void SuperFrameParser::convertImageToPointCloud (const sensor_msgs::ImagePtr& depth_msg, const pcl::PointCloud<pcl::PointXYZ>::Ptr &cloud)
{
cloud->height = depth_msg->height;
cloud->width = depth_msg->width;
cloud->resize (cloud->height * cloud->width);
// Use correct principal point from calibration
float center_x = depth_info_->K[2]; // c_x
float center_y = depth_info_->K[5]; // c_y
// Combine unit conversion (if necessary) with scaling by focal length for computing (X,Y)
double unit_scaling = 0.001f;
float constant_x = unit_scaling / depth_info_->K[0]; // f_x
float constant_y = unit_scaling / depth_info_->K[4]; // f_y
float bad_point = std::numeric_limits<float>::quiet_NaN ();
pcl::PointCloud<pcl::PointXYZ>::iterator pt_iter = cloud->begin ();
const uint16_t* depth_row = reinterpret_cast<const uint16_t*> (&depth_msg->data[0]);
int row_step = depth_msg->step / sizeof (uint16_t);
for (int v = 0; v < (int)depth_msg->height; ++v, depth_row += row_step)
{
for (int u = 0; u < (int)depth_msg->width; ++u)
{
pcl::PointXYZ& pt = *pt_iter++;
uint16_t depth = depth_row[u];
// Missing points denoted by NaNs
if (depth == 0.0f)
{
pt.x = pt.y = pt.z = bad_point;
continue;
}
// Fill in XYZ
pt.x = (u - center_x) * depth * constant_x;
pt.y = (v - center_y) * depth * constant_y;
pt.z = depth * 0.001f;
}
}
}
void
pcl::StereoMatching::getVisualMap (pcl::PointCloud<pcl::RGB>::Ptr vMap)
{
if ( static_cast<int> (vMap->width) != width_ || static_cast<int> (vMap->height) != height_)
{
vMap->resize(width_*height_);
vMap->width = width_;
vMap->height = height_;
}
if ( vMap->is_dense)
vMap->is_dense = false;
pcl::RGB invalid_val;
invalid_val.r = 0;
invalid_val.g = 255;
invalid_val.b = 0;
float scale = 255.0f / (16.0f * static_cast<float> (max_disp_));
for (int y = 0; y<height_; y++)
{
for (int x = 0; x<width_; x++)
{
if (disp_map_[y * width_+ x] <= 0)
{
vMap->at (x,y) = invalid_val;
}
else
{
unsigned char val = static_cast<unsigned char> (floor (scale*disp_map_[y * width_+ x]));
vMap->at (x, y).r = val;
vMap->at (x, y).g = val;
vMap->at (x, y).b = val;
}
}
}
}
template <typename PointInT, typename PointOutT> void
pcl_1_8::Edge<PointInT, PointOutT>::detectEdgeRoberts (pcl::PointCloud<PointOutT> &output)
{
convolution_.setInputCloud (input_);
pcl::PointCloud<pcl::PointXYZI>::Ptr kernel_x (new pcl::PointCloud<pcl::PointXYZI>);
pcl::PointCloud<pcl::PointXYZI>::Ptr magnitude_x (new pcl::PointCloud<pcl::PointXYZI>);
kernel_.setKernelType (kernel<pcl::PointXYZI>::ROBERTS_X);
kernel_.fetchKernel (*kernel_x);
convolution_.setKernel (*kernel_x);
convolution_.filter (*magnitude_x);
pcl::PointCloud<pcl::PointXYZI>::Ptr kernel_y (new pcl::PointCloud<pcl::PointXYZI>);
pcl::PointCloud<pcl::PointXYZI>::Ptr magnitude_y (new pcl::PointCloud<pcl::PointXYZI>);
kernel_.setKernelType (kernel<pcl::PointXYZI>::ROBERTS_Y);
kernel_.fetchKernel (*kernel_y);
convolution_.setKernel (*kernel_y);
convolution_.filter (*magnitude_y);
