--- /dev/null
+/*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.
+//
+//
+// Intel License Agreement
+// For Open Source Computer Vision Library
+//
+// Copyright (C) 2009, Xavier Delacour, 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 Intel Corporation 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*/
+
+// 2009-01-12, Xavier Delacour <xavier.delacour@gmail.com>
+
+
+// * hash perf could be improved
+// * in particular, implement integer only (converted fixed from float input)
+
+// * number of hash functions could be reduced (andoni paper)
+
+// * redundant distance computations could be suppressed
+
+// * rework CvLSHOperations interface-- move some of the loops into it to
+// * allow efficient async storage
+
+
+// Datar, M., Immorlica, N., Indyk, P., and Mirrokni, V. S. 2004. Locality-sensitive hashing
+// scheme based on p-stable distributions. In Proceedings of the Twentieth Annual Symposium on
+// Computational Geometry (Brooklyn, New York, USA, June 08 - 11, 2004). SCG '04. ACM, New York,
+// NY, 253-262. DOI= http://doi.acm.org/10.1145/997817.997857
+
+#include "_cv.h"
+#include "cv.hpp"
+#include "cxmisc.h"
+#include <math.h>
+#include <vector>
+#include <algorithm>
+#include <limits>
+
+template <class T>
+class memory_hash_ops : public CvLSHOperations {
+ int d;
+ std::vector<T> data;
+ std::vector<int> free_data;
+ struct node {
+ int i, h2, next;
+ };
+ std::vector<node> nodes;
+ std::vector<int> free_nodes;
+ std::vector<int> bins;
+
+public:
+ memory_hash_ops(int _d, int n) : d(_d) {
+ bins.resize(n, -1);
+ }
+
+ virtual int vector_add(const void* _p) {
+ const T* p = (const T*)_p;
+ int i;
+ if (free_data.empty()) {
+ i = (int)data.size();
+ data.insert(data.end(), d, 0);
+ } else {
+ i = free_data.end()[-1];
+ free_data.pop_back();
+ }
+ std::copy(p, p + d, data.begin() + i);
+ return i / d;
+ }
+ virtual void vector_remove(int i) {
+ free_data.push_back(i * d);
+ }
+ virtual const void* vector_lookup(int i) {
+ return &data[i * d];
+ }
+ virtual void vector_reserve(int n) {
+ data.reserve(n * d);
+ }
+ virtual unsigned int vector_count() {
+ return (unsigned)(data.size() / d - free_data.size());
+ }
+
+ virtual void hash_insert(lsh_hash h, int /*l*/, int i) {
+ int ii;
+ if (free_nodes.empty()) {
+ ii = (int)nodes.size();
+ nodes.push_back(node());
+ } else {
+ ii = free_nodes.end()[-1];
+ free_nodes.pop_back();
+ }
+ node& n = nodes[ii];
+ int h1 = h.h1 % bins.size();
+ n.i = i;
+ n.h2 = h.h2;
+ n.next = bins[h1];
+ bins[h1] = ii;
+ }
+ virtual void hash_remove(lsh_hash h, int /*l*/, int i) {
+ int h1 = h.h1 % bins.size();
+ for (int ii = bins[h1], iin, iip = -1; ii != -1; iip = ii, ii = iin) {
+ iin = nodes[ii].next;
+ if (nodes[ii].h2 == h.h2 && nodes[ii].i == i) {
+ free_nodes.push_back(ii);
+ if (iip == -1)
+ bins[h1] = iin;
+ else
+ nodes[iip].next = iin;
+ }
+ }
+ }
+ virtual int hash_lookup(lsh_hash h, int /*l*/, int* ret_i, int ret_i_max) {
+ int h1 = h.h1 % bins.size();
+ int k = 0;
+ for (int ii = bins[h1]; ii != -1 && k < ret_i_max; ii = nodes[ii].next)
+ if (nodes[ii].h2 == h.h2)
