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sebastic-guest pushed a commit to branch upstream-master
in repository pktools.
commit e6880149d7a9001a440ed6af22df6b56fe66c614
Author: Pieter Kempeneers <kempe...@gmail.com>
Date: Fri Oct 24 13:00:35 2014 +0200
worked on pkkalman
---
ChangeLog | 6 ++++--
src/algorithms/Filter.h | 4 ++--
src/algorithms/Filter2d.cc | 2 +-
src/algorithms/Filter2d.h | 6 +++---
src/apps/pkextract.cc | 16 ++++++++--------
src/apps/pkfilter.cc | 8 ++++++--
src/apps/pkkalman.cc | 17 +++++++++++------
7 files changed, 35 insertions(+), 24 deletions(-)
diff --git a/ChangeLog b/ChangeLog
index dc0f1f6..0e626a5 100755
--- a/ChangeLog
+++ b/ChangeLog
@@ -301,7 +301,7 @@ version 2.5.2
version 2.5.3
- pklas2img
libLAS 1.8.0 support (ticket #42951)
- - myfann_cpp.h
+ - myfann_cpp.h
Fix build failure on ARM due to va_list usage in myfann_cpp.h
@@ -311,6 +311,7 @@ version 2.5.4
- pkfilter
Support filtering and statistics in spectral domain (see ticket #43252)
Support different padding strategies (currently only supported for
spectral/temporal filtering)
+ Renamed "majority" to "mode" for majority voting filter
- pkextract
bug fix for proportion rule
support standard deviation rule (stdev) for polygon features (ticket
#43193)
@@ -330,7 +331,8 @@ version 2.5.4
replaced options s|start and e|end with bs|bstart and be|bend
- ImgWriteOgr
overwrite existing ogr datasets per default
-Next versions:
+
+Todo:
- todo for API
ImgReaderGdal (ImgWriterGdal) open in update mode (check gdal_edit.py:
http://searchcode.com/codesearch/view/18938404)
Img[Reader|Writer]Ogr replace OGRDataSource with GDALDataset conform to
GDAL API 2.x
diff --git a/src/algorithms/Filter.h b/src/algorithms/Filter.h
index 9b12410..705bfc6 100644
--- a/src/algorithms/Filter.h
+++ b/src/algorithms/Filter.h
@@ -33,7 +33,7 @@ extern "C" {
namespace filter
{
- enum FILTER_TYPE { median=0, var=1 , min=2, max=3, sum=4, mean=5, minmax=6,
dilate=7, erode=8, close=9, open=10, homog=11, sobelx=12, sobely=13,
sobelxy=14, sobelyx=-14, smooth=15, density=16, majority=17, mixed=18,
smoothnodata=19, threshold=20, ismin=21, ismax=22, heterog=23, order=24,
stdev=25, dwt=26, dwti=27, dwt_cut=28, dwt_cut_from=29};
+ enum FILTER_TYPE { median=0, var=1 , min=2, max=3, sum=4, mean=5, minmax=6,
dilate=7, erode=8, close=9, open=10, homog=11, sobelx=12, sobely=13,
sobelxy=14, sobelyx=-14, smooth=15, density=16, mode=17, mixed=18,
smoothnodata=19, threshold=20, ismin=21, ismax=22, heterog=23, order=24,
stdev=25, dwt=26, dwti=27, dwt_cut=28, dwt_cut_from=29};
enum PADDING { symmetric=0, replicate=1, circular=2, constant=3};
@@ -116,7 +116,7 @@ private:
m_filterMap["sobelyx"]=filter::sobelyx;
m_filterMap["smooth"]=filter::smooth;
m_filterMap["density"]=filter::density;
- m_filterMap["majority"]=filter::majority;
+ m_filterMap["mode"]=filter::mode;
m_filterMap["mixed"]=filter::mixed;
m_filterMap["smoothnodata"]=filter::smoothnodata;
m_filterMap["threshold"]=filter::threshold;
diff --git a/src/algorithms/Filter2d.cc b/src/algorithms/Filter2d.cc
index 0fc643a..2d02182 100644
--- a/src/algorithms/Filter2d.cc
+++ b/src/algorithms/Filter2d.cc
