/***********************************************************************
* Software License Agreement (BSD License)
*
* Copyright 2008-2009 Marius Muja (mariusm@cs.ubc.ca). All rights reserved.
* Copyright 2008-2009 David G. Lowe (lowe@cs.ubc.ca). All rights reserved.
*
* THE BSD LICENSE
*
* Redistribution and use in source and binary forms, with or without
* modification, are permitted provided that the following conditions
* are met:
*
* 1. Redistributions of source code must retain the above copyright
* notice, this list of conditions and the following disclaimer.
* 2. Redistributions 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.
*
* THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``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 AUTHOR 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.
*************************************************************************/
/***********************************************************************
* Author: Vincent Rabaud
*************************************************************************/
#ifndef OPENCV_FLANN_LSH_TABLE_H_
#define OPENCV_FLANN_LSH_TABLE_H_
#include <algorithm>
#include <iostream>
#include <iomanip>
#include <limits.h>
// TODO as soon as we use C++0x, use the code in USE_UNORDERED_MAP
#ifdef __GXX_EXPERIMENTAL_CXX0X__
# define USE_UNORDERED_MAP 1
#else
# define USE_UNORDERED_MAP 0
#endif
#if USE_UNORDERED_MAP
#include <unordered_map>
#else
#include <map>
#endif
#include <math.h>
#include <stddef.h>
#include "dynamic_bitset.h"
#include "matrix.h"
namespace cvflann
{
namespace lsh
{
////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
/** What is stored in an LSH bucket
*/
typedef uint32_t FeatureIndex;
/** The id from which we can get a bucket back in an LSH table
*/
typedef unsigned int BucketKey;
/** A bucket in an LSH table
*/
typedef std::vector<FeatureIndex> Bucket;
////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
/** POD for stats about an LSH table
*/
struct LshStats
{
std::vector<unsigned int> bucket_sizes_;
size_t n_buckets_;
size_t bucket_size_mean_;
size_t bucket_size_median_;
size_t bucket_size_min_;
size_t bucket_size_max_;
size_t bucket_size_std_dev;
/** Each contained vector contains three value: beginning/end for interval, number of elements in the bin
*/
std::vector<std::vector<unsigned int> > size_histogram_;
};
/** Overload the << operator for LshStats
* @param out the streams
* @param stats the stats to display
* @return the streams
*/
inline std::ostream& operator <<(std::ostream& out, const LshStats& stats)
{
int w = 20;
out << "Lsh Table Stats:\n" << std::setw(w) << std::setiosflags(std::ios::right) << "N buckets : "
<< stats.n_buckets_ << "\n" << std::setw(w) << std::setiosflags(std::ios::right) << "mean size : "
<< std::setiosflags(std::ios::left) << stats.bucket_size_mean_ << "\n" << std::setw(w)
<< std::setiosflags(std::ios::right) << "median size : " << stats.bucket_size_median_ << "\n" << std::setw(w)
<< std::setiosflags(std::ios::right) << "min size : " << std::setiosflags(std::ios::left)
<< stats.bucket_size_min_ << "\n" << std::setw(w) << std::setiosflags(std::ios::right) << "max size : "
<< std::setiosflags(std::ios::left) << stats.bucket_size_max_;
// Display the histogram
out << std::endl << std::setw(w) << std::setiosflags(std::ios::right) << "histogram : "
<< std::setiosflags(std::ios::left);
for (std::vector<std::vector<unsigned int> >::const_iterator iterator = stats.size_histogram_.begin(), end =
stats.size_histogram_.end(); iterator != end; ++iterator) out << (*iterator)[0] << "-" << (*iterator)[1] << ": " << (*iterator)[2] << ", ";
return out;
}
////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
/** Lsh hash table. As its key is a sub-feature, and as usually
* the size of it is pretty small, we keep it as a continuous memory array.
* The value is an index in the corpus of features (we keep it as an unsigned
* int for pure memory reasons, it could be a size_t)
*/
template<typename ElementType>
class LshTable
{
public:
/** A container of all the feature indices. Optimized for space
*/
#if USE_UNORDERED_MAP
typedef std::unordered_map<BucketKey, Bucket> BucketsSpace;
#else
typedef std::map<BucketKey, Bucket> BucketsSpace;
#endif
/** A container of all the feature indices. Optimized for speed
*/
typedef std::vector<Bucket> BucketsSpeed;
/** Default constructor
*/
LshTable()
{
}
/** Default constructor
* Create the mask and allocate the memory
* @param feature_size is the size of the feature (considered as a ElementType[])
* @param key_size is the number of bits that are turned on in the feature
*/
LshTable(unsigned int feature_size, unsigned int key_size)
{
(void)feature_size;
(void)key_size;
std::cerr << "LSH is not implemented for that type" << std::endl;
assert(0);
}
/** Add a feature to the table
* @param value the value to store for that feature
* @param feature the feature itself
*/
void add(unsigned int value, const ElementType* feature)
{
// Add the value to the corresponding bucket
BucketKey key = (lsh::BucketKey)getKey(feature);
switch (speed_level_) {
case kArray:
// That means we get the buckets from an array
buckets_speed_[key].push_back(value);
break;
case kBitsetHash:
// That means we can check the bitset for the presence of a key
key_bitset_.set(key);
buckets_space_[key].push_back(value);
break;
case kHash:
{
// That means we have to check for the hash table for the presence of a key
buckets_space_[key].push_back(value);
break;
}
}
}
/** Add a set of features to the table
* @param dataset the values to store
*/
void add(Matrix<ElementType> dataset)
{
#if USE_UNORDERED_MAP
buckets_space_.rehash((buckets_space_.size() + dataset.rows) * 1.2);
#endif
// Add the features to the table
for (unsigned int i = 0; i < dataset.rows; ++i) add(i, dataset[i]);
// Now that the table is full, optimize it for speed/space
optimize();
}
/** Get a bucket given the key
* @param key
* @return
*/
inline const Bucket* getBucketFromKey(BucketKey key) const
{
// Generate other buckets
switch (speed_level_) {
case kArray:
// That means we get the buckets from an array
return &buckets_speed_[key];
break;
case kBitsetHash:
// That means we can check the bitset for the presence of a key
if (key_bitset_.test(key)) return &buckets_space_.find(key)->second;
else return 0;
break;
case kHash:
{
// That means we have to check for the hash table for the presence of a key
BucketsSpace::const_iterator bucket_it, bucket_end = buckets_space_.end();
bucket_it = buckets_space_.find(key);
// Stop here if that bucket does not exist
if (bucket_it == bucket_end) return 0;
else return &bucket_it->second;
break;
}
}
return 0;
}
/** Compute the sub-signature of a feature
*/
size_t getKey(const ElementType* /*feature*/) const
{
std::cerr << "LSH is not implemented for that type" << std::endl;
assert(0);
return 1;
}
/** Get statistics about the table
* @return
*/
LshStats getStats() const;
private:
/** defines the speed fo the implementation
* kArray uses a vector for storing data
* kBitsetHash uses a hash map but checks for the validity of a key with a bitset
* kHash uses a hash map only
*/
enum SpeedLevel
{
kArray, kBitsetHash, kHash
};
/** Initialize some variables
*/
void initialize(size_t key_size)
{
const size_t key_size_lower_bound = 1;
//a value (size_t(1) << key_size) must fit the size_t type so key_size has to be strictly less than size of size_t
const size_t key_size_upper_bound = std::min(sizeof(BucketKey) * CHAR_BIT + 1, sizeof(size_t) * CHAR_BIT);
if (key_size < key_size_lower_bound || key_size >= key_size_upper_bound)
{
CV_Error(cv::Error::StsBadArg, cv::format("Invalid key_size (=%d). Valid values for your system are %d <= key_size < %d.", (int)key_size, (int)key_size_lower_bound, (int)key_size_upper_bound));
}
speed_level_ = kHash;
key_size_ = (unsigned)key_size;
}
/** Optimize the table for speed/space
*/
void optimize()
{
// If we are already using the fast storage, no need to do anything
if (speed_level_ == kArray) return;
// Use an array if it will be more than half full
if (buckets_space_.size() > ((size_t(1) << key_size_) / 2)) {
speed_level_ = kArray;
// Fill the array version of it
buckets_speed_.resize(size_t(1) << key_size_);
for (BucketsSpace::const_iterator key_bucket = buckets_space_.begin(); key_bucket != buckets_space_.end(); ++key_bucket) buckets_speed_[key_bucket->first] = key_bucket->second;
// Empty the hash table
buckets_space_.clear();
return;
}
// If the bitset is going to use less than 10% of the RAM of the hash map (at least 1 size_t for the key and two
// for the vector) or less than 512MB (key_size_ <= 30)
if (((std::max(buckets_space_.size(), buckets_speed_.size()) * CHAR_BIT * 3 * sizeof(BucketKey)) / 10
>= (size_t(1) << key_size_)) || (key_size_ <= 32)) {
speed_level_ = kBitsetHash;
key_bitset_.resize(size_t(1) << key_size_);
key_bitset_.reset();
// Try with the BucketsSpace
for (BucketsSpace::const_iterator key_bucket = buckets_space_.begin(); key_bucket != buckets_space_.end(); ++key_bucket) key_bitset_.set(key_bucket->first);
}
else {
speed_level_ = kHash;
key_bitset_.clear();
}
}
/** The vector of all the buckets if they are held for speed
*/
BucketsSpeed buckets_speed_;
/** The hash table of all the buckets in case we cannot use the speed version
*/
BucketsSpace buckets_space_;
/** What is used to store the data */
SpeedLevel speed_level_;
/** If the subkey is small enough, it will keep track of which subkeys are set through that bitset
* That is just a speedup so that we don't look in the hash table (which can be mush slower that checking a bitset)
*/
DynamicBitset key_bitset_;
/** The size of the sub-signature in bits
*/
unsigned int key_size_;
// Members only used for the unsigned char specialization
/** The mask to apply to a feature to get the hash key
* Only used in the unsigned char case
*/
std::vector<size_t> mask_;
};
////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
// Specialization for unsigned char
template<>
inline LshTable<unsigned char>::LshTable(unsigned int feature_size, unsigned int subsignature_size)
{
initialize(subsignature_size);
// Allocate the mask
mask_ = std::vector<size_t>((size_t)ceil((float)(feature_size * sizeof(char)) / (float)sizeof(size_t)), 0);
// A bit brutal but fast to code
std::vector<size_t> indices(feature_size * CHAR_BIT);
for (size_t i = 0; i < feature_size * CHAR_BIT; ++i) indices[i] = i;
std::random_shuffle(indices.begin(), indices.end());
// Generate a random set of order of subsignature_size_ bits
for (unsigned int i = 0; i < key_size_; ++i) {
size_t index = indices[i];
// Set that bit in the mask
size_t divisor = CHAR_BIT * sizeof(size_t);
size_t idx = index / divisor; //pick the right size_t index
mask_[idx] |= size_t(1) << (index % divisor); //use modulo to find the bit offset
}
// Set to 1 if you want to display the mask for debug
#if 0
{
size_t bcount = 0;
BOOST_FOREACH(size_t mask_block, mask_){
out << std::setw(sizeof(size_t) * CHAR_BIT / 4) << std::setfill('0') << std::hex << mask_block
<< std::endl;
bcount += __builtin_popcountll(mask_block);
}
out << "bit count : " << std::dec << bcount << std::endl;
out << "mask size : " << mask_.size() << std::endl;
return out;
}
#endif
}
/** Return the Subsignature of a feature
* @param feature the feature to analyze
*/
template<>
inline size_t LshTable<unsigned char>::getKey(const unsigned char* feature) const
{
// no need to check if T is dividable by sizeof(size_t) like in the Hamming
// distance computation as we have a mask
const size_t* feature_block_ptr = reinterpret_cast<const size_t*> ((const void*)feature);
// Figure out the subsignature of the feature
// Given the feature ABCDEF, and the mask 001011, the output will be
// 000CEF
size_t subsignature = 0;
size_t bit_index = 1;
for (std::vector<size_t>::const_iterator pmask_block = mask_.begin(); pmask_block != mask_.end(); ++pmask_block) {
// get the mask and signature blocks
size_t feature_block = *feature_block_ptr;
size_t mask_block = *pmask_block;
while (mask_block) {
// Get the lowest set bit in the mask block
size_t lowest_bit = mask_block & (-(ptrdiff_t)mask_block);
// Add it to the current subsignature if necessary
subsignature += (feature_block & lowest_bit) ? bit_index : 0;
// Reset the bit in the mask block
mask_block ^= lowest_bit;
// increment the bit index for the subsignature
bit_index <<= 1;
}
// Check the next feature block
++feature_block_ptr;
}
return subsignature;
}
template<>
inline LshStats LshTable<unsigned char>::getStats() const
{
LshStats stats;
stats.bucket_size_mean_ = 0;
if ((buckets_speed_.empty()) && (buckets_space_.empty())) {
stats.n_buckets_ = 0;
stats.bucket_size_median_ = 0;
stats.bucket_size_min_ = 0;
stats.bucket_size_max_ = 0;
return stats;
}
if (!buckets_speed_.empty()) {
for (BucketsSpeed::const_iterator pbucket = buckets_speed_.begin(); pbucket != buckets_speed_.end(); ++pbucket) {
stats.bucket_sizes_.push_back((lsh::FeatureIndex)pbucket->size());
stats.bucket_size_mean_ += pbucket->size();
}
stats.bucket_size_mean_ /= buckets_speed_.size();
stats.n_buckets_ = buckets_speed_.size();
}
else {
for (BucketsSpace::const_iterator x = buckets_space_.begin(); x != buckets_space_.end(); ++x) {
stats.bucket_sizes_.push_back((lsh::FeatureIndex)x->second.size());
stats.bucket_size_mean_ += x->second.size();
}
stats.bucket_size_mean_ /= buckets_space_.size();
stats.n_buckets_ = buckets_space_.size();
}
std::sort(stats.bucket_sizes_.begin(), stats.bucket_sizes_.end());
// BOOST_FOREACH(int size, stats.bucket_sizes_)
// std::cout << size << " ";
// std::cout << std::endl;
stats.bucket_size_median_ = stats.bucket_sizes_[stats.bucket_sizes_.size() / 2];
stats.bucket_size_min_ = stats.bucket_sizes_.front();
stats.bucket_size_max_ = stats.bucket_sizes_.back();
// TODO compute mean and std
/*float mean, stddev;
stats.bucket_size_mean_ = mean;
stats.bucket_size_std_dev = stddev;*/
// Include a histogram of the buckets
unsigned int bin_start = 0;
unsigned int bin_end = 20;
bool is_new_bin = true;
for (std::vector<unsigned int>::iterator iterator = stats.bucket_sizes_.begin(), end = stats.bucket_sizes_.end(); iterator
!= end; )
if (*iterator < bin_end) {
if (is_new_bin) {
stats.size_histogram_.push_back(std::vector<unsigned int>(3, 0));
stats.size_histogram_.back()[0] = bin_start;
stats.size_histogram_.back()[1] = bin_end - 1;
is_new_bin = false;
}
++stats.size_histogram_.back()[2];
++iterator;
}
else {
bin_start += 20;
bin_end += 20;
is_new_bin = true;
}
return stats;
}
// End the two namespaces
}
}
////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
#endif /* OPENCV_FLANN_LSH_TABLE_H_ */