Class EpipolarTransformer
- java.lang.Object
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- algorithms.imageProcessing.transform.EpipolarTransformer
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public class EpipolarTransformer extends java.lang.Object
class to solve for the epipoles for two images with stereo projection and apply the solution.The fundamental matrix is the projective solution for transformation between 2 images of the same objects in pixel coordinates. Present below is the solution for having 7 matched points between images and the solution for having 8 or more matched points between the images. Both use numerical conditioning and recipes suggested by Hartley (see reference below). For the 8-point algorithm: IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, VOL. 19, NO. 6, JUNE 1997 "In Defense of the Eight-Point Algorithm" by Richard Hartley The 8-point algorithm matrix represents epipolar geometry, and can be used with data from cameras whose characteristics are not known to solve up to the projective transformation. ...a simple transformation (translation and scaling) of the points in the image before formulating the linear equations leads to an enormous improvement in the condition of the problem and hence of the stability of the result... Some definitions: vectors are treated as columns unless noted otherwise. vector as a row uses notation u^T. u^T*v represents the inner product (= dot product). result is a scalar. u*v^T represents the outer product. result is a matrix. u_1 is the (x,y) points from image 1 and u_2 are the matched (x,y) points from image 2. Epipolar Plane: An epipolar plane is defined by the principal points o1, and o2 of the cameras and the point p. Epipoles: The projection of one camera center onto the other camera's image plane is an epipole. (the epipole can lie outside the image boundaries) in the Essential Matrix: e2 ~ T and e1 ~ R*T up to a scalar factor. in general: e1 is the left null space of E and e2 is the right null space of E. e2^T*E = 0 E*e1 = 0 and e1 = SVD(E).u <-- last column, then divided by last item e2 = SVD(E).v <-- last row, then divided by last item Epipolar line of p: intersection of the epipolar place of p with an image place is the epipolar line of p. l2 ~ E*x1 l1 ~ E^T*x2 l*e=0 for (l1,e1) and also (l2,e2) l^T*x=0 for (l1,x1) and also (l2,x2) the fundamental matrix is defined: u_2^T * F * u_1 = 0 where u are the x,y points in images _1 and _2 and F is a 3 × 3 matrix of rank 2 An explanation of the derivation of the fundamental matrix can be found in Zhengyou Zhang. "Determining the Epipolar Geometry and its Uncertainty: A Review". RR-2927, INRIA. 1996. ffinria-00073771 equations (1) and (2) u_1 = (x_1, y_1, 1)^T u_2 = (x_2, y_2, 1)^T u_1 = x_1_i x_1_i+1 ... y_1_i y_1_i+1 ... 1 1 ... u_2^T = x_2_i y_2_i 1 x_2_i+1 y_2_i+1 1 ... ... ... x_1*x_2*F_1_1 + x_1*y_2*F_2_1 + x_1*F_3_1 + y_1*x_2*F_1_2 + y_1*y_2*F_2_2 + y_1*F_3_2 + x_2*F_1_3 + y_2*F_2_3 + F_3_3 = 0 A * f = 0 where A = x_1*x_2, x_1*y_2, x_1, y_1*x_2, y_1*y_2, y_1, x_2, y_2, 1 (which is each element of column 0 of u_1 dotted separately with row 0 of u2_T) A is nData X 9 and f is a nine-vector (size 9X1) containing the entries of the matrix F To avoid the trivial scale, ||f|| = 1 where f is the norm of f the rank of A is 8, but noise and other errors make it 9. if A is longer than 8, the system is over-determined (over specified) and so must be solved using least squares. the set may be over determined and not have a zero solution. we want the vector f that minimizes ||A*f|| subject to the constraint that ||f|| = f^T*f = 1 the solution is the unit eigenvector corresponding to the smallest eigenvalue of A^T*A. Since A^T*A is positive semi-definite and symmetric, all of its eigenvectors are real and positive or zero. This eigenvector is what he calls the least eigenvector of A^T*A and it is found via the Jacobi algorithm or Singular Value Decomposition. NOTE: SVD(A).U == SVD(A^T).V == SVD(AA^T).U == SVD(AA^T).V SVD(A).V == SVD(A^T).U == SVD(A^TA).V == SVD(A^TA).U The solved for matrix will in general not have rank 2 and needs to, so further corrections are necessary: matrix F is replaced by F' that minimizes the Frobenius norm ||F - F'|| subject to the condition det F' = 0. A convenient method of doing this is to use the Singular Value Decomposition (SVD). let F = U*D*V^T be the SVD of F, where D is diagonal matrix D = diag(r, s, t) satisfying r >= s >= t. let F' = U*diag(r, s, 0)*V^T. (1) Transforming the coordinates: Normalization for isotropic scaling. 1) The points are translated so that their centroid is at the origin. 2) The points are then scaled so that the average distance from the origin is equal to 2 . 3) This transformation is applied to each of the two images independently. NOTE: if needed to use non-isotropic scaling (e.g. rectangular pixels, etc): transform the points so that 1) Their centroid is at the origin. 2) The principal moments are both equal to unity scaling the coordinate so that the homogeneous coordinates are on the average equal to unity will improve the condition of the matrix A^T*A. I talso allows use of error calculations such as Sampson's. (2) build matrix A with the normalized x,y points (3) compute linear least square solution to the least eigenvector of f: solve A = U * D * V^T for A*f = [..x...]*f = 0 A has rank 8. F has rank 2. calculate [U,D,V] from svd(A) (4) make the fundamental matrix have a rank of 2 by performing a svd and then reconstructing with the two largest singular values (similar to dimensionality reduction) [U,D,V] = svd(F,0); F = U * diag([D(1,1) D(2,2) 0]) * V^T; (5) denormalize the fundamental matrix The related part of the normalization equation: inv(T_2) * F * inv(T_1) so denormalizing is: F = (T_2)^T * F * T_1 (6) estimate the error in the fundamental matrix by calculating epipolar lines for points in image 1 and find their nearest points in image 2 and measure the perpendicular distance from the epipolar line for those nearest points. NOTE: This is best done using normalized coordinates and fundamental matrix, after step (4) and before step (5). For the 7-point algorithm: references are: the the Hartley & Zisserman matlab code vgg_F_from_7pts_2img from a version of http://www.robots.ox.ac.uk/~vgg/hzbook/code/ which is part of the supplementary material for their book "Multiple View Geometry in Computer Vision Second Edition" and Hartley, R. I. (1994a). Projective reconstruction and invariants from multiple images. PAMI, 16(10):1036–1041 and Torr, P. H. S. and Murray, D. (1997). "The development and comparison of robust meth- ods for estimating the fundamental matrix. International Journal of Computer Vision", 24(3):271–300. The 7-point algorithm solves for the null space of the fundamental matrix and results in one or 3 solutions which can for some geometries be reduced to a single solution. The nullspace is where Ax=0 in reduced echelon, that is, the free variable rows. The normalization and denormalization steps before and following the solution, are the same as in the 8-point solution. this from comments in the Hartley & Zisserman matlab code vgg_F_from_7pts_2img: Solutions for the 7-points are pruned by requirement that scalars s in all equations s * cross(e1,u_1) == F*u_2 are positive. In case of multiple solutions, F has one dimension more such that F(:,:,n) is the n-th solution. note: because translation is not a linear transformation (see Strang Chap 7) one has to keep it as a separate transformation matrix when performing operations on a sequence of matrices such as inverse and transpose operations. translation matrix: inverse changes the signs of the translation elements, but not the diagonal rotation matrix: inverse is the transpose of rotation matrix. scaling matrix: inverse is performed on each element, that is, the reciprocal. (A*B*C)^-1 = (C^-1) * (B^-1) * (A^-1) also, when A * A^(-1) = I, one can use: 1 A^(-1) = ------ C^(T) where C_ij = cofactor of a_ij det A NOTE: the normalization suggested by Hartley is explored further in Chojnacki et al. 2003, "Revisiting Hartley’s Normalized Eight-Point Algorithm" Some excerpts: xc is the centroid of x coordinates. yc is the centroid of y coordinates. s is the root mean square distance of (x-xc,y-yc) to origin divided by sqrt(2) u1_normalized = T1 * u1 u2_normalized = T2 * u2 denormalized F = T2^T * F_normalized * T1 denormalized u1 = T1^-1 * u1_normalized and similar foru2 FM_normalized = inverse(transpose(T2)) * FM * inverse(T1) denormalized FM = transpose(T2) * FM_normalized * T1 u2^T * FM * u1 = u2_normalized^T * FM_normalized * u1_normalized = residual | 1/s 0 0 | | 1 0 -xc | | 1/s 0 -xc/s | T = | 0 1/s 0 | * | 0 1 -yc | = | 0 1/s -yc/s | | 0 0 1 | | 0 0 1 | | 0 0 1 | | 1 0 xc | | s 0 0 | | s 0 xc | T^-1 = | 0 1 yc | * | 0 s 0 | = | 0 s yc | | 0 0 1 | | 0 0 1 | | 0 0 1 | | 1 0 xc | | s^2 0 0 | | 1 0 0 | T^-1 * T^-T = | 0 1 yc | * | 0 s^2 0 | * | 0 1 0 | | 0 0 1 | | 0 0 1 | | xc yc 1 | | s^2 + xc^2 xc*yc xc | = | yc*xc s^2 + yc^2 yc | | xc yc 1 | | s 0 0 | | 1 0 0 | | s 0 0 | from that can see T^-T = | 0 s 0 | * | 0 1 0 | = | 0 s 0 | | 0 0 1 | | xc yc 1 | | xc yc 1 | | 1 0 0 | | 1/s 0 0 | | 1/s 0 0 | T^T = | 0 1 0 | * | 0 1/s 0 | = | 0 1/s 0 | |-xc -yc 1 | | 0 0 1 | | -xc/s -yc/s 1 | ( | 1/s 0 0 | )^-1 | 1 0 0 | | s 0 0 | (T^T)^-1 = ( | 0 1/s 0 | ) * | 0 1 0 | = | 0 s 0 | ( | 0 0 1 | ) | xc yc 1 | | xc yc 1 | can see that (T^-1)^T = (T^T)^-1
The plunder-dl scoring can be used for comparison between different models. for example, comparing results of the 7-point and 8-point solutions or comparing 7-point projection to 6-point affine, etc. plunder-dl is from equation 33 of Torr, Zisserman, & Maybank 1996, “Robust Detection of Degenerate Configurations whilst Estimating the Fundamental Matrix" https://www.robots.ox.ac.uk/~phst/Papers/CVIU97/m.ps.gz EQN 33: PL = DOF + (4*n_o + n_i dimension of model) where n_i = number of inliers n_o = number of outliers DOF = 7 for this solver n=7 PL = DOF + 4*n_o + n_i* (model_dimension) ni=7, no=0 PL = 7 + 0 + 0 * md ni=5, no=2 PL = 7 + 8 + 8 * md ni=4, no=3 PL = 7 + 12 + 28 * md PLUNDER stands for Pick Least UNDEgenerate Randomly, Description Length For nPoints=8, model_dimension = 1. for nPoints=7 amd only 1 solution in the cubic constraints, model_dimension=2, else for nPoints=7, model_dimension = 3.
A summary of epipolar geometry is in chapter 5 of Ma, Soatto, Kosecká, and Sastry 2012, "An Invitation to 3-D Vision". e2 when normalized by 3rd coord is in coord space of left image and it is the location of the right camera center. e2 is the last column of svd.u e2 is the left null space of F (e2^T*F = 0 e2^T*E = 0) ==> e2~T where T is translation and ~ is equality up to a scale factor e1 is the last row of svd.vt e1 is the right null space of F (F*e1 = 0 E*e1 = 0) ==> e1~R^T*T where R is rotation and T is translation l2 = E*x1 l1 = E^T*x2 (x1^T*l1=0 and l1^T*e1=0) (x2^T*l2=0 and l2^T*e2=0)
NOTE: For "7-point" correspondences, consider implementing MLESAC. "MLESAC: A new robust estimator with application to estimating image geometry" by P. H. S. Torr and A. Zisserman 1996 http://www.robots.ox.ac.uk/~vgg/publications/papers/torr00.pdf TODO: implement Nister's 5-point algorithm to determine the essential matrix for the case where arguments are the camera intrinsic parameters and the correspondence.- Author:
- nichole
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Nested Class Summary
Nested Classes Modifier and Type Class Description static class
EpipolarTransformer.NormalizationTransformations
static class
EpipolarTransformer.NormalizedXY
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Constructor Summary
Constructors Constructor Description EpipolarTransformer()
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Method Summary
All Methods Static Methods Instance Methods Concrete Methods Modifier and Type Method Description no.uib.cipr.matrix.DenseMatrix
calculateEpipolarProjection(no.uib.cipr.matrix.DenseMatrix leftXY, no.uib.cipr.matrix.DenseMatrix rightXY, boolean calibrated)
calculate the epipolar projection for a set of 8 or more matched points.double[][]
calculateEpipolarProjection2(double[][] leftXY, double[][] rightXY, boolean calibrated)
calculate the epipolar projection.java.util.List<no.uib.cipr.matrix.DenseMatrix>
calculateEpipolarProjectionFor7Points(no.uib.cipr.matrix.DenseMatrix leftXY, no.uib.cipr.matrix.DenseMatrix rightXY)
NOT READY FOR USE YET calculate the epipolar projection for 7 correspondences and filter with a chirality check.double[][]
calculateEpipolarProjectionUsing7Points(double[][] leftXY, double[][] rightXY)
calculate the epipolar projection given 7 pairs of points.(package private) double[][]
calculateEpipoles(no.uib.cipr.matrix.DenseMatrix fundamentalMatrix)
calculate the epipoles of the fundamental matrix and return them as an array with left epipole in column 0 and right epipole in column 1.static double[][]
createEssentialFromFundamentalMatrix(double[][] k1, double[][] k2, double[][] fm)
convert the fundamental matrix into the essential matrix.static double[][]
createFundamentalFromEssentialMatrix(double[][] k1, double[][] k2, double[][] em)
convert the essential matrix into the fundamental matrix using F= K2^-T * E * K1^-1(package private) double[][]
createFundamentalMatrix(double[][] normXY1, double[][] normXY2)
a = xLeft ⊗ xRight(package private) double[][]
createFundamentalMatrix(no.uib.cipr.matrix.DenseMatrix normXY1, no.uib.cipr.matrix.DenseMatrix normXY2)
a = xLeft ⊗ xRightprotected static EpipolarTransformer.NormalizationTransformations
createScaleTranslationMatrices(double scale, double centroidX, double centroidY)
create 2 scale and translation matrices to normalize homogeneous 2D coordinates by multiplying on the left side, and to de-normalize the normalized coordinates by multiplying on the left side.static no.uib.cipr.matrix.DenseMatrix
denormalizeTheFundamentalMatrix(no.uib.cipr.matrix.DenseMatrix normalizedFundamentalMatrix, EpipolarTransformer.NormalizationTransformations leftNT, EpipolarTransformer.NormalizationTransformations rightNT)
(package private) no.uib.cipr.matrix.DenseMatrix
denormalizeTheFundamentalMatrix(no.uib.cipr.matrix.DenseMatrix normalizedFundamentalMatrix, EpipolarTransformer.NormalizedXY normalizedLeftXY, EpipolarTransformer.NormalizedXY normalizedRightXY)
static double
estimateToleranceForDOF1(double standardDeviation)
if assume gaussian errors and chi-squared statistics, for 1 degree of freedom (i.e.static double
estimateToleranceForDOF2(double standardDeviation)
if assume gaussian errors and chi-squared statistics, for 2 degree of freedom (i.e.static double
estimateToleranceForDOF3(double standardDeviation)
if assume gaussian errors and chi-squared statistics, for 2 degree of freedom (i.e.static double[][]
extractIndices(double[][] m, java.util.List<java.lang.Integer> inlierIndexes)
static no.uib.cipr.matrix.DenseMatrix
extractIndices(no.uib.cipr.matrix.DenseMatrix m, java.util.List<java.lang.Integer> inlierIndexes)
algorithms.util.PairIntArray
getEpipolarLine(no.uib.cipr.matrix.DenseMatrix epipolarLines, int imgWidth, int imgHeight, int pointNumber)
static EpipolarTransformer.NormalizedXY
normalize(no.uib.cipr.matrix.DenseMatrix xy)
normalize the x,y coordinates as recommended by Hartley 1997 and return the matrix and coordinates.static double[]
normalizeUsingUnitStandard(double[][] xy)
perform unit standard normalization of the points xy to result in a mean of 0 for xy and a standard deviation of 1, and return the mean of x, mean of y, st.static EpipolarTransformer.NormalizedXY
normalizeUsingUnitStandard(no.uib.cipr.matrix.DenseMatrix xy)
normalize the x,y coordinates as recommended by Hartley 1997 and return the matrix and coordinates.no.uib.cipr.matrix.DenseMatrix