@@ -581,7 +581,7 @@ void filter2d::Filter2d::doit(const ImgReaderGdal& input,
ImgWriterGdal& output,
outBuffer[x/down]=(m_noDataValues.size())? m_noDataValues[0] : 0;
break;
}
- case(filter2d::majority):{
+ case(filter2d::mode):{
if(occurrence.size()){
std::map<long int,int>::const_iterator maxit=occurrence.begin();
for(std::map<long int,int>::const_iterator
mit=occurrence.begin();mit!=occurrence.end();++mit){
diff --git a/src/algorithms/Filter2d.h b/src/algorithms/Filter2d.h
index deb27d8..d4e1917 100644
--- a/src/algorithms/Filter2d.h
+++ b/src/algorithms/Filter2d.h
@@ -58,7 +58,7 @@ extern "C" {
namespace filter2d
{
- enum FILTER_TYPE { median=100, var=101 , min=102, max=103, sum=104,
mean=105, minmax=106, dilate=107, erode=108, close=109, open=110, homog=111,
sobelx=112, sobely=113, sobelxy=114, sobelyx=115, smooth=116, density=117,
majority=118, mixed=119, threshold=120, ismin=121, ismax=122, heterog=123,
order=124, stdev=125, mrf=126, dwt=127, dwti=128, dwt_cut=129, scramble=130,
shift=131, linearfeature=132, smoothnodata=133, countid=134, dwt_cut_from=135};
+ enum FILTER_TYPE { median=100, var=101 , min=102, max=103, sum=104,
mean=105, minmax=106, dilate=107, erode=108, close=109, open=110, homog=111,
sobelx=112, sobely=113, sobelxy=114, sobelyx=115, smooth=116, density=117,
mode=118, mixed=119, threshold=120, ismin=121, ismax=122, heterog=123,
order=124, stdev=125, mrf=126, dwt=127, dwti=128, dwt_cut=129, scramble=130,
shift=131, linearfeature=132, smoothnodata=133, countid=134, dwt_cut_from=135};
enum RESAMPLE { NEAR = 0, BILINEAR = 1, BICUBIC = 2 };//bicubic not
supported yet...
@@ -149,7 +149,7 @@ private:
m_filterMap["sobelyx"]=filter2d::sobelyx;
m_filterMap["smooth"]=filter2d::smooth;
m_filterMap["density"]=filter2d::density;
- m_filterMap["majority"]=filter2d::majority;
+ m_filterMap["mode"]=filter2d::mode;
m_filterMap["mixed"]=filter2d::mixed;
m_filterMap["smoothnodata"]=filter2d::smoothnodata;
m_filterMap["threshold"]=filter2d::threshold;
@@ -453,7 +453,7 @@ template<class T1, class T2> void Filter2d::doit(const
Vector2d<T1>& inputVector
outBuffer[x/down]=noDataValue;
break;
}
- case(filter2d::majority):{
+ case(filter2d::mode):{
if(occurrence.size()){
std::map<int,int>::const_iterator maxit=occurrence.begin();
for(std::map<int,int>::const_iterator
mit=occurrence.begin();mit!=occurrence.end();++mit){
diff --git a/src/apps/pkextract.cc b/src/apps/pkextract.cc
index 759782b..8ec5bd6 100644
--- a/src/apps/pkextract.cc
+++ b/src/apps/pkextract.cc
@@ -35,7 +35,7 @@ along with pktools. If not, see
<http://www.gnu.org/licenses/>.
#endif
namespace rule{
- enum RULE_TYPE {point=0, mean=1, proportion=2, custom=3, min=4, max=5,
maxvote=6, centroid=7, sum=8, median=9, stdev=10};
+ enum RULE_TYPE {point=0, mean=1, proportion=2, custom=3, min=4, max=5,
mode=6, centroid=7, sum=8, median=9, stdev=10};
}
using namespace std;
@@ -48,14 +48,14 @@ int main(int argc, char *argv[])
Optionpk<unsigned int> random_opt("rand", "random", "Create simple random
sample of points. Provide number of points to generate");
Optionpk<double> grid_opt("grid", "grid", "Create systematic grid of points.
