455 lines
14 KiB
C++
455 lines
14 KiB
C++
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/*M///////////////////////////////////////////////////////////////////////////////////////
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//
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// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
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//
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// By downloading, copying, installing or using the software you agree to this license.
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// If you do not agree to this license, do not download, install,
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// copy or use the software.
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//
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//
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// Intel License Agreement
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// For Open Source Computer Vision Library
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//
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// Copyright (C) 2009, Xavier Delacour, all rights reserved.
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// Third party copyrights are property of their respective owners.
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//
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// Redistribution and use in source and binary forms, with or without modification,
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// are permitted provided that the following conditions are met:
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//
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// * Redistribution's of source code must retain the above copyright notice,
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// this list of conditions and the following disclaimer.
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//
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// * Redistribution's in binary form must reproduce the above copyright notice,
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// this list of conditions and the following disclaimer in the documentation
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// and/or other materials provided with the distribution.
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//
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// * The name of Intel Corporation may not be used to endorse or promote products
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// derived from this software without specific prior written permission.
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//
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// This software is provided by the copyright holders and contributors "as is" and
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// any express or implied warranties, including, but not limited to, the implied
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// warranties of merchantability and fitness for a particular purpose are disclaimed.
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// In no event shall the Intel Corporation or contributors be liable for any direct,
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// indirect, incidental, special, exemplary, or consequential damages
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// (including, but not limited to, procurement of substitute goods or services;
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// loss of use, data, or profits; or business interruption) however caused
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// and on any theory of liability, whether in contract, strict liability,
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// or tort (including negligence or otherwise) arising in any way out of
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// the use of this software, even if advised of the possibility of such damage.
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//
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//M*/
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// 2009-01-12, Xavier Delacour <xavier.delacour@gmail.com>
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// * hash perf could be improved
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// * in particular, implement integer only (converted fixed from float input)
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// * number of hash functions could be reduced (andoni paper)
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// * redundant distance computations could be suppressed
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// * rework CvLSHOperations interface-- move some of the loops into it to
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// * allow efficient async storage
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// Datar, M., Immorlica, N., Indyk, P., and Mirrokni, V. S. 2004. Locality-sensitive hashing
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// scheme based on p-stable distributions. In Proceedings of the Twentieth Annual Symposium on
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// Computational Geometry (Brooklyn, New York, USA, June 08 - 11, 2004). SCG '04. ACM, New York,
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// NY, 253-262. DOI= http://doi.acm.org/10.1145/997817.997857
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#include "precomp.hpp"
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#include <math.h>
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#include <vector>
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#include <algorithm>
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#include <limits>
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template <class T>
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class memory_hash_ops : public CvLSHOperations {
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int d;
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std::vector<T> data;
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std::vector<int> free_data;
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struct node {
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int i, h2, next;
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};
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std::vector<node> nodes;
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std::vector<int> free_nodes;
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std::vector<int> bins;
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public:
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memory_hash_ops(int _d, int n) : d(_d) {
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bins.resize(n, -1);
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}
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virtual int vector_add(const void* _p) {
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const T* p = (const T*)_p;
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int i;
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if (free_data.empty()) {
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i = (int)data.size();
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data.insert(data.end(), d, 0);
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} else {
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i = free_data.end()[-1];
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free_data.pop_back();