const int height = input_->height;
const int width = input_->width;
output.resize (height * width);
output.height = height;
output.width = width;
for (size_t i = 0; i < output.size (); ++i)
{
output[i].magnitude_x = (*magnitude_x)[i].intensity;
output[i].magnitude_y = (*magnitude_y)[i].intensity;
output[i].magnitude =
sqrtf ((*magnitude_x)[i].intensity * (*magnitude_x)[i].intensity +
(*magnitude_y)[i].intensity * (*magnitude_y)[i].intensity);
output[i].direction =
atan2f ((*magnitude_y)[i].intensity, (*magnitude_x)[i].intensity);
}
}
int computeNormalsAndEigenvalues(const typename pcl::PointCloud<PointInT>::Ptr
&in_cloud,
const float &radius, const int &n_threads,
pcl::PointCloud<PointOutT> &out_cloud)
{
size_t n_points = in_cloud->size();
// resize the out cloud
out_cloud.resize(n_points);
// build the kdtree
pcl::KdTreeFLANN<PointInT> flann;
flann.setInputCloud(in_cloud);
// place for neighs ids and distances
std::vector<int> indices;
std::vector<float> distances;
#ifdef USE_OPENMP
#pragma omp parallel for shared(out_cloud) private(indices, distances) \
num_threads(n_threads)
#endif
for (int i = 0; i < n_points; ++i) {
// get neighbors for this point
flann.radiusSearch((*in_cloud)[i], radius, indices, distances);
// estimate normal and eigenvalues
computePointNormal(*in_cloud, indices, out_cloud[i].normal_x,
out_cloud[i].normal_y, out_cloud[i].normal_z,
out_cloud[i].lam0, out_cloud[i].lam1,
out_cloud[i].lam2);
flipNormal
<PointInT>((*(in_cloud))[i], 0.0, 0.0, 0.0, out_cloud[i].normal_x,
out_cloud[i].normal_y, out_cloud[i].normal_z);
}
return 1;
}
void
FeatureFactory::extractRangeFeature (cv_bridge::CvImagePtr cv_ptr, pcl::PointCloud<feature_factory::FeaturePoint<
RANGE_FEATURES_HISTOGRAM> > &range_features, FeatureType feature_type)
{
double max_feature_val;
if (feature_type == DEPTH)
max_feature_val = MAX_DEPTH_FEATURE_VAL;
else
max_feature_val = MAX_CONF_AMP_FEATURE_VAL;
const uint16_t n_grids_per_row = RANGE_IMAGE_WIDTH / grid_edge_size;
const uint16_t n_grids_per_col = RANGE_IMAGE_HEIGHT / grid_edge_size;
const uint16_t n_grids = n_grids_per_col * n_grids_per_row;
range_features.resize (n_grids);
for (uint16_t gri = 0; gri < n_grids_per_col; gri++)
for (uint16_t gci = 0; gci < n_grids_per_row; gci++)
{
// Hint: Rect(top_left_x, top_left_y, width, height)
range_features[gri * n_grids_per_row + gci]
= feature_factory::FeaturePoint<RANGE_FEATURES_HISTOGRAM>::calculate (cv_ptr->image, cv::Rect (gci
* grid_edge_size, gri * grid_edge_size, grid_edge_size, grid_edge_size), 0, max_feature_val);
}
}
//////////////////////////////////////////////////////////////////////////////
//const pcl::PointCloud<pcl::PointXYZRGBA>::Ptr pcl::StereoMatching::getPointCloud(float uC, float vC, float focal, float baseline)
bool
pcl::StereoMatching::getPointCloud (
float u_c, float v_c, float focal, float baseline,
pcl::PointCloud<pcl::PointXYZ>::Ptr cloud)
{
//disp map has not been computed yet..
