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

modules/calib3d/src/circlesgrid.hpp

@@ -132,7 +132,7 @@ public:
 private:
   void computeRNG(Graph &rng, std::vector<cv::Point2f> &vectors, cv::Mat *drawImage = 0) const;
   void rng2gridGraph(Graph &rng, std::vector<cv::Point2f> &vectors) const;
-  void eraseUsedGraph(vector<Graph> &basisGraphs) const;
+  void eraseUsedGraph(std::vector<Graph> &basisGraphs) const;
   void filterOutliersByDensity(const std::vector<cv::Point2f> &samples, std::vector<cv::Point2f> &filteredSamples);
   void findBasis(const std::vector<cv::Point2f> &samples, std::vector<cv::Point2f> &basis,
                  std::vector<Graph> &basisGraphs);
@@ -164,12 +164,12 @@ private:
 
   //if endpoint is on a segment then function return false
   static bool areSegmentsIntersecting(Segment seg1, Segment seg2);
-  static bool doesIntersectionExist(const vector<Segment> &corner, const vector<vector<Segment> > &segments);
+  static bool doesIntersectionExist(const std::vector<Segment> &corner, const std::vector<std::vector<Segment> > &segments);
-  void getCornerSegments(const vector<vector<size_t> > &points, vector<vector<Segment> > &segments,
+  void getCornerSegments(const std::vector<std::vector<size_t> > &points, std::vector<std::vector<Segment> > &segments,
-                         vector<cv::Point> &cornerIndices, vector<cv::Point> &firstSteps,
+                         std::vector<cv::Point> &cornerIndices, std::vector<cv::Point> &firstSteps,
-                         vector<cv::Point> &secondSteps) const;
+                         std::vector<cv::Point> &secondSteps) const;
-  size_t getFirstCorner(vector<cv::Point> &largeCornerIndices, vector<cv::Point> &smallCornerIndices,
+  size_t getFirstCorner(std::vector<cv::Point> &largeCornerIndices, std::vector<cv::Point> &smallCornerIndices,
-                        vector<cv::Point> &firstSteps, vector<cv::Point> &secondSteps) const;
+                        std::vector<cv::Point> &firstSteps, std::vector<cv::Point> &secondSteps) const;
   static double getDirection(cv::Point2f p1, cv::Point2f p2, cv::Point2f p3);
 
   std::vector<cv::Point2f> keypoints;

modules/contrib/src/chamfermatching.cpp

@@ -53,11 +53,11 @@ namespace cv
 
 using std::queue;
     
-typedef pair<int,int> coordinate_t;
+typedef std::pair<int,int> coordinate_t;
-typedef float orientation_t;;
+typedef float orientation_t;
-typedef vector<coordinate_t> template_coords_t;
+typedef std::vector<coordinate_t> template_coords_t;
-typedef vector<orientation_t> template_orientations_t;
+typedef std::vector<orientation_t> template_orientations_t;
-typedef pair<Point, float> location_scale_t;
+typedef std::pair<Point, float> location_scale_t;
 
 class ChamferMatcher
 {
@@ -106,7 +106,7 @@ private:
     
     class LocationImageRange : public ImageRange
     {
-        const vector<Point>& locations_;
+        const std::vector<Point>& locations_;
         
         int scales_;
         float min_scale_;
@@ -116,7 +116,7 @@ private:
 		LocationImageRange& operator=(const LocationImageRange&);
         
     public:
-        LocationImageRange(const vector<Point>& locations, int scales = 5, float min_scale = 0.6, float max_scale = 1.6) :
+        LocationImageRange(const std::vector<Point>& locations, int scales = 5, float min_scale = 0.6, float max_scale = 1.6) :
         locations_(locations), scales_(scales), min_scale_(min_scale), max_scale_(max_scale)
         {
         }
@@ -130,13 +130,13 @@ private:
     
     class LocationScaleImageRange : public ImageRange
     {
-        const vector<Point>& locations_;
+        const std::vector<Point>& locations_;
-        const vector<float>& scales_;
+        const std::vector<float>& scales_;
         
