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Merge branch 'fix/matlab_examples_wrapper' into develop

release/4.3a0
lvzhaoyang 2014-12-19 11:27:20 -05:00
parent 73a281974a bcfcf8be8e
commit 3363edf77c
19 changed files with 179 additions and 61 deletions
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3
gtsam.h
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@ -1730,6 +1730,7 @@ class Values {
void insert(size_t j, const gtsam::Cal3DS2& t);
void insert(size_t j, const gtsam::Cal3Bundler& t);
void insert(size_t j, const gtsam::EssentialMatrix& t);
void insert(size_t j, const gtsam::SimpleCamera& t);
void insert(size_t j, const gtsam::imuBias::ConstantBias& t);
void insert(size_t j, Vector t);
void insert(size_t j, Matrix t);
@ -2149,7 +2150,7 @@ class NonlinearISAM {
#include <gtsam/geometry/StereoPoint2.h>
#include <gtsam/slam/PriorFactor.h>
template<T = { gtsam::Point2, gtsam::StereoPoint2, gtsam::Point3, gtsam::Rot2, gtsam::Rot3, gtsam::Pose2, gtsam::Pose3, gtsam::Cal3_S2,gtsam::CalibratedCamera, gtsam::SimpleCamera, gtsam::imuBias::ConstantBias}>
template<T = {Vector, gtsam::Point2, gtsam::StereoPoint2, gtsam::Point3, gtsam::Rot2, gtsam::Rot3, gtsam::Pose2, gtsam::Pose3, gtsam::Cal3_S2,gtsam::CalibratedCamera, gtsam::SimpleCamera, gtsam::imuBias::ConstantBias}>
virtual class PriorFactor : gtsam::NoiseModelFactor {
PriorFactor(size_t key, const T& prior, const gtsam::noiseModel::Base* noiseModel);
T prior() const;

4
matlab/gtsam_examples/IMUKittiExampleGPS.m
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@ -33,7 +33,7 @@ GPSskip = 10; % Skip this many GPS measurements each time
%% Get initial conditions for the estimated trajectory
currentPoseGlobal = Pose3(Rot3, GPS_data(firstGPSPose).Position); % initial pose is the reference frame (navigation frame)
currentVelocityGlobal = LieVector([0;0;0]); % the vehicle is stationary at the beginning
currentVelocityGlobal = [0;0;0]; % the vehicle is stationary at the beginning
currentBias = imuBias.ConstantBias(zeros(3,1), zeros(3,1));
sigma_init_x = noiseModel.Isotropic.Precisions([ 0.0; 0.0; 0.0; 1; 1; 1 ]);
sigma_init_v = noiseModel.Isotropic.Sigma(3, 1000.0);
@ -72,7 +72,7 @@ for measurementIndex = firstGPSPose:length(GPS_data)
newValues.insert(currentVelKey, currentVelocityGlobal);
newValues.insert(currentBiasKey, currentBias);
newFactors.add(PriorFactorPose3(currentPoseKey, currentPoseGlobal, sigma_init_x));
newFactors.add(PriorFactorLieVector(currentVelKey, currentVelocityGlobal, sigma_init_v));
newFactors.add(PriorFactorVector(currentVelKey, currentVelocityGlobal, sigma_init_v));
newFactors.add(PriorFactorConstantBias(currentBiasKey, currentBias, sigma_init_b));
else
t_previous = GPS_data(measurementIndex-1, 1).Time;

4
matlab/gtsam_examples/MonocularVOExample.m
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@ -40,7 +40,7 @@ end
%% Create initial estimate
initial = Values;
trueE = EssentialMatrix(aRb,Sphere2(aTb));
trueE = EssentialMatrix(aRb,Unit3(aTb));
initialE = trueE.retract([0.1, -0.1, 0.1, 0.1, -0.1]');
initial.insert(1, initialE);
@ -52,5 +52,5 @@ result = optimizer.optimize();
%% Print result (as essentialMatrix) and error
error = graph.error(result)
E = result.at(1)
E = result.atEssentialMatrix(1)

