void blobFromProto(const caffe::BlobProto &pbBlob, cv::Mat &dstBlob)
{
MatShape shape;
blobShapeFromProto(pbBlob, shape);
dstBlob.create((int)shape.size(), &shape[0], CV_32F);
if (pbBlob.data_size())
{
// Single precision floats.
CV_Assert(pbBlob.data_size() == (int)dstBlob.total());
CV_DbgAssert(pbBlob.GetDescriptor()->FindFieldByLowercaseName("data")->cpp_type() == FieldDescriptor::CPPTYPE_FLOAT);
Mat(dstBlob.dims, &dstBlob.size[0], CV_32F, (void*)pbBlob.data().data()).copyTo(dstBlob);
}
else
{
CV_Assert(pbBlob.has_raw_data());
const std::string& raw_data = pbBlob.raw_data();
if (pbBlob.raw_data_type() == caffe::FLOAT16)
{
// Half precision floats.
CV_Assert(raw_data.size() / 2 == (int)dstBlob.total());
Mat halfs((int)shape.size(), &shape[0], CV_16SC1, (void*)raw_data.c_str());
convertFp16(halfs, dstBlob);
}
else if (pbBlob.raw_data_type() == caffe::FLOAT)
{
CV_Assert(raw_data.size() / 4 == (int)dstBlob.total());
Mat((int)shape.size(), &shape[0], CV_32FC1, (void*)raw_data.c_str()).copyTo(dstBlob);
}
else
CV_Error(Error::StsNotImplemented, "Unexpected blob data type");
}
}
void blobFromProto(const caffe::BlobProto &pbBlob, cv::Mat &dstBlob)
{
MatShape shape;
blobShapeFromProto(pbBlob, shape);
dstBlob.create((int)shape.size(), &shape[0], CV_32F);
float *dstData = dstBlob.ptr<float>();
if (pbBlob.data_size())
{
// Single precision floats.
CV_Assert(pbBlob.data_size() == (int)dstBlob.total());
CV_DbgAssert(pbBlob.GetDescriptor()->FindFieldByLowercaseName("data")->cpp_type() == FieldDescriptor::CPPTYPE_FLOAT);
for (int i = 0; i < pbBlob.data_size(); i++)
dstData[i] = pbBlob.data(i);
}
else
{
// Half precision floats.
CV_Assert(pbBlob.raw_data_type() == caffe::FLOAT16);
std::string raw_data = pbBlob.raw_data();
CV_Assert(raw_data.size() / 2 == (int)dstBlob.total());
Mat halfs((int)shape.size(), &shape[0], CV_16SC1, (void*)raw_data.c_str());
convertFp16(halfs, dstBlob);
}
}
double __aruco_solve_pnp(const std::vector<cv::Point3f>& p3d, const std::vector<cv::Point2f>& p2d,
const cv::Mat& cam_matrix, const cv::Mat& dist, cv::Mat& r_io, cv::Mat& t_io)
{
assert(r_io.type() == CV_32F);
assert(t_io.type() == CV_32F);
assert(t_io.total() == r_io.total());
assert(t_io.total() == 3);
auto toSol = [](const cv::Mat& r, const cv::Mat& t) {
typename LevMarq<T>::eVector sol(6);
for (int i = 0; i < 3; i++)
{
sol(i) = r.ptr<float>(0)[i];
sol(i + 3) = t.ptr<float>(0)[i];
}
return sol;
};
auto fromSol = [](const typename LevMarq<T>::eVector& sol, cv::Mat& r, cv::Mat& t) {
r.create(1, 3, CV_32F);
t.create(1, 3, CV_32F);
for (int i = 0; i < 3; i++)
{
r.ptr<float>(0)[i] = sol(i);
t.ptr<float>(0)[i] = sol(i + 3);
}
};
cv::Mat Jacb;
auto err_f = [&](const typename LevMarq<T>::eVector& sol, typename LevMarq<T>::eVector& err) {
std::vector<cv::Point2f> p2d_rej;
cv::Mat r, t;
fromSol(sol, r, t);
cv::projectPoints(p3d, r, t, cam_matrix, dist, p2d_rej, Jacb);
err.resize(p3d.size() * 2);
int err_idx = 0;
for (size_t i = 0; i < p3d.size(); i++)
{
err(err_idx++) = p2d_rej[i].x - p2d[i].x;
err(err_idx++) = p2d_rej[i].y - p2d[i].y;
}
};
auto jac_f = [&](const typename LevMarq<T>::eVector& sol, Eigen::Matrix<T, Eigen::Dynamic, Eigen::Dynamic>& J) {
(void)(sol);
J.resize(p3d.size() * 2, 6);
for (size_t i = 0; i < p3d.size() * 2; i++)
{
double* jacb = Jacb.ptr<double>(i);
for (int j = 0; j < 6; j++)
J(i, j) = jacb[j];
}
};
LevMarq<T> solver;
