I have a camera and a 3D object, I have calculated the camera matrix as given in this. The image captured using the camera is having size 1600x1200. But in the camera matrix I am not getting the value of image centers properly. Instead of 800, 600 I am getting some other values. What will be the possible reasons for this.
#include <iostream>
#include "opencv2/imgproc/imgproc.hpp"
#include "opencv2/highgui/highgui.hpp"
#include <math.h>
using namespace std;
using namespace cv;
int main()
{
int numberofpoints=30;
float p2d[30][2] =
{{72, 169}, {72, 184}, {71, 264}, {96, 168}, {94, 261}, {94, 276}, {257, 133},
{254, 322}, {254, 337}, {278, 132}, {278, 146}, {275, 321}, {439, 228},
{439, 243}, {437, 328}, {435, 431}, {461, 226}, {459, 326}, {459, 342},
{457, 427}, {457, 444}, {612, 196}, {609, 291}, {605, 392}, {605, 406},
{634, 191}, {633, 206}, {630, 288}, {630, 303}, {627, 390}};
float p3d[30][3] =
{{0, 0, 98}, {0, 0, 94}, {0, 0, 75}, {4, 4, 98}, {4, 4, 75}, {4, 4, 71},
{0, 96, 75}, {0, 96, 25}, {0, 96, 21}, {4, 100, 75},
{4, 100, 71}, {4, 100, 25}, {96, 0, 98}, {96, 0, 94},
{96, 0, 75}, {96, 0, 50}, {100, 4, 98}, {100, 4, 75},
{100, 4, 71}, {100, 4, 50}, {100, 4, 46}, {96, 96, 75},
{96, 96, 50}, {96, 96, 25}, {96, 96, 21}, {100, 100, 75},
{100, 100, 71}, {100, 100, 50}, {100, 100, 46}, {100, 100, 25}};
Mat Matrix2D= Mat(30, 2, CV_64FC1, &p2d);
Mat Matrix3D= Mat(30, 3, CV_64FC1, &p3d);
Mat X_3D=Matrix3D.col(0);
Mat Y_3D=Matrix3D.col(1);
Mat Z_3D=Matrix3D.col(2);
Mat X_2D=Matrix2D.col(0);
Mat Y_2D=Matrix2D.col(1);
Mat z = Mat::zeros(numberofpoints,4, CV_64FC1);
Mat o = Mat::ones(numberofpoints,1, CV_64FC1);
Mat temp,temp1,C,D,AoddRows;
hconcat( X_3D, Y_3D,temp);
hconcat(Z_3D ,o,temp1);
hconcat(z, -X_2D.mul(X_3D),C);
hconcat(-X_2D.mul(Y_3D),-X_2D.mul(Z_3D),D);
hconcat(temp,temp1,AoddRows);
hconcat(AoddRows,C,AoddRows);
hconcat(AoddRows,D,AoddRows);
hconcat(AoddRows,-X_2D,AoddRows);
Mat A1,B1,C1,AevenRows;
hconcat(z,Matrix3D,A1);
hconcat(o,-Y_2D.mul(X_3D) ,B1);
hconcat(-Y_2D.mul(Y_3D),-Y_2D.mul(Z_3D),C1);
hconcat(A1,B1,AevenRows);
hconcat(AevenRows,C1,AevenRows);
hconcat(AevenRows,-Y_2D,AevenRows);
Mat A;
vconcat(AoddRows,AevenRows,A);
Mat w, u, EigenVectors;
SVD::compute(A, w, u, EigenVectors);
Mat EigenVector_SmallestEigenvalue=EigenVectors.row(EigenVectors.rows-1);
Mat P=Mat::zeros(3,4, CV_64FC1);
int k=0;
for(int i=0;i<P.rows;i++)
{
for(int j=0;j<P.cols;j++)
{
P.at<double>(i, j) = EigenVector_SmallestEigenvalue.at<double>(0,k);
k++;
}
}
double abs_lambda = sqrt(P.at<double>(2,0) * P.at<double>(2,0) + P.at<double>(2,1) * P.at<double>(2,1) + P.at<double>(2,2) * P.at<double>(2,2));
P = P / abs_lambda;
Mat ProjectionMatrix = P;
Mat T= Mat::zeros(3,1, CV_64FC1);
Mat R = Mat::zeros(3,3, CV_64FC1);
Mat B = Mat::zeros(3,3, CV_64FC1);
Mat b = Mat::zeros(3,1,CV_64FC1);
P.row(0).colRange(0,3).copyTo(B.row(0));
P.row(1).colRange(0,3).copyTo(B.row(1));
P.row(2).colRange(0,3).copyTo(B.row(2));
P.col(3).copyTo(b.col(0));
Mat K=B*B.t();
double Center_x = K.at<double>(0,2);
double Center_y = K.at<double>(1,2);
double beta = sqrt(K.at<double>(1,1)-Center_y*Center_y);
double gemma = (K.at<double>(0,1)-Center_x*Center_y)/beta;
double alpha = sqrt(K.at<double>(0,0)-Center_x*Center_x-gemma*gemma);
Mat CameraMatrix=Mat::zeros(3,3, CV_64FC1);
CameraMatrix.at<double>(0,0)=alpha;
CameraMatrix.at<double>(0,1)=gemma;
CameraMatrix.at<double>(0,2)=Center_x;
CameraMatrix.at<double>(1,1)=beta;
CameraMatrix.at<double>(1,2)=Center_y;
CameraMatrix.at<double>(2,2)=1;
R = CameraMatrix.inv()*B;
T = CameraMatrix.inv()*b;
Mat newMatrix2D=Mat::zeros(numberofpoints,2, CV_64FC1);
for(int i=0;i<numberofpoints;i++)
{
double num_u = P.at<double>(0,0) * Matrix3D.at<double>(i,0)
+ P.at<double>(0,1) * Matrix3D.at<double>(i,1)
+ P.at<double>(0,2) * Matrix3D.at<double>(i,2)
+ P.at<double>(0,3);
double num_v = P.at<double>(1,0) * Matrix3D.at<double>(i,0)
+ P.at<double>(1,1) * Matrix3D.at<double>(i,1)
+ P.at<double>(1,2) * Matrix3D.at<double>(i,2)
+ P.at<double>(1,3);
double den = P.at<double>(2,0) * Matrix3D.at<double>(i,0)
+ P.at<double>(2,1) * Matrix3D.at<double>(i,1)
+ P.at<double>(2,2) * Matrix3D.at<double>(i,2)
+ P.at<double>(2,3);
newMatrix2D.at<double>(i,0)=num_u/den;
newMatrix2D.at<double>(i,1)=num_v/den;
}
Mat reprojMatrix=newMatrix2D;
Mat errorDiff=reprojMatrix-Matrix2D;
int size=errorDiff.rows;
double sum=0;
for(int i=0;i<errorDiff.rows;i++)
{
sum=sum + sqrt(errorDiff.at<double>(i,0) * errorDiff.at<double>(i,0)
+ errorDiff.at<double>(i,1) * errorDiff.at<double>(i,1));
}
sum=sum/size;
cout<<"Average Error="<<sum<<endl;
cout<<"Translation Matrix="<<T<<endl<<endl;
cout<<"Rotational Matrix="<<R<<endl<<endl;
cout<<"Camera Matrix="<<CameraMatrix<<endl;
return 0;
}
SAMPLE 1:
const int numberofpoints = 30;
float p2d[numberofpoints][2] =
{{72, 169}, {72, 184}, {71, 264}, {96, 168}, {94, 261}, {94, 276}, {257, 133},
{254, 322}, {254, 337}, {278, 132}, {278, 146}, {275, 321}, {439, 228},
{439, 243}, {437, 328}, {435, 431}, {461, 226}, {459, 326}, {459, 342},
{457, 427}, {457, 444}, {612, 196}, {609, 291}, {605, 392}, {605, 406},
{634, 191}, {633, 206}, {630, 288}, {630, 303}, {627, 390}};
float p3d[numberofpoints][3] =
{{0, 0, 98}, {0, 0, 94}, {0, 0, 75}, {4, 4, 98}, {4, 4, 75}, {4, 4, 71},
{0, 96, 75}, {0, 96, 25}, {0, 96, 21}, {4, 100, 75},
{4, 100, 71}, {4, 100, 25}, {96, 0, 98}, {96, 0, 94},
{96, 0, 75}, {96, 0, 50}, {100, 4, 98}, {100, 4, 75},
{100, 4, 71}, {100, 4, 50}, {100, 4, 46}, {96, 96, 75},
{96, 96, 50}, {96, 96, 25}, {96, 96, 21}, {100, 100, 75},
{100, 100, 71}, {100, 100, 50}, {100, 100, 46}, {100, 100, 25}};
SAMPLE 2:
const int numberofpoints = 24;
float p2d[24][2] =
{{41, 291}, {41, 494}, {41, 509}, {64, 285}, {64, 303},
{64, 491}, {195, 146}, {216, 144}, {216, 160}, {431, 337},
{431, 441}, {431, 543}, {431, 558}, {452, 336}, {452, 349},
{452, 438}, {452, 457}, {452, 539}, {568, 195}, {566, 291},
{588, 190}, {587, 209}, {586, 289}, {586, 302}};
float p3d[24][3] =
{{0, 0, 75}, {0, 0, 25}, {0, 0, 21}, {4, 4, 75}, {4, 4, 71}, {4, 4, 25},
{0, 96, 75 }, {4, 100, 75}, {4, 100, 71}, {96, 0, 75}, {96, 0, 50}, {96, 0, 25},
{96, 0, 21}, {100, 4, 75}, {100, 4, 71}, {100, 4, 50}, {100, 4, 46}, {100, 4, 25}, {96, 96, 75 }, {96, 96, 50 }, {100, 100, 75}, {100, 100, 71}, {100, 100, 50}, {100, 100, 46}};
SAMPLE 3:
const int numberofpoints = 33;
float p2d[numberofpoints][2] =
{{45, 310}, {43, 412}, {41, 513}, {41, 530}, {68, 305},
{68, 321}, {66, 405}, {66, 423}, {64, 509}, {201, 70}, {201, 84},
{200, 155}, {198, 257}, {218, 259}, {218, 271}, {441, 364},
{439, 464}, {437, 569}, {437, 586}, {462, 361}, {462, 376},
{460, 462}, {460, 479}, {459, 565}, {583, 117}, {583, 131},
{580, 215}, {576, 313}, {603, 113}, {600, 214}, {599, 229},
{596, 311}, {596, 323}};
float p3d[numberofpoints][3] =
{{0, 0, 75}, {0, 0, 50}, {0, 0, 25}, {0, 0, 21}, {4, 4, 75}, {4, 4, 71},
{4, 4, 50}, {4, 4, 46}, {4, 4, 25}, {0, 96, 98}, {0, 96, 94}, {0, 96, 75},
{0, 96, 50}, {4, 100,75}, {4, 100,71}, {96, 0, 75}, {96, 0, 50}, {96, 0, 25},
{96, 0, 21}, {100, 4,75}, {100, 4,71}, {100, 4,50}, {100, 4,46}, {100, 4,25},{96, 96,98}, {96, 96,94}, {96, 96,75}, {96, 96,50}, {100, 100, 98}, {100, 100, 75},{100, 100, 71}, {100, 100, 50}, {100, 100, 46}};
EDIT:
I've taken one of the 3D coordinate point sets you've provided, and then tried to project them on a virtual camera (to prevent any errors with 3d-2d point correspondences). The alpha, beta and gamma are estimated correctly in this case, but u and v (that you are looking for) were for some reason negative in all of my experiments. Perhaps there's a bug in my code, but maybe this is due to inaccuracy of OpenCV solveZ implementation (I've had problems with OpenCV's eigenvalue/vector computation before and try to use OpenBLAS when high accuracy is needed).
Another possible reason - as authors state, this approach performs algebraic minimization, which may produce physically meaningless results. Perhaps you should check remaining part of paper or try a different approach.
Here's the updated code:
#include <opencv2/opencv.hpp>
#include <iostream>
int main()
{
// generate random 3d points
const int numberofpoints = 33;
float p3d_[numberofpoints][3] =
{{0, 0, 75}, {0, 0, 50}, {0, 0, 25}, {0, 0, 21}, {4, 4, 75}, {4, 4, 71},
{4, 4, 50}, {4, 4, 46}, {4, 4, 25}, {0, 96, 98}, {0, 96, 94}, {0, 96, 75},
{0, 96, 50}, {4, 100,75}, {4, 100,71}, {96, 0, 75}, {96, 0, 50}, {96, 0, 25},
{96, 0, 21}, {100, 4,75}, {100, 4,71}, {100, 4,50}, {100, 4,46}, {100, 4,25},{96, 96,98}, {96, 96,94}, {96, 96,75}, {96, 96,50}, {100, 100, 98}, {100, 100, 75},{100, 100, 71}, {100, 100, 50}, {100, 100, 46}};
std::vector<cv::Point3d> p3d;
for (int i = 0; i < numberofpoints; i++) {
cv::Point3d X(p3d_[i][0], p3d_[i][1], p3d_[i][2]);
p3d.push_back(X);
}
// set up virtual camera
const cv::Size imgSize(1600, 1200);
const double fx = 100;
const double fy = 120;
cv::Mat1d K = (cv::Mat1d(3, 3) << fx, 0, imgSize.width/2,
0, fy, imgSize.height/2,
0, 0, 1);
std::cout << "K = " << std::endl;
std::cout << K << std::endl << std::endl;
cv::Mat1d t = (cv::Mat1d(3, 1) << 10, 20, 20);
cv::Mat1d rvecDeg = (cv::Mat1d(3, 1) << 10, 20, 30);
// project point on camera
std::vector<cv::Point2d> p2d;
cv::projectPoints(p3d,
rvecDeg*CV_PI/180,
t,
K,
cv::Mat(),
p2d);
// estimate projection
cv::Mat1d G = cv::Mat1d::zeros(numberofpoints*2, 12);
for (int i = 0; i < numberofpoints; i++) {
const double X = p3d[i].x;
const double Y = p3d[i].y;
const double Z = p3d[i].z;
