[pktools] 337/375: replaced options start and end with bstart and bend in pksvm and pkann
Bas Couwenberg
sebastic at xs4all.nl
Wed Dec 3 21:54:27 UTC 2014
This is an automated email from the git hooks/post-receive script.
sebastic-guest pushed a commit to branch upstream-master
in repository pktools.
commit 15b59bd67ff029c6d8097467bba9448ecd5b11d7
Author: Pieter Kempeneers <kempenep at gmail.com>
Date: Sun Oct 12 22:09:26 2014 +0200
replaced options start and end with bstart and bend in pksvm and pkann
---
ChangeLog | 11 ++++++++++-
src/apps/pkann.cc | 12 ++++++------
src/apps/pkfsann.cc | 12 ++++++------
src/apps/pkfssvm.cc | 12 ++++++------
src/apps/pkoptsvm.cc | 12 ++++++------
src/apps/pksvm.cc | 12 ++++++------
6 files changed, 40 insertions(+), 31 deletions(-)
diff --git a/ChangeLog b/ChangeLog
index 7dda69c..6a522b6 100755
--- a/ChangeLog
+++ b/ChangeLog
@@ -317,9 +317,18 @@ version 2.5.4
support statistic rules (mean, stdev, median, etc.) for point features by taking into account buffer (default= 3 by 3 pixels). If option -polygon is set, output ogr features are polygons defining the buffer.
changed names for maximum and minumum rule to max and min respectively
new options -rand and -grid to support simple random sampling and systematic grid (do not provide sample vector dataset)
+ - pksvm
+ replaced options s|start and e|end with bs|bstart and be|bend
+ - pkann
+ replaced options s|start and e|end with bs|bstart and be|bend
+ - pkfssvm
+ replaced options s|start and e|end with bs|bstart and be|bend
+ - pkfsann
+ replaced options s|start and e|end with bs|bstart and be|bend
+ - pkoptsvm
+ replaced options s|start and e|end with bs|bstart and be|bend
- ImgWriteOgr
overwrite existing ogr datasets per default
-
Next versions:
- todo for API
ImgReaderGdal (ImgWriterGdal) open in update mode (check gdal_edit.py: http://searchcode.com/codesearch/view/18938404)
diff --git a/src/apps/pkann.cc b/src/apps/pkann.cc
index bccd66d..64c34e4 100644
--- a/src/apps/pkann.cc
+++ b/src/apps/pkann.cc
@@ -45,9 +45,9 @@ int main(int argc, char *argv[])
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,2);
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);
- Optionpk<double> start_opt("s", "start", "start band sequence number",0);
- Optionpk<double> end_opt("e", "end", "end band sequence number (set to 0 to include bands)", 0);
Optionpk<short> band_opt("b", "band", "band index (starting from 0, either use band option or use start to end)");
+ Optionpk<double> bstart_opt("bs", "bstart", "start band sequence number",0);
+ Optionpk<double> bend_opt("be", "bend", "end band sequence number (set to 0 to include bands)", 0);
Optionpk<double> offset_opt("\0", "offset", "offset value for each spectral band input features: refl[band]=(DN[band]-offset[band])/scale[band]", 0.0);
Optionpk<double> scale_opt("\0", "scale", "scale value for each spectral band input features: refl=(DN[band]-offset[band])/scale[band] (use 0 if scale min and max in each band to -1.0 and 1.0)", 0.0);
Optionpk<unsigned short> aggreg_opt("a", "aggreg", "how to combine aggregated classifiers, see also rc option (1: sum rule, 2: max rule).",1);
@@ -89,9 +89,9 @@ int main(int argc, char *argv[])
balance_opt.retrieveOption(argc,argv);
random_opt.retrieveOption(argc,argv);
minSize_opt.retrieveOption(argc,argv);
- start_opt.retrieveOption(argc,argv);
- end_opt.retrieveOption(argc,argv);
band_opt.retrieveOption(argc,argv);
+ bstart_opt.retrieveOption(argc,argv);
+ bend_opt.retrieveOption(argc,argv);
offset_opt.retrieveOption(argc,argv);
scale_opt.retrieveOption(argc,argv);
