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opencv/modules/features2d/src/calonder.cpp

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//*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of the copyright holders may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
#include "precomp.hpp"
#include <cstdio>
#include <iostream>
#include <fstream>
using namespace std;
class CSMatrixGenerator {
public:
typedef enum { PDT_GAUSS=1, PDT_BERNOULLI, PDT_DBFRIENDLY } PHI_DISTR_TYPE;
~CSMatrixGenerator();
static float* getCSMatrix(int m, int n, PHI_DISTR_TYPE dt); // do NOT free returned pointer
private:
static float *cs_phi_; // matrix for compressive sensing
static int cs_phi_m_, cs_phi_n_;
};
float* CSMatrixGenerator::getCSMatrix(int m, int n, PHI_DISTR_TYPE dt)
{
assert(m <= n);
if (cs_phi_m_!=m || cs_phi_n_!=n || cs_phi_==NULL) {
if (cs_phi_) delete [] cs_phi_;
cs_phi_ = new float[m*n];
}
#if 0 // debug - load the random matrix from a file (for reproducability of results)
//assert(m == 176);
//assert(n == 500);
//const char *phi = "/u/calonder/temp/dim_red/kpca_phi.txt";
const char *phi = "/u/calonder/temp/dim_red/debug_phi.txt";
std::ifstream ifs(phi);
for (size_t i=0; i<m*n; ++i) {
if (!ifs.good()) {
printf("[ERROR] RandomizedTree::makeRandomMeasMatrix: problem reading '%s'\n", phi);
exit(0);
}
ifs >> cs_phi[i];
}
ifs.close();
static bool warned=false;
if (!warned) {
printf("[NOTE] RT: reading %ix%i PHI matrix from '%s'...\n", m, n, phi);
warned=true;
}
return;
#endif
float *cs_phi = cs_phi_;
if (m == n) {
// special case - set to 0 for safety
memset(cs_phi, 0, m*n*sizeof(float));
printf("[WARNING] %s:%i: square CS matrix (-> no reduction)\n", __FILE__, __LINE__);
}
else {
cv::RNG rng(23);
// par is distr param, cf 'Favorable JL Distributions' (Baraniuk et al, 2006)
if (dt == PDT_GAUSS) {
float par = (float)(1./m);
for (int i=0; i<m*n; ++i)
*cs_phi++ = (float)rng.gaussian(par);
}
else if (dt == PDT_BERNOULLI) {
float par = (float)(1./sqrt((float)m));
for (int i=0; i<m*n; ++i)
*cs_phi++ = (rng(2)==0 ? par : -par);
}
else if (dt == PDT_DBFRIENDLY) {
float par = (float)sqrt(3./m);
for (int i=0; i<m*n; ++i) {
int r = rng(6);
*cs_phi++ = (r==0 ? par : (r==1 ? -par : 0.f));
}
}
else
throw("PHI_DISTR_TYPE not implemented.");
}
return cs_phi_;
}
CSMatrixGenerator::~CSMatrixGenerator()
{
if (cs_phi_) delete [] cs_phi_;
cs_phi_ = NULL;
}
float *CSMatrixGenerator::cs_phi_ = NULL;
int CSMatrixGenerator::cs_phi_m_ = 0;
int CSMatrixGenerator::cs_phi_n_ = 0;
inline void addVec(int size, const float* src1, const float* src2, float* dst)
{
while(--size >= 0) {
*dst = *src1 + *src2;
++dst; ++src1; ++src2;
}
}
// sum up 50 byte vectors of length 176
// assume 4 bits max for input vector values
// final shift is 2 bits right
// temp buffer should be twice as long as signature
// sig and buffer need not be initialized
inline void sum_50t_176c(uchar **pp, uchar *sig, unsigned short *temp)
{
#if CV_SSE2
__m128i acc, *acc1, *acc2, *acc3, *acc4, tzero;
__m128i *ssig, *ttemp;
ssig = (__m128i *)sig;
ttemp = (__m128i *)temp;
// empty ttemp[]
tzero = _mm_set_epi32(0, 0, 0, 0);
for (int i=0; i<22; i++)
ttemp[i] = tzero;
for (int j=0; j<48; j+=16)
{
// empty ssig[]
for (int i=0; i<11; i++)
ssig[i] = tzero;
for (int i=j; i<j+16; i+=4) // 4 columns at a time, to 16
{
acc1 = (__m128i *)pp[i];
acc2 = (__m128i *)pp[i+1];
acc3 = (__m128i *)pp[i+2];
acc4 = (__m128i *)pp[i+3];
// add next four columns
acc = _mm_adds_epu8(acc1[0],acc2[0]);
acc = _mm_adds_epu8(acc,acc3[0]);
acc = _mm_adds_epu8(acc,acc4[1]);
