_params.weak_count = 1;
}
+ std::cout << "WARNING: " << sampleIdx << std::endl;
+
bool update = false;
return cv::Boost::train(trainData, CV_COL_SAMPLE, _responses, varIdx, sampleIdx, varType, missingDataMask, _params,
update);
public:
Preprocessor(int shr) : shrinkage(shr) {}
- void apply(const Mat& frame, Mat integrals)
+ void apply(const Mat& frame, Mat& integrals)
{
CV_Assert(frame.type() == CV_8UC3);
dprintf("Process candidate positive image %s\n", curr.c_str());
cv::Mat sample = cv::imread(curr);
- cv::Mat channels = integrals.col(total).reshape(0, h + 1);
+ cv::Mat channels = integrals.row(total).reshape(0, h + 1);
prepocessor.apply(sample, channels);
responses.ptr<float>(total)[0] = 1.f;
sft::Random::engine eng;
sft::Random::engine idxEng;
+ int w = 64 * pow(2, logScale) /shrinkage;
+ int h = 128 * pow(2, logScale) /shrinkage * 10;
+
Preprocessor prepocessor(shrinkage);
int nimages = (int)dataset.neg.size();
Mat frame = cv::imread(dataset.neg[curr]);
prepocessor.apply(frame, sum);
- int maxW = frame.cols - 2 * boundingBox.x - boundingBox.width;
- int maxH = frame.rows - 2 * boundingBox.y - boundingBox.height;
+ std::cout << "WARNING: " << frame.cols << " " << frame.rows << std::endl;
+ std::cout << "WARNING: " << frame.cols / shrinkage << " " << frame.rows / shrinkage << std::endl;
- sft::Random::uniform wRand(0, maxW);
- sft::Random::uniform hRand(0, maxH);
+ int maxW = frame.cols / shrinkage - 2 * boundingBox.x - boundingBox.width;
+ int maxH = frame.rows / shrinkage - 2 * boundingBox.y - boundingBox.height;
+
+ std::cout << "WARNING: " << maxW << " " << maxH << std::endl;
+
+ sft::Random::uniform wRand(0, maxW -1);
+ sft::Random::uniform hRand(0, maxH -1);
int dx = wRand(eng);
int dy = hRand(eng);
- sum = sum(cv::Rect(dx, dy, boundingBox.width, boundingBox.height));
+ std::cout << "WARNING: " << dx << " " << dy << std::endl;
+ std::cout << "WARNING: " << dx + boundingBox.width + 1 << " " << dy + boundingBox.height + 1 << std::endl;
+ std::cout << "WARNING: " << sum.cols << " " << sum.rows << std::endl;
+
+ sum = sum(cv::Rect(dx, dy, boundingBox.width + 1, boundingBox.height * 10 + 1));
dprintf("generated %d %d\n", dx, dy);
- if (predict(sum))
+ // if (predict(sum))
{
responses.ptr<float>(i)[0] = 0.f;
- sum = sum.reshape(0, 1);
- sum.copyTo(integrals.col(i));
+ // sum = sum.reshape(0, 1);
+ sum.copyTo(integrals.row(i).reshape(0, h + 1));
++i;
}
}
// 3. only sumple case (all samples used)
int nsamples = npositives + nnegatives;
cv::Mat sampleIdx(1, nsamples, CV_32SC1);
- ptr = varIdx.ptr<int>(0);
+ ptr = sampleIdx.ptr<int>(0);
for (int x = 0; x < nsamples; ++x)
ptr[x] = x;
cv::Mat missingMask;
- return train(trainData, responses, varIdx, sampleIdx, varType, missingMask);
+ bool ok = train(trainData, responses, varIdx, sampleIdx, varType, missingMask);
+ if (!ok)
+ std::cout << "ERROR:tree couldnot be trained" << std::endl;
+ return ok;
}
#include <sft/common.hpp>
#include <sft/octave.hpp>
+#include <sft/config.hpp>
int main(int argc, char** argv)
{
-// hard coded now
- int nfeatures = 50;
- int npositives = 10;
- int nnegatives = 10;
+ using namespace sft;
- int shrinkage = 4;
- int octave = 0;
+ const string keys =
+ "{help h usage ? | | print this message }"
+ "{config c | | path to configuration xml }"
+ ;
- int nsamples = npositives + nnegatives;
- cv::Size model(64, 128);
- std::string path = "/home/kellan/cuda-dev/opencv_extra/testdata/sctrain/rescaled-train-2012-10-27-19-02-52";
+ cv::CommandLineParser parser(argc, argv, keys);
+ parser.about("Soft cascade training application.");
- cv::Rect boundingBox(5, 5 ,16, 32);
- sft::Octave boost(boundingBox, npositives, nnegatives, octave, shrinkage);
+ if (parser.has("help"))
+ {
+ parser.printMessage();
+ return 0;
+ }
- sft::FeaturePool pool(model, nfeatures);
- sft::Dataset dataset(path, boost.logScale);
+ if (!parser.check())
+ {
+ parser.printErrors();
+ return 1;
+ }
- boost.train(dataset, pool);
+ string configPath = parser.get<string>("config");
+ if (configPath.empty())
+ {
+ std::cout << "Configuration file is missing or empty. Could not start training." << std::endl << std::flush;
