Open Source Computer Vision Library https://opencv.org/
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/*M///////////////////////////////////////////////////////////////////////////////////////
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// License Agreement
// For Open Source Computer Vision Library
//
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#include <sft/octave.hpp>
#include <sft/random.hpp>
#if defined VISUALIZE_GENERATION
# define show(a, b) \
do { \
cv::imshow(a,b); \
cv::waitkey(0); \
} while(0)
#else
# define show(a, b)
#endif
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#include <glob.h>
#include <opencv2/imgproc/imgproc.hpp>
#include <opencv2/highgui/highgui.hpp>
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// ============ Octave ============ //
sft::Octave::Octave(cv::Rect bb, int np, int nn, int ls, int shr)
: logScale(ls), boundingBox(bb), npositives(np), nnegatives(nn), shrinkage(shr)
{
int maxSample = npositives + nnegatives;
responses.create(maxSample, 1, CV_32FC1);
}
sft::Octave::~Octave(){}
bool sft::Octave::train( const cv::Mat& trainData, const cv::Mat& _responses, const cv::Mat& varIdx,
const cv::Mat& sampleIdx, const cv::Mat& varType, const cv::Mat& missingDataMask)
{
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CvBoostParams _params;
{
// tree params
_params.max_categories = 10;
_params.max_depth = 2;
_params.cv_folds = 0;
_params.truncate_pruned_tree = false;
_params.use_surrogates = false;
_params.use_1se_rule = false;
_params.regression_accuracy = 0.0;
// boost params
_params.boost_type = CvBoost::GENTLE;
_params.split_criteria = CvBoost::SQERR;
_params.weight_trim_rate = 0.95;
/// ToDo: move to params
_params.min_sample_count = 2;
_params.weak_count = 1;
}
std::cout << "WARNING: " << sampleIdx << std::endl;
bool update = false;
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return cv::Boost::train(trainData, CV_COL_SAMPLE, _responses, varIdx, sampleIdx, varType, missingDataMask, _params,
update);
}
namespace {
using namespace sft;
class Preprocessor
{
public:
Preprocessor(int shr) : shrinkage(shr) {}
void apply(const Mat& frame, Mat& integrals)
{
CV_Assert(frame.type() == CV_8UC3);
int h = frame.rows;
int w = frame.cols;
cv::Mat channels, gray;
channels.create(h * BINS, w, CV_8UC1);
channels.setTo(0);
cvtColor(frame, gray, CV_BGR2GRAY);
cv::Mat df_dx, df_dy, mag, angle;
cv::Sobel(gray, df_dx, CV_32F, 1, 0);
cv::Sobel(gray, df_dy, CV_32F, 0, 1);
cv::cartToPolar(df_dx, df_dy, mag, angle, true);
mag *= (1.f / (8 * sqrt(2.f)));
cv::Mat nmag;
mag.convertTo(nmag, CV_8UC1);
angle *= 6 / 360.f;
for (int y = 0; y < h; ++y)
{
uchar* magnitude = nmag.ptr<uchar>(y);
float* ang = angle.ptr<float>(y);
for (int x = 0; x < w; ++x)
{
channels.ptr<uchar>(y + (h * (int)ang[x]))[x] = magnitude[x];
}
}
cv::Mat luv, shrunk;
cv::cvtColor(frame, luv, CV_BGR2Luv);
std::vector<cv::Mat> splited;
for (int i = 0; i < 3; ++i)
splited.push_back(channels(cv::Rect(0, h * (7 + i), w, h)));
split(luv, splited);
cv::resize(channels, shrunk, cv::Size(), 1.0 / shrinkage, 1.0 / shrinkage, CV_INTER_AREA);
cv::integral(shrunk, integrals, cv::noArray(), CV_32S);
}
int shrinkage;
enum {BINS = 10};
};
}
// ToDo: parallelize it
// ToDo: sunch model size and shrinced model size usage/ Now model size mean already shrinked model
void sft::Octave::processPositives(const Dataset& dataset, const FeaturePool& pool)
{
Preprocessor prepocessor(shrinkage);
int w = 64 * pow(2, logScale) /shrinkage;
int h = 128 * pow(2, logScale) /shrinkage * 10;
integrals.create(pool.size(), (w + 1) * (h + 1), CV_32SC1);
int total = 0;
for (svector::const_iterator it = dataset.pos.begin(); it != dataset.pos.end(); ++it)
{
const string& curr = *it;
dprintf("Process candidate positive image %s\n", curr.c_str());
cv::Mat sample = cv::imread(curr);
cv::Mat channels = integrals.row(total).reshape(0, h + 1);
prepocessor.apply(sample, channels);
responses.ptr<float>(total)[0] = 1.f;
if (++total >= npositives) break;
}
dprintf("Processing positives finished:\n\trequested %d positives, collected %d samples.\n", npositives, total);
npositives = total;
nnegatives = cvRound(nnegatives * total / (double)npositives);
}
void sft::Octave::generateNegatives(const Dataset& dataset)
{
// ToDo: set seed, use offsets
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();
sft::Random::uniform iRand(0, nimages - 1);
int total = 0;
Mat sum;
for (int i = npositives; i < nnegatives + npositives; ++total)
{
int curr = iRand(idxEng);
dprintf("View %d-th sample\n", curr);
dprintf("Process %s\n", dataset.neg[curr].c_str());
Mat frame = cv::imread(dataset.neg[curr]);
prepocessor.apply(frame, sum);
std::cout << "WARNING: " << frame.cols << " " << frame.rows << std::endl;
std::cout << "WARNING: " << frame.cols / shrinkage << " " << frame.rows / shrinkage << std::endl;
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);
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))
{
responses.ptr<float>(i)[0] = 0.f;
// sum = sum.reshape(0, 1);
sum.copyTo(integrals.row(i).reshape(0, h + 1));
++i;
}
}
dprintf("Processing negatives finished:\n\trequested %d negatives, viewed %d samples.\n", nnegatives, total);
}
bool sft::Octave::train(const Dataset& dataset, const FeaturePool& pool)
{
// 1. fill integrals and classes
processPositives(dataset, pool);
generateNegatives(dataset);
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// 2. only sumple case (all features used)
int nfeatures = pool.size();
cv::Mat varIdx(1, nfeatures, CV_32SC1);
int* ptr = varIdx.ptr<int>(0);
for (int x = 0; x < nfeatures; ++x)
ptr[x] = x;
// 3. only sumple case (all samples used)
int nsamples = npositives + nnegatives;
cv::Mat sampleIdx(1, nsamples, CV_32SC1);
ptr = sampleIdx.ptr<int>(0);
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for (int x = 0; x < nsamples; ++x)
ptr[x] = x;
// 4. ICF has an orderable responce.