G(i*2 + 0, 0) = X;
G(i*2 + 0, 1) = Y;
G(i*2 + 0, 2) = Z;
G(i*2 + 0, 3) = 1;
G(i*2 + 1, 4) = X;
G(i*2 + 1, 5) = Y;
G(i*2 + 1, 6) = Z;
G(i*2 + 1, 7) = 1;
const double u = p2d[i].x;
const double v = p2d[i].y;
G(i*2 + 0, 8) = u*X;
G(i*2 + 0, 9) = u*Y;
G(i*2 + 0, 10) = u*Z;
G(i*2 + 0, 11) = u;
G(i*2 + 1, 8) = v*X;
G(i*2 + 1, 9) = v*Y;
G(i*2 + 1, 10) = v*Z;
G(i*2 + 1, 11) = v;
}
std::cout << "G = " << std::endl;
std::cout << G << std::endl << std::endl;
cv::Mat1d p;
cv::SVD::solveZ(G, p);
cv::Mat1d P = p.reshape(0, 3).clone();
P /= P(2, 3);
std::cout << "p = " << std::endl;
std::cout << p << std::endl << std::endl;
std::cout << "P = " << std::endl;
std::cout << P << std::endl << std::endl;
cv::Mat1d B(3, 3);
cv::Mat1d b(3, 1);
P.colRange(0, 3).copyTo(B);
P.col(3).copyTo(b);
std::cout << "B = " << std::endl;
std::cout << B << std::endl << std::endl;
std::cout << "b = " << std::endl;
std::cout << b << std::endl << std::endl;
cv::Mat1d K_ = B*B.t();
K_ /= K_(2, 2);
std::cout << "K_ = " << std::endl;
std::cout << K_ << std::endl << std::endl;
const double u = K_(0, 2);
const double v = K_(1, 2);
const double ku = K_(0, 0);
const double kv = K_(1, 1);
const double kc = K_(0, 1);
const double beta = sqrt(kv - v*v);
const double gamma = (kc - u*v)/beta;
const double alpha = sqrt(ku - u*u - gamma*gamma);
cv::Mat1d A = (cv::Mat1d(3, 3) << alpha, gamma, u,
0, beta, v,
0, 0, 1);
std::cout << "A = " << std::endl;
std::cout << A << std::endl << std::endl;
return 0;
}
And output:
K =
[100, 0, 800;
0, 120, 600;
0, 0, 1]
G =
[0, 0, 75, 1, 0, 0, 0, 0, 0, 0, 63156.71331987197, 842.0895109316264;
0, 0, 0, 0, 0, 0, 75, 1, 0, 0, 46451.70410142483, 619.3560546856644;
0, 0, 50, 1, 0, 0, 0, 0, 0, 0, 42144.23132613153, 842.8846265226306;
0, 0, 0, 0, 0, 0, 50, 1, 0, 0, 31473.60945095979, 629.4721890191959;
0, 0, 25, 1, 0, 0, 0, 0, 0, 0, 21113.32803626328, 844.5331214505311;
0, 0, 0, 0, 0, 0, 25, 1, 0, 0, 16261.14346086196, 650.4457384344785;
0, 0, 21, 1, 0, 0, 0, 0, 0, 0, 17744.50634900177, 844.9764928096083;
0, 0, 0, 0, 0, 0, 21, 1, 0, 0, 13777.82037617329, 656.0866845796805;
4, 4, 75, 1, 0, 0, 0, 0, 3374.871998456306, 3374.871998456306, 63278.84997105574, 843.7179996140766;
0, 0, 0, 0, 4, 4, 75, 1, 2506.953106676701, 2506.953106676701, 47005.37075018814, 626.7382766691752;
4, 4, 71, 1, 0, 0, 0, 0, 3375.547822963469, 3375.547822963469, 59915.97385760157, 843.8869557408673;
0, 0, 0, 0, 4, 4, 71, 1, 2513.243523511581, 2513.243523511581, 44610.07254233056, 628.3108808778952;
4, 4, 50, 1, 0, 0, 0, 0, 3380.337247599766, 3380.337247599766, 42254.21559499707, 845.0843118999414;
0, 0, 0, 0, 4, 4, 50, 1, 2557.822370597248, 2557.822370597248, 31972.7796324656, 639.4555926493119;
4, 4, 46, 1, 0, 0, 0, 0, 3381.587616222724, 3381.587616222724, 38888.25758656133, 845.396904055681;
0, 0, 0, 0, 4, 4, 46, 1, 2569.460510014068, 2569.460510014068, 29548.79586516178, 642.365127503517;
4, 4, 25, 1, 0, 0, 0, 0, 3391.684639092943, 3391.684639092943, 21198.02899433089, 847.9211597732357;
0, 0, 0, 0, 4, 4, 25, 1, 2663.441243136111, 2663.441243136111, 16646.50776960069, 665.8603107840277;
0, 96, 98, 1, 0, 0, 0, 0, 0, 76956.82972653268, 78560.09701250211, 801.6336429847155;
0, 0, 0, 0, 0, 96, 98, 1, 0, 65682.8899983677, 67051.28354000035, 684.1967708163302;
0, 96, 94, 1, 0, 0, 0, 0, 0, 76853.25603860538, 75252.14653780111, 800.5547504021395;
0, 0, 0, 0, 0, 96, 94, 1, 0, 65937.37528926283, 64563.67997073651, 686.8476592631544;
0, 96, 75, 1, 0, 0, 0, 0, 0, 76268.94727818707, 59585.11506108365, 794.4682008144487;
0, 0, 0, 0, 0, 96, 75, 1, 0, 67373.04865228917, 52635.19425960092, 701.8025901280123;
0, 96, 50, 1, 0, 0, 0, 0, 0, 75153.33695072016, 39142.36299516675, 782.8472599033349;
0, 0, 0, 0, 0, 96, 50, 1, 0, 70114.15427855898, 36517.78868674947, 730.3557737349894;
4, 100, 75, 1, 0, 0, 0, 0, 3182.802966382433, 79570.07415956081, 59677.55561967062, 795.7007415956082;
0, 0, 0, 0, 4, 100, 75, 1, 2830.826944396773, 70770.67360991932, 53078.00520743949, 707.7067360991932;
4, 100, 71, 1, 0, 0, 0, 0, 3176.842851499092, 79421.07128747732, 56388.96061410889, 794.2107128747731;
0, 0, 0, 0, 4, 100, 71, 1, 2846.678125949429, 71166.95314873573, 50528.53673560237, 711.6695314873573;
96, 0, 75, 1, 0, 0, 0, 0, 94501.57967461165, 0, 73829.35912079035, 984.3914549438714;
0, 0, 0, 0, 96, 0, 75, 1, 69392.97525695754, 0, 54213.26191949808, 722.8434922599744;
96, 0, 50, 1, 0, 0, 0, 0, 102665.427189569, 0, 53471.57666123386, 1069.431533224677;
0, 0, 0, 0, 96, 0, 50, 1, 76872.21110081286, 0, 40037.60994834002, 800.7521989668005;
96, 0, 25, 1, 0, 0, 0, 0, 134150.4060603281, 0, 34935.00157821043, 1397.400063128417;
0, 0, 0, 0, 96, 0, 25, 1, 105716.8927391909, 0, 27530.44081749762, 1101.217632699905;
