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in repository pktools.
commit 655c1250cc1e51903af0b25395382bdf33d4a69c
Author: Pieter Kempeneers <kempe...@gmail.com>
Date: Sat Mar 29 23:02:51 2014 +0100
support layer name in read training data in ogr
---
ChangeLog | 2 +-
doc/{faq_pkcomposit.dox => faq_pkcomposite.dox} | 0
src/apps/pkann.cc | 18 ++++++++++--------
src/apps/pkfsann.cc | 18 ++++++++++--------
src/apps/pkfssvm.cc | 16 +++++++++-------
src/apps/pkoptsvm.cc | 16 +++++++++-------
src/apps/pksvm.cc | 18 ++++++++++--------
src/imageclasses/ImgReaderOgr.cc | 14 ++++++++++++--
src/imageclasses/ImgReaderOgr.h | 24 +++++++++++++-----------
9 files changed, 74 insertions(+), 52 deletions(-)
diff --git a/ChangeLog b/ChangeLog
index d0ef20a..2d4767b 100755
--- a/ChangeLog
+++ b/ChangeLog
@@ -278,7 +278,7 @@ version 2.5.2
removed underscore for QProcess in Windows
- pkopt_svm -> pkoptsvm
removed underscore for QProcess in Windows
- - pkmosaic -> pkcomposit
+ - pkmosaic -> pkcomposite
name was confusing as also compositing is supported (unlike
gdal_merge.py and gdalwarp)
- version control for libraries
thanks to suggestion of Francesco Paolo Lovergine
diff --git a/doc/faq_pkcomposit.dox b/doc/faq_pkcomposite.dox
similarity index 100%
rename from doc/faq_pkcomposit.dox
rename to doc/faq_pkcomposite.dox
diff --git a/src/apps/pkann.cc b/src/apps/pkann.cc
index b3cdb16..15c1464 100644
--- a/src/apps/pkann.cc
+++ b/src/apps/pkann.cc
@@ -39,8 +39,9 @@ int main(int argc, char *argv[])
//--------------------------- command line options
------------------------------------
Optionpk<string> input_opt("i", "input", "input image");
- Optionpk<string> training_opt("t", "training", "training shape file. A
single shape file contains all training features (must be set as: B0, B1,
B2,...) for all classes (class numbers identified by label option). Use
multiple training files for bootstrap aggregation (alternative to the bag and
bsize options, where a random subset is taken from a single training file)");
- Optionpk<string> label_opt("label", "label", "identifier for class label in
training shape file.","label");
+ Optionpk<string> training_opt("t", "training", "training vector file. A
single vector file contains all training features (must be set as: B0, B1,
B2,...) for all classes (class numbers identified by label option). Use
multiple training files for bootstrap aggregation (alternative to the bag and
bsize options, where a random subset is taken from a single training file)");
+ Optionpk<string> tlayer_opt("tln", "tln", "training layer name","");
+ Optionpk<string> label_opt("label", "label", "identifier for class label in
training vector file.","label");
Optionpk<unsigned int> balance_opt("bal", "balance", "balance the input data
to this number of samples for each class", 0);
Optionpk<bool> random_opt("random", "random", "in case of balance, randomize
input data", true);
Optionpk<int> minSize_opt("min", "min", "if number of training pixels is
less then min, do not take this class into account (0: consider all classes)",
0);
@@ -83,6 +84,7 @@ int main(int argc, char *argv[])
try{
doProcess=input_opt.retrieveOption(argc,argv);
training_opt.retrieveOption(argc,argv);
+ tlayer_opt.retrieveOption(argc,argv);
label_opt.retrieveOption(argc,argv);
balance_opt.retrieveOption(argc,argv);
random_opt.retrieveOption(argc,argv);
@@ -137,7 +139,7 @@ int main(int argc, char *argv[])
if(mask_opt.size())