 		LocationScaleImageRange(const LocationScaleImageRange&);
 		LocationScaleImageRange& operator=(const LocationScaleImageRange&);
     public:
-        LocationScaleImageRange(const vector<Point>& locations, const vector<float>& scales) :
+        LocationScaleImageRange(const std::vector<Point>& locations, const std::vector<float>& scales) :
         locations_(locations), scales_(scales)
         {
             assert(locations.size()==scales.size());
@@ -162,8 +162,8 @@ public:
         
         
     public:
-        vector<Template*> scaled_templates;
+        std::vector<Template*> scaled_templates;
-        vector<int> addr;
+        std::vector<int> addr;
         int addr_width;
         float scale;
         template_coords_t coords;
@@ -200,7 +200,7 @@ public:
          */
         Template* rescale(float scale);
         
-        vector<int>& getTemplateAddresses(int width);
+        std::vector<int>& getTemplateAddresses(int width);
     };
     
     
@@ -217,7 +217,7 @@ public:
         const Template* tpl;
     };
     
-    typedef vector<Match> Matches;
+    typedef std::vector<Match> Matches;
     
 private:
     /**
@@ -230,7 +230,7 @@ private:
         float truncate_;
         bool use_orientation_;
         
-        vector<Template*> templates;
+        std::vector<Template*> templates;
     public:
         Matching(bool use_orientation = true, float truncate = 10) : truncate_(truncate), use_orientation_(use_orientation)
         {
@@ -347,7 +347,7 @@ private:
     
     class LocationImageIterator : public ImageIterator
     {
-        const vector<Point>& locations_;
+        const std::vector<Point>& locations_;
         
         size_t iter_;
         
@@ -365,7 +365,7 @@ private:
 		LocationImageIterator& operator=(const LocationImageIterator&);
         
     public:
-        LocationImageIterator(const vector<Point>& locations, int scales, float min_scale, float max_scale);
+        LocationImageIterator(const std::vector<Point>& locations, int scales, float min_scale, float max_scale);
         
         bool hasNext() const {
             return has_next_;
@@ -376,8 +376,8 @@ private:
     
     class LocationScaleImageIterator : public ImageIterator
     {
-        const vector<Point>& locations_;
+        const std::vector<Point>& locations_;
-        const vector<float>& scales_;
+        const std::vector<float>& scales_;
         
         size_t iter_;
         
@@ -387,7 +387,7 @@ private:
 		LocationScaleImageIterator& operator=(const LocationScaleImageIterator&);
         
     public:
-        LocationScaleImageIterator(const vector<Point>& locations, const vector<float>& scales) :
+        LocationScaleImageIterator(const std::vector<Point>& locations, const std::vector<float>& scales) :
         locations_(locations), scales_(scales)
         {
             assert(locations.size()==scales.size());
@@ -511,7 +511,7 @@ ChamferMatcher::SlidingWindowImageIterator::SlidingWindowImageIterator( int widt
 
 location_scale_t ChamferMatcher::SlidingWindowImageIterator::next()
 {
-	location_scale_t next_val = make_pair(Point(x_,y_),scale_);
+	location_scale_t next_val = std::make_pair(Point(x_,y_),scale_);
 
 	x_ += x_step_;
 
@@ -544,7 +544,7 @@ ChamferMatcher::ImageIterator* ChamferMatcher::SlidingWindowImageRange::iterator
 
 
 
-ChamferMatcher::LocationImageIterator::LocationImageIterator(const vector<Point>& locations, 
+ChamferMatcher::LocationImageIterator::LocationImageIterator(const std::vector<Point>& locations, 
 																int scales = 5, 
 																float min_scale = 0.6, 
 																float max_scale = 1.6) :
@@ -562,7 +562,7 @@ ChamferMatcher::LocationImageIterator::LocationImageIterator(const vector<Point>
 
 location_scale_t ChamferMatcher::LocationImageIterator:: next()
 {
-	location_scale_t next_val = make_pair(locations_[iter_],scale_);
+	location_scale_t next_val = std::make_pair(locations_[iter_],scale_);
 
 	iter_ ++;
 	if (iter_==locations_.size()) {
@@ -583,7 +583,7 @@ location_scale_t ChamferMatcher::LocationImageIterator:: next()
 
 location_scale_t ChamferMatcher::LocationScaleImageIterator::next()
 {
-	location_scale_t next_val = make_pair(locations_[iter_],scales_[iter_]);
+	location_scale_t next_val = std::make_pair(locations_[iter_],scales_[iter_]);
 
 	iter_ ++;
 	if (iter_==locations_.size()) {
@@ -738,7 +738,7 @@ void ChamferMatcher::Matching::findContourOrientations(const template_coords_t&
 	const int M = 5;
 	int coords_size = coords.size();
 