cv::Mat varType(1, nfeatures + 1, CV_8UC1);
uchar* uptr = varType.ptr<uchar>(0);
for (int x = 0; x < nfeatures; ++x)
uptr[x] = CV_VAR_ORDERED;
uptr[nfeatures] = CV_VAR_CATEGORICAL;
cv::Mat trainData(nfeatures, nsamples, CV_32FC1);
for (int fi = 0; fi < nfeatures; ++fi)
{
float* dptr = trainData.ptr<float>(fi);
for (int si = 0; si < nsamples; ++si)
{
dptr[si] = pool.apply(fi, si, integrals);
}
}
cv::Mat missingMask;
bool ok = train(trainData, responses, varIdx, sampleIdx, varType, missingMask);
if (!ok)
std::cout << "ERROR:tree couldnot be trained" << std::endl;
return ok;
}
// ========= FeaturePool ========= //
sft::FeaturePool::FeaturePool(cv::Size m, int n) : model(m), nfeatures(n)
{
CV_Assert(m != cv::Size() && n > 0);
fill(nfeatures);
}
sft::FeaturePool::~FeaturePool(){}
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float sft::FeaturePool::apply(int fi, int si, const Mat& integrals) const
{
return 0.f;
}
void sft::FeaturePool::fill(int desired)
{
int mw = model.width;
int mh = model.height;
int maxPoolSize = (mw -1) * mw / 2 * (mh - 1) * mh / 2 * N_CHANNELS;
nfeatures = std::min(desired, maxPoolSize);
pool.reserve(nfeatures);
sft::Random::engine eng(seed);
sft::Random::engine eng_ch(seed);
sft::Random::uniform chRand(0, N_CHANNELS - 1);
sft::Random::uniform xRand(0, model.width - 2);
sft::Random::uniform yRand(0, model.height - 2);
sft::Random::uniform wRand(1, model.width - 1);
sft::Random::uniform hRand(1, model.height - 1);
while (pool.size() < size_t(nfeatures))
{
int x = xRand(eng);
int y = yRand(eng);
int w = 1 + wRand(eng, model.width - x - 1);
int h = 1 + hRand(eng, model.height - y - 1);
CV_Assert(w > 0);
CV_Assert(h > 0);
CV_Assert(w + x < model.width);
CV_Assert(h + y < model.height);
int ch = chRand(eng_ch);
sft::ICF f(x, y, w, h, ch);
if (std::find(pool.begin(), pool.end(),f) == pool.end())
pool.push_back(f);
}
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}
// ============ Dataset ============ //
namespace {
using namespace sft;
string itoa(long i)
{
char s[65];
sprintf(s, "%ld", i);
return std::string(s);
}
void glob(const string& path, svector& ret)
{
glob_t glob_result;
glob(path.c_str(), GLOB_TILDE, 0, &glob_result);
ret.clear();
ret.reserve(glob_result.gl_pathc);
for(uint i = 0; i < glob_result.gl_pathc; ++i)
{
ret.push_back(std::string(glob_result.gl_pathv[i]));
dprintf("%s\n", ret[i].c_str());
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}
globfree(&glob_result);
}
}
// in the default case data folders should be alligned as following:
// 1. positives: <train or test path>/octave_<octave number>/pos/*.png
// 2. negatives: <train or test path>/octave_<octave number>/neg/*.png
Dataset::Dataset(const string& path, const int oct)
{
dprintf("%s\n", "get dataset file names...");
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dprintf("%s\n", "Positives globbing...");
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glob(path + "/pos/octave_" + itoa(oct) + "/*.png", pos);
dprintf("%s\n", "Negatives globbing...");
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glob(path + "/neg/octave_" + itoa(oct) + "/*.png", neg);
// Check: files not empty
CV_Assert(pos.size() != size_t(0));
CV_Assert(neg.size() != size_t(0));
}