pFromF(no.uib.cipr.matrix.DenseMatrix f)
Determine the camera matrices of an image pair up to a scene homography, given their fundamental matrix using algorithm of Hartley and Zisserman 2004.(package private) no.uib.cipr.matrix.DenseMatrix[]
solveFor7Point(double[][] ff1, double[][] ff2)
(package private) no.uib.cipr.matrix.DenseMatrix
validateSolution(no.uib.cipr.matrix.DenseMatrix solution, no.uib.cipr.matrix.DenseMatrix leftXY, no.uib.cipr.matrix.DenseMatrix rightXY)
The validation of the 7-point algorithm follows source code adapted from this site and license: based upon code within www.robots.ox.ac.uk/~vgg/hzbook/code/ MIT License License for "MATLAB Functions for Multiple View Geometry" Copyright (c) 1995-2005 Visual Geometry Group Department of Engineering Science University of Oxford http://www.robots.ox.ac.uk/~vgg/ Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
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Method Detail
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calculateEpipolarProjection2
public double[][] calculateEpipolarProjection2(double[][] leftXY, double[][] rightXY, boolean calibrated) throws java.io.IOException
calculate the epipolar projection. all normalizations and de-normalizations are handled internally. if the camera calibration is known, the essential matrix is returned.- Parameters:
leftXY
- the left image coordinates of the feature correspondencesrightXY
- the right image coordinates of the feature correspondencescalibrated
- whether or not the camera calibration is known. if true, the essential matrix is returned.- Returns:
- the fundamental matrix (or essential matrix) of the epipolar projection, else null if SVD failed.
- Throws:
java.io.IOException
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calculateEpipolarProjectionUsing7Points
public double[][] calculateEpipolarProjectionUsing7Points(double[][] leftXY, double[][] rightXY) throws java.io.IOException
calculate the epipolar projection given 7 pairs of points. all normalizations and de-normalizations are handled internally. if the camera calibration is known, the essential matrix is returned.- Parameters:
leftXY
- the left image coordinates of the feature correspondencesrightXY
- the right image coordinates of the feature correspondences- Returns:
- the fundamental matrix (or essential matrix) of the epipolar projection.
- Throws:
java.io.IOException
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calculateEpipolarProjection
public no.uib.cipr.matrix.DenseMatrix calculateEpipolarProjection(no.uib.cipr.matrix.DenseMatrix leftXY, no.uib.cipr.matrix.DenseMatrix rightXY, boolean calibrated)
calculate the epipolar projection for a set of 8 or more matched points. NOTE that for best results, the method should be given unit standard normalized coordinates.- Parameters:
leftXY
- left image correspondence in format of double array with x points on row 0, y points on row 1, and 1's on row 2. the number of columns is the number of data points.rightXY
- right image correspondence in format of double array with x points on row 0, y points on row 1, and 1's on row 2. the number of columns is the number of data points.calibrated
- if true, solves for the Essential Matrix, else solves for the Fundamental matrix. The difference is only in the diagonal used for dimension reduction.- Returns:
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calculateEpipolarProjectionFor7Points
public java.util.List<no.uib.cipr.matrix.DenseMatrix> calculateEpipolarProjectionFor7Points(no.uib.cipr.matrix.DenseMatrix leftXY, no.uib.cipr.matrix.DenseMatrix rightXY)
NOT READY FOR USE YET calculate the epipolar projection for 7 correspondences and filter with a chirality check. returns a list of the filtered solutions. NOTE that for best results, the method should be given unit standard normalized coordinates. references are: (1) the the Hartley & Zisserman matlab code vgg_F_from_7pts_2img from a version of http://www.robots.ox.ac.uk/~vgg/hzbook/code/ which is part of the supplementary material for their book "Multiple View Geometry in Computer Vision Second Edition" (2) Section IVa of Hartley, R. I. (1994a). Projective reconstruction and invariants from multiple images. PAMI, 16(10):1036–1041 (3) Torr, P. H. S. and Murray, D. (1997). "The development and comparison of robust methods for estimating the fundamental matrix. International Journal of Computer Vision", 24(3):271–300.- Parameters:
leftXY
-rightXY
-- Returns:
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validateSolution
no.uib.cipr.matrix.DenseMatrix validateSolution(no.uib.cipr.matrix.DenseMatrix solution, no.uib.cipr.matrix.DenseMatrix leftXY, no.uib.cipr.matrix.DenseMatrix rightXY)
The validation of the 7-point algorithm follows source code adapted from this site and license: based upon code within www.robots.ox.ac.uk/~vgg/hzbook/code/ MIT License License for "MATLAB Functions for Multiple View Geometry" Copyright (c) 1995-2005 Visual Geometry Group Department of Engineering Science University of Oxford http://www.robots.ox.ac.uk/~vgg/ Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. The software is provided "as is", without warranty of any kind, express or implied, including but not limited to the warranties of merchantability, fitness for a particular purpose and noninfringement. In no event shall the authors or copyright holders be liable for any claim, damages or other liability, whether in an action of contract, tort or otherwise, arising from, out of or in connection with the software or the use or other dealings in the software. vgg_multiview/vgg_F_from_7pts_2img.m The method "signs_OK" validates the solution matrices: for i = 1:length(a) Fi = a(i)*FF{1} + (1-a(i))*FF{2}; %for n = 1:7, disp(norm(x(:,n,1)'*Fi*x(:,n,2))), end % test code if signs_OK(Fi,x1,x2) F = cat(3, F, Fi); end end return %%%%%%%%%%%%%%%%%%%%%%%%% % Checks sign consistence of F and x function OK = signs_OK(F,x1,x2) [u,s,v] = svd(F'); e1 = v(:,3); l1 = vgg_contreps(e1)*x1; s = sum( (F*x2) .* l1 ); OK = all(s>0) | all(s less than 0); return More on the subject is present in "Cheirality in Epipolar Geometry" by Werner & Pajdla, 2000 regarding realizability of two images. http://citeseerx.ist.psu.edu/viewdoc/download?doi=<IP_ADDRESS>.9013&rep=rep1&type=pdf Very clear paper on cheirality: http://users.cecs.anu.edu.au/~hartley/Papers/cheiral/revision/cheiral.pdf The cheirality of a point is whether it lies in front of or behind a given camera. It's used to distinguish between four different possible scene reconstructions from two views. A transform is cheirality-reversing for a given point if it swaps the point from the front to the back of the camera, or vice-versa. Otherwise it is called cheirality-preserving. note: alternate spelling is chirality.
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pFromF
public no.uib.cipr.matrix.DenseMatrix pFromF(no.uib.cipr.matrix.DenseMatrix f)
Determine the camera matrices of an image pair up to a scene homography, given their fundamental matrix using algorithm of Hartley and Zisserman 2004.If x2'*F*x1 = 0 for any pair of image points x1 and x2, then the camera matrices of the image pair are P1 = [I 0] (as 3x4 matrix) and P2 = vgg_P_from_F(F), up to a scene homography.
algorithm follows source code for vgg_P_from_F.m adapted from this site and license: based upon code within www.robots.ox.ac.uk/~vgg/hzbook/code/ MIT License License for "MATLAB Functions for Multiple View Geometry" Copyright (c) 1995-2005 Visual Geometry Group Department of Engineering Science University of Oxford http://www.robots.ox.ac.uk/~vgg/ Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. The software is provided "as is", without warranty of any kind, express or implied, including but not limited to the warranties of merchantability, fitness for a particular purpose and noninfringement. In no event shall the authors or copyright holders be liable for any claim, damages or other liability, whether in an action of contract, tort or otherwise, arising from, out of or in connection with the software or the use or other dealings in the software. %%%%%%%%%%%%%%%%%%%%%%%%%- Parameters:
f
- fundamental matrix in format 3x3- Returns:
- camera matrices in format 3x4
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solveFor7Point
no.uib.cipr.matrix.DenseMatrix[] solveFor7Point(double[][] ff1, double[][] ff2)
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denormalizeTheFundamentalMatrix
no.uib.cipr.matrix.DenseMatrix denormalizeTheFundamentalMatrix(no.uib.cipr.matrix.DenseMatrix normalizedFundamentalMatrix, EpipolarTransformer.NormalizedXY normalizedLeftXY, EpipolarTransformer.NormalizedXY normalizedRightXY)
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normalize
public static EpipolarTransformer.NormalizedXY normalize(no.uib.cipr.matrix.DenseMatrix xy)
normalize the x,y coordinates as recommended by Hartley 1997 and return the matrix and coordinates. does not modify the state of this transformer instance. the normalized coordinates have an origin = centroid of the points. the mean square distance of the normalized points from the origin is sqrt(2) pixels. normalized xy = T * xy- Parameters:
xy
-- Returns:
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normalizeUsingUnitStandard
public static EpipolarTransformer.NormalizedXY normalizeUsingUnitStandard(no.uib.cipr.matrix.DenseMatrix xy)
normalize the x,y coordinates as recommended by Hartley 1997 and return the matrix and coordinates. does not modify the state of this transformer instance. the normalized coordinates have an origin = centroid of the points and a scale equal to the standard deviation of the mean subtracted coordinates.- Parameters:
xy
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normalizeUsingUnitStandard
public static double[] normalizeUsingUnitStandard(double[][] xy)
perform unit standard normalization of the points xy to result in a mean of 0 for xy and a standard deviation of 1, and return the mean of x, mean of y, st. dev of x and st dev of y. note that no normalization is performed on the z coordinates. note that the normalization alters the input.- Parameters:
xy
- data points in format [3 X n]. e.g. xy[0][0] is x, xy[1][0] is y, xy[2][0] is z for point 0.- Returns:
- mean of x, mean of y, st. dev of x and st dev of y
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createScaleTranslationMatrices
protected static EpipolarTransformer.NormalizationTransformations createScaleTranslationMatrices(double scale, double centroidX, double centroidY)
create 2 scale and translation matrices to normalize homogeneous 2D coordinates by multiplying on the left side, and to de-normalize the normalized coordinates by multiplying on the left side. The normalization matrix transforms x by (x - centroidX)/scale and similarly transforms y. The de-normalization matrix can be used to multiply by scale and add the centroid.- Parameters:
scale
-centroidX
-centroidY
-- Returns:
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denormalizeTheFundamentalMatrix
public static no.uib.cipr.matrix.DenseMatrix denormalizeTheFundamentalMatrix(no.uib.cipr.matrix.DenseMatrix normalizedFundamentalMatrix, EpipolarTransformer.NormalizationTransformations leftNT, EpipolarTransformer.NormalizationTransformations rightNT)
- Parameters:
normalizedFundamentalMatrix
- the normalized fundamental matrix, that is the solution for the fundamental matrix using the normalized correspondence.leftNT
- the transformations created for normalization and de-normalization of the left image coordinates.rightNT
- the transformations created for normalization and de-normalization of the right image coordinates.- Returns:
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createFundamentalMatrix
double[][] createFundamentalMatrix(no.uib.cipr.matrix.DenseMatrix normXY1, no.uib.cipr.matrix.DenseMatrix normXY2)
a = xLeft ⊗ xRight- Parameters:
normXY1
- a matrix of size 3 x nPoints, where 1st row is x, second is y.normXY2
- a matrix of size 3 x nPoints, where 1st row is x, second is y.- Returns:
- fundamental matrix of size [nPoints x 9]
-
createFundamentalMatrix
double[][] createFundamentalMatrix(double[][] normXY1, double[][] normXY2)
a = xLeft ⊗ xRight- Parameters:
normXY1
- a matrix of size 3 x nPoints, where 1st row is x, second is y.normXY2
- a matrix of size 3 x nPoints, where 1st row is x, second is y.- Returns:
- fundamental matrix of size [nPoints x 9]
-
calculateEpipoles
double[][] calculateEpipoles(no.uib.cipr.matrix.DenseMatrix fundamentalMatrix)
calculate the epipoles of the fundamental matrix and return them as an array with left epipole in column 0 and right epipole in column 1. Epipolar Plane: An epipolar plane is defined by the principal points o1, and o2 of the cameras and the point p. Epipoles: In the epipolar plane, the point where the camera principal point intersects it image is the epipole. The location of the epipole in the other image can be thought of as the projection of the other camera center onto its image. (the epipole can lie outside the image boundaries) e1 is the left null space of E and e2 is the right null space of E. e2^T*E = 0 E*e1 = 0 and e1 = SVD(E).u <-- last column, then divided by last item e2 = SVD(E).v <-- last row, then divided by last item for the essential matrix: e2 ~ T and e1 ~ R*T up to a scalar factor.- Parameters:
fundamentalMatrix
-- Returns:
- a matrix holding whose first row holds e1 and 2nd row holds e2
-
createEssentialFromFundamentalMatrix
public static double[][] createEssentialFromFundamentalMatrix(double[][] k1, double[][] k2, double[][] fm) throws no.uib.cipr.matrix.NotConvergedException
convert the fundamental matrix into the essential matrix.- Parameters:
k1
- camera 1 intrinsics matrix.k2
- camera 2 intrinsics matrix.fm
- the fundamental matrix in the reference frame of the original image space of pixel coordinates.- Returns:
- the essential matrix in the reference frame of normalized image coordinates (origin is optical center of the image).
- Throws:
no.uib.cipr.matrix.NotConvergedException
-
createFundamentalFromEssentialMatrix
public static double[][] createFundamentalFromEssentialMatrix(double[][] k1, double[][] k2, double[][] em)
convert the essential matrix into the fundamental matrix using F= K2^-T * E * K1^-1- Parameters:
k1
- camera 1 intrinsics matrix.k2
- camera 2 intrinsics matrix.em
- the essential matrix in the reference frame of normalized image coordinates (origin is optical center of the image)- Returns:
- the fundamental matrix .
-
getEpipolarLine
public algorithms.util.PairIntArray getEpipolarLine(no.uib.cipr.matrix.DenseMatrix epipolarLines, int imgWidth, int imgHeight, int pointNumber)
-
extractIndices
public static no.uib.cipr.matrix.DenseMatrix extractIndices(no.uib.cipr.matrix.DenseMatrix m, java.util.List<java.lang.Integer> inlierIndexes)
-
extractIndices
public static double[][] extractIndices(double[][] m, java.util.List<java.lang.Integer> inlierIndexes)
-
estimateToleranceForDOF1
public static double estimateToleranceForDOF1(double standardDeviation)
if assume gaussian errors and chi-squared statistics, for 1 degree of freedom (i.e. fitting a line, fundamental matrix, d^2 = 3.84*(st.dev^2)- Parameters:
standardDeviation
-- Returns:
-
estimateToleranceForDOF2
public static double estimateToleranceForDOF2(double standardDeviation)
if assume gaussian errors and chi-squared statistics, for 2 degree of freedom (i.e. fitting a line, fundamental matrix, d^2 = 5.99*(st.dev^2)- Parameters:
standardDeviation
-- Returns:
-
estimateToleranceForDOF3
public static double estimateToleranceForDOF3(double standardDeviation)
if assume gaussian errors and chi-squared statistics, for 2 degree of freedom (i.e. fitting a line, fundamental matrix, d^2 = 7.82*(st.dev^2)- Parameters:
standardDeviation
-- Returns:
-
-