cout << "mask filename: " << mask_opt[0] << endl;
if(training_opt.size()){
- cout << "training shape file: " << endl;
+ cout << "training vector file: " << endl;
for(int ifile=0;ifile<training_opt.size();++ifile)
cout << training_opt[ifile] << endl;
}
@@ -207,13 +209,13 @@ int main(int argc, char *argv[])
trainingMap.clear();
trainingPixels.clear();
if(verbose_opt[0]>=1)
- cout << "reading imageShape file " << training_opt[0] << endl;
+ cout << "reading imageVector file " << training_opt[0] << endl;
try{
ImgReaderOgr trainingReaderBag(training_opt[ibag]);
if(band_opt.size())
-
totalSamples=trainingReaderBag.readDataImageShape(trainingMap,fields,band_opt,label_opt[0],verbose_opt[0]);
+
totalSamples=trainingReaderBag.readDataImageOgr(trainingMap,fields,band_opt,label_opt[0],tlayer_opt[0],verbose_opt[0]);
else
-
totalSamples=trainingReaderBag.readDataImageShape(trainingMap,fields,start_opt[0],end_opt[0],label_opt[0],verbose_opt[0]);
+
totalSamples=trainingReaderBag.readDataImageOgr(trainingMap,fields,start_opt[0],end_opt[0],label_opt[0],tlayer_opt[0],verbose_opt[0]);
if(trainingMap.size()<2){
string errorstring="Error: could not read at least two classes from
training file, did you provide class labels in training sample (see option
label)?";
throw(errorstring);
@@ -962,9 +964,9 @@ int main(int argc, char *argv[])
classImageBag.close();
classImageOut.close();
}
- else{//classify shape file
+ else{//classify vector file
cm.clearResults();
- //notice that fields have already been set by readDataImageShape (taking
into account appropriate bands)
+ //notice that fields have already been set by readDataImageOgr (taking
into account appropriate bands)
for(int ivalidation=0;ivalidation<input_opt.size();++ivalidation){
if(output_opt.size())
assert(output_opt.size()==input_opt.size());
diff --git a/src/apps/pkfsann.cc b/src/apps/pkfsann.cc
index 777925d..b115dcb 100644
--- a/src/apps/pkfsann.cc
+++ b/src/apps/pkfsann.cc
@@ -197,8 +197,9 @@ int main(int argc, char *argv[])
//--------------------------- command line options
------------------------------------
Optionpk<string> input_opt("i", "input", "input test set (leave empty to
perform a cross validation based on training only)");
- Optionpk<string> training_opt("t", "training", "training shape file. A
single shape file contains all training features (must be set as: B0, B1,
B2,...) for all classes (class numbers identified by label option). Use
multiple training files for bootstrap aggregation (alternative to the bag and
bsize options, where a random subset is taken from a single training file)");
- Optionpk<string> label_opt("\0", "label", "identifier for class label in
training shape file.","label");
+ Optionpk<string> training_opt("t", "training", "training vector file. A
single vector file contains all training features (must be set as: B0, B1,
B2,...) for all classes (class numbers identified by label option). Use
multiple training files for bootstrap aggregation (alternative to the bag and
bsize options, where a random subset is taken from a single training file)");
+ Optionpk<string> tlayer_opt("tln", "tln", "training layer name","");
+ Optionpk<string> label_opt("\0", "label", "identifier for class label in
training vector file.","label");
Optionpk<unsigned short> maxFeatures_opt("n", "nf", "number of features to
select (0 to select optimal number, see also ecost option)", 0);
Optionpk<unsigned int> balance_opt("\0", "balance", "balance the input data