18
matlab/gtsam_tests/testPriorFactor.m Normal file
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@ -0,0 +1,18 @@
% test wrapping of Values
import gtsam.*;
values = Values;
key = 5;
priorPose3 = Pose3;
model = noiseModel.Unit.Create(6);
factor = PriorFactorPose3(key, priorPose3, model);
values.insert(key, priorPose3);
EXPECT('error', factor.error(values) == 0);
key = 3;
priorVector = [0,0,0]';
model = noiseModel.Unit.Create(3);
factor = PriorFactorVector(key, priorVector, model);
values.insert(key, priorVector);
EXPECT('error', factor.error(values) == 0);

3
matlab/gtsam_tests/test_gtsam.m
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@ -1,5 +1,8 @@
% Test runner script - runs each test
% display 'Starting: testPriorFactor'
% testPriorFactor
display 'Starting: testValues'
testValues

12
matlab/unstable_examples/FlightCameraTransformIMU.m
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@ -57,7 +57,7 @@ isamParams.setFactorization('QR');
isam = ISAM2(isamParams);
%% Get initial conditions for the estimated trajectory
currentVelocityGlobal = LieVector([10;0;0]); % (This is slightly wrong!)
currentVelocityGlobal = [10;0;0]; % (This is slightly wrong!) Zhaoyang: Fixed
currentBias = imuBias.ConstantBias(zeros(3,1), zeros(3,1));
sigma_init_v = noiseModel.Isotropic.Sigma(3, 1.0);
@ -101,7 +101,7 @@ axis equal;
for i=1:size(trajectory)-1
xKey = symbol('x',i);
pose = trajectory.at(xKey); % GT pose
pose = trajectory.atPose3(xKey); % GT pose
pose_t = pose.translation(); % GT pose-translation
if exist('h_cursor','var')
@ -165,13 +165,13 @@ for i=1:size(trajectory)-1
if i > 1
xKey_prev = symbol('x',i-1);
pose_prev = trajectory.at(xKey_prev);
pose_prev = trajectory.atPose3(xKey_prev);
step = pose_prev.between(pose);
% insert estimate for current pose with some normal noise on
% translation
initial.insert(xKey,result.at(xKey_prev).compose(step.retract([0; 0; 0; normrnd(0,0.2,3,1)])));
initial.insert(xKey,result.atPose3(xKey_prev).compose(step.retract([0; 0; 0; normrnd(0,0.2,3,1)])));
% visual measurements
if measurements.size > 0 && use_camera
@ -181,12 +181,12 @@ for i=1:size(trajectory)-1
zKey = measurementKeys.at(zz);
lKey = symbol('l',symbolIndex(zKey));
fg.add(TransformCalProjectionFactorCal3_S2(measurements.at(zKey), ...
fg.add(TransformCalProjectionFactorCal3_S2(measurements.atPoint2(zKey), ...
z_cov, xKey, transformKey, lKey, calibrationKey, false, true));
% only add landmark to values if doesn't exist yet
if ~result.exists(lKey)
noisy_landmark = landmarks.at(lKey).compose(Point3(normrnd(0,landmark_noise,3,1)));
noisy_landmark = landmarks.atPoint3(lKey).compose(Point3(normrnd(0,landmark_noise,3,1)));
initial.insert(lKey, noisy_landmark);
% and add a prior since its position is known

