#3D DIAG MATLAB PLUS#
So, with this new PLUS setting, all sensors refer back to Reference, and got all its value corrected to match the NDI. The reason behind this is due to - PLUS setting that does not use Reference. Thank you.Ī) Only Reference value for NDI, Slicer and Unity is the sameī) All other sensors for both Slicer and Unity which share the same value contradicts with NDI. It is a great help in getting us a useful result. If I am multiplying them with IJKToRAS - none of the values are the same in all 3 Apps. I am getting different values in Unity, compare to the value from NDI Track App.Īnd, the value I am getting from NDI Track App is the same for Slicer and Unity.ĭoes it mean that I need to set the Needle origin in Slicer or can I set this in PLUS server setting? so that I get same values for Needle coordinates in NDI Track, Slicer and Unity?įYI - The reference coordinate is correct and similar for all three - NDI Track, Slicer and Unity (if I am using the matrix and not the IJKToRAS) It looks like the steps for conversion is not complete there. = LPS_to_RAS * inv( fromParent_LPS ) * RAS_to_LPS IJKToRAS.SetRow(3, new Vector4(0.0f, 0.0f, 0.0f, 1.0f)) Ĭomparing to the steps that you described above Matrix.SetRow(2, new Vector4(m, m, m, m / scaleMultiplier)) Matrix.SetRow(1, new Vector4(m, m, m, m / scaleMultiplier)) Matrix.SetRow(0, new Vector4(m, m, m, m / scaleMultiplier))
#3D DIAG MATLAB CODE#
Return np.asarray(A), np.squeeze(np.Refering to Unity Code (OpenIGTLinkConnect.cs) taken from this Github ( OpenIGTLink-Unity/OpenIGTLink-Unity/Assets/Scripts at master Q = np.column_stack((points, np.ones(N))).TĪ = la.inv(points.T*np.diag(u)*points - c.T*c)/d Return A, c where the equation for the ellipse given in "center form" is Since readability counts, you may wish to do the main computation with NumPy matrices: def mvee(points, tol = 0.001): np.dot(c, c))/dīecomes A = la.inv(points.T*np.diag(u)*points - c.T*c)/d For example, A = la.inv(np.dot(np.dot(points.T, np.diag(u)), points) If we convert the NumPy arrays into NumPy matrices, then matrix multiplication can be expressed with *. Step_size = (M-d-1.0)/((d+1)*(M-1.0))Ī = la.inv(np.dot(np.dot(points.T, np.diag(u)), points)Īx.plot_surface(x, y, z, cstride = 1, rstride = 1, alpha = 0.05)īy the way, this computation uses a lot of matrix multiplication, which when using np.dot looks rather verbose. More debugging print statements on the Octave side yield (P*u) =īut on the Python side, c = np.dot(points.T,u) This agrees with the result for u found by the Python function mvee. Using Octave, I found that after the while-loop in MinVolEllipse ends, u = X,y,z = np.dot(np.transpose(V),np.array(,y,z])) + centroidĪx = fig.add_subplot(111, projection='3d')Īx.scatter(points,points,points)Īx.plot_surface(x, y, z, cstride = 1, rstride = 1, alpha = 0.1) The corresponding test code: from mpl_toolkits.mplot3d import Axes3Dįrom mpl_3d import Pol圓DCollection M = np.diag( np.dot(np.dot(np.transpose(Q), la.inv(X)),Q)) X = np.dot(np.dot(Q, np.diag(u)), np.transpose(Q))
Now, here is my attempt at port this code to Python (2.7): from _future_ import division Which will produce the the covariance matrix: A = Here is some MATLAB test code: points = , D diag (v) D 5×5 2 0 0 0 0 0 1 0 0 0 0 0 -1 0 0 0 0 0 -2 0 0 0 0 0 -5 Create a matrix with the elements of v on the first super diagonal ( k1 ).
Step_size = (maximum - d -1)/((d+1)*(maximum-1)) Ī = (1/d) * inv(P * U * P' - (P * u)*(P*u)' ) v 2 1 -1 -2 -5 Use diag to create a matrix with the elements of v on the main diagonal. I'll paste the relevant part for convenience: function = MinVolEllipse(P, tolerance) MATLAB code exists to find the so-called "minimum volume enclosing ellipsoid" (e.g.
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