Ten unique apprenticeships available from ghost hunter to chocolate maker
Whatever your interests, there is an apprenticeship out there for you
by Nathalie GannonYou are never too old to start an apprenticeship.
If you are aged 16 or above, and not in full time education, you can take part in an apprenticeship scheme. They are the perfect opportunity to start down a new career path, and they give you the freedom to learn, work, and get paid.
Apprenticeships are a convenient route into a profession that doesn’t land you with a hefty student debt, and there are thousands of different schemes to choose from that could take you down many diverse paths.
And we mean thousands. Many people think apprentices only cater to a handful of different career options but that couldn’t be further from the truth.
And here are ten unique apprenticeships on offer to prove it:
Nuclear Scientist
Looking for a brand spanking nu-clear career? This apprenticeship lands you with a degree and some cash when it’s all finished (it even offers annual pay increases). If you’re a science or maths whizz there are loads of apprenticeships that offer you money to study.
Puppy Pamperer
Love a wet nose and the pitter-patter of tiny paws? Being an animal groomer doesn’t sound too ruff. This apprenticeship will bring you the most loveable clientele, that will always be happy to see you, and will have bundles of pawsitivity.
Martial Artist
Tae Kwon-Do or Tae Kwon-Don’t? Take that fighting spirit and apply it to an apprenticeship that will condition both your body and mind and give you the opportunity to transform into a role model for others in the process.
All about...The BristolLive and BathLive Apprenticeship Awards
The BristolLive and BathLive Apprenticeship Awards is an annual campaign seeking our cities’ best and brightest apprentices across all different industries and sectors.
It is also a chance to commend the businesses investing in fresh talent and the schemes that put apprentices on a path to success.
If you know someone who you think deserves some recognition, either as an apprentice or an apprenticeship provider, we want to hear from you.
To find out more about the awards visit the website
Sandwich Artist
Are you someone that goes against the (whole)grain? If you fancy yourself a Dali with the deli meats and have great customer service skills, then an apprenticeship as a Sandwich Artist might be for you.
Guitar Maker
Fret-ting about your career? Don’t get strung up looking for a degree and learn a niche craft. This independent company offers courses in guitar building and apprenticeships in making precision tools for guitar making.
Submarine Engineer
Student debt can sometimes get you into deep water. This apprenticeship does the complete opposite, offering a healthy salary with a lot of growth opportunity. A varied role with multiple responsibilities, this is one apprenticeship in which you could rise to the top.
Pizzaiolo
Do you know your prosciutto from your pepperoni? Get a pizza the action and earn some dough! This is an apprenticeship that will definitely make you popular amongst your friends, because pizza is life.
Chocolate Maker
Satisfy your sweet tooth and choose from a multitude of roles on offer from Nestlé. They are one of the world’s largest food and drink producers, and have a range of apprenticeship roles from factory floor to corporate finance.
Boat Maker
Knot shore what you want out of a career? Maybe boatmaking will be your lega-sea. This apprenticeship might float your boat and earn you qualifications in marine engineering.
Ghost Hunter
If you aren’t afraid of things that go bump in the night, and like the sound of being a specter spectator this might be the one for you. Gloucester’s resident Ghost Hunter was searching for an apprentice, and although that role has now been filled, you can see that trainee paranormal investigators have avoided landing in a dead-end job!
As you can see, the number of opportunities available are limitless! If these roles have peaked your interest have an explore of the different industries available and earn whilst you learn!


