PointViewSet LocateFilter::run(PointViewPtr inView)
{
PointViewSet viewSet;
if (!inView->size())
return viewSet;
PointId minidx, maxidx;
double minval = (std::numeric_limits<double>::max)();
double maxval = std::numeric_limits<double>::lowest();
for (PointId idx = 0; idx < inView->size(); idx++)
{
double val = inView->getFieldAs<double>(m_dimId, idx);
if (val > maxval)
{
maxval = val;
maxidx = idx;
}
if (val < minval)
{
minval = val;
minidx = idx;
}
}
PointViewPtr outView = inView->makeNew();
if (Utils::iequals("min", m_minmax))
outView->appendPoint(*inView.get(), minidx);
if (Utils::iequals("max", m_minmax))
outView->appendPoint(*inView.get(), maxidx);
viewSet.insert(outView);
return viewSet;
}
TEST(Ilvis2ReaderTest, testReadHigh)
{
Option filename("filename",
Support::datapath("ilvis2/ILVIS2_TEST_FILE.TXT"));
Options options(filename);
options.add("mapping","high");
std::shared_ptr<Ilvis2Reader> reader(new Ilvis2Reader);
reader->setOptions(options);
PointTable table;
reader->prepare(table);
PointViewSet viewSet = reader->execute(table);
EXPECT_EQ(viewSet.size(), 1u);
PointViewPtr view = *viewSet.begin();
EXPECT_EQ(view->size(), 3u);
checkPoint(*view.get(), 0, 42504.48313,
78.307672,-58.785213,1956.777
);
checkPoint(*view.get(), 1, 42504.48512,
78.307592, 101.215097, 1956.588
);
checkPoint(*view.get(), 2, 42504.48712,
78.307512, -58.78459, 2956.667
);
}
PointViewSet DecimationFilter::run(PointViewPtr inView)
{
PointViewSet viewSet;
PointViewPtr outView = inView->makeNew();
decimate(*inView.get(), *outView.get());
viewSet.insert(outView);
return viewSet;
}
void BpfWriter::writeView(const PointViewPtr dataShared)
{
setAutoXForm(dataShared);
// Avoid reference count overhead internally.
const PointView* data(dataShared.get());
// We know that X, Y and Z are dimensions 0, 1 and 2.
m_dims[0].m_offset = m_xXform.m_offset;
m_dims[1].m_offset = m_yXform.m_offset;
m_dims[2].m_offset = m_zXform.m_offset;
switch (m_header.m_pointFormat)
{
case BpfFormat::PointMajor:
writePointMajor(data);
break;
case BpfFormat::DimMajor:
writeDimMajor(data);
break;
case BpfFormat::ByteMajor:
writeByteMajor(data);
break;
}
m_header.m_numPts += data->size();
}
void LasWriter::writeView(const PointViewPtr view)
{
Utils::writeProgress(m_progressFd, "READYVIEW",
std::to_string(view->size()));
m_scaling.setAutoXForm(view);
point_count_t pointLen = m_lasHeader.pointLen();
// Make a buffer of at most a meg.
m_pointBuf.resize(std::min((point_count_t)1000000, pointLen * view->size()));
const PointView& viewRef(*view.get());
point_count_t remaining = view->size();
PointId idx = 0;
while (remaining)
{
point_count_t filled = fillWriteBuf(viewRef, idx, m_pointBuf);
idx += filled;
remaining -= filled;
if (m_compression == LasCompression::LasZip)
writeLasZipBuf(m_pointBuf.data(), pointLen, filled);
else if (m_compression == LasCompression::LazPerf)
writeLazPerfBuf(m_pointBuf.data(), pointLen, filled);
else
m_ostream->write(m_pointBuf.data(), filled * pointLen);
}
Utils::writeProgress(m_progressFd, "DONEVIEW",
std::to_string(view->size()));
}
MetadataNode InfoKernel::dumpQuery(PointViewPtr inView) const
{
int count;
std::string location;
// See if there's a provided point count.
