGrid.java | EllipseClustersIntoRegularGrid.checkGridSize | static boolean checkGridSize(List<List<NodeInfo>> grid ,
int clusterSize ) {
int total = 0;
int expected = grid.get(0).size();
for (int i = 0; i < grid.size(); i++) {
if( expected != grid.get(i).size() )
return false;
total += grid.get(i).size();
}
return total == clusterSize;
} | java | static boolean checkGridSize(List<List<NodeInfo>> grid ,
int clusterSize ) {
int total = 0;
int expected = grid.get(0).size();
for (int i = 0; i < grid.size(); i++) {
if( expected != grid.get(i).size() )
return false;
total += grid.get(i).size();
}
return total == clusterSize;
} | [
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lessthanoptimal/BoofCV | main/boofcv-geo/src/main/java/boofcv/alg/geo/triangulate/PixelDepthLinearMetric.java | PixelDepthLinearMetric.depthNView | public double depthNView( List<Point2D_F64> obs ,
List<Se3_F64> motion )
{
double top = 0, bottom = 0;
Point2D_F64 a = obs.get(0);
for( int i = 1; i < obs.size(); i++ ) {
Se3_F64 se = motion.get(i-1);
Point2D_F64 b = obs.get(i);
GeometryMath_F64.multCrossA(b, se.getR(), temp0);
GeometryMath_F64.mult(temp0,a,temp1);
GeometryMath_F64.cross(b, se.getT(), temp2);
top += temp2.x+temp2.y+temp2.z;
bottom += temp1.x+temp1.y+temp1.z;
}
return -top/bottom;
} | java | public double depthNView( List<Point2D_F64> obs ,
List<Se3_F64> motion )
{
double top = 0, bottom = 0;
Point2D_F64 a = obs.get(0);
for( int i = 1; i < obs.size(); i++ ) {
Se3_F64 se = motion.get(i-1);
Point2D_F64 b = obs.get(i);
GeometryMath_F64.multCrossA(b, se.getR(), temp0);
GeometryMath_F64.mult(temp0,a,temp1);
GeometryMath_F64.cross(b, se.getT(), temp2);
top += temp2.x+temp2.y+temp2.z;
bottom += temp1.x+temp1.y+temp1.z;
}
return -top/bottom;
} | [
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@param obs List of observations on a single feature in normalized coordinates
@param motion List of camera motions. Each index 'i' is the motion from view 0 to view i+1.
@return depth of the pixels | [
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lessthanoptimal/BoofCV | main/boofcv-geo/src/main/java/boofcv/alg/geo/triangulate/PixelDepthLinearMetric.java | PixelDepthLinearMetric.depth2View | public double depth2View( Point2D_F64 a , Point2D_F64 b , Se3_F64 fromAtoB )
{
DMatrixRMaj R = fromAtoB.getR();
Vector3D_F64 T = fromAtoB.getT();
GeometryMath_F64.multCrossA(b, R, temp0);
GeometryMath_F64.mult(temp0,a,temp1);
GeometryMath_F64.cross(b, T, temp2);
return -(temp2.x+temp2.y+temp2.z)/(temp1.x+temp1.y+temp1.z);
} | java | public double depth2View( Point2D_F64 a , Point2D_F64 b , Se3_F64 fromAtoB )
{
DMatrixRMaj R = fromAtoB.getR();
Vector3D_F64 T = fromAtoB.getT();
GeometryMath_F64.multCrossA(b, R, temp0);
GeometryMath_F64.mult(temp0,a,temp1);
GeometryMath_F64.cross(b, T, temp2);
return -(temp2.x+temp2.y+temp2.z)/(temp1.x+temp1.y+temp1.z);
} | [
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@param b Observation in second frame. In calibrated coordinates. Not modified.
@param fromAtoB Transform from frame a to frame b.
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lessthanoptimal/BoofCV | main/boofcv-geo/src/main/java/boofcv/alg/geo/structure/ProjectiveStructureByFactorization.java | ProjectiveStructureByFactorization.initialize | public void initialize( int numFeatures , int numViews ) {
depths.reshape(numViews,numFeatures);
pixels.reshape(numViews*2,numFeatures);
pixelScale = 0;
} | java | public void initialize( int numFeatures , int numViews ) {
depths.reshape(numViews,numFeatures);
pixels.reshape(numViews*2,numFeatures);
pixelScale = 0;
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lessthanoptimal/BoofCV | main/boofcv-geo/src/main/java/boofcv/alg/geo/structure/ProjectiveStructureByFactorization.java | ProjectiveStructureByFactorization.setPixels | public void setPixels(int view , List<Point2D_F64> pixelsInView ) {
if( pixelsInView.size() != pixels.numCols )
throw new IllegalArgumentException("Pixel count must be constant and match "+pixels.numCols);
int row = view*2;
for (int i = 0; i < pixelsInView.size(); i++) {
Point2D_F64 p = pixelsInView.get(i);
pixels.set(row,i,p.x);
pixels.set(row+1,i,p.y);
pixelScale = Math.max(Math.abs(p.x),Math.abs(p.y));
}
} | java | public void setPixels(int view , List<Point2D_F64> pixelsInView ) {
if( pixelsInView.size() != pixels.numCols )
throw new IllegalArgumentException("Pixel count must be constant and match "+pixels.numCols);
int row = view*2;
for (int i = 0; i < pixelsInView.size(); i++) {
Point2D_F64 p = pixelsInView.get(i);
pixels.set(row,i,p.x);
pixels.set(row+1,i,p.y);
pixelScale = Math.max(Math.abs(p.x),Math.abs(p.y));
}
} | [
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lessthanoptimal/BoofCV | main/boofcv-geo/src/main/java/boofcv/alg/geo/structure/ProjectiveStructureByFactorization.java | ProjectiveStructureByFactorization.setDepths | public void setDepths( int view , double featureDepths[] ) {
if( featureDepths.length < depths.numCols )
throw new IllegalArgumentException("Pixel count must be constant and match "+pixels.numCols);
int N = depths.numCols;
for (int i = 0; i < N; i++) {
depths.set(view,i, featureDepths[i]);
}
} | java | public void setDepths( int view , double featureDepths[] ) {
if( featureDepths.length < depths.numCols )
throw new IllegalArgumentException("Pixel count must be constant and match "+pixels.numCols);
int N = depths.numCols;
for (int i = 0; i < N; i++) {
depths.set(view,i, featureDepths[i]);
}
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lessthanoptimal/BoofCV | main/boofcv-geo/src/main/java/boofcv/alg/geo/structure/ProjectiveStructureByFactorization.java | ProjectiveStructureByFactorization.setDepthsFrom3D | public void setDepthsFrom3D(int view , List<Point3D_F64> locations ) {
if( locations.size() != pixels.numCols )
throw new IllegalArgumentException("Pixel count must be constant and match "+pixels.numCols);
int N = depths.numCols;
for (int i = 0; i < N; i++) {
depths.set(view,i, locations.get(i).z );
}
} | java | public void setDepthsFrom3D(int view , List<Point3D_F64> locations ) {
if( locations.size() != pixels.numCols )
throw new IllegalArgumentException("Pixel count must be constant and match "+pixels.numCols);
int N = depths.numCols;
for (int i = 0; i < N; i++) {
depths.set(view,i, locations.get(i).z );
}
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lessthanoptimal/BoofCV | main/boofcv-geo/src/main/java/boofcv/alg/geo/structure/ProjectiveStructureByFactorization.java | ProjectiveStructureByFactorization.process | public boolean process() {
int numViews = depths.numRows;
int numFeatures = depths.numCols;
P.reshape(3*numViews,4);
X.reshape(4,numFeatures);
A.reshape(numViews*3,numFeatures);
B.reshape(numViews*3,numFeatures);
// Scale depths so that they are close to unity
normalizeDepths(depths);
// Compute the initial A matirx
assignValuesToA(A);
for (int iter = 0; iter < maxIterations; iter++) {
if( !svd.decompose(A) )
return false;
svd.getU(U,false);
svd.getV(Vt,true);
double sv[] = svd.getSingularValues();
SingularOps_DDRM.descendingOrder(U,false,sv,A.numCols,Vt,true);
// This is equivalent to forcing the rank to be 4
CommonOps_DDRM.extract(U,0,0,P);
CommonOps_DDRM.multCols(P,sv);
CommonOps_DDRM.extract(Vt,0,0,X);
// Compute the new value of A
CommonOps_DDRM.mult(P,X,B);
// See how much change there is
double delta = SpecializedOps_DDRM.diffNormF(A,B)/(A.numCols*A.numRows);
// swap arrays for the next iteration
DMatrixRMaj tmp = A;
A = B;
B = tmp;
// exit if converged
if( delta <= minimumChangeTol )
break;
}
return true;
} | java | public boolean process() {
int numViews = depths.numRows;
int numFeatures = depths.numCols;
P.reshape(3*numViews,4);
X.reshape(4,numFeatures);
A.reshape(numViews*3,numFeatures);
B.reshape(numViews*3,numFeatures);
// Scale depths so that they are close to unity
normalizeDepths(depths);
// Compute the initial A matirx
assignValuesToA(A);
for (int iter = 0; iter < maxIterations; iter++) {
if( !svd.decompose(A) )
return false;
svd.getU(U,false);
svd.getV(Vt,true);
double sv[] = svd.getSingularValues();
SingularOps_DDRM.descendingOrder(U,false,sv,A.numCols,Vt,true);
// This is equivalent to forcing the rank to be 4
CommonOps_DDRM.extract(U,0,0,P);
CommonOps_DDRM.multCols(P,sv);
CommonOps_DDRM.extract(Vt,0,0,X);
// Compute the new value of A
CommonOps_DDRM.mult(P,X,B);
// See how much change there is