14
matlab/unstable_examples/IMUKittiExampleAdvanced.m
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@ -7,7 +7,7 @@ disp('Example of application of ISAM2 for visual-inertial navigation on the KITT
%% Read metadata and compute relative sensor pose transforms
% IMU metadata
disp('-- Reading sensor metadata')
IMU_metadata = importdata('KittiEquivBiasedImu_metadata.txt');
IMU_metadata = importdata(findExampleDataFile('KittiEquivBiasedImu_metadata.txt'));
IMU_metadata = cell2struct(num2cell(IMU_metadata.data), IMU_metadata.colheaders, 2);
IMUinBody = Pose3.Expmap([IMU_metadata.BodyPtx; IMU_metadata.BodyPty; IMU_metadata.BodyPtz;
IMU_metadata.BodyPrx; IMU_metadata.BodyPry; IMU_metadata.BodyPrz; ]);
@ -16,14 +16,14 @@ if ~IMUinBody.equals(Pose3, 1e-5)
end
% VO metadata
VO_metadata = importdata('KittiRelativePose_metadata.txt');
VO_metadata = importdata(findExampleDataFile('KittiRelativePose_metadata.txt'));
VO_metadata = cell2struct(num2cell(VO_metadata.data), VO_metadata.colheaders, 2);
VOinBody = Pose3.Expmap([VO_metadata.BodyPtx; VO_metadata.BodyPty; VO_metadata.BodyPtz;
VO_metadata.BodyPrx; VO_metadata.BodyPry; VO_metadata.BodyPrz; ]);
VOinIMU = IMUinBody.inverse().compose(VOinBody);
% GPS metadata
GPS_metadata = importdata('KittiGps_metadata.txt');
GPS_metadata = importdata(findExampleDataFile('KittiGps_metadata.txt'));
GPS_metadata = cell2struct(num2cell(GPS_metadata.data), GPS_metadata.colheaders, 2);
GPSinBody = Pose3.Expmap([GPS_metadata.BodyPtx; GPS_metadata.BodyPty; GPS_metadata.BodyPtz;
GPS_metadata.BodyPrx; GPS_metadata.BodyPry; GPS_metadata.BodyPrz; ]);
@ -32,7 +32,7 @@ GPSinIMU = IMUinBody.inverse().compose(GPSinBody);
%% Read data and change coordinate frame of GPS and VO measurements to IMU frame
disp('-- Reading sensor data from file')
% IMU data
IMU_data = importdata('KittiEquivBiasedImu.txt');
IMU_data = importdata(findExampleDataFile('KittiEquivBiasedImu.txt'));
IMU_data = cell2struct(num2cell(IMU_data.data), IMU_data.colheaders, 2);
imum = cellfun(@(x) x', num2cell([ [IMU_data.accelX]' [IMU_data.accelY]' [IMU_data.accelZ]' [IMU_data.omegaX]' [IMU_data.omegaY]' [IMU_data.omegaZ]' ], 2), 'UniformOutput', false);
[IMU_data.acc_omega] = deal(imum{:});
@ -40,7 +40,7 @@ imum = cellfun(@(x) x', num2cell([ [IMU_data.accelX]' [IMU_data.accelY]' [IMU_da
clear imum
% VO data
VO_data = importdata('KittiRelativePose.txt');
VO_data = importdata(findExampleDataFile('KittiRelativePose.txt'));
VO_data = cell2struct(num2cell(VO_data.data), VO_data.colheaders, 2);
% Merge relative pose fields and convert to Pose3
logposes = [ [VO_data.dtx]' [VO_data.dty]' [VO_data.dtz]' [VO_data.drx]' [VO_data.dry]' [VO_data.drz]' ];
@ -71,7 +71,7 @@ clear logposes relposes
%% Get initial conditions for the estimated trajectory
% % % currentPoseGlobal = Pose3(Rot3, GPS_data(firstGPSPose).Position); % initial pose is the reference frame (navigation frame)
currentPoseGlobal = Pose3;
currentVelocityGlobal = LieVector([0;0;0]); % the vehicle is stationary at the beginning
currentVelocityGlobal = [0;0;0]; % the vehicle is stationary at the beginning
currentBias = imuBias.ConstantBias(zeros(3,1), zeros(3,1));
sigma_init_x = noiseModel.Isotropic.Sigma(6, 0.01);
sigma_init_v = noiseModel.Isotropic.Sigma(3, 1000.0);
@ -120,7 +120,7 @@ for measurementIndex = 1:length(timestamps)
newValues.insert(currentVelKey, currentVelocityGlobal);
newValues.insert(currentBiasKey, currentBias);
newFactors.add(PriorFactorPose3(currentPoseKey, currentPoseGlobal, sigma_init_x));
newFactors.add(PriorFactorLieVector(currentVelKey, currentVelocityGlobal, sigma_init_v));
newFactors.add(PriorFactorVector(currentVelKey, currentVelocityGlobal, sigma_init_v));
newFactors.add(PriorFactorConstantBias(currentBiasKey, currentBias, sigma_init_b));
else
t_previous = timestamps(measurementIndex-1, 1);