Provide cell grid size (in projected units, e.g,. m)");
Optionpk<string> output_opt("o", "output", "Output sample dataset");
- Optionpk<int> class_opt("c", "class", "Class(es) to extract from input
sample image. Leave empty to extract all valid data pixels from sample dataset.
Make sure to set classes if rule is set to maxvote or proportion");
+ Optionpk<int> class_opt("c", "class", "Class(es) to extract from input
sample image. Leave empty to extract all valid data pixels from sample dataset.
Make sure to set classes if rule is set to mode or proportion");
Optionpk<float> threshold_opt("t", "threshold", "Probability threshold for
selecting samples (randomly). Provide probability in percentage (>0) or
absolute (<0). Use a single threshold for vector sample datasets. If using
raster land cover maps as a sample dataset, you can provide a threshold value
for each class (e.g. -t 80 -t 60). Use value 100 to select all pixels for
selected class(es)", 100);
Optionpk<string> ogrformat_opt("f", "f", "Output sample dataset
format","SQLite");
Optionpk<string> ftype_opt("ft", "ftype", "Field type (only Real or
Integer)", "Real");
Optionpk<string> ltype_opt("lt", "ltype", "Label type: In16 or String",
"Integer");
Optionpk<bool> polygon_opt("polygon", "polygon", "Create OGRPolygon as
geometry instead of OGRPoint.", false);
Optionpk<int> band_opt("b", "band", "Band index(es) to extract. Leave empty
to use all bands");
- Optionpk<string> rule_opt("r", "rule", "Rule how to report image information
per feature (only for vector sample). point (value at each point or at centroid
if polygon), centroid, mean, stdev, median, proportion, min, max, maxvote,
sum.", "centroid");
+ Optionpk<string> rule_opt("r", "rule", "Rule how to report image information
per feature (only for vector sample). point (value at each point or at centroid
if polygon), centroid, mean, stdev, median, proportion, min, max, mode, sum.",
"centroid");
Optionpk<double> srcnodata_opt("srcnodata", "srcnodata", "Invalid value(s)
for input image");
Optionpk<int> bndnodata_opt("bndnodata", "bndnodata", "Band(s) in input
image to check if pixel is valid (used for srcnodata)", 0);
Optionpk<float> polythreshold_opt("tp", "thresholdPolygon", "(absolute)
threshold for selecting samples in each polygon");
@@ -118,7 +118,7 @@ int main(int argc, char *argv[])
ruleMap["min"]=rule::min;
ruleMap["max"]=rule::max;
ruleMap["custom"]=rule::custom;
- ruleMap["maxvote"]=rule::maxvote;
+ ruleMap["mode"]=rule::mode;
ruleMap["sum"]=rule::sum;
if(srcnodata_opt.size()){
@@ -771,7 +771,7 @@ int main(int argc, char *argv[])
break;
}
case(rule::custom):
- case(rule::maxvote):
+ case(rule::mode):
ogrWriter.createField(label_opt[0],fieldType,ilayerWrite);
if(test_opt.size())
ogrTestWriter.createField(label_opt[0],fieldType,ilayerWrite);
@@ -1259,7 +1259,7 @@ int main(int argc, char *argv[])
writePolygonFeature->SetField(label_opt[0].c_str(),static_cast<int>(20));
}
}
- else if(ruleMap[rule_opt[0]]==rule::maxvote){
+ else if(ruleMap[rule_opt[0]]==rule::mode){
//maximum votes in polygon
if(verbose_opt[0])
std::cout << "number of points in window: " << nPointWindow
<< std::endl;
@@ -1720,7 +1720,7 @@ int main(int argc, char *argv[])
writePolygonFeature->SetField(label_opt[0].c_str(),static_cast<int>(20));
}
}
- else if(ruleMap[rule_opt[0]]==rule::maxvote){
+ else if(ruleMap[rule_opt[0]]==rule::mode){
//maximum votes in polygon
if(verbose_opt[0])
std::cout << "number of points in polygon: " <<
nPointPolygon << std::endl;