to this number of samples for each class", 0);
Optionpk<bool> random_opt("random","random", "in case of balance, randomize
input data", true);
@@ -218,6 +219,7 @@ int main(int argc, char *argv[])
doProcess=input_opt.retrieveOption(argc,argv);
training_opt.retrieveOption(argc,argv);
maxFeatures_opt.retrieveOption(argc,argv);
+ tlayer_opt.retrieveOption(argc,argv);
label_opt.retrieveOption(argc,argv);
balance_opt.retrieveOption(argc,argv);
random_opt.retrieveOption(argc,argv);
@@ -261,7 +263,7 @@ int main(int argc, char *argv[])
if(input_opt.size())
cv_opt[0]=0;
if(verbose_opt[0]>=1)
- std::cout << "training shape file: " << training_opt[0] << std::endl;
+ std::cout << "training vector file: " << training_opt[0] << std::endl;
unsigned int totalSamples=0;
unsigned int totalTestSamples=0;
@@ -304,22 +306,22 @@ int main(int argc, char *argv[])
map<string,Vector2d<float> > trainingMap;
map<string,Vector2d<float> > testMap;
if(verbose_opt[0]>=1)
- std::cout << "reading imageShape file " << training_opt[0] << std::endl;
+ std::cout << "reading imageVector file " << training_opt[0] << std::endl;
try{
ImgReaderOgr trainingReader(training_opt[0]);
if(band_opt.size()){
-
totalSamples=trainingReader.readDataImageShape(trainingMap,fields,band_opt,label_opt[0],verbose_opt[0]);
+
totalSamples=trainingReader.readDataImageOgr(trainingMap,fields,band_opt,label_opt[0],tlayer_opt[0],verbose_opt[0]);
if(input_opt.size()){
ImgReaderOgr inputReader(input_opt[0]);
-
totalTestSamples=trainingReader.readDataImageShape(testMap,fields,band_opt,label_opt[0],verbose_opt[0]);
+
totalTestSamples=trainingReader.readDataImageOgr(testMap,fields,band_opt,label_opt[0],tlayer_opt[0],verbose_opt[0]);
inputReader.close();
}
}
else{
-
totalSamples=trainingReader.readDataImageShape(trainingMap,fields,start_opt[0],end_opt[0],label_opt[0],verbose_opt[0]);
+
totalSamples=trainingReader.readDataImageOgr(trainingMap,fields,start_opt[0],end_opt[0],label_opt[0],tlayer_opt[0],verbose_opt[0]);
if(input_opt.size()){
ImgReaderOgr inputReader(input_opt[0]);
-
totalTestSamples=trainingReader.readDataImageShape(testMap,fields,start_opt[0],end_opt[0],label_opt[0],verbose_opt[0]);
+
totalTestSamples=trainingReader.readDataImageOgr(testMap,fields,start_opt[0],end_opt[0],label_opt[0],tlayer_opt[0],verbose_opt[0]);
inputReader.close();
}
}
diff --git a/src/apps/pkfssvm.cc b/src/apps/pkfssvm.cc
index f1bcc61..4dcf05b 100644
--- a/src/apps/pkfssvm.cc
+++ b/src/apps/pkfssvm.cc
@@ -221,8 +221,9 @@ int main(int argc, char *argv[])
//--------------------------- command line options
------------------------------------
Optionpk<string> input_opt("i", "input", "input test set (leave empty to
perform a cross validation based on training only)");
- Optionpk<string> training_opt("t", "training", "training shape file. A
single shape file contains all training features (must be set as: B0, B1,
B2,...) for all classes (class numbers identified by label option).");
- Optionpk<string> label_opt("\0", "label", "identifier for class label in
training shape file.","label");
+ Optionpk<string> training_opt("t", "training", "training vector file. A
single vector file contains all training features (must be set as: B0, B1,
B2,...) for all classes (class numbers identified by label option).");
+ Optionpk<string> tlayer_opt("tln", "tln", "training layer name","");
+ Optionpk<string> label_opt("\0", "label", "identifier for class label in
training vector file.","label");