double delta = SpecializedOps_DDRM.diffNormF(A,B)/(A.numCols*A.numRows);
// swap arrays for the next iteration
DMatrixRMaj tmp = A;
A = B;
B = tmp;
// exit if converged
if( delta <= minimumChangeTol )
break;
}
return true;
} | [
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lessthanoptimal/BoofCV | main/boofcv-geo/src/main/java/boofcv/alg/geo/structure/ProjectiveStructureByFactorization.java | ProjectiveStructureByFactorization.getCameraMatrix | public void getCameraMatrix(int view , DMatrixRMaj cameraMatrix ) {
cameraMatrix.reshape(3,4);
CommonOps_DDRM.extract(P,view*3,0,cameraMatrix);
for (int col = 0; col < 4; col++) {
cameraMatrix.data[cameraMatrix.getIndex(0,col)] *= pixelScale;
cameraMatrix.data[cameraMatrix.getIndex(1,col)] *= pixelScale;
}
} | java | public void getCameraMatrix(int view , DMatrixRMaj cameraMatrix ) {
cameraMatrix.reshape(3,4);
CommonOps_DDRM.extract(P,view*3,0,cameraMatrix);
for (int col = 0; col < 4; col++) {
cameraMatrix.data[cameraMatrix.getIndex(0,col)] *= pixelScale;
cameraMatrix.data[cameraMatrix.getIndex(1,col)] *= pixelScale;
}
} | [
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lessthanoptimal/BoofCV | main/boofcv-geo/src/main/java/boofcv/alg/geo/structure/ProjectiveStructureByFactorization.java | ProjectiveStructureByFactorization.getFeature3D | public void getFeature3D( int feature , Point4D_F64 out ) {
out.x = X.get(0,feature);
out.y = X.get(1,feature);
out.z = X.get(2,feature);
out.w = X.get(3,feature);
} | java | public void getFeature3D( int feature , Point4D_F64 out ) {
out.x = X.get(0,feature);
out.y = X.get(1,feature);
out.z = X.get(2,feature);
out.w = X.get(3,feature);
} | [
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lessthanoptimal/BoofCV | main/boofcv-feature/src/main/java/boofcv/alg/feature/disparity/impl/ImplDisparityScoreSadRectFive_U8.java | ImplDisparityScoreSadRectFive_U8.computeScoreFive | protected void computeScoreFive( int top[] , int middle[] , int bottom[] , int score[] , int width ) {
// disparity as the outer loop to maximize common elements in inner loops, reducing redundant calculations
for( int d = minDisparity; d < maxDisparity; d++ ) {
// take in account the different in image border between the sub-regions and the effective region
int indexSrc = (d-minDisparity)*width + (d-minDisparity) + radiusX;
int indexDst = (d-minDisparity)*width + (d-minDisparity);
int end = indexSrc + (width-d-4*radiusX);
while( indexSrc < end ) {
int s = 0;
// sample four outer regions at the corners around the center region
int val0 = top[indexSrc-radiusX];
int val1 = top[indexSrc+radiusX];
int val2 = bottom[indexSrc-radiusX];
int val3 = bottom[indexSrc+radiusX];
// select the two best scores from outer for regions
if( val1 < val0 ) {
int temp = val0;
val0 = val1;
val1 = temp;
}
if( val3 < val2 ) {
int temp = val2;
val2 = val3;
val3 = temp;
}
if( val3 < val0 ) {
s += val2;
s += val3;
} else if( val2 < val1 ) {
s += val2;
s += val0;
} else {
s += val0;
s += val1;
}
score[indexDst++] = s + middle[indexSrc++];
}
}
} | java | protected void computeScoreFive( int top[] , int middle[] , int bottom[] , int score[] , int width ) {
// disparity as the outer loop to maximize common elements in inner loops, reducing redundant calculations
for( int d = minDisparity; d < maxDisparity; d++ ) {
// take in account the different in image border between the sub-regions and the effective region
int indexSrc = (d-minDisparity)*width + (d-minDisparity) + radiusX;
int indexDst = (d-minDisparity)*width + (d-minDisparity);
int end = indexSrc + (width-d-4*radiusX);
while( indexSrc < end ) {
int s = 0;
// sample four outer regions at the corners around the center region
int val0 = top[indexSrc-radiusX];
int val1 = top[indexSrc+radiusX];
int val2 = bottom[indexSrc-radiusX];
int val3 = bottom[indexSrc+radiusX];
// select the two best scores from outer for regions
if( val1 < val0 ) {
int temp = val0;
val0 = val1;
val1 = temp;
}
if( val3 < val2 ) {
int temp = val2;
val2 = val3;
val3 = temp;
}
if( val3 < val0 ) {
s += val2;
s += val3;
} else if( val2 < val1 ) {
s += val2;
s += val0;
} else {
s += val0;
s += val1;
}
score[indexDst++] = s + middle[indexSrc++];
}
}
} | [
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lessthanoptimal/BoofCV | main/boofcv-geo/src/main/java/boofcv/alg/geo/trifocal/TrifocalTransfer.java | TrifocalTransfer.setTrifocal | public void setTrifocal(TrifocalTensor tensor ) {
this.tensor = tensor;
extract.setTensor(tensor);
extract.extractFundmental(F21,F31);
} | java | public void setTrifocal(TrifocalTensor tensor ) {
this.tensor = tensor;
extract.setTensor(tensor);
extract.extractFundmental(F21,F31);
} | [
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@param tensor tensor | [
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] | f01c0243da0ec086285ee722183804d5923bc3ac | https://github.com/lessthanoptimal/BoofCV/blob/f01c0243da0ec086285ee722183804d5923bc3ac/main/boofcv-geo/src/main/java/boofcv/alg/geo/trifocal/TrifocalTransfer.java#L59-L63 | train |
lessthanoptimal/BoofCV | main/boofcv-geo/src/main/java/boofcv/alg/geo/trifocal/TrifocalTransfer.java | TrifocalTransfer.transfer_1_to_3 | public void transfer_1_to_3(double x1 , double y1 ,
double x2 , double y2 , Point3D_F64 p3)
{
// Adjust the observations so that they lie on the epipolar lines exactly
adjuster.process(F21,x1,y1,x2,y2,pa,pb);
GeometryMath_F64.mult(F21,pa,la);
// line through pb and perpendicular to la
l.x = la.y;
l.y = -la.x;
l.z = -pb.x*la.y + pb.y*la.x;
MultiViewOps.transfer_1_to_3(tensor,pa,l,p3);
} | java | public void transfer_1_to_3(double x1 , double y1 ,
double x2 , double y2 , Point3D_F64 p3)
{
// Adjust the observations so that they lie on the epipolar lines exactly
adjuster.process(F21,x1,y1,x2,y2,pa,pb);
GeometryMath_F64.mult(F21,pa,la);
// line through pb and perpendicular to la
l.x = la.y;
l.y = -la.x;
l.z = -pb.x*la.y + pb.y*la.x;
MultiViewOps.transfer_1_to_3(tensor,pa,l,p3);
} | [
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lessthanoptimal/BoofCV | main/boofcv-geo/src/main/java/boofcv/alg/geo/trifocal/TrifocalTransfer.java | TrifocalTransfer.transfer_1_to_2 | public void transfer_1_to_2(double x1 , double y1 ,
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{
// Adjust the observations so that they lie on the epipolar lines exactly
adjuster.process(F31,x1,y1,x3,y3,pa,pb);
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l.x = la.y;
l.y = -la.x;
l.z = -pb.x*la.y + pb.y*la.x;
MultiViewOps.transfer_1_to_2(tensor,pa,l,p2);
} | java | public void transfer_1_to_2(double x1 , double y1 ,
double x3 , double y3 , Point3D_F64 p2)
{
// Adjust the observations so that they lie on the epipolar lines exactly
adjuster.process(F31,x1,y1,x3,y3,pa,pb);
GeometryMath_F64.multTran(F31,pa,la);
// line through pb and perpendicular to la
l.x = la.y;
l.y = -la.x;
l.z = -pb.x*la.y + pb.y*la.x;
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lessthanoptimal/BoofCV | main/boofcv-recognition/src/main/java/boofcv/deepboof/BaseImageClassifier.java | BaseImageClassifier.classify | @Override
public void classify(Planar<GrayF32> image) {
DataManipulationOps.imageToTensor(preprocess(image),tensorInput,0);
innerProcess(tensorInput);
} | java | @Override
public void classify(Planar<GrayF32> image) {
DataManipulationOps.imageToTensor(preprocess(image),tensorInput,0);
innerProcess(tensorInput);
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lessthanoptimal/BoofCV | main/boofcv-feature/src/main/java/boofcv/alg/feature/disparity/impl/UtilDisparityScore.java | UtilDisparityScore.computeScoreRow | public static void computeScoreRow(GrayU8 left, GrayU8 right, int row, int[] scores,
int minDisparity , int maxDisparity , int regionWidth ,
int elementScore[] ) {
// disparity as the outer loop to maximize common elements in inner loops, reducing redundant calculations
for( int d = minDisparity; d < maxDisparity; d++ ) {
int dispFromMin = d - minDisparity;
// number of individual columns the error is computed in
final int colMax = left.width-d;
// number of regions that a score/error is computed in
final int scoreMax = colMax-regionWidth;
// indexes that data is read to/from for different data structures
int indexScore = left.width*dispFromMin + dispFromMin;
int indexLeft = left.startIndex + left.stride*row + d;
int indexRight = right.startIndex + right.stride*row;