StringList parts = Utils::split2(m_queryPoint, '/');
if (parts.size() == 2)
{
location = parts[0];
count = atoi(parts[1].c_str());
}
else if (parts.size() == 1)
{
location = parts[0];
count = inView->size();
}
else
count = 0;
if (count == 0)
throw pdal_error("Invalid location specificiation. "
"--query=\"X,Y[/count]\"");
auto seps = [](char c){ return (c == ',' || c == '|' || c == ' '); };
std::vector<std::string> tokens = Utils::split2(location, seps);
std::vector<double> values;
for (auto ti = tokens.begin(); ti != tokens.end(); ++ti)
{
double d;
if (Utils::fromString(*ti, d))
values.push_back(d);
}
if (values.size() != 2 && values.size() != 3)
throw pdal_error("--points must be two or three values");
PointViewPtr outView = inView->makeNew();
std::vector<PointId> ids;
if (values.size() >= 3)
{
KD3Index kdi(*inView);
kdi.build();
ids = kdi.neighbors(values[0], values[1], values[2], count);
}
else
{
KD2Index kdi(*inView);
kdi.build();
ids = kdi.neighbors(values[0], values[1], count);
}
for (auto i = ids.begin(); i != ids.end(); ++i)
outView->appendPoint(*inView.get(), *i);
return outView->toMetadata();
}
TEST(PLangTest, PLangTest_array)
{
PointTable table;
PointViewPtr view = makeTestView(table, 40);
plang::Array array;
array.update(view);
verifyTestView(*view.get(), 4);
}
PointViewSet MergeFilter::run(PointViewPtr in)
{
PointViewSet viewSet;
// If the SRS of all the point views aren't the same, print a warning
// unless we're explicitly overriding the SRS.
if (getSpatialReference().empty() &&
(in->spatialReference() != m_view->spatialReference()))
log()->get(LogLevel::Warning) << getName() << ": merging points "
"with inconsistent spatial references." << std::endl;
m_view->append(*in.get());
viewSet.insert(m_view);
return viewSet;
}
TEST(SbetReaderTest, testRead)
{
Option filename("filename", Support::datapath("sbet/2-points.sbet"), "");
Options options(filename);
std::shared_ptr<SbetReader> reader(new SbetReader);
reader->setOptions(options);
PointTable table;
reader->prepare(table);
PointViewSet viewSet = reader->execute(table);
EXPECT_EQ(viewSet.size(), 1u);
PointViewPtr view = *viewSet.begin();
EXPECT_EQ(view->size(), 2u);
checkPoint(*view.get(), 0,
1.516310028360710e+05, 5.680211852972264e-01,
-2.041654392303940e+00, 1.077152953296560e+02,
-2.332420866600025e+00, -3.335067504871401e-01,
-3.093961631767838e-02, -2.813407149321339e-02,
-2.429905393889139e-02, 3.046773230278662e+00,
-2.198414736922658e-02, 7.859639737752390e-01,
7.849084719295495e-01, -2.978807916450262e-01,
6.226807982589819e-05, 9.312162756440178e-03,
7.217812320996525e-02);
checkPoint(*view.get(), 1,
1.516310078318641e+05, 5.680211834722869e-01,
-2.041654392034053e+00, 1.077151424357507e+02,
-2.336228229691271e+00, -3.324663118952635e-01,
-3.022948961008987e-02, -2.813856631423094e-02,
-2.425215669392169e-02, 3.047131105236811e+00,
-2.198416007932108e-02, 8.397590491636475e-01,
3.252165276637165e-01, -1.558883225990844e-01,
8.379685112283802e-04, 7.372886784718076e-03,
7.179027672314571e-02);
}
void LasWriter::write(const PointViewPtr view)
{
setAutoOffset(view);
size_t pointLen = m_lasHeader.pointLen();
// Make a buffer of at most a meg.
std::vector<char> buf(std::min((size_t)1000000, pointLen * view->size()));
const PointView& viewRef(*view.get());
//ABELL - Removed callback handling for now.
