24 #include "imageclasses/ImgReaderGdal.h"
25 #include "imageclasses/ImgWriterGdal.h"
26 #include "imageclasses/ImgReaderOgr.h"
27 #include "imageclasses/ImgWriterOgr.h"
28 #include "base/Optionpk.h"
29 #include "base/PosValue.h"
30 #include "algorithms/ConfusionMatrix.h"
31 #include "algorithms/svm.h"
119 enum SVM_TYPE {C_SVC=0, nu_SVC=1,one_class=2, epsilon_SVR=3, nu_SVR=4};
120 enum KERNEL_TYPE {linear=0,polynomial=1,radial=2,sigmoid=3};
123 #define Malloc(type,n) (type *)malloc((n)*sizeof(type))
127 int main(
int argc,
char *argv[])
129 vector<double> priors;
133 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)");
135 Optionpk<string> label_opt(
"label",
"label",
"Attribute name for class label in training vector file.",
"label");
136 Optionpk<unsigned int> balance_opt(
"bal",
"balance",
"Balance the input data to this number of samples for each class", 0);
137 Optionpk<bool> random_opt(
"random",
"random",
"Randomize training data for balancing and bagging",
true, 2);
138 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);
139 Optionpk<unsigned short> band_opt(
"b",
"band",
"Band index (starting from 0, either use band option or use start to end)");
142 Optionpk<double> offset_opt(
"offset",
"offset",
"Offset value for each spectral band input features: refl[band]=(DN[band]-offset[band])/scale[band]", 0.0);
143 Optionpk<double> scale_opt(
"scale",
"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);
144 Optionpk<double> priors_opt(
"prior",
"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);
145 Optionpk<string> priorimg_opt(
"pim",
"priorimg",
"Prior probability image (multi-band img with band for each class",
"",2);
147 Optionpk<string> cmformat_opt(
"cmf",
"cmf",
"Format for confusion matrix (ascii or latex)",
"ascii");
148 Optionpk<std::string> svm_type_opt(
"svmt",
"svmtype",
"Type of SVM (C_SVC, nu_SVC,one_class, epsilon_SVR, nu_SVR)",
"C_SVC");
149 Optionpk<std::string> kernel_type_opt(
"kt",
"kerneltype",
"Type of kernel function (linear,polynomial,radial,sigmoid) ",
"radial");
151 Optionpk<float> gamma_opt(
"g",
"gamma",
"Gamma in kernel function",1.0);
152 Optionpk<float> coef0_opt(
"c0",
"coef0",
"Coef0 in kernel function",0);
153 Optionpk<float> ccost_opt(
"cc",
"ccost",
"The parameter C of C_SVC, epsilon_SVR, and nu_SVR",1000);
154 Optionpk<float> nu_opt(
"nu",
"nu",
"The parameter nu of nu_SVC, one_class SVM, and nu_SVR",0.5);
155 Optionpk<float> epsilon_loss_opt(
"eloss",
"eloss",
"The epsilon in loss function of epsilon_SVR",0.1);
156 Optionpk<int> cache_opt(
"cache",
"cache",
"Cache memory size in MB",100);
157 Optionpk<float> epsilon_tol_opt(
"etol",
"etol",
"The tolerance of termination criterion",0.001);
158 Optionpk<bool> shrinking_opt(
"shrink",
"shrink",
"Whether to use the shrinking heuristics",
false);
159 Optionpk<bool> prob_est_opt(
"pe",
"probest",
"Whether to train a SVC or SVR model for probability estimates",
true,2);
161 Optionpk<unsigned short> comb_opt(
"comb",
"comb",
"How to combine bootstrap aggregation classifiers (0: sum rule, 1: product rule, 2: max rule). Also used to aggregate classes with rc option.",0);
163 Optionpk<int> bagSize_opt(
"bagsize",
"bagsize",
"Percentage of features used from available training features for each bootstrap aggregation (one size for all classes, or a different size for each class respectively", 100);
164 Optionpk<string> classBag_opt(
"cb",
"classbag",
"Output for each individual bootstrap aggregation");
165 Optionpk<string> mask_opt(
"m",
"mask",
"Only classify within specified mask (vector or raster). For raster mask, set nodata values with the option msknodata.");
166 Optionpk<short> msknodata_opt(
"msknodata",
"msknodata",
"Mask value(s) not to consider for classification (use negative values if only these values should be taken into account). Values will be taken over in classification image.", 0);
169 Optionpk<string> oformat_opt(
"of",
"oformat",
"Output image format (see also gdal_translate). Empty string: inherit from input image");
170 Optionpk<string> option_opt(
"co",
"co",
"Creation option for output file. Multiple options can be specified.");
171 Optionpk<string> colorTable_opt(
"ct",
"ct",
"Color table in ASCII format having 5 columns: id R G B ALFA (0: transparent, 255: solid)");
173 Optionpk<string> entropy_opt(
"entropy",
"entropy",
"Entropy image (measure for uncertainty of classifier output",
"",2);
174 Optionpk<string> active_opt(
"active",
"active",
"Ogr output for active training sample.",
"",2);
175 Optionpk<string> ogrformat_opt(
"f",
"f",
"Output ogr format for active training sample",
"SQLite");
178 Optionpk<short> classvalue_opt(
"r",
"reclass",
"List of class values (use same order as in class opt).");
182 bstart_opt.setHide(1);
184 balance_opt.setHide(1);
185 minSize_opt.setHide(1);
187 bagSize_opt.setHide(1);
189 classBag_opt.setHide(1);