ssig[0] = _mm_adds_epu8(acc,ssig[0]);
// add four columns
acc = _mm_adds_epu8(acc1[1],acc2[1]);
acc = _mm_adds_epu8(acc,acc3[1]);
acc = _mm_adds_epu8(acc,acc4[1]);
ssig[1] = _mm_adds_epu8(acc,ssig[1]);
// add four columns
acc = _mm_adds_epu8(acc1[2],acc2[2]);
acc = _mm_adds_epu8(acc,acc3[2]);
acc = _mm_adds_epu8(acc,acc4[2]);
ssig[2] = _mm_adds_epu8(acc,ssig[2]);
// add four columns
acc = _mm_adds_epu8(acc1[3],acc2[3]);
acc = _mm_adds_epu8(acc,acc3[3]);
acc = _mm_adds_epu8(acc,acc4[3]);
ssig[3] = _mm_adds_epu8(acc,ssig[3]);
// add four columns
acc = _mm_adds_epu8(acc1[4],acc2[4]);
acc = _mm_adds_epu8(acc,acc3[4]);
acc = _mm_adds_epu8(acc,acc4[4]);
ssig[4] = _mm_adds_epu8(acc,ssig[4]);
// add four columns
acc = _mm_adds_epu8(acc1[5],acc2[5]);
acc = _mm_adds_epu8(acc,acc3[5]);
acc = _mm_adds_epu8(acc,acc4[5]);
ssig[5] = _mm_adds_epu8(acc,ssig[5]);
// add four columns
acc = _mm_adds_epu8(acc1[6],acc2[6]);
acc = _mm_adds_epu8(acc,acc3[6]);
acc = _mm_adds_epu8(acc,acc4[6]);
ssig[6] = _mm_adds_epu8(acc,ssig[6]);
// add four columns
acc = _mm_adds_epu8(acc1[7],acc2[7]);
acc = _mm_adds_epu8(acc,acc3[7]);
acc = _mm_adds_epu8(acc,acc4[7]);
ssig[7] = _mm_adds_epu8(acc,ssig[7]);
// add four columns
acc = _mm_adds_epu8(acc1[8],acc2[8]);
acc = _mm_adds_epu8(acc,acc3[8]);
acc = _mm_adds_epu8(acc,acc4[8]);
ssig[8] = _mm_adds_epu8(acc,ssig[8]);
// add four columns
acc = _mm_adds_epu8(acc1[9],acc2[9]);
acc = _mm_adds_epu8(acc,acc3[9]);
acc = _mm_adds_epu8(acc,acc4[9]);
ssig[9] = _mm_adds_epu8(acc,ssig[9]);
// add four columns
acc = _mm_adds_epu8(acc1[10],acc2[10]);
acc = _mm_adds_epu8(acc,acc3[10]);
acc = _mm_adds_epu8(acc,acc4[10]);
ssig[10] = _mm_adds_epu8(acc,ssig[10]);
}
// unpack to ttemp buffer and add
ttemp[0] = _mm_add_epi16(_mm_unpacklo_epi8(ssig[0],tzero),ttemp[0]);
ttemp[1] = _mm_add_epi16(_mm_unpackhi_epi8(ssig[0],tzero),ttemp[1]);
ttemp[2] = _mm_add_epi16(_mm_unpacklo_epi8(ssig[1],tzero),ttemp[2]);
ttemp[3] = _mm_add_epi16(_mm_unpackhi_epi8(ssig[1],tzero),ttemp[3]);
ttemp[4] = _mm_add_epi16(_mm_unpacklo_epi8(ssig[2],tzero),ttemp[4]);
ttemp[5] = _mm_add_epi16(_mm_unpackhi_epi8(ssig[2],tzero),ttemp[5]);
ttemp[6] = _mm_add_epi16(_mm_unpacklo_epi8(ssig[3],tzero),ttemp[6]);
ttemp[7] = _mm_add_epi16(_mm_unpackhi_epi8(ssig[3],tzero),ttemp[7]);
ttemp[8] = _mm_add_epi16(_mm_unpacklo_epi8(ssig[4],tzero),ttemp[8]);
ttemp[9] = _mm_add_epi16(_mm_unpackhi_epi8(ssig[4],tzero),ttemp[9]);
ttemp[10] = _mm_add_epi16(_mm_unpacklo_epi8(ssig[5],tzero),ttemp[10]);
ttemp[11] = _mm_add_epi16(_mm_unpackhi_epi8(ssig[5],tzero),ttemp[11]);
ttemp[12] = _mm_add_epi16(_mm_unpacklo_epi8(ssig[6],tzero),ttemp[12]);
ttemp[13] = _mm_add_epi16(_mm_unpackhi_epi8(ssig[6],tzero),ttemp[13]);
ttemp[14] = _mm_add_epi16(_mm_unpacklo_epi8(ssig[7],tzero),ttemp[14]);
ttemp[15] = _mm_add_epi16(_mm_unpackhi_epi8(ssig[7],tzero),ttemp[15]);
ttemp[16] = _mm_add_epi16(_mm_unpacklo_epi8(ssig[8],tzero),ttemp[16]);
ttemp[17] = _mm_add_epi16(_mm_unpackhi_epi8(ssig[8],tzero),ttemp[17]);
ttemp[18] = _mm_add_epi16(_mm_unpacklo_epi8(ssig[9],tzero),ttemp[18]);
ttemp[19] = _mm_add_epi16(_mm_unpackhi_epi8(ssig[9],tzero),ttemp[19]);
ttemp[20] = _mm_add_epi16(_mm_unpacklo_epi8(ssig[10],tzero),ttemp[20]);
ttemp[21] = _mm_add_epi16(_mm_unpackhi_epi8(ssig[10],tzero),ttemp[21]);
}
// create ssignature from 16-bit result
ssig[0] =_mm_packus_epi16(_mm_srai_epi16(ttemp[0],2),_mm_srai_epi16(ttemp[1],2));
ssig[1] =_mm_packus_epi16(_mm_srai_epi16(ttemp[2],2),_mm_srai_epi16(ttemp[3],2));
ssig[2] =_mm_packus_epi16(_mm_srai_epi16(ttemp[4],2),_mm_srai_epi16(ttemp[5],2));