aggreg_opt.retrieveOption(argc,argv);
@@ -222,7 +222,7 @@ int main(int argc, char *argv[])
if(band_opt.size())
totalSamples=trainingReaderBag.readDataImageOgr(trainingMap,fields,band_opt,label_opt[0],tlayer_opt,verbose_opt[0]);
else
- totalSamples=trainingReaderBag.readDataImageOgr(trainingMap,fields,start_opt[0],end_opt[0],label_opt[0],tlayer_opt,verbose_opt[0]);
+ totalSamples=trainingReaderBag.readDataImageOgr(trainingMap,fields,bstart_opt[0],bend_opt[0],label_opt[0],tlayer_opt,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);
@@ -723,7 +723,7 @@ int main(int argc, char *argv[])
}
}
else{
- for(int iband=start_opt[0];iband<start_opt[0]+nband;++iband){
+ for(int iband=bstart_opt[0];iband<bstart_opt[0]+nband;++iband){
if(verbose_opt[0]==2)
std::cout << "reading band " << iband << std::endl;
assert(iband>=0);
diff --git a/src/apps/pkfsann.cc b/src/apps/pkfsann.cc
index 9bee67d..d155158 100644
--- a/src/apps/pkfsann.cc
+++ b/src/apps/pkfsann.cc
@@ -179,9 +179,9 @@ int main(int argc, char *argv[])
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);
Optionpk<int> minSize_opt("min", "min", "if number of training pixels is less then min, do not take this class into account", 0);
- Optionpk<double> start_opt("s", "start", "start band sequence number",0);
- Optionpk<double> end_opt("e", "end", "end band sequence number (set to 0 to include all bands)", 0);
Optionpk<short> band_opt("b", "band", "band index (starting from 0, either use band option or use start to end)");
+ Optionpk<double> bstart_opt("bs", "bstart", "start band sequence number",0);
+ Optionpk<double> bend_opt("be", "bend", "end band sequence number (set to 0 to include all bands)", 0);
Optionpk<double> offset_opt("\0", "offset", "offset value for each spectral band input features: refl[band]=(DN[band]-offset[band])/scale[band]", 0.0);
Optionpk<double> scale_opt("\0", "scale", "scale value for each spectral band input features: refl=(DN[band]-offset[band])/scale[band] (use 0 if scale min and max in each band to -1.0 and 1.0)", 0.0);
Optionpk<unsigned short> aggreg_opt("a", "aggreg", "how to combine aggregated classifiers, see also rc option (0: no aggregation, 1: sum rule, 2: max rule).",0);
@@ -208,9 +208,9 @@ int main(int argc, char *argv[])
balance_opt.retrieveOption(argc,argv);
random_opt.retrieveOption(argc,argv);
minSize_opt.retrieveOption(argc,argv);
- start_opt.retrieveOption(argc,argv);
- end_opt.retrieveOption(argc,argv);
band_opt.retrieveOption(argc,argv);
+ bstart_opt.retrieveOption(argc,argv);
+ bend_opt.retrieveOption(argc,argv);
offset_opt.retrieveOption(argc,argv);
scale_opt.retrieveOption(argc,argv);
aggreg_opt.retrieveOption(argc,argv);
@@ -316,10 +316,10 @@ int main(int argc, char *argv[])
}
}
else{
- totalSamples=trainingReader.readDataImageOgr(trainingMap,fields,start_opt[0],end_opt[0],label_opt[0],tlayer_opt,verbose_opt[0]);
+ totalSamples=trainingReader.readDataImageOgr(trainingMap,fields,bstart_opt[0],bend_opt[0],label_opt[0],tlayer_opt,verbose_opt[0]);
if(input_opt.size()){
ImgReaderOgr inputReader(input_opt[0]);
- totalTestSamples=trainingReader.readDataImageOgr(testMap,fields,start_opt[0],end_opt[0],label_opt[0],tlayer_opt,verbose_opt[0]);
+ totalTestSamples=trainingReader.readDataImageOgr(testMap,fields,bstart_opt[0],bend_opt[0],label_opt[0],tlayer_opt,verbose_opt[0]);
inputReader.close();
}
}
diff --git a/src/apps/pkfssvm.cc b/src/apps/pkfssvm.cc
index 3a8a222..67c96d0 100644
--- a/src/apps/pkfssvm.cc
+++ b/src/apps/pkfssvm.cc
@@ -205,9 +205,9 @@ int main(int argc, char *argv[])
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);