Optionpk<unsigned short> maxFeatures_opt("n", "nf", "number of features to
select (0 to select optimal number, see also ecost option)", 0);
Optionpk<unsigned int> balance_opt("\0", "balance", "balance the input data
to this number of samples for each class", 0);
Optionpk<bool> random_opt("random","random", "in case of balance, randomize
input data", true);
@@ -240,6 +241,7 @@ int main(int argc, char *argv[])
doProcess=input_opt.retrieveOption(argc,argv);
training_opt.retrieveOption(argc,argv);
maxFeatures_opt.retrieveOption(argc,argv);
+ tlayer_opt.retrieveOption(argc,argv);
label_opt.retrieveOption(argc,argv);
balance_opt.retrieveOption(argc,argv);
random_opt.retrieveOption(argc,argv);
@@ -285,7 +287,7 @@ int main(int argc, char *argv[])
if(verbose_opt[0]>=1){
if(input_opt.size())
std::cout << "input filename: " << input_opt[0] << std::endl;
- std::cout << "training shape file: " << std::endl;
+ std::cout << "training vector file: " << std::endl;
for(int ifile=0;ifile<training_opt.size();++ifile)
std::cout << training_opt[ifile] << std::endl;
std::cout << "verbose: " << verbose_opt[0] << std::endl;
@@ -346,18 +348,18 @@ int main(int argc, char *argv[])
try{
ImgReaderOgr trainingReader(training_opt[0]);
if(band_opt.size()){
-
totalSamples=trainingReader.readDataImageShape(trainingMap,fields,band_opt,label_opt[0],verbose_opt[0]);
+
totalSamples=trainingReader.readDataImageOgr(trainingMap,fields,band_opt,label_opt[0],tlayer_opt[0],verbose_opt[0]);
if(input_opt.size()){
ImgReaderOgr inputReader(input_opt[0]);
-
totalTestSamples=inputReader.readDataImageShape(testMap,fields,band_opt,label_opt[0],verbose_opt[0]);
+
totalTestSamples=inputReader.readDataImageOgr(testMap,fields,band_opt,label_opt[0],tlayer_opt[0],verbose_opt[0]);
inputReader.close();
}
}
else{
-
totalSamples=trainingReader.readDataImageShape(trainingMap,fields,start_opt[0],end_opt[0],label_opt[0],verbose_opt[0]);
+
totalSamples=trainingReader.readDataImageOgr(trainingMap,fields,start_opt[0],end_opt[0],label_opt[0],tlayer_opt[0],verbose_opt[0]);
if(input_opt.size()){
ImgReaderOgr inputReader(input_opt[0]);
-
totalTestSamples=inputReader.readDataImageShape(testMap,fields,start_opt[0],end_opt[0],label_opt[0],verbose_opt[0]);
+
totalTestSamples=inputReader.readDataImageOgr(testMap,fields,start_opt[0],end_opt[0],label_opt[0],tlayer_opt[0],verbose_opt[0]);
inputReader.close();
}
}
diff --git a/src/apps/pkoptsvm.cc b/src/apps/pkoptsvm.cc
index e63f37d..ce77a17 100644
--- a/src/apps/pkoptsvm.cc
+++ b/src/apps/pkoptsvm.cc
@@ -231,8 +231,9 @@ int main(int argc, char *argv[])
map<short,int> reclassMap;
vector<int> vreclass;
Optionpk<string> input_opt("i", "input", "input image");
- Optionpk<string> training_opt("t", "training", "training shape file. A
single shape file contains all training features (must be set as: B0, B1,
B2,...) for all classes (class numbers identified by label option).");
- Optionpk<string> label_opt("\0", "label", "identifier for class label in
training shape file.","label");
+ Optionpk<string> training_opt("t", "training", "training vector file. A
single vector file contains all training features (must be set as: B0, B1,
B2,...) for all classes (class numbers identified by label option).");
+ Optionpk<string> tlayer_opt("tln", "tln", "training layer name","");
+ Optionpk<string> label_opt("\0", "label", "identifier for class label in
training vector file.","label");
// Optionpk<unsigned short> reclass_opt("\0", "rc", "reclass code (e.g.