ssig[3] =_mm_packus_epi16(_mm_srai_epi16(ttemp[6],2),_mm_srai_epi16(ttemp[7],2));
ssig[4] =_mm_packus_epi16(_mm_srai_epi16(ttemp[8],2),_mm_srai_epi16(ttemp[9],2));
ssig[5] =_mm_packus_epi16(_mm_srai_epi16(ttemp[10],2),_mm_srai_epi16(ttemp[11],2));
ssig[6] =_mm_packus_epi16(_mm_srai_epi16(ttemp[12],2),_mm_srai_epi16(ttemp[13],2));
ssig[7] =_mm_packus_epi16(_mm_srai_epi16(ttemp[14],2),_mm_srai_epi16(ttemp[15],2));
ssig[8] =_mm_packus_epi16(_mm_srai_epi16(ttemp[16],2),_mm_srai_epi16(ttemp[17],2));
ssig[9] =_mm_packus_epi16(_mm_srai_epi16(ttemp[18],2),_mm_srai_epi16(ttemp[19],2));
ssig[10] =_mm_packus_epi16(_mm_srai_epi16(ttemp[20],2),_mm_srai_epi16(ttemp[21],2));
#else
CV_Error( CV_StsNotImplemented, "Not supported without SSE2" );
#endif
}
namespace cv
{
RandomizedTree::RandomizedTree()
: posteriors_(NULL), posteriors2_(NULL)
{
}
RandomizedTree::~RandomizedTree()
{
freePosteriors(3);
}
void RandomizedTree::createNodes(int num_nodes, RNG &rng)
{
nodes_.reserve(num_nodes);
for (int i = 0; i < num_nodes; ++i) {
nodes_.push_back( RTreeNode((uchar)rng(RandomizedTree::PATCH_SIZE),
(uchar)rng(RandomizedTree::PATCH_SIZE),
(uchar)rng(RandomizedTree::PATCH_SIZE),
(uchar)rng(RandomizedTree::PATCH_SIZE)) );
}
}
int RandomizedTree::getIndex(uchar* patch_data) const
{
int index = 0;
for (int d = 0; d < depth_; ++d) {
int child_offset = nodes_[index](patch_data);
index = 2*index + 1 + child_offset;
}
return (int)(index - nodes_.size());
}
void RandomizedTree::train(std::vector<BaseKeypoint> const& base_set,
RNG &rng, int depth, int views, size_t reduced_num_dim,
int num_quant_bits)
{
PatchGenerator make_patch;
train(base_set, rng, make_patch, depth, views, reduced_num_dim, num_quant_bits);
}
void RandomizedTree::train(std::vector<BaseKeypoint> const& base_set,
RNG &rng, PatchGenerator &make_patch,
int depth, int views, size_t reduced_num_dim,
int num_quant_bits)
{
init((int)base_set.size(), depth, rng);
Mat patch;
// Estimate posterior probabilities using random affine views
std::vector<BaseKeypoint>::const_iterator keypt_it;
int class_id = 0;
Size patchSize(PATCH_SIZE, PATCH_SIZE);
for (keypt_it = base_set.begin(); keypt_it != base_set.end(); ++keypt_it, ++class_id) {
for (int i = 0; i < views; ++i) {
make_patch( Mat(keypt_it->image), Point(keypt_it->x, keypt_it->y ), patch, patchSize, rng );
IplImage iplPatch = patch;
addExample(class_id, getData(&iplPatch));
}
}
finalize(reduced_num_dim, num_quant_bits);
}
void RandomizedTree::allocPosteriorsAligned(int num_leaves, int num_classes)
{
freePosteriors(3);
posteriors_ = new float*[num_leaves]; //(float**) malloc(num_leaves*sizeof(float*));
for (int i=0; i<num_leaves; ++i) {
posteriors_[i] = (float*)cvAlloc(num_classes*sizeof(posteriors_[i][0]));
memset(posteriors_[i], 0, num_classes*sizeof(float));
}
posteriors2_ = new uchar*[num_leaves];
for (int i=0; i<num_leaves; ++i) {
posteriors2_[i] = (uchar*)cvAlloc(num_classes*sizeof(posteriors2_[i][0]));
memset(posteriors2_[i], 0, num_classes*sizeof(uchar));
}
classes_ = num_classes;
}
void RandomizedTree::freePosteriors(int which)
{
if (posteriors_ && (which&1)) {
for (int i=0; i<num_leaves_; ++i)
if (posteriors_[i])
cvFree( &posteriors_[i] );
delete [] posteriors_;
posteriors_ = NULL;
}
if (posteriors2_ && (which&2)) {
for (int i=0; i<num_leaves_; ++i)
cvFree( &posteriors2_[i] );
delete [] posteriors2_;
posteriors2_ = NULL;
}
classes_ = -1;
}
void RandomizedTree::init(int num_classes, int depth, RNG &rng)
{
depth_ = depth;
num_leaves_ = 1 << depth; // 2**d
