void
AAKR::normalize(Math::Matrix& mean, Math::Matrix& std)
{
// Resize mean and standard deviation variables.
mean.resizeAndFill(1, sampleSize(), 0);
std.resizeAndFill(1, sampleSize(), 0);
// Compute mean.
for (unsigned i = 0; i < sampleSize(); i++)
mean(i) = sum(m_data.get(0, m_num_values - 1, i, i)) / m_num_values;
// Compute standard deviation.
for (unsigned j = 0; j < sampleSize(); j++)
{
double sum = 0;
// Sum of the power of two difference
// between the value and the mean.
for (unsigned i = 0; i < m_num_values; i++)
sum += std::pow(m_data(i, j) - mean(j), 2);
// Standard deviation.
std(j) = std::sqrt(sum / m_num_values);
// Normalize each member of the data set.
for (unsigned i = 0; i < m_num_values; i++)
{
if (std(j))
m_norm(i, j) = (m_data(i, j) - mean(j)) / std(j);
else
m_norm(i, j) = 0;
}
}
}
static int lua_stoprec_DSP(lua_State *L){
int argc = lua_gettop(L);
#ifndef SKIP_ERROR_HANDLING
if (argc != 0) return luaL_error(L, "wrong number of arguments");
#endif
bool isSampling;
MICU_IsSampling(&isSampling);
u32 offset;
if (isSampling){
offset = micGetLastSampleOffset();
MICU_StopSampling();
}else offset = micsize;
u32 samplerate = sampleSize(srate);
u32 mute_size = samplerate<<1;
u8* bytebuf = (u8*)linearAlloc(offset);
memcpy(bytebuf, (u8*)micbuf, offset);
micExit();
free(micbuf);
Music* songFile = (Music*)malloc(sizeof(Music));
songFile->audiobuf = bytebuf;
songFile->audiobuf2 = NULL;
songFile->big_endian = false;
songFile->mem_size = 0;
songFile->ch = 0xDEADBEEF;
songFile->size = offset;
songFile->samplerate = samplerate;
songFile->encoding = CSND_ENCODING_PCM16;
strcpy(songFile->author,"");
strcpy(songFile->title,"");
songFile->isPlaying = false;
songFile->bytepersample = 2;
songFile->magic = 0x4C534E44;
lua_pushinteger(L,(u32)songFile);
return 1;
}
Math::Matrix
AAKR::estimate(Math::Matrix query, double variance)
{
Math::Matrix mean;
Math::Matrix std;
normalize(mean, std);
// Use normalized query vector.
computeDistance((query - mean) / std);
computeWeights(variance);
double s = sum(m_weights);
// Avoid division by zero.
if (!s)
return query;
// Combine with weights.
Math::Matrix result = ((transpose(m_weights) * m_norm) / s);
// Normalize.
for (unsigned i = 0; i < sampleSize(); i++)
result(i) = result(i) * std(i) + mean(i);
return result;
}
/*!
Returns the number of bytes required to represent one frame (a sample in each channel) in this format.
Returns 0 if this format is invalid.
*/
int QAudioFormat::bytesPerFrame() const
{
if (!isValid())
return 0;
return (sampleSize() * channelCount()) / 8;
}
void
AAKR::add(Math::Matrix v)
{
if (dataSize() == 0)
throw std::runtime_error("unable to add: data window size is undefined.");
if (v.rows() != 1)
throw std::runtime_error("unable to add: new sample is not a row vector.");
if (sampleSize() == 0)
m_data.resize(dataSize(), v.columns());
if ((unsigned)v.columns() != sampleSize())
throw std::runtime_error("unable to add: sample size does not match.");
// Write to the data set.
m_data.set(m_index, m_index, 0, sampleSize() - 1, v);
// Increment data set index.
increment();
}
void
AAKR::resize(unsigned r, unsigned c)
{
m_index = 0;
m_num_values = 0;
if (r == dataSize() && c == sampleSize())
return;
m_data.resizeAndFill(r, c, 0.0);
m_norm = m_data;
m_distances.resizeAndFill(r, 1, 0.0);
m_weights.resizeAndFill(r, 1, 0.0);
}
void
AAKR::computeDistance(Math::Matrix query)
{
if (query.rows() != 1)
throw std::runtime_error("unable to compute distance: reference is not row vector.");
if ((unsigned)query.columns() != sampleSize())
throw std::runtime_error("unable to compute distance: sample size does not match.");
m_distances.fill(0.0);
// Fill distances vector.
for (unsigned i = 0; i < m_num_values; i++)
{
Math::Matrix q = query - m_norm.row(i);
m_distances(i) = std::sqrt(sum(q * transpose(q)));
};
}
/**
* Checks if the Haar-like features generated by haargen.cpp conform to Pavani's restrictions.