Graduated from the University of California, Santa Cruz, Justin Zhu began his work with non-profits to help the community around him. After years of working with the community, he developed and realized his passion for programming and web development. Justin spent years studying from an Object Oriented Language like Java to web-based language like Javascript. On Justin's free time, he continues to acquire new skills, design personal projects, and give back to the community.
Justin is a Front-End Developer who has created, built, and designed visually appealing sites for entrepreneurs and small local companies. While being skilled in the latest web technologies (HTML5, CSS3, and Javascript), Justin's strength lies in his "whatever it takes" attitude and burning passion to learn. He believes that technology is ever-evolving and maintenance of knowledge is an important aspect of keeping up with the latest trends.


Rotation 4: Laser Cutting and Engraving and Vinyl Cutting
Project Description:
The first project of this cycle was to create a laser cut box with both rastering and engraving. I made my box in corel draw a three by three inch box on cardboard. I put my name and heart on one side, Jason Kipnis on another, Snapchat, a sloth. a softball, and the state of Ohio. This project was my second time using the laser cutter.
The second project of this cycle was designing three different shapes that when laser printed will connect together. This project I also designed in Corel Draw. I made the shapes by creating a shape on corel then making a hairline rectangle that was the same on each shape that would cut out to connect them. This idea worked well.
The third project of this cycle was desiging and laser printing on anything we wanted. I chose a glass. I made two glasses, both with images from google on corel. The dolphin cup turned out better than the pineapple cup because it was a clearer image. I learned after the first try with the pineapple that using a clip art image with no background came out very clear and not pixely.


Dedicated to inspiring individuals, businesses and communities to create their future - Australia
A planned lab with a focus on sustainable development in Auckland - New Zealand
A volunteer run and home to a diverse community of people with a passion for making things - Australia
A community-based space with a focus on empowering people to become makers and creators - New Zealand
Nurturing resilience through hands-on experimentation and lifelong learning in global open design ecosystems - New Zealand
On a mission to equip our whole community with 21st century skills, and make Wairarapa a place where hi-tech people live! - New Zealand


This is a boilerplate of angular4-django with login functionality.
FrontEnd Features
- Ready to go, statically typed build system using Gulp for working with TypeScript.
- Production and development builds.
- Sample unit tests with Jasmine and Karma including code coverage via Istanbul.
- End-to-end tests with Protractor.
- Development server with live reload.
- TypeScript definition management using Types.
- Basic Service Worker, which implements "Cache then network strategy".
- MeView service so that only authenticated ones can view the home page else go to login page using router canActivate
BackendEnd Features
- Knox login-logout functionality
- Only frontend server allowed on allowed_hosts for backend accessibility
- Need to add a local.py file for backend functionality for development purposes which includes the secret key and is a replica of local_template.py
- Used SQLite database
- Waitress can be configured to nginx configuration


Its a dead man's party! We are on a mission from God to provide you with the most entertainmnet you have had in four score and seven years! Each category is a timed event. Go add rest of instructions and funniness.


Casts
Discover, search and listen to your favorite podcasts.
Search
Search all podcasts and episodes on this site by title or description.
Podcast
Episode


Autocryp is the place to buy and sell Bitcoin, Ethereum and various kinds of Digital Assets in Indonesia. Then, book your weekly, bi-weekly or one-off clean online with imminent confirmation.
Your account is protected by Multifactor Authentication using a combination of email, SMS and Google Authenticator.
You only need 1 account to do all Bitcoin transactions whether it is buying, selling, investing or trading.
You only need 1 account to do all Bitcoin transactions whether it is buying, selling, investing or trading.


Postponed for snow... yeah, actual flakes were coming down, here in Portland.
12/1/2007, 10:27:15 AM
Favs: 0
Retweets: 0
link--- layout: tweet ---
Postponed for snow... yeah, actual flakes were coming down, here in Portland.
12/1/2007, 10:27:15 AM
Favs: 0
Retweets: 0
link


Simple Runnable to test as a standaloneJob.
More...
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| def | param_defaults (self) |
| |
| def | fetch_input (self) |
| |
| def | run (self) |
| |
| def | write_output (self) |
| |
| def | __init__ (self, read_fileno, write_fileno) |
| |
| def | warning (self, message, is_error=False) |
| | Store a message in the log_message table with is_error indicating whether the warning is actually an error or not. More...
|
| |
| def | dataflow (self, output_ids, branch_name_or_code=1) |
| | Dataflows the output_id(s) on a given branch (default 1). More...
|
| |
| def | worker_temp_directory (self) |
| | Returns the full path of the temporary directory created by the worker. More...
|
| |
| def | param_defaults (self) |
| | Returns the defaults parameters for this runnable. More...
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| |
| def | param_required (self, param_name) |
| | Returns the value of the parameter "param_name" or raises an exception if anything wrong happens. More...
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| |
| def | param (self, param_name, args) |
| | When called as a setter: sets the value of the parameter "param_name". More...
|
| |
| def | param_exists (self, param_name) |
| | Returns True or False, whether the parameter exists (it doesn't mean it can be successfully substituted) More...
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| |
| def | param_is_defined (self, param_name) |
| | Returns True or False, whether the parameter exists, can be successfully substituted, and is not None. More...
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| |
Simple Runnable to test as a standaloneJob.
Definition at line 6 of file TestRunnable.py.
◆ fetch_input()
| def TestRunnable.TestRunnable.fetch_input |
( |
|
self | ) |
|
◆ param_defaults()
| def TestRunnable.TestRunnable.param_defaults |
( |
|
self | ) |
|
◆ run()
| def TestRunnable.TestRunnable.run |
( |
|
self | ) |
|
◆ write_output()
| def TestRunnable.TestRunnable.write_output |
( |
|
self | ) |
|
The documentation for this class was generated from the following file:


{% extends "common/docs/base.html" %} {% block nav %} {% include "common/docs/sidebar.html" with active_id="recommender" %} {% endblock %} {% block content %}
Use these endpoints to update and access various course recommendations generated by the FireRoad recommendation engine.
Sends a subject rating to the FireRoad server. The body of the request should be a JSON list of dictionaries, each containing s (subject ID) and v (rating value). Updates the ratings for each item.
Takes an optional parameter t indicating the type of recommendation to return. Returns a dictionary of recommendation types mapped to JSON strings indicating the recommended subjects and their rating values.
{% endblock %}


Pick a cat to pet!
Every time you click on a cat, they receive a nice scratch behind the ears.
Name:
URL:
Clicks:
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cached
Profile Saving... Please Wait !
Choose your Leadership Profile as instructed by your facilitator
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3 writes to ExecutionStatus
System.Management.Automation (3)
engine\hostifaces\History.cs (3)
38
ExecutionStatus
= status;
53
ExecutionStatus
= history.ExecutionStatus;
125
internal void SetStatus(PipelineState status) =>
ExecutionStatus
= status;
2 references to ExecutionStatus
System.Management.Automation (2)
engine\hostifaces\History.cs (2)
53
ExecutionStatus = history.
ExecutionStatus
;
1400
infoToAdd.
ExecutionStatus
,


The laplace transform of \(f\) is \[ F(s) := \int_{0}^{\infty} e^{-st} f(t) dt \]
LT exists for f(t) if it is
- Piecewise Continuous
- Exponential Order
Piecewise contiuous if:
- Has finite number of jump discontinuities
- f(t) approaches finite limit at each discontinuity
Exponential Order of \(\alpha\) if it does not grow faster than \(e^{\alpha t}\): \[|f(t)| \leq Me^{\alpha t} \quad\text{for all } t \geq T\] where \(M, T\) are positive constants
The Laplace Transform is linear \[ \mathscr{L}\{f_1 + f_2\} = \mathscr{L}\{f_1\} + \mathscr{L}\{f_2\} \] \[ \mathscr{L}\{cf_1\} = c\mathscr{L}\{f_1\} \]
\(f(t)\) is said to have a jump discontinuity at \(t_0 \in (a, b)\) if \(f(t)\) is discontinuous at \(t_0\) but the one-sided limits \[\lim{t\to t_0^-} f(t) and \lim{t\to t_0^+} f(t)\] exists as finite numbers.
If the discontinuity occurs at an endpoint, \(t_0 = a \ (or \ b)\), a jump discontinuity occurs if the one sided limit of \(f(t)\) as \(t\to a^+ or\ (t \to b^-)\) exists as a finite number.
\[ \mathscr{L}\{f^{(n)}\}(s) = s^n\mathscr{L}\{f\}(s) - s^{n-1}f(0) - s^{n-2}f'(0) - \cdots - f^{(n - 1)}(0) \]
\[ \begin{align*} \mathscr{L}(e^{at}f) &= \int_0^{\infty} e^{-st} e^{at} f(t) dt \\ &= \int_0^{\infty} e^{(a-s)t} f(t) dt \\ &= F(s-a) \end{align*} \]
ex.
\[\frac{x^2+15}{(x+3)^2(x^2+3)} = \frac{A_1}{x+3} + \frac{A_2}{(x+3)^2} + \frac{Bx+C}{x^2+3}\]
\[ \begin{align} \frac{As+B}{as^2 + bs +c} &= \frac{\frac{A}{a}s + \frac{B}{a}}{s^2 + \frac{b}{a}s + \frac{c}{a}} \\ &= \frac{E(s-\alpha) + D\beta}{(s-\alpha)^2 + \beta^2} \end{align} \]
where
- \(\alpha = -\frac{b}{2a}\)
- \(\beta = \frac{\sqrt{4ac-b^2}}{2a}\)
Then inverse each term to \(e^{\alpha t}\cos(bt)\) and \(e^{at}\sin(bt)\)
\[h(t) = (f * g)(t) = \int_{0}^{t} f(\tau)g(t-\tau) d\tau\]
\[ M \cdot H(t-a) = \left\{ \begin{array}{ll} 0 & \quad t < a \\ M & \quad t > a \end{array} \right. \]
\[ M \cdot (H(t-a) - H(t-b) = \left\{ \begin{array}{ll} 0 & \quad t < a \\ M & \quad a < t < b \\ 0 & \quad b < t \end{array} \right. \]
\[ \begin{align} \mathscr{L} \{H(t-a)f(t-a)\} &= e^{-as} \mathscr{L} \{f(t)\} \\ \mathscr{L} \{H(t-a)f(t)\} &= e^{-as} \mathscr{L} \{f(t+a)\} \end{align} \]
\[\int e^{bx}cos(ax)dx = e^{bx}\frac{a sin(ax) + b cos (ax)}{a^2 + b^2}\] \[\int e^{bx}sin(ax)dx = e^{bx}\frac{b sin(ax) - a cos(ax)}{a^2 + b^2}\]
http://tutorial.math.lamar.edu/pdf/Laplace_Table.pdf


--- date: 2010-03-27T00:00:00Z published: true title: An ex-CIA spy explains Iran’s quest for nuclear weapons url: /2010/03/27/an-ex-cia-spy-explains-iran-s-quest-for-nuclear-weapons/ ---
Khamenei considers the Koran to be the ultimate source of guidance. One Koranic tenet is that you should deceive your enemies until you are strong enough to destroy them. Khamenei is employing this when he makes his declarations to the West.


Megiorni (2011) 2.10(1.05,4.20) 17/102 20/230 20.72 Zhang (2010) 1.44(0.78,268) 11132 1571/26501 20.99 Tao (2011) —> 1953(1036,33.14) 17112 545845 21.12 Akar (2002) 0.89(028,282) 7165 650 18.61 Barbosa (2016) 2.04(0.63,659) 5339112. 18.56 DeL Overall (I-squared = 92.4%, p = 0.000) 2.60(0.74,9.14) 57/444 1660/32538 100.00 M-H Overall 2.44 (1.75, 3.42)
NOTE: Weights are from random effects analysis


Notting Hill, known for its artistic and boho vibes, is probably the neighbourhood that most epitomises London’s diversity and eccentricity. It’s also one of the most well-known areas in the city, famous for its Portobello Road market, its yearly street carnival and the stage of novels and romantic comedy Notting Hill starring Hugh Grant and Julia Roberts in 1999.
Nestled between Regent’s Park and Primrose Hill and boasting canals and the charms of Little Venice, the St John’s Wood/Maida Vale neighbourhood provides a village atmosphere that appeals to young professionals and families alike.Being well connected to public transport via the Jubilee Line (St John’s Wood station), Bakerloo line (Warwick Avenue & Maida Vale stations) and various bus lines serving Central and North West London, the neighbourhood is ideally located.
Canary Wharf, located in Greater London, is highly regarded as a commercial centre. The 97-acre area has over 1.5 million square metres of retail and office space. Being the second largest financial district, it has some of the tallest and most spectacular buildings to be found in Europe. In fact, the skyline is a sight to behold and looks quite spectacular at night when the buildings are lit up.