solver.setParams(100, 0.01, 0.01);
// solver.verbose()=true;
typename LevMarq<T>::eVector sol = toSol(r_io, t_io);
auto err = solver.solve(sol, err_f, jac_f);
fromSol(sol, r_io, t_io);
return err;
}
void CameraParameters::setParams(cv::Mat cameraMatrix, cv::Mat distorsionCoeff, cv::Size size) {
CV_Assert(cameraMatrix.rows == 3 && cameraMatrix.cols == 3);
CV_Assert(distorsionCoeff.total() >= 4 && distorsionCoeff.total() < 7);
cameraMatrix.convertTo(CameraMatrix, CV_32FC1);
distorsionCoeff.convertTo(Distorsion, CV_32FC1);
CamSize = size;
}
int Hamming::fitness(cv::Mat &A, cv::Mat &B) const
{
cv::Mat C;
cv::bitwise_xor(A, B, C);
int measure = cv::countNonZero(C); //czarne pixele
int inpNonZero = A.total() - cv::countNonZero(A); //czarne pixele wejsciowe
int refNonZero = B.total() - cv::countNonZero(B); //czarne pixele referencyjne
if(inpNonZero < refNonZero)
{
measure += (refNonZero - inpNonZero) * 10;
}
return measure;
}
/*!
*
*/
void CameraParameters::setParams(cv::Mat &cameraMatrix, cv::Mat &distorsionCoeff, cv::Size &size)
throw(cv::Exception)
{
if (cameraMatrix.rows!=3 || cameraMatrix.cols!=3)
throw cv::Exception(9000,"invalid input cameraMatrix","CameraParameters::setParams",
__FILE__,__LINE__);
cameraMatrix.convertTo(_k,CV_32FC1);
if ( distorsionCoeff.total()<4 || distorsionCoeff.total()>=7 )
throw cv::Exception(9000,"invalid input distorsionCoeff","CameraParameters::setParams",
__FILE__,__LINE__);
distorsionCoeff.convertTo(_distor, CV_32FC1);
_size=size;
}
void WriteToMesh::writeDisparityToMeshProcessed(cv::Mat point_cloud, cv::Mat disparity, std::string filename){
std::ofstream plyfile(filename.c_str());
if(!plyfile.is_open()){
throw std::runtime_error("File not found on saveComputedPoints");
}
int failed_pixel = 0;
for(int y=0; y<disparity.rows; y++)
for(int x=0; x<disparity.cols; x++){
if(disparity.at<uchar>(y,x)==0){
failed_pixel++;
}
}
int point_num = (int)point_cloud.total();
plyfile << "ply" << std::endl;
plyfile << "format ascii 1.0" << std::endl;
plyfile << "element vertex " << point_num - failed_pixel << std::endl;
plyfile << "property float x" << std::endl;
plyfile << "property float y" << std::endl;
plyfile << "property float z" << std::endl;
plyfile << "end_header" << std::endl;
cv::Point3f point;
for(int y=0; y<point_cloud.rows; y++)
for(int x=0; x<point_cloud.cols; x++){
if(disparity.at<uchar>(y,x)==0){
continue;
}
point = point_cloud.at<cv::Point3f>(y,x);
plyfile << point.x << " " << point.y << " " << point.z << std::endl;
}
plyfile.close();
}
std::vector<unsigned long> create_histogram(const cv::Mat& mat, int channel)
{
assert(channel >= 0 && channel < mat.channels());
assert(mat.depth() == CV_8U);
return create_histogram(mat.data + channel, mat.total(), mat.channels());
}
void plotHistogram(const cv::Mat& histogram, cv::Mat& canvas, const cv::Scalar& color) {
Check("Histogram", histogram).notEmpty().hasDepth(CV_32F);
Check("Canvas", canvas).notEmpty().hasType(CV_8UC3);
const auto N = histogram.total();
const auto bar_width = canvas.cols / N;
const auto bar_height = canvas.rows / histogram.channels();
assert(bar_width > 0);
assert(bar_height > 0);
double max = 0;
cv::minMaxLoc(histogram, nullptr, &max, nullptr, nullptr);
std::vector<cv::Mat> histogram_channels;
cv::split(histogram, histogram_channels);
const cv::Scalar* COLORS = histogram.channels() > 1 ? utils::colors::BGR : &color;
