[pktools] 268/375: default SQLite in pkdiff
Bas Couwenberg
sebastic at xs4all.nl
Wed Dec 3 21:54:20 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 88204b80caed0fa9ec3428f42424106dc56bd94a
Author: Pieter Kempeneers <kempenep at gmail.com>
Date: Tue May 13 23:06:48 2014 +0200
default SQLite in pkdiff
---
src/apps/pkdiff.cc | 32 ++++++++++++++++----------------
src/apps/pkextract.cc | 10 ++++++----
src/apps/pkoptsvm.cc | 22 ++++++++++------------
3 files changed, 32 insertions(+), 32 deletions(-)
diff --git a/src/apps/pkdiff.cc b/src/apps/pkdiff.cc
index da98e98..c7bb770 100644
--- a/src/apps/pkdiff.cc
+++ b/src/apps/pkdiff.cc
@@ -32,18 +32,18 @@ int main(int argc, char *argv[])
Optionpk<string> input_opt("i", "input", "Input image file.");
Optionpk<string> reference_opt("ref", "reference", "Reference image file");
Optionpk<string> output_opt("o", "output", "Output image file. Default is empty: no output image, only report difference or identical.");
- Optionpk<string> ogrformat_opt("f", "f", "Output sample file format","ESRI Shapefile");
+ Optionpk<string> ogrformat_opt("f", "f", "Output sample file format","SQLite");
Optionpk<string> mask_opt("m", "mask", "Mask image file. A single mask is supported only, but several mask values can be used. See also msknodata option. (default is empty)");
Optionpk<int> masknodata_opt("msknodata", "msknodata", "Mask value(s) where image is invalid. Use negative value for valid data (example: use -t -1: if only -1 is valid value)", 0);
Optionpk<string> colorTable_opt("ct", "ct", "color table (file with 5 columns: id R G B ALFA (0: transparent, 255: solid)");
- Optionpk<short> valueE_opt("\0", "correct", "Value for correct pixels", 0,1);
- Optionpk<short> valueO_opt("\0", "omission", "Value for omission errors: input label > reference label", 1,1);
+ Optionpk<short> valueE_opt("\0", "correct", "Value for correct pixels", 0,2);
+ Optionpk<short> valueO_opt("\0", "omission", "Value for omission errors: input label > reference label", 1,2);
Optionpk<short> valueC_opt("\0", "commission", "Value for commission errors: input label < reference label", 2,1);
Optionpk<short> nodata_opt("nodata", "nodata", "No value flag(s)");
Optionpk<short> band_opt("b", "band", "Input band", 0);
Optionpk<bool> confusion_opt("cm", "confusion", "create confusion matrix (to std out)", false);
- Optionpk<string> labelref_opt("lr", "lref", "name of the reference label in case reference is shape file", "label");
- Optionpk<string> labelclass_opt("lc", "lclass", "name of the classified label in case output is shape file", "class");
+ Optionpk<string> labelref_opt("lr", "lref", "name of the reference label in case reference is OGR vector file", "label");
+ Optionpk<string> labelclass_opt("lc", "lclass", "name of the classified label in case output is OGR vector file", "class");
Optionpk<short> boundary_opt("bnd", "boundary", "boundary for selecting the sample", 1,1);
Optionpk<bool> disc_opt("circ", "circular", "use circular disc kernel boundary)", false,1);
Optionpk<bool> homogeneous_opt("hom", "homogeneous", "only take homogeneous regions into account", false,1);
@@ -55,27 +55,27 @@ int main(int argc, char *argv[])
bool doProcess;//stop process when program was invoked with help option (-h --help)
try{
doProcess=input_opt.retrieveOption(argc,argv);
- output_opt.retrieveOption(argc,argv);
- ogrformat_opt.retrieveOption(argc,argv);
- option_opt.retrieveOption(argc,argv);
reference_opt.retrieveOption(argc,argv);
+ confusion_opt.retrieveOption(argc,argv);
+ labelref_opt.retrieveOption(argc,argv);
+ classname_opt.retrieveOption(argc,argv);
+ classvalue_opt.retrieveOption(argc,argv);
+ nodata_opt.retrieveOption(argc,argv);
mask_opt.retrieveOption(argc,argv);
+ masknodata_opt.retrieveOption(argc,argv);
+ output_opt.retrieveOption(argc,argv);
+ ogrformat_opt.retrieveOption(argc,argv);
+ labelclass_opt.retrieveOption(argc,argv);
colorTable_opt.retrieveOption(argc,argv);
+ option_opt.retrieveOption(argc,argv);
valueE_opt.retrieveOption(argc,argv);
valueO_opt.retrieveOption(argc,argv);
valueC_opt.retrieveOption(argc,argv);
- nodata_opt.retrieveOption(argc,argv);
- masknodata_opt.retrieveOption(argc,argv);
band_opt.retrieveOption(argc,argv);
- confusion_opt.retrieveOption(argc,argv);
- labelref_opt.retrieveOption(argc,argv);
- labelclass_opt.retrieveOption(argc,argv);
// class_opt.retrieveOption(argc,argv);
boundary_opt.retrieveOption(argc,argv);
disc_opt.retrieveOption(argc,argv);