+ ret_i[k++] = nodes[ii].i;
+ return k;
+ }
+};
+
+template <class T,int cvtype>
+class pstable_l2_func {
+ CvMat *a, *b, *r1, *r2;
+ int d, k;
+ double r;
+ pstable_l2_func(const pstable_l2_func& x);
+ pstable_l2_func& operator= (const pstable_l2_func& rhs);
+public:
+ typedef T scalar_type;
+ typedef T accum_type;
+ pstable_l2_func(int _d, int _k, double _r, CvRNG& rng)
+ : d(_d), k(_k), r(_r) {
+ assert(sizeof(T) == CV_ELEM_SIZE1(cvtype));
+ a = cvCreateMat(k, d, cvtype);
+ b = cvCreateMat(k, 1, cvtype);
+ r1 = cvCreateMat(k, 1, CV_32SC1);
+ r2 = cvCreateMat(k, 1, CV_32SC1);
+ cvRandArr(&rng, a, CV_RAND_NORMAL, cvScalar(0), cvScalar(1));
+ cvRandArr(&rng, b, CV_RAND_UNI, cvScalar(0), cvScalar(r));
+ cvRandArr(&rng, r1, CV_RAND_UNI,
+ cvScalar(std::numeric_limits<int>::min()),
+ cvScalar(std::numeric_limits<int>::max()));
+ cvRandArr(&rng, r2, CV_RAND_UNI,
+ cvScalar(std::numeric_limits<int>::min()),
+ cvScalar(std::numeric_limits<int>::max()));
+ }
+ ~pstable_l2_func() {
+ cvReleaseMat(&a);
+ cvReleaseMat(&b);
+ cvReleaseMat(&r1);
+ cvReleaseMat(&r2);
+ }
+
+ // * factor all L functions into this (reduces number of matrices to 4 total;
+ // * simpler syntax in lsh_table). give parameter l here that tells us which
+ // * row to use etc.
+
+ lsh_hash operator() (const T* x) const {
+ const T* aj = (const T*)a->data.ptr;
+ const T* bj = (const T*)b->data.ptr;
+
+ lsh_hash h;
+ h.h1 = h.h2 = 0;
+ for (int j = 0; j < k; ++j) {
+ accum_type s = 0;
+ for (int jj = 0; jj < d; ++jj)
+ s += aj[jj] * x[jj];
+ s += *bj;
+ s = accum_type(s/r);
+ int si = int(s);
+ h.h1 += r1->data.i[j] * si;
+ h.h2 += r2->data.i[j] * si;
+
+ aj += d;
+ bj++;
+ }
+ return h;
+ }
+ accum_type distance(const T* p, const T* q) const {
+ accum_type s = 0;
+ for (int j = 0; j < d; ++j) {
+ accum_type d1 = p[j] - q[j];
+ s += d1 * d1;
+ }
+ return s;
+ }
+};
+
+template <class H>
+class lsh_table {
+public:
+ typedef typename H::scalar_type scalar_type;
+ typedef typename H::accum_type accum_type;
+private:
+ std::vector<H*> g;
+ CvLSHOperations* ops;
+ int d, L, k;
+ double r;
+
+ static accum_type comp_dist(const std::pair<int,accum_type>& x,
+ const std::pair<int,accum_type>& y) {
+ return x.second < y.second;
+ }
+
+ lsh_table(const lsh_table& x);
+ lsh_table& operator= (const lsh_table& rhs);
+public:
+ lsh_table(CvLSHOperations* _ops, int _d, int _L, int _k, double _r, CvRNG& rng)
+ : ops(_ops), d(_d), L(_L), k(_k), r(_r) {
+ g.resize(L);
+ for (int j = 0; j < L; ++j)
+ g[j] = new H(d, k, r, rng);
+ }
+ ~lsh_table() {
+ for (int j = 0; j < L; ++j)
+ delete g[j];
+ delete ops;
+ }
+
+ int dims() const {
+ return d;
+ }
+ unsigned int size() const {
+ return ops->vector_count();
+ }
+
+ void add(const scalar_type* data, int n, int* ret_indices = 0) {
+ for (int j=0;j<n;++j) {
+ const scalar_type* x = data+j*d;
+ int i = ops->vector_add(x);
+ if (ret_indices)
+ ret_indices[j] = i;
+
+ for (int l = 0; l < L; ++l) {
+ lsh_hash h = (*g[l])(x);
+ ops->hash_insert(h, l, i);
+ }
+ }
+ }
+ void remove(const int* indices, int n) {
+ for (int j = 0; j < n; ++j) {