point_count_t remaining = view->size();
PointId idx = 0;
while (remaining)
{
point_count_t filled = fillWriteBuf(viewRef, idx, buf);
idx += filled;
remaining -= filled;
#ifdef PDAL_HAVE_LASZIP
if (m_lasHeader.compressed())
{
char *pos = buf.data();
for (point_count_t i = 0; i < filled; i++)
{
memcpy(m_zipPoint->m_lz_point_data.data(), pos, pointLen);
if (!m_zipper->write(m_zipPoint->m_lz_point))
{
std::ostringstream oss;
const char* err = m_zipper->get_error();
if (err == NULL)
err = "(unknown error)";
oss << "Error writing point: " << std::string(err);
throw pdal_error(oss.str());
}
pos += pointLen;
}
}
else
m_ostream->write(buf.data(), filled * pointLen);
#else
m_ostream->write(buf.data(), filled * pointLen);
#endif
}
m_numPointsWritten = view->size() - remaining;
}
void LasWriter::writeView(const PointViewPtr view)
{
Utils::writeProgress(m_progressFd, "READYVIEW",
std::to_string(view->size()));
m_scaling.setAutoXForm(view);
point_count_t pointLen = m_lasHeader.pointLen();
// Since we use the LASzip API, we can't benefit from building
// a buffer of multiple points, so loop.
if (m_compression == LasCompression::LasZip)
{
PointRef point(*view, 0);
for (PointId idx = 0; idx < view->size(); ++idx)
{
point.setPointId(idx);
processPoint(point);
}
}
else
{
// Make a buffer of at most a meg.
m_pointBuf.resize((std::min)((point_count_t)1000000,
pointLen * view->size()));
const PointView& viewRef(*view.get());
point_count_t remaining = view->size();
PointId idx = 0;
while (remaining)
{
point_count_t filled = fillWriteBuf(viewRef, idx, m_pointBuf);
idx += filled;
remaining -= filled;
if (m_compression == LasCompression::LazPerf)
writeLazPerfBuf(m_pointBuf.data(), pointLen, filled);
else
m_ostream->write(m_pointBuf.data(), filled * pointLen);
}
}
Utils::writeProgress(m_progressFd, "DONEVIEW",
std::to_string(view->size()));
}
PointViewSet SplitterFilter::run(PointViewPtr inView)
{
PointViewSet viewSet;
if (!inView->size())
return viewSet;
// Use the location of the first point as the origin, unless specified.
// (!= test == isnan(), which doesn't exist on windows)
if (m_xOrigin != m_xOrigin)
m_xOrigin = inView->getFieldAs<double>(Dimension::Id::X, 0);
if (m_yOrigin != m_yOrigin)
m_yOrigin = inView->getFieldAs<double>(Dimension::Id::Y, 0);
// Overlay a grid of squares on the points (m_length sides). Each square
// corresponds to a new point buffer. Place the points falling in the
// each square in the corresponding point buffer.
for (PointId idx = 0; idx < inView->size(); idx++)
{
double x = inView->getFieldAs<double>(Dimension::Id::X, idx);
x -= m_xOrigin;
int xpos = x / m_length;
if (x < 0)
xpos--;
double y = inView->getFieldAs<double>(Dimension::Id::Y, idx);
y -= m_yOrigin;
int ypos = y / m_length;
if (y < 0)
ypos--;
Coord loc(xpos, ypos);
PointViewPtr& outView = m_viewMap[loc];
if (!outView)
outView = inView->makeNew();
outView->appendPoint(*inView.get(), idx);
}
// Pull the buffers out of the map and stick them in the standard
// output set.