if ( disp_map_ == NULL)
{
PCL_ERROR(
"[pcl::StereoMatching::getPointCloud] Error: a disparity map has not been computed yet. The resulting cloud can not be computed..\n"
);
return false;
}
//cloud needs to be re-allocated
if (static_cast<int> (cloud->width) != width_ || static_cast<int> (cloud->height) != height_)
{
cloud->resize(width_*height_);
cloud->width = width_;
cloud->height = height_;
cloud->is_dense = false;
}
if ( cloud->is_dense)
cloud->is_dense = false;
//Loop
pcl::PointXYZ temp_point;
pcl::PointXYZ nan_point;
nan_point.x = std::numeric_limits<float>::quiet_NaN();
nan_point.y = std::numeric_limits<float>::quiet_NaN();
nan_point.z = std::numeric_limits<float>::quiet_NaN();
//nan_point.intensity = std::numeric_limits<float>::quiet_NaN();
//all disparities are multiplied by a constant equal to 16;
//this must be taken into account when computing z values
float depth_scale = baseline * focal * 16.0f;
for ( int j=0; j<height_; j++)
{
for ( int i=0; i<width_; i++)
{
if ( disp_map_[ j*width_ + i] > 0 )
{
temp_point.z = depth_scale / disp_map_[j * width_ + i];
temp_point.x = ((static_cast<float> (i) - u_c) * temp_point.z) / focal;
temp_point.y = ((static_cast<float> (j) - v_c) * temp_point.z) / focal;
//temp_point.intensity = 255;
(*cloud)[j * width_ + i] = temp_point;
}
//adding NaN value
else
{
(*cloud)[j * width_ + i] = nan_point;
}
}
}
return (true);
}
bool
pcl::StereoMatching::getPointCloud (
float u_c, float v_c, float focal, float baseline,
pcl::PointCloud<pcl::PointXYZRGB>::Ptr cloud,
pcl::PointCloud<pcl::RGB>::Ptr texture)
{
//disp map has not been computed yet..
if (disp_map_ == NULL)
{
PCL_ERROR ("[pcl::StereoMatching::getPointCloud] Error: a disparity map has not been computed yet. The resulting cloud can not be computed..\n");
return (false);
}
if (static_cast<int> (texture->width) != width_ || static_cast<int> (texture->height) != height_)
{
PCL_ERROR("[pcl::StereoMatching::getPointCloud] Error: the size of the texture cloud does not match that of the computed range map. The resulting cloud can not be computed..\n");
return (false);
}
//cloud needs to be re-allocated
if (static_cast<int> (cloud->width) != width_ || static_cast<int> (cloud->height) != height_)
{
//cloud.reset(new pcl::PointCloud<pcl::PointXYZRGBA>(width_, height_) );
cloud->resize (width_ * height_);
cloud->width = width_;
cloud->height = height_;
cloud->is_dense = false;
}
//Loop
pcl::PointXYZRGB temp_point;
/*pcl::PointXYZRGB nan_point;
nan_point.x = std::numeric_limits<float>::quiet_NaN();
nan_point.y = std::numeric_limits<float>::quiet_NaN();
nan_point.z = std::numeric_limits<float>::quiet_NaN();
nan_point.r = std::numeric_limits<unsigned char>::quiet_NaN();
nan_point.g = std::numeric_limits<unsigned char>::quiet_NaN();
nan_point.b = std::numeric_limits<unsigned char>::quiet_NaN();*/
//all disparities are multiplied by a constant equal to 16;
//this must be taken into account when computing z values
float depth_scale = baseline * focal * 16.0f;
for (int j = 0; j < height_; j++)
{
for (int i = 0; i < width_; i++)
{
if (disp_map_[ j*width_ + i] > 0)
{
temp_point.z = (depth_scale) / (disp_map_[ j*width_ + i]);
temp_point.x = ((static_cast<float> (i) - u_c) * temp_point.z) / focal;
temp_point.y = ((static_cast<float> (j) - v_c) * temp_point.z) / focal;
//temp_point.intensity = ( texture->at(j*width_+i).r +texture->at(j*width_+i).g + texture->at(j*width_+i).b) / 3.0f;
temp_point.r = texture->at (j * width_ + i).r;
