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Removing 'using namespace std' from header files, closes bugs #730 and #846

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This commit is contained in:
Marius Muja
2011-02-16 06:36:15 +00:00
parent 6b34532901
commit 7d42dbdd71
22 changed files with 110 additions and 126 deletions
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12
modules/flann/include/opencv2/flann/autotuned_index.h
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@@ -224,8 +224,8 @@ private:
float totalCost;
};
typedef pair<CostData, KDTreeIndexParams> KDTreeCostData;
typedef pair<CostData, KMeansIndexParams> KMeansCostData;
typedef std::pair<CostData, KDTreeIndexParams> KDTreeCostData;
typedef std::pair<CostData, KMeansIndexParams> KMeansCostData;
void evaluate_kmeans(CostData& cost, const KMeansIndexParams& kmeans_params)
@@ -338,7 +338,7 @@ private:
int kmeansParamSpaceSize = ARRAY_LEN(maxIterations)*ARRAY_LEN(branchingFactors);
vector<KMeansCostData> kmeansCosts(kmeansParamSpaceSize);
std::vector<KMeansCostData> kmeansCosts(kmeansParamSpaceSize);
// CostData* kmeansCosts = new CostData[kmeansParamSpaceSize];
@@ -417,7 +417,7 @@ private:
int testTrees[] = { 1, 4, 8, 16, 32 };
size_t kdtreeParamSpaceSize = ARRAY_LEN(testTrees);
vector<KDTreeCostData> kdtreeCosts(kdtreeParamSpaceSize);
std::vector<KDTreeCostData> kdtreeCosts(kdtreeParamSpaceSize);
// evaluate kdtree for all parameter combinations
int cnt = 0;
@@ -484,7 +484,7 @@ private:
IndexParams* estimateBuildParams()
{
int sampleSize = int(index_params.sample_fraction*dataset.rows);
int testSampleSize = min(sampleSize/10, 1000);
int testSampleSize = std::min(sampleSize/10, 1000);
logger().info("Entering autotuning, dataset size: %d, sampleSize: %d, testSampleSize: %d\n",dataset.rows, sampleSize, testSampleSize);
@@ -550,7 +550,7 @@ private:
float speedup = 0;
int samples = (int)min(dataset.rows/10, SAMPLE_COUNT);
int samples = (int)std::min(dataset.rows/10, SAMPLE_COUNT);
if (samples>0) {
Matrix<ELEM_TYPE> testDataset = random_sample(dataset,samples);

1
modules/flann/include/opencv2/flann/dist.h
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@@ -32,7 +32,6 @@
#define _OPENCV_DIST_H_
#include <cmath>
using namespace std;
#include "opencv2/flann/general.h"

6
modules/flann/include/opencv2/flann/flann_base.hpp
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@@ -109,7 +109,7 @@ public:
template<typename T>
NNIndex<T>* load_saved_index(const Matrix<T>& dataset, const string& filename)
NNIndex<T>* load_saved_index(const Matrix<T>& dataset, const std::string& filename)
{
FILE* fin = fopen(filename.c_str(), "rb");
if (fin==NULL) {
@@ -208,7 +208,7 @@ int Index<T>::radiusSearch(const Matrix<T>& query, Matrix<int>& indices, Matrix<
// TODO: optimise here
int* neighbors = resultSet.getNeighbors();
float* distances = resultSet.getDistances();
size_t count_nn = min(resultSet.size(), indices.cols);
size_t count_nn = std::min(resultSet.size(), indices.cols);
assert (dists.cols>=count_nn);
@@ -222,7 +222,7 @@ int Index<T>::radiusSearch(const Matrix<T>& query, Matrix<int>& indices, Matrix<
template<typename T>
void Index<T>::save(string filename)
void Index<T>::save(std::string filename)
{
FILE* fout = fopen(filename.c_str(), "wb");
if (fout==NULL) {

4
modules/flann/include/opencv2/flann/ground_truth.h
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@@ -66,8 +66,8 @@ void find_nearest(const Matrix<T>& dataset, T* query, int* matches, int nn, int
int j = dcnt-1;
// bubble up
while (j>=1 && dists[j]<dists[j-1]) {
swap(dists[j],dists[j-1]);
swap(match[j],match[j-1]);
std::swap(dists[j],dists[j-1]);
std::swap(match[j],match[j-1]);
j--;
}
}