191 priorimg_opt.setHide(1);
192 offset_opt.setHide(1);
193 scale_opt.setHide(1);
194 svm_type_opt.setHide(1);
195 kernel_type_opt.setHide(1);
196 kernel_degree_opt.setHide(1);
197 coef0_opt.setHide(1);
199 epsilon_loss_opt.setHide(1);
200 cache_opt.setHide(1);
201 epsilon_tol_opt.setHide(1);
202 shrinking_opt.setHide(1);
203 prob_est_opt.setHide(1);
204 entropy_opt.setHide(1);
205 active_opt.setHide(1);
206 nactive_opt.setHide(1);
207 random_opt.setHide(1);
209 verbose_opt.setHide(2);
213 doProcess=training_opt.retrieveOption(argc,argv);
214 input_opt.retrieveOption(argc,argv);
215 output_opt.retrieveOption(argc,argv);
216 cv_opt.retrieveOption(argc,argv);
217 cmformat_opt.retrieveOption(argc,argv);
218 tlayer_opt.retrieveOption(argc,argv);
219 classname_opt.retrieveOption(argc,argv);
220 classvalue_opt.retrieveOption(argc,argv);
221 oformat_opt.retrieveOption(argc,argv);
222 ogrformat_opt.retrieveOption(argc,argv);
223 option_opt.retrieveOption(argc,argv);
224 colorTable_opt.retrieveOption(argc,argv);
225 label_opt.retrieveOption(argc,argv);
226 priors_opt.retrieveOption(argc,argv);
227 gamma_opt.retrieveOption(argc,argv);
228 ccost_opt.retrieveOption(argc,argv);
229 mask_opt.retrieveOption(argc,argv);
230 msknodata_opt.retrieveOption(argc,argv);
231 nodata_opt.retrieveOption(argc,argv);
233 band_opt.retrieveOption(argc,argv);
234 bstart_opt.retrieveOption(argc,argv);
235 bend_opt.retrieveOption(argc,argv);
236 balance_opt.retrieveOption(argc,argv);
237 minSize_opt.retrieveOption(argc,argv);
238 bag_opt.retrieveOption(argc,argv);
239 bagSize_opt.retrieveOption(argc,argv);
240 comb_opt.retrieveOption(argc,argv);
241 classBag_opt.retrieveOption(argc,argv);
242 prob_opt.retrieveOption(argc,argv);
243 priorimg_opt.retrieveOption(argc,argv);
244 offset_opt.retrieveOption(argc,argv);
245 scale_opt.retrieveOption(argc,argv);
246 svm_type_opt.retrieveOption(argc,argv);
247 kernel_type_opt.retrieveOption(argc,argv);
248 kernel_degree_opt.retrieveOption(argc,argv);
249 coef0_opt.retrieveOption(argc,argv);
250 nu_opt.retrieveOption(argc,argv);
251 epsilon_loss_opt.retrieveOption(argc,argv);
252 cache_opt.retrieveOption(argc,argv);
253 epsilon_tol_opt.retrieveOption(argc,argv);
254 shrinking_opt.retrieveOption(argc,argv);
255 prob_est_opt.retrieveOption(argc,argv);
256 entropy_opt.retrieveOption(argc,argv);
257 active_opt.retrieveOption(argc,argv);
258 nactive_opt.retrieveOption(argc,argv);
259 verbose_opt.retrieveOption(argc,argv);
260 random_opt.retrieveOption(argc,argv);
262 catch(
string predefinedString){
263 std::cout << predefinedString << std::endl;
268 cout <<
"Usage: pksvm -t training [-i input -o output] [-cv value]" << endl;
270 std::cout <<
"short option -h shows basic options only, use long option --help to show all options" << std::endl;
274 if(entropy_opt[0]==
"")
276 if(active_opt[0]==
"")
278 if(priorimg_opt[0]==
"")
279 priorimg_opt.clear();
282 std::map<std::string, svm::SVM_TYPE> svmMap;
284 svmMap[
"C_SVC"]=svm::C_SVC;
285 svmMap[
"nu_SVC"]=svm::nu_SVC;
286 svmMap[
"one_class"]=svm::one_class;
287 svmMap[
"epsilon_SVR"]=svm::epsilon_SVR;
288 svmMap[
"nu_SVR"]=svm::nu_SVR;
290 std::map<std::string, svm::KERNEL_TYPE> kernelMap;
292 kernelMap[
"linear"]=svm::linear;
293 kernelMap[
"polynomial"]=svm::polynomial;
294 kernelMap[
"radial"]=svm::radial;
295 kernelMap[
"sigmoid;"]=svm::sigmoid;
297 assert(training_opt.size());
299 if(verbose_opt[0]>=1){
301 std::cout <<
"input filename: " << input_opt[0] << std::endl;
303 std::cout <<
"mask filename: " << mask_opt[0] << std::endl;
304 std::cout <<
"training vector file: " << std::endl;
305 for(
int ifile=0;ifile<training_opt.size();++ifile)
306 std::cout << training_opt[ifile] << std::endl;
307 std::cout <<
"verbose: " << verbose_opt[0] << std::endl;
309 unsigned short nbag=(training_opt.size()>1)?training_opt.size():bag_opt[0];
310 if(verbose_opt[0]>=1)
311 std::cout <<
"number of bootstrap aggregations: " << nbag << std::endl;
321 bool maskIsVector=
false;
324 extentReader.open(mask_opt[0]);
326 readLayer = extentReader.getDataSource()->GetLayer(0);
327 if(!(extentReader.getExtent(ulx,uly,lrx,lry))){
328 cerr <<
"Error: could not get extent from " << mask_opt[0] << endl;
332 catch(
string errorString){
338 if(active_opt.size()){
339 prob_est_opt[0]=
true;
341 activeWriter.open(active_opt[0],ogrformat_opt[0]);
342 activeWriter.createLayer(active_opt[0],trainingReader.getProjection(),wkbPoint,NULL);
343 activeWriter.copyFields(trainingReader);
345 vector<PosValue> activePoints(nactive_opt[0]);
346 for(
int iactive=0;iactive<activePoints.size();++iactive){
347 activePoints[iactive].value=1.0;
348 activePoints[iactive].posx=0.0;
349 activePoints[iactive].posy=0.0;
352 unsigned int totalSamples=0;
353 unsigned int nactive=0;
354 vector<struct svm_model*>
svm(nbag);
355 vector<struct svm_parameter> param(nbag);