for (int c = 0; c < histogram.channels(); ++c) {
cv::Mat roi(canvas, cv::Rect(0, (histogram.channels() - 1 - c) * bar_height, N * bar_width, bar_height));
roi.setTo(255);
for (unsigned int b = 0; b < N; b++) {
auto value = histogram_channels[c].at<float>(b);
auto height = std::round(value * bar_height / max);
if (height > 0)
cv::rectangle(roi, cv::Point(b * bar_width, bar_height - 1),
cv::Point((b + 1) * bar_width - 1, bar_height - 1 - height), COLORS[c], -1);
}
}
}
void SensorData::setUserData(const cv::Mat & userData)
{
if(!userData.empty() && (!_userDataCompressed.empty() || !_userDataRaw.empty()))
{
UWARN("Cannot write new user data (%d bytes) over existing user "
"data (%d bytes, %d compressed). Set user data of %d to null "
"before setting a new one.",
int(userData.total()*userData.elemSize()),
int(_userDataRaw.total()*_userDataRaw.elemSize()),
_userDataCompressed.cols,
this->id());
return;
}
_userDataRaw = cv::Mat();
_userDataCompressed = cv::Mat();
if(!userData.empty())
{
if(userData.type() == CV_8UC1) // Bytes
{
_userDataCompressed = userData; // assume compressed
}
else
{
_userDataRaw = userData;
_userDataCompressed = compressData2(userData);
}
}
}
cv::Mat compressData2(const cv::Mat & data)
{
cv::Mat bytes;
if(!data.empty())
{
uLong sourceLen = uLong(data.total())*uLong(data.elemSize());
uLong destLen = compressBound(sourceLen);
bytes = cv::Mat(1, destLen+3*sizeof(int), CV_8UC1);
int errCode = compress(
(Bytef *)bytes.data,
&destLen,
(const Bytef *)data.data,
sourceLen);
bytes = cv::Mat(bytes, cv::Rect(0,0, destLen+3*sizeof(int), 1));
*((int*)&bytes.data[destLen]) = data.rows;
*((int*)&bytes.data[destLen+sizeof(int)]) = data.cols;
*((int*)&bytes.data[destLen+2*sizeof(int)]) = data.type();
if(errCode == Z_MEM_ERROR)
{
UERROR("Z_MEM_ERROR : Insufficient memory.");
}
else if(errCode == Z_BUF_ERROR)
{
UERROR("Z_BUF_ERROR : The buffer dest was not large enough to hold the uncompressed data.");
}
}
return bytes;
}
//
// showPropertiesOfMat
//
// ...displays all properties of specified Mat.
//
void showPropertiesOfMat (const cv::Mat &src_mat)
{
// 行数
std::cout << "rows:" << src_mat.rows <<std::endl;
// 列数
std::cout << "cols:" << src_mat.cols << std::endl;
// 次元数
std::cout << "dims:" << src_mat.dims << std::endl;
// サイズ（2次元の場合）
std::cout << "size[]:" << src_mat.size().width << "," << src_mat.size().height << "[byte]" << std::endl;
// ビット深度ID
std::cout << "depth (ID):" << src_mat.depth() << "(=" << CV_64F << ")" << std::endl;
// チャンネル数
std::cout << "channels:" << src_mat.channels() << std::endl;
// 1要素内の1チャンネル分のサイズ [バイト単位]
std::cout << "elemSize1 (elemSize/channels):" << src_mat.elemSize1() << "[byte]" << std::endl;
// 要素の総数
std::cout << "total:" << src_mat.total() << std::endl;
// ステップ数 [バイト単位]
std::cout << "step:" << src_mat.step << "[byte]" << std::endl;
// 1ステップ内のチャンネル総数
std::cout << "step1 (step/elemSize1):" << src_mat.step1() << std::endl;
// データは連続か？
std::cout << "isContinuous:" << (src_mat.isContinuous()?"true":"false") << std::endl;
// 部分行列か？
std::cout << "isSubmatrix:" << (src_mat.isSubmatrix()?"true":"false") << std::endl;
// データは空か？
std::cout << "empty:" << (src_mat.empty()?"true":"false") << std::endl;
}
std::vector<unsigned char> compressData(const cv::Mat & data)
{
std::vector<unsigned char> bytes;
if(!data.empty())
{
uLong sourceLen = uLong(data.total())*uLong(data.elemSize());
uLong destLen = compressBound(sourceLen);