for (auto bi = m_viewMap.begin(); bi != m_viewMap.end(); ++bi)
viewSet.insert(bi->second);
return viewSet;
}
point_count_t PgReader::readPgPatch(PointViewPtr view, point_count_t numPts)
{
point_count_t numRemaining = m_patch.remaining;
PointId nextId = view->size();
point_count_t numRead = 0;
size_t offset = (m_patch.count - m_patch.remaining) * packedPointSize();
char *pos = (char *)(m_patch.binary.data() + offset);
while (numRead < numPts && numRemaining > 0)
{
writePoint(*view.get(), nextId, pos);
pos += packedPointSize();
numRemaining--;
nextId++;
numRead++;
}
m_patch.remaining = numRemaining;
return numRead;
}
PointViewSet GroupByFilter::run(PointViewPtr inView)
{
PointViewSet viewSet;
if (!inView->size())
return viewSet;
for (PointId idx = 0; idx < inView->size(); idx++)
{
uint64_t val = inView->getFieldAs<uint64_t>(m_dimId, idx);
PointViewPtr& outView = m_viewMap[val];
if (!outView)
outView = inView->makeNew();
outView->appendPoint(*inView.get(), idx);
}
// Pull the buffers out of the map and stick them in the standard
// output set.
for (auto bi = m_viewMap.begin(); bi != m_viewMap.end(); ++bi)
viewSet.insert(bi->second);
return viewSet;
}
// Test reprojecting UTM 15 to DD with a filter
TEST(ReprojectionFilterTest, ReprojectionFilterTest_test_1)
{
const char* epsg4326_wkt = "GEOGCS[\"WGS 84\",DATUM[\"WGS_1984\",SPHEROID[\"WGS 84\",6378137,298.257223563,AUTHORITY[\"EPSG\",\"7030\"]],AUTHORITY[\"EPSG\",\"6326\"]],PRIMEM[\"Greenwich\",0],UNIT[\"degree\",0.0174532925199433],AUTHORITY[\"EPSG\",\"4326\"]]";
PointTable table;
const double postX = -93.351563;
const double postY = 41.577148;
const double postZ = 16.000000;
{
const SpatialReference out_ref(epsg4326_wkt);
Options ops1;
ops1.add("filename", Support::datapath("las/utm15.las"));
LasReader reader;
reader.setOptions(ops1);
Options options;
options.add("out_srs", out_ref.getWKT());
ReprojectionFilter reprojectionFilter;
reprojectionFilter.setOptions(options);
reprojectionFilter.setInput(reader);
reprojectionFilter.prepare(table);
PointViewSet viewSet = reprojectionFilter.execute(table);
EXPECT_EQ(viewSet.size(), 1u);
PointViewPtr view = *viewSet.begin();
double x, y, z;
getPoint(*view.get(), x, y, z);
EXPECT_FLOAT_EQ(x, postX);
EXPECT_FLOAT_EQ(y, postY);
EXPECT_FLOAT_EQ(z, postZ);
}
}
MetadataNode InfoKernel::dumpPoints(PointViewPtr inView) const
{
MetadataNode root;
PointViewPtr outView = inView->makeNew();
// Stick points in a inViewfer.
std::vector<PointId> points = getListOfPoints(m_pointIndexes);
for (size_t i = 0; i < points.size(); ++i)
{
PointId id = (PointId)points[i];
if (id < inView->size())
outView->appendPoint(*inView.get(), id);
}
MetadataNode tree = outView->toMetadata();
std::string prefix("point ");
for (size_t i = 0; i < outView->size(); ++i)
{
MetadataNode n = tree.findChild(std::to_string(i));
n.add("PointId", points[i]);
root.add(n.clone("point"));
}
return root;
}
std::vector<PointId> PMFFilter::processGroundApprox(PointViewPtr view)
{
using namespace Eigen;
BOX2D bounds;
view->calculateBounds(bounds);
double extent_x = floor(bounds.maxx) - ceil(bounds.minx);
double extent_y = floor(bounds.maxy) - ceil(bounds.miny);
int cols = static_cast<int>(ceil(extent_x/m_cellSize)) + 1;
int rows = static_cast<int>(ceil(extent_y/m_cellSize)) + 1;
// Compute the series of window sizes and height thresholds
std::vector<float> htvec;
std::vector<float> wsvec;
int iter = 0;
float ws = 0.0f;
float ht = 0.0f;
while (ws < m_maxWindowSize)
{
// Determine the initial window size.
if (1) // exponential
ws = m_cellSize * (2.0f * std::pow(2, iter) + 1.0f);
else
ws = m_cellSize * (2.0f * (iter+1) * 2 + 1.0f);
// Calculate the height threshold to be used in the next iteration.