int num_nodes = num_leaves_ - 1; // 2**d - 1
// Initialize probabilities and counts to 0
allocPosteriorsAligned(num_leaves_, num_classes); // will set classes_ correctly
for (int i = 0; i < num_leaves_; ++i)
memset((void*)posteriors_[i], 0, num_classes*sizeof(float));
leaf_counts_.resize(num_leaves_);
for (int i = 0; i < num_leaves_; ++i)
memset((void*)posteriors2_[i], 0, num_classes*sizeof(uchar));
createNodes(num_nodes, rng);
}
void RandomizedTree::addExample(int class_id, uchar* patch_data)
{
int index = getIndex(patch_data);
float* posterior = getPosteriorByIndex(index);
++leaf_counts_[index];
++posterior[class_id];
}
// returns the p% percentile of data (length n vector)
static float percentile(float *data, int n, float p)
{
assert(n>0);
assert(p>=0 && p<=1);
std::vector<float> vec(data, data+n);
std::sort(vec.begin(), vec.end());
int ix = (int)(p*(n-1));
return vec[ix];
}
void RandomizedTree::finalize(size_t reduced_num_dim, int num_quant_bits)
{
// Normalize by number of patches to reach each leaf
for (int index = 0; index < num_leaves_; ++index) {
float* posterior = posteriors_[index];
assert(posterior != NULL);
int count = leaf_counts_[index];
if (count != 0) {
float normalizer = 1.0f / count;
for (int c = 0; c < classes_; ++c) {
*posterior *= normalizer;
++posterior;
}
}
}
leaf_counts_.clear();
// apply compressive sensing
if ((int)reduced_num_dim != classes_)
compressLeaves(reduced_num_dim);
else {
static bool notified = false;
if (!notified)
printf("\n[OK] NO compression to leaves applied, dim=%i\n", (int)reduced_num_dim);
notified = true;
}
// convert float-posteriors to char-posteriors (quantization step)
makePosteriors2(num_quant_bits);
}
void RandomizedTree::compressLeaves(size_t reduced_num_dim)
{
static bool warned = false;
if (!warned) {
printf("\n[OK] compressing leaves with phi %i x %i\n", (int)reduced_num_dim, (int)classes_);
warned = true;
}
static bool warned2 = false;
if ((int)reduced_num_dim == classes_) {
if (!warned2)
printf("[WARNING] RandomizedTree::compressLeaves: not compressing because reduced_dim == classes()\n");
warned2 = true;
return;
}
// DO NOT FREE RETURNED POINTER
float *cs_phi = CSMatrixGenerator::getCSMatrix((int)reduced_num_dim, classes_, CSMatrixGenerator::PDT_BERNOULLI);
float *cs_posteriors = new float[num_leaves_ * reduced_num_dim]; // temp, num_leaves_ x reduced_num_dim
for (int i=0; i<num_leaves_; ++i) {
float *post = getPosteriorByIndex(i);
float *prod = &cs_posteriors[i*reduced_num_dim];
Mat A( (int)reduced_num_dim, classes_, CV_32FC1, cs_phi );
Mat X( classes_, 1, CV_32FC1, post );
Mat Y( (int)reduced_num_dim, 1, CV_32FC1, prod );
Y = A*X;
}
// copy new posteriors
freePosteriors(3);
allocPosteriorsAligned(num_leaves_, (int)reduced_num_dim);
for (int i=0; i<num_leaves_; ++i)
memcpy(posteriors_[i], &cs_posteriors[i*reduced_num_dim], reduced_num_dim*sizeof(float));
classes_ = (int)reduced_num_dim;
delete [] cs_posteriors;
}
void RandomizedTree::makePosteriors2(int num_quant_bits)
{
int N = (1<<num_quant_bits) - 1;
float perc[2];
estimateQuantPercForPosteriors(perc);
assert(posteriors_ != NULL);
for (int i=0; i<num_leaves_; ++i)
quantizeVector(posteriors_[i], classes_, N, perc, posteriors2_[i]);
// printf("makePosteriors2 quantization bounds: %.3e, %.3e (num_leaves=%i, N=%i)\n",
// perc[0], perc[1], num_leaves_, N);
}
void RandomizedTree::estimateQuantPercForPosteriors(float perc[2])
{
// _estimate_ percentiles for this tree
// TODO: do this more accurately
assert(posteriors_ != NULL);
perc[0] = perc[1] = .0f;
for (int i=0; i<num_leaves_; i++) {