+ int i = indices[n];
+ const scalar_type* x = (const scalar_type*)ops->vector_lookup(i);
+
+ for (int l = 0; l < L; ++l) {
+ lsh_hash h = (*g[l])(x);
+ ops->hash_remove(h, l, i);
+ }
+ ops->vector_remove(i);
+ }
+ }
+ void query(const scalar_type* q, int k0, int emax, double* dist, int* results) {
+ int* tmp = (int*)cvStackAlloc(sizeof(int) * emax);
+ typedef std::pair<int, accum_type> dr_type; // * swap int and accum_type here, for naming consistency
+ dr_type* dr = (dr_type*)cvStackAlloc(sizeof(dr_type) * k0);
+ int k1 = 0;
+
+ // * handle k0 >= emax, in which case don't track max distance
+
+ for (int l = 0; l < L && emax > 0; ++l) {
+ lsh_hash h = (*g[l])(q);
+ int m = ops->hash_lookup(h, l, tmp, emax);
+ for (int j = 0; j < m && emax > 0; ++j, --emax) {
+ int i = tmp[j];
+ const scalar_type* p = (const scalar_type*)ops->vector_lookup(i);
+ accum_type pd = (*g[l]).distance(p, q);
+ if (k1 < k0) {
+ dr[k1++] = std::make_pair(i, pd);
+ std::push_heap(dr, dr + k1, comp_dist);
+ } else if (pd < dr[0].second) {
+ std::pop_heap(dr, dr + k0, comp_dist);
+ dr[k0 - 1] = std::make_pair(i, pd);
+ std::push_heap(dr, dr + k0, comp_dist);
+ }
+ }
+ }
+
+ for (int j = 0; j < k1; ++j)
+ dist[j] = dr[j].second, results[j] = dr[j].first;
+ std::fill(dist + k1, dist + k0, 0);
+ std::fill(results + k1, results + k0, -1);
+ }
+ void query(const scalar_type* data, int n, int k0, int emax, double* dist, int* results) {
+ for (int j = 0; j < n; ++j) {
+ query(data, k0, emax, dist, results);
+ data += d; // * this may not agree with step for some scalar_types
+ dist += k0;
+ results += k0;
+ }
+ }
+};
+
+typedef lsh_table<pstable_l2_func<float, CV_32FC1> > lsh_pstable_l2_32f;
+typedef lsh_table<pstable_l2_func<double, CV_64FC1> > lsh_pstable_l2_64f;
+
+struct CvLSH {
+ int type;
+ union {
+ lsh_pstable_l2_32f* lsh_32f;
+ lsh_pstable_l2_64f* lsh_64f;
+ } u;
+};
+
+CvLSH* cvCreateLSH(CvLSHOperations* ops, int d, int L, int k, int type, double r, int64 seed) {
+ CvLSH* lsh = 0;
+ CvRNG rng = cvRNG(seed);
+
+ __BEGIN__;
+ CV_FUNCNAME("cvCreateLSH");
+
+ if (type != CV_32FC1 && type != CV_64FC1)
+ CV_ERROR(CV_StsUnsupportedFormat, "vectors must be either CV_32FC1 or CV_64FC1");
+ lsh = new CvLSH;
+ lsh->type = type;
+ switch (type) {
+ case CV_32FC1: lsh->u.lsh_32f = new lsh_pstable_l2_32f(ops, d, L, k, r, rng); break;
+ case CV_64FC1: lsh->u.lsh_64f = new lsh_pstable_l2_64f(ops, d, L, k, r, rng); break;
+ }
+
+ __END__;
+ return lsh;
+}
+
+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;
+
+ __BEGIN__;
+ CV_FUNCNAME("cvLSHAdd");
+
+ 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;
+ }
+ __END__;
+}
+
+void cvLSHRemove(CvLSH* lsh, const CvMat* indices) {
+ int n;
+
+ __BEGIN__;
+ CV_FUNCNAME("cvLSHRemove");
+
+ 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;
+ }
+ __END__;
+}
+
+void cvLSHQuery(CvLSH* lsh, const CvMat* data, CvMat* indices, CvMat* dist, int k, int emax) {
+ int dims;
+
+ __BEGIN__;
+ CV_FUNCNAME("cvLSHQuery");
+
+ 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;
+ }
+ __END__;
+}
projects / opencv / blobdiff