if (iter == 0)
ht = m_initialDistance;
else
ht = m_slope * (ws - wsvec[iter-1]) * m_cellSize + m_initialDistance;
// Enforce max distance on height threshold
if (ht > m_maxDistance)
ht = m_maxDistance;
wsvec.push_back(ws);
htvec.push_back(ht);
iter++;
}
std::vector<PointId> groundIdx;
for (PointId i = 0; i < view->size(); ++i)
groundIdx.push_back(i);
MatrixXd ZImin = eigen::createDSM(*view.get(), rows, cols, m_cellSize,
bounds);
// Progressively filter ground returns using morphological open
for (size_t j = 0; j < wsvec.size(); ++j)
{
log()->get(LogLevel::Debug) << "Iteration " << j
<< " (height threshold = " << htvec[j]
<< ", window size = " << wsvec[j]
<< ")...\n";
MatrixXd mo = eigen::matrixOpen(ZImin, 0.5*(wsvec[j]-1));
std::vector<PointId> groundNewIdx;
for (auto p_idx : groundIdx)
{
double x = view->getFieldAs<double>(Dimension::Id::X, p_idx);
double y = view->getFieldAs<double>(Dimension::Id::Y, p_idx);
double z = view->getFieldAs<double>(Dimension::Id::Z, p_idx);
int r = static_cast<int>(std::floor((y-bounds.miny) / m_cellSize));
int c = static_cast<int>(std::floor((x-bounds.minx) / m_cellSize));
float diff = z - mo(r, c);
if (diff < htvec[j])
groundNewIdx.push_back(p_idx);
}
ZImin.swap(mo);
groundIdx.swap(groundNewIdx);
log()->get(LogLevel::Debug) << "Ground now has " << groundIdx.size()
<< " points.\n";
}
return groundIdx;
}
std::vector<PointId> SMRFilter::processGround(PointViewPtr view)
{
log()->get(LogLevel::Info) << "processGround: Running SMRF...\n";
// The algorithm consists of four conceptually distinct stages. The first is
// the creation of the minimum surface (ZImin). The second is the processing
// of the minimum surface, in which grid cells from the raster are
// identified as either containing bare earth (BE) or objects (OBJ). This
// second stage represents the heart of the algorithm. The third step is the
// creation of a DEM from these gridded points. The fourth step is the
// identification of the original LIDAR points as either BE or OBJ based on
// their relationship to the interpolated
std::vector<PointId> groundIdx;
BOX2D bounds;
view->calculateBounds(bounds);
SpatialReference srs(view->spatialReference());
// Determine the number of rows and columns at the given cell size.
m_numCols = ((bounds.maxx - bounds.minx) / m_cellSize) + 1;
m_numRows = ((bounds.maxy - bounds.miny) / m_cellSize) + 1;
MatrixXd cx(m_numRows, m_numCols);
MatrixXd cy(m_numRows, m_numCols);
for (auto c = 0; c < m_numCols; ++c)
{
for (auto r = 0; r < m_numRows; ++r)
{
cx(r, c) = bounds.minx + (c + 0.5) * m_cellSize;
cy(r, c) = bounds.miny + (r + 0.5) * m_cellSize;
}
}
// STEP 1:
// As with many other ground filtering algorithms, the first step is
// generation of ZImin from the cell size parameter and the extent of the
// data. The two vectors corresponding to [min:cellSize:max] for each
// coordinate – xi and yi – may be supplied by the user or may be easily and
// automatically calculated from the data. Without supplied ranges, the SMRF
// algorithm creates a raster from the ceiling of the minimum to the floor
// of the maximum values for each of the (x,y) dimensions. If the supplied
// cell size parameter is not an integer, the same general rule applies to
// values evenly divisible by the cell size. For example, if cell size is
// equal to 0.5 m, and the x values range from 52345.6 to 52545.4, the range
// would be [52346 52545].