362 if(priors_opt.size()>1){
363 priors.resize(priors_opt.size());
365 for(
short iclass=0;iclass<priors_opt.size();++iclass){
366 priors[iclass]=priors_opt[iclass];
367 normPrior+=priors[iclass];
370 for(
short iclass=0;iclass<priors_opt.size();++iclass)
371 priors[iclass]/=normPrior;
376 if(bstart_opt.size()){
377 if(bend_opt.size()!=bstart_opt.size()){
378 string errorstring=
"Error: options for start and end band indexes must be provided as pairs, missing end band";
382 for(
int ipair=0;ipair<bstart_opt.size();++ipair){
383 if(bend_opt[ipair]<=bstart_opt[ipair]){
384 string errorstring=
"Error: index for end band must be smaller then start band";
387 for(
int iband=bstart_opt[ipair];iband<=bend_opt[ipair];++iband)
388 band_opt.push_back(iband);
393 cerr << error << std::endl;
398 std::sort(band_opt.begin(),band_opt.end());
400 map<string,short> classValueMap;
401 vector<std::string> nameVector;
402 if(classname_opt.size()){
403 assert(classname_opt.size()==classvalue_opt.size());
404 for(
int iclass=0;iclass<classname_opt.size();++iclass)
405 classValueMap[classname_opt[iclass]]=classvalue_opt[iclass];
410 vector< vector<double> > offset(nbag);
411 vector< vector<double> > scale(nbag);
412 map<string,Vector2d<float> > trainingMap;
413 vector< Vector2d<float> > trainingPixels;
414 vector<string> fields;
416 vector<struct svm_problem> prob(nbag);
417 vector<struct svm_node *> x_space(nbag);
419 for(
int ibag=0;ibag<nbag;++ibag){
421 if(ibag<training_opt.size()){
423 trainingPixels.clear();
424 if(verbose_opt[0]>=1)
425 std::cout <<
"reading imageVector file " << training_opt[0] << std::endl;
429 totalSamples=trainingReaderBag.readDataImageOgr(trainingMap,fields,band_opt,label_opt[0],tlayer_opt,verbose_opt[0]);
431 totalSamples=trainingReaderBag.readDataImageOgr(trainingMap,fields,0,0,label_opt[0],tlayer_opt,verbose_opt[0]);
432 if(trainingMap.size()<2){
433 string errorstring=
"Error: could not read at least two classes from training file, did you provide class labels in training sample (see option label)?";
436 trainingReaderBag.close();
439 cerr << error << std::endl;
442 catch(std::exception& e){
443 std::cerr <<
"Error: ";
444 std::cerr << e.what() << std::endl;
445 std::cerr << CPLGetLastErrorMsg() << std::endl;
449 cerr <<
"error catched" << std::endl;
456 std::cout <<
"training pixels: " << std::endl;
457 map<string,Vector2d<float> >::iterator mapit=trainingMap.begin();
458 while(mapit!=trainingMap.end()){
460 if((mapit->second).size()<minSize_opt[0]){
461 trainingMap.erase(mapit);
464 trainingPixels.push_back(mapit->second);
466 std::cout << mapit->first <<
": " << (mapit->second).size() <<
" samples" << std::endl;
470 nclass=trainingPixels.size();
471 if(classname_opt.size())
472 assert(nclass==classname_opt.size());
473 nband=trainingPixels[0][0].size()-2;
476 assert(nclass==trainingPixels.size());
477 assert(nband==trainingPixels[0][0].size()-2);
482 if(balance_opt[0]>0){
483 while(balance_opt.size()<nclass)
484 balance_opt.push_back(balance_opt.back());
488 for(
short iclass=0;iclass<nclass;++iclass){
489 if(trainingPixels[iclass].size()>balance_opt[iclass]){
490 while(trainingPixels[iclass].size()>balance_opt[iclass]){
491 int index=rand()%trainingPixels[iclass].size();
492 trainingPixels[iclass].erase(trainingPixels[iclass].begin()+index);
496 int oldsize=trainingPixels[iclass].size();
497 for(
int isample=trainingPixels[iclass].size();isample<balance_opt[iclass];++isample){
498 int index = rand()%oldsize;
499 trainingPixels[iclass].push_back(trainingPixels[iclass][index]);
502 totalSamples+=trainingPixels[iclass].size();
507 offset[ibag].resize(nband);
508 scale[ibag].resize(nband);
509 if(offset_opt.size()>1)
510 assert(offset_opt.size()==nband);
511 if(scale_opt.size()>1)
512 assert(scale_opt.size()==nband);
513 for(
int iband=0;iband<nband;++iband){
514 if(verbose_opt[0]>=1)
515 std::cout <<
"scaling for band" << iband << std::endl;
516 offset[ibag][iband]=(offset_opt.size()==1)?offset_opt[0]:offset_opt[iband];
517 scale[ibag][iband]=(scale_opt.size()==1)?scale_opt[0]:scale_opt[iband];
519 if(scale[ibag][iband]<=0){
520 float theMin=trainingPixels[0][0][iband+startBand];
521 float theMax=trainingPixels[0][0][iband+startBand];
522 for(
short iclass=0;iclass<nclass;++iclass){
523 for(
int isample=0;isample<trainingPixels[iclass].size();++isample){
524 if(theMin>trainingPixels[iclass][isample][iband+startBand])
525 theMin=trainingPixels[iclass][isample][iband+startBand];
526 if(theMax<trainingPixels[iclass][isample][iband+startBand])
527 theMax=trainingPixels[iclass][isample][iband+startBand];
530 offset[ibag][iband]=theMin+(theMax-theMin)/2.0;
531 scale[ibag][iband]=(theMax-theMin)/2.0;
532 if(verbose_opt[0]>=1){
533 std::cout <<
"Extreme image values for band " << iband <<
": [" << theMin <<
"," << theMax <<
"]" << std::endl;