bytes.resize(destLen);
int errCode = compress(
(Bytef *)bytes.data(),
&destLen,
(const Bytef *)data.data,
sourceLen);
bytes.resize(destLen+3*sizeof(int));
*((int*)&bytes[destLen]) = data.rows;
*((int*)&bytes[destLen+sizeof(int)]) = data.cols;
*((int*)&bytes[destLen+2*sizeof(int)]) = data.type();
if(errCode == Z_MEM_ERROR)
{
UERROR("Z_MEM_ERROR : Insufficient memory.");
}
else if(errCode == Z_BUF_ERROR)
{
UERROR("Z_BUF_ERROR : The buffer dest was not large enough to hold the uncompressed data.");
}
}
return bytes;
}
camera_packet_t(cv::Mat mat):camera_packet_t(){
rows=mat.rows;
cols=mat.cols;
data=mat.data;
size = (unsigned int) (mat.total() * mat.elemSize());
isMat = true;
}
WarpCost::WarpCost(const Warper& warper,
const cv::Mat& J,
const cv::Mat& I,
const cv::Mat& dIdx,
const cv::Mat& dIdy,
const cv::Mat& mask,
int interpolation,
bool check_condition,
double max_condition)
: warper_(&warper),
J_(&J),
I_(&I),
dIdx_(&dIdx),
dIdy_(&dIdy),
mask_(&mask),
interpolation_(interpolation),
check_condition_(check_condition),
max_condition_(max_condition) {
// Check that we have a square patch.
CHECK(J.rows == J.cols);
// Check that mask is correct size.
CHECK(mask.size() == J.size());
// Set the number of inputs (configure as a single block).
mutable_parameter_block_sizes()->push_back(warper_->numParams());
// Set the number of outputs.
set_num_residuals(J.total());
}
cv::Mat MedianFilter::process_frame(const cv::Mat &frame)
{
// Get grayscale image to do sorting with
cv::Mat gray_frame;
cv::cvtColor(frame, gray_frame, cv::COLOR_BGR2GRAY);
// Initialize the filter if we haven't yet
if ( ! is_init )
init_median_lists(frame.total());
assert( median_lists.size() == frame.total() );
// Init iterators
cv::MatConstIterator_<cv::Vec3b> bgr_it = frame.begin<cv::Vec3b>(),
bgr_end = frame.end <cv::Vec3b>();
cv::MatConstIterator_<uchar> gray_it = gray_frame.begin<uchar>();
auto med_it = median_lists.begin();
// Matrix to stuff the "current frame" into.
cv::Mat dst(frame.rows, frame.cols, CV_8UC3);
cv::MatIterator_<cv::Vec3b> dst_it = dst.begin<cv::Vec3b>();
// Iterate through all pixels in each image together
for ( ; bgr_it != bgr_end; bgr_it++, gray_it++, dst_it++, med_it++)
{
Pixel pixel(*bgr_it, *gray_it);
// NOTE: because quickselect partially sorts, we're not really
// getting rid of the min/max values, but this should be fine
if (pop_front)
(*med_it)[0] = pixel;
else
(*med_it)[filter_length-1] = pixel;
int med_idx = (*med_it).size()/2;
std::nth_element((*med_it).begin(), (*med_it).begin()+med_idx, (*med_it).end());
*dst_it = (*med_it)[med_idx].color;
}
pop_front = ! pop_front;
return dst;
}
bool write_bgr_image(const cv::Mat& output_image, const std::string& output_filename)
{
std::cout << "Output Image: name=" << output_filename
<< ", width=" << output_image.cols << ", height=" << output_image.rows
<< ", channels=" << output_image.channels() << ", total=" << output_image.total()
<< std::endl;
return cv::imwrite(output_filename, output_image);
}
void CameraParameters::setParams(cv::Mat cameraMatrix,cv::Mat distorsionCoeff,cv::Size size)
{
if (cameraMatrix.rows!=3 || cameraMatrix.cols!=3)
throw cv::Exception(9000,"invalid input cameraMatrix","CameraParameters::setParams",__FILE__,__LINE__);
cameraMatrix.convertTo(CameraMatrix,CV_32FC1);
if ( distorsionCoeff.total()<4 || distorsionCoeff.total()>5 )