// The minimum surface grid ZImin defined by vectors (xi,yi) is filled with
// the nearest, lowest elevation from the original point cloud (x,y,z)
// values, provided that the distance to the nearest point does not exceed
// the supplied cell size parameter. This provision means that some grid
// points of ZImin will go unfilled. To fill these values, we rely on
// computationally inexpensive image inpainting techniques. Image inpainting
// involves the replacement of the empty cells in an image (or matrix) with
// values calculated from other nearby values. It is a type of interpolation
// technique derived from artistic replacement of damaged portions of
// photographs and paintings, where preservation of texture is an important
// concern (Bertalmio et al., 2000). When empty values are spread through
// the image, and the ratio of filled to empty pixels is quite high, most
// methods of inpainting will produce satisfactory results. In an evaluation
// of inpainting methods on ground identification from the final terrain
// model, we found that Laplacian techniques produced error rates nearly
// three times higher than either an average of the eight nearest neighbors
// or D’Errico’s spring-metaphor inpainting technique (D’Errico, 2004). The
// spring-metaphor technique imagines springs connecting each cell with its
// eight adjacent neighbors, where the inpainted value corresponds to the
// lowest energy state of the set, and where the entire (sparse) set of
// linear equations is solved using partial differential equations. Both of
// these latter techniques were nearly the same with regards to total error,
// with the spring technique performing slightly better than the k-nearest
// neighbor (KNN) approach.
MatrixXd ZImin = eigen::createMinMatrix(*view.get(), m_numRows, m_numCols,
m_cellSize, bounds);
// MatrixXd ZImin_painted = inpaintKnn(cx, cy, ZImin);
// MatrixXd ZImin_painted = TPS(cx, cy, ZImin);
MatrixXd ZImin_painted = expandingTPS(cx, cy, ZImin);
if (!m_outDir.empty())
{
std::string filename = FileUtils::toAbsolutePath("zimin.tif", m_outDir);
eigen::writeMatrix(ZImin, filename, "GTiff", m_cellSize, bounds, srs);
filename = FileUtils::toAbsolutePath("zimin_painted.tif", m_outDir);
eigen::writeMatrix(ZImin_painted, filename, "GTiff", m_cellSize, bounds, srs);
}
ZImin = ZImin_painted;
// STEP 2:
// The second stage of the ground identification algorithm involves the
// application of a progressive morphological filter to the minimum surface
// grid (ZImin). At the first iteration, the filter applies an image opening
// operation to the minimum surface. An opening operation consists of an
// application of an erosion filter followed by a dilation filter. The
// erosion acts to snap relative high values to relative lows, where a
// supplied window radius and shape (or structuring element) defines the
// search neighborhood. The dilation uses the same window radius and
// structuring element, acting to outwardly expand relative highs. Fig. 2
// illustrates an opening operation on a cross section of a transect from
// Sample 1–1 in the ISPRS LIDAR reference dataset (Sithole and Vosselman,
// 2003), following Zhang et al. (2003).
// paper has low point happening later, i guess it doesn't matter too much, this is where he does it in matlab code
MatrixXi Low = progressiveFilter(-ZImin, m_cellSize, 5.0, 1.0);
// matlab code has net cutting occurring here
MatrixXd ZInet = ZImin;
MatrixXi isNetCell = MatrixXi::Zero(m_numRows, m_numCols);
if (m_cutNet > 0.0)
{
MatrixXd bigOpen = eigen::matrixOpen(ZImin, 2*std::ceil(m_cutNet / m_cellSize));
for (auto c = 0; c < m_numCols; c += std::ceil(m_cutNet/m_cellSize))
{
for (auto r = 0; r < m_numRows; ++r)
{
isNetCell(r, c) = 1;
}
}
for (auto c = 0; c < m_numCols; ++c)
{
for (auto r = 0; r < m_numRows; r += std::ceil(m_cutNet/m_cellSize))
{
isNetCell(r, c) = 1;
}
}
for (auto c = 0; c < m_numCols; ++c)
{
for (auto r = 0; r < m_numRows; ++r)
{
if (isNetCell(r, c)==1)
ZInet(r, c) = bigOpen(r, c);
}
}
}
// and finally object detection
MatrixXi Obj = progressiveFilter(ZInet, m_cellSize, m_percentSlope, m_maxWindow);
// STEP 3:
// The end result of the iteration process described above is a binary grid
// where each cell is classified as being either bare earth (BE) or object
// (OBJ). The algorithm then applies this mask to the starting minimum
// surface to eliminate nonground cells. These cells are then inpainted
// according to the same process described previously, producing a
// provisional DEM (ZIpro).