-	vector<float> angles(2*M);
+    std::vector<float> angles(2*M);
         orientations.insert(orientations.begin(), coords_size, float(-3*CV_PI)); // mark as invalid in the beginning
 
 	if (coords_size<2*M+1) {  // if contour not long enough to estimate orientations, abort
@@ -948,7 +948,7 @@ void ChamferMatcher::Matching::computeDistanceTransform(Mat& edges_img, Mat& dis
 	int w = s.width;
 	int h = s.height;
 	// set distance to the edge pixels to 0 and put them in the queue
-	queue<pair<int,int> > q;
+    std::queue<std::pair<int,int> > q;
 
 
 
@@ -957,7 +957,7 @@ void ChamferMatcher::Matching::computeDistanceTransform(Mat& edges_img, Mat& dis
 
 			unsigned char edge_val = edges_img.at<uchar>(y,x);
 			if ( (edge_val!=0) ) {
-				q.push(make_pair(x,y));
+				q.push(std::make_pair(x,y));
 				dist_img.at<float>(y,x)= 0;
 
 				if (&annotate_img!=NULL) {
@@ -972,7 +972,7 @@ void ChamferMatcher::Matching::computeDistanceTransform(Mat& edges_img, Mat& dis
 	}
 
 	// breadth first computation of distance transform
-	pair<int,int> crt;
+    std::pair<int,int> crt;
 	while (!q.empty()) {
 		crt = q.front();
 		q.pop();
@@ -1000,7 +1000,7 @@ void ChamferMatcher::Matching::computeDistanceTransform(Mat& edges_img, Mat& dis
 
 			if (dt==-1 || dt>dist) {
 				dist_img.at<float>(ny,nx) = dist;
-				q.push(make_pair(nx,ny));
+				q.push(std::make_pair(nx,ny));
 
 				if (&annotate_img!=NULL) {
 					annotate_img.at<Vec2i>(ny,nx)[0]=annotate_img.at<Vec2i>(y,x)[0];
@@ -1082,7 +1082,7 @@ ChamferMatcher::Match* ChamferMatcher::Matching::localChamferDistance(Point offs
 
 	float beta = 1-alpha;
 
-	vector<int>& addr = tpl->getTemplateAddresses(dist_img.cols);
+    std::vector<int>& addr = tpl->getTemplateAddresses(dist_img.cols);
 
 	float* ptr = dist_img.ptr<float>(y)+x;
 	
@@ -1271,7 +1271,7 @@ void ChamferMatcher::addMatch(float cost, Point offset, const Template* tpl)
 void ChamferMatcher::showMatch(Mat& img, int index)
 {
 	if (index>=count) {
-		cout << "Index too big.\n" << endl;
+        std::cout << "Index too big.\n" << std::endl;
 	}
 
 	assert(img.channels()==3);
@@ -1347,7 +1347,7 @@ const ChamferMatcher::Matches& ChamferMatcher::matching(Template& tpl, Mat& imag
 
     
 int chamerMatching( Mat& img, Mat& templ,
-                    vector<vector<Point> >& results, vector<float>& costs,
+                    std::vector<std::vector<Point> >& results, std::vector<float>& costs,
                     double templScale, int maxMatches, double minMatchDistance, int padX,
                     int padY, int scales, double minScale, double maxScale,
                     double orientationWeight, double truncate )
@@ -1381,7 +1381,7 @@ int chamerMatching( Mat& img, Mat& templ,
         costs[i] = (float)cval;
         
         const template_coords_t& templ_coords = match.tpl->coords;
-        vector<Point>& templPoints = results[i];
+        std::vector<Point>& templPoints = results[i];
         size_t j, npoints = templ_coords.size();
         templPoints.resize(npoints);
         

modules/flann/include/opencv2/flann/autotuned_index.h

@@ -224,8 +224,8 @@ private:
         float totalCost;
     };
 