// Fill elementScore with scores for individual elements for this row at disparity d
computeScoreRowSad(left, right, colMax, indexLeft, indexRight, elementScore);
// score at the first column
int score = 0;
for( int i = 0; i < regionWidth; i++ )
score += elementScore[i];
scores[indexScore++] = score;
// scores for the remaining columns
for( int col = 0; col < scoreMax; col++ , indexScore++ ) {
scores[indexScore] = score += elementScore[col+regionWidth] - elementScore[col];
}
}
} | java | public static void computeScoreRow(GrayU8 left, GrayU8 right, int row, int[] scores,
int minDisparity , int maxDisparity , int regionWidth ,
int elementScore[] ) {
// disparity as the outer loop to maximize common elements in inner loops, reducing redundant calculations
for( int d = minDisparity; d < maxDisparity; d++ ) {
int dispFromMin = d - minDisparity;
// number of individual columns the error is computed in
final int colMax = left.width-d;
// number of regions that a score/error is computed in
final int scoreMax = colMax-regionWidth;
// indexes that data is read to/from for different data structures
int indexScore = left.width*dispFromMin + dispFromMin;
int indexLeft = left.startIndex + left.stride*row + d;
int indexRight = right.startIndex + right.stride*row;
// Fill elementScore with scores for individual elements for this row at disparity d
computeScoreRowSad(left, right, colMax, indexLeft, indexRight, elementScore);
// score at the first column
int score = 0;
for( int i = 0; i < regionWidth; i++ )
score += elementScore[i];
scores[indexScore++] = score;
// scores for the remaining columns
for( int col = 0; col < scoreMax; col++ , indexScore++ ) {
scores[indexScore] = score += elementScore[col+regionWidth] - elementScore[col];
}
}
} | [
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lessthanoptimal/BoofCV | main/boofcv-feature/src/main/java/boofcv/alg/feature/disparity/impl/UtilDisparityScore.java | UtilDisparityScore.computeScoreRowSad | public static void computeScoreRowSad(GrayF32 left, GrayF32 right,
int elementMax, int indexLeft, int indexRight,
float elementScore[])
{
for( int rCol = 0; rCol < elementMax; rCol++ ) {
float diff = (left.data[ indexLeft++ ]) - (right.data[ indexRight++ ]);
elementScore[rCol] = Math.abs(diff);
}
} | java | public static void computeScoreRowSad(GrayF32 left, GrayF32 right,
int elementMax, int indexLeft, int indexRight,
float elementScore[])
{
for( int rCol = 0; rCol < elementMax; rCol++ ) {
float diff = (left.data[ indexLeft++ ]) - (right.data[ indexRight++ ]);
elementScore[rCol] = Math.abs(diff);
}
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lessthanoptimal/BoofCV | examples/src/main/java/boofcv/examples/sfm/ExamplePnP.java | ExamplePnP.estimateOutliers | public Se3_F64 estimateOutliers( List<Point2D3D> observations ) {
// We can no longer trust that each point is a real observation. Let's use RANSAC to separate the points
// You will need to tune the number of iterations and inlier threshold!!!
ModelMatcherMultiview<Se3_F64,Point2D3D> ransac =
FactoryMultiViewRobust.pnpRansac(new ConfigPnP(),new ConfigRansac(300,1.0));
ransac.setIntrinsic(0,intrinsic);
// Observations must be in normalized image coordinates! See javadoc of pnpRansac
if( !ransac.process(observations) )
throw new RuntimeException("Probably got bad input data with NaN inside of it");
System.out.println("Inlier size "+ransac.getMatchSet().size());
Se3_F64 worldToCamera = ransac.getModelParameters();
// You will most likely want to refine this solution too. Can make a difference with real world data
RefinePnP refine = FactoryMultiView.pnpRefine(1e-8,200);
Se3_F64 refinedWorldToCamera = new Se3_F64();
// notice that only the match set was passed in
if( !refine.fitModel(ransac.getMatchSet(),worldToCamera,refinedWorldToCamera) )
throw new RuntimeException("Refined failed! Input probably bad...");
return refinedWorldToCamera;
} | java | public Se3_F64 estimateOutliers( List<Point2D3D> observations ) {
// We can no longer trust that each point is a real observation. Let's use RANSAC to separate the points
// You will need to tune the number of iterations and inlier threshold!!!
ModelMatcherMultiview<Se3_F64,Point2D3D> ransac =
FactoryMultiViewRobust.pnpRansac(new ConfigPnP(),new ConfigRansac(300,1.0));
ransac.setIntrinsic(0,intrinsic);
// Observations must be in normalized image coordinates! See javadoc of pnpRansac
if( !ransac.process(observations) )
throw new RuntimeException("Probably got bad input data with NaN inside of it");
System.out.println("Inlier size "+ransac.getMatchSet().size());
Se3_F64 worldToCamera = ransac.getModelParameters();
// You will most likely want to refine this solution too. Can make a difference with real world data
RefinePnP refine = FactoryMultiView.pnpRefine(1e-8,200);
Se3_F64 refinedWorldToCamera = new Se3_F64();
// notice that only the match set was passed in
if( !refine.fitModel(ransac.getMatchSet(),worldToCamera,refinedWorldToCamera) )
throw new RuntimeException("Refined failed! Input probably bad...");
return refinedWorldToCamera;
} | [
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lessthanoptimal/BoofCV | examples/src/main/java/boofcv/examples/sfm/ExamplePnP.java | ExamplePnP.addOutliers | public void addOutliers( List<Point2D3D> observations , int total ) {
int size = observations.size();
for (int i = 0; i < total; i++) {
// outliers will be created by adding lots of noise to real observations
Point2D3D p = observations.get(rand.nextInt(size));
Point2D3D o = new Point2D3D();
o.observation.set(p.observation);
o.location.x = p.location.x + rand.nextGaussian()*5;
o.location.y = p.location.y + rand.nextGaussian()*5;
o.location.z = p.location.z + rand.nextGaussian()*5;
observations.add(o);
}
// randomize the order
Collections.shuffle(observations,rand);
} | java | public void addOutliers( List<Point2D3D> observations , int total ) {
int size = observations.size();
for (int i = 0; i < total; i++) {
// outliers will be created by adding lots of noise to real observations
Point2D3D p = observations.get(rand.nextInt(size));
Point2D3D o = new Point2D3D();
o.observation.set(p.observation);
o.location.x = p.location.x + rand.nextGaussian()*5;
o.location.y = p.location.y + rand.nextGaussian()*5;
o.location.z = p.location.z + rand.nextGaussian()*5;
observations.add(o);
}
// randomize the order
Collections.shuffle(observations,rand);
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lessthanoptimal/BoofCV | integration/boofcv-swing/src/main/java/boofcv/gui/SelectAlgorithmAndInputPanel.java | SelectAlgorithmAndInputPanel.loadInputData | @Override
public void loadInputData(String fileName) {
Reader r = media.openFile(fileName);
List<PathLabel> refs = new ArrayList<>();
try {
BufferedReader reader = new BufferedReader(r);
String line;
while( (line = reader.readLine()) != null ) {
String[]z = line.split(":");
String[] names = new String[z.length-1];
for( int i = 1; i < z.length; i++ ) {
names[i-1] = baseDirectory+z[i];
}
refs.add(new PathLabel(z[0],names));
}
setInputList(refs);
} catch (IOException e) {
throw new RuntimeException(e);
}
} | java | @Override
public void loadInputData(String fileName) {
Reader r = media.openFile(fileName);
List<PathLabel> refs = new ArrayList<>();
try {
BufferedReader reader = new BufferedReader(r);
String line;
while( (line = reader.readLine()) != null ) {
String[]z = line.split(":");
String[] names = new String[z.length-1];
for( int i = 1; i < z.length; i++ ) {
names[i-1] = baseDirectory+z[i];
}
refs.add(new PathLabel(z[0],names));
}
setInputList(refs);
} catch (IOException e) {
throw new RuntimeException(e);
}
} | [
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lessthanoptimal/BoofCV | integration/boofcv-swing/src/main/java/boofcv/gui/SelectAlgorithmAndInputPanel.java | SelectAlgorithmAndInputPanel.addToToolbar | public void addToToolbar( JComponent comp ) {
toolbar.add(comp,1+algBoxes.length);
toolbar.revalidate();
addedComponents.add(comp);
} | java | public void addToToolbar( JComponent comp ) {
toolbar.add(comp,1+algBoxes.length);
toolbar.revalidate();
addedComponents.add(comp);
} | [
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lessthanoptimal/BoofCV | integration/boofcv-swing/src/main/java/boofcv/gui/SelectAlgorithmAndInputPanel.java | SelectAlgorithmAndInputPanel.setMainGUI | public void setMainGUI( final Component gui ) {
postAlgorithmEvents = true;
this.gui = gui;
