void BotClickFollowArea()
{
//—юды
int ypos = 0;
int xpos = 1;
generator.seed(std::time(0)); // seed with the current time
NumberDistribution distributiony(-50, 50);
Generator numberGeneratory(generator, distributiony);
int randomy = numberGeneratory();
NumberDistribution distributionx(-30, 30);
Generator numberGeneratorx(generator, distributionx);
int randomx = numberGeneratorx();
NumberDistribution distributionsleep(-280, 100);
Generator numberGeneratorSleep(generator, distributionsleep);
int randomsleep = numberGeneratorSleep();
if(ypos + randomy > 680)
{
randomy = 0;
}
SetCursorPosition(xpos + randomx, ypos + randomy);
mouse_event(MOUSEEVENTF_LEFTDOWN,0,0,0,0); // нажали левую кнопку мыши
mouse_event(MOUSEEVENTF_LEFTUP,0,0,0,0); // отпустили левую кнопку мыши ( Ќ≈ «јЅџ¬ј“№ )
BotSleep(0,0,0,0,0,0,0,0,0,0,0,0,300 + randomsleep);
}
void randomfollow()
{
generator.seed(std::time(0)); // seed with the current time
NumberDistribution distributiony(-15, 160);
Generator numberGeneratory(generator, distributiony);
randomfollowy = numberGeneratory();
NumberDistribution distributionx(-80, 120);
Generator numberGeneratorx(generator, distributionx);
randomfollowx = numberGeneratorx();
}
std::unique_ptr<Map> SceneGenerator::generateFromPoses(const std::vector<Eigen::Matrix3fr> &posesR, const std::vector<Eigen::Vector3f> &posesCenter)
{
std::unique_ptr<Map> newMap(new Map());
newMap->mGroundTruthCamera.reset(new CameraModel(*mCamera));
cv::Mat3b nullImg3(mCamera->getImageSize()[1], mCamera->getImageSize()[0]);
cv::Mat1b nullImg1(mCamera->getImageSize()[1], mCamera->getImageSize()[0]);
Eigen::Matrix<uchar, 1, 32> nullDescr;
nullDescr.setZero();
float expectedPixelNoiseStd = std::max(0.3f, mNoiseStd);
//Features
const int kFeatureCount = 1000;
std::uniform_real_distribution<float> distributionx(0, mCamera->getImageSize()[0]);
std::uniform_real_distribution<float> distributiony(0, mCamera->getImageSize()[1]);
for (int i = 0; i < kFeatureCount; i++)
{
std::unique_ptr<Feature> newFeature(new Feature());
//Get position
Eigen::Vector2f imagePos(distributionx(mRandomDevice), distributiony(mRandomDevice));
Eigen::Vector2f xn = mCamera->unprojectToWorld(imagePos).hnormalized();
newFeature->mGroundTruthPosition3D = Eigen::Vector3f(xn[0], xn[1], 0);
newFeature->mPosition3D = newFeature->mGroundTruthPosition3D;
//newFeature->setPosition(imagePos);
newMap->addFeature(std::move(newFeature));
}
//Matlab log of poses
if (mLogScene)
{
//Eigen::MatrixX3f points(newMap->getFeatures().size(), 3);
//for (int i = 0; i < points.rows(); i++)
//{
// points.row(i) = newMap->getFeatures()[i]->mPosition3D.transpose();
//}
//MatlabDataLog::Instance().AddValue("K", mCamera->getK());
//MatlabDataLog::Instance().AddValue("X", points.transpose());
//for (int i = 0; i < posesR.size(); i++)
//{
// MatlabDataLog::Instance().AddCell("R", posesR[i]);
// MatlabDataLog::Instance().AddCell("center", posesCenter[i]);
//}
//return std::unique_ptr<Map>();
}
//Frames
std::normal_distribution<float> error_distribution(0, mNoiseStd);
for (int i = 0; i < (int)posesR.size(); i++)
{
std::unique_ptr<Keyframe> newFrame(new Keyframe());
newFrame->init(nullImg3, nullImg1);
Eigen::Vector3f poseT = -posesR[i] * posesCenter[i];
newFrame->mGroundTruthPose3DR = posesR[i];
newFrame->mGroundTruthPose3DT = poseT;
//Stats
std::vector<float> distortionError;
std::vector<float> noiseError;
//Points
std::vector<Eigen::Vector2f> refPoints, imgPoints;
for (int j = 0; j< (int)newMap->getFeatures().size(); j++)
{
auto &feature = *newMap->getFeatures()[j];
//Project
Eigen::Vector3f xn = posesR[i] * feature.mPosition3D + poseT;
Eigen::Vector2f imagePosClean = mCamera->projectFromWorld(xn);
Eigen::Vector2f noise(error_distribution(mRandomDevice), error_distribution(mRandomDevice));
Eigen::Vector2f imagePos = imagePosClean + noise;
//Position
if (i == 0)
feature.setPosition(imagePos);
//Skip if outside of image
if (imagePos[0] < 0 || imagePos[1] < 0 || imagePos[0] > mCamera->getImageSize()[0] || imagePos[1] > mCamera->getImageSize()[1])
continue;
//Save distortion and noise errors
if (mVerbose)
{
//Eigen::Vector2f imagePosNoDistortion = mCamera->projectFromDistorted(xn.hnormalized());
//distortionError.push_back((imagePosClean - imagePosNoDistortion).norm());
//noiseError.push_back(noise.norm());
}
//Measurement
std::unique_ptr<FeatureMeasurement> m(new FeatureMeasurement(&feature, newFrame.get(), imagePos, 0, nullDescr.data()));
newFrame->getMeasurements().push_back(m.get());
feature.getMeasurements().push_back(std::move(m));
//Save match
refPoints.push_back(feature.getPosition());
imgPoints.push_back(imagePos);
}
//Write stats
if (mVerbose)
{
MYAPP_LOG << "Frame " << i << "\n";
if (newFrame->getMeasurements().empty())
MYAPP_LOG << "AAAAHHHH NO MEASUREMENTS!\n";
Eigen::Map<Eigen::ArrayXf> _distortionError(distortionError.data(), distortionError.size());
Eigen::Map<Eigen::ArrayXf> _noiseError(noiseError.data(), noiseError.size());
MYAPP_LOG << " Measurement count: " << newFrame->getMeasurements().size() << "\n";
MYAPP_LOG << " Max distortion error: " << _distortionError.maxCoeff() << "\n";
MYAPP_LOG << " Max noise error: " << _noiseError.maxCoeff() << "\n";
}
//Get homography
Eigen::Matrix<uchar, Eigen::Dynamic, 1> mask(refPoints.size());
cv::Mat1b mask_cv(refPoints.size(), 1, mask.data());
HomographyRansac ransac;
ransac.setParams(3 * expectedPixelNoiseStd, 10, 100, (int)(0.9f * newFrame->getMeasurements().size()));
ransac.setData(newFrame->getMeasurements());
ransac.doRansac();
Eigen::Matrix3fr H = ransac.getBestModel().cast<float>();
//Refine
HomographyEstimation hest;
std::vector<bool> inliersVec;
std::vector<float> scales(imgPoints.size(), 1);
H = hest.estimateCeres(H, imgPoints, refPoints, scales, 3 * expectedPixelNoiseStd, inliersVec);
//Set final
newFrame->setPose(H);
//Add
newMap->addKeyframe(std::move(newFrame));
}
return std::move(newMap);
}