-    typedef pair<CostData, KDTreeIndexParams> KDTreeCostData;
+    typedef std::pair<CostData, KDTreeIndexParams> KDTreeCostData;
-    typedef pair<CostData, KMeansIndexParams> KMeansCostData;
+    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);
 

modules/flann/include/opencv2/flann/dist.h

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

modules/flann/include/opencv2/flann/flann_base.hpp

@@ -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) {

modules/flann/include/opencv2/flann/ground_truth.h

@@ -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]);
+            std::swap(dists[j],dists[j-1]);
-            swap(match[j],match[j-1]);
+            std::swap(match[j],match[j-1]);
             j--;
         }
     }

modules/flann/include/opencv2/flann/heap.h

@@ -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);
 		}
 	}

modules/flann/include/opencv2/flann/index_testing.h

@@ -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];

modules/flann/include/opencv2/flann/kdtree_index.h

@@ -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");

modules/flann/include/opencv2/flann/kmeans_index.h

@@ -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]);
+                    std::swap(indices[i],indices[end]);
-					swap(belongs_to[i],belongs_to[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) {

modules/flann/include/opencv2/flann/logger.h

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

modules/flann/include/opencv2/flann/nn_index.h

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

modules/flann/include/opencv2/flann/random.h

@@ -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;

modules/flann/include/opencv2/flann/result_set.h

@@ -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]);
+            std::swap(indices[i],indices[i-1]);
-			swap(dists[i],dists[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;

modules/flann/include/opencv2/flann/sampling.h

@@ -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++;
             }

modules/flann/src/flann.cpp

@@ -89,7 +89,7 @@ int countCorrectMatches(int* neighbors, int* groundTruth, int n)
     return count;
 }
 
-// ----------------------- logger().cpp ---------------------------
+// ----------------------- logger.cpp ---------------------------
 
 Logger logger_;
 

modules/imgproc/src/gcgraph.hpp

@@ -42,8 +42,6 @@
 #ifndef _CV_GCGRAPH_H_
 #define _CV_GCGRAPH_H_
 
-using namespace std;
-
 template <class TWeight> class GCGraph
 {
 public:
@@ -76,8 +74,8 @@ private:
         TWeight weight;
     };
 
-    vector<Vtx> vtcs;
+    std::vector<Vtx> vtcs;
-    vector<Edge> edges;
+    std::vector<Edge> edges;
     TWeight flow;
 };
 
@@ -158,7 +156,7 @@ TWeight GCGraph<TWeight>::maxFlow()
     Vtx *vtxPtr = &vtcs[0];
     Edge *edgePtr = &edges[0];
 
-    vector<Vtx*> orphans;
+    std::vector<Vtx*> orphans;
 
     // initialize the active queue and the graph vertices
     for( int i = 0; i < (int)vtcs.size(); i++ )

samples/gpu/driver_api_multi.cpp

@@ -16,11 +16,11 @@
 int main()
 {
 #if !defined(HAVE_CUDA)
-    cout << "CUDA support is required (CMake key 'WITH_CUDA' must be true).\n";
+    std::cout << "CUDA support is required (CMake key 'WITH_CUDA' must be true).\n";
 #endif
 
 #if !defined(HAVE_TBB)
-    cout << "TBB support is required (CMake key 'WITH_TBB' must be true).\n";
+    std::cout << "TBB support is required (CMake key 'WITH_TBB' must be true).\n";
 #endif
 
     return 0;
@@ -44,7 +44,7 @@ inline void safeCall_(int code, const char* expr, const char* file, int line)
 {
     if (code != CUDA_SUCCESS)
     {
-        cout << "CUDA driver API error: code " << code << ", expr " << expr
+        std::cout << "CUDA driver API error: code " << code << ", expr " << expr
             << ", file " << file << ", line " << line << endl;
         destroyContexts();
         exit(-1);
@@ -59,7 +59,7 @@ int main()
     int num_devices = getCudaEnabledDeviceCount();
     if (num_devices < 2)
     {
-        cout << "Two or more GPUs are required\n";
+        std::cout << "Two or more GPUs are required\n";
         return -1;
     }
 
@@ -68,7 +68,7 @@ int main()
         DeviceInfo dev_info(i);
         if (!dev_info.isCompatible())
         {
-            cout << "GPU module isn't built for GPU #" << i << " ("
+            std::cout << "GPU module isn't built for GPU #" << i << " ("
                  << dev_info.name() << ", CC " << dev_info.majorVersion()
                  << dev_info.minorVersion() << "\n";
             return -1;
@@ -122,7 +122,7 @@ void Worker::operator()(int device_id) const
 
     // Check results
     bool passed = norm(dst - Mat(d_dst), NORM_INF) < 1e-3;
-    cout << "GPU #" << device_id << " (" << DeviceInfo().name() << "): "
+    std::cout << "GPU #" << device_id << " (" << DeviceInfo().name() << "): "
         << (passed ? "passed" : "FAILED") << endl;
 