--rc=12 --rc=23 to reclass first two classes to 12 and 23 resp.).", 0);
Optionpk<unsigned int> balance_opt("\0", "balance", "balance the input data
to this number of samples for each class", 0);
Optionpk<bool> random_opt("random","random", "in case of balance, randomize
input data", true);
@@ -253,6 +254,7 @@ int main(int argc, char *argv[])
try{
doProcess=input_opt.retrieveOption(argc,argv);
training_opt.retrieveOption(argc,argv);
+ tlayer_opt.retrieveOption(argc,argv);
label_opt.retrieveOption(argc,argv);
// reclass_opt.retrieveOption(argc,argv);
balance_opt.retrieveOption(argc,argv);
@@ -301,7 +303,7 @@ int main(int argc, char *argv[])
if(verbose_opt[0]>=1){
if(input_opt.size())
std::cout << "input filename: " << input_opt[0] << std::endl;
- std::cout << "training shape file: " << std::endl;
+ std::cout << "training vector file: " << std::endl;
for(int ifile=0;ifile<training_opt.size();++ifile)
std::cout << training_opt[ifile] << std::endl;
std::cout << "verbose: " << verbose_opt[0] << std::endl;
@@ -355,18 +357,18 @@ int main(int argc, char *argv[])
try{
ImgReaderOgr trainingReader(training_opt[0]);
if(band_opt.size()){
-
totalSamples=trainingReader.readDataImageShape(trainingMap,fields,band_opt,label_opt[0],verbose_opt[0]);
+
totalSamples=trainingReader.readDataImageOgr(trainingMap,fields,band_opt,label_opt[0],tlayer_opt[0],verbose_opt[0]);
if(input_opt.size()){
ImgReaderOgr inputReader(input_opt[0]);
-
totalTestSamples=inputReader.readDataImageShape(testMap,fields,band_opt,label_opt[0],verbose_opt[0]);
+
totalTestSamples=inputReader.readDataImageOgr(testMap,fields,band_opt,label_opt[0],tlayer_opt[0],verbose_opt[0]);
inputReader.close();
}
}
else{
-
totalSamples=trainingReader.readDataImageShape(trainingMap,fields,start_opt[0],end_opt[0],label_opt[0],verbose_opt[0]);
+
totalSamples=trainingReader.readDataImageOgr(trainingMap,fields,start_opt[0],end_opt[0],label_opt[0],tlayer_opt[0],verbose_opt[0]);
if(input_opt.size()){
ImgReaderOgr inputReader(input_opt[0]);
-
totalTestSamples=inputReader.readDataImageShape(testMap,fields,start_opt[0],end_opt[0],label_opt[0],verbose_opt[0]);
+
totalTestSamples=inputReader.readDataImageOgr(testMap,fields,start_opt[0],end_opt[0],label_opt[0],tlayer_opt[0],verbose_opt[0]);
inputReader.close();
}
trainingReader.close();
diff --git a/src/apps/pksvm.cc b/src/apps/pksvm.cc
index cead84e..38e835f 100644
--- a/src/apps/pksvm.cc
+++ b/src/apps/pksvm.cc
@@ -49,8 +49,9 @@ int main(int argc, char *argv[])
//--------------------------- command line options
------------------------------------
Optionpk<string> input_opt("i", "input", "input image");
- Optionpk<string> training_opt("t", "training", "training shape file. A
single shape file contains all training features (must be set as: B0, B1,
B2,...) for all classes (class numbers identified by label option). Use
multiple training files for bootstrap aggregation (alternative to the bag and
bsize options, where a random subset is taken from a single training file)");
- Optionpk<string> label_opt("label", "label", "identifier for class label in
training shape file.","label");
+ Optionpk<string> training_opt("t", "training", "training vector file. A
single vector file contains all training features (must be set as: B0, B1,
B2,...) for all classes (class numbers identified by label option). Use
multiple training files for bootstrap aggregation (alternative to the bag and
bsize options, where a random subset is taken from a single training file)");
+ Optionpk<string> tlayer_opt("tln", "tln", "training layer name","");