4
matlab/unstable_examples/SmartRangeFactorExample.m
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@ -82,8 +82,8 @@ for ind_pose = 2:7
key_prev = poseKey(ind_pose-1);
key_curr = poseKey(ind_pose);
prev_pose = smartValues.at(key_prev);
curr_pose = smartValues.at(key_curr);
prev_pose = smartValues.atPose2(key_prev);
curr_pose = smartValues.atPose2(key_curr);
GTDeltaPose = prev_pose.between(curr_pose);
noisePose = Pose2([t_noise*rand; t_noise*rand; r_noise*rand]);
NoisyDeltaPose = GTDeltaPose.compose(noisePose);

24
matlab/unstable_examples/TransformCalProjectionFactorExampleISAM.m
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@ -96,17 +96,17 @@ for i=1:20
if i > 1
if i < 11
initial.insert(i,result.at(i-1).compose(move_forward));
initial.insert(i,result.atPose3(i-1).compose(move_forward));
fg.add(BetweenFactorPose3(i-1,i, move_forward, covariance));
else
initial.insert(i,result.at(i-1).compose(move_circle));
initial.insert(i,result.atPose3(i-1).compose(move_circle));
fg.add(BetweenFactorPose3(i-1,i, move_circle, covariance));
end
end
% generate some camera measurements
cam_pose = initial.at(i).compose(actual_transform);
cam_pose = initial.atPose3(i).compose(actual_transform);
% gtsam.plotPose3(cam_pose);
cam = SimpleCamera(cam_pose,K);
i
@ -137,10 +137,10 @@ for i=1:20
hold on;
%% plot results
result_camera_transform = result.at(transformKey);
result_camera_transform = result.atPose3(transformKey);
for j=1:i
gtsam.plotPose3(result.at(j),[],0.5);
gtsam.plotPose3(result.at(j).compose(result_camera_transform),[],0.5);
gtsam.plotPose3(result.atPose3(j),[],0.5);
gtsam.plotPose3(result.atPose3(j).compose(result_camera_transform),[],0.5);
end
xlabel('x (m)');
@ -153,12 +153,12 @@ for i=1:20
% axis equal
for l=101:100+nrPoints
plotPoint3(result.at(l),'g');
plotPoint3(result.atPoint3(l),'g');
end
ty = result.at(transformKey).translation().y();
fx = result.at(calibrationKey).fx();
fy = result.at(calibrationKey).fy();
ty = result.atPose3(transformKey).translation().y();
fx = result.atCal3_S2(calibrationKey).fx();
fy = result.atCal3_S2(calibrationKey).fy();
text(1,5,5,sprintf('Y-Transform(0.0): %0.2f',ty));
text(1,5,3,sprintf('fx(900): %.0f',fx));
text(1,5,1,sprintf('fy(900): %.0f',fy));
@ -180,10 +180,10 @@ end
fprintf('Cheirality Exception count: %d\n', cheirality_exception_count);
disp('Transform after optimization');
result.at(transformKey)
result.atPose3(transformKey)
disp('Calibration after optimization');
result.at(calibrationKey)
result.atCal3_S2(calibrationKey)

4
matlab/unstable_examples/TransformCalProjectionFactorIMUExampleISAM.m
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@ -89,7 +89,7 @@ isam = ISAM2(isamParams);
currentIMUPoseGlobal = Pose3();
%% Get initial conditions for the estimated trajectory
currentVelocityGlobal = LieVector([1;0;0]); % the vehicle is stationary at the beginning
currentVelocityGlobal = [1;0;0]; % the vehicle is stationary at the beginning
currentBias = imuBias.ConstantBias(zeros(3,1), zeros(3,1));
sigma_init_v = noiseModel.Isotropic.Sigma(3, 1.0);
@ -127,7 +127,7 @@ for i=1:steps
fg.add(PriorFactorCal3_S2(calibrationKey,K_corrupt,K_cov));
% velocity and bias evolution
fg.add(PriorFactorLieVector(currentVelKey, currentVelocityGlobal, sigma_init_v));
fg.add(PriorFactorVector(currentVelKey, currentVelocityGlobal, sigma_init_v));
fg.add(PriorFactorConstantBias(currentBiasKey, currentBias, sigma_init_b));
result = initial;