perc[0] += percentile(posteriors_[i], classes_, GET_LOWER_QUANT_PERC());
perc[1] += percentile(posteriors_[i], classes_, GET_UPPER_QUANT_PERC());
}
perc[0] /= num_leaves_;
perc[1] /= num_leaves_;
}
float* RandomizedTree::getPosterior(uchar* patch_data)
{
return const_cast<float*>(const_cast<const RandomizedTree*>(this)->getPosterior(patch_data));
}
const float* RandomizedTree::getPosterior(uchar* patch_data) const
{
return getPosteriorByIndex( getIndex(patch_data) );
}
uchar* RandomizedTree::getPosterior2(uchar* patch_data)
{
return const_cast<uchar*>(const_cast<const RandomizedTree*>(this)->getPosterior2(patch_data));
}
const uchar* RandomizedTree::getPosterior2(uchar* patch_data) const
{
return getPosteriorByIndex2( getIndex(patch_data) );
}
void RandomizedTree::quantizeVector(float *vec, int dim, int N, float bnds[2], int clamp_mode)
{
float map_bnd[2] = {0.f,(float)N}; // bounds of quantized target interval we're mapping to
for (int k=0; k<dim; ++k, ++vec) {
*vec = float(int((*vec - bnds[0])/(bnds[1] - bnds[0])*(map_bnd[1] - map_bnd[0]) + map_bnd[0]));
// 0: clamp both, lower and upper values
if (clamp_mode == 0) *vec = (*vec<map_bnd[0]) ? map_bnd[0] : ((*vec>map_bnd[1]) ? map_bnd[1] : *vec);
// 1: clamp lower values only
else if (clamp_mode == 1) *vec = (*vec<map_bnd[0]) ? map_bnd[0] : *vec;
// 2: clamp upper values only
else if (clamp_mode == 2) *vec = (*vec>map_bnd[1]) ? map_bnd[1] : *vec;
// 4: no clamping
else if (clamp_mode == 4) ; // yep, nothing
else {
printf("clamp_mode == %i is not valid (%s:%i).\n", clamp_mode, __FILE__, __LINE__);
exit(1);
}
}
}
void RandomizedTree::quantizeVector(float *vec, int dim, int N, float bnds[2], uchar *dst)
{
int map_bnd[2] = {0, N}; // bounds of quantized target interval we're mapping to
int tmp;
for (int k=0; k<dim; ++k) {
tmp = int((*vec - bnds[0])/(bnds[1] - bnds[0])*(map_bnd[1] - map_bnd[0]) + map_bnd[0]);
*dst = (uchar)((tmp<0) ? 0 : ((tmp>N) ? N : tmp));
++vec;
++dst;
}
}
void RandomizedTree::read(const char* file_name, int num_quant_bits)
{
std::ifstream file(file_name, std::ifstream::binary);
read(file, num_quant_bits);
file.close();
}
void RandomizedTree::read(std::istream &is, int num_quant_bits)
{
is.read((char*)(&classes_), sizeof(classes_));
is.read((char*)(&depth_), sizeof(depth_));
num_leaves_ = 1 << depth_;
int num_nodes = num_leaves_ - 1;
nodes_.resize(num_nodes);
is.read((char*)(&nodes_[0]), num_nodes * sizeof(nodes_[0]));
//posteriors_.resize(classes_ * num_leaves_);
//freePosteriors(3);
//printf("[DEBUG] reading: %i leaves, %i classes\n", num_leaves_, classes_);
allocPosteriorsAligned(num_leaves_, classes_);
for (int i=0; i<num_leaves_; i++)
is.read((char*)posteriors_[i], classes_ * sizeof(*posteriors_[0]));
// make char-posteriors from float-posteriors
makePosteriors2(num_quant_bits);
}
void RandomizedTree::write(const char* file_name) const
{
std::ofstream file(file_name, std::ofstream::binary);
write(file);
file.close();
}
void RandomizedTree::write(std::ostream &os) const
{
if (!posteriors_) {
printf("WARNING: Cannot write float posteriors (posteriors_ = NULL).\n");
return;
}
os.write((char*)(&classes_), sizeof(classes_));
os.write((char*)(&depth_), sizeof(depth_));
os.write((char*)(&nodes_[0]), (int)(nodes_.size() * sizeof(nodes_[0])));
for (int i=0; i<num_leaves_; i++) {
os.write((char*)posteriors_[i], classes_ * sizeof(*posteriors_[0]));
}
}
void RandomizedTree::savePosteriors(std::string url, bool append)
{
std::ofstream file(url.c_str(), (append?std::ios::app:std::ios::out));
for (int i=0; i<num_leaves_; i++) {