throw cv::Exception(9000,"invalid input distorsionCoeff","CameraParameters::setParams",__FILE__,__LINE__);
cv::Mat auxD;
distorsionCoeff.convertTo( auxD,CV_32FC1);
//now, get only the 4 first elements
Distorsion.create(1,4,CV_32FC1);
for (int i=0;i<4;i++)
Distorsion.ptr<float>(0)[i]=auxD.ptr<float>(0)[i];
CamSize=size;
}
void printImageData(const cv::Mat& input, std::string description){
std::cout << description << std::endl;
std::cout << "matrix type: " << input.type() << std::endl;
std::cout << "matrix depth: " << input.depth() << std::endl;
std::cout << "channels: " << input.channels() << std::endl;
std::cout << "size: " << input.size() << std::endl;
std::cout << "total elem: " << input.total() << std::endl;
std::cout << std::endl;
}
void CameraParameters::setParams(cv::Mat cameraMatrix,cv::Mat distorsionCoeff,cv::Size size)
{
if (cameraMatrix.rows!=3 || cameraMatrix.cols!=3)
return;
cameraMatrix.convertTo(CameraMatrix,CV_32FC1);
if ( distorsionCoeff.total()<4 || distorsionCoeff.total()>=7 )
return;
cv::Mat auxD;
distorsionCoeff.convertTo( Distorsion,CV_32FC1);
// Distorsion.create(1,4,CV_32FC1);
// for (int i=0;i<4;i++)
// Distorsion.ptr<float>(0)[i]=auxD.ptr<float>(0)[i];
CamSize=size;
}
cv::Mat plotHistogram(const cv::Mat& histogram, unsigned int bar_width, unsigned int height, const cv::Scalar& color) {
assert(bar_width > 0);
assert(height > 0);
cv::Mat canvas(height * histogram.channels(), bar_width * histogram.total(), CV_8UC3);
canvas.setTo(0);
plotHistogram(histogram, canvas, color);
return canvas;
}
cv::Mat createMultiChannelMask(const cv::Mat& img, const cv::Mat& mask) {
cv::Mat out(img.size(), CV_8UC4, cv::Scalar::all(0));
for (int i = 0; i < img.total(); i++) {
for (int j = 0; j < 3; j++)
out.ptr<cv::Vec4b>()[i][j] = img.ptr<cv::Vec3b>()[i][j];
if (mask.size() == img.size())
out.ptr<cv::Vec4b>()[i][3] = mask.ptr<unsigned char>()[i];
}
return out;
}
template <typename Tp, int cn> KDTree <Tp, cn>::
KDTree(const cv::Mat &img, const int _leafNumber, const int _zeroThresh)
: height(img.rows), width(img.cols),
leafNumber(_leafNumber), zeroThresh(_zeroThresh)
///////////////////////////////////////////////////
{
int imgch = img.channels();
CV_Assert( img.isContinuous() && imgch <= cn);
for(size_t i = 0; i < img.total(); i++)
{
cv::Vec<Tp, cn> v = cv::Vec<Tp, cn>::all((Tp)0);
for (int c = 0; c < imgch; c++)
{
v[c] = *((Tp*)(img.data) + i*imgch + c);
}
data.push_back(v);
}
generate_seq( std::back_inserter(idx), 0, int(data.size()) );
std::fill_n( std::back_inserter(nodes),
int(data.size()), cv::Point2i(0, 0) );
std::stack <int> left, right;
left.push( 0 );
right.push( int(idx.size()) );
while ( !left.empty() )
{
int _left = left.top(); left.pop();
int _right = right.top(); right.pop();
if ( _right - _left <= leafNumber)
{
for (int i = _left; i < _right; ++i)
nodes[idx[i]] = cv::Point2i(_left, _right);
continue;
}
int nth = _left + (_right - _left)/2;
int dimIdx = getMaxSpreadN(_left, _right);
KDTreeComparator comp( this, dimIdx );
std::nth_element(/**/
idx.begin() + _left,
idx.begin() + nth,
idx.begin() + _right, comp
/**/);
left.push(_left); right.push(nth + 1);
left.push(nth + 1); right.push(_right);
}
}
//! Write cv::Mat to binary file
void writeMatBinary(std::ofstream& ofs, const cv::Mat& out_mat)
{
if(!ofs.is_open()){
throw new orArgException("not opened");
}
int type = out_mat.type();
ofs.write((const char*)(&out_mat.rows), sizeof(int));