temp_point.g = texture->at (j * width_ + i).g;
temp_point.b = texture->at (j * width_ + i).b;
(*cloud)[j*width_ + i] = temp_point;
}
//adding NaN value
else
{
temp_point.x = std::numeric_limits<float>::quiet_NaN();
temp_point.y = std::numeric_limits<float>::quiet_NaN();
temp_point.z = std::numeric_limits<float>::quiet_NaN();
temp_point.r = texture->at (j * width_ + i).r;
temp_point.g = texture->at (j * width_ + i).g;
temp_point.b = texture->at (j * width_ + i).b;
(*cloud)[j*width_ + i] = temp_point;
}
}
}
return (true);
}
template <typename PointNT> void
pcl::Poisson<PointNT>::performReconstruction (pcl::PointCloud<PointNT> &points,
std::vector<pcl::Vertices> &polygons)
{
poisson::CoredVectorMeshData mesh;
poisson::Point3D<float> center;
float scale = 1.0f;
switch (degree_)
{
case 1:
{
execute<1> (mesh, center, scale);
break;
}
case 2:
{
execute<2> (mesh, center, scale);
break;
}
case 3:
{
execute<3> (mesh, center, scale);
break;
}
case 4:
{
execute<4> (mesh, center, scale);
break;
}
case 5:
{
execute<5> (mesh, center, scale);
break;
}
default:
{
PCL_ERROR (stderr, "Degree %d not supported\n", degree_);
}
}
// Write output PolygonMesh
// Write vertices
points.resize (int (mesh.outOfCorePointCount () + mesh.inCorePoints.size ()));
poisson::Point3D<float> p;
for (int i = 0; i < int(mesh.inCorePoints.size ()); i++)
{
p = mesh.inCorePoints[i];
points.points[i].x = p.coords[0]*scale+center.coords[0];
points.points[i].y = p.coords[1]*scale+center.coords[1];
points.points[i].z = p.coords[2]*scale+center.coords[2];
}
for (int i = int(mesh.inCorePoints.size()); i < int (mesh.outOfCorePointCount() + mesh.inCorePoints.size ()); i++)
{
mesh.nextOutOfCorePoint (p);
points.points[i].x = p.coords[0]*scale+center.coords[0];
points.points[i].y = p.coords[1]*scale+center.coords[1];
points.points[i].z = p.coords[2]*scale+center.coords[2];
}
polygons.resize (mesh.polygonCount ());
// Write faces
std::vector<poisson::CoredVertexIndex> polygon;
for (int p_i = 0; p_i < mesh.polygonCount (); p_i++)
{
pcl::Vertices v;
mesh.nextPolygon (polygon);
v.vertices.resize (polygon.size ());
for (int i = 0; i < static_cast<int> (polygon.size ()); ++i)
if (polygon[i].inCore )
v.vertices[i] = polygon[i].idx;
else
v.vertices[i] = polygon[i].idx + int (mesh.inCorePoints.size ());
polygons[p_i] = v;
}
}
template <typename PointT> void
pcl::copyPointCloud (const pcl::PointCloud<PointT> &cloud_in, pcl::PointCloud<PointT> &cloud_out,
int top, int bottom, int left, int right, pcl::InterpolationType border_type, const PointT& value)
{
if (top < 0 || left < 0 || bottom < 0 || right < 0)
{
std::string faulty = (top < 0) ? "top" : (left < 0) ? "left" : (bottom < 0) ? "bottom" : "right";
PCL_THROW_EXCEPTION (pcl::BadArgumentException, "[pcl::copyPointCloud] error: " << faulty << " must be positive!");
return;
}
if (top == 0 && left == 0 && bottom == 0 && right == 0)
cloud_out = cloud_in;
else
{
// Allocate enough space and copy the basics
cloud_out.header = cloud_in.header;
cloud_out.width = cloud_in.width + left + right;
cloud_out.height = cloud_in.height + top + bottom;
if (cloud_out.size () != cloud_out.width * cloud_out.height)
cloud_out.resize (cloud_out.width * cloud_out.height);
cloud_out.is_dense = cloud_in.is_dense;
cloud_out.sensor_orientation_ = cloud_in.sensor_orientation_;
cloud_out.sensor_origin_ = cloud_in.sensor_origin_;
if (border_type == pcl::BORDER_TRANSPARENT)
{
const PointT* in = &(cloud_in.points[0]);