5
modules/flann/include/opencv2/flann/heap.h
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@@ -33,7 +33,6 @@
#include <algorithm>
using namespace std;
namespace cvflann
{
@@ -162,7 +161,7 @@ public:
}
/* Switch first node with last. */
swap(heap[1],heap[count]);
std::swap(heap[1],heap[count]);
count -= 1;
heapify(1); /* Move new node 1 to right position. */
@@ -197,7 +196,7 @@ public:
/* If a child was smaller, than swap parent with it and Heapify. */
if (minloc != parent) {
swap(heap[parent],heap[minloc]);
std::swap(heap[parent],heap[minloc]);
heapify(minloc);
}
}

3
modules/flann/include/opencv2/flann/index_testing.h
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@@ -41,7 +41,6 @@
#include "opencv2/flann/timer.h"
using namespace std;
namespace cvflann
{
@@ -207,7 +206,7 @@ float test_index_precisions(NNIndex<ELEM_TYPE>& index, const Matrix<ELEM_TYPE>&
const float SEARCH_EPS = 0.001;
// make sure precisions array is sorted
sort(precisions, precisions+precisions_length);
std::sort(precisions, precisions+precisions_length);
int pindex = 0;
float precision = precisions[pindex];

14
modules/flann/include/opencv2/flann/kdtree_index.h
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@@ -45,8 +45,6 @@
#include "opencv2/flann/random.h"
#include "opencv2/flann/saving.h"
using namespace std;
namespace cvflann
{
@@ -232,7 +230,7 @@ public:
/* Randomize the order of vectors to allow for unbiased sampling. */
for (int j = (int)size_; j > 0; --j) {
int rnd = rand_int(j);
swap(vind[j-1], vind[rnd]);
std::swap(vind[j-1], vind[rnd]);
}
trees[i] = divideTree(0, (int)size_ - 1);
}
@@ -384,7 +382,7 @@ private:
/* Compute mean values. Only the first SAMPLE_MEAN values need to be
sampled to get a good estimate.
*/
int end = min(first + SAMPLE_MEAN, last);
int end = std::min(first + SAMPLE_MEAN, last);
for (int j = first; j <= end; ++j) {
ELEM_TYPE* v = dataset[vind[j]];
for (size_t k=0; k<veclen_; ++k) {
@@ -432,7 +430,7 @@ private:
/* Bubble end value down to right location by repeated swapping. */
int j = num - 1;
while (j > 0 && v[topind[j]] > v[topind[j-1]]) {
swap(topind[j], topind[j-1]);
std::swap(topind[j], topind[j-1]);
--j;
}
}
@@ -459,7 +457,7 @@ private:
++i;
} else {
/* Move to end of list by swapping vind i and j. */
swap(vind[i], vind[j]);
std::swap(vind[i], vind[j]);
--j;
}
}
@@ -506,7 +504,7 @@ private:
int checkCount = 0;
Heap<BranchSt>* heap = new Heap<BranchSt>((int)size_);
vector<bool> checked(size_,false);
std::vector<bool> checked(size_,false);
/* Search once through each tree down to root. */
for (i = 0; i < numTrees; ++i) {
@@ -530,7 +528,7 @@ private:
* at least "mindistsq".
*/
void searchLevel(ResultSet<ELEM_TYPE>& result, const ELEM_TYPE* vec, Tree node, float mindistsq, int& checkCount, int maxCheck,
Heap<BranchSt>* heap, vector<bool>& checked)
Heap<BranchSt>* heap, std::vector<bool>& checked)
{
if (result.worstDist()<mindistsq) {
// printf("Ignoring branch, too far\n");