534 std::cout <<
"Using offset, scale: " << offset[ibag][iband] <<
", " << scale[ibag][iband] << std::endl;
535 std::cout <<
"scaled values for band " << iband <<
": [" << (theMin-offset[ibag][iband])/scale[ibag][iband] <<
"," << (theMax-offset[ibag][iband])/scale[ibag][iband] <<
"]" << std::endl;
541 offset[ibag].resize(nband);
542 scale[ibag].resize(nband);
543 for(
int iband=0;iband<nband;++iband){
544 offset[ibag][iband]=offset[0][iband];
545 scale[ibag][iband]=scale[0][iband];
550 if(priors_opt.size()==1){
551 priors.resize(nclass);
552 for(
short iclass=0;iclass<nclass;++iclass)
553 priors[iclass]=1.0/nclass;
555 assert(priors_opt.size()==1||priors_opt.size()==nclass);
558 while(bagSize_opt.size()<nclass)
559 bagSize_opt.push_back(bagSize_opt.back());
561 if(verbose_opt[0]>=1){
562 std::cout <<
"number of bands: " << nband << std::endl;
563 std::cout <<
"number of classes: " << nclass << std::endl;
564 if(priorimg_opt.empty()){
565 std::cout <<
"priors:";
566 for(
short iclass=0;iclass<nclass;++iclass)
567 std::cout <<
" " << priors[iclass];
568 std::cout << std::endl;
571 map<string,Vector2d<float> >::iterator mapit=trainingMap.begin();
574 while(mapit!=trainingMap.end()){
575 nameVector.push_back(mapit->first);
576 if(classValueMap.size()){
578 if(classValueMap[mapit->first]>0){
579 if(cm.getClassIndex(type2string<short>(classValueMap[mapit->first]))<0){
580 cm.pushBackClassName(type2string<short>(classValueMap[mapit->first]),doSort);
584 std::cerr <<
"Error: names in classname option are not complete, please check names in training vector and make sure classvalue is > 0" << std::endl;
589 cm.pushBackClassName(mapit->first,doSort);
594 std::cerr <<
"Error: did you provide class pairs names (-c) and integer values (-r) for each class in training vector?" << std::endl;
597 if(classname_opt.empty()){
599 for(
int iclass=0;iclass<nclass;++iclass){
601 std::cout << iclass <<
" " << cm.getClass(iclass) <<
" -> " << string2type<short>(cm.getClass(iclass)) << std::endl;
602 classValueMap[cm.getClass(iclass)]=string2type<short>(cm.getClass(iclass));
614 vector< Vector2d<float> > trainingFeatures(nclass);
615 for(
short iclass=0;iclass<nclass;++iclass){
617 if(verbose_opt[0]>=1)
618 std::cout <<
"calculating features for class " << iclass << std::endl;
621 nctraining=(bagSize_opt[iclass]<100)? trainingPixels[iclass].size()/100.0*bagSize_opt[iclass] : trainingPixels[iclass].size();
624 assert(nctraining<=trainingPixels[iclass].size());
626 if(bagSize_opt[iclass]<100)
627 random_shuffle(trainingPixels[iclass].begin(),trainingPixels[iclass].end());
629 std::cout <<
"nctraining (class " << iclass <<
"): " << nctraining << std::endl;
630 trainingFeatures[iclass].resize(nctraining);
631 for(
int isample=0;isample<nctraining;++isample){
633 for(
int iband=0;iband<nband;++iband){
634 float value=trainingPixels[iclass][isample][iband+startBand];
635 trainingFeatures[iclass][isample].push_back((value-offset[ibag][iband])/scale[ibag][iband]);
638 assert(trainingFeatures[iclass].size()==nctraining);
641 unsigned int nFeatures=trainingFeatures[0][0].size();
642 if(verbose_opt[0]>=1)
643 std::cout <<
"number of features: " << nFeatures << std::endl;
644 unsigned int ntraining=0;
645 for(
short iclass=0;iclass<nclass;++iclass)
646 ntraining+=trainingFeatures[iclass].size();
647 if(verbose_opt[0]>=1)
648 std::cout <<
"training size over all classes: " << ntraining << std::endl;
650 prob[ibag].l=ntraining;
651 prob[ibag].y = Malloc(
double,prob[ibag].l);
652 prob[ibag].x = Malloc(
struct svm_node *,prob[ibag].l);
653 x_space[ibag] = Malloc(
struct svm_node,(nFeatures+1)*ntraining);
654 unsigned long int spaceIndex=0;
656 for(
short iclass=0;iclass<nclass;++iclass){
657 for(
int isample=0;isample<trainingFeatures[iclass].size();++isample){
658 prob[ibag].x[lIndex]=&(x_space[ibag][spaceIndex]);
659 for(
int ifeature=0;ifeature<nFeatures;++ifeature){
660 x_space[ibag][spaceIndex].index=ifeature+1;
661 x_space[ibag][spaceIndex].value=trainingFeatures[iclass][isample][ifeature];
664 x_space[ibag][spaceIndex++].index=-1;
665 prob[ibag].y[lIndex]=iclass;
669 assert(lIndex==prob[ibag].l);
672 param[ibag].svm_type = svmMap[svm_type_opt[0]];
673 param[ibag].kernel_type = kernelMap[kernel_type_opt[0]];
674 param[ibag].degree = kernel_degree_opt[0];
675 param[ibag].gamma = (gamma_opt[0]>0)? gamma_opt[0] : 1.0/nFeatures;
676 param[ibag].coef0 = coef0_opt[0];
677 param[ibag].nu = nu_opt[0];
678 param[ibag].cache_size = cache_opt[0];
679 param[ibag].C = ccost_opt[0];
680 param[ibag].eps = epsilon_tol_opt[0];
681 param[ibag].p = epsilon_loss_opt[0];
682 param[ibag].shrinking = (shrinking_opt[0])? 1 : 0;
683 param[ibag].probability = (prob_est_opt[0])? 1 : 0;
684 param[ibag].nr_weight = 0;
685 param[ibag].weight_label = NULL;
686 param[ibag].weight = NULL;
687 param[ibag].verbose=(verbose_opt[0]>1)?