SwingUtilities.invokeLater(new Runnable() {
public void run() {
add(gui,BorderLayout.CENTER);
}});
} | java | public void setMainGUI( final Component gui ) {
postAlgorithmEvents = true;
this.gui = gui;
SwingUtilities.invokeLater(new Runnable() {
public void run() {
add(gui,BorderLayout.CENTER);
}});
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] | f01c0243da0ec086285ee722183804d5923bc3ac | https://github.com/lessthanoptimal/BoofCV/blob/f01c0243da0ec086285ee722183804d5923bc3ac/integration/boofcv-swing/src/main/java/boofcv/gui/SelectAlgorithmAndInputPanel.java#L165-L172 | train |
lessthanoptimal/BoofCV | integration/boofcv-swing/src/main/java/boofcv/gui/SelectAlgorithmAndInputPanel.java | SelectAlgorithmAndInputPanel.setInputImage | public void setInputImage( BufferedImage image ) {
inputImage = image;
SwingUtilities.invokeLater(new Runnable() {
public void run() {
if( inputImage == null ) {
originalCheck.setEnabled(false);
} else {
originalCheck.setEnabled(true);
origPanel.setImage(inputImage);
origPanel.setPreferredSize(new Dimension(inputImage.getWidth(),inputImage.getHeight()));
origPanel.repaint();
}
}});
} | java | public void setInputImage( BufferedImage image ) {
inputImage = image;
SwingUtilities.invokeLater(new Runnable() {
public void run() {
if( inputImage == null ) {
originalCheck.setEnabled(false);
} else {
originalCheck.setEnabled(true);
origPanel.setImage(inputImage);
origPanel.setPreferredSize(new Dimension(inputImage.getWidth(),inputImage.getHeight()));
origPanel.repaint();
}
}});
} | [
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lessthanoptimal/BoofCV | integration/boofcv-swing/src/main/java/boofcv/gui/SelectAlgorithmAndInputPanel.java | SelectAlgorithmAndInputPanel.setInputList | public void setInputList(final List<PathLabel> inputRefs) {
this.inputRefs = inputRefs;
SwingUtilities.invokeLater(new Runnable() {
public void run() {
for( int i = 0; i < inputRefs.size(); i++ ) {
imageBox.addItem(inputRefs.get(i).getLabel());
}
}});
} | java | public void setInputList(final List<PathLabel> inputRefs) {
this.inputRefs = inputRefs;
SwingUtilities.invokeLater(new Runnable() {
public void run() {
for( int i = 0; i < inputRefs.size(); i++ ) {
imageBox.addItem(inputRefs.get(i).getLabel());
}
}});
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lessthanoptimal/BoofCV | integration/boofcv-swing/src/main/java/boofcv/gui/SelectAlgorithmAndInputPanel.java | SelectAlgorithmAndInputPanel.getAlgorithmCookie | protected <T> T getAlgorithmCookie( int indexFamily ) {
return (T)algCookies[indexFamily].get( algBoxes[indexFamily].getSelectedIndex() );
} | java | protected <T> T getAlgorithmCookie( int indexFamily ) {
return (T)algCookies[indexFamily].get( algBoxes[indexFamily].getSelectedIndex() );
} | [
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] | f01c0243da0ec086285ee722183804d5923bc3ac | https://github.com/lessthanoptimal/BoofCV/blob/f01c0243da0ec086285ee722183804d5923bc3ac/integration/boofcv-swing/src/main/java/boofcv/gui/SelectAlgorithmAndInputPanel.java#L266-L268 | train |
lessthanoptimal/BoofCV | main/boofcv-recognition/src/main/java/boofcv/alg/fiducial/square/BaseDetectFiducialSquare.java | BaseDetectFiducialSquare.checkSideSize | private boolean checkSideSize( Polygon2D_F64 p ) {
double max=0,min=Double.MAX_VALUE;
for (int i = 0; i < p.size(); i++) {
double l = p.getSideLength(i);
max = Math.max(max,l);
min = Math.min(min,l);
}
// See if a side is too small to decode
if( min < 10 )
return false;
// see if it's under extreme perspective distortion and unlikely to be readable
return !(min / max < thresholdSideRatio);
} | java | private boolean checkSideSize( Polygon2D_F64 p ) {
double max=0,min=Double.MAX_VALUE;
for (int i = 0; i < p.size(); i++) {
double l = p.getSideLength(i);
max = Math.max(max,l);
min = Math.min(min,l);
}
// See if a side is too small to decode
if( min < 10 )
return false;
// see if it's under extreme perspective distortion and unlikely to be readable
return !(min / max < thresholdSideRatio);
} | [
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lessthanoptimal/BoofCV | main/boofcv-recognition/src/main/java/boofcv/alg/fiducial/square/BaseDetectFiducialSquare.java | BaseDetectFiducialSquare.computeFractionBoundary | protected double computeFractionBoundary( float pixelThreshold ) {
// TODO ignore outer pixels from this computation. Will require 8 regions (4 corners + top/bottom + left/right)
final int w = square.width;
int radius = (int) (w * borderWidthFraction);
int innerWidth = w-2*radius;
int total = w*w - innerWidth*innerWidth;
int count = 0;
for (int y = 0; y < radius; y++) {
int indexTop = y*w;
int indexBottom = (w - radius + y)*w;
for (int x = 0; x < w; x++) {
if( square.data[indexTop++] < pixelThreshold )
count++;
if( square.data[indexBottom++] < pixelThreshold )
count++;
}
}
for (int y = radius; y < w-radius; y++) {
int indexLeft = y*w;
int indexRight = y*w + w - radius;
for (int x = 0; x < radius; x++) {
if( square.data[indexLeft++] < pixelThreshold )
count++;
if( square.data[indexRight++] < pixelThreshold )
count++;
}
}
return count/(double)total;
} | java | protected double computeFractionBoundary( float pixelThreshold ) {
// TODO ignore outer pixels from this computation. Will require 8 regions (4 corners + top/bottom + left/right)
final int w = square.width;
int radius = (int) (w * borderWidthFraction);
int innerWidth = w-2*radius;
int total = w*w - innerWidth*innerWidth;
int count = 0;
for (int y = 0; y < radius; y++) {
int indexTop = y*w;
int indexBottom = (w - radius + y)*w;
for (int x = 0; x < w; x++) {
if( square.data[indexTop++] < pixelThreshold )
count++;
if( square.data[indexBottom++] < pixelThreshold )
count++;
}
}
for (int y = radius; y < w-radius; y++) {
int indexLeft = y*w;
int indexRight = y*w + w - radius;
for (int x = 0; x < radius; x++) {
if( square.data[indexLeft++] < pixelThreshold )
count++;
if( square.data[indexRight++] < pixelThreshold )
count++;
}
}
return count/(double)total;
} | [
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lessthanoptimal/BoofCV | main/boofcv-recognition/src/main/java/boofcv/alg/fiducial/square/BaseDetectFiducialSquare.java | BaseDetectFiducialSquare.prepareForOutput | private void prepareForOutput(Polygon2D_F64 imageShape, Result result) {
// the rotation estimate, apply in counter clockwise direction
// since result.rotation is a clockwise rotation in the visual sense, which
// is CCW on the grid
int rotationCCW = (4-result.rotation)%4;
for (int j = 0; j < rotationCCW; j++) {
UtilPolygons2D_F64.shiftUp(imageShape);
}
// save the results for output
FoundFiducial f = found.grow();
f.id = result.which;
for (int i = 0; i < 4; i++) {
Point2D_F64 a = imageShape.get(i);
undistToDist.compute(a.x, a.y, f.distortedPixels.get(i));
}
} | java | private void prepareForOutput(Polygon2D_F64 imageShape, Result result) {
// the rotation estimate, apply in counter clockwise direction
// since result.rotation is a clockwise rotation in the visual sense, which
// is CCW on the grid
int rotationCCW = (4-result.rotation)%4;
for (int j = 0; j < rotationCCW; j++) {
UtilPolygons2D_F64.shiftUp(imageShape);
}
// save the results for output
FoundFiducial f = found.grow();
f.id = result.which;
for (int i = 0; i < 4; i++) {
Point2D_F64 a = imageShape.get(i);
undistToDist.compute(a.x, a.y, f.distortedPixels.get(i));
}
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lessthanoptimal/BoofCV | main/boofcv-feature/src/main/java/boofcv/alg/segmentation/ms/MergeRegionMeanShift.java | MergeRegionMeanShift.process | public void process( GrayS32 pixelToRegion ,
GrowQueue_I32 regionMemberCount,
FastQueue<float[]> regionColor ,
FastQueue<Point2D_I32> modeLocation ) {
stopRequested = false;
initializeMerge(regionMemberCount.size);
markMergeRegions(regionColor,modeLocation,pixelToRegion);
if( stopRequested )
return;
performMerge(pixelToRegion, regionMemberCount);
} | java | public void process( GrayS32 pixelToRegion ,
GrowQueue_I32 regionMemberCount,
FastQueue<float[]> regionColor ,
FastQueue<Point2D_I32> modeLocation ) {
stopRequested = false;
initializeMerge(regionMemberCount.size);
markMergeRegions(regionColor,modeLocation,pixelToRegion);
if( stopRequested )
return;
performMerge(pixelToRegion, regionMemberCount);
} | [
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@param regionMemberCount (Input/output) Number of pixels in each region. Modified.