+ Optionpk<string> label_opt("label", "label", "identifier for class label in
training vector file.","label");
Optionpk<unsigned int> balance_opt("bal", "balance", "balance the input data
to this number of samples for each class", 0);
Optionpk<bool> random_opt("random", "random", "in case of balance, randomize
input data", true);
Optionpk<int> minSize_opt("min", "min", "if number of training pixels is
less then min, do not take this class into account (0: consider all classes)",
0);
@@ -99,6 +100,7 @@ int main(int argc, char *argv[])
try{
doProcess=input_opt.retrieveOption(argc,argv);
training_opt.retrieveOption(argc,argv);
+ tlayer_opt.retrieveOption(argc,argv);
label_opt.retrieveOption(argc,argv);
balance_opt.retrieveOption(argc,argv);
random_opt.retrieveOption(argc,argv);
@@ -174,7 +176,7 @@ int main(int argc, char *argv[])
std::cout << "input filename: " << input_opt[0] << std::endl;
if(mask_opt.size())
std::cout << "mask filename: " << mask_opt[0] << std::endl;
- std::cout << "training shape file: " << std::endl;
+ std::cout << "training vector file: " << std::endl;
for(int ifile=0;ifile<training_opt.size();++ifile)
std::cout << training_opt[ifile] << std::endl;
std::cout << "verbose: " << verbose_opt[0] << std::endl;
@@ -249,13 +251,13 @@ int main(int argc, char *argv[])
trainingMap.clear();
trainingPixels.clear();
if(verbose_opt[0]>=1)
- std::cout << "reading imageShape file " << training_opt[0] <<
std::endl;
+ std::cout << "reading imageVector file " << training_opt[0] <<
std::endl;
try{
ImgReaderOgr trainingReaderBag(training_opt[ibag]);
if(band_opt.size())
-
totalSamples=trainingReaderBag.readDataImageShape(trainingMap,fields,band_opt,label_opt[0],verbose_opt[0]);
+
totalSamples=trainingReaderBag.readDataImageOgr(trainingMap,fields,band_opt,label_opt[0],tlayer_opt[0],verbose_opt[0]);
else
-
totalSamples=trainingReaderBag.readDataImageShape(trainingMap,fields,start_opt[0],end_opt[0],label_opt[0],verbose_opt[0]);
+
totalSamples=trainingReaderBag.readDataImageOgr(trainingMap,fields,start_opt[0],end_opt[0],tlayer_opt[0],label_opt[0],verbose_opt[0]);
if(trainingMap.size()<2){
string errorstring="Error: could not read at least two classes from
training file, did you provide class labels in training sample (see option
label)?";
throw(errorstring);
@@ -960,9 +962,9 @@ int main(int argc, char *argv[])
classImageBag.close();
classImageOut.close();
}
- else{//classify shape file
+ else{//classify vector file
cm.clearResults();
- //notice that fields have already been set by readDataImageShape (taking
into account appropriate bands)
+ //notice that fields have already been set by readDataImageOgr (taking
into account appropriate bands)
for(int ivalidation=0;ivalidation<input_opt.size();++ivalidation){
if(output_opt.size())
assert(output_opt.size()==input_opt.size());
diff --git a/src/imageclasses/ImgReaderOgr.cc b/src/imageclasses/ImgReaderOgr.cc
index 500dac9..8472796 100644
--- a/src/imageclasses/ImgReaderOgr.cc
+++ b/src/imageclasses/ImgReaderOgr.cc
@@ -217,10 +217,11 @@ std::ostream& operator<<(std::ostream& theOstream,
ImgReaderOgr& theImageReader)
// }
// }
-unsigned int
ImgReaderOgr::readDataImageShape(std::map<std::string,Vector2d<float> >
&mapPixels, //[classNr][pixelNr][bandNr],
+unsigned int
ImgReaderOgr::readDataImageOgr(std::map<std::string,Vector2d<float> >
&mapPixels, //[classNr][pixelNr][bandNr],
std::vector<std::string>& fields,
const std::vector<short>& bands,
const std::string& label,
+ const std::string& layername,
int verbose)
{
mapPixels.clear();
@@ -230,6 +231,10 @@ unsigned int
ImgReaderOgr::readDataImageShape(std::map<std::string,Vector2d<floa