PointT* out = &(cloud_out.points[0]);
PointT* out_inner = out + cloud_out.width*top + left;
for (uint32_t i = 0; i < cloud_in.height; i++, out_inner += cloud_out.width, in += cloud_in.width)
{
if (out_inner != in)
memcpy (out_inner, in, cloud_in.width * sizeof (PointT));
}
}
else
{
// Copy the data
if (border_type != pcl::BORDER_CONSTANT)
{
try
{
std::vector<int> padding (cloud_out.width - cloud_in.width);
int right = cloud_out.width - cloud_in.width - left;
int bottom = cloud_out.height - cloud_in.height - top;
for (int i = 0; i < left; i++)
padding[i] = pcl::interpolatePointIndex (i-left, cloud_in.width, border_type);
for (int i = 0; i < right; i++)
padding[i+left] = pcl::interpolatePointIndex (cloud_in.width+i, cloud_in.width, border_type);
const PointT* in = &(cloud_in.points[0]);
PointT* out = &(cloud_out.points[0]);
PointT* out_inner = out + cloud_out.width*top + left;
for (uint32_t i = 0; i < cloud_in.height; i++, out_inner += cloud_out.width, in += cloud_in.width)
{
if (out_inner != in)
memcpy (out_inner, in, cloud_in.width * sizeof (PointT));
for (int j = 0; j < left; j++)
out_inner[j - left] = in[padding[j]];
for (int j = 0; j < right; j++)
out_inner[j + cloud_in.width] = in[padding[j + left]];
}
for (int i = 0; i < top; i++)
{
int j = pcl::interpolatePointIndex (i - top, cloud_in.height, border_type);
memcpy (out + i*cloud_out.width,
out + (j+top) * cloud_out.width,
sizeof (PointT) * cloud_out.width);
}
for (int i = 0; i < bottom; i++)
{
int j = pcl::interpolatePointIndex (i + cloud_in.height, cloud_in.height, border_type);
memcpy (out + (i + cloud_in.height + top)*cloud_out.width,
out + (j+top)*cloud_out.width,
cloud_out.width * sizeof (PointT));
}
}
catch (pcl::BadArgumentException &e)
{
PCL_ERROR ("[pcl::copyPointCloud] Unhandled interpolation type %d!\n", border_type);
}
}
else
{
int right = cloud_out.width - cloud_in.width - left;
int bottom = cloud_out.height - cloud_in.height - top;
std::vector<PointT> buff (cloud_out.width, value);
PointT* buff_ptr = &(buff[0]);
const PointT* in = &(cloud_in.points[0]);
PointT* out = &(cloud_out.points[0]);
PointT* out_inner = out + cloud_out.width*top + left;
for (uint32_t i = 0; i < cloud_in.height; i++, out_inner += cloud_out.width, in += cloud_in.width)
{
if (out_inner != in)
memcpy (out_inner, in, cloud_in.width * sizeof (PointT));
memcpy (out_inner - left, buff_ptr, left * sizeof (PointT));
memcpy (out_inner + cloud_in.width, buff_ptr, right * sizeof (PointT));
}
for (int i = 0; i < top; i++)
{
memcpy (out + i*cloud_out.width, buff_ptr, cloud_out.width * sizeof (PointT));
}
for (int i = 0; i < bottom; i++)
{
memcpy (out + (i + cloud_in.height + top)*cloud_out.width,
buff_ptr,
cloud_out.width * sizeof (PointT));
}
}
}
}
}
template <typename PointInT, typename PointOutT> void
pcl_1_8::Edge<PointInT, PointOutT>::canny (
const pcl::PointCloud<PointInT> &input_x,
const pcl::PointCloud<PointInT> &input_y,
pcl::PointCloud<PointOutT> &output)
{
float tHigh = hysteresis_threshold_high_;
float tLow = hysteresis_threshold_low_;
const int height = input_x.height;
const int width = input_x.width;
output.resize (height * width);
output.height = height;
output.width = width;
// Noise reduction using gaussian blurring
pcl::PointCloud<pcl::PointXYZI>::Ptr gaussian_kernel (new pcl::PointCloud<pcl::PointXYZI>);
kernel_.setKernelSize (3);
kernel_.setKernelSigma (1.0);
kernel_.setKernelType (kernel<pcl::PointXYZI>::GAUSSIAN);
kernel_.fetchKernel (*gaussian_kernel);