ofs.write((const char*)(&out_mat.cols), sizeof(int));
ofs.write((const char*)(&type), sizeof(int));
ofs.write((const char*)(out_mat.data), out_mat.elemSize() * out_mat.total());
}
bool Core_EigenTest::check_pairs_order(const cv::Mat& eigen_values)
{
switch (eigen_values.type())
{
case CV_32FC1:
{
for (int i = 0; i < (int)(eigen_values.total() - 1); ++i)
if (!(eigen_values.at<float>(i, 0) > eigen_values.at<float>(i+1, 0)))
{
std::cout << endl; std::cout << "Checking order of eigen values vector " << eigen_values << "..." << endl;
std::cout << "Pair of indexes with non ascending of eigen values: (" << i << ", " << i+1 << ")." << endl;
std::cout << endl;
CV_Error(CORE_EIGEN_ERROR_ORDER, MESSAGE_ERROR_ORDER);
return false;
}
break;
}
case CV_64FC1:
{
for (int i = 0; i < (int)(eigen_values.total() - 1); ++i)
if (!(eigen_values.at<double>(i, 0) > eigen_values.at<double>(i+1, 0)))
{
std::cout << endl; std::cout << "Checking order of eigen values vector " << eigen_values << "..." << endl;
std::cout << "Pair of indexes with non ascending of eigen values: (" << i << ", " << i+1 << ")." << endl;
std::cout << endl;
CV_Error(CORE_EIGEN_ERROR_ORDER, "Eigen values are not sorted in ascending order.");
return false;
}
break;
}
default:;
}
return true;
}
bool PointSelection::getDebugIndex(const size_t& level, cv::Mat& dbg_idx)
{
if(debug_ && storage_.size() > level)
{
dbg_idx = storage_[level].debug_idx;
return dbg_idx.total() > 0;
}
else
{
return false;
}
}
void cv::CircleDetector::Context::debug_buffer(const cv::Mat& image, cv::Mat& out)
{
out.create(height, width, CV_8UC3);
cv::Vec3b* out_ptr = out.ptr<cv::Vec3b>(0);
const cv::Vec3b* im_ptr = image.ptr<cv::Vec3b>(0);
out = cv::Scalar(128,128,128);
for (uint i = 0; i < out.total(); i++, ++out_ptr, ++im_ptr) {
/*if (buffer[i] == -1) *ptr = cv::Vec3b(0,0,0);
else if (buffer[i] == -2) *ptr = cv::Vec3b(255,255,255);*/
//else if (buffer[i] < 0) *ptr = cv::Vec3b(0, 255, 0);
if (buffer[i] > 0) *out_ptr = cv::Vec3b(255, 0, 255);
else *out_ptr = *im_ptr;
}
}
//! Read cv::Mat from binary
void readMatBinary(std::ifstream& ifs, cv::Mat& in_mat)
{
if(!ifs.is_open()){
throw new orArgException("Not opened");
}
int rows, cols, type;
ifs.read((char*)(&rows), sizeof(int));
ifs.read((char*)(&cols), sizeof(int));
ifs.read((char*)(&type), sizeof(int));
in_mat.release();
in_mat.create(rows, cols, type);
ifs.read((char*)(in_mat.data), in_mat.elemSize() * in_mat.total());
}
void hulo::readMatBin(std::string filename, cv::Mat& mat) {
std::ifstream ifs(filename, std::ios::binary);
CV_Assert(ifs.is_open());
int rows, cols, type;
ifs.read((char*) (&rows), sizeof(int));
if (rows == 0) {
return;
}
ifs.read((char*) (&cols), sizeof(int));
ifs.read((char*) (&type), sizeof(int));
mat.release();
mat.create(rows, cols, type);
ifs.read((char*) (mat.data), mat.elemSize() * mat.total());
}
void SensorData::setUserDataRaw(const cv::Mat & userDataRaw)
{
if(!userDataRaw.empty() && (!_userDataCompressed.empty() || !_userDataRaw.empty()))
{
UWARN("Cannot write new user data (%d bytes) over existing user "
"data (%d bytes, %d compressed). Set user data of %d to null "
"before setting a new one.",
int(userDataRaw.total()*userDataRaw.elemSize()),
int(_userDataRaw.total()*_userDataRaw.elemSize()),
_userDataCompressed.cols,
this->id());
return;
}
_userDataRaw = userDataRaw;
_userDataCompressed = cv::Mat();
}