@param regionColor (Input/output) Color of each region. Modified.
@param modeLocation (Input) Location of each region's mode. Not modified. | [
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lessthanoptimal/BoofCV | main/boofcv-feature/src/main/java/boofcv/alg/segmentation/ms/MergeRegionMeanShift.java | MergeRegionMeanShift.markMergeRegions | protected void markMergeRegions(FastQueue<float[]> regionColor,
FastQueue<Point2D_I32> modeLocation,
GrayS32 pixelToRegion ) {
for( int targetId = 0; targetId < modeLocation.size &&!stopRequested; targetId++ ) {
float[] color = regionColor.get(targetId);
Point2D_I32 location = modeLocation.get(targetId);
int x0 = location.x-searchRadius;
int x1 = location.x+searchRadius+1;
int y0 = location.y-searchRadius;
int y1 = location.y+searchRadius+1;
// ensure that all pixels it examines are inside the image
if( x0 < 0 ) x0 = 0;
if( x1 > pixelToRegion.width ) x1 = pixelToRegion.width;
if( y0 < 0 ) y0 = 0;
if( y1 > pixelToRegion.height ) y1 = pixelToRegion.height;
// look at the local neighborhood
for( int y = y0; y < y1; y++ ) {
for( int x = x0; x < x1; x++ ) {
int candidateId = pixelToRegion.unsafe_get(x,y);
// see if it is the same region
if( candidateId == targetId )
continue;
// see if the mode is near by
Point2D_I32 p = modeLocation.get(candidateId);
if( p.distance2(location) <= maxSpacialDistanceSq ) {
// see if the color is similar
float[] candidateColor = regionColor.get(candidateId);
float colorDistance = SegmentMeanShiftSearch.distanceSq(color,candidateColor);
if( colorDistance <= maxColorDistanceSq ) {
// mark the two regions as merged
markMerge(targetId, candidateId);
}
}
}
}
}
} | java | protected void markMergeRegions(FastQueue<float[]> regionColor,
FastQueue<Point2D_I32> modeLocation,
GrayS32 pixelToRegion ) {
for( int targetId = 0; targetId < modeLocation.size &&!stopRequested; targetId++ ) {
float[] color = regionColor.get(targetId);
Point2D_I32 location = modeLocation.get(targetId);
int x0 = location.x-searchRadius;
int x1 = location.x+searchRadius+1;
int y0 = location.y-searchRadius;
int y1 = location.y+searchRadius+1;
// ensure that all pixels it examines are inside the image
if( x0 < 0 ) x0 = 0;
if( x1 > pixelToRegion.width ) x1 = pixelToRegion.width;
if( y0 < 0 ) y0 = 0;
if( y1 > pixelToRegion.height ) y1 = pixelToRegion.height;
// look at the local neighborhood
for( int y = y0; y < y1; y++ ) {
for( int x = x0; x < x1; x++ ) {
int candidateId = pixelToRegion.unsafe_get(x,y);
// see if it is the same region
if( candidateId == targetId )
continue;
// see if the mode is near by
Point2D_I32 p = modeLocation.get(candidateId);
if( p.distance2(location) <= maxSpacialDistanceSq ) {
// see if the color is similar
float[] candidateColor = regionColor.get(candidateId);
float colorDistance = SegmentMeanShiftSearch.distanceSq(color,candidateColor);
if( colorDistance <= maxColorDistanceSq ) {
// mark the two regions as merged
markMerge(targetId, candidateId);
}
}
}
}
}
} | [
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lessthanoptimal/BoofCV | integration/boofcv-jcodec/src/main/java/boofcv/io/jcodec/UtilJCodec.java | UtilJCodec.convertToBoof | public static void convertToBoof(Picture input, ImageBase output) {
if( input.getColor() == ColorSpace.RGB ) {
ImplConvertJCodecPicture.RGB_to_PLU8(input, (Planar) output);
} else if( input.getColor() == ColorSpace.YUV420 ) {
if( output instanceof Planar) {
Planar ms = (Planar)output;
if( ms.getImageType().getDataType() == ImageDataType.U8 ) {
ImplConvertJCodecPicture.yuv420_to_PlRgb_U8(input, ms);
} else if( ms.getImageType().getDataType() == ImageDataType.F32 ) {
ImplConvertJCodecPicture.yuv420_to_PlRgb_F32(input, ms);
}
} else if( output instanceof GrayU8) {
ImplConvertJCodecPicture.yuv420_to_U8(input, (GrayU8) output);
} else if( output instanceof GrayF32) {
ImplConvertJCodecPicture.yuv420_to_F32(input, (GrayF32) output);
} else {
throw new RuntimeException("Unexpected output image type");
}
}
} | java | public static void convertToBoof(Picture input, ImageBase output) {
if( input.getColor() == ColorSpace.RGB ) {
ImplConvertJCodecPicture.RGB_to_PLU8(input, (Planar) output);
} else if( input.getColor() == ColorSpace.YUV420 ) {
if( output instanceof Planar) {
Planar ms = (Planar)output;
if( ms.getImageType().getDataType() == ImageDataType.U8 ) {
ImplConvertJCodecPicture.yuv420_to_PlRgb_U8(input, ms);
} else if( ms.getImageType().getDataType() == ImageDataType.F32 ) {
ImplConvertJCodecPicture.yuv420_to_PlRgb_F32(input, ms);
}
} else if( output instanceof GrayU8) {
ImplConvertJCodecPicture.yuv420_to_U8(input, (GrayU8) output);
} else if( output instanceof GrayF32) {
ImplConvertJCodecPicture.yuv420_to_F32(input, (GrayF32) output);
} else {
throw new RuntimeException("Unexpected output image type");
}
}
} | [
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lessthanoptimal/BoofCV | main/boofcv-geo/src/main/java/boofcv/alg/geo/pose/PositionFromPairLinear2.java | PositionFromPairLinear2.process | public boolean process( DMatrixRMaj R , List<Point3D_F64> worldPts , List<Point2D_F64> observed )
{
if( worldPts.size() != observed.size() )
throw new IllegalArgumentException("Number of worldPts and observed must be the same");
if( worldPts.size() < 2 )
throw new IllegalArgumentException("A minimum of two points are required");
int N = worldPts.size();
A.reshape(3*N,3); b.reshape(A.numRows, 1);
for( int i = 0; i < N; i++ ) {
Point3D_F64 X = worldPts.get(i);
Point2D_F64 o = observed.get(i);
int indexA = i*3*3;
int indexB = i*3;
A.data[indexA+1] = -1;
A.data[indexA+2] = o.y;
A.data[indexA+3] = 1;
A.data[indexA+5] = -o.x;
A.data[indexA+6] = -o.y;
A.data[indexA+7] = o.x;
GeometryMath_F64.mult(R,X,RX);
b.data[indexB++] = 1*RX.y - o.y*RX.z;
b.data[indexB++] = -1*RX.x + o.x*RX.z;
b.data[indexB ] = o.y*RX.x - o.x*RX.y;
}
if( !solver.setA(A) )
return false;
solver.solve(b,x);
T.x = x.data[0];
T.y = x.data[1];
T.z = x.data[2];
return true;
} | java | public boolean process( DMatrixRMaj R , List<Point3D_F64> worldPts , List<Point2D_F64> observed )
{
if( worldPts.size() != observed.size() )
throw new IllegalArgumentException("Number of worldPts and observed must be the same");
if( worldPts.size() < 2 )
throw new IllegalArgumentException("A minimum of two points are required");
int N = worldPts.size();
A.reshape(3*N,3); b.reshape(A.numRows, 1);
for( int i = 0; i < N; i++ ) {
Point3D_F64 X = worldPts.get(i);
Point2D_F64 o = observed.get(i);
int indexA = i*3*3;
int indexB = i*3;
A.data[indexA+1] = -1;
A.data[indexA+2] = o.y;
A.data[indexA+3] = 1;
A.data[indexA+5] = -o.x;
A.data[indexA+6] = -o.y;
A.data[indexA+7] = o.x;
GeometryMath_F64.mult(R,X,RX);
b.data[indexB++] = 1*RX.y - o.y*RX.z;
b.data[indexB++] = -1*RX.x + o.x*RX.z;
b.data[indexB ] = o.y*RX.x - o.x*RX.y;
}
if( !solver.setA(A) )
return false;
solver.solve(b,x);
T.x = x.data[0];
T.y = x.data[1];
T.z = x.data[2];
return true;
} | [
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@param R Rotation matrix. World to view.