@@ -2238,7 +2238,7 @@ int main(int argc, char *argv[])
writePolygonFeature->SetField(label_opt[0].c_str(),static_cast<int>(20));
}
}
- else if(ruleMap[rule_opt[0]]==rule::maxvote){
+ else if(ruleMap[rule_opt[0]]==rule::mode){
//maximum votes in polygon
if(verbose_opt[0])
std::cout << "number of points in polygon: " <<
nPointPolygon << std::endl;
diff --git a/src/apps/pkfilter.cc b/src/apps/pkfilter.cc
index 21d43cc..2645f1b 100644
--- a/src/apps/pkfilter.cc
+++ b/src/apps/pkfilter.cc
@@ -42,7 +42,7 @@ int main(int argc,char **argv) {
Optionpk<std::string> tmpdir_opt("tmp", "tmp", "Temporary
directory","/tmp",2);
Optionpk<bool> disc_opt("circ", "circular", "circular disc kernel for
dilation and erosion", false);
// Optionpk<double> angle_opt("a", "angle", "angle used for directional
filtering in dilation (North=0, East=90, South=180, West=270).");
- Optionpk<std::string> method_opt("f", "filter", "filter function (median,
var, min, max, sum, mean, dilate, erode, close, open, homog (central pixel must
be identical to all other pixels within window), heterog, sobelx (horizontal
edge detection), sobely (vertical edge detection), sobelxy (diagonal edge
detection NE-SW),sobelyx (diagonal edge detection NW-SE), smooth, density,
countid, majority voting (only for classes), smoothnodata (smooth nodata values
only) values, threshold local [...]
+ Optionpk<std::string> method_opt("f", "filter", "filter function (median,
var, min, max, sum, mean, dilate, erode, close, open, homog (central pixel must
be identical to all other pixels within window), heterog, sobelx (horizontal
edge detection), sobely (vertical edge detection), sobelxy (diagonal edge
detection NE-SW),sobelyx (diagonal edge detection NW-SE), smooth, density,
countid, mode (majority voting, only for classes), smoothnodata (smooth nodata
values only) values, threshold [...]
Optionpk<std::string> resample_opt("r", "resampling-method", "Resampling
method for shifting operation (near: nearest neighbour, bilinear: bi-linear
interpolation).", "near");
Optionpk<double> dimX_opt("dx", "dx", "filter kernel size in x, better use
odd value to avoid image shift", 3);
Optionpk<double> dimY_opt("dy", "dy", "filter kernel size in y, better use
odd value to avoid image shift", 3);
@@ -191,7 +191,10 @@ int main(int argc,char **argv) {
output.open(output_opt[0],(input.nrOfCol()+down_opt[0]-1)/down_opt[0],(input.nrOfRow()+down_opt[0]-1)/down_opt[0],nband,theType,imageType,option_opt);
}
else{
- int nband=(dimZ_opt[0]==1)? 1 : input.nrOfBand();
+ int nband=input.nrOfBand();
+ if(dimZ_opt.size())
+ if(dimZ_opt[0]==1)
+ nband=1;
output.open(output_opt[0],(input.nrOfCol()+down_opt[0]-1)/down_opt[0],(input.nrOfRow()+down_opt[0]-1)/down_opt[0],nband,theType,imageType,option_opt);
}
}
@@ -241,6 +244,7 @@ int main(int argc,char **argv) {
if(verbose_opt[0])
std::cout<< std::endl;
}
+
if(nodata_opt.size()){
if(verbose_opt[0])
std::cout<< "mask values: ";
diff --git a/src/apps/pkkalman.cc b/src/apps/pkkalman.cc
index bbc7475..24dd44b 100644
--- a/src/apps/pkkalman.cc
+++ b/src/apps/pkkalman.cc
@@ -50,6 +50,7 @@ int main(int argc,char **argv) {
Optionpk<double> eps_opt("eps", "eps", "epsilon for non zero division",
0.00001);
Optionpk<double> uncertModel_opt("um", "uncertmodel", "Multiply this value
with std dev of first model image to obtain uncertainty of model",2);
Optionpk<double> uncertObs_opt("uo", "uncertobs", "Uncertainty of valid
observations",0);
+ Optionpk<double> deltaObs_opt("dobs", "deltaobs", "Threshold for relative