96, 0, 21, 1, 0, 0, 0, 0, 150007.0124893856, 0, 32814.03398205309, 1562.573046764433;
0, 0, 0, 0, 96, 0, 21, 1, 120243.7808209231, 0, 26303.32705457694, 1252.539383551283;
100, 4, 75, 1, 0, 0, 0, 0, 98699.75231231008, 3947.990092492403, 74024.81423423256, 986.9975231231008;
0, 0, 0, 0, 100, 4, 75, 1, 73361.21263523313, 2934.448505409325, 55020.90947642484, 733.6121263523312;
100, 4, 71, 1, 0, 0, 0, 0, 99628.75712124966, 3985.150284849986, 70736.41755608725, 996.2875712124966;
0, 0, 0, 0, 100, 4, 71, 1, 74265.21217550985, 2970.608487020394, 52728.30064461199, 742.6521217550985;
100, 4, 50, 1, 0, 0, 0, 0, 107383.6098967354, 4295.344395869415, 53691.80494836769, 1073.836098967354;
0, 0, 0, 0, 100, 4, 50, 1, 81811.33387099921, 3272.453354839968, 40905.6669354996, 818.113338709992;
100, 4, 46, 1, 0, 0, 0, 0, 109823.0621321851, 4392.922485287403, 50518.60858080514, 1098.230621321851;
0, 0, 0, 0, 100, 4, 46, 1, 84185.12534995917, 3367.405013998367, 38725.15766098122, 841.8512534995917;
100, 4, 25, 1, 0, 0, 0, 0, 141059.7182762867, 5642.388731051467, 35264.92956907167, 1410.597182762867;
0, 0, 0, 0, 100, 4, 25, 1, 114581.0097655446, 4583.240390621784, 28645.25244138615, 1145.810097655446;
96, 96, 98, 1, 0, 0, 0, 0, 83904.83905262669, 83904.83905262669, 85652.85653288974, 874.0087401315279;
0, 0, 0, 0, 96, 96, 98, 1, 72995.44277461393, 72995.44277461393, 74516.18116575171, 760.3691955688951;
96, 96, 94, 1, 0, 0, 0, 0, 84021.59669072446, 84021.59669072446, 82271.1467596677, 875.2249655283798;
0, 0, 0, 0, 96, 96, 94, 1, 73575.81721996517, 73575.81721996517, 72042.98769454923, 766.4147627079706;
96, 96, 75, 1, 0, 0, 0, 0, 84712.67045349024, 84712.67045349024, 66181.77379178925, 882.4236505571899;
0, 0, 0, 0, 96, 96, 75, 1, 77010.98050603706, 77010.98050603706, 60164.82852034145, 802.1977136045526;
96, 96, 50, 1, 0, 0, 0, 0, 86206.34878960505, 86206.34878960505, 44899.13999458597, 897.9827998917193;
0, 0, 0, 0, 96, 96, 50, 1, 84435.70020728504, 84435.70020728504, 43976.92719129429, 879.5385438258859;
100, 100, 98, 1, 0, 0, 0, 0, 87539.46210370047, 87539.46210370047, 85788.67286162646, 875.3946210370046;
0, 0, 0, 0, 100, 100, 98, 1, 76664.75192364832, 76664.75192364832, 75131.45688517536, 766.6475192364833;
100, 100, 75, 1, 0, 0, 0, 0, 88416.27638616599, 88416.27638616599, 66312.20728962449, 884.16276386166;
0, 0, 0, 0, 100, 100, 75, 1, 81008.14162095707, 81008.14162095707, 60756.10621571781, 810.0814162095708;
100, 100, 71, 1, 0, 0, 0, 0, 88614.85267838663, 88614.85267838663, 62916.5454016545, 886.1485267838663;
0, 0, 0, 0, 100, 100, 71, 1, 81991.80970097007, 81991.80970097007, 58214.18488768875, 819.9180970097007;
100, 100, 50, 1, 0, 0, 0, 0, 90038.77512167525, 90038.77512167525, 45019.38756083763, 900.3877512167526;
0, 0, 0, 0, 100, 100, 50, 1, 89045.35592350175, 89045.35592350175, 44522.67796175087, 890.4535592350175;
100, 100, 46, 1, 0, 0, 0, 0, 90415.3917433265, 90415.3917433265, 41591.08020193018, 904.153917433265;
0, 0, 0, 0, 100, 100, 46, 1, 90910.96508045508, 90910.96508045508, 41819.04393700934, 909.1096508045508]
p =
[0.006441365659365744;
-0.006935698812720837;
-0.03488896901596035;
-0.7622591852949104;
0.004771957238156334;
-0.01133107207303337;
-0.02452697177582621;
-0.6456783687203944;
-1.258567018901194e-05;
1.123537587555102e-05;
4.154374841821949e-05;
0.0008967755121116598]
P =
[7.182807260423624, -7.734041261217285, -38.90490824599617, -849.9999999999995;
5.321239455925471, -12.63534956072988, -27.35018011148848, -719.9999999999993;
-0.0140343597913107, 0.01252863813051139, 0.04632569451009583, 1]
B =
[7.182807260423624, -7.734041261217285, -38.90490824599617;
5.321239455925471, -12.63534956072988, -27.35018011148848;
-0.0140343597913107, 0.01252863813051139, 0.04632569451009583]
b =
[-849.9999999999995;
-719.9999999999993;
1]
K_ =
[649999.9999999985, 479999.9999999995, -799.9999999999992;
479999.9999999995, 374400.0000000002, -600.0000000000002;
-799.9999999999992, -600.0000000000002, 1]
A =
[99.99999999999883, -1.940255363782255e-12, -799.9999999999992;
0, 119.9999999999995, -600.0000000000002;
0, 0, 1]
What other values are you getting? Generally, the camera center is never exactly at the center of the image, but it should be reasonably close.
Related
I'm trying to create a driver for my usb device, using iOS and DriverKit.
I'm basing my code in the example used in WWDC: https://github.com/knightsc/USBApp
My driver starts fine when the device is connected and the readCompleted method is called fine, but the action->GetReference() gets only \0 characteres.
Also in order to know that the usb device is actually working I've connected it to my mac first and using PyUSB I can see that it's returning data in chunks of 1024 bytes in the interface 0.