Technology
This extension to help users can control youtube videos easily just by your hand gestures.
Data
Many thank to our friends who supported the videos to make the model training data. We also thank to IU Computer Vision Lab and Google team providing Egohand dataset and HMD dataset.
SSD MobileNet
SSD (Single Shot MultiBox Detector) is a popular algorithm in object detection. It’s generally faster than Faster RCNN.
VGG - 16
VGG - 16 is a convolutional neural network model proposed by K. Simonyan and A. Zisserman from the University of Oxford in the paper"Very Deep Convolutional Networks for Large-Scale Image Recognition".
Chrome extension
Chrome extension is a small software module for customizing your browser Chrome. Chrome typically allow a variety of extensions, including user interface modifications, ad blocking, and cookie management.


Your computer seems to be disconnected from the network. Therefore, we cannot connect to the server to handle your submission request. Please verify that your network connection and proxy settings are fine.


Relation Graph
Country-Country
All Supplier-Customer
FullDetail
Select Country
AR
MY
PH
ID
Separated link
One more separated link


eReferendum
Referendums
Questions
Welcome to eReferendum! 👋
Use the links above to navigate through the application.


|
Audiometry
|
Files | |
| file | AblbModel.cs |
| file | AirCondAidDataModel.cs |
| file | AirCondMskDataModel.cs |
| file | AirCondUmskDataModel.cs |
| file | BoneCondMskDataModel.cs |
| file | BoneCondUmskDataModel.cs |
| file | LoudDiscomLvlDataModel.cs |
| file | PureToneDataModel.cs |
| file | SisiModel.cs |
| file | SoundFieldDataModel.cs |
| file | StengerModel.cs |
| file | SubTabModel.cs |
| file | ToneDecayModel.cs |


CHAPTER 469
UNDERTAKERS, EMBALMERS, FUNERAL DIRECTORS
Section
469-1 License
469-2 Rules
469-3 Renewal of license
469-4 Registration of apprentices
469-5 Disposition of fees
469-6 Revocation of license
469-7 Powers of department to subpoena witnesses, etc.
469-8 Penalty
Cross References
Disposition of a decedent's remains, control of, see chapter 531B.


Implementation of the Open Perimetry Interface (OPI) for simulating and controlling visual field machines using R. The OPI is a standard for interfacing with visual field testing machines (perimeters). It specifies basic functions that allow many visual field tests to be constructed. As of October 2017 it is fully implemented on the Octopus 900 and partially on the Heidelberg Edge Perimeter, the Kowa AP 7000, the CrewT imo and the Centervue Compass. It also has a cousin: the R package 'visualFields', which has tools for analysing and manipulating visual field data.
| Version: | 2.7 |
| Depends: | methods |
| Published: | 2017-10-25 |
| Author: | Andrew Turpin [cre, aut, cph], David Lawson [ctb, cph], Matthias Muller [ctb], Jonathan Dennis [ctb, cph], Astrid Zeman [ctb] |
| Maintainer: | Andrew Turpin <aturpin at unimelb.edu.au> |
| License: | GPL-3 |
| URL: | http://people.eng.unimelb.edu.au/aturpin/opi/index.html |
| NeedsCompilation: | no |
| Materials: | ChangeLog |
| CRAN checks: | OPI results |
| Reference manual: | OPI.pdf |
| Package source: | OPI_2.7.tar.gz |
| Windows binaries: | r-devel: OPI_2.7.zip, r-release: OPI_2.7.zip, r-oldrel: OPI_2.7.zip |
| OS X binaries: | r-release: OPI_2.7.tgz, r-oldrel: OPI_2.7.tgz |
| Old sources: | OPI archive |
Please use the canonical form https://CRAN.R-project.org/package=OPI to link to this page.


RoboBears
Home
About
Portfolio
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Portfolio
Check our latest work
Fidget Killer
Fidget Killer
Lil’ Chungus
Lil' Chungus
Pig's Foot
Pig's Foot
The Imposter
The Crewmates
Bigweld
Jawbreaker
UwU
The Khila
Sheila


Clipboard Sync Options
Thanks for installing Clipboard Sync. You may want to choose a friendlier name for this installation.
Client name:
Human-readable name shown in sync notifications.
Dismiss notifications after:
30 seconds
1 minute
2 minutes
5 minutes
never
Auto-close "clipboard pushed" notifications after this interval.


Notice that clicking on the Forward button here will not result in any action, because there is no next URL in the history list.


Tile Scaling
This extension defines tile scaling behaviors for tile tables. When tiles at a requested zoom level are not present, alternate zoom level tiles may be scaled in place. The extension configures the allowable zoom directions and distance from the requested tile.
National Geospatial-Intelligence Agency, author_name nga
nga_tile_scaling
New Requirement
This extension applies to the table names of tile user tables (2.2.8).
Read-write
The Tile Scaling extension is registered into the gpkg_extensions
table as follows:
| table_name | column_name | extension_name | definition | scope |
|---|---|---|---|---|
user tile table name |
null |
|
URL to this extension description |
read-write |
The Tile Scaling Table contains tile table names and the desired scaling behavior configurations.
| Column Name | Type | Description | Null | Default | Key |
|---|---|---|---|---|---|
|
TEXT |
Tile Table name, foreign key to |
no |
PK, FK |
|
|
TEXT |
The scaling behavior type: ('in','out','in_out','out_in','closest_in_out','closest_out_in') |
no |
||
|
INTEGER |
Max zoom levels in (higher zoom level) to search from a requested zoom level, null for no limit, ignored when |
yes |
||
|
INTEGER |
Max zoom levels out (lower zoom level) to search from a requested zoom level, null for no limit, ignored when |
yes |
CREATE TABLE nga_tile_scaling (
table_name TEXT PRIMARY KEY NOT NULL,
scaling_type TEXT NOT NULL,
zoom_in INTEGER,
zoom_out INTEGER,
CONSTRAINT fk_nts_gtms_tn FOREIGN KEY('table_name') REFERENCES gpkg_tile_matrix_set('table_name')
CHECK (scaling_type in ('in','out','in_out','out_in','closest_in_out','closest_out_in'))
);
The Tile Scaling Type defines the zoom search directions and order from a requested tile.
| Name | Description |
|---|---|
in |
Search for tiles by zooming in |
out |
Search for tiles by zooming out |
in_out |
Search for tiles by zooming in first, and then zooming out |
out_in |
Search for tiles by zooming out first, and then zooming in |
closest_in_out |
Search for tiles in closest zoom level order, equal zoom level distance prefers zoom in before zoom out |
closest_out_in |
Search for tiles in closest zoom level order, equal zoom level distance prefers zoom out before zoom in |
None
None
The rivers GeoPackage has a rivers_tiles
tile table that is configured for tile scaling.
The example GeoPackage has tile tables configured for tile scaling.


Global Distribution
Global Relation
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| 008:075 Khan | : |
And those who believed afterwards, and emigrated and strove hard along with you, (in the Cause of Allah) they are of you. But kindred by blood are nearer to one another regarding inheritance in the decree ordained by Allah. Verily, Allah is the All-Knower of everything. |
| 008:075 Maulana | : |
And those who believed afterwards and fled and struggled hard along with you, they are of you. And the relatives are nearer one to another in ordinance of Allah. Surely Allah is Knower of all things |
| 008:075 Pickthal | : |
And those who afterwards believed and left their homes and strove along with you, they are of you; and those who are akin are nearer one to another in the ordinance of Allah. Lo! Allah is Knower of all things. |
| 008:075 Rashad | : |
Those who believed afterwards, and emigrated, and strove with you, they belong with you. Those who are related to each other shall be the first to support each other, in accordance with GOD's commandments. GOD is fully aware of all things. |
| 008:075 Sarwar | : |
Those who accepted the faith later, left their homes and strove with you for the cause of God are also your people. They relatives are nearer to each other according to the Book of God. God has knowledge of all things. |
| 008:075 Shakir | : |
And (as for) those who believed afterwards and fled and struggled hard along with you, they are of you; and the possessors of relationships are nearer to each other in the ordinance of Allah; surely Allah knows all things. |
| 008:075 Sherali | : |
And those who believe after this and leave their homes and strive for the cause of ALLAH along with you - these are of you; and as to blood relations, they are nearer one to another in the Book of ALLAH. Surely, ALLAH knows all things well. |
| 008:075 Yusufali | : |
And those who accept Faith subsequently, and adopt exile, and fight for the Faith in your company,- they are of you. But kindred by blood have prior rights against each other in the Book of Allah. Verily Allah is well-acquainted with all things. |
Concepts/Themes Covered: أركان الإسلام > (محمد (صلى الله عليه وسلم > تزكية أمته وصحابته Pillars of Islam> The Blessed Muhammad(PBUH)> His Nation and Companions' Attest of Him أركان الإسلام > (محمد (صلى الله عليه وسلم > هجرته ومنزلة المهاجرين Pillars of Islam> The Blessed Muhammad(PBUH)> His Immigration and the Immigrants Rank الجهاد > الجهاد في سبيل الإسلام > تفضيل المجاهدين Jihad> The Call to Jihad>Preferring the Strugglers الجهاد > الجهاد في سبيل الإسلام > مدح الجهاد Jihad> The Call to Jihad>Praising the Holy War الجهاد > الهجرة > ثواب المهاجرين Jihad> The Immigration> The Immigrants' Reward الإنسان والعلاقات الاجتماعية > صلة ذوي القربى Man and The Social Relations>Kinship تنظيم العلاقات المالية > الميراث Organizing Financial Relationships>Inheritance
This tool uses eight English parallel translations for the Holy Quran imported from Mr. Clay Smith. You can also visit Qurany Concepts for browsing the Quran themes or topics. If you want to search the Holy Quran for keywords, please visit Qurany Keywords.


Police chief urges children to use social media carefully
‘Prevention of Child Abuse Day’ observed
by Special CorrespondentCity Police Commissioner A.K.Viswanathan on Tuesday advised schoolchildren not to accept or send friend requests to unknown persons on social media networks.
‘Prevention of Child Abuse Day’ was observed by the city police on Tuesday. On the occasion, the police’s special wing for women and children organised a programme at the Presidency Higher Secondary School For Girls, to raise social awareness among schoolchildren.
Inaugurating the programme, Mr. Viswanathan said “Young students should not to send any ‘friend request’ or chat with unknown persons over social media networks.”
“They should be aware of the dangers of sharing personal and sensitive information/pictures online.” he added.
Vigilance imperative
Advising girls to be more vigilant, he said statistics showed that crimes against children were committed mostly by persons who were known to them.
Young boys can inform their friends or teachers if they sense any danger, he said.
Over 2.5 lakh closed circuit television (CCTV) cameras have been installed across the city with a view to enhance women’s security, the city police have launched Pink Police Patrol vehicles, he said.
Chennai is a safer city for women and children, and a special wing of the police was functioning here to handle crimes against women and children, he said.