// we currently aren't checking for net cells or empty cells (haven't i already marked empty cells as NaNs?)
MatrixXd ZIpro = ZImin;
for (int i = 0; i < Obj.size(); ++i)
{
if (Obj(i) == 1 || Low(i) == 1 || isNetCell(i) == 1)
ZIpro(i) = std::numeric_limits<double>::quiet_NaN();
}
// MatrixXd ZIpro_painted = inpaintKnn(cx, cy, ZIpro);
// MatrixXd ZIpro_painted = TPS(cx, cy, ZIpro);
MatrixXd ZIpro_painted = expandingTPS(cx, cy, ZIpro);
if (!m_outDir.empty())
{
std::string filename = FileUtils::toAbsolutePath("zilow.tif", m_outDir);
eigen::writeMatrix(Low.cast<double>(), filename, "GTiff", m_cellSize, bounds, srs);
filename = FileUtils::toAbsolutePath("zinet.tif", m_outDir);
eigen::writeMatrix(ZInet, filename, "GTiff", m_cellSize, bounds, srs);
filename = FileUtils::toAbsolutePath("ziobj.tif", m_outDir);
eigen::writeMatrix(Obj.cast<double>(), filename, "GTiff", m_cellSize, bounds, srs);
filename = FileUtils::toAbsolutePath("zipro.tif", m_outDir);
eigen::writeMatrix(ZIpro, filename, "GTiff", m_cellSize, bounds, srs);
filename = FileUtils::toAbsolutePath("zipro_painted.tif", m_outDir);
eigen::writeMatrix(ZIpro_painted, filename, "GTiff", m_cellSize, bounds, srs);
}
ZIpro = ZIpro_painted;
// STEP 4:
// The final step of the algorithm is the identification of ground/object
// LIDAR points. This is accomplished by measuring the vertical distance
// between each LIDAR point and the provisional DEM, and applying a
// threshold calculation. While many authors use a single value for the
// elevation threshold, we suggest that a second parameter be used to
// increase the threshold on steep slopes, transforming the threshold to a
// slope-dependent value. The total permissible distance is then equal to a
// fixed elevation threshold plus the scaling value multiplied by the slope
// of the DEM at each LIDAR point. The rationale behind this approach is
// that small horizontal and vertical displacements yield larger errors on
// steep slopes, and as a result the BE/OBJ threshold distance should be
// more per- missive at these points.
// The calculation requires that both elevation and slope are interpolated
// from the provisional DEM. There are any number of interpolation
// techniques that might be used, and even nearest neighbor approaches work
// quite well, so long as the cell size of the DEM nearly corresponds to the
// resolution of the LIDAR data. A comparison of how well these different
// methods of interpolation perform is given in the next section. Based on
// these results, we find that a splined cubic interpolation provides the
// best results.
// It is common in LIDAR point clouds to have a small number of outliers
// which may be either above or below the terrain surface. While
// above-ground outliers (e.g., a random return from a bird in flight) are
// filtered during the normal algorithm routine, the below-ground outliers
// (e.g., those caused by a reflection) require a separate approach. Early
// in the routine and along a separate processing fork, the minimum surface
// is checked for low outliers by inverting the point cloud in the z-axis
// and applying the filter with parameters (slope = 500%, maxWindowSize =
// 1). The resulting mask is used to flag low outlier cells as OBJ before
// the inpainting of the provisional DEM. This outlier identification
// methodology is functionally the same as that of Zhang et al. (2003).
// The provisional DEM (ZIpro), created by removing OBJ cells from the
// original minimum surface (ZImin) and then inpainting, tends to be less
// smooth than one might wish, especially when the surfaces are to be used
// to create visual products like immersive geographic virtual environments.