if(verbose)
std::cout << "reading shape file " << m_filename << std::endl;
for(int ilayer=0;ilayer<getLayerCount();++ilayer){
+ std::string currentLayername=getLayer(ilayer)->GetName();
+ if(!layername.empty())
+ if(currentLayername!=layername)
+ continue;
try{
//only retain bands in fields
getFields(fields,ilayer);
@@ -295,11 +300,12 @@ unsigned int
ImgReaderOgr::readDataImageShape(std::map<std::string,Vector2d<floa
return totalSamples;
}
-unsigned int
ImgReaderOgr::readDataImageShape(std::map<std::string,Vector2d<float> >
&mapPixels, //[classNr][pixelNr][bandNr],
+unsigned int
ImgReaderOgr::readDataImageOgr(std::map<std::string,Vector2d<float> >
&mapPixels, //[classNr][pixelNr][bandNr],
std::vector<std::string>& fields,
double start,
double end,
const std::string& label,
+ const std::string& layername,
int verbose)
{
mapPixels.clear();
@@ -309,6 +315,10 @@ unsigned int
ImgReaderOgr::readDataImageShape(std::map<std::string,Vector2d<floa
if(verbose)
std::cout << "reading shape file " << m_filename << std::endl;
for(int ilayer=0;ilayer<getLayerCount();++ilayer){
+ std::string currentLayername=getLayer(ilayer)->GetName();
+ if(!layername.empty())
+ if(currentLayername!=layername)
+ continue;
try{
//only retain bands in fields
getFields(fields,ilayer);
diff --git a/src/imageclasses/ImgReaderOgr.h b/src/imageclasses/ImgReaderOgr.h
index 36653e6..12e48c4 100644
--- a/src/imageclasses/ImgReaderOgr.h
+++ b/src/imageclasses/ImgReaderOgr.h
@@ -48,18 +48,20 @@ public:
template <typename T> int readData(Vector2d<T>& data, const OGRFieldType&
fieldType, std::vector<std::string>& fields, int layer=0, bool pos=false, bool
verbose=false);//default layer 0 and no pos information in data
template <typename T> int readData(std::map<int,Vector2d<T> >& data, const
OGRFieldType& fieldType, std::vector<std::string>& fields, const std::string&
label, int layer=0, bool pos=false, bool verbose=false);//default layer 0 and
no pos information in data
template <typename T> int readData(std::map<std::string,Vector2d<T> >& data,
const OGRFieldType& fieldType, std::vector<std::string>& fields, const
std::string& label, int layer=0, bool pos=false, bool verbose=false);//default
layer 0 and no pos information in data
- unsigned int readDataImageShape(std::map<std::string,Vector2d<float> >
&mapPixels, //[classNr][pixelNr][bandNr],
-
std::vector<std::string>& fields,
- const
std::vector<short>& bands,
- const std::string& label,
- int verbose=false);
+ unsigned int readDataImageOgr(std::map<std::string,Vector2d<float> >
&mapPixels, //[classNr][pixelNr][bandNr],
+ std::vector<std::string>& fields,
+ const std::vector<short>& bands,
+ const std::string& label,
+ const std::string& layername=std::string(),
+ int verbose=false);
- unsigned int readDataImageShape(std::map<std::string,Vector2d<float> >
&mapPixels, //[classNr][pixelNr][bandNr],
-
std::vector<std::string>& fields,
- double start,
- double end,
- const std::string& label,
- int verbose=false);
+ unsigned int readDataImageOgr(std::map<std::string,Vector2d<float> >
&mapPixels, //[classNr][pixelNr][bandNr],
+ std::vector<std::string>& fields,
+ double start,
+ double end,
+ const std::string& label,
+ const std::string& layername=std::string(),
+ int verbose=false);
void shape2ascii(std::ostream& theOstream, const std::string& pointname, int
layer=0, bool verbose=false);
unsigned long int getFeatureCount(int layer=0) const;
--
Alioth's /usr/local/bin/git-commit-notice on
/srv/git.debian.org/git/pkg-grass/pktools.git
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