This is the data I get in PyUSB:
array('B', [6, 0, 1, 0, 2, 0, 3, 0, 4, 0, 5, 0, 6, 0, 7, 0, 8, 0, 9, 0, 10, 0, 11, 0, 12, 0, 13, 0, 14, 0, 15, 0, 16, 0, 17, 0, 18, 0, 19, 0, 20, 0, 21, 0, 22, 0, 23, 0, 24, 0, 25, 0, 26, 0, 27, 0, 28, 0, 29, 0, 30, 0, 31, 0, 32, 0, 33, 0, 34, 0, 35, 0, 36, 0, 37, 0, 38, 0, 39, 0, 40, 0, 41, 0, 42, 0, 43, 0, 44, 0, 45, 0, 46, 0, 47, 0, 48, 0, 49, 0, 50, 0, 51, 0, 52, 0, 53, 0, 54, 0, 55, 0, 56, 0, 57, 0, 58, 0, 59, 0, 60, 0, 61, 0, 62, 0, 63, 0, 64, 0, 65, 0, 66, 0, 67, 0, 68, 0, 69, 0, 70, 0, 71, 0, 72, 0, 73, 0, 74, 0, 75, 0, 76, 0, 77, 0, 78, 0, 79, 0, 80, 0, 81, 0, 82, 0, 83, 0, 84, 0, 85, 0, 86, 0, 87, 0, 88, 0, 89, 0, 90, 0, 91, 0, 92, 0, 93, 0, 94, 0, 95, 0, 96, 0, 97, 0, 98, 0, 99, 0, 100, 0, 101, 0, 102, 0, 103, 0, 104, 0, 105, 0, 106, 0, 107, 0, 108, 0, 109, 0, 110, 0, 111, 0, 112, 0, 113, 0, 114, 0, 115, 0, 116, 0, 117, 0, 118, 0, 119, 0, 120, 0, 121, 0, 122, 0, 123, 0, 124, 0, 125, 0, 126, 0, 127, 0, 128, 0, 129, 0, 130, 0, 131, 0, 132, 0, 133, 0, 134, 0, 135, 0, 136, 0, 137, 0, 138, 0, 139, 0, 140, 0, 141, 0, 142, 0, 143, 0, 144, 0, 145, 0, 146, 0, 147, 0, 148, 0, 149, 0, 150, 0, 151, 0, 152, 0, 153, 0, 154, 0, 155, 0, 156, 0, 157, 0, 158, 0, 159, 0, 160, 0, 161, 0, 162, 0, 163, 0, 164, 0, 165, 0, 166, 0, 167, 0, 168, 0, 169, 0, 170, 0, 171, 0, 172, 0, 173, 0, 174, 0, 175, 0, 176, 0, 177, 0, 178, 0, 179, 0, 180, 0, 181, 0, 182, 0, 183, 0, 184, 0, 185, 0, 186, 0, 187, 0, 188, 0, 189, 0, 190, 0, 191, 0, 192, 0, 193, 0, 194, 0, 195, 0, 196, 0, 197, 0, 198, 0, 199, 0, 200, 0, 201, 0, 202, 0, 203, 0, 204, 0, 205, 0, 206, 0, 207, 0, 208, 0, 209, 0, 210, 0, 211, 0, 212, 0, 213, 0, 214, 0, 215, 0, 216, 0, 217, 0, 218, 0, 219, 0, 220, 0, 221, 0, 222, 0, 223, 0, 224, 0, 225, 0, 226, 0, 227, 0, 228, 0, 229, 0, 230, 0, 231, 0, 232, 0, 233, 0, 234, 0, 235, 0, 236, 0, 237, 0, 238, 0, 239, 0, 240, 0, 241, 0, 242, 0, 243, 0, 244, 0, 245, 0, 246, 0, 247, 0, 248, 0, 249, 0, 250, 0, 251, 0, 252, 0, 253, 0, 254, 0, 255, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0])
This is the Ivars:
struct Mk1dDriver_IVars
{
IOUSBHostInterface *interface;
IOUSBHostPipe *inPipe;
OSAction *ioCompleteCallback;
IOBufferMemoryDescriptor *inData;
uint16_t maxPacketSize;
};
This is the Start method:
kern_return_t
IMPL(Mk1dDriver, Start)
{
kern_return_t ret;
IOUSBStandardEndpointDescriptors descriptors;
ret = Start(provider, SUPERDISPATCH);
__Require(kIOReturnSuccess == ret, Exit);
ret = RegisterService();
if (ret != kIOReturnSuccess)
{
Log("Start() - Failed to register service with error: 0x%08x.", ret);
goto Exit;
}
ivars->interface = OSDynamicCast(IOUSBHostInterface, provider);
__Require_Action(NULL != ivars->interface, Exit, ret = kIOReturnNoDevice);
ret = ivars->interface->Open(this, 0, NULL);
__Require(kIOReturnSuccess == ret, Exit);
ret = ivars->interface->CopyPipe(kMyEndpointAddress, &ivars->inPipe);
__Require(kIOReturnSuccess == ret, Exit);
ret = ivars->interface->CreateIOBuffer(kIOMemoryDirectionIn,
1024,
&ivars->inData);
__Require(kIOReturnSuccess == ret, Exit);
ret = OSAction::Create(this,
Mk1dDriver_ReadComplete_ID,
IOUSBHostPipe_CompleteAsyncIO_ID,
0,
&ivars->ioCompleteCallback);
__Require(kIOReturnSuccess == ret, Exit);
ret = ivars->inPipe->AsyncIO(ivars->inData,
ivars->maxPacketSize,
ivars->ioCompleteCallback,
0);
__Require(kIOReturnSuccess == ret, Exit);
os_log(OS_LOG_DEFAULT,"Finish");
// WWDC slides don't show the full function
// i.e. this is still unfinished
Exit:
return ret;
}
The only difference in this compared with the code from Apple is that I set capacity in the method CreateIOBuffer to 1024. This is because if I leave it to 0 it will return an error that memory could not be allocated: kIOReturnNoMemory
And the ReadComplete method:
void
IMPL(Mk1dDriver, ReadComplete)
{
char output[1024];
memcpy(action->GetReference(), &output, 1024);
os_log(OS_LOG_DEFAULT,"ReadComplete");
If I put a breakpoint in the log, I can see all the positions in output will be \0
Any idea what I'm doing wrong?
Thanks
You need to store some reference to the IOBufferMemoryDescriptor* that you asked AsyncIO to write to when the data from the device is received (ivars->inData) so that you can access it when the completion callback ReadComplete is called. You can store this in the memory that you can access with GetReference().
You should set the size of the custom memory that should be allocated for you. Currently you are allocating 0 bytes. See OSAction::Create.
In ReadComplete you can then call GetReference() to access the memory. Since you know that this memory contains a reference to the IOBufferMemoryDescriptor that data has been written to, you can then use it with memcpy.
Something like this:
ret = OSAction::Create(this,
Mk1dDriver_ReadComplete_ID,
IOUSBHostPipe_CompleteAsyncIO_ID,
sizeof(IOBufferMemoryDescriptor*),
&ivars->ioCompleteCallback);
memcpy(ivars->ioCompleteCallback->GetReference(),
ivars->inData, sizeof(IOBufferMemoryDescriptor*));
First parameter to memcpy is the destination.
void IMPL(Mk1dDriver, ReadComplete)
{
IOBufferMemoryDescriptor* ptr;
memcpy(ptr, action->GetReference(), sizeof(IOBufferMemoryDescriptor*));
IOAddressSegment addressSegement{};
ptr->GetAddressRange(&addressSegement);
char output[1024];
memcpy(output, addressSegement.address, addressSegement.length);
}
I ran a LIWC analysis and it gives me the following results (below). I would like to turn the result into a pandas dataframe. If anyone can chip in, that would be wonderful.