Definition at line 15 of file DAORelationsTrait.php.
◆ _getAll()
| static _getAll |
( |
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$className, |
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ConditionParser |
$conditionParser, |
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$included = true , |
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$useCache = NULL |
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staticabstractprotected |
◆ _getIncludedNext()
| static _getIncludedNext |
( |
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$included, |
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$member |
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) |
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staticprivate |
◆ _initRelationFields()
| static _initRelationFields |
( |
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$included, |
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$metaDatas, |
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& |
$invertedJoinColumns, |
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$oneToManyFields, |
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$manyToManyFields |
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) |
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staticprotected |
◆ generateManyToManyParser()
| static generateManyToManyParser |
( |
ManyToManyParser |
$parser, |
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& |
$myPkValues |
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) |
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staticprivate |
◆ getIncludedForStep()
| static getIncludedForStep |
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$included | ) |
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◆ getInvertedJoinColumns()
| static getInvertedJoinColumns |
( |
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$included, |
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$invertedJoinColumns |
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) |
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staticprivate |
◆ getManyToManyFromArrayIds()
| static getManyToManyFromArrayIds |
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$objectClass, |
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$relationObjects, |
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$ids |
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◆ getToManyFields()
| static getToManyFields |
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$included, |
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$toManyFields |
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staticprivate |
◆ parseEncludeMember()
| static parseEncludeMember |
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staticprivate |
The documentation for this class was generated from the following file:


Ocopea's key feature is the ability to create and restore full application copies of cloud native apps. Ocopea is able to do this thanks to two key ingredients:
- Understanding the cloud native app topology
- Abstraction of the service consumption API (Ocopea Brokers)
In order for Ocopea to take application copies, it first needs to understand the application topology:
what components it is built of and what is the interaction between them. Ocopea differentiates between
two types of components: stateless components (app service) and
stateful components (infrastructure services).
Mostly thanks to the ability to easily scale stateless microservices this has become a common
distinction for cloud native apps.
In addition, while it is popular to run the stateless microservices using container orchestrators/PaaS -
many organizations prefer delegating the standard infrastructure services to
either SaaS (e.g. AWS RDS) or DevOps (e.g. Oracle database on premises).
Lets walk through an example:
The application topology diagram above describes our basic sample hackathon application.
The application is built of one app service: The "hackathon service", written in java delivered as a
docker container, and has two infrastructure service dependencies:
an S3 service for storing documents and a Postgres database for storing hackathon ideas metadata.
The hackathon service is the application entry point -
meaning application users can interact with it via the web.
In order to make a copy of the entire hackathon application state and later restore it on a different
environment,
Ocopea needs the ability to make copies of each individual component configuration and data:
- The hackathon docker image version that is used including the configuration (port bindings, environment variables etc.)
- The postgres database - hackathon schemas, users and the configuration allowing the hackathon service to access it (bindings)
- The S3 service - hackathon buckets data, and the configuration allowing the hackathon service to access it (bindings)
The ability to both create copies and later restore individual components falls under the responsibility of the "Ocopea Brokers".
Ocopea brokers are the components that allow Ocopea to interact with the world.
Deployment of containers, provisioning and creation of infrastructure services copies is
all delegated to brokers.
Brokers are managed by the ocopea component called the Site.
Every Ocopea site is responsible for a local geographical location - either a public cloud availability zone or an on premises datacenter.
Sites can be aggregated by a central component called the Ocopea Hub.
There are three types of brokers:
- PSB - PaaS/Container Orchestrator brokers
- DSB - Data service brokers
- CRB - Copy repository brokers
The PSB is responsible for communicating with the PaaS or the container orchestrator. It is used by
the Ocopea site to deploy the stateless containers and bind them to infrastructure services
(Data Services)
Ocopea has built in PSBs for both
Kubernetes and
Cloud Foundry
The DSB is responsible for communicating with stateful services. Stateful services can run within the
boundaries of the platform the app is running, or externally.
The two main tasks of a DSB are:
- Provision a new stateful service instance
- Create a copy of the service to a copy repository
Every service that implement the DSB API can be used by Ocopea. The API and samples can be found
here
Examples of services that require DSBs:
- AWS S3 DSB
- Oracle DSB
- Docker volume mount DSB
As you can see in our standardization page, we keep our eye open for in the field of service consumption.
The CRB is responsible for listing and providing connectivity with copy repositories.
Copy repositories are where Ocopea stateful services store copies. Ocopea has out-of-the box a postgres
and a filesystem implementation of CRB.
Examples of services that require CRBs:
- AWS Glacier/S3 CRB
- VTL DSB
For more information regarding data path protocols and API see the CRB project.


DimMatrix (mA, rows, columns, preserve_flag )
Sets the dimensions of matrix mA to the specified number of rows and columns. If preserve_flag is true then it will preserve the matrix structure after resize.
Example:
DimMatrix ( 0, 4, 4, true )
This resizes matrix 0 to a 4x4 matrix and preserves the data in it within the previous organized structure.


For mvc hangfire is configure via the Startup.cs Configuration method
using Hangfire;
// ...
public void Configuration(IAppBuilder app)
{
GlobalConfiguration.Configuration.UseSqlServerStorage("connection string or its name");
app.UseHangfireDashboard();
app.UseHangfireServer();
}
You can open the dash board by navigating to http://your-site/hangfire. I use this for seeing job status and debugging issues with jobs
Every running instance will create a server with queues
Its important to note if your job uses local resource they will need to be accessible from the server the jobs is running on
Hangfire can process multiple queues. In order to assure your job is process on the same queue as your server include a options object when setting up hangfire in the Startup.cs
I've setup my instance with a preprocessor to create a different queue based on development or production
#if DEBUG
// debug mode use dashboard and set queue to a unique queue
GlobalConfiguration.Configuration
.UseSqlServerStorage("Hangfire")
.UseDashboardMetric(SqlServerStorage.ActiveConnections)
.UseDashboardMetric(SqlServerStorage.TotalConnections)
.UseDashboardMetric(DashboardMetrics.FailedCount);
app.UseHangfireDashboard();
var options = new BackgroundJobServerOptions
{
Queues = new[] { "development"}
};
app.UseHangfireServer(options);
#else
// release mode dont' use dashboard and set queue to support
GlobalConfiguration.Configuration
.UseSqlServerStorage("Hangfire")
var options = new BackgroundJobServerOptions
{
Queues = new[] { "support"}
};
app.UseHangfireServer(options);
#endif
The hangfire library allow us to process jobs in the background safe from any app domain shutdowns
Jobs Type:
Running jobs on a specific queue is a simple as provinding the Queue("") attribute
#if DEBUG
public const string queue = "development";
#else
public const string queue = "support";
#endif
[Queue(queue)]
public void DummyJob()
{
System.Threading.Thread.Sleep(500);
}
SignalR is a websockets framework build for .Net
The most common use of SignalR is using the Hubs
Hubs provide a abstraction for groups, topics and defining methods that both server and client can use
// Server
public class NotificationHub : Hub
{
public override Task OnConnected()
{
string name = Context.User.Identity.Name;
Groups.Add(Context.ConnectionId, name);
return base.OnConnected();
}
public void SendNotificationMessage(NotificationMessage message)
{
Clients.Caller.SendNotificationMessage(message);
}
}
// Client
var notificationHub = $.connection.notificationHub;
if ($.connection.hub && $.connection.hub.state === $.signalR.connectionState.disconnected) {
$.connection.hub.start()
.done(function (state) {
// websocket up and running!
});
}
notificationHub.client.sendNotificationMessage = function (message) {
// message received use it!
};
/


|
Home >
Enterprise >
Participants >
Symantec >
Command and Control (TA0011)
|
|
Carbanak+FIN7 |
||||||||
| Step | ATT&CK Pattern |
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1.A.10
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Technique Application Layer Protocol (T1071) Subtechnique Application Layer Protocol: Web Protocols (T1071.001) |
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1.A.11
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Technique Encrypted Channel (T1573) Subtechnique Encrypted Channel: Asymmetric Cryptography (T1573.002) |
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2.B.1
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Technique Ingress Tool Transfer (T1105) |
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3.B.1
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Technique Ingress Tool Transfer (T1105) |
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3.B.7
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Technique Non-Application Layer Protocol (T1095) |
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4.B.1
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Technique Ingress Tool Transfer (T1105) |
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4.B.2
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Technique Ingress Tool Transfer (T1105) |
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5.A.1
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Technique Ingress Tool Transfer (T1105) |
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5.A.2
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Technique Ingress Tool Transfer (T1105) |
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5.A.3
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Technique Ingress Tool Transfer (T1105) |
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5.A.4
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Technique Ingress Tool Transfer (T1105) |
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5.A.5
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Technique Ingress Tool Transfer (T1105) |
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7.A.1
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Technique Ingress Tool Transfer (T1105) |
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7.A.3
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Technique Application Layer Protocol (T1071) |
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7.C.1
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Technique Ingress Tool Transfer (T1105) |
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7.C.3
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Technique Ingress Tool Transfer (T1105) |
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8.A.2
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Technique Application Layer Protocol (T1071) Subtechnique Application Layer Protocol: Web Protocols (T1071.001) |
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8.A.3
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Technique Encrypted Channel (T1573) Subtechnique Encrypted Channel: Asymmetric Cryptography (T1573.002) |
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|
9.A.1
|
Technique Ingress Tool Transfer (T1105) |
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9.B.1
|
Technique Ingress Tool Transfer (T1105) |
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10.A.1
|
Technique Ingress Tool Transfer (T1105) |
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10.A.2
|
Technique Ingress Tool Transfer (T1105) |
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10.B.1
|
Technique Remote Access Software (T1219) |
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12.A.3
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Technique Application Layer Protocol (T1071) |
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12.B.1
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Technique Ingress Tool Transfer (T1105) |
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13.B.1
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Technique Ingress Tool Transfer (T1105) |
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14.A.6
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Technique Application Layer Protocol (T1071) Subtechnique Application Layer Protocol: Web Protocols (T1071.001) |
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14.A.7
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Technique Encrypted Channel (T1573) Subtechnique Encrypted Channel: Asymmetric Cryptography (T1573.002) |
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15.A.2
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Technique Ingress Tool Transfer (T1105) |
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15.A.3
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Technique Ingress Tool Transfer (T1105) |
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16.A.1
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Technique Ingress Tool Transfer (T1105) |
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16.A.2
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Technique Ingress Tool Transfer (T1105) |
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16.A.8
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Technique Application Layer Protocol (T1071) Subtechnique Application Layer Protocol: Web Protocols (T1071.001) |
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16.A.9
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Technique Encrypted Channel (T1573) Subtechnique Encrypted Channel: Asymmetric Cryptography (T1573.002) |
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17.A.1
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Technique Ingress Tool Transfer (T1105) |
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17.A.5
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Technique Application Layer Protocol (T1071) Subtechnique Application Layer Protocol: Web Protocols (T1071.001) |
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17.A.6
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Technique Encrypted Channel (T1573) Subtechnique Encrypted Channel: Asymmetric Cryptography (T1573.002) |
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19.A.3
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Technique Proxy (T1090) |
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19.B.3
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Technique Ingress Tool Transfer (T1105) |
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19.B.4
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Technique Ingress Tool Transfer (T1105) |
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20.A.3
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Technique Application Layer Protocol (T1071) Subtechnique Application Layer Protocol: Web Protocols (T1071.001) |
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20.A.4
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Technique Encrypted Channel (T1573) Subtechnique Encrypted Channel: Asymmetric Cryptography (T1573.002) |
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20.B.1
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Technique Ingress Tool Transfer (T1105) |
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20.B.3
|
Technique Ingress Tool Transfer (T1105) |
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APT29 |
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| Step | ATT&CK Pattern |
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1.A.3
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Technique Non-Application Layer Protocol (T1095) |
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1.A.4
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Technique Encrypted Channel (T1573) Subtechnique Encrypted Channel: Symmetric Cryptography (T1573.001) |
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3.A.1
|
Technique Ingress Tool Transfer (T1105) |
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3.B.3
|
Technique Commonly Used Port (T1043) |
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3.B.4
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Technique Application Layer Protocol (T1071) Subtechnique Application Layer Protocol: Web Protocols (T1071.001) |
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3.B.5
|
Technique Encrypted Channel (T1573) Subtechnique Encrypted Channel: Asymmetric Cryptography (T1573.002) |
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4.A.1
|
Technique Ingress Tool Transfer (T1105) |
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8.B.1
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Technique Ingress Tool Transfer (T1105) |
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9.A.1
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Technique Ingress Tool Transfer (T1105) |
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9.A.2
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Technique Ingress Tool Transfer (T1105) |
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9.B.8
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11.A.13
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Technique Commonly Used Port (T1043) |
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11.A.14
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Technique Application Layer Protocol (T1071) Subtechnique Application Layer Protocol: Web Protocols (T1071.001) |
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11.A.15
|
Technique Encrypted Channel (T1573) Subtechnique Encrypted Channel: Asymmetric Cryptography (T1573.002) |
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14.B.3
|
Technique Ingress Tool Transfer (T1105) |
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18.A.1
|
Technique Web Service (T1102) |
|
Procedure
Used RC4 stream cipher to encrypt C2 (<IP_ADDRESS>) traffic
Criteria
Evidence that the network data sent over the C2 channel is encrypted
Procedure
Dropped stage 2 payload (monkey.png) to disk
Criteria
The rcs.3aka3.doc process creating the file monkey.png
Footnotes
- The telemetry was acquired by manually initiating an export of endpoint-stored event data that was not automatically sent to the analysis system, which caused this detection to receive Delayed (Manual).
Procedure
Read and downloaded ZIP (working.zip on Desktop) over C2 channel (<IP_ADDRESS> over TCP port 8443)
Criteria
python.exe reading the file working.zip while connected to the C2 channel
Procedure
Established C2 channel (<IP_ADDRESS>) via PowerShell payload over port 443
Criteria
Established network channel over port 443
Footnotes
- The telemetry was acquired by manually initiating an export of endpoint-stored event data that was not automatically sent to the analysis system, which caused this detection to receive Delayed (Manual).
Procedure
Used HTTPS to transport C2 (<IP_ADDRESS>) traffic
Criteria
Established network channel over the HTTPS protocol
Footnotes
- The telemetry was acquired by manually initiating an export of endpoint-stored event data that was not automatically sent to the analysis system, which caused this detection to receive Delayed (Manual).
Procedure
Used HTTPS to encrypt C2 (<IP_ADDRESS>) traffic
Criteria
Evidence that the network data sent over the C2 channel is encrypted
Footnotes
- The telemetry was acquired by manually initiating an export of endpoint-stored event data that was not automatically sent to the analysis system, which caused this detection to receive Delayed (Manual).
Procedure
Mapped a network drive to an online OneDrive account using PowerShell
Criteria
net.exe with command-line arguments then making a network connection to a public IP over port 443
Footnotes
- The telemetry was acquired by manually initiating an export of endpoint-stored event data that was not automatically sent to the analysis system, which caused this detection to receive Delayed (Manual).
[2]
[3]