float *post = posteriors_[i];
char buf[20];
for (int i=0; i<classes_; i++) {
sprintf(buf, "%.10e", *post++);
file << buf << ((i<classes_-1) ? " " : "");
}
file << std::endl;
}
file.close();
}
void RandomizedTree::savePosteriors2(std::string url, bool append)
{
std::ofstream file(url.c_str(), (append?std::ios::app:std::ios::out));
for (int i=0; i<num_leaves_; i++) {
uchar *post = posteriors2_[i];
for (int i=0; i<classes_; i++)
file << int(*post++) << (i<classes_-1?" ":"");
file << std::endl;
}
file.close();
}
////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
RTreeClassifier::RTreeClassifier()
: classes_(0)
{
posteriors_ = NULL;
}
void RTreeClassifier::train(std::vector<BaseKeypoint> const& base_set,
RNG &rng, int num_trees, int depth,
int views, size_t reduced_num_dim,
int num_quant_bits)
{
PatchGenerator make_patch;
train(base_set, rng, make_patch, num_trees, depth, views, reduced_num_dim, num_quant_bits);
}
// Single-threaded version of train(), with progress output
void RTreeClassifier::train(std::vector<BaseKeypoint> const& base_set,
RNG &rng, PatchGenerator &make_patch, int num_trees,
int depth, int views, size_t reduced_num_dim,
int num_quant_bits)
{
if (reduced_num_dim > base_set.size()) {
printf("INVALID PARAMS in RTreeClassifier::train: reduced_num_dim{%i} > base_set.size(){%i}\n",
(int)reduced_num_dim, (int)base_set.size());
return;
}
num_quant_bits_ = num_quant_bits;
classes_ = (int)reduced_num_dim; // base_set.size();
original_num_classes_ = (int)base_set.size();
trees_.resize(num_trees);
printf("[OK] Training trees: base size=%i, reduced size=%i\n", (int)base_set.size(), (int)reduced_num_dim);
int count = 1;
printf("[OK] Trained 0 / %i trees", num_trees); fflush(stdout);
for( int ti = 0; ti < num_trees; ti++ ) {
trees_[ti].train(base_set, rng, make_patch, depth, views, reduced_num_dim, num_quant_bits_);
printf("\r[OK] Trained %i / %i trees", count++, num_trees);
fflush(stdout);
}
printf("\n");
countZeroElements();
printf("\n\n");
}
void RTreeClassifier::getSignature(IplImage* patch, float *sig) const
{
// Need pointer to 32x32 patch data
uchar buffer[RandomizedTree::PATCH_SIZE * RandomizedTree::PATCH_SIZE];
uchar* patch_data;
if (patch->widthStep != RandomizedTree::PATCH_SIZE) {
//printf("[INFO] patch is padded, data will be copied (%i/%i).\n",
// patch->widthStep, RandomizedTree::PATCH_SIZE);
uchar* data = getData(patch);
patch_data = buffer;
for (int i = 0; i < RandomizedTree::PATCH_SIZE; ++i) {
memcpy((void*)patch_data, (void*)data, RandomizedTree::PATCH_SIZE);
data += patch->widthStep;
patch_data += RandomizedTree::PATCH_SIZE;
}
patch_data = buffer;
}
else {
patch_data = getData(patch);
}
memset((void*)sig, 0, classes_ * sizeof(float));
std::vector<RandomizedTree>::const_iterator tree_it;
// get posteriors
float **posteriors = new float*[trees_.size()]; // TODO: move alloc outside this func
float **pp = posteriors;
for (tree_it = trees_.begin(); tree_it != trees_.end(); ++tree_it, pp++) {
*pp = const_cast<float*>(tree_it->getPosterior(patch_data));
assert(*pp != NULL);
}
// sum them up
pp = posteriors;
for (tree_it = trees_.begin(); tree_it != trees_.end(); ++tree_it, pp++)
addVec(classes_, sig, *pp, sig);
delete [] posteriors;
posteriors = NULL;
// full quantization (experimental)
#if 0
int n_max = 1<<8 - 1;
int sum_max = (1<<4 - 1)*trees_.size();
int shift = 0;
while ((sum_max>>shift) > n_max) shift++;
for (int i = 0; i < classes_; ++i) {
sig[i] = int(sig[i] + .5) >> shift;
if (sig[i]>n_max) sig[i] = n_max;
}
static bool warned = false;
