void print_feature_vectors(feature_vectors* fs)
{
printf("FEATURE VECTORS PACKET {{{{{{{{{{{{\n");
for(int i = 0; i < fs->num_vectors; i++)
print_feature(&fs->features[i]);
printf("}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}\n\n");
}
int main_train(int argc, char **argv)
{
char *posSplFile = NULL;
char *negSplFile = NULL;
char *modelFile = NULL;
int stage = 15;
int width = 0, height = 0;
int numPos = 0, numNeg = 0;
const int numVal = 400;
float tarfpr = 0.05;
float maxfnr = 0.05;
std::vector<std::string> negImgList;
if((argc - 1) / 2 != 10)
{
print_train_usage(argv[0]);
return 1;
}
for(int i = 1; i < argc; i++)
{
if(strcmp(argv[i], "--stage") == 0)
stage = atoi(argv[++i]);
else if(strcmp(argv[i], "-X") == 0)
width = atoi(argv[++i]);
else if(strcmp(argv[i], "-Y") == 0)
height = atoi(argv[++i]);
else if(strcmp(argv[i], "--false_alarm_rate") == 0)
tarfpr = atof(argv[++i]);
else if(strcmp(argv[i], "--missing_rate") == 0)
maxfnr = atof(argv[++i]);
else if(strcmp(argv[i], "--pos") == 0)
posSplFile = argv[++i];
else if(strcmp(argv[i], "--neg") == 0)
negSplFile = argv[++i];
else if(strcmp(argv[i], "-m") == 0)
modelFile = argv[++i];
else if(strcmp(argv[i], "--numPos"))
numPos = atoi(argv[++i]);
else if(strcmp(argv[i], "--numNeg"))
numNeg = atoi(argv[++i]);
else
{
printf("Can't recognize params %s\n", argv[i]);
print_train_usage(argv[0]);
return 1;
}
}
if(posSplFile == NULL || negSplFile == NULL || width == 0 || height == 0 || numPos <= 0 || numNeg <= 0){
print_train_usage(argv[0]);
return 1;
}
std::list<float *> positiveSet, negativeSet, validateSet;
int ret;
std::vector<Feature*> featureSet;
float *stepFPR;
float npRatio = 1.0 * numNeg / numPos;
CascadeClassifier *cc = new CascadeClassifier();
StrongClassifier* sc;
float maxfpr = 1.0;
std::list<StrongClassifier *> scs;
init_cascade_classifier(cc, scs, width, height);
printf("GENERATE POSITIVE SAMPLES\n");
ret = generate_positive_samples(posSplFile, positiveSet, width, height, numPos);
if(ret != 0) return 2;
printf("GENERATE NEGATIVE SAMPLES\n");
read_image_list(negSplFile, negImgList);
for(int i = 0; i < numNeg; i ++)
{
float *data = NULL;
read_neg_sample_from_file(negImgList, width, height, &data);
#ifdef USE_HAAR_FEATURE
integral_image(data, width, height);
#endif
negativeSet.push_back(data);
}
printf("GENERATE VALIDATE SAMPLES\n");
generate_validate_samples(negImgList, width, height, validateSet, numPos);
printf("Positive sample size: %ld\n", positiveSet.size());
printf("Negative sample size: %ld\n", negativeSet.size());
printf("Validate sample size: %d\n", numVal);
printf("GENERATE FEATURE TEMPLATE\n");
generate_feature_set(featureSet, width, height);
printf("SELECT FEATURE TEMPLATE\n");
select_feature(featureSet, positiveSet, validateSet, width);
init_steps_false_positive(&stepFPR, stage, tarfpr);
clock_t startTime = clock();
char outname[128];
for(int i = 0; i < stage; i++)
{
printf("\n--------------cascade stage %d-----------------\n", i+1);
int correctSize = 0;
std::list<float*>::iterator iter, iterEnd;
numNeg = numPos * npRatio;
printf("READ NEGATIVE SAMPLES\n");
ret = generate_negative_samples(negImgList, width, height, cc, negativeSet, numNeg);
if(ret != numNeg) {
printf("Can't generate enough negatvie samples %d:%d\n", ret, numNeg);
break;
}
printf("READ VALIDATE SAMPLES\n");
ret = generate_negative_samples(negImgList, width, height, cc, validateSet, numVal);
if(ret != numVal) {
printf("Can't generate enough validate samples %d:%d\n", ret, numVal);
break;
}
maxfpr *= stepFPR[i];
printf("Positive sample size: %d\n", numPos);
printf("Negative sample size: %d\n", numNeg);
printf("Target false positive rate: %f\n", maxfpr);
printf("Target false negative rate: %f\n", maxfnr);
sc = adaboost_learning(cc, positiveSet, numPos, negativeSet, numNeg, validateSet, featureSet, maxfpr, maxfnr);
add(cc, sc);
iter = positiveSet.begin();
iterEnd = positiveSet.end();
while(iter != iterEnd)
{
if(classify(cc, *iter, width, 0, 0) == 0)
{
std::list<float*>::iterator iterTmp = iter;
iter++;
delete[] (*iterTmp);
positiveSet.erase(iterTmp);
iter--;
}
iter++;
}
numPos = positiveSet.size();
printf("cascade TP: %d\n", numPos);
iter = negativeSet.begin();
iterEnd = negativeSet.end();
correctSize = negativeSet.size();
while(iter != iterEnd)
{
if(classify(cc, *iter, width, 0, 0) == 0)
{
std::list<float*>::iterator iterTmp = iter;
iter++;
delete[] (*iterTmp);
negativeSet.erase(iterTmp);
iter--;
}
iter++;
}
printf("cascade TN: %ld\n", correctSize - negativeSet.size());
iter = validateSet.begin();
iterEnd = validateSet.end();
while(iter != iterEnd)
{
if(classify(cc, *iter, width, 0, 0) == 0)
{
std::list<float*>::iterator iterTmp = iter;
iter++;
delete[] (*iterTmp);
validateSet.erase(iterTmp);
iter--;
}
iter++;
}
printf("----------------------------------------\n");
sprintf(outname, "model/cascade_%d.dat", i+1);
save(cc, outname);
#ifdef SHOW_FEATURE
print_feature(cc);
#endif
}
save(cc, modelFile);
clock_t trainTime = clock() - startTime;
printf("Train time:");
print_time(trainTime);
printf("\n");
clear(cc);
clear_list(positiveSet);
clear_list(negativeSet);
clear_list(validateSet);
clear_features(featureSet);
return 0;
}