convolution_.setKernel (*gaussian_kernel);
PointCloudIn smoothed_cloud_x;
convolution_.setInputCloud (input_x.makeShared());
convolution_.filter (smoothed_cloud_x);
PointCloudIn smoothed_cloud_y;
convolution_.setInputCloud (input_y.makeShared());
convolution_.filter (smoothed_cloud_y);
// Edge detection usign Sobel
pcl::PointCloud<PointXYZIEdge>::Ptr edges (new pcl::PointCloud<PointXYZIEdge>);
sobelMagnitudeDirection (smoothed_cloud_x, smoothed_cloud_y, *edges.get ());
// Edge discretization
discretizeAngles (*edges);
pcl::PointCloud<pcl::PointXYZI>::Ptr maxima (new pcl::PointCloud<pcl::PointXYZI>);
suppressNonMaxima (*edges, *maxima, tLow);
// Edge tracing
for (int i = 0; i < height; i++)
{
for (int j = 0; j < width; j++)
{
if ((*maxima)(j, i).intensity < tHigh || (*maxima)(j, i).intensity == std::numeric_limits<float>::max ())
continue;
(*maxima)(j, i).intensity = std::numeric_limits<float>::max ();
cannyTraceEdge ( 1, 0, i, j, *maxima);
cannyTraceEdge (-1, 0, i, j, *maxima);
cannyTraceEdge ( 1, 1, i, j, *maxima);
cannyTraceEdge (-1, -1, i, j, *maxima);
cannyTraceEdge ( 0, -1, i, j, *maxima);
cannyTraceEdge ( 0, 1, i, j, *maxima);
cannyTraceEdge (-1, 1, i, j, *maxima);
cannyTraceEdge ( 1, -1, i, j, *maxima);
}
}
// Final thresholding
for (int i = 0; i < height; i++)
{
for (int j = 0; j < width; j++)
{
if ((*maxima)(j, i).intensity == std::numeric_limits<float>::max ())
output (j, i).magnitude = 255;
else
output (j, i).magnitude = 0;
}
}
}
template<typename PointInT, typename PointOutT> void
pcl_1_8::Edge<PointInT, PointOutT>::detectEdgeCanny (pcl::PointCloud<PointOutT> &output)
{
float tHigh = hysteresis_threshold_high_;
float tLow = hysteresis_threshold_low_;
const int height = input_->height;
const int width = input_->width;
output.resize (height * width);
output.height = height;
output.width = width;
//pcl::console::TicToc tt;
//tt.tic ();
// Noise reduction using gaussian blurring
pcl::PointCloud<pcl::PointXYZI>::Ptr gaussian_kernel (new pcl::PointCloud<pcl::PointXYZI>);
PointCloudInPtr smoothed_cloud (new PointCloudIn);
kernel_.setKernelSize (3);
kernel_.setKernelSigma (1.0);
kernel_.setKernelType (kernel<pcl::PointXYZI>::GAUSSIAN);
kernel_.fetchKernel (*gaussian_kernel);
convolution_.setKernel (*gaussian_kernel);
convolution_.setInputCloud (input_);
convolution_.filter (*smoothed_cloud);
//PCL_ERROR ("Gaussian blur: %g\n", tt.toc ()); tt.tic ();
// Edge detection usign Sobel
pcl::PointCloud<PointXYZIEdge>::Ptr edges (new pcl::PointCloud<PointXYZIEdge>);
setInputCloud (smoothed_cloud);
detectEdgeSobel (*edges);
//PCL_ERROR ("Sobel: %g\n", tt.toc ()); tt.tic ();
// Edge discretization
discretizeAngles (*edges);
//PCL_ERROR ("Discretize: %g\n", tt.toc ()); tt.tic ();
// tHigh and non-maximal supression
pcl::PointCloud<pcl::PointXYZI>::Ptr maxima (new pcl::PointCloud<pcl::PointXYZI>);
suppressNonMaxima (*edges, *maxima, tLow);
//PCL_ERROR ("NM suppress: %g\n", tt.toc ()); tt.tic ();
// Edge tracing
for (int i = 0; i < height; i++)
{
for (int j = 0; j < width; j++)
{
if ((*maxima)(j, i).intensity < tHigh || (*maxima)(j, i).intensity == std::numeric_limits<float>::max ())
continue;
(*maxima)(j, i).intensity = std::numeric_limits<float>::max ();
cannyTraceEdge ( 1, 0, i, j, *maxima);
cannyTraceEdge (-1, 0, i, j, *maxima);
cannyTraceEdge ( 1, 1, i, j, *maxima);
cannyTraceEdge (-1, -1, i, j, *maxima);
cannyTraceEdge ( 0, -1, i, j, *maxima);
cannyTraceEdge ( 0, 1, i, j, *maxima);