Thanks in advance :)
Best,
David
resp = requests.post(url, auth=(api_key, api_secret), data=data)
resp1 = resp
print(resp.json())
{'plan_usage': {'call_limit': 1000, 'calls_made': 6, 'calls_remaining': 994, 'percent_used': 0.6, 'start_date': '2020-12-09T03:05:57.779556Z', 'end_date': '2020-12-23T03:05:57.779556Z'}, 'results': [{'response_id': 'd1382f42-5c28-4528-ab2e-81b80ba185e2', 'request_id': 'req-1', 'language': 'en', 'version': 'v1.0.0', 'summary': {'word_count': 57, 'words_per_sentence': 11.4, 'sentence_count': 5, 'six_plus_words': 0.2982456140350877, 'emojis': 0, 'emoticons': 0, 'hashtags': 0, 'urls': 0}, 'liwc': {'scores': {'analytical_thinking': 80.77394876079086, 'authentic': 38.8220872694557, 'clout': 50, 'emotional_tone': 97.58138119866139, 'dictionary_words': 0.8771929824561403, 'categories': {'achievement': 0, 'adjectives': 0.017543859649122806, 'adverbs': 0.03508771929824561, 'affect': 0.05263157894736842, 'affiliation': 0.017543859649122806, 'all_punctuation': 0.10526315789473684, 'anger_words': 0, 'anxiety_words': 0, 'apostrophes': 0, 'articles': 0.12280701754385964, 'assent': 0, 'auxiliary_verbs': 0.14035087719298245, 'biological_processes': 0, 'body': 0, 'causation': 0, 'certainty': 0, 'cognitive_processes': 0.05263157894736842, 'colons': 0, 'commas': 0.017543859649122806, 'comparisons': 0, 'conjunctions': 0.07017543859649122, 'dashes': 0, 'death': 0, 'differentiation': 0, 'discrepancies': 0.017543859649122806, 'drives': 0.03508771929824561, 'exclamations': 0, 'family': 0, 'feel': 0, 'female': 0, 'filler_words': 0, 'focus_future': 0, 'focus_past': 0, 'focus_present': 0.14035087719298245, 'friends': 0.017543859649122806, 'function_words': 0.543859649122807, 'health': 0, 'hear': 0, 'home': 0, 'i': 0.03508771929824561, 'impersonal_pronouns': 0.03508771929824561, 'informal_language': 0, 'ingestion': 0, 'insight': 0, 'interrogatives': 0.017543859649122806, 'leisure': 0.14035087719298245, 'male': 0, 'money': 0, 'motion': 0.05263157894736842, 'negations': 0, 'negative_emotion_words': 0, 'netspeak': 0, 'nonfluencies': 0, 'numbers': 0, 'other_grammar': 0.2807017543859649, 'other_punctuation': 0, 'parentheses': 0, 'perceptual_processes': 0.017543859649122806, 'periods': 0.08771929824561403, 'personal_concerns': 0.14035087719298245, 'personal_pronouns': 0.03508771929824561, 'positive_emotion_words': 0.05263157894736842, 'power': 0, 'prepositions': 0.10526315789473684, 'pronouns': 0.07017543859649122, 'quantifiers': 0.05263157894736842, 'question_marks': 0, 'quotes': 0, 'relativity': 0.17543859649122806, 'religion': 0, 'reward': 0.017543859649122806, 'risk': 0, 'sad_words': 0, 'see': 0.017543859649122806, 'semicolons': 0, 'sexual': 0, 'she_he': 0, 'social': 0.03508771929824561, 'space': 0.10526315789473684, 'swear_words': 0, 'tentative': 0.03508771929824561, 'they': 0, 'time': 0.017543859649122806, 'time_orientation': 0.14035087719298245, 'verbs': 0.19298245614035087, 'we': 0, 'work': 0, 'you': 0}}}, 'sallee': {'counts': {'emotions': {'admiration': 5, 'amusement': 0, 'anger': 0, 'boredom': 0, 'calmness': 0, 'curiosity': 0, 'desire': 0, 'disgust': 0, 'excitement': 0.375, 'fear': 0, 'gratitude': 2, 'joy': 6.375, 'love': 5, 'pain': 0, 'sadness': 0, 'surprise': 0}, 'goodfeel': 13.375, 'ambifeel': 0, 'badfeel': 0, 'emotionality': 13.375, 'sentiment': 13.375, 'non_emotion': None}, 'scores': {'emotions': {'admiration': 0.3333333333333333, 'amusement': 0, 'anger': 0, 'boredom': 0, 'calmness': 0, 'curiosity': 0, 'desire': 0, 'disgust': 0, 'excitement': 0.03614457831325301, 'fear': 0, 'gratitude': 0.16666666666666666, 'joy': 0.3893129770992366, 'love': 0.3333333333333333, 'pain': 0, 'sadness': 0, 'surprise': 0}, 'goodfeel': 0.2015065913370998, 'ambifeel': 0, 'badfeel': 0, 'emotionality': 0.2015065913370998, 'sentiment': 0.6541600137038615, 'non_emotion': 0.7984934086629002}, 'emotion_word_count': 4}}]}
js = resp.json()
df = pd.json_normalize(js['results'][0])
df.columns
Index(['response_id', 'request_id', 'language', 'version',
'summary.word_count', 'summary.words_per_sentence',
'summary.sentence_count', 'summary.six_plus_words', 'summary.emojis',
'summary.emoticons',
...
'sallee.scores.emotions.pain', 'sallee.scores.emotions.sadness',
'sallee.scores.emotions.surprise', 'sallee.scores.goodfeel',
'sallee.scores.ambifeel', 'sallee.scores.badfeel',
'sallee.scores.emotionality', 'sallee.scores.sentiment',
'sallee.scores.non_emotion', 'sallee.emotion_word_count'],
dtype='object', length=150)
df.iloc[0]
response_id d1382f42-5c28-4528-ab2e-81b80ba185e2
request_id req-1
language en
version v1.0.0
summary.word_count 57
...
sallee.scores.badfeel 0
sallee.scores.emotionality 0.202
sallee.scores.sentiment 0.654
sallee.scores.non_emotion 0.798
sallee.emotion_word_count 4
Name: 0, Length: 150, dtype: object
I've got numpy array with shape of (3, 50):
data = np.array([[0, 3, 0, 2, 0, 0, 1, 2, 2, 0, 1, 0, 0, 0, 0, 0, 0, 2, 1, 2, 0, 0,
0, 0, 0, 0, 0, 0, 0, 2, 1, 0, 0, 0, 0, 0, 1, 0, 0, 7, 0, 0, 0, 0,
1, 1, 2, 0, 0, 2],
[0, 0, 0, 0, 0, 3, 0, 1, 6, 1, 1, 0, 0, 0, 0, 2, 0, 0, 1, 0, 1, 0,
3, 0, 0, 0, 0, 0, 0, 5, 2, 2, 2, 1, 0, 0, 1, 0, 1, 3, 2, 0, 0, 0,
0, 0, 2, 0, 0, 0],
[1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1,
0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 2, 0, 1, 0, 0, 0, 1, 0,
0, 0, 0, 0, 0, 0]])
and the following column names:
new_cols = [f'description_word_{i+1}_count' for i in range(50)]
I'm trying to add new columns in already existing dataframe in such way:
df[new_cols] = data
but get the error:
KeyError: "None of [Index(['description_word_1_count',
'description_word_2_count',\n 'description_word_3_count',
'description_word_4_count',\n 'description_word_5_count',
'description_word_6_count',\n 'description_word_7_count',
'description_word_8_count',\n 'description_word_9_count',
'description_word_10_count',\n 'description_word_11_count',
'description_word_12_count',\n 'description_word_13_count',
'description_word_14_count',\n 'description_word_15_count',
'description_word_16_count',\n 'description_word_17_count',
'description_word_18_count',\n 'description_word_19_count',
'description_word_20_count',\n 'description_word_21_count',
'description_word_22_count',\n 'description_word_23_count',
'description_word_24_count',\n 'description_word_25_count',
'description_word_26_count',\n 'description_word_27_count',
'description_word_28_count',\n 'description_word_29_count',
'description_word_30_count',\n 'description_word_31_count',
'description_word_32_count',\n 'description_word_33_count',
'description_word_34_count',\n 'description_word_35_count',
'description_word_36_count',\n 'description_word_37_count',
'description_word_38_count',\n 'description_word_39_count',
'description_word_40_count',\n 'description_word_41_count',
'description_word_42_count',\n 'description_word_43_count',
'description_word_44_count',\n 'description_word_45_count',
'description_word_46_count',\n 'description_word_47_count',
'description_word_48_count',\n 'description_word_49_count',
'description_word_50_count'],\n dtype='object')] are in the
[columns]"
Also I don't know where it finds a '\n' symbols in my column names.