if (!warned) {
printf("[WARNING] Using full quantization (RTreeClassifier::getSignature)! shift=%i\n", shift);
warned = true;
}
#else
// TODO: get rid of this multiply (-> number of trees is known at train
// time, exploit it in RandomizedTree::finalize())
float normalizer = 1.0f / trees_.size();
for (int i = 0; i < classes_; ++i)
sig[i] *= normalizer;
#endif
}
void RTreeClassifier::getSignature(IplImage* patch, uchar *sig) const
{
// Need pointer to 32x32 patch data
uchar buffer[RandomizedTree::PATCH_SIZE * RandomizedTree::PATCH_SIZE];
uchar* patch_data;
if (patch->widthStep != RandomizedTree::PATCH_SIZE) {
//printf("[INFO] patch is padded, data will be copied (%i/%i).\n",
// patch->widthStep, RandomizedTree::PATCH_SIZE);
uchar* data = getData(patch);
patch_data = buffer;
for (int i = 0; i < RandomizedTree::PATCH_SIZE; ++i) {
memcpy((void*)patch_data, (void*)data, RandomizedTree::PATCH_SIZE);
data += patch->widthStep;
patch_data += RandomizedTree::PATCH_SIZE;
}
patch_data = buffer;
} else {
patch_data = getData(patch);
}
std::vector<RandomizedTree>::const_iterator tree_it;
// get posteriors
if (posteriors_ == NULL)
{
posteriors_ = (uchar**)cvAlloc( trees_.size()*sizeof(posteriors_[0]) );
ptemp_ = (unsigned short*)cvAlloc( classes_*sizeof(ptemp_[0]) );
}
/// @todo What is going on in the next 4 lines?
uchar **pp = posteriors_;
for (tree_it = trees_.begin(); tree_it != trees_.end(); ++tree_it, pp++)
*pp = const_cast<uchar*>(tree_it->getPosterior2(patch_data));
pp = posteriors_;
#if 1
// SSE2 optimized code
sum_50t_176c(pp, sig, ptemp_); // sum them up
#else
static bool warned = false;
memset((void*)sig, 0, classes_ * sizeof(sig[0]));
unsigned short *sig16 = new unsigned short[classes_]; // TODO: make member, no alloc here
memset((void*)sig16, 0, classes_ * sizeof(sig16[0]));
for (tree_it = trees_.begin(); tree_it != trees_.end(); ++tree_it, pp++)
addVec(classes_, sig16, *pp, sig16);
// squeeze signatures into an uchar
const bool full_shifting = true;
int shift;
if (full_shifting) {
float num_add_bits_f = log((float)trees_.size())/log(2.f); // # additional bits required due to summation
int num_add_bits = int(num_add_bits_f);
if (num_add_bits_f != float(num_add_bits)) ++num_add_bits;
shift = num_quant_bits_ + num_add_bits - 8*sizeof(uchar);
//shift = num_quant_bits_ + num_add_bits - 2;
//shift = 6;
if (shift>0)
for (int i = 0; i < classes_; ++i)
sig[i] = (sig16[i] >> shift); // &3 cut off all but lowest 2 bits, 3(dec) = 11(bin)
if (!warned)
printf("[OK] RTC: quantizing by FULL RIGHT SHIFT, shift = %i\n", shift);
}
else {
printf("[ERROR] RTC: not implemented!\n");
exit(0);
}
if (!warned)
printf("[WARNING] RTC: unoptimized signature computation\n");
warned = true;
#endif
}
void RTreeClassifier::getSparseSignature(IplImage *patch, float *sig, float thresh) const
{
getFloatSignature(patch, sig);
for (int i=0; i<classes_; ++i, sig++)
if (*sig < thresh) *sig = 0.f;
}
int RTreeClassifier::countNonZeroElements(float *vec, int n, double tol)
{
int res = 0;
while (n-- > 0)
res += (fabs(*vec++) > tol);
return res;
}
void RTreeClassifier::read(const char* file_name)
{
std::ifstream file(file_name, std::ifstream::binary);
if( file.is_open() )
{
read(file);
file.close();
}
}
void RTreeClassifier::read(std::istream &is)
{
int num_trees = 0;
is.read((char*)(&num_trees), sizeof(num_trees));
is.read((char*)(&classes_), sizeof(classes_));
is.read((char*)(&original_num_classes_), sizeof(original_num_classes_));
is.read((char*)(&num_quant_bits_), sizeof(num_quant_bits_));
if (num_quant_bits_<1 || num_quant_bits_>8) {