@param worldPts Location of features in world coordinates.
@param observed Observations of point in current view. Normalized coordinates.
@return true if it succeeded. | [
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] | f01c0243da0ec086285ee722183804d5923bc3ac | https://github.com/lessthanoptimal/BoofCV/blob/f01c0243da0ec086285ee722183804d5923bc3ac/main/boofcv-geo/src/main/java/boofcv/alg/geo/pose/PositionFromPairLinear2.java#L78-L121 | train |
lessthanoptimal/BoofCV | main/boofcv-feature/src/main/java/boofcv/alg/shapes/ellipse/BinaryEllipseDetectorPixel.java | BinaryEllipseDetectorPixel.process | public void process( GrayU8 binary ) {
found.reset();
labeled.reshape(binary.width, binary.height);
contourFinder.process(binary, labeled);
List<ContourPacked> blobs = contourFinder.getContours();
for (int i = 0; i < blobs.size(); i++) {
ContourPacked c = blobs.get(i);
contourFinder.loadContour(c.externalIndex,contourTmp);
proccessContour(contourTmp.toList());
if(internalContour) {
for( int j = 0; j < c.internalIndexes.size(); j++ ) {
contourFinder.loadContour(c.internalIndexes.get(j),contourTmp);
proccessContour(contourTmp.toList());
}
}
}
} | java | public void process( GrayU8 binary ) {
found.reset();
labeled.reshape(binary.width, binary.height);
contourFinder.process(binary, labeled);
List<ContourPacked> blobs = contourFinder.getContours();
for (int i = 0; i < blobs.size(); i++) {
ContourPacked c = blobs.get(i);
contourFinder.loadContour(c.externalIndex,contourTmp);
proccessContour(contourTmp.toList());
if(internalContour) {
for( int j = 0; j < c.internalIndexes.size(); j++ ) {
contourFinder.loadContour(c.internalIndexes.get(j),contourTmp);
proccessContour(contourTmp.toList());
}
}
}
} | [
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... | Finds all valid ellipses in the binary image
@param binary binary image | [
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] | f01c0243da0ec086285ee722183804d5923bc3ac | https://github.com/lessthanoptimal/BoofCV/blob/f01c0243da0ec086285ee722183804d5923bc3ac/main/boofcv-feature/src/main/java/boofcv/alg/shapes/ellipse/BinaryEllipseDetectorPixel.java#L132-L152 | train |
lessthanoptimal/BoofCV | main/boofcv-feature/src/main/java/boofcv/alg/shapes/ellipse/BinaryEllipseDetectorPixel.java | BinaryEllipseDetectorPixel.adjustElipseForBinaryBias | protected void adjustElipseForBinaryBias( EllipseRotated_F64 ellipse ) {
ellipse.center.x += 0.5;
ellipse.center.y += 0.5;
ellipse.a += 0.5;
ellipse.b += 0.5;
} | java | protected void adjustElipseForBinaryBias( EllipseRotated_F64 ellipse ) {
ellipse.center.x += 0.5;
ellipse.center.y += 0.5;
ellipse.a += 0.5;
ellipse.b += 0.5;
} | [
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... | In a binary image the contour on the right and bottom is off by one pixel. This is because the block region
extends the entire pixel not just the lower extent which is where it is indexed from. | [
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lessthanoptimal/BoofCV | main/boofcv-feature/src/main/java/boofcv/alg/shapes/ellipse/BinaryEllipseDetectorPixel.java | BinaryEllipseDetectorPixel.undistortContour | void undistortContour(List<Point2D_I32> external, FastQueue<Point2D_F64> pointsF ) {
for (int j = 0; j < external.size(); j++) {
Point2D_I32 p = external.get(j);
if( distToUndist != null ) {
distToUndist.compute(p.x,p.y,distortedPoint);
pointsF.grow().set( distortedPoint.x , distortedPoint.y );
} else {
pointsF.grow().set(p.x, p.y);
}
}
} | java | void undistortContour(List<Point2D_I32> external, FastQueue<Point2D_F64> pointsF ) {
for (int j = 0; j < external.size(); j++) {
Point2D_I32 p = external.get(j);
if( distToUndist != null ) {
distToUndist.compute(p.x,p.y,distortedPoint);
pointsF.grow().set( distortedPoint.x , distortedPoint.y );
} else {
pointsF.grow().set(p.x, p.y);
}
}
} | [
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"... | Undistort the contour points and convert into a floating point format for the fitting operation
@param external The external contour
@param pointsF Output of converted points | [
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] | f01c0243da0ec086285ee722183804d5923bc3ac | https://github.com/lessthanoptimal/BoofCV/blob/f01c0243da0ec086285ee722183804d5923bc3ac/main/boofcv-feature/src/main/java/boofcv/alg/shapes/ellipse/BinaryEllipseDetectorPixel.java#L239-L250 | train |
lessthanoptimal/BoofCV | main/boofcv-feature/src/main/java/boofcv/alg/shapes/ellipse/BinaryEllipseDetectorPixel.java | BinaryEllipseDetectorPixel.isApproximatelyElliptical | boolean isApproximatelyElliptical(EllipseRotated_F64 ellipse , List<Point2D_F64> points , int maxSamples ) {
closestPoint.setEllipse(ellipse);
double maxDistance2 = maxDistanceFromEllipse*maxDistanceFromEllipse;
if( points.size() <= maxSamples ) {
for( int i = 0; i < points.size(); i++ ) {
Point2D_F64 p = points.get(i);
closestPoint.process(p);
double d = closestPoint.getClosest().distance2(p);
if( d > maxDistance2 ) {
return false;
}
}
} else {
for (int i = 0; i < maxSamples; i++) {
Point2D_F64 p = points.get( i*points.size()/maxSamples );
closestPoint.process(p);
double d = closestPoint.getClosest().distance2(p);
if( d > maxDistance2 ) {
return false;
}
}
}
return true;
} | java | boolean isApproximatelyElliptical(EllipseRotated_F64 ellipse , List<Point2D_F64> points , int maxSamples ) {
closestPoint.setEllipse(ellipse);
double maxDistance2 = maxDistanceFromEllipse*maxDistanceFromEllipse;
if( points.size() <= maxSamples ) {
for( int i = 0; i < points.size(); i++ ) {
Point2D_F64 p = points.get(i);
closestPoint.process(p);
double d = closestPoint.getClosest().distance2(p);
if( d > maxDistance2 ) {
return false;
}
}
} else {
for (int i = 0; i < maxSamples; i++) {
Point2D_F64 p = points.get( i*points.size()/maxSamples );
closestPoint.process(p);
double d = closestPoint.getClosest().distance2(p);
if( d > maxDistance2 ) {
return false;
}
}
}
return true;
} | [
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lessthanoptimal/BoofCV | main/boofcv-recognition/src/main/java/boofcv/alg/fiducial/calib/chess/ChessboardPolygonHelper.java | ChessboardPolygonHelper.filterPixelPolygon | @Override
public boolean filterPixelPolygon(Polygon2D_F64 undistorted , Polygon2D_F64 distorted,
GrowQueue_B touches, boolean touchesBorder) {
if( touchesBorder ) {
if( distorted.size() < 3)
return false;
int totalRegular = distorted.size();
for (int i = 0; i < distorted.size(); i++) {
if( touches.get(i) )
totalRegular--;
}
return totalRegular > 0;
// Would be 3 if it was filled in, but local binary can cause external contour to be concave
} else {
return distorted.size() == 4;
}
} | java | @Override
public boolean filterPixelPolygon(Polygon2D_F64 undistorted , Polygon2D_F64 distorted,
GrowQueue_B touches, boolean touchesBorder) {
if( touchesBorder ) {