- Optionpk<double> start_opt("s", "start", "start band sequence number",0);
- Optionpk<double> end_opt("e", "end", "end band sequence number (set to 0 to include all bands)", 0);
Optionpk<short> band_opt("b", "band", "band index (starting from 0, either use band option or use start to end)");
+ Optionpk<double> bstart_opt("bs", "bstart", "start band sequence number",0);
+ Optionpk<double> bend_opt("be", "bend", "end band sequence number (set to 0 to include all bands)", 0);
Optionpk<double> offset_opt("\0", "offset", "offset value for each spectral band input features: refl[band]=(DN[band]-offset[band])/scale[band]", 0.0);
Optionpk<double> scale_opt("\0", "scale", "scale value for each spectral band input features: refl=(DN[band]-offset[band])/scale[band] (use 0 if scale min and max in each band to -1.0 and 1.0)", 0.0);
Optionpk<string> selector_opt("sm", "sm", "feature selection method (sffs=sequential floating forward search,sfs=sequential forward search, sbs, sequential backward search ,bfs=brute force search)","sffs");
@@ -240,9 +240,9 @@ int main(int argc, char *argv[])
balance_opt.retrieveOption(argc,argv);
random_opt.retrieveOption(argc,argv);
minSize_opt.retrieveOption(argc,argv);
- start_opt.retrieveOption(argc,argv);
- end_opt.retrieveOption(argc,argv);
band_opt.retrieveOption(argc,argv);
+ bstart_opt.retrieveOption(argc,argv);
+ bend_opt.retrieveOption(argc,argv);
offset_opt.retrieveOption(argc,argv);
scale_opt.retrieveOption(argc,argv);
// priors_opt.retrieveOption(argc,argv);
@@ -350,10 +350,10 @@ int main(int argc, char *argv[])
}
}
else{
- totalSamples=trainingReader.readDataImageOgr(trainingMap,fields,start_opt[0],end_opt[0],label_opt[0],tlayer_opt,verbose_opt[0]);
+ totalSamples=trainingReader.readDataImageOgr(trainingMap,fields,bstart_opt[0],bend_opt[0],label_opt[0],tlayer_opt,verbose_opt[0]);
if(input_opt.size()){
ImgReaderOgr inputReader(input_opt[0]);
- totalTestSamples=inputReader.readDataImageOgr(testMap,fields,start_opt[0],end_opt[0],label_opt[0],tlayer_opt,verbose_opt[0]);
+ totalTestSamples=inputReader.readDataImageOgr(testMap,fields,bstart_opt[0],bend_opt[0],label_opt[0],tlayer_opt,verbose_opt[0]);
inputReader.close();
}
}
diff --git a/src/apps/pkoptsvm.cc b/src/apps/pkoptsvm.cc
index 83fd8cf..0452469 100644
--- a/src/apps/pkoptsvm.cc
+++ b/src/apps/pkoptsvm.cc
@@ -266,9 +266,9 @@ int main(int argc, char *argv[])
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);
- Optionpk<double> start_opt("s", "start", "start band sequence number",0);
- Optionpk<double> end_opt("e", "end", "end band sequence number (set to 0 to include all bands)", 0);
Optionpk<short> band_opt("b", "band", "band index (starting from 0, either use band option or use start to end)");
+ Optionpk<double> bstart_opt("bs", "bstart", "start band sequence number",0);
+ Optionpk<double> bend_opt("be", "bend", "bend band sequence number (set to 0 to include all bands)", 0);
Optionpk<double> offset_opt("\0", "offset", "offset value for each spectral band input features: refl[band]=(DN[band]-offset[band])/scale[band]", 0.0);
Optionpk<double> scale_opt("\0", "scale", "scale value for each spectral band input features: refl=(DN[band]-offset[band])/scale[band] (use 0 if scale min and max in each band to -1.0 and 1.0)", 0.0);
Optionpk<float> gamma_opt("g", "gamma", "min max boundaries for gamma in kernel function (optional: initial value)",0);
@@ -288,9 +288,9 @@ int main(int argc, char *argv[])
balance_opt.retrieveOption(argc,argv);
random_opt.retrieveOption(argc,argv);
minSize_opt.retrieveOption(argc,argv);
- start_opt.retrieveOption(argc,argv);