true:
false;
690 std::cout <<
"checking parameters" << std::endl;
691 svm_check_parameter(&prob[ibag],¶m[ibag]);
693 std::cout <<
"parameters ok, training" << std::endl;
694 svm[ibag]=svm_train(&prob[ibag],¶m[ibag]);
696 std::cout <<
"SVM is now trained" << std::endl;
699 std::cout <<
"Cross validating" << std::endl;
700 double *target = Malloc(
double,prob[ibag].l);
701 svm_cross_validation(&prob[ibag],¶m[ibag],cv_opt[0],target);
702 assert(param[ibag].svm_type != EPSILON_SVR&¶m[ibag].svm_type != NU_SVR);
704 for(
int i=0;i<prob[ibag].l;i++){
705 string refClassName=nameVector[prob[ibag].y[i]];
706 string className=nameVector[target[i]];
707 if(classValueMap.size())
708 cm.incrementResult(type2string<short>(classValueMap[refClassName]),type2string<short>(classValueMap[className]),1.0/nbag);
710 cm.incrementResult(cm.getClass(prob[ibag].y[i]),cm.getClass(target[i]),1.0/nbag);
719 assert(cm.nReference());
720 cm.setFormat(cmformat_opt[0]);
721 cm.reportSE95(
false);
722 std::cout << cm << std::endl;
741 if(input_opt.empty())
744 const char* pszMessage;
745 void* pProgressArg=NULL;
746 GDALProgressFunc pfnProgress=GDALTermProgress;
749 pfnProgress(progress,pszMessage,pProgressArg);
751 bool inputIsRaster=
false;
754 imgReaderOgr.open(input_opt[0]);
755 imgReaderOgr.close();
757 catch(
string errorString){
763 if(verbose_opt[0]>=1)
764 std::cout <<
"opening image " << input_opt[0] << std::endl;
765 testImage.open(input_opt[0]);
768 cerr << error << std::endl;
772 if(priorimg_opt.size()){
774 if(verbose_opt[0]>=1)
775 std::cout <<
"opening prior image " << priorimg_opt[0] << std::endl;
776 priorReader.open(priorimg_opt[0]);
777 assert(priorReader.nrOfCol()==testImage.nrOfCol());
778 assert(priorReader.nrOfRow()==testImage.nrOfRow());
781 cerr << error << std::endl;
785 cerr <<
"error catched" << std::endl;
790 int nrow=testImage.nrOfRow();
791 int ncol=testImage.nrOfCol();
792 if(option_opt.findSubstring(
"INTERLEAVE=")==option_opt.end()){
793 string theInterleave=
"INTERLEAVE=";
794 theInterleave+=testImage.getInterleave();
795 option_opt.push_back(theInterleave);
797 vector<char> classOut(ncol);
805 string imageType=testImage.getImageType();
806 if(oformat_opt.size())
807 imageType=oformat_opt[0];
809 assert(output_opt.size());
810 if(verbose_opt[0]>=1)
811 std::cout <<
"opening class image for writing output " << output_opt[0] << std::endl;
812 if(classBag_opt.size()){
813 classImageBag.open(classBag_opt[0],ncol,nrow,nbag,GDT_Byte,imageType,option_opt);
814 classImageBag.GDALSetNoDataValue(nodata_opt[0]);
815 classImageBag.copyGeoTransform(testImage);
816 classImageBag.setProjection(testImage.getProjection());
818 classImageOut.open(output_opt[0],ncol,nrow,1,GDT_Byte,imageType,option_opt);
819 classImageOut.GDALSetNoDataValue(nodata_opt[0]);
820 classImageOut.copyGeoTransform(testImage);
821 classImageOut.setProjection(testImage.getProjection());
822 if(colorTable_opt.size())
823 classImageOut.setColorTable(colorTable_opt[0],0);
825 probImage.open(prob_opt[0],ncol,nrow,nclass,GDT_Byte,imageType,option_opt);
826 probImage.GDALSetNoDataValue(nodata_opt[0]);
827 probImage.copyGeoTransform(testImage);
828 probImage.setProjection(testImage.getProjection());
830 if(entropy_opt.size()){
831 entropyImage.open(entropy_opt[0],ncol,nrow,1,GDT_Byte,imageType,option_opt);
832 entropyImage.GDALSetNoDataValue(nodata_opt[0]);
833 entropyImage.copyGeoTransform(testImage);
834 entropyImage.setProjection(testImage.getProjection());
838 cerr << error << std::endl;
845 maskWriter.open(
"/vsimem/mask.tif",ncol,nrow,1,GDT_Float32,imageType,option_opt);
846 maskWriter.GDALSetNoDataValue(nodata_opt[0]);
847 maskWriter.copyGeoTransform(testImage);
848 maskWriter.setProjection(testImage.getProjection());
849 vector<double> burnValues(1,1);
850 maskWriter.rasterizeOgr(extentReader,burnValues);
851 extentReader.close();
855 cerr << error << std::endl;
859 cerr <<
"error catched" << std::endl;
863 mask_opt.push_back(
"/vsimem/mask.tif");
868 if(verbose_opt[0]>=1)
869 std::cout <<
"opening mask image file " << mask_opt[0] << std::endl;
870 maskReader.open(mask_opt[0]);
873 cerr << error << std::endl;
877 cerr <<
"error catched" << std::endl;
882 for(
int iline=0;iline<nrow;++iline){
883 vector<float> buffer(ncol);
884 vector<short> lineMask;
886 if(priorimg_opt.size())