// As a result, it is often worthwhile to reinter- polate a final DEM from
// the identified ground points of the original LIDAR data (ZIfin). Surfaces
// created from these data tend to be smoother and more visually satisfying
// than those derived from the provisional DEM.
// Very large (>40m in length) buildings can sometimes prove troublesome to
// remove on highly differentiated terrain. To accommodate the removal of
// such objects, we implemented a feature in the published SMRF algorithm
// which is helpful in removing such features. We accomplish this by
// introducing into the initial minimum surface a ‘‘net’’ of minimum values
// at a spacing equal to the maximum window diameter, where these minimum
// values are found by applying a morphological open operation with a disk
// shaped structuring element of radius (2?wkmax). Since only one example in
// this dataset had features this large (Sample 4–2, a trainyard) we did not
// include this portion of the algorithm in the formal testing procedure,
// though we provide a brief analysis of the effect of using this net filter
// in the next section.
MatrixXd scaled = ZIpro / m_cellSize;
MatrixXd gx = eigen::gradX(scaled);
MatrixXd gy = eigen::gradY(scaled);
MatrixXd gsurfs = (gx.cwiseProduct(gx) + gy.cwiseProduct(gy)).cwiseSqrt();
// MatrixXd gsurfs_painted = inpaintKnn(cx, cy, gsurfs);
// MatrixXd gsurfs_painted = TPS(cx, cy, gsurfs);
MatrixXd gsurfs_painted = expandingTPS(cx, cy, gsurfs);
if (!m_outDir.empty())
{
std::string filename = FileUtils::toAbsolutePath("gx.tif", m_outDir);
eigen::writeMatrix(gx, filename, "GTiff", m_cellSize, bounds, srs);
filename = FileUtils::toAbsolutePath("gy.tif", m_outDir);
eigen::writeMatrix(gy, filename, "GTiff", m_cellSize, bounds, srs);
filename = FileUtils::toAbsolutePath("gsurfs.tif", m_outDir);
eigen::writeMatrix(gsurfs, filename, "GTiff", m_cellSize, bounds, srs);
filename = FileUtils::toAbsolutePath("gsurfs_painted.tif", m_outDir);
eigen::writeMatrix(gsurfs_painted, filename, "GTiff", m_cellSize, bounds, srs);
}
gsurfs = gsurfs_painted;
MatrixXd thresh = (m_threshold + m_scalar * gsurfs.array()).matrix();
if (!m_outDir.empty())
{
std::string filename = FileUtils::toAbsolutePath("thresh.tif", m_outDir);
eigen::writeMatrix(thresh, filename, "GTiff", m_cellSize, bounds, srs);
}
for (PointId i = 0; i < view->size(); ++i)
{
using namespace Dimension;
double x = view->getFieldAs<double>(Id::X, i);
double y = view->getFieldAs<double>(Id::Y, i);
double z = view->getFieldAs<double>(Id::Z, i);
int c = Utils::clamp(static_cast<int>(floor(x - bounds.minx) / m_cellSize), 0, m_numCols-1);
int r = Utils::clamp(static_cast<int>(floor(y - bounds.miny) / m_cellSize), 0, m_numRows-1);
// author uses spline interpolation to get value from ZIpro and gsurfs
if (std::isnan(ZIpro(r, c)))
continue;
// not sure i should just brush this under the rug...
if (std::isnan(gsurfs(r, c)))
continue;
double ez = ZIpro(r, c);
// double ez = interp2(r, c, cx, cy, ZIpro);
// double si = gsurfs(r, c);
// double si = interp2(r, c, cx, cy, gsurfs);
// double reqVal = m_threshold + 1.2 * si;
if (std::abs(ez - z) > thresh(r, c))
continue;
// if (std::abs(ZIpro(r, c) - z) > m_threshold)
// continue;
groundIdx.push_back(i);
}
return groundIdx;
}
TEST(PointViewTest, getSet)
{
PointTable table;
PointViewPtr view = makeTestView(table, 1);
verifyTestView(*view.get(), 1);
}