difference (in percentage) in observation and model pixel");
Optionpk<double> uncertNodata_opt("unodata", "uncertnodata", "Uncertainty in
case of no-data values in observation", 10000);
// Optionpk<double> regTime_opt("rt", "regtime", "Relative Weight for
regression in time series", 1.0);
// Optionpk<double> regSensor_opt("rs", "regsensor", "Relative Weight for
regression in sensor series", 1.0);
@@ -57,7 +58,7 @@ int main(int argc,char **argv) {
Optionpk<float> threshold_opt("th", "threshold", "threshold for selecting
samples (randomly). Provide probability in percentage (>0) or absolute (<0).",
0);
Optionpk<int> minreg_opt("minreg", "minreg", "Minimum number of pixels to
take into account for regression", 5, 2);
// Optionpk<bool> regObs_opt("regobs", "regobs", "Perform regression between
modeled and observed value",false);
- Optionpk<bool> checkDiff_opt("diff", "diff", "Flag observation as invalid if
difference with model is above uncertainty",false);
+ // Optionpk<double> checkDiff_opt("diff", "diff", "Flag observation as
invalid if difference with model is above uncertainty",false);
Optionpk<unsigned short> window_opt("win", "window", "window size for
calculating regression (use 0 for global)", 0);
Optionpk<string> oformat_opt("of", "oformat", "Output image format (see
also gdal_translate). Empty string: inherit from input image","GTiff",2);
Optionpk<string> option_opt("co", "co", "Creation option for output file.
Multiple options can be specified.");
@@ -83,6 +84,7 @@ int main(int argc,char **argv) {
eps_opt.retrieveOption(argc,argv);
uncertModel_opt.retrieveOption(argc,argv);
uncertObs_opt.retrieveOption(argc,argv);
+ deltaObs_opt.retrieveOption(argc,argv);
uncertNodata_opt.retrieveOption(argc,argv);
// regTime_opt.retrieveOption(argc,argv);
// regSensor_opt.retrieveOption(argc,argv);
@@ -90,7 +92,7 @@ int main(int argc,char **argv) {
threshold_opt.retrieveOption(argc,argv);
minreg_opt.retrieveOption(argc,argv);
// regObs_opt.retrieveOption(argc,argv);
- checkDiff_opt.retrieveOption(argc,argv);
+ // checkDiff_opt.retrieveOption(argc,argv);
window_opt.retrieveOption(argc,argv);
oformat_opt.retrieveOption(argc,argv);
option_opt.retrieveOption(argc,argv);
@@ -613,13 +615,14 @@ int main(int argc,char **argv) {
//observation update
if(update&&!imgReaderObs.isNoData(obsLineBuffer[icol])){
bool doUpdate=true;
- if(checkDiff_opt[0]){
+ if(deltaObs_opt.size()){
statfactory::StatFactory statobs;
statobs.setNoDataValues(obsnodata_opt);
double obsMeanValue=statobs.mean(obsWindowBuffer);
double difference=(obsMeanValue-c0obs)/c1obs-modValue;
+ difference/=modValue;//make relative difference
difference*=difference;
- doUpdate=(sqrt(difference)<uncertModel_opt[0]*stdDev);
+ doUpdate=(100*sqrt(difference)<deltaObs_opt[0]);
}
if(doUpdate){
double kalmanGain=1;
@@ -1035,13 +1038,15 @@ int main(int argc,char **argv) {
//observation update
if(update&&!imgReaderObs.isNoData(obsLineBuffer[icol])){
bool doUpdate=true;
- if(checkDiff_opt[0]){
+ if(deltaObs_opt.size()){
statfactory::StatFactory statobs;
statobs.setNoDataValues(obsnodata_opt);
double obsMeanValue=statobs.mean(obsWindowBuffer);
double difference=(obsMeanValue-c0obs)/c1obs-modValue;
difference*=difference;
- doUpdate=(sqrt(difference)<uncertModel_opt[0]*stdDev);
+ difference/=modValue;//make relative difference
+ difference*=difference;
+ doUpdate=(100*sqrt(difference)<deltaObs_opt[0]);
}
if(doUpdate){
double kalmanGain=1;
--
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