if( distorted.size() < 3)
return false;
int totalRegular = distorted.size();
for (int i = 0; i < distorted.size(); i++) {
if( touches.get(i) )
totalRegular--;
}
return totalRegular > 0;
// Would be 3 if it was filled in, but local binary can cause external contour to be concave
} else {
return distorted.size() == 4;
}
} | [
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at least 3 corners not touching the border. 7 corners at most. If there were 8 then all sides of a square
would be touching the border. No more than 3 corners since that's the most number of non-border corners
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lessthanoptimal/BoofCV | integration/boofcv-ffmpeg/src/main/java/org/bytedeco/copiedstuff/Java2DFrameConverter.java | Java2DFrameConverter.getFrame | public Frame getFrame(BufferedImage image, double gamma, boolean flipChannels) {
if (image == null) {
return null;
}
SampleModel sm = image.getSampleModel();
int depth = 0, numChannels = sm.getNumBands();
switch (image.getType()) {
case BufferedImage.TYPE_INT_RGB:
case BufferedImage.TYPE_INT_ARGB:
case BufferedImage.TYPE_INT_ARGB_PRE:
case BufferedImage.TYPE_INT_BGR:
depth = Frame.DEPTH_UBYTE;
numChannels = 4;
break;
}
if (depth == 0 || numChannels == 0) {
switch (sm.getDataType()) {
case DataBuffer.TYPE_BYTE: depth = Frame.DEPTH_UBYTE; break;
case DataBuffer.TYPE_USHORT: depth = Frame.DEPTH_USHORT; break;
case DataBuffer.TYPE_SHORT: depth = Frame.DEPTH_SHORT; break;
case DataBuffer.TYPE_INT: depth = Frame.DEPTH_INT; break;
case DataBuffer.TYPE_FLOAT: depth = Frame.DEPTH_FLOAT; break;
case DataBuffer.TYPE_DOUBLE: depth = Frame.DEPTH_DOUBLE; break;
default: assert false;
}
}
if (frame == null || frame.imageWidth != image.getWidth() || frame.imageHeight != image.getHeight()
|| frame.imageDepth != depth || frame.imageChannels != numChannels) {
frame = new Frame(image.getWidth(), image.getHeight(), depth, numChannels);
}
copy(image, frame, gamma, flipChannels, null);
return frame;
} | java | public Frame getFrame(BufferedImage image, double gamma, boolean flipChannels) {
if (image == null) {
return null;
}
SampleModel sm = image.getSampleModel();
int depth = 0, numChannels = sm.getNumBands();
switch (image.getType()) {
case BufferedImage.TYPE_INT_RGB:
case BufferedImage.TYPE_INT_ARGB:
case BufferedImage.TYPE_INT_ARGB_PRE:
case BufferedImage.TYPE_INT_BGR:
depth = Frame.DEPTH_UBYTE;
numChannels = 4;
break;
}
if (depth == 0 || numChannels == 0) {
switch (sm.getDataType()) {
case DataBuffer.TYPE_BYTE: depth = Frame.DEPTH_UBYTE; break;
case DataBuffer.TYPE_USHORT: depth = Frame.DEPTH_USHORT; break;
case DataBuffer.TYPE_SHORT: depth = Frame.DEPTH_SHORT; break;
case DataBuffer.TYPE_INT: depth = Frame.DEPTH_INT; break;
case DataBuffer.TYPE_FLOAT: depth = Frame.DEPTH_FLOAT; break;
case DataBuffer.TYPE_DOUBLE: depth = Frame.DEPTH_DOUBLE; break;
default: assert false;
}
}
if (frame == null || frame.imageWidth != image.getWidth() || frame.imageHeight != image.getHeight()
|| frame.imageDepth != depth || frame.imageChannels != numChannels) {
frame = new Frame(image.getWidth(), image.getHeight(), depth, numChannels);
}
copy(image, frame, gamma, flipChannels, null);
return frame;
} | [
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lessthanoptimal/BoofCV | main/boofcv-recognition/src/main/java/boofcv/alg/fiducial/qrcode/GaliosFieldOps.java | GaliosFieldOps.multiply | public static int multiply( int x , int y , int primitive , int domain ) {
int r = 0;
while( y > 0 ) {
if( (y&1) != 0 ) {
r = r ^ x;
}
y = y >> 1;
x = x << 1;
if( x >= domain) {
x ^= primitive;
}
}
return r;
} | java | public static int multiply( int x , int y , int primitive , int domain ) {
int r = 0;
while( y > 0 ) {
if( (y&1) != 0 ) {
r = r ^ x;
}
y = y >> 1;
x = x << 1;
if( x >= domain) {
x ^= primitive;
}
}
return r;
} | [
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as the inputs, provided primitive is an appropriate irreducible polynomial for that field.
Uses 'Russian Peasant Multiplication' that should be a faster algorithm.
@param x polynomial
@param y polynomial
@param primitive Primitive polynomial which is irreducible.
@param domain Value of a the largest possible value plus 1. E.g. GF(2**8) would be 256
@return result polynomial | [
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lessthanoptimal/BoofCV | main/boofcv-recognition/src/main/java/boofcv/alg/fiducial/calib/circle/DetectCircleHexagonalGrid.java | DetectCircleHexagonalGrid.isClockWise | private static boolean isClockWise( Grid g ) {
EllipseRotated_F64 v00 = g.get(0,0);
EllipseRotated_F64 v02 = g.columns<3?g.get(1,1):g.get(0,2);
EllipseRotated_F64 v20 = g.rows<3?g.get(1,1):g.get(2,0);
double a_x = v02.center.x - v00.center.x;
double a_y = v02.center.y - v00.center.y;
double b_x = v20.center.x - v00.center.x;
double b_y = v20.center.y - v00.center.y;
return a_x * b_y - a_y * b_x < 0;
} | java | private static boolean isClockWise( Grid g ) {
EllipseRotated_F64 v00 = g.get(0,0);
EllipseRotated_F64 v02 = g.columns<3?g.get(1,1):g.get(0,2);
EllipseRotated_F64 v20 = g.rows<3?g.get(1,1):g.get(2,0);
double a_x = v02.center.x - v00.center.x;
double a_y = v02.center.y - v00.center.y;
double b_x = v20.center.x - v00.center.x;
double b_y = v20.center.y - v00.center.y;
return a_x * b_y - a_y * b_x < 0;
} | [
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] | f01c0243da0ec086285ee722183804d5923bc3ac | https://github.com/lessthanoptimal/BoofCV/blob/f01c0243da0ec086285ee722183804d5923bc3ac/main/boofcv-recognition/src/main/java/boofcv/alg/fiducial/calib/circle/DetectCircleHexagonalGrid.java#L136-L148 | train |
lessthanoptimal/BoofCV | main/boofcv-ip/src/main/java/boofcv/abst/distort/FDistort.java | FDistort.init | public FDistort init(ImageBase input, ImageBase output) {
this.input = input;
this.output = output;
inputType = input.getImageType();
interp(InterpolationType.BILINEAR);
border(0);
cached = false;
distorter = null;
outputToInput = null;
return this;
} | java | public FDistort init(ImageBase input, ImageBase output) {
this.input = input;
this.output = output;
inputType = input.getImageType();
interp(InterpolationType.BILINEAR);
border(0);
cached = false;
distorter = null;
outputToInput = null;
return this;
} | [
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lessthanoptimal/BoofCV | main/boofcv-ip/src/main/java/boofcv/abst/distort/FDistort.java | FDistort.setRefs | public FDistort setRefs( ImageBase input, ImageBase output ) {
this.input = input;
this.output = output;
inputType = input.getImageType();
return this;
} | java | public FDistort setRefs( ImageBase input, ImageBase output ) {
this.input = input;
this.output = output;
inputType = input.getImageType();
return this;
} | [
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user to correctly update everything else.