cannyTraceEdge (-1, 1, i, j, *maxima);
cannyTraceEdge ( 1, -1, i, j, *maxima);
}
}
//PCL_ERROR ("Edge tracing: %g\n", tt.toc ());
// Final thresholding
for (size_t i = 0; i < input_->size (); ++i)
{
if ((*maxima)[i].intensity == std::numeric_limits<float>::max ())
output[i].magnitude = 255;
else
output[i].magnitude = 0;
}
}
template <typename PointInT, typename PointOutT> void
pcl_1_8::Edge<PointInT, PointOutT>::suppressNonMaxima (
const pcl::PointCloud<PointXYZIEdge> &edges,
pcl::PointCloud<pcl::PointXYZI> &maxima, float tLow)
{
const int height = edges.height;
const int width = edges.width;
maxima.height = height;
maxima.width = width;
maxima.resize (height * width);
for (size_t i = 0; i < maxima.size (); ++i)
maxima[i].intensity = 0.0f;
// tHigh and non-maximal supression
for (int i = 1; i < height - 1; i++)
{
for (int j = 1; j < width - 1; j++)
{
const PointXYZIEdge &ptedge = edges (j, i);
pcl::PointXYZI &ptmax = maxima (j, i);
if (ptedge.magnitude < tLow)
continue;
//maxima (j, i).intensity = 0;
switch (int (ptedge.direction))
{
case 0:
{
if (ptedge.magnitude >= edges (j - 1, i).magnitude &&
ptedge.magnitude >= edges (j + 1, i).magnitude)
ptmax.intensity = ptedge.magnitude;
break;
}
case 45:
{
if (ptedge.magnitude >= edges (j - 1, i - 1).magnitude &&
ptedge.magnitude >= edges (j + 1, i + 1).magnitude)
ptmax.intensity = ptedge.magnitude;
break;
}
case 90:
{
if (ptedge.magnitude >= edges (j, i - 1).magnitude &&
ptedge.magnitude >= edges (j, i + 1).magnitude)
ptmax.intensity = ptedge.magnitude;
break;
}
case 135:
{
if (ptedge.magnitude >= edges (j + 1, i - 1).magnitude &&
ptedge.magnitude >= edges (j - 1, i + 1).magnitude)
ptmax.intensity = ptedge.magnitude;
break;
}
}
}
}
}
void get_en_image(pcl::PointCloud<pcl::PointXYZ> &cloud)
{
char flag = 'g';
int i = 0;
while(flag != 'q')
{
ostringstream conv;
conv << i;
cout<<"Capturing new calibration image from the ensenso stereo vision camera."<<endl;
///Read the Ensenso stereo cameras:
try {
// Initialize NxLib and enumerate cameras
nxLibInitialize(true);
// Reference to the first camera in the node BySerialNo
NxLibItem root;
NxLibItem camera = root[itmCameras][itmBySerialNo][0];
// Open the Ensenso
NxLibCommand open(cmdOpen);
open.parameters()[itmCameras] = camera[itmSerialNumber].asString();
open.execute();
// Capture an image
NxLibCommand (cmdCapture).execute();
// Stereo matching task
NxLibCommand (cmdComputeDisparityMap).execute ();
// Convert disparity map into XYZ data for each pixel
NxLibCommand (cmdComputePointMap).execute ();
// Get info about the computed point map and copy it into a std::vector
double timestamp;
std::vector<float> pointMap;
int width, height;
camera[itmImages][itmRaw][itmLeft].getBinaryDataInfo (0, 0, 0, 0, 0, ×tamp); // Get raw image timestamp
camera[itmImages][itmPointMap].getBinaryDataInfo (&width, &height, 0, 0, 0, 0);
camera[itmImages][itmPointMap].getBinaryData (pointMap, 0);
// Copy point cloud and convert in meters
//cloud.header.stamp = getPCLStamp (timestamp);
cloud.resize (height * width);
cloud.width = width;
cloud.height = height;
cloud.is_dense = false;
// Copy data in point cloud (and convert milimeters in meters)
for (size_t i = 0; i < pointMap.size (); i += 3)
{
cloud.points[i / 3].x = pointMap[i] / 1000.0;
cloud.points[i / 3].y = pointMap[i + 1] / 1000.0;
cloud.points[i / 3].z = pointMap[i + 2] / 1000.0;
}
NxLibCommand (cmdRectifyImages).execute();
// Save images
NxLibCommand saveImage(cmdSaveImage);
// raw left