| File | Statements | Branches | Functions | Lines | |||||
|---|---|---|---|---|---|---|---|---|---|
| avatar.js | 75% | 9/12 | 25% | 4/16 | 33.33% | 1/3 | 75% | 6/8 | |
| profile.js | 80% | 16/20 | 50% | 6/12 | 25% | 1/4 | 76.92% | 10/13 | |
| profileActions.js | 21.7% | 23/106 | 0% | 0/44 | 14.81% | 4/27 | 18.81% | 19/101 | |
| profileActions.spec.js | 74.29% | 26/35 | 75% | 6/8 | 60% | 6/10 | 70.97% | 22/31 | |
| profileForm.js | 47.37% | 9/19 | 100% | 4/4 | 11.11% | 1/9 | 40% | 6/15 | |
| profileNav.js | 60% | 9/15 | 66.67% | 4/6 | 16.67% | 1/6 | 54.55% | 6/11 | |
| profileReducer.js | 65.52% | 19/29 | 52.17% | 12/23 | 100% | 3/3 | 44.44% | 8/18 | |
| profileReducer.spec.js | 100% | 13/13 | 100% | 4/4 | 100% | 3/3 | 100% | 10/10 |


The Annotation class¶
A pylidc.Annotation
object belongs to a pylidc.Scan
object:
import pylidc as pl
ann = pl.query(pl.Annotation).first()
print(ann.scan.patient_id)
# => LIDC-IDRI-0078
Jump to:
Querying annotation feature values¶
The queryable attributes for Annotation objects are the characteristic values assigned by the particular annotating radiologist:
anns = pl.query(pl.Annotation).filter(pl.Annotation.spiculation == 5,
pl.Annotation.malignancy == 5)
print(anns.count())
# => 91
Note
Not all of the class attributes are queryable! Some attributes are actually computed properties. You can determine this by, for example, examining the object type for the class:
print(type(pl.Annotation.lobulation))
# => <class 'sqlalchemy.orm.attributes.InstrumentedAttribute'>
# => ^^^ queryable because it's an sqlalchemy attribute.
# Whereas ...
print(type(pl.Annotation.Lobulation))
# => <type 'property'>
# ^^^ not queryable because it's a computed property
The lobulation attribute is the numerical value assigned by the annotating radiologist, whereas the Lobulation attribute is a property that encodes the numerical value to the semantic string representation.
Each of the characteristic values are the numerical values assigned by the annotating radiologist. Alongside each of these attributes is a corresponding computed property that gives the semantic interpretation of the numerical value for a given characteristic:
ann = pl.query(pl.Annotation)\
.filter(pl.Annotation.malignancy == 5).first()
print(ann.malignancy, ann.Malignancy)
# => 5, 'Highly Suspicious'
print(ann.margin, ann.Margin)
# => 2, 'Near Poorly Defined'
The names of these characteristics are found in pl.annotation_feature_names:
('subtlety',
'internalStructure',
'calcification',
'sphericity',
'margin',
'lobulation',
'spiculation',
'texture',
'malignancy')
All of these characteristics values and strings can be quickly viewed by:
ann.print_formatted_feature_table()
# => Feature Meaning #
# => - - -
# => Subtlety | Obvious | 5
# => Internalstructure | Soft Tissue | 1
# => Calcification | Absent | 6
# => Sphericity | Ovoid/Round | 4
# => Margin | Near Poorly Defined | 2
# => Lobulation | Near Marked Lobulation | 4
# => Spiculation | No Spiculation | 1
# => Texture | Solid | 5
# => Malignancy | Highly Suspicious | 5
We can also query directly for the attributes themselves, rather than the entire Annotation object:
svals = pl.query(pl.Annotation.spiculation)\
.filter(pl.Annotation.spiculation > 3)
print(svals[0])
# => (4,)
print(all([s[0] > 3 for s in svals]))
# => True
Contour-derived data¶
The pylidc.Contour
class is almost never used directly, but only
through the pylidc.Annotation
object to which the contours belong.
They can be accessed directly, however, via:
ann = pl.query(pl.Annotation).first()
contours = ann.contours
print(contours[0])
# => Contour(id=21,annotation_id=1)
The diameter, surface_area, and volume attributes are all, for example, computed properties that use the contour data for a particular Annotation:
print("%.2f mm, %.2f mm^2, %.2f mm^3" % (ann.diameter,
ann.surface_area,
ann.volume))
# => 20.84 mm, 1242.74 mm^2, 2439.30 mm^3
A boolean-valued volume can be obtained that is 1 to indicate nodule and 0 to indicate non-nodule:
mask = ann.boolean_mask()
print(mask.shape, mask.dtype)
# => (34, 27, 6), dtype('bool')
Note by the shape of the volume that the boolean-valued mask does not occupy the entire extent of the CT image volume (which is 512 x 512 x num slices). Rather, the boolean mask sits within the computed “bounding box” of the nodule, which is the computed extent of the contour indices of the annotation.
The pylidc.Annotation.bbox()
method returns a tuple of slices
corresponding to the nodule bounding box indices. This can be used to
easily index into the NumPy CT image volume:
bbox = ann.bbox()
print(bbox)
# => (slice(151, 185, None), slice(349, 376, None), slice(44, 50, None))
vol = ann.scan.to_volume()
print(vol[bbox].shape)
# => (34, 27, 6)
Note that both the pylidc.Annotation.boolean_mask()
and the
pylidc.Annotation.bbox()
methods accept a pad argument which
can be used for adding context about the nodule.
We can also get the physical dimensions of the bounding box:
print(ann.bbox_dims())
# => [21.45, 16.90, 15.0]
Annotation visualization¶
The pylidc.Annotation
class provides two direct convenience functions
for visualization annotation data which we will show below; however,
let’s first continue the
pylidc.Annotation.boolean_mask()
and
pylidc.Annotation.bbox()
examples from above to provide a
visualization of the annotated nodule:
import pylidc as pl
import matplotlib.pyplot as plt
ann = pl.query(pl.Annotation).first()
vol = ann.scan.to_volume()
padding = [(30,10), (10,25), (0,0)]
mask = ann.boolean_mask(pad=padding)
bbox = ann.bbox(pad=padding)
fig,ax = plt.subplots(1,2,figsize=(5,3))
ax[0].imshow(vol[bbox][:,:,2], cmap=plt.cm.gray)
ax[0].axis('off')
ax[1].imshow(mask[:,:,2], cmap=plt.cm.gray)
ax[1].axis('off')
plt.tight_layout()
#plt.savefig("../images/mask_bbox.png", bbox_inches="tight")
plt.show()
Surface visualization¶
The nodule surface (as obtained by a single annotator) can be visualized in three dimensions by invoking the following:
ann = pl.query(pl.Annotation)\
.filter(pl.Annotation.lobulation == 5).first()
ann.visualize_in_3d()
which appears as:
CT visualization¶
The contours can be visualized interactively on top of the CT image data:
ann = pl.query(pl.Annotation).first()
ann.visualize_in_scan()
which appears as:


Create a new file called hello.tomb with the following sample code as the content
contract hello
{
import Token;
import Runtime;
global admin: address;
constructor(owner:address)
{
admin := owner;
}
trigger onUpgrade(from:address) {
Runtime.expect(Runtime.isWitness(admin), "witness failed");
return;
}
public callMe(from:address):number
{
Runtime.expect(Runtime.isWitness(from), "witness failed");
local thisAddr:address := $THIS_ADDRESS;
Token.transfer(from, thisAddr, "KCAL", Decimal.convert(10, 5));
}
}