18
modules/flann/include/opencv2/flann/kmeans_index.h
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@@ -46,12 +46,10 @@
#include "opencv2/flann/allocator.h"
#include "opencv2/flann/random.h"
using namespace std;
namespace cvflann
{
struct CV_EXPORTS KMeansIndexParams : public IndexParams {
KMeansIndexParams(int branching_ = 32, int iterations_ = 11,
flann_centers_init_t centers_init_ = CENTERS_RANDOM, float cb_index_ = 0.2 ) :
@@ -353,7 +351,7 @@ class KMeansIndex : public NNIndex<ELEM_TYPE>
// Compute the new potential
double newPot = 0;
for (int i = 0; i < n; i++)
newPot += min( flann_dist(dataset[indices[i]], dataset[indices[i]] + dataset.cols, dataset[indices[index]]), closestDistSq[i] );
newPot += std::min( flann_dist(dataset[indices[i]], dataset[indices[i]] + dataset.cols, dataset[indices[index]]), closestDistSq[i] );
// Store the best result
if (bestNewPot < 0 || newPot < bestNewPot) {
@@ -366,7 +364,7 @@ class KMeansIndex : public NNIndex<ELEM_TYPE>
centers[centerCount] = indices[bestNewIndex];
currentPot = bestNewPot;
for (int i = 0; i < n; i++)
closestDistSq[i] = min( flann_dist(dataset[indices[i]], dataset[indices[i]]+dataset.cols, dataset[indices[bestNewIndex]]), closestDistSq[i] );
closestDistSq[i] = std::min( flann_dist(dataset[indices[i]], dataset[indices[i]]+dataset.cols, dataset[indices[bestNewIndex]]), closestDistSq[i] );
}
centers_length = centerCount;
@@ -402,7 +400,7 @@ public:
branching = params.branching;
max_iter = params.iterations;
if (max_iter<0) {
max_iter = numeric_limits<int>::max();
max_iter = (std::numeric_limits<int>::max)();
}
flann_centers_init_t centersInit = params.centers_init;
@@ -711,7 +709,7 @@ private:
if (indices_length < branching) {
node->indices = indices;
sort(node->indices,node->indices+indices_length);
std::sort(node->indices,node->indices+indices_length);
node->childs = NULL;
return;
}
@@ -722,7 +720,7 @@ private:
if (centers_length<branching) {
node->indices = indices;
sort(node->indices,node->indices+indices_length);
std::sort(node->indices,node->indices+indices_length);
node->childs = NULL;
return;
}
@@ -859,8 +857,8 @@ private:
double d = flann_dist(dataset[indices[i]],dataset[indices[i]]+veclen_,zero());
variance += d;
mean_radius += sqrt(d);
swap(indices[i],indices[end]);
swap(belongs_to[i],belongs_to[end]);
std::swap(indices[i],indices[end]);
std::swap(belongs_to[i],belongs_to[end]);
end++;
}
}
@@ -1072,7 +1070,7 @@ private:
float meanVariance = root->variance*root->size;
while (clusterCount<clusters_length) {
float minVariance = numeric_limits<float>::max();
float minVariance = (std::numeric_limits<float>::max)();
int splitIndex = -1;
for (int i=0;i<clusterCount;++i) {

1
modules/flann/include/opencv2/flann/logger.h
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@@ -36,7 +36,6 @@
#include <stdarg.h>
#include "opencv2/flann/general.h"
using namespace std;
namespace cvflann
{

2
modules/flann/include/opencv2/flann/nn_index.h
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@@ -36,8 +36,6 @@
#include "opencv2/flann/general.h"
#include "opencv2/flann/matrix.h"
using namespace std;
namespace cvflann
{

3
modules/flann/include/opencv2/flann/random.h
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@@ -35,7 +35,6 @@
#include <cstdlib>
#include <cassert>
using namespace std;
namespace cvflann
{
@@ -109,7 +108,7 @@ public:
// int rand = cast(int) (drand48() * n);
int rnd = rand_int(i);
assert(rnd >=0 && rnd < i);
swap(vals[i-1], vals[rnd]);
std::swap(vals[i-1], vals[rnd]);
}
counter = 0;

10
modules/flann/include/opencv2/flann/result_set.h
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@@ -37,8 +37,6 @@
#include <vector>
#include "opencv2/flann/dist.h"
using namespace std;
namespace cvflann
{
@@ -181,8 +179,8 @@ public:
// bubble up
while (i>=1 && (dists[i]<dists[i-1] || (dists[i]==dists[i-1] && indices[i]<indices[i-1]) ) ) {
// while (i>=1 && (dists[i]<dists[i-1]) ) {
swap(indices[i],indices[i-1]);
swap(dists[i],dists[i-1]);
std::swap(indices[i],indices[i-1]);
std::swap(dists[i],dists[i-1]);
i--;
}
@@ -191,7 +189,7 @@ public:
float worstDist() const
{
return (count<capacity) ? numeric_limits<float>::max() : dists[count-1];
return (count<capacity) ? (std::numeric_limits<float>::max)() : dists[count-1];
}
};
@@ -215,7 +213,7 @@ class RadiusResultSet : public ResultSet<ELEM_TYPE>
}
};
vector<Item> items;
std::vector<Item> items;
float radius;
bool sorted;

2
modules/flann/include/opencv2/flann/sampling.h
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@@ -56,7 +56,7 @@ Matrix<T> random_sample(Matrix<T>& srcMatrix, long size, bool remove = false)
dest = srcMatrix[srcMatrix.rows-i-1];
src = srcMatrix[r];
for (size_t j=0;j<srcMatrix.cols;++j) {
swap(*src,*dest);
std::swap(*src,*dest);
src++;
dest++;
}