     // Deallocate data here, otherwise deallocation will be performed

samples/gpu/driver_api_stereo_multi.cpp

@@ -18,11 +18,11 @@
 int main()
 {
 #if !defined(HAVE_CUDA)
-    cout << "CUDA support is required (CMake key 'WITH_CUDA' must be true).\n";
+    std::cout << "CUDA support is required (CMake key 'WITH_CUDA' must be true).\n";
 #endif
 
 #if !defined(HAVE_TBB)
-    cout << "TBB support is required (CMake key 'WITH_TBB' must be true).\n";
+    std::cout << "TBB support is required (CMake key 'WITH_TBB' must be true).\n";
 #endif
 
     return 0;
@@ -46,7 +46,7 @@ inline void safeCall_(int code, const char* expr, const char* file, int line)
 {
     if (code != CUDA_SUCCESS)
     {
-        cout << "CUDA driver API error: code " << code << ", expr " << expr
+        std::cout << "CUDA driver API error: code " << code << ", expr " << expr
             << ", file " << file << ", line " << line << endl;
         destroyContexts();
         exit(-1);
@@ -80,14 +80,14 @@ int main(int argc, char** argv)
 {
     if (argc < 3)
     {
-        cout << "Usage: stereo_multi_gpu <left_image> <right_image>\n";
+        std::cout << "Usage: stereo_multi_gpu <left_image> <right_image>\n";
         return -1;
     }
 
     int num_devices = getCudaEnabledDeviceCount();
     if (num_devices < 2)
     {
-        cout << "Two or more GPUs are required\n";
+        std::cout << "Two or more GPUs are required\n";
         return -1;
     }
 
@@ -96,7 +96,7 @@ int main(int argc, char** argv)
         DeviceInfo dev_info(i);
         if (!dev_info.isCompatible())
         {
-            cout << "GPU module isn't built for GPU #" << i << " ("
+            std::cout << "GPU module isn't built for GPU #" << i << " ("
                  << dev_info.name() << ", CC " << dev_info.majorVersion()
                  << dev_info.minorVersion() << "\n";
             return -1;
@@ -108,12 +108,12 @@ int main(int argc, char** argv)
     Mat right = imread(argv[2], CV_LOAD_IMAGE_GRAYSCALE);
     if (left.empty())
     {
-        cout << "Cannot open '" << argv[1] << "'\n";
+        std::cout << "Cannot open '" << argv[1] << "'\n";
         return -1;
     }
     if (right.empty())
     {
-        cout << "Cannot open '" << argv[2] << "'\n";
+        std::cout << "Cannot open '" << argv[2] << "'\n";
         return -1;
     }
 
@@ -180,7 +180,7 @@ void Worker::operator()(int device_id) const
     bm[device_id]->operator()(d_left[device_id], d_right[device_id],
                               d_result[device_id]);
 
-    cout << "GPU #" << device_id << " (" << DeviceInfo().name()
+    std::cout << "GPU #" << device_id << " (" << DeviceInfo().name()
         << "): finished\n";
 
     contextOff();

samples/gpu/multi.cpp

@@ -16,11 +16,11 @@
 int main()
 {
 #if !defined(HAVE_CUDA)
-    cout << "CUDA support is required (CMake key 'WITH_CUDA' must be true).\n";
+    std::cout << "CUDA support is required (CMake key 'WITH_CUDA' must be true).\n";
 #endif
 
 #if !defined(HAVE_TBB)
-    cout << "TBB support is required (CMake key 'WITH_TBB' must be true).\n";
+    std::cout << "TBB support is required (CMake key 'WITH_TBB' must be true).\n";
 #endif
 
     return 0;
@@ -43,7 +43,7 @@ int main()
     int num_devices = getCudaEnabledDeviceCount();
     if (num_devices < 2)
     {
-        cout << "Two or more GPUs are required\n";
+        std::cout << "Two or more GPUs are required\n";
         return -1;
     }
     for (int i = 0; i < num_devices; ++i)
@@ -51,7 +51,7 @@ int main()
         DeviceInfo dev_info(i);
         if (!dev_info.isCompatible())
         {
-            cout << "GPU module isn't built for GPU #" << i << " ("
+            std::cout << "GPU module isn't built for GPU #" << i << " ("
                  << dev_info.name() << ", CC " << dev_info.majorVersion()
                  << dev_info.minorVersion() << "\n";
             return -1;
@@ -88,7 +88,7 @@ void Worker::operator()(int device_id) const
 