homogeneous_opt.retrieveOption(argc,argv);
- classname_opt.retrieveOption(argc,argv);
- classvalue_opt.retrieveOption(argc,argv);
verbose_opt.retrieveOption(argc,argv);
}
catch(string predefinedString){
@@ -539,7 +539,7 @@ int main(int argc, char *argv[])
if(verbose_opt[0])
cout << "creating feature" << endl;
if(writeLayer->CreateFeature( writeFeature ) != OGRERR_NONE ){
- string errorString="Failed to create feature in shapefile";
+ string errorString="Failed to create feature in OGR vector file";
throw(errorString);
}
}
diff --git a/src/apps/pkextract.cc b/src/apps/pkextract.cc
index 56231e5..e771a18 100644
--- a/src/apps/pkextract.cc
+++ b/src/apps/pkextract.cc
@@ -124,10 +124,12 @@ int main(int argc, char *argv[])
ruleMap["maxvote"]=rule::maxvote;
ruleMap["sum"]=rule::sum;
- while(srcnodata_opt.size()<bndnodata_opt.size())
- srcnodata_opt.push_back(srcnodata_opt[0]);
- while(bndnodata_opt.size()<srcnodata_opt.size())
- bndnodata_opt.push_back(bndnodata_opt[0]);
+ if(srcnodata_opt.size()){
+ while(srcnodata_opt.size()<bndnodata_opt.size())
+ srcnodata_opt.push_back(srcnodata_opt[0]);
+ while(bndnodata_opt.size()<srcnodata_opt.size())
+ bndnodata_opt.push_back(bndnodata_opt[0]);
+ }
if(verbose_opt[0])
std::cout << class_opt << std::endl;
diff --git a/src/apps/pkoptsvm.cc b/src/apps/pkoptsvm.cc
index 864ccaf..ffcc488 100644
--- a/src/apps/pkoptsvm.cc
+++ b/src/apps/pkoptsvm.cc
@@ -101,7 +101,7 @@ double objFunction(const std::vector<double> &x, std::vector<double> &grad, void
ntest+=nctest[iclass];
}
if(ntest)
- assert(!cv_opt[0]);
+ cv_opt[0]=0;
if(!cv_opt[0])
assert(ntest);
// ntraining+=(*tf)[iclass].size();
@@ -248,8 +248,7 @@ int main(int argc, char *argv[])
Optionpk<unsigned int> maxit_opt("maxit","maxit","maximum number of iterations",500);
Optionpk<string> algorithm_opt("a", "algorithm", "GRID, or any optimization algorithm from http://ab-initio.mit.edu/wiki/index.php/NLopt_Algorithms","GRID");
Optionpk<double> tolerance_opt("tol","tolerance","relative tolerance for stopping criterion",0.0001);
- Optionpk<double> stepcc_opt("stepcc","stepcc","multiplicative step for ccost in GRID search",2);
- Optionpk<double> stepg_opt("stepg","stepg","multiplicative step for gamma in GRID search",2);
+ Optionpk<double> step_opt("step","step","multiplicative step for ccost and gamma in GRID search",2);
bool doProcess;//stop process when program was invoked with help option (-h --help)
try{
@@ -259,7 +258,7 @@ int main(int argc, char *argv[])
label_opt.retrieveOption(argc,argv);
// reclass_opt.retrieveOption(argc,argv);
balance_opt.retrieveOption(argc,argv);
- random_opt.retrieveOption(argc,argv);
+ random_opt.retrieveOption(argc,argv);
minSize_opt.retrieveOption(argc,argv);
start_opt.retrieveOption(argc,argv);
end_opt.retrieveOption(argc,argv);
@@ -282,8 +281,7 @@ int main(int argc, char *argv[])
costfunction_opt.retrieveOption(argc,argv);
maxit_opt.retrieveOption(argc,argv);
tolerance_opt.retrieveOption(argc,argv);
- stepcc_opt.retrieveOption(argc,argv);
- stepg_opt.retrieveOption(argc,argv);
+ step_opt.retrieveOption(argc,argv);
algorithm_opt.retrieveOption(argc,argv);
classname_opt.retrieveOption(argc,argv);
classvalue_opt.retrieveOption(argc,argv);
@@ -596,8 +594,8 @@ int main(int argc, char *argv[])
std::vector<double> x(2);
if(algorithm_opt[0]=="GRID"){
- // if(step_opt.size()<2)//[0] for cost, [1] for gamma
- // step_opt.push_back(step_opt.back());
+ if(step_opt.size()<2)//[0] for cost, [1] for gamma
+ step_opt.push_back(step_opt.back());
double minError=1000;
double minCost=0;
double minGamma=0;
@@ -607,10 +605,10 @@ int main(int argc, char *argv[])
double progress=0;
if(!verbose_opt[0])
pfnProgress(progress,pszMessage,pProgressArg);
- double ncost=log(ccost_opt[1])/log(10.0)-log(ccost_opt[0])/log(10.0);
- double ngamma=log(gamma_opt[1])/log(10.0)-log(gamma_opt[0])/log(10.0);
- for(double ccost=ccost_opt[0];ccost<=ccost_opt[1];ccost*=stepcc_opt[0]){
- for(double gamma=gamma_opt[0];gamma<=gamma_opt[1];gamma*=stepg_opt[0]){
+ double ncost=log(ccost_opt[1])/log(step_opt[0])-log(ccost_opt[0])/log(step_opt[0]);
+ double ngamma=log(gamma_opt[1])/log(step_opt[1])-log(gamma_opt[0])/log(step_opt[1]);
+ for(double ccost=ccost_opt[0];ccost<=ccost_opt[1];ccost*=step_opt[0]){
+ for(double gamma=gamma_opt[0];gamma<=gamma_opt[1];gamma*=step_opt[1]){
x[0]=ccost;
x[1]=gamma;
std::vector<double> theGrad;
--
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