+ return 0;
+ }
- cv::Mat train_data(nfeatures, nsamples, CV_32FC1);
- cv::RNG rng;
+ std::cout << "Read configuration from file " << configPath << std::endl;
+ cv::FileStorage fs(configPath, cv::FileStorage::READ);
+ if(!fs.isOpened())
+ {
+ std::cout << "Configuration file " << configPath << " can't be opened." << std::endl << std::flush;
+ return 1;
+ }
- for (int y = 0; y < nfeatures; ++y)
- for (int x = 0; x < nsamples; ++x)
- train_data.at<float>(y, x) = rng.uniform(0.f, 1.f);
-// +
- int tflag = CV_COL_SAMPLE;
- cv::Mat responses(nsamples, 1, CV_32FC1);
- for (int y = 0; y < nsamples; ++y)
- responses.at<float>(y, 0) = (y < npositives) ? 1.f : 0.f;
+ // 1. load config
+ sft::Config cfg;
+ fs["config"] >> cfg;
+ std::cout << std::endl << "Training will be executed for configuration:" << std::endl << cfg << std::endl;
+ // 2. check and open output file
+ cv::FileStorage fso(cfg.outXmlPath, cv::FileStorage::WRITE);
+ if(!fs.isOpened())
+ {
+ std::cout << "Training stopped. Output classifier Xml file " << cfg.outXmlPath << " can't be opened." << std::endl << std::flush;
+ return 1;
+ }
- cv::Mat var_idx(1, nfeatures, CV_32SC1);
- for (int x = 0; x < nfeatures; ++x)
- var_idx.at<int>(0, x) = x;
+ // ovector strong;
+ // strong.reserve(cfg.octaves.size());
- // Mat sample_idx;
- cv::Mat sample_idx(1, nsamples, CV_32SC1);
- for (int x = 0; x < nsamples; ++x)
- sample_idx.at<int>(0, x) = x;
+ // fso << "softcascade" << "{" << "octaves" << "[";
- cv::Mat var_type(1, nfeatures + 1, CV_8UC1);
- for (int x = 0; x < nfeatures; ++x)
- var_type.at<uchar>(0, x) = CV_VAR_ORDERED;
+ // 3. Train all octaves
+ for (ivector::const_iterator it = cfg.octaves.begin(); it != cfg.octaves.end(); ++it)
+ {
+ int nfeatures = cfg.poolSize;
+ int npositives = cfg.positives;
+ int nnegatives = cfg.negatives;
- var_type.at<uchar>(0, nfeatures) = CV_VAR_CATEGORICAL;
+ int shrinkage = cfg.shrinkage;
+ int octave = *it;
- cv::Mat missing_mask;
+ cv::Size model = cfg.modelWinSize;
+ std::string path = cfg.trainPath;
- CvBoostParams params;
- {
- params.max_categories = 10;
- params.max_depth = 2;
- params.min_sample_count = 2;
- params.cv_folds = 0;
- params.truncate_pruned_tree = false;
-
- /// ??????????????????
- params.regression_accuracy = 0.01;
- params.use_surrogates = false;
- params.use_1se_rule = false;
-
- ///////// boost params
- params.boost_type = CvBoost::GENTLE;
- params.weak_count = 1;
- params.split_criteria = CvBoost::SQERR;
- params.weight_trim_rate = 0.95;
+ cv::Rect boundingBox(cfg.offset.x / cfg.shrinkage, cfg.offset.y / cfg.shrinkage,
+ cfg.modelWinSize.width / cfg.shrinkage, cfg.modelWinSize.height / cfg.shrinkage);
+
+ sft::Octave boost(boundingBox, npositives, nnegatives, octave, shrinkage);
+
+ sft::FeaturePool pool(model, nfeatures);
+ sft::Dataset dataset(path, boost.logScale);
+
+ if (boost.train(dataset, pool))
+ {
+ }
+ std::cout << "Octave " << octave << " was successfully trained..." << std::endl;
+ // // d. crain octave
+ // if (octave.train(pool, cfg.positives, cfg.negatives, cfg.weaks))
+ // {
+ // strong.push_back(octave);
+ // }
}
- bool update = false;
+ // fso << "]" << "}";
+
+// // 3. create Soft Cascade
+// // sft::SCascade cascade(cfg.modelWinSize, cfg.octs, cfg.shrinkage);
+
+// // // 4. Generate feature pool
+// // std::vector<sft::ICF> pool;
+// // sft::fillPool(pool, cfg.poolSize, cfg.modelWinSize / cfg.shrinkage, cfg.seed);
+
+// // // 5. Train all octaves
+// // cascade.train(cfg.trainPath);
+
+// // // 6. Set thresolds
+// // cascade.prune();
- // boost.train(train_data, responses, var_idx, sample_idx, var_type, missing_mask);
+// // // 7. Postprocess
+// // cascade.normolize();
- // CvFileStorage* fs = cvOpenFileStorage( "/home/kellan/train_res.xml", 0, CV_STORAGE_WRITE );
- // boost.write(fs, "test_res");
+// // // 8. Write result xml
+// // cv::FileStorage ofs(cfg.outXmlPath, cv::FileStorage::WRITE);
+// // ofs << cfg.cascadeName << cascade;
- // cvReleaseFileStorage( &fs );
+ std::cout << "Training complete..." << std::endl;
+ return 0;
}
\ No newline at end of file
projects / profile / ivi / opencv.git / commitdiff