At the same time creating a new dataframe with the data is OK:
new_df = pd.DataFrame(data=data, columns=new_cols)
Does anyone know what is causing the error?
Suppose you have a df like this:
df = pd.DataFrame({'person': [1,1,1], 'event': ['A','B','C']})
You can add new columns like this:
import pandas as pd
import numpy as np
data = np.array([[0, 3, 0, 2, 0, 0, 1, 2, 2, 0, 1, 0, 0, 0, 0, 0, 0, 2, 1, 2, 0, 0,
0, 0, 0, 0, 0, 0, 0, 2, 1, 0, 0, 0, 0, 0, 1, 0, 0, 7, 0, 0, 0, 0,
1, 1, 2, 0, 0, 2],
[0, 0, 0, 0, 0, 3, 0, 1, 6, 1, 1, 0, 0, 0, 0, 2, 0, 0, 1, 0, 1, 0,
3, 0, 0, 0, 0, 0, 0, 5, 2, 2, 2, 1, 0, 0, 1, 0, 1, 3, 2, 0, 0, 0,
0, 0, 2, 0, 0, 0],
[1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1,
0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 2, 0, 1, 0, 0, 0, 1, 0,
0, 0, 0, 0, 0, 0]])
new_cols = [f'description_word_{i+1}_count' for i in range(50)]
df[new_cols] = pd.DataFrame(data, index=df.index)
I think the problem is that you are using a syntax to create series, when you actually need to create several series. In other words, a dataframe.
// Connections
int mreset = 10;
int shift = 11;
int store = 12;
int data = 13;
int taster = 9;
int a, tasterstatus = 0;
// Delays
int tasterdelay = 250;
int fast = 5;
int stage1 = 1000;
int stage2 = 1000;
unsigned long time_now = 0;
// Pattern
int firstlevel[24] = {1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0};
int secondlevel[24] = {0, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0};
int thirdlevel[24] = {0, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0};
int fourthlevel[24] = {0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0};
int leftside[24] = {1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0};
int ndleftside[24] = {1, 1, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0};
int ndrightside[24] = {1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0};
int rightside[24] = {1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0};
int backside[24] = {1, 1, 1, 1, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0};
int ndbackside[24] = {1, 1, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0};
int ndfrontside[24] = {1, 1, 1, 1, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0};
int frontside[24] = {1, 1, 1, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0};
int cube1[24] = {1, 1, 0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0};
int cube2_1[24] = {1, 0, 1, 0, 1, 1, 1, 0, 1, 0, 1, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0};
int cube2_2[24] = {0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0};
int bigcube1_1[24] = {1, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0};
int bigcube1_2[24] = {0, 1, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0};
int cube3_1[24] = {0, 1, 0, 1, 0, 0, 0, 0, 1, 1, 1, 0, 1, 0, 1, 0, 1, 1, 1, 0, 0, 0, 0, 0};
int cube3_2[24] = {0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0};
int cube4[24] = {0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0};
int cube5_1[24] = {1, 0, 1, 0, 0, 1, 1, 1, 0, 1, 0, 1, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0};
int cube5_2[24] = {0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0};
int cube6[24] = {1, 1, 0, 0, 0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0};
int cube7_1[24] = {0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 1, 1, 0, 1, 0, 1, 0, 1, 1, 1, 0, 0, 0, 0};
int cube7_2[24] = {0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0};
int cube8[24] = {0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 0, 0};
int custom1[24] = {1, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0};
int custom2[24] = {0, 1, 1, 0, 1, 0, 0, 1, 0, 1, 1, 0, 0, 1, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0};
void setup() {
//Set Outputs
pinMode(shift , OUTPUT);
pinMode(store , OUTPUT);
pinMode(data , OUTPUT);
pinMode(mreset , OUTPUT);
pinMode(taster , INPUT);
// Set Shift Register Master Reset On/Off
digitalWrite(mreset, LOW);
digitalWrite(store, HIGH);
digitalWrite(mreset, HIGH);
digitalWrite(store, LOW);
}
void loop() {
//Waiting for Button PRess
tasterstatus = digitalRead(taster);
if (tasterstatus == HIGH) {
a++;
delay(tasterdelay);
}
//Choose Pattern
if (a == 1) {
pattern1();
}
if (a == 2) {
pattern2();
}
}
void pattern1() {
//Equate Millis with time_now
time_now = millis();
// First Sample for LED Cube
for (int i = 0; i < 24; i++) {
digitalWrite(shift, LOW);
digitalWrite(data, firstlevel[i]);
digitalWrite(shift, HIGH);
}
digitalWrite(store, HIGH);
digitalWrite(store, LOW);
// "Delay"
while (millis() < time_now + stage1) {
tasterstatus = digitalRead(taster);
// Checking Button State
if (tasterstatus == HIGH) {
delay(tasterdelay);
a++;
pattern2();
}
}
// Clearing Shift Registers with Master Reset
digitalWrite(mreset, LOW);
digitalWrite(store, HIGH);
digitalWrite(mreset, HIGH);
digitalWrite(store, LOW);
time_now = millis();
for (int i = 0; i < 24; i++) {
digitalWrite(shift, LOW);
digitalWrite(data, secondlevel[i]);
digitalWrite(shift, HIGH);
}
digitalWrite(store, HIGH);
digitalWrite(store, LOW);
while (millis() < time_now + stage1) {
tasterstatus = digitalRead(taster);
if (tasterstatus == HIGH) {
delay(tasterdelay);
a++;
pattern2();
}
}
digitalWrite(mreset, LOW);
digitalWrite(store, HIGH);
digitalWrite(mreset, HIGH);
digitalWrite(store, LOW);
time_now = millis();
for (int i = 0; i < 24; i++) {
digitalWrite(shift, LOW);
digitalWrite(data, thirdlevel[i]);
digitalWrite(shift, HIGH);
}
digitalWrite(store, HIGH);
digitalWrite(store, LOW);
while (millis() < time_now + stage1) {
tasterstatus = digitalRead(taster);
if (tasterstatus == HIGH) {
delay(tasterdelay);
a++;
pattern2();
}
}
digitalWrite(mreset, LOW);
digitalWrite(store, HIGH);
digitalWrite(mreset, HIGH);
digitalWrite(store, LOW);
time_now = millis();
for (int i = 0; i < 24; i++) {
digitalWrite(shift, LOW);
digitalWrite(data, fourthlevel[i]);
digitalWrite(shift, HIGH);
}
digitalWrite(store, HIGH);
digitalWrite(store, LOW);
while (millis() < time_now + stage1) {
tasterstatus = digitalRead(taster);
if (tasterstatus == HIGH) {
delay(tasterdelay);
a++;
pattern2();
}
}
digitalWrite(mreset, LOW);
digitalWrite(store, HIGH);
digitalWrite(mreset, HIGH);
digitalWrite(store, LOW);
}
void pattern2() {
time_now = millis();
for (int i = 0; i < 24; i++) {
digitalWrite(shift, LOW);
digitalWrite(data, cube1[i]);
digitalWrite(shift, HIGH);
}
digitalWrite(store, HIGH);
digitalWrite(store, LOW);
}`enter code here
My Problem Is that when i press the button in Pattern1, Pattern2 Starts 1x time, after that Pattern1 continues where i pressed the Button, runs 1x trough, and after that pattern 2 repeats normaly. Any ideas how to fix it ? Like when i press the Button First Pattern1 repeats infinity as it should. But when i press it again, while Pattern1 is running, its goes to Pattern2 as it should, but it only repeats Pattern2 1x time, than it resumes pattern1, where i pressed the button, and then it repeats pattern2 as it should.