887 linePrior.resize(nclass,ncol);
890 vector<float> entropy(ncol);
892 if(classBag_opt.size())
893 classBag.resize(nbag,ncol);
896 for(
int iband=0;iband<band_opt.size();++iband){
897 if(verbose_opt[0]==2)
898 std::cout <<
"reading band " << band_opt[iband] << std::endl;
899 assert(band_opt[iband]>=0);
900 assert(band_opt[iband]<testImage.nrOfBand());
901 testImage.readData(buffer,GDT_Float32,iline,band_opt[iband]);
902 for(
int icol=0;icol<ncol;++icol)
903 hpixel[icol].push_back(buffer[icol]);
907 for(
int iband=0;iband<nband;++iband){
908 if(verbose_opt[0]==2)
909 std::cout <<
"reading band " << iband << std::endl;
911 assert(iband<testImage.nrOfBand());
912 testImage.readData(buffer,GDT_Float32,iline,iband);
913 for(
int icol=0;icol<ncol;++icol)
914 hpixel[icol].push_back(buffer[icol]);
918 catch(
string theError){
919 cerr <<
"Error reading " << input_opt[0] <<
": " << theError << std::endl;
923 cerr <<
"error catched" << std::endl;
926 assert(nband==hpixel[0].size());
928 std::cout <<
"used bands: " << nband << std::endl;
930 if(priorimg_opt.size()){
932 for(
short iclass=0;iclass<nclass;++iclass){
933 if(verbose_opt.size()>1)
934 std::cout <<
"Reading " << priorimg_opt[0] <<
" band " << iclass <<
" line " << iline << std::endl;
935 priorReader.readData(linePrior[iclass],GDT_Float32,iline,iclass);
938 catch(
string theError){
939 std::cerr <<
"Error reading " << priorimg_opt[0] <<
": " << theError << std::endl;
943 cerr <<
"error catched" << std::endl;
947 double oldRowMask=-1;
949 for(
int icol=0;icol<ncol;++icol){
950 assert(hpixel[icol].size()==nband);
951 bool doClassify=
true;
957 testImage.image2geo(icol,iline,geox,geoy);
959 if(uly>=geoy&&lry<=geoy&&ulx<=geox&&lrx>=geox){
968 testImage.image2geo(icol,iline,geox,geoy);
969 maskReader.geo2image(geox,geoy,colMask,rowMask);
970 colMask=
static_cast<int>(colMask);
971 rowMask=
static_cast<int>(rowMask);
972 if(rowMask>=0&&rowMask<maskReader.nrOfRow()&&colMask>=0&&colMask<maskReader.nrOfCol()){
973 if(static_cast<int>(rowMask)!=
static_cast<int>(oldRowMask)){
974 assert(rowMask>=0&&rowMask<maskReader.nrOfRow());
977 maskReader.readData(lineMask,GDT_Int16,static_cast<int>(rowMask));
979 catch(
string errorstring){
980 cerr << errorstring << endl;
984 cerr <<
"error catched" << std::endl;
990 for(
short ivalue=0;ivalue<msknodata_opt.size();++ivalue){
991 if(msknodata_opt[ivalue]>=0){
992 if(lineMask[colMask]==msknodata_opt[ivalue]){
993 theMask=lineMask[colMask];
999 if(lineMask[colMask]!=-msknodata_opt[ivalue]){
1000 theMask=lineMask[colMask];
1010 if(classBag_opt.size())
1011 for(
int ibag=0;ibag<nbag;++ibag)
1012 classBag[ibag][icol]=theMask;
1013 classOut[icol]=theMask;
1018 for(
int iband=0;iband<hpixel[icol].size();++iband){
1019 if(hpixel[icol][iband]){
1027 for(
short iclass=0;iclass<nclass;++iclass)
1028 probOut[iclass][icol]=0;
1030 if(classBag_opt.size())
1031 for(
int ibag=0;ibag<nbag;++ibag)
1032 classBag[ibag][icol]=nodata_opt[0];
1033 classOut[icol]=nodata_opt[0];
1036 if(verbose_opt[0]>1)
1037 std::cout <<
"begin classification " << std::endl;
1039 for(
int ibag=0;ibag<nbag;++ibag){
1040 vector<double> result(nclass);
1043 for(
int iband=0;iband<nband;++iband){
1044 x[iband].index=iband+1;
1045 x[iband].value=(hpixel[icol][iband]-offset[ibag][iband])/scale[ibag][iband];
1048 double predict_label=0;
1049 vector<float> prValues(nclass);
1051 if(!prob_est_opt[0]){
1052 predict_label = svm_predict(
svm[ibag],x);
1053 for(
short iclass=0;iclass<nclass;++iclass){
1054 if(iclass==static_cast<short>(predict_label))
1061 assert(svm_check_probability_model(
svm[ibag]));
1062 predict_label = svm_predict_probability(
svm[ibag],x,&(result[0]));
1065 if(classBag_opt.size()){
1068 classBag[ibag][icol]=0;
1071 if(priorimg_opt.size()){
1072 for(
short iclass=0;iclass<nclass;++iclass)
1073 normPrior+=linePrior[iclass][icol];
1075 for(
short iclass=0;iclass<nclass;++iclass){
1076 if(priorimg_opt.size())
1077 priors[iclass]=linePrior[iclass][icol]/normPrior;
1078 switch(comb_opt[0]){
1081 probOut[iclass][icol]+=result[iclass]*priors[iclass];
1084 probOut[iclass][icol]*=pow(static_cast<float>(priors[iclass]),static_cast<float>(1.0-nbag)/nbag)*result[iclass];
1087 if(priors[iclass]*result[iclass]>probOut[iclass][icol])
1088 probOut[iclass][icol]=priors[iclass]*result[iclass];