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}
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std::copy(p, p + d, data.begin() + i);
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return i / d;
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}
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virtual void vector_remove(int i) {
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free_data.push_back(i * d);
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}
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virtual const void* vector_lookup(int i) {
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return &data[i * d];
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}
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virtual void vector_reserve(int n) {
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data.reserve(n * d);
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}
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virtual unsigned int vector_count() {
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return (unsigned)(data.size() / d - free_data.size());
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}
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virtual void hash_insert(lsh_hash h, int /*l*/, int i) {
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int ii;
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if (free_nodes.empty()) {
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ii = (int)nodes.size();
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nodes.push_back(node());
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} else {
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ii = free_nodes.end()[-1];
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free_nodes.pop_back();
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}
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node& n = nodes[ii];
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int h1 = h.h1 % bins.size();
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n.i = i;
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n.h2 = h.h2;
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n.next = bins[h1];
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bins[h1] = ii;
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}
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virtual void hash_remove(lsh_hash h, int /*l*/, int i) {
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int h1 = h.h1 % bins.size();
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for (int ii = bins[h1], iin, iip = -1; ii != -1; iip = ii, ii = iin) {
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iin = nodes[ii].next;
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if (nodes[ii].h2 == h.h2 && nodes[ii].i == i) {
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free_nodes.push_back(ii);
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if (iip == -1)
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bins[h1] = iin;
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else
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nodes[iip].next = iin;
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}
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}
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}
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virtual int hash_lookup(lsh_hash h, int /*l*/, int* ret_i, int ret_i_max) {
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int h1 = h.h1 % bins.size();
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int k = 0;
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for (int ii = bins[h1]; ii != -1 && k < ret_i_max; ii = nodes[ii].next)
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if (nodes[ii].h2 == h.h2)
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ret_i[k++] = nodes[ii].i;
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return k;
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}
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};
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template <class T,int cvtype>
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class pstable_l2_func {
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CvMat *a, *b, *r1, *r2;
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int d, k;
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double r;
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pstable_l2_func(const pstable_l2_func& x);
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pstable_l2_func& operator= (const pstable_l2_func& rhs);
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public:
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typedef T scalar_type;
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typedef T accum_type;
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pstable_l2_func(int _d, int _k, double _r, CvRNG& rng)
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: d(_d), k(_k), r(_r) {
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assert(sizeof(T) == CV_ELEM_SIZE1(cvtype));
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a = cvCreateMat(k, d, cvtype);
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b = cvCreateMat(k, 1, cvtype);
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r1 = cvCreateMat(k, 1, CV_32SC1);
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r2 = cvCreateMat(k, 1, CV_32SC1);
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cvRandArr(&rng, a, CV_RAND_NORMAL, cvScalar(0), cvScalar(1));
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cvRandArr(&rng, b, CV_RAND_UNI, cvScalar(0), cvScalar(r));
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cvRandArr(&rng, r1, CV_RAND_UNI,
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cvScalar(std::numeric_limits<int>::min()),
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cvScalar(std::numeric_limits<int>::max()));
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cvRandArr(&rng, r2, CV_RAND_UNI,
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cvScalar(std::numeric_limits<int>::min()),
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cvScalar(std::numeric_limits<int>::max()));
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}
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~pstable_l2_func() {
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cvReleaseMat(&a);
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cvReleaseMat(&b);
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cvReleaseMat(&r1);
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cvReleaseMat(&r2);
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}
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// * factor all L functions into this (reduces number of matrices to 4 total;
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// * simpler syntax in lsh_table). give parameter l here that tells us which
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// * row to use etc.
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lsh_hash operator() (const T* x) const {
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const T* aj = (const T*)a->data.ptr;
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const T* bj = (const T*)b->data.ptr;
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lsh_hash h;
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h.h1 = h.h2 = 0;
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for (int j = 0; j < k; ++j) {
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accum_type s = 0;