*/
int main(int argc, char * args[])
{
if (argc != 2) {
return 1;
}
cv::Size sampleSize(SAMPLE_SIZE, SAMPLE_SIZE); //size in pixels of the trainning images
//load the wavelets
std::vector<HaarWavelet> wavelets;
std::cout << "Loading Haar wavelets from " << args[1] << std::endl;
loadHaarWavelets(args[1], wavelets);
std::cout << "Loaded " << wavelets.size() << " wavelets." << std::endl;
//STATS
int dimensions[3] = {0, 0, 0}; //2, 3 and 4 dimensions of the wavelets
int regionsHistogram[3][3]; //x regions are 8 - 4 - 8
//y regions are 7 - 6 - 7
int widthHistogram[20], heightHistogram[20];
int totalRectangles;
{
for(int i = 0; i < 20; ++i)
{
widthHistogram[i] = heightHistogram[i] = 0;
}
for (int i = 0; i < 3; ++i)
{
for (int j = 0; j < 3; ++j)
{
regionsHistogram[i][j] = 0;
}
}
std::vector<HaarWavelet>::iterator it = wavelets.begin();
const std::vector<HaarWavelet>::iterator end = wavelets.end();
for(;it != end; ++it)
{
dimensions[it->dimensions() - 2]++;
std::vector<cv::Rect>::const_iterator itr = it->rects_begin();
const std::vector<cv::Rect>::const_iterator endr = it->rects_end();
for(; itr != endr; ++itr)
{
const cv::Rect r = *itr;
totalRectangles++;
widthHistogram[r.width - 1]++;
heightHistogram[r.height - 1]++;
float horizontalMean = r.x + r.width / 2.0f;
float verticalMean = r.y + r.height / 2.0f;
const int xIndex = horizontalMean < 8 ? 0 :
8 <= horizontalMean && horizontalMean < 12 ? 1 :
2;
const int yIndex = verticalMean < 7 ? 0 :
7 <= verticalMean && verticalMean < 13 ? 1 :
2;
regionsHistogram[xIndex][yIndex]++;
}
}
std::cout << "Total 2D/3D/4D wavelets: " << dimensions[0] << "/" << dimensions[1] << "/" << dimensions[2] << std::endl;
std::cout << "Total rectangles: " << totalRectangles << std::endl;
std::stringstream wHist, hHist;
wHist << "Width histogram: ";
hHist << "Height histogram: ";
for(int i = 0; i < 20; ++i)
{
wHist << widthHistogram[i] << " ";
hHist << heightHistogram[i] << " ";
}
std::cout << wHist.str() << std::endl;
std::cout << hHist.str() << std::endl;
std::cout << "Rectangles mean position 2D histogram: " << std::endl;
for (int i = 0; i < 3; ++i)
{
for (int j = 0; j < 3; ++j)
{
std::cout << regionsHistogram[j][i] << " ";
}
std::cout << std::endl;
}
}
{//double checks for wavelets with overlapping rectangles
std::cout << "Checking for overlapped rectangles..." << std::endl;
std::vector<HaarWavelet>::iterator it = wavelets.begin();
const std::vector<HaarWavelet>::iterator end = wavelets.end();
for(;it != end; ++it)
{
if (hasOverlappingRectangles(*it))
{
std::cout << "Overlaps ==> ";
it->write(std::cout);
std::cout << std::endl;
}
}
}
{//double checks if any rect has x or y at position 20 or more
std::cout << "Checking for problems with rectangle sizes..." << std::endl;
std::vector<HaarWavelet>::iterator it = wavelets.begin();
const std::vector<HaarWavelet>::iterator end = wavelets.end();
for(;it != end; ++it)
{
std::vector<cv::Rect>::const_iterator itr = it->rects_begin();
const std::vector<cv::Rect>::const_iterator endr = it->rects_end();
for(; itr != endr; ++itr)
{
if (itr->x >= 20 || itr->y >= 20 || itr->x < 0 || itr->y < 0 || itr->x + itr->width > 20 || itr->y + itr->height > 20 || itr->width < 3 || itr->height < 3)
{
std::cout << "Size problem ==> ";
it->write(std::cout);
std::cout << std::endl;
break;
}
}
}
}
{//double checks for repeated rects in a haar wavelet through brute force
std::cout << "Checking duplicated rects in each haar wavelet..." << std::endl;
std::vector<HaarWavelet>::iterator it = wavelets.begin();
const std::vector<HaarWavelet>::iterator end = wavelets.end();
for(;it != end; ++it)
{
std::vector<HaarWavelet>::iterator it2 = it + 1;
for(;it2 != end; ++it2)
{
if( same(*it, *it2) )
{
std::cout << "Repeats ==> ";
it->write(std::cout);
std::cout << std::endl;
}
}
}
}
return 0;
}
qint64 OutputALSA::latency()
{
return m_prebuf_fill * 1000 / sampleRate() / channels() / sampleSize();
}
int AudioParameters::sampleSize() const
{
return sampleSize(m_format);
}