1091 if(classBag_opt.size()){
1097 if(result[iclass]>maxP){
1098 maxP=result[iclass];
1099 classBag[ibag][icol]=iclass;
1110 for(
short iclass=0;iclass<nclass;++iclass){
1111 if(probOut[iclass][icol]>maxBag1){
1112 maxBag1=probOut[iclass][icol];
1113 classOut[icol]=classValueMap[nameVector[iclass]];
1115 else if(probOut[iclass][icol]>maxBag2)
1116 maxBag2=probOut[iclass][icol];
1117 normBag+=probOut[iclass][icol];
1121 for(
short iclass=0;iclass<nclass;++iclass){
1122 float prv=probOut[iclass][icol];
1124 entropy[icol]-=prv*log(prv)/log(2.0);
1127 probOut[iclass][icol]=
static_cast<short>(prv+0.5);
1132 entropy[icol]/=log(static_cast<double>(nclass))/log(2.0);
1133 entropy[icol]=
static_cast<short>(100*entropy[icol]+0.5);
1134 if(active_opt.size()){
1135 if(entropy[icol]>activePoints.back().value){
1136 activePoints.back().value=entropy[icol];
1137 activePoints.back().posx=icol;
1138 activePoints.back().posy=iline;
1141 std::cout << activePoints.back().posx <<
" " << activePoints.back().posy <<
" " << activePoints.back().value << std::endl;
1146 if(classBag_opt.size())
1147 for(
int ibag=0;ibag<nbag;++ibag)
1148 classImageBag.writeData(classBag[ibag],GDT_Byte,iline,ibag);
1149 if(prob_opt.size()){
1150 for(
short iclass=0;iclass<nclass;++iclass)
1151 probImage.writeData(probOut[iclass],GDT_Float32,iline,iclass);
1153 if(entropy_opt.size()){
1154 entropyImage.writeData(entropy,GDT_Float32,iline);
1156 classImageOut.writeData(classOut,GDT_Byte,iline);
1157 if(!verbose_opt[0]){
1158 progress=
static_cast<float>(iline+1.0)/classImageOut.nrOfRow();
1159 pfnProgress(progress,pszMessage,pProgressArg);
1163 if(active_opt.size()){
1164 for(
int iactive=0;iactive<activePoints.size();++iactive){
1165 std::map<string,double> pointMap;
1166 for(
int iband=0;iband<testImage.nrOfBand();++iband){
1168 testImage.readData(value,GDT_Float64,static_cast<int>(activePoints[iactive].posx),static_cast<int>(activePoints[iactive].posy),iband);
1171 pointMap[fs.str()]=value;
1173 pointMap[label_opt[0]]=0;
1175 testImage.image2geo(activePoints[iactive].posx,activePoints[iactive].posy,x,y);
1176 std::string fieldname=
"id";
1177 activeWriter.addPoint(x,y,pointMap,fieldname,++nactive);
1184 if(priorimg_opt.size())
1185 priorReader.close();
1188 if(entropy_opt.size())
1189 entropyImage.close();
1190 if(classBag_opt.size())
1191 classImageBag.close();
1192 classImageOut.close();
1197 for(
int ivalidation=0;ivalidation<input_opt.size();++ivalidation){
1198 if(output_opt.size())
1199 assert(output_opt.size()==input_opt.size());
1201 std::cout <<
"opening img reader " << input_opt[ivalidation] << std::endl;
1202 imgReaderOgr.open(input_opt[ivalidation]);
1205 if(output_opt.size()){
1207 std::cout <<
"opening img writer and copying fields from img reader" << output_opt[ivalidation] << std::endl;
1208 imgWriterOgr.open(output_opt[ivalidation],imgReaderOgr);
1211 cout <<
"number of layers in input ogr file: " << imgReaderOgr.getLayerCount() << endl;
1212 for(
int ilayer=0;ilayer<imgReaderOgr.getLayerCount();++ilayer){
1214 cout <<
"processing input layer " << ilayer << endl;
1215 if(output_opt.size()){
1217 std::cout <<
"creating field class" << std::endl;
1218 if(classValueMap.size())
1219 imgWriterOgr.createField(
"class",OFTInteger,ilayer);
1221 imgWriterOgr.createField(
"class",OFTString,ilayer);
1223 unsigned int nFeatures=imgReaderOgr.getFeatureCount(ilayer);
1224 unsigned int ifeature=0;
1226 pfnProgress(progress,pszMessage,pProgressArg);
1227 OGRFeature *poFeature;
1228 while( (poFeature = imgReaderOgr.getLayer(ilayer)->GetNextFeature()) != NULL ){
1229 if(verbose_opt[0]>1)
1230 std::cout <<
"feature " << ifeature << std::endl;
1231 if( poFeature == NULL ){
1232 cout <<
"Warning: could not read feature " << ifeature <<
" in layer " << imgReaderOgr.getLayerName(ilayer) << endl;
1235 OGRFeature *poDstFeature = NULL;
1236 if(output_opt.size()){
1237 poDstFeature=imgWriterOgr.createFeature(ilayer);
1238 if( poDstFeature->SetFrom( poFeature, TRUE ) != OGRERR_NONE ){
1239 CPLError( CE_Failure, CPLE_AppDefined,
1240 "Unable to translate feature %d from layer %s.\n",
1241 poFeature->GetFID(), imgWriterOgr.getLayerName(ilayer).c_str() );
1242 OGRFeature::DestroyFeature( poFeature );
1243 OGRFeature::DestroyFeature( poDstFeature );
1246 vector<float> validationPixel;
1247 vector<float> validationFeature;