Gateway
BOT
05-Jun-19 02:25 PM
[dscrd-hush] <dukeleto> If you still have funds on Agama, please let us help you get founds out
[dscrd-hush] <dukeleto> jl777cToday at 7:21 AM
with our fast action, we rescued about 8 million KMD and over 100 BTC, but as of now it seems about a million KMD has been stolen. I am working on a plan that I hope will be an acceptable outcome to most. Please be aware that even if your funds got swept, there are others that have nothing left and i would like to come up with a plan where nobody is wiped out. in order to do this, we need as accurate of a damage assessment as possible. So please make sure to file a claim with your details as soon as possible so we can get a handle on what actually happened.
once we quantify the actual problem at hand, then we can make a specific plan. I would like to make all the smaller accounts 100% whole, but there isnt enough funds for all accounts to be 100%. I think if the big amounts ends up around 80% to 90%. As long as the damage is less than a million KMD, then I will be able to find the funds to cover it, but if it is more than a million, well we will cross that path if it turns out that way.
So for now, we need to be able to assess the scope of the damage, it could be that we wont have 100% of people claiming and the 5% rewards (400,000 per year) can end up covering most of the claims if they are coming in over time. What does it mean to be a small account? A big account? I dont know yet. I am without sleep for over 30+ hours and I dont want to make any decisions in this state.
Please be patient and file your claims, that is what will help the most. The general plan is to protect the smaller accounts fully, make sure nobody is wiped out, use 5% rewards to cover some of the costs and I will also personally put in half a million KMD. Until we have the accurate numbers, we can only guess as to the specific plan.
[dscrd-hush] <dukeleto> TLDR: About 1 million KMD stolen
[dscrd-hush] <dukeleto> @everyone The time to move all your funds out of Agama is right now. Please ask for help on Discord and read pinned posts! If you ignore this message, you could lose all funds in your addresses. Please heed this advice
[dscrd-hush] <dukeleto> !tip a random person a coffee
[dscrd-hush] <Discordtip> :money_mouth: @dukeleto sent @texastyman97 1 cup of coffee (= $1.50 = 24.71 HUSH).
[dscrd-hush] <dukeleto> lots of people will need coffee for this
[dscrd-hush] <yefuchen> Thanks
[dscrd-hush] <Pepin the Healer> whoa
[dscrd-hush] <dukeleto> This is not a joke, not a drill. This is real. Agama wallet is compromised and NOT SAFU
[dscrd-hush] <dukeleto> I don't have all details, but I suspect there is a poisoned nodeJS dependency
[dscrd-hush] <dukeleto> other coins could be having this same issue right now, as well. Be very safe!
[dscrd-hush] <dukeleto> If you use nodeJS wallets on any other coin (many have them), be very cautious
[dscrd-hush] <Pepin the Healer> use open source libs, they said
don't re-invent the wheel, they said
[dscrd-hush] <CryptoPope> Asking for help in this thread or another ?
[dscrd-hush] <ott> What to do? Where to move HUSH and BTCH??
[dscrd-hush] <Oso> Wow coffee
[dscrd-hush] <CryptoPope> I have a small amount in the agama wallet, I think 2600ish. What should I do when I get home tonight ?
[dscrd-hush] <Pepin the Healer> agama WAS the go-to wallet. i've been here for quite some time and i still have no idea what to use as a replacement
[dscrd-hush] <Pepin the Healer> maybe deposit to the tipbot?
[dscrd-hush] <unko> anyone got more info on the vuln?
[dscrd-hush] <dukeleto> @unko i don't know much, but seems like a compromised nodeJS dependency, weaponized to steal funds
[dscrd-hush] <unko> kinda wanna know which one
[dscrd-hush] <dukeleto> lots of people do
[dscrd-hush] <Pepin the Healer> it's probably some dumb shit like 'capitalize-string.js'
[dscrd-hush] <dukeleto> if this hacker went after KMD, they will go after many other coins
[dscrd-hush] <dukeleto> @Pepin the Healer yep, sounds about right
[dscrd-hush] <dukeleto> add-one-to-an-integer.js
[dscrd-hush] <dukeleto> the default nodeJS application has 1700 dependencies
[dscrd-hush] <dukeleto> 1700!!!!!
[dscrd-hush] <Pepin the Healer> after the left-pad fiasco, i checked our inhouse app (dayjob)
it had left-pad included in 4 different root dependencies
made me lose faith in humanity
[dscrd-hush] <Pepin the Healer> for what it's worth, php has the best package manager of all the ecosystems i've worked with
[dscrd-hush] <Pepin the Healer> npm is a broken kid's toy by comparison
[dscrd-hush] <Roxin> hello, where we can move the hush coins ?
[dscrd-hush] <Pepin the Healer> similar story with pip
[dscrd-hush] <Pepin the Healer> @Roxin i was only half-joking when i suggested sending it to your tipbot address
[dscrd-hush] <Roxin> @dukeleto @heguli97 why we have to move hush from agama wallet ?
[dscrd-hush] <Pepin the Healer> because it's been compromised and you might lose everything?
[dscrd-hush] <Hammerstein> @dukeleto apologize if you dealt with this elsewhere, but when I try to access the mobile wallet where my funds are located, it says “build removed” and I can no longer access the funds, what should I do?
[dscrd-hush] <Roxin> @Pepin the Healer where we have to move ?
IMPORTANT @KomodoPlatform has become aware of a compromised nodeJS dependency in the Agama Wallet. If you have funds in Agama, $KMD, $HUSH or any coin, move it now or risk losing funds!
More details: https://t.co/ZR77NqNgfE
PROTIP: Address asset viewer
https://t.co...
[dscrd-hush] <Pepin the Healer> ffs
[dscrd-hush] <dukeleto> @Hammerstein #community-support in KMD discord will know the most, that is their code. We can try to help as much as we can
[dscrd-hush] <dukeleto> old Agama builds have been removed from Github
[dscrd-hush] <dukeleto> you can't download them anymore
[dscrd-hush] <dukeleto> Some of you might be stuck in a situation where your Agama is too old to talk to current servers. Speak up if that is you
[dscrd-hush] <Roxin> @dukeleto mate we can move coins where ? on some exchange or ? the agama wallet will not work anymore ?
[dscrd-hush] <Hammerstein> @dukeleto , so.... am I fucked lol...
[dscrd-hush] <dukeleto> !tip @Berg a coffee
[dscrd-hush] <dukeleto> not even enough for coffee, ouch
[dscrd-hush] <dukeleto> !tip @Berg 10 hush
[dscrd-hush] <Discordtip> @dukeleto sent @Berg 10.00 HUSH (≈ $0.60).
[dscrd-hush] <dukeleto> for that timely blockfolio signal that just went out


The following pictures show the same OpenEXR image; yet
the amount of detail you can see when comparing the three is
substantially different. The view out the window is obscured
by the bright sunlight, although there is, in fact, a
building next door with a rust-colored roof; the sky is
blue, and there are even power lines visible above the
building. Using the Viewer, you can adjust the amount of
exposure on the fly to see more details emerge, as the
followin images show.
ILM developed the OpenEXR format in response to the demand for higher color
fidelity in the visual effects industry. When the project began in
2000, ILM evaluated existing file formats, but rejected them for
various reasons:
- 8- and 10-bit formats lack the dynamic range necessary to store
high-contras images captured from HDR devices.
- 16-bit integer-based formats typically represent color component
values from 0 ("black") to 1 ("white"), but don't account for
over-range values (e.g., a chrome highlight) that can be captured by film
negative or other HDR devices. Fo images intended only for
display or print reproduction, clamping at "white" may be sufficient;
but for image processing in a visual effects house, highlights often
need to be preserved in the image data. Preserving over-range values in
the source image allows an artist to change the apparent
exposure of the image with minimal loss of data, for example.
- Conversely, 32-bit floating-point TIFF is often overkill for visual
effects work. 32-bit FP TIFF provides more than sufficient precision
and dynamic range for VF images, but it comes at the cost of storage,
both on disk and in memory. When creating background plates for VFX
work, film is usually scanned in at 2k pixels wide or more, and
24p H images are 1920x1080, so background plates are already quite
large relative to "typical" digita images.
ILM decided to develop a new HDR file format with
16-bit floating-point color component values. Since the IEEE-754
floating-point specification does not define a 16-bit format, ILM
created the "half" format. Half values have 1 sign bit, 5 exponent
bits, and 10 mantissa bits. For linea images, this format provides
1024 (210) values per color component per f-stop, and 30 f-stops (25 - 2),
with an additional 10 f-stops with reduced precision at the
low end (denormals).
The half format supports denormalized numbers, positive and negative
infinities, and NaNs. It is identical to the half data type in
NVIDIA's Cg graphics language, allowing a developer to process values
from an OpenEXR image directly on current NVIDIA GPUs such as the GeForce FX family.
In addition to the half data type, OpenEXR supports 32-bit unsigned
integer and 32-bit floating-point data types. OpenEX images can have
an arbitrary number of channels, each with a different data type.
The current release of OpenEXR supports several lossless compression
methods, some of which can achieve compression ratios of about 2:1 for
images with film grain. OpenEXR is extensible, so developers can easily
add new compression methods (lossless or lossy).
OpenEX images can be annotated with an arbitrary number of attributes, e.g., with color balance information from a camera.
OpenEX images are able to support stereoscopic workflows; version 1.7.0 has support for multiple views resident in the image files.
OpenEXR v2 introduces support for storing "Deep Data" buffers wherein pixels are capable of holding variable number of data samples, particularly as pertaining to depth. The implementation is capable of both hard surface and volumetric representation requirements for deep compositing workflows. OpenEXR v2 also introduces the concept of "Multi-Part" files that contain a number of separate, but related images in one file. Access to any part is independent of the others.
Please see the documentation pages for further detail.
© Copyright OpenEXR a Series of LF Projects, LLC and/or its contributors. All documentation on this website made available under a Creative Commons Attribution 4.0 International License.


The Bad Girl by Mario Vargas Llosa
9780374182434 A translator pursues the world-wandering beauty who has captured his heart but remains tantalizingly out of his reach, changing her identity with each new encounter.
The Brutal Telling by Louise Penny
9780312377038 In the Quebec village of Three Pines, Chief Inspector Armand Gamache and his team investigate the death of an unknown old man.
Day After Night by Anita Diamant
9780743299848 Four young women in 1945 Israel, escapees from Nazi Europe, find healing in shared friendship and the challenge of recreating themselves in a strange new country.
The Deportees and Other Stories by Roddy Doyle
9780670018451 Doyle paints a multicultural portrait of a changing Ireland in stories of immigrants experiencing anger, awe, and alienation.
Half Broke Horses: A True-Life Novel by Jeannette Walls
9781416586289 A tough pioneer spirit helps a remarkable woman endure poverty and hard times and persevere through misfortunes and tragedies on the Western frontier.
How to Read the Air by Dinaw Mengestu
9781594487705 The Woldemariam family emigrates from Ethiopia to America, where Mariam and Yosef’s troubled marriage affects the life of their son, Jonas.
In the Kitchen by Monica Ali
9781416571681 A London chef, already dealing with pressure from his customers, his investors, his girlfriend, and his multicultural staff, now has the added worry of a murder in the basement.
Island Beneath the Sea by Isabel Allende
9780061988240 Tete, a young mulatto, desperately tries to preserve her identity amid the cruel confines of slavery.
Major Pettigrew’s Last Stand by Helen Simonson
9781400068937 The clash of culture, religion, and tradition threatens the relationship of an unlikely English widower and Pakistani widow.
The Man from Beijing by Henning Mankell
9780307271860 A red ribbon is the only clue to 19 brutal murders in a small Swedish village.
Pirate Latitudes by Michael Crichton
9780061929373 A vicious captain’s thirst for gold triggers a bloody battle in 17th-century Jamaica.
Remarkable Creatures by Tracy Chevalier
9780525951452 An amazing scientific discovery of the 19th century fosters a close friendship between two very different women.
Room by Emma Donoghue
9780316098335 Five-year-old Jack and his Ma are kept prisoner in a soundproof shed. When Jack begins to understand their predicament, Ma plots an escape.
Shanghai Girls by Lisa See
9781400067114 Forced to leave Shanghai when their father sells them to California suitors, sisters May and Pearl struggle to adapt to life in 1930s Los Angeles.
The Thousand Autumns of Jacob de Zoet by David Mitchell
9781400065455 In 1799, a Dutch clerk is sent to Japan and begins a forbidden courtship of a young Japanese midwife.
The Immortal Life of Henrietta Lacks by Rebecca Skloot
9781400052172 A young African-American mother struck down with an aggressive cancer unknowingly provides the cells used in groundbreaking medical research.
The Lost City of Z: A Tale of Deadly Obsession in the Amazon by David Grann
9780385513531 An obsession with the disappearance of British explorer Percy Fawcett unearths hidden mysteries of the Amazon jungle.
The Wave: In Pursuit of the Rogues, Freaks and Giants of the Ocean by Susan Casey
9780767928847 A chilling look at the world’s most ferocious waves and the surfers destined to chase them.
A Week at the Airport by Alain de Botton
9780307739674 De Botton “lived” at London’s Heathrow Airport for a week to examine the unique world of arrival and departure.
The World in a City: Traveling the Globe through the Neighborhoods of the New New York by Joseph Berger


This is the content of the index.html file.
You can also see the content of the dynamic route at /hi.