saveImage.parameters()[itmNode] = camera[itmImages][itmRaw][itmLeft].path;
saveImage.parameters()[itmFilename] = "calib_en/raw_left" + conv.str()+".png";
saveImage.execute();
// raw right
/*saveImage.parameters()[itmNode] = camera[itmImages][itmRaw][itmRight].path;
saveImage.parameters()[itmFilename] = "calib_en/raw_right.png";
saveImage.execute();
// rectified left
saveImage.parameters()[itmNode] = camera[itmImages][itmRectified][itmLeft].path;
saveImage.parameters()[itmFilename] = "calib_en/rectified_left.png";
saveImage.execute();
// rectified right
saveImage.parameters()[itmNode] = camera[itmImages][itmRectified][itmRight].path;
saveImage.parameters()[itmFilename] = "calib_en/rectified_right.png";
saveImage.execute();*/
} catch (NxLibException& e) { // Display NxLib API exceptions, if any
printf("An NxLib API error with code %d (%s) occurred while accessing item %s.\n", e.getErrorCode(), e.getErrorText().c_str(), e.getItemPath().c_str());
if (e.getErrorCode() == NxLibExecutionFailed) printf("/Execute:\n%s\n", NxLibItem(itmExecute).asJson(true).c_str());
}
/*catch (NxLibException &ex)
{
ensensoExceptionHandling (ex, "grabSingleCloud");
}*/
catch (...) { // Display other exceptions
printf("Something, somewhere went terribly wrong!\n");
}
/*cout<<"Plug in the RGB camera and press any key to continue."<<endl;
cin.ignore();
cin.get();*/
cout<<"Capturing new calibration image from the ensenso RGB camera."<<endl;
///Read the IDS RGB Camera attached to the Ensenso stereo camera
HIDS hCam = 0;
printf("Success-Code: %d\n",IS_SUCCESS);
//Kamera öffnen
INT nRet = is_InitCamera (&hCam, NULL);
printf("Status Init %d\n",nRet);
//Pixel-Clock setzen
UINT nPixelClockDefault = 9;
nRet = is_PixelClock(hCam, IS_PIXELCLOCK_CMD_SET,
(void*)&nPixelClockDefault,
sizeof(nPixelClockDefault));
printf("Status is_PixelClock %d\n",nRet);
//Farbmodus der Kamera setzen
//INT colorMode = IS_CM_CBYCRY_PACKED;
INT colorMode = IS_CM_BGR8_PACKED;
nRet = is_SetColorMode(hCam,colorMode);
printf("Status SetColorMode %d\n",nRet);
UINT formatID = 4;
//Bildgröße einstellen -> 2592x1944
nRet = is_ImageFormat(hCam, IMGFRMT_CMD_SET_FORMAT, &formatID, 4);
printf("Status ImageFormat %d\n",nRet);
//Speicher für Bild alloziieren
char* pMem = NULL;
int memID = 0;
nRet = is_AllocImageMem(hCam, 1280, 1024, 24, &pMem, &memID);
printf("Status AllocImage %d\n",nRet);
//diesen Speicher aktiv setzen
nRet = is_SetImageMem(hCam, pMem, memID);
printf("Status SetImageMem %d\n",nRet);
//Bilder im Kameraspeicher belassen
INT displayMode = IS_SET_DM_DIB;
nRet = is_SetDisplayMode (hCam, displayMode);
printf("Status displayMode %d\n",nRet);
//Bild aufnehmen
nRet = is_FreezeVideo(hCam, IS_WAIT);
printf("Status is_FreezeVideo %d\n",nRet);
//Bild aus dem Speicher auslesen und als Datei speichern
String path = "./calib_en/snap_BGR"+conv.str()+".png";
std::wstring widepath;
for(int i = 0; i < path.length(); ++i)
widepath += wchar_t (path[i] );
IMAGE_FILE_PARAMS ImageFileParams;
ImageFileParams.pwchFileName = &widepath[0];
ImageFileParams.pnImageID = NULL;
ImageFileParams.ppcImageMem = NULL;
ImageFileParams.nQuality = 0;
ImageFileParams.nFileType = IS_IMG_PNG;
nRet = is_ImageFile(hCam, IS_IMAGE_FILE_CMD_SAVE, (void*) &ImageFileParams, sizeof(ImageFileParams));
printf("Status is_ImageFile %d\n",nRet);
//Kamera wieder freigeben
is_ExitCamera(hCam);
cout<<"To quit capturing calibration images, choose q. Else, choose any other letter."<<endl;
cin >> flag;
i++;
}
}