12
matlab/unstable_examples/TransformProjectionFactorExample.m
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@ -61,17 +61,17 @@ cheirality_exception_count = 0;
for i=1:20
if i > 1
if i < 11
initial.insert(i,initial.at(i-1).compose(move_forward));
initial.insert(i,initial.atPose3(i-1).compose(move_forward));
fg.add(BetweenFactorPose3(i-1,i, move_forward, covariance));
else
initial.insert(i,initial.at(i-1).compose(move_circle));
initial.insert(i,initial.atPose3(i-1).compose(move_circle));
fg.add(BetweenFactorPose3(i-1,i, move_circle, covariance));
end
end
% generate some camera measurements
cam_pose = initial.at(i).compose(actual_transform);
cam_pose = initial.atPose3(i).compose(actual_transform);
gtsam.plotPose3(cam_pose);
cam = SimpleCamera(cam_pose,K);
i
@ -93,14 +93,14 @@ fprintf('Cheirality Exception count: %d\n', cheirality_exception_count);
%% camera plotting
for i=1:20
gtsam.plotPose3(initial.at(i).compose(camera_transform));
gtsam.plotPose3(initial.atPose3(i).compose(camera_transform));
end
xlabel('x (m)');
ylabel('y (m)');
disp('Transform before optimization');
initial.at(1000)
initial.atPose3(1000)
params = LevenbergMarquardtParams;
params.setAbsoluteErrorTol(1e-15);
@ -112,7 +112,7 @@ optimizer = LevenbergMarquardtOptimizer(fg, initial, params);
result = optimizer.optimizeSafely();
disp('Transform after optimization');
result.at(1000)
result.atPose3(1000) % results is empty here. optimizer doesn't generate result?
axis([0 25 0 25 0 10]);
axis equal

18
matlab/unstable_examples/TransformProjectionFactorExampleISAM.m
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@ -86,17 +86,17 @@ for i=1:20
if i > 1
if i < 11
initial.insert(i,result.at(i-1).compose(move_forward));
initial.insert(i,result.atPose3(i-1).compose(move_forward));
fg.add(BetweenFactorPose3(i-1,i, move_forward, covariance));
else
initial.insert(i,result.at(i-1).compose(move_circle));
initial.insert(i,result.atPose3(i-1).compose(move_circle));
fg.add(BetweenFactorPose3(i-1,i, move_circle, covariance));
end
end
% generate some camera measurements
cam_pose = initial.at(i).compose(actual_transform);
cam_pose = initial.atPose3(i).compose(actual_transform);
% gtsam.plotPose3(cam_pose);
cam = SimpleCamera(cam_pose,K);
i
@ -127,10 +127,10 @@ for i=1:20
hold on;
%% plot results
result_camera_transform = result.at(1000);
result_camera_transform = result.atPose3(1000);
for j=1:i
gtsam.plotPose3(result.at(j));
gtsam.plotPose3(result.at(j).compose(result_camera_transform),[],0.5);
gtsam.plotPose3(result.atPose3(j));
gtsam.plotPose3(result.atPose3(j).compose(result_camera_transform),[],0.5);
end
xlabel('x (m)');
@ -143,10 +143,10 @@ for i=1:20
% axis equal
for l=101:100+nrPoints
plotPoint3(result.at(l),'g');
plotPoint3(result.atPoint3(l),'g');
end
ty = result.at(1000).translation().y();
ty = result.atPose3(1000).translation().y();
text(5,5,5,sprintf('Y-Transform: %0.2g',ty));
if(write_video)
@ -168,7 +168,7 @@ fprintf('Cheirality Exception count: %d\n', cheirality_exception_count);
disp('Transform after optimization');
result.at(1000)
result.atPose3(1000)

2
matlab/unstable_examples/plot_projected_landmarks.m
View File

@ -23,7 +23,7 @@ z = zeros(1,nrMeasurements);
for i = 0:measurement_keys.size-1
key = measurement_keys.at(i);
key_index = gtsam.symbolIndex(key);
p = landmarks.at(gtsam.symbol('l',key_index));
p = landmarks.atPoint3(gtsam.symbol('l',key_index));
x(i+1) = p.x;
y(i+1) = p.y;