     // Check results
     bool passed = norm(dst - Mat(d_dst), NORM_INF) < 1e-3;
-    cout << "GPU #" << device_id << " (" << DeviceInfo().name() << "): "
+    std::cout << "GPU #" << device_id << " (" << DeviceInfo().name() << "): "
         << (passed ? "passed" : "FAILED") << endl;
 
     // Deallocate data here, otherwise deallocation will be performed

samples/gpu/stereo_multi.cpp

@@ -18,11 +18,11 @@
 int main()
 {
 #if !defined(HAVE_CUDA)
-    cout << "CUDA support is required (CMake key 'WITH_CUDA' must be true).\n";
+    std::cout << "CUDA support is required (CMake key 'WITH_CUDA' must be true).\n";
 #endif
 
 #if !defined(HAVE_TBB)
-    cout << "TBB support is required (CMake key 'WITH_TBB' must be true).\n";
+    std::cout << "TBB support is required (CMake key 'WITH_TBB' must be true).\n";
 #endif
 
     return 0;
@@ -53,14 +53,14 @@ int main(int argc, char** argv)
 {
     if (argc < 3)
     {
-        cout << "Usage: stereo_multi_gpu <left_image> <right_image>\n";
+        std::cout << "Usage: stereo_multi_gpu <left_image> <right_image>\n";
         return -1;
     }
 
     int num_devices = getCudaEnabledDeviceCount();
     if (num_devices < 2)
     {
-        cout << "Two or more GPUs are required\n";
+        std::cout << "Two or more GPUs are required\n";
         return -1;
     }
     for (int i = 0; i < num_devices; ++i)
@@ -68,7 +68,7 @@ int main(int argc, char** argv)
         DeviceInfo dev_info(i);
         if (!dev_info.isCompatible())
         {
-            cout << "GPU module isn't built for GPU #" << i << " ("
+            std::cout << "GPU module isn't built for GPU #" << i << " ("
                  << dev_info.name() << ", CC " << dev_info.majorVersion()
                  << dev_info.minorVersion() << "\n";
             return -1;
@@ -80,12 +80,12 @@ int main(int argc, char** argv)
     Mat right = imread(argv[2], CV_LOAD_IMAGE_GRAYSCALE);
     if (left.empty())
     {
-        cout << "Cannot open '" << argv[1] << "'\n";
+        std::cout << "Cannot open '" << argv[1] << "'\n";
         return -1;
     }
     if (right.empty())
     {
-        cout << "Cannot open '" << argv[2] << "'\n";
+        std::cout << "Cannot open '" << argv[2] << "'\n";
         return -1;
     }
 
@@ -139,7 +139,7 @@ void Worker::operator()(int device_id) const
     bm[device_id]->operator()(d_left[device_id], d_right[device_id],
                               d_result[device_id]);
 
-    cout << "GPU #" << device_id << " (" << DeviceInfo().name()
+    std::cout << "GPU #" << device_id << " (" << DeviceInfo().name()
         << "): finished\n";
 
     multi_gpu_mgr.gpuOff();

tests/ml/src/mltest.h

@@ -55,7 +55,6 @@
 #include <string>
 #include <iostream>
 
-using namespace std;
 using namespace cv;
 
 #define CV_NBAYES   "nbayes"
@@ -83,13 +82,13 @@ protected:
     virtual int validate_test_results( int testCaseIdx ) = 0;
 
     int train( int testCaseIdx );
-    float get_error( int testCaseIdx, int type, vector<float> *resp = 0 );
+    float get_error( int testCaseIdx, int type, std::vector<float> *resp = 0 );
     void save( const char* filename );
     void load( const char* filename );
 
     CvMLData data;
     string modelName, validationFN;
-    vector<string> dataSetNames;
+    std::vector<string> dataSetNames;
     FileStorage validationFS;
 
     // MLL models
@@ -103,7 +102,7 @@ protected:
     CvRTrees* rtrees;
     CvERTrees* ertrees;
 
-    map<int, int> cls_map;
+    std::map<int, int> cls_map;
 
     int64 initSeed;
 };
@@ -125,7 +124,7 @@ protected:
     virtual int run_test_case( int testCaseIdx );
     virtual int validate_test_results( int testCaseIdx );
 
-    vector<float> test_resps1, test_resps2; // predicted responses for test data
+    std::vector<float> test_resps1, test_resps2; // predicted responses for test data
     char fname1[50], fname2[50];
 };