So the Main Problem is that Pattern1 Runs 1x time in pattern2;
Thanks for help
i fixed it by putting a return; after pattern()2; ;)
I trained a BERT model based on this notebook.
I export it as a tf SavedModel this way:
def serving_input_fn():
receiver_tensors = {
"input_ids": tf.placeholder(dtype=tf.int32, shape=[1, MAX_SEQ_LENGTH])
}
features = {
"input_ids": receiver_tensors['input_ids'],
"input_mask": 1 - tf.cast(tf.equal(receiver_tensors['input_ids'], 0), dtype=tf.int32),
"segment_ids": tf.zeros(dtype=tf.int32, shape=[1, MAX_SEQ_LENGTH]),
"label_ids": tf.placeholder(tf.int32, [None], name='label_ids')
}
return tf.estimator.export.ServingInputReceiver(features, receiver_tensors)
estimator._export_to_tpu = False
estimator.export_saved_model("export", serving_input_fn)
Then if I try to use the saved model locally it works:
from tensorflow.contrib import predictor
predict_fn = predictor.from_saved_model("export/1575241274/")
print(predict_fn({
"input_ids": [[101, 10468, 99304, 11496, 171, 112, 10176, 22873, 119, 102, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]
}))
# {'probabilities': array([[-0.01023898, -4.5866656 ]], dtype=float32), 'labels': 0}
Then I uploaded the SavedModel to a bucket and created a model and a model version on gcloud this way:
gcloud alpha ai-platform versions create v1gpu --model [...] --origin=[...] --python-version=3.5 --runtime-version=1.14 --accelerator=^:^count=1:type=nvidia-tesla-k80 --machine-type n1-highcpu-4
No issue there, the model is deployed and displayed as working in the console.
But if I try to get predictions, as such:
import googleapiclient.discovery
service = googleapiclient.discovery.build('ml', 'v1')
name = 'projects/[project_name]/models/[model_name]/versions/v1gpu'
response = service.projects().predict(
name=name,
body={'instances': [{
"input_ids": [[101, 10468, 99304, 11496, 171, 112, 10176, 22873, 119, 102, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]
}]}
).execute()
print(response["predictions"])
All I get is the following error:
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/usr/local/lib/python3.6/dist-packages/googleapiclient/_helpers.py", line 130, in positional_wrapper
return wrapped(*args, **kwargs)
File "/usr/local/lib/python3.6/dist-packages/googleapiclient/http.py", line 851, in execute
raise HttpError(resp, content, uri=self.uri)
googleapiclient.errors.HttpError: <HttpError 400 when requesting https://ml.googleapis.com/v1/projects/[project_name]/models/[model_name]/versions/v1gpu:predict?alt=json returned "Bad Request">
I get the same error if I test the model from the gcloud console using the "Test your model with sample input data" feature.
Edit:
The saved_model has a tagset "serve" and signature_def "serving_default".
Output of "saved_model_cli show --dir 1575241274/ --tag_set serve --signature_def serving_default":
The given SavedModel SignatureDef contains the following input(s):
inputs['input_ids'] tensor_info:
dtype: DT_INT32
shape: (1, 128)
name: Placeholder:0
The given SavedModel SignatureDef contains the following output(s):
outputs['labels'] tensor_info:
dtype: DT_INT32
shape: ()
name: loss/Squeeze:0
outputs['probabilities'] tensor_info:
dtype: DT_FLOAT
shape: (1, 2)
name: loss/LogSoftmax:0
Method name is: tensorflow/serving/predict
The body of the request sent to the API has the form:
{"instances": [<instance 1>, <instance 2>, ...]}
As specified in documentation we need something like this:
{
"instances": [
<object>
...
]
}
In this case you have:
{
"instances": [
{
"input_ids":
[ <object> ]
}
...
]
}
You need to replace input_ids to instances:
{
"instances":
[
[101, 10468, 99304, 11496, 171, 112, 10176, 22873, 119, 102, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
]
}
Note If you can show the saved_model_cli will be great.
Also gcloud local predict command is also a good option for testing.
It depend of the signature of the model. In my case I have the following signature (just keeping the input part):
The given SavedModel SignatureDef contains the following input(s):
inputs['attention_mask'] tensor_info:
dtype: DT_INT32
shape: (-1, 128)
name: serving_default_attention_mask:0
inputs['input_ids'] tensor_info:
dtype: DT_INT32
shape: (-1, 128)
name: serving_default_input_ids:0
inputs['token_type_ids'] tensor_info:
dtype: DT_INT32
shape: (-1, 128)
name: serving_default_token_type_ids:0
and I need to pass data in the following format (in this case 2 examples):
{'instances':
[
{'input_ids': [101, 143, 18267, 15470, 90395, ...],
'attention_mask': [1, 1, 1, 1, 1, 1, 1, 1, .....],
'token_type_ids': [0, 0, 0, 0, 0, 0, 0, 0, .....]
},
{'input_ids': [101, 17664, 143, 30728, .........],
'attention_mask': [1, 1, 1, 1, 1, 1, 1, .......],
'token_type_ids': [0, 0, 0, 0, 0, 0, 0, 0, ....]
}
]
}
I am using it with a Keras model with Tensorflow 2.2.0
I guess in your case you need (for 2 examples):
{'instances':
[
{'input_ids': [101, 143, 18267, 15470, 90395, ...]},
{'input_ids': [101, 17664, 143, 30728, .........]}
]
}