2
matlab/unstable_examples/project_landmarks.m
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@ -8,7 +8,7 @@ function [ measurements ] = project_landmarks( pose, landmarks, K )
measurements = Values;
for i=1:size(landmarks)-1
z = camera.project(landmarks.at(symbol('l',i)));
z = camera.project(landmarks.atPoint3(symbol('l',i)));
% check bounding box
if z.x < 0 || z.x > 1280

12
matlab/unstable_examples/testTSAMFactors.m
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@ -48,12 +48,12 @@ optimizer = LevenbergMarquardtOptimizer(graph, initial, params);
result = optimizer.optimize();
% Check result
CHECK('error',result.at(100).equals(b1,1e-5))
CHECK('error',result.at(10).equals(origin,1e-5))
CHECK('error',result.at(1).equals(Point2(0,1),1e-5))
CHECK('error',result.at(2).equals(Point2(0,1),1e-5))
CHECK('error',result.at(20).equals(origin,1e-5))
CHECK('error',result.at(200).equals(b2,1e-5))
CHECK('error',result.atPose2(100).equals(b1,1e-5))
CHECK('error',result.atPose2(10).equals(origin,1e-5))
CHECK('error',result.atPoint2(1).equals(Point2(0,1),1e-5))
CHECK('error',result.atPoint2(2).equals(Point2(0,1),1e-5))
CHECK('error',result.atPose2(20).equals(origin,1e-5))
CHECK('error',result.atPose2(200).equals(b2,1e-5))
% Check error
CHECK('error',abs(graph.error(result))<1e-9)

6
wrap/Argument.cpp
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@ -78,11 +78,11 @@ void Argument::matlab_unwrap(FileWriter& file, const string& matlabName) const {
string cppType = type.qualifiedName("::");
string matlabUniqueType = type.qualifiedName();
if (is_ptr)
if (is_ptr && type.category != Qualified::EIGEN)
// A pointer: emit an "unwrap_shared_ptr" call which returns a pointer
file.oss << "boost::shared_ptr<" << cppType << "> " << name
<< " = unwrap_shared_ptr< ";
else if (is_ref)
else if (is_ref && type.category != Qualified::EIGEN)
// A reference: emit an "unwrap_shared_ptr" call and de-reference the pointer
file.oss << cppType << "& " << name << " = *unwrap_shared_ptr< ";
else
@ -94,7 +94,7 @@ void Argument::matlab_unwrap(FileWriter& file, const string& matlabName) const {
file.oss << cppType << " " << name << " = unwrap< ";
file.oss << cppType << " >(" << matlabName;
if (is_ptr || is_ref)
if( (is_ptr || is_ref) && type.category != Qualified::EIGEN)
file.oss << ", \"ptr_" << matlabUniqueType << "\"";
file.oss << ");" << endl;
}

17
wrap/matlab.h
View File

@ -425,3 +425,20 @@ boost::shared_ptr<Class> unwrap_shared_ptr(const mxArray* obj, const string& pro
boost::shared_ptr<Class>* spp = *reinterpret_cast<boost::shared_ptr<Class>**> (mxGetData(mxh));
return *spp;
}
//// throw an error if unwrap_shared_ptr is attempted for an Eigen Vector
//template <>
//Vector unwrap_shared_ptr<Vector>(const mxArray* obj, const string& propertyName) {
// bool unwrap_shared_ptr_Vector_attempted = false;
// BOOST_STATIC_ASSERT(unwrap_shared_ptr_Vector_attempted, "Vector cannot be unwrapped as a shared pointer");
// return Vector();
//}
//// throw an error if unwrap_shared_ptr is attempted for an Eigen Matrix
//template <>
//Matrix unwrap_shared_ptr<Matrix>(const mxArray* obj, const string& propertyName) {
// bool unwrap_shared_ptr_Matrix_attempted = false;
// BOOST_STATIC_ASSERT(unwrap_shared_ptr_Matrix_attempted, "Matrix cannot be unwrapped as a shared pointer");
// return Matrix();
//}