1249 imgReaderOgr.readData(validationPixel,OFTReal,fields,poFeature,ilayer);
1250 assert(validationPixel.size()==nband);
1251 vector<float> probOut(nclass);
1252 for(
short iclass=0;iclass<nclass;++iclass)
1254 for(
int ibag=0;ibag<nbag;++ibag){
1255 for(
int iband=0;iband<nband;++iband){
1256 validationFeature.push_back((validationPixel[iband]-offset[ibag][iband])/scale[ibag][iband]);
1257 if(verbose_opt[0]==2)
1258 std::cout <<
" " << validationFeature.back();
1260 if(verbose_opt[0]==2)
1261 std::cout << std::endl;
1262 vector<double> result(nclass);
1264 x = (
struct svm_node *) malloc((validationFeature.size()+1)*
sizeof(
struct svm_node));
1265 for(
int i=0;i<validationFeature.size();++i){
1267 x[i].value=validationFeature[i];
1270 x[validationFeature.size()].index=-1;
1271 double predict_label=0;
1272 if(!prob_est_opt[0]){
1273 predict_label = svm_predict(
svm[ibag],x);
1274 for(
short iclass=0;iclass<nclass;++iclass){
1275 if(iclass==static_cast<short>(predict_label))
1282 assert(svm_check_probability_model(
svm[ibag]));
1283 predict_label = svm_predict_probability(
svm[ibag],x,&(result[0]));
1285 if(verbose_opt[0]>1){
1286 std::cout <<
"predict_label: " << predict_label << std::endl;
1287 for(
int iclass=0;iclass<result.size();++iclass)
1288 std::cout << result[iclass] <<
" ";
1289 std::cout << std::endl;
1293 for(
short iclass=0;iclass<nclass;++iclass){
1294 switch(comb_opt[0]){
1297 probOut[iclass]+=result[iclass]*priors[iclass];
1300 probOut[iclass]*=pow(static_cast<float>(priors[iclass]),static_cast<float>(1.0-nbag)/nbag)*result[iclass];
1303 if(priors[iclass]*result[iclass]>probOut[iclass])
1304 probOut[iclass]=priors[iclass]*result[iclass];
1314 string classOut=
"Unclassified";
1315 for(
short iclass=0;iclass<nclass;++iclass){
1316 if(verbose_opt[0]>1)
1317 std::cout << probOut[iclass] <<
" ";
1318 if(probOut[iclass]>maxBag){
1319 maxBag=probOut[iclass];
1320 classOut=nameVector[iclass];
1324 if(verbose_opt[0]>1){
1325 if(classValueMap.size())
1326 std::cout <<
"->" << classValueMap[classOut] << std::endl;
1328 std::cout <<
"->" << classOut << std::endl;
1330 if(output_opt.size()){
1331 if(classValueMap.size())
1332 poDstFeature->SetField(
"class",classValueMap[classOut]);
1334 poDstFeature->SetField(
"class",classOut.c_str());
1335 poDstFeature->SetFID( poFeature->GetFID() );
1337 int labelIndex=poFeature->GetFieldIndex(label_opt[0].c_str());
1339 string classRef=poFeature->GetFieldAsString(labelIndex);
1341 if(classValueMap.size())
1342 cm.incrementResult(type2string<short>(classValueMap[classRef]),type2string<short>(classValueMap[classOut]),1);
1344 cm.incrementResult(classRef,classOut,1);
1348 if(output_opt.size()){
1349 if(imgWriterOgr.createFeature(poDstFeature,ilayer) != OGRERR_NONE){
1350 CPLError( CE_Failure, CPLE_AppDefined,
1351 "Unable to translate feature %d from layer %s.\n",
1352 poFeature->GetFID(), imgWriterOgr.getLayerName(ilayer).c_str() );
1353 OGRFeature::DestroyFeature( poDstFeature );
1354 OGRFeature::DestroyFeature( poDstFeature );
1358 if(!verbose_opt[0]){
1359 progress=
static_cast<float>(ifeature+1.0)/nFeatures;
1360 pfnProgress(progress,pszMessage,pProgressArg);
1362 OGRFeature::DestroyFeature( poFeature );
1363 OGRFeature::DestroyFeature( poDstFeature );
1366 imgReaderOgr.close();
1367 if(output_opt.size())
1368 imgWriterOgr.close();
1370 if(cm.nReference()){
1371 std::cout << cm << std::endl;
1372 cout <<
"class #samples userAcc prodAcc" << endl;
1379 for(
short iclass=0;iclass<cm.nClasses();++iclass){
1380 dua=cm.ua_pct(cm.getClass(iclass),&se95_ua);
1381 dpa=cm.pa_pct(cm.getClass(iclass),&se95_pa);
1382 cout << cm.getClass(iclass) <<
" " << cm.nReference(cm.getClass(iclass)) <<
" " << dua <<
" (" << se95_ua <<
")" <<
" " << dpa <<
" (" << se95_pa <<
")" << endl;
1384 std::cout <<
"Kappa: " << cm.kappa() << std::endl;
1385 doa=cm.oa(&se95_oa);
1386 std::cout <<
"Overall Accuracy: " << 100*doa <<
" (" << 100*se95_oa <<
")" << std::endl;
1390 if(active_opt.size())
1391 activeWriter.close();
1393 catch(
string errorString){
1394 std::cerr <<
"Error: errorString" << std::endl;
1397 for(
int ibag=0;ibag<nbag;++ibag){
1399 svm_destroy_param(¶m[ibag]);
1402 free(x_space[ibag]);
1403 svm_free_and_destroy_model(&(
svm[ibag]));
throw this class when syntax error in command line option