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for (int jj = 0; jj < d; ++jj)
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s += aj[jj] * x[jj];
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s += *bj;
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s = accum_type(s/r);
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int si = int(s);
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h.h1 += r1->data.i[j] * si;
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h.h2 += r2->data.i[j] * si;
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aj += d;
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bj++;
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}
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return h;
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}
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accum_type distance(const T* p, const T* q) const {
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accum_type s = 0;
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for (int j = 0; j < d; ++j) {
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accum_type d1 = p[j] - q[j];
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s += d1 * d1;
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}
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return s;
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}
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};
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template <class H>
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class lsh_table {
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public:
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typedef typename H::scalar_type scalar_type;
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typedef typename H::accum_type accum_type;
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private:
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std::vector<H*> g;
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CvLSHOperations* ops;
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int d, L, k;
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double r;
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static accum_type comp_dist(const std::pair<int,accum_type>& x,
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const std::pair<int,accum_type>& y) {
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return x.second < y.second;
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}
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lsh_table(const lsh_table& x);
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lsh_table& operator= (const lsh_table& rhs);
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public:
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lsh_table(CvLSHOperations* _ops, int _d, int Lval, int _k, double _r, CvRNG& rng)
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: ops(_ops), d(_d), L(Lval), k(_k), r(_r) {
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g.resize(L);
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for (int j = 0; j < L; ++j)
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g[j] = new H(d, k, r, rng);
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}
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~lsh_table() {
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for (int j = 0; j < L; ++j)
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delete g[j];
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delete ops;
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}
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int dims() const {
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return d;
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}
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unsigned int size() const {
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return ops->vector_count();
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}
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void add(const scalar_type* data, int n, int* ret_indices = 0) {
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for (int j=0;j<n;++j) {
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const scalar_type* x = data+j*d;
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int i = ops->vector_add(x);
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if (ret_indices)
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ret_indices[j] = i;
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for (int l = 0; l < L; ++l) {
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lsh_hash h = (*g[l])(x);
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ops->hash_insert(h, l, i);
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}
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}
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}
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void remove(const int* indices, int n) {
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for (int j = 0; j < n; ++j) {
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int i = indices[n];
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const scalar_type* x = (const scalar_type*)ops->vector_lookup(i);
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for (int l = 0; l < L; ++l) {
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lsh_hash h = (*g[l])(x);
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ops->hash_remove(h, l, i);
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}
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ops->vector_remove(i);
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}
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}
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void query(const scalar_type* q, int k0, int emax, double* dist, int* results) {
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int* tmp = (int*)cvStackAlloc(sizeof(int) * emax);
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typedef std::pair<int, accum_type> dr_type; // * swap int and accum_type here, for naming consistency
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dr_type* dr = (dr_type*)cvStackAlloc(sizeof(dr_type) * k0);
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int k1 = 0;
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// * handle k0 >= emax, in which case don't track max distance
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for (int l = 0; l < L && emax > 0; ++l) {
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lsh_hash h = (*g[l])(q);
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int m = ops->hash_lookup(h, l, tmp, emax);
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for (int j = 0; j < m && emax > 0; ++j, --emax) {
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int i = tmp[j];
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const scalar_type* p = (const scalar_type*)ops->vector_lookup(i);
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accum_type pd = (*g[l]).distance(p, q);
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if (k1 < k0) {
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dr[k1++] = std::make_pair(i, pd);
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std::push_heap(dr, dr + k1, comp_dist);
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} else if (pd < dr[0].second) {
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std::pop_heap(dr, dr + k0, comp_dist);
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dr[k0 - 1] = std::make_pair(i, pd);