- end_opt.retrieveOption(argc,argv);
band_opt.retrieveOption(argc,argv);
+ bstart_opt.retrieveOption(argc,argv);
+ bend_opt.retrieveOption(argc,argv);
offset_opt.retrieveOption(argc,argv);
scale_opt.retrieveOption(argc,argv);
svm_type_opt.retrieveOption(argc,argv);
@@ -393,10 +393,10 @@ int main(int argc, char *argv[])
}
}
else{
- totalSamples=trainingReader.readDataImageOgr(trainingMap,fields,start_opt[0],end_opt[0],label_opt[0],tlayer_opt,verbose_opt[0]);
+ totalSamples=trainingReader.readDataImageOgr(trainingMap,fields,bstart_opt[0],bend_opt[0],label_opt[0],tlayer_opt,verbose_opt[0]);
if(input_opt.size()){
ImgReaderOgr inputReader(input_opt[0]);
- totalTestSamples=inputReader.readDataImageOgr(testMap,fields,start_opt[0],end_opt[0],label_opt[0],tlayer_opt,verbose_opt[0]);
+ totalTestSamples=inputReader.readDataImageOgr(testMap,fields,bstart_opt[0],bend_opt[0],label_opt[0],tlayer_opt,verbose_opt[0]);
inputReader.close();
}
trainingReader.close();
diff --git a/src/apps/pksvm.cc b/src/apps/pksvm.cc
index 12ce0e6..d5532f6 100644
--- a/src/apps/pksvm.cc
+++ b/src/apps/pksvm.cc
@@ -55,9 +55,9 @@ int main(int argc, char *argv[])
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", "Randomize training data for balancing and bagging", true, 2);
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);
- Optionpk<double> start_opt("s", "start", "Start band sequence number",0);
- Optionpk<double> end_opt("e", "end", "End band sequence number (set to 0 to include all bands)", 0);
Optionpk<short> band_opt("b", "band", "Band index (starting from 0, either use band option or use start to end)");
+ Optionpk<double> bstart_opt("bs", "bstart", "Start band sequence number",0);
+ Optionpk<double> bend_opt("be", "bend", "End band sequence number (set to 0 to include all bands)", 0);
Optionpk<double> offset_opt("\0", "offset", "Offset value for each spectral band input features: refl[band]=(DN[band]-offset[band])/scale[band]", 0.0);
Optionpk<double> scale_opt("\0", "scale", "Scale value for each spectral band input features: refl=(DN[band]-offset[band])/scale[band] (use 0 if scale min and max in each band to -1.0 and 1.0)", 0.0);
Optionpk<double> priors_opt("p", "prior", "Prior probabilities for each class (e.g., -p 0.3 -p 0.3 -p 0.2 ). Used for input only (ignored for cross validation)", 0.0);
@@ -117,8 +117,8 @@ int main(int argc, char *argv[])
msknodata_opt.retrieveOption(argc,argv);
nodata_opt.retrieveOption(argc,argv);
band_opt.retrieveOption(argc,argv);
- start_opt.retrieveOption(argc,argv);
- end_opt.retrieveOption(argc,argv);
+ bstart_opt.retrieveOption(argc,argv);
+ bend_opt.retrieveOption(argc,argv);
balance_opt.retrieveOption(argc,argv);
minSize_opt.retrieveOption(argc,argv);
bag_opt.retrieveOption(argc,argv);
@@ -265,7 +265,7 @@ int main(int argc, char *argv[])
if(band_opt.size())
totalSamples=trainingReaderBag.readDataImageOgr(trainingMap,fields,band_opt,label_opt[0],tlayer_opt,verbose_opt[0]);
else
- totalSamples=trainingReaderBag.readDataImageOgr(trainingMap,fields,start_opt[0],end_opt[0],label_opt[0],tlayer_opt,verbose_opt[0]);
+ totalSamples=trainingReaderBag.readDataImageOgr(trainingMap,fields,bstart_opt[0],bend_opt[0],label_opt[0],tlayer_opt,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);
@@ -716,7 +716,7 @@ int main(int argc, char *argv[])
}
}
else{
- for(int iband=start_opt[0];iband<start_opt[0]+nband;++iband){
+ for(int iband=bstart_opt[0];iband<bstart_opt[0]+nband;++iband){
if(verbose_opt[0]==2)
std::cout << "reading band " << iband << std::endl;
assert(iband>=0);
--
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