If called the first time you need to do the following
<pre>
1) specify the interpolation method
2) specify the transform
3) specify the border
</pre>
If called again and the image shape has changed you need to do the following:
<pre>
1) Update the transform
</pre> | [
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lessthanoptimal/BoofCV | main/boofcv-ip/src/main/java/boofcv/abst/distort/FDistort.java | FDistort.input | public FDistort input( ImageBase input ) {
if( this.input == null || this.input.width != input.width || this.input.height != input.height ) {
distorter = null;
}
this.input = input;
inputType = input.getImageType();
return this;
} | java | public FDistort input( ImageBase input ) {
if( this.input == null || this.input.width != input.width || this.input.height != input.height ) {
distorter = null;
}
this.input = input;
inputType = input.getImageType();
return this;
} | [
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lessthanoptimal/BoofCV | main/boofcv-ip/src/main/java/boofcv/abst/distort/FDistort.java | FDistort.output | public FDistort output( ImageBase output ) {
if( this.output == null || this.output.width != output.width || this.output.height != output.height ) {
distorter = null;
}
this.output = output;
return this;
} | java | public FDistort output( ImageBase output ) {
if( this.output == null || this.output.width != output.width || this.output.height != output.height ) {
distorter = null;
}
this.output = output;
return this;
} | [
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] | f01c0243da0ec086285ee722183804d5923bc3ac | https://github.com/lessthanoptimal/BoofCV/blob/f01c0243da0ec086285ee722183804d5923bc3ac/main/boofcv-ip/src/main/java/boofcv/abst/distort/FDistort.java#L153-L159 | train |
lessthanoptimal/BoofCV | main/boofcv-ip/src/main/java/boofcv/abst/distort/FDistort.java | FDistort.border | public FDistort border( BorderType type ) {
if( borderType == type )
return this;
borderType = type;
return border(FactoryImageBorder.generic(type, inputType));
} | java | public FDistort border( BorderType type ) {
if( borderType == type )
return this;
borderType = type;
return border(FactoryImageBorder.generic(type, inputType));
} | [
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] | f01c0243da0ec086285ee722183804d5923bc3ac | https://github.com/lessthanoptimal/BoofCV/blob/f01c0243da0ec086285ee722183804d5923bc3ac/main/boofcv-ip/src/main/java/boofcv/abst/distort/FDistort.java#L172-L177 | train |
lessthanoptimal/BoofCV | main/boofcv-ip/src/main/java/boofcv/abst/distort/FDistort.java | FDistort.border | public FDistort border( double value ) {
// to recycle here the value also needs to be saved
// if( borderType == BorderType.VALUE )
// return this;
borderType = BorderType.ZERO;
return border(FactoryImageBorder.genericValue(value, inputType));
} | java | public FDistort border( double value ) {
// to recycle here the value also needs to be saved
// if( borderType == BorderType.VALUE )
// return this;
borderType = BorderType.ZERO;
return border(FactoryImageBorder.genericValue(value, inputType));
} | [
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] | f01c0243da0ec086285ee722183804d5923bc3ac | https://github.com/lessthanoptimal/BoofCV/blob/f01c0243da0ec086285ee722183804d5923bc3ac/main/boofcv-ip/src/main/java/boofcv/abst/distort/FDistort.java#L182-L188 | train |
lessthanoptimal/BoofCV | main/boofcv-ip/src/main/java/boofcv/abst/distort/FDistort.java | FDistort.interp | public FDistort interp(InterpolationType type) {
distorter = null;
this.interp = FactoryInterpolation.createPixel(0, 255, type, BorderType.EXTENDED, inputType);
return this;
} | java | public FDistort interp(InterpolationType type) {
distorter = null;
this.interp = FactoryInterpolation.createPixel(0, 255, type, BorderType.EXTENDED, inputType);
return this;
} | [
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] | f01c0243da0ec086285ee722183804d5923bc3ac | https://github.com/lessthanoptimal/BoofCV/blob/f01c0243da0ec086285ee722183804d5923bc3ac/main/boofcv-ip/src/main/java/boofcv/abst/distort/FDistort.java#L215-L220 | train |
lessthanoptimal/BoofCV | main/boofcv-ip/src/main/java/boofcv/abst/distort/FDistort.java | FDistort.affine | public FDistort affine(double a11, double a12, double a21, double a22,
double dx, double dy) {
PixelTransformAffine_F32 transform;
if( outputToInput != null && outputToInput instanceof PixelTransformAffine_F32 ) {
transform = (PixelTransformAffine_F32)outputToInput;
} else {
transform = new PixelTransformAffine_F32();
}
Affine2D_F32 m = new Affine2D_F32();
m.a11 = (float)a11;
m.a12 = (float)a12;
m.a21 = (float)a21;
m.a22 = (float)a22;
m.tx = (float)dx;
m.ty = (float)dy;
m.invert(transform.getModel());
return transform(transform);
} | java | public FDistort affine(double a11, double a12, double a21, double a22,
double dx, double dy) {
PixelTransformAffine_F32 transform;
if( outputToInput != null && outputToInput instanceof PixelTransformAffine_F32 ) {
transform = (PixelTransformAffine_F32)outputToInput;
} else {
transform = new PixelTransformAffine_F32();
}
Affine2D_F32 m = new Affine2D_F32();
m.a11 = (float)a11;
m.a12 = (float)a12;
m.a21 = (float)a21;
m.a22 = (float)a22;
m.tx = (float)dx;
m.ty = (float)dy;
m.invert(transform.getModel());
return transform(transform);
} | [
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] | f01c0243da0ec086285ee722183804d5923bc3ac | https://github.com/lessthanoptimal/BoofCV/blob/f01c0243da0ec086285ee722183804d5923bc3ac/main/boofcv-ip/src/main/java/boofcv/abst/distort/FDistort.java#L256-L279 | train |
lessthanoptimal/BoofCV | main/boofcv-ip/src/main/java/boofcv/abst/distort/FDistort.java | FDistort.rotate | public FDistort rotate( double angleInputToOutput ) {
PixelTransform<Point2D_F32> outputToInput = DistortSupport.transformRotate(input.width/2,input.height/2,
output.width/2,output.height/2,(float)angleInputToOutput);
return transform(outputToInput);
} | java | public FDistort rotate( double angleInputToOutput ) {
PixelTransform<Point2D_F32> outputToInput = DistortSupport.transformRotate(input.width/2,input.height/2,
output.width/2,output.height/2,(float)angleInputToOutput);
return transform(outputToInput);
} | [
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lessthanoptimal/BoofCV | main/boofcv-ip/src/main/java/boofcv/abst/distort/FDistort.java | FDistort.apply | public void apply() {
// see if the distortion class needs to be created again
if( distorter == null ) {
Class typeOut = output.getImageType().getImageClass();
switch( input.getImageType().getFamily() ) {
case GRAY:
distorter = FactoryDistort.distortSB(cached, (InterpolatePixelS)interp, typeOut);
break;
case PLANAR:
distorter = FactoryDistort.distortPL(cached, (InterpolatePixelS)interp, typeOut);
break;
case INTERLEAVED:
distorter = FactoryDistort.distortIL(cached, (InterpolatePixelMB) interp, output.getImageType());
break;
default:
throw new IllegalArgumentException("Unsupported image type");
}
}
distorter.setModel(outputToInput);
distorter.apply(input,output);
} | java | public void apply() {
// see if the distortion class needs to be created again
if( distorter == null ) {
Class typeOut = output.getImageType().getImageClass();
switch( input.getImageType().getFamily() ) {
case GRAY:
distorter = FactoryDistort.distortSB(cached, (InterpolatePixelS)interp, typeOut);
break;
case PLANAR:
distorter = FactoryDistort.distortPL(cached, (InterpolatePixelS)interp, typeOut);
break;
case INTERLEAVED:
distorter = FactoryDistort.distortIL(cached, (InterpolatePixelMB) interp, output.getImageType());
break;
default:
throw new IllegalArgumentException("Unsupported image type");
}
}
distorter.setModel(outputToInput);
distorter.apply(input,output);
} | [
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] | f01c0243da0ec086285ee722183804d5923bc3ac | https://github.com/lessthanoptimal/BoofCV/blob/f01c0243da0ec086285ee722183804d5923bc3ac/main/boofcv-ip/src/main/java/boofcv/abst/distort/FDistort.java#L326-L350 | train |
lessthanoptimal/BoofCV | main/boofcv-geo/src/main/java/boofcv/alg/geo/impl/ProjectiveToIdentity.java | ProjectiveToIdentity.process | public boolean process( DMatrixRMaj P ) {
if( !svd.decompose(P) )
return false;
svd.getU(Ut,true);
svd.getV(V,false);
double sv[] = svd.getSingularValues();
SingularOps_DDRM.descendingOrder(Ut,true,sv,3,V,false);
// compute W+, which is transposed and non-negative inverted
for (int i = 0; i < 3; i++) {
Wt.unsafe_set(i,i, 1.0/sv[i]);
}
// get the pseudo inverse
// A+ = V*(W+)*U'
CommonOps_DDRM.mult(V,Wt,tmp);
CommonOps_DDRM.mult(tmp, Ut,PA);
// Vector U, which is P*U = 0
SpecializedOps_DDRM.subvector(V,0,3,V.numRows,false,0,ns);
return true;
} | java | public boolean process( DMatrixRMaj P ) {
if( !svd.decompose(P) )
return false;
svd.getU(Ut,true);
svd.getV(V,false);
double sv[] = svd.getSingularValues();
SingularOps_DDRM.descendingOrder(Ut,true,sv,3,V,false);
// compute W+, which is transposed and non-negative inverted
for (int i = 0; i < 3; i++) {
Wt.unsafe_set(i,i, 1.0/sv[i]);
}
// get the pseudo inverse
// A+ = V*(W+)*U'
CommonOps_DDRM.mult(V,Wt,tmp);
CommonOps_DDRM.mult(tmp, Ut,PA);
// Vector U, which is P*U = 0
SpecializedOps_DDRM.subvector(V,0,3,V.numRows,false,0,ns);
return true;
} | [
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@param P (Input) 3x4 camera matrix
@return true if no errors | [
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] | f01c0243da0ec086285ee722183804d5923bc3ac | https://github.com/lessthanoptimal/BoofCV/blob/f01c0243da0ec086285ee722183804d5923bc3ac/main/boofcv-geo/src/main/java/boofcv/alg/geo/impl/ProjectiveToIdentity.java#L52-L76 | train |
lessthanoptimal/BoofCV | main/boofcv-geo/src/main/java/boofcv/alg/geo/impl/ProjectiveToIdentity.java | ProjectiveToIdentity.computeH | public void computeH( DMatrixRMaj H ) {
H.reshape(4,4);
CommonOps_DDRM.insert(PA,H,0,0);
for (int i = 0; i < 4; i++) {
H.unsafe_set(i,3,ns.data[i]);
}
} | java | public void computeH( DMatrixRMaj H ) {
H.reshape(4,4);
CommonOps_DDRM.insert(PA,H,0,0);
for (int i = 0; i < 4; i++) {
H.unsafe_set(i,3,ns.data[i]);
}
} | [
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"i"... | Retrieve projective transform H | [
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] | f01c0243da0ec086285ee722183804d5923bc3ac | https://github.com/lessthanoptimal/BoofCV/blob/f01c0243da0ec086285ee722183804d5923bc3ac/main/boofcv-geo/src/main/java/boofcv/alg/geo/impl/ProjectiveToIdentity.java#L81-L90 | train |
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