Fix android build warnings

modules/contrib/src/bowmsctrainer.cpp

@@ -81,46 +81,46 @@ Mat BOWMSCTrainer::cluster() const {
     return cluster(mergedDescriptors);
 }
 
-Mat BOWMSCTrainer::cluster(const Mat& descriptors) const {
+Mat BOWMSCTrainer::cluster(const Mat& _descriptors) const {
 
-    CV_Assert(!descriptors.empty());
+    CV_Assert(!_descriptors.empty());
 
     // TODO: sort the descriptors before clustering.
 
 
-    Mat icovar = Mat::eye(descriptors.cols,descriptors.cols,descriptors.type());
+    Mat icovar = Mat::eye(_descriptors.cols,_descriptors.cols,_descriptors.type());
 
     vector<Mat> initialCentres;
-    initialCentres.push_back(descriptors.row(0));
-    for (int i = 1; i < descriptors.rows; i++) {
+    initialCentres.push_back(_descriptors.row(0));
+    for (int i = 1; i < _descriptors.rows; i++) {
         double minDist = DBL_MAX;
         for (size_t j = 0; j < initialCentres.size(); j++) {
             minDist = std::min(minDist,
-                cv::Mahalanobis(descriptors.row(i),initialCentres[j],
+                cv::Mahalanobis(_descriptors.row(i),initialCentres[j],
                 icovar));
         }
         if (minDist > clusterSize)
-            initialCentres.push_back(descriptors.row(i));
+            initialCentres.push_back(_descriptors.row(i));
     }
 
     std::vector<std::list<cv::Mat> > clusters;
     clusters.resize(initialCentres.size());
-    for (int i = 0; i < descriptors.rows; i++) {
+    for (int i = 0; i < _descriptors.rows; i++) {
         int index = 0; double dist = 0, minDist = DBL_MAX;
         for (size_t j = 0; j < initialCentres.size(); j++) {
-            dist = cv::Mahalanobis(descriptors.row(i),initialCentres[j],icovar);
+            dist = cv::Mahalanobis(_descriptors.row(i),initialCentres[j],icovar);
             if (dist < minDist) {
                 minDist = dist;
                 index = (int)j;
             }
         }
-        clusters[index].push_back(descriptors.row(i));
+        clusters[index].push_back(_descriptors.row(i));
     }
 
     // TODO: throw away small clusters.
 
     Mat vocabulary;
-    Mat centre = Mat::zeros(1,descriptors.cols,descriptors.type());
+    Mat centre = Mat::zeros(1,_descriptors.cols,_descriptors.type());
     for (size_t i = 0; i < clusters.size(); i++) {
         centre.setTo(0);
         for (std::list<cv::Mat>::iterator Ci = clusters[i].begin(); Ci != clusters[i].end(); Ci++) {

modules/contrib/src/openfabmap.cpp

@@ -63,7 +63,7 @@ namespace of2 {
 static double logsumexp(double a, double b) {
     return a > b ? log(1 + exp(b - a)) + a : log(1 + exp(a - b)) + b;
 }
-    
+
 FabMap::FabMap(const Mat& _clTree, double _PzGe,
         double _PzGNe, int _flags, int _numSamples) :
     clTree(_clTree), PzGe(_PzGe), PzGNe(_PzGNe), flags(
@@ -445,16 +445,16 @@ FabMap1::~FabMap1() {
 }
 
 void FabMap1::getLikelihoods(const Mat& queryImgDescriptor,
-        const vector<Mat>& testImgDescriptors, vector<IMatch>& matches) {
+        const vector<Mat>& testImageDescriptors, vector<IMatch>& matches) {
 
-    for (size_t i = 0; i < testImgDescriptors.size(); i++) {
+    for (size_t i = 0; i < testImageDescriptors.size(); i++) {
         bool zq, zpq, Lzq;
         double logP = 0;
         for (int q = 0; q < clTree.cols; q++) {
 
             zq = queryImgDescriptor.at<float>(0,q) > 0;
             zpq = queryImgDescriptor.at<float>(0,pq(q)) > 0;
-            Lzq = testImgDescriptors[i].at<float>(0,q) > 0;
+            Lzq = testImageDescriptors[i].at<float>(0,q) > 0;
 
             logP += log((this->*PzGL)(q, zq, zpq, Lzq));
 
@@ -490,16 +490,16 @@ FabMapLUT::~FabMapLUT() {
 }
 
 void FabMapLUT::getLikelihoods(const Mat& queryImgDescriptor,
-        const vector<Mat>& testImgDescriptors, vector<IMatch>& matches) {
+        const vector<Mat>& testImageDescriptors, vector<IMatch>& matches) {
 
     double precFactor = (double)pow(10.0, -precision);
 
-    for (size_t i = 0; i < testImgDescriptors.size(); i++) {
+    for (size_t i = 0; i < testImageDescriptors.size(); i++) {
         unsigned long long int logP = 0;
         for (int q = 0; q < clTree.cols; q++) {
             logP += table[q][(queryImgDescriptor.at<float>(0,pq(q)) > 0) +
             ((queryImgDescriptor.at<float>(0, q) > 0) << 1) +
-            ((testImgDescriptors[i].at<float>(0,q) > 0) << 2)];
+            ((testImageDescriptors[i].at<float>(0,q) > 0) << 2)];
         }
         matches.push_back(IMatch(0,(int)i,-precFactor*(double)logP,0));
     }
@@ -518,7 +518,7 @@ FabMapFBO::~FabMapFBO() {
 }
 
 void FabMapFBO::getLikelihoods(const Mat& queryImgDescriptor,
-        const vector<Mat>& testImgDescriptors, vector<IMatch>& matches) {
+        const vector<Mat>& testImageDescriptors, vector<IMatch>& matches) {
 
     std::multiset<WordStats> wordData;
     setWordStatistics(queryImgDescriptor, wordData);
@@ -526,7 +526,7 @@ void FabMapFBO::getLikelihoods(const Mat& queryImgDescriptor,
     vector<int> matchIndices;
     vector<IMatch> queryMatches;
 
-    for (size_t i = 0; i < testImgDescriptors.size(); i++) {
+    for (size_t i = 0; i < testImageDescriptors.size(); i++) {
         queryMatches.push_back(IMatch(0,(int)i,0,0));
         matchIndices.push_back((int)i);
     }
@@ -543,7 +543,7 @@ void FabMapFBO::getLikelihoods(const Mat& queryImgDescriptor,
 
         for (size_t i = 0; i < matchIndices.size(); i++) {
             bool Lzq =
-                testImgDescriptors[matchIndices[i]].at<float>(0,wordIter->q) > 0;
+                testImageDescriptors[matchIndices[i]].at<float>(0,wordIter->q) > 0;
             queryMatches[matchIndices[i]].likelihood +=
                 log((this->*PzGL)(wordIter->q,zq,zpq,Lzq));
             currBest =
@@ -689,17 +689,17 @@ void FabMap2::add(const vector<Mat>& queryImgDescriptors) {
 }
 
 void FabMap2::getLikelihoods(const Mat& queryImgDescriptor,
-        const vector<Mat>& testImgDescriptors, vector<IMatch>& matches) {
+        const vector<Mat>& testImageDescriptors, vector<IMatch