2
wrap/tests/expected2/geometry_wrapper.cpp
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@ -679,7 +679,7 @@ void MyTemplatePoint2_templatedMethod_55(int nargout, mxArray *out[], int nargin
typedef boost::shared_ptr<MyTemplatePoint2> Shared;
checkArguments("templatedMethodMatrix",nargout,nargin-1,1);
Shared obj = unwrap_shared_ptr<MyTemplatePoint2>(in[0], "ptr_MyTemplatePoint2");
Matrix& t = *unwrap_shared_ptr< Matrix >(in[1], "ptr_Matrix");
Matrix& t = unwrap< Matrix >(in[1]);
out[0] = wrap< Matrix >(obj->templatedMethod<Matrix>(t));
}

79
wrap/tests/testClass.cpp
View File

@ -160,6 +160,85 @@ TEST( Class, Grammar ) {
EXPECT_LONGS_EQUAL(ReturnType::EIGEN, rv4.type1.category);
}
//******************************************************************************
TEST( Class, TemplateSubstition ) {
using classic::space_p;
// Create type grammar that will place result in cls
Class cls;
Template t;
ClassGrammar g(cls, t);
string markup(
string("template<T = {void, double, Matrix, Point3}> class Point2 { \n")
+ string(" T myMethod(const T& t) const; \n")
+ string("};"));
EXPECT(parse(markup.c_str(), g, space_p).full);
// Method 2
Method m2 = cls.method("myMethod");
EXPECT(assert_equal("myMethod", m2.name()));
EXPECT(m2.isConst());
LONGS_EQUAL(1, m2.nrOverloads());
ReturnValue rv2 = m2.returnValue(0);
EXPECT(!rv2.isPair);
EXPECT(!rv2.type1.isPtr);
EXPECT(assert_equal("T", rv2.type1.name()));
EXPECT_LONGS_EQUAL(ReturnType::CLASS, rv2.type1.category);
EXPECT_LONGS_EQUAL(4, t.nrValues());
EXPECT(t.argName()=="T");
EXPECT(t[0]==Qualified("void",Qualified::VOID));
EXPECT(t[1]==Qualified("double",Qualified::BASIS));
EXPECT(t[2]==Qualified("Matrix",Qualified::EIGEN));
EXPECT(t[3]==Qualified("Point3",Qualified::CLASS));
vector<Class> classes = cls.expandTemplate(t.argName(),
t.argValues());
// check the number of new classes is four
EXPECT_LONGS_EQUAL(4, classes.size());
// check return types
EXPECT(classes[0].method("myMethod").returnValue(0).type1 == Qualified("void",Qualified::VOID));
EXPECT(classes[1].method("myMethod").returnValue(0).type1 == Qualified("double",Qualified::BASIS));
EXPECT(classes[2].method("myMethod").returnValue(0).type1 == Qualified("Matrix",Qualified::EIGEN));
EXPECT(classes[3].method("myMethod").returnValue(0).type1 == Qualified("Point3",Qualified::CLASS));
// check the argument types
EXPECT(classes[0].method("myMethod").argumentList(0)[0].type == Qualified("void",Qualified::VOID));
EXPECT(classes[1].method("myMethod").argumentList(0)[0].type == Qualified("double",Qualified::BASIS));
EXPECT(classes[2].method("myMethod").argumentList(0)[0].type == Qualified("Matrix",Qualified::EIGEN));
EXPECT(classes[3].method("myMethod").argumentList(0)[0].type == Qualified("Point3",Qualified::CLASS));
}
TEST(Class, Template) {
using classic::space_p;
// Create type grammar that will place result in cls
Class cls;
Template t;
ClassGrammar g(cls, t);
string markup(
string("template<T = {Vector, Matrix}>"
" virtual class PriorFactor : gtsam::NoiseModelFactor {"
" PriorFactor(size_t key, const T& prior, const gtsam::noiseModel::Base* noiseModel); "
" T prior() const; "
" void serialize() const; "
"};" ));
EXPECT(parse(markup.c_str(), g, space_p).full);
}
/* ************************************************************************* */
int main() {
TestResult tr;