printf("[WARNING] RTC: suspicious value num_quant_bits_=%i found; setting to %i.\n",
num_quant_bits_, (int)DEFAULT_NUM_QUANT_BITS);
num_quant_bits_ = DEFAULT_NUM_QUANT_BITS;
}
trees_.resize(num_trees);
std::vector<RandomizedTree>::iterator tree_it;
for (tree_it = trees_.begin(); tree_it != trees_.end(); ++tree_it) {
tree_it->read(is, num_quant_bits_);
}
printf("[OK] Loaded RTC, quantization=%i bits\n", num_quant_bits_);
countZeroElements();
}
void RTreeClassifier::write(const char* file_name) const
{
std::ofstream file(file_name, std::ofstream::binary);
write(file);
file.close();
}
void RTreeClassifier::write(std::ostream &os) const
{
int num_trees = (int)trees_.size();
os.write((char*)(&num_trees), sizeof(num_trees));
os.write((char*)(&classes_), sizeof(classes_));
os.write((char*)(&original_num_classes_), sizeof(original_num_classes_));
os.write((char*)(&num_quant_bits_), sizeof(num_quant_bits_));
printf("RTreeClassifier::write: num_quant_bits_=%i\n", num_quant_bits_);
std::vector<RandomizedTree>::const_iterator tree_it;
for (tree_it = trees_.begin(); tree_it != trees_.end(); ++tree_it)
tree_it->write(os);
}
void RTreeClassifier::saveAllFloatPosteriors(std::string url)
{
printf("[DEBUG] writing all float posteriors to %s...\n", url.c_str());
for (int i=0; i<(int)trees_.size(); ++i)
trees_[i].savePosteriors(url, (i==0 ? false : true));
printf("[DEBUG] done\n");
}
void RTreeClassifier::saveAllBytePosteriors(std::string url)
{
printf("[DEBUG] writing all byte posteriors to %s...\n", url.c_str());
for (int i=0; i<(int)trees_.size(); ++i)
trees_[i].savePosteriors2(url, (i==0 ? false : true));
printf("[DEBUG] done\n");
}
void RTreeClassifier::setFloatPosteriorsFromTextfile_176(std::string url)
{
std::ifstream ifs(url.c_str());
for (int i=0; i<(int)trees_.size(); ++i) {
int num_classes = trees_[i].classes_;
assert(num_classes == 176); // TODO: remove this limitation (arose due to SSE2 optimizations)
for (int k=0; k<trees_[i].num_leaves_; ++k) {
float *post = trees_[i].getPosteriorByIndex(k);
for (int j=0; j<num_classes; ++j, ++post)
ifs >> *post;
assert(ifs.good());
}
}
classes_ = 176;
//setQuantization(num_quant_bits_);
ifs.close();
printf("[EXPERIMENTAL] read entire tree from '%s'\n", url.c_str());
}
float RTreeClassifier::countZeroElements()
{
size_t flt_zeros = 0;
size_t ui8_zeros = 0;
size_t num_elem = trees_[0].classes();
for (int i=0; i<(int)trees_.size(); ++i)
for (int k=0; k<(int)trees_[i].num_leaves_; ++k) {
float *p = trees_[i].getPosteriorByIndex(k);
uchar *p2 = trees_[i].getPosteriorByIndex2(k);
assert(p); assert(p2);
for (int j=0; j<(int)num_elem; ++j, ++p, ++p2) {
if (*p == 0.f) flt_zeros++;
if (*p2 == 0) ui8_zeros++;
}
}
num_elem = trees_.size()*trees_[0].num_leaves_*num_elem;
float flt_perc = 100.f*flt_zeros/num_elem;
float ui8_perc = 100.f*ui8_zeros/num_elem;
printf("[OK] RTC: overall %i/%i (%.3f%%) zeros in float leaves\n", (int)flt_zeros, (int)num_elem, flt_perc);
printf(" overall %i/%i (%.3f%%) zeros in uint8 leaves\n", (int)ui8_zeros, (int)num_elem, ui8_perc);
return flt_perc;
}
void RTreeClassifier::setQuantization(int num_quant_bits)
{
for (int i=0; i<(int)trees_.size(); ++i)
trees_[i].applyQuantization(num_quant_bits);
printf("[OK] signature quantization is now %i bits (before: %i)\n", num_quant_bits, num_quant_bits_);
num_quant_bits_ = num_quant_bits;
}
void RTreeClassifier::discardFloatPosteriors()
{
for (int i=0; i<(int)trees_.size(); ++i)
trees_[i].discardFloatPosteriors();
printf("[OK] RTC: discarded float posteriors of all trees\n");
}
}