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std::push_heap(dr, dr + k0, comp_dist);
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}
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}
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}
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for (int j = 0; j < k1; ++j)
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dist[j] = dr[j].second, results[j] = dr[j].first;
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std::fill(dist + k1, dist + k0, 0);
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std::fill(results + k1, results + k0, -1);
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}
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void query(const scalar_type* data, int n, int k0, int emax, double* dist, int* results) {
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for (int j = 0; j < n; ++j) {
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query(data, k0, emax, dist, results);
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data += d; // * this may not agree with step for some scalar_types
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dist += k0;
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results += k0;
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}
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}
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};
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typedef lsh_table<pstable_l2_func<float, CV_32FC1> > lsh_pstable_l2_32f;
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typedef lsh_table<pstable_l2_func<double, CV_64FC1> > lsh_pstable_l2_64f;
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struct CvLSH {
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int type;
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union {
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lsh_pstable_l2_32f* lsh_32f;
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lsh_pstable_l2_64f* lsh_64f;
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} u;
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};
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CvLSH* cvCreateLSH(CvLSHOperations* ops, int d, int L, int k, int type, double r, int64 seed) {
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CvLSH* lsh = 0;
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CvRNG rng = cvRNG(seed);
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if (type != CV_32FC1 && type != CV_64FC1)
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CV_Error(CV_StsUnsupportedFormat, "vectors must be either CV_32FC1 or CV_64FC1");
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lsh = new CvLSH;
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lsh->type = type;
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switch (type) {
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case CV_32FC1: lsh->u.lsh_32f = new lsh_pstable_l2_32f(ops, d, L, k, r, rng); break;
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case CV_64FC1: lsh->u.lsh_64f = new lsh_pstable_l2_64f(ops, d, L, k, r, rng); break;
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}
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return lsh;
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}
|
||
|
|
|
||
|
|
CvLSH* cvCreateMemoryLSH(int d, int n, int L, int k, int type, double r, int64 seed) {
|
||
|
|
CvLSHOperations* ops = 0;
|
||
|
|
|
||
|
|
switch (type) {
|
||
|
|
case CV_32FC1: ops = new memory_hash_ops<float>(d,n); break;
|
||
|
|
case CV_64FC1: ops = new memory_hash_ops<double>(d,n); break;
|
||
|
|
}
|
||
|
|
return cvCreateLSH(ops, d, L, k, type, r, seed);
|
||
|
|
}
|
||
|
|
|
||
|
|
void cvReleaseLSH(CvLSH** lsh) {
|
||
|
|
switch ((*lsh)->type) {
|
||
|
|
case CV_32FC1: delete (*lsh)->u.lsh_32f; break;
|
||
|
|
case CV_64FC1: delete (*lsh)->u.lsh_64f; break;
|
||
|
|
default: assert(0);
|
||
|
|
}
|
||
|
|
delete *lsh;
|
||
|
|
*lsh = 0;
|
||
|
|
}
|
||
|
|
|
||
|
|
unsigned int LSHSize(CvLSH* lsh) {
|
||
|
|
switch (lsh->type) {
|
||
|
|
case CV_32FC1: return lsh->u.lsh_32f->size(); break;
|
||
|
|
case CV_64FC1: return lsh->u.lsh_64f->size(); break;
|
||
|
|
default: assert(0);
|
||
|
|
}
|
||
|
|
return 0;
|
||
|
|
}
|
||
|
|
|
||
|
|
|
||
|
|
void cvLSHAdd(CvLSH* lsh, const CvMat* data, CvMat* indices) {
|
||
|
|
int dims, n;
|
||
|
|
int* ret_indices = 0;
|
||
|
|
|
||
|
|
switch (lsh->type) {
|
||
|
|
case CV_32FC1: dims = lsh->u.lsh_32f->dims(); break;
|
||
|
|
case CV_64FC1: dims = lsh->u.lsh_64f->dims(); break;
|
||
|
|
default: assert(0); return;
|
||
|
|
}
|
||
|
|
|
||
|
|
n = data->rows;
|
||
|
|
|
||
|
|
if (dims != data->cols)
|
||
|
|
CV_Error(CV_StsBadSize, "data must be n x d, where d is what was used to construct LSH");
|
||
|
|
|
||
|
|
if (CV_MAT_TYPE(data->type) != lsh->type)
|
||
|
|
CV_Error(CV_StsUnsupportedFormat, "type of data and constructed LSH must agree");
|
||
|
|
if (indices) {
|
||
|
|
if (CV_MAT_TYPE(indices->type) != CV_32SC1)
|
||
|
|
CV_Error(CV_StsUnsupportedFormat, "indices must be CV_32SC1");
|
||
|
|
if (indices->rows * indices->cols != n)
|
||
|
|
CV_Error(CV_StsBadSize, "indices must be n x 1 or 1 x n for n x d data");
|
||
|
|
ret_indices = indices->data.i;
|
||
|
|
}
|
||
|
|
|
||
|
|
switch (lsh->type) {
|
||
|
|
case CV_32FC1: lsh->u.lsh_32f->add(data->data.fl, n, ret_indices); break;
|
||
|
|
case CV_64FC1: lsh->u.lsh_64f->add(data->data.db, n, ret_indices); break;
|
||
|
|
default: assert(0); return;
|
||
|
|
}
|
||
|
|
}
|
||
|
|
|
||
|
|
void cvLSHRemove(CvLSH* lsh, const CvMat* indices) {
|
||
|
|
int n;
|
||
|
|
|
||
|
|
if (CV_MAT_TYPE(indices->type) != CV_32SC1)
|
||
|
|
CV_Error(CV_StsUnsupportedFormat, "indices must be CV_32SC1");
|
||
|
|
n = indices->rows * indices->cols;
|
||
|
|
switch (lsh->type) {
|
||
|
|
case CV_32FC1: lsh->u.lsh_32f->remove(indices->data.i, n); break;
|
||
|
|
case CV_64FC1: lsh->u.lsh_64f->remove(indices->data.i, n); break;
|
||
|
|
default: assert(0); return;
|
||
|
|
}
|
||
|
|
}
|
||
|
|
|
||
|
|
void cvLSHQuery(CvLSH* lsh, const CvMat* data, CvMat* indices, CvMat* dist, int k, int emax) {
|
||
|
|
int dims;
|
||
|
|
|
||
|
|
switch (lsh->type) {
|
||
|
|
case CV_32FC1: dims = lsh->u.lsh_32f->dims(); break;
|
||
|
|
case CV_64FC1: dims = lsh->u.lsh_64f->dims(); break;
|
||
|
|
default: assert(0); return;
|
||
|
|
}
|
||
|
|
|
||
|
|
if (k<1)
|
||
|
|
CV_Error(CV_StsOutOfRange, "k must be positive");
|
||
|
|
if (CV_MAT_TYPE(data->type) != lsh->type)
|
||
|
|
CV_Error(CV_StsUnsupportedFormat, "type of data and constructed LSH must agree");
|
||
|
|
if (dims != data->cols)
|
||
|
|
CV_Error(CV_StsBadSize, "data must be n x d, where d is what was used to construct LSH");
|
||
|
|
if (dist->rows != data->rows || dist->cols != k)
|
||
|
|
CV_Error(CV_StsBadSize, "dist must be n x k for n x d data");
|
||
|
|
if (dist->rows != indices->rows || dist->cols != indices->cols)
|
||
|
|
CV_Error(CV_StsBadSize, "dist and indices must be same size");
|
||
|
|
if (CV_MAT_TYPE(dist->type) != CV_64FC1)
|
||
|
|
CV_Error(CV_StsUnsupportedFormat, "dist must be CV_64FC1");
|
||
|
|
if (CV_MAT_TYPE(indices->type) != CV_32SC1)
|
||
|
|
CV_Error(CV_StsUnsupportedFormat, "indices must be CV_32SC1");
|
||
|
|
|
||
|
|
switch (lsh->type) {
|
||
|
|
case CV_32FC1: lsh->u.lsh_32f->query(data->data.fl, data->rows,
|
||
|
|
k, emax, dist->data.db, indices->data.i); break;
|
||
|
|
case CV_64FC1: lsh->u.lsh_64f->query(data->data.db, data->rows,
|
||
|
|
k, emax, dist->data.db, indices->data.i); break;
|
||
|
|
default: assert(0); return;
|
||
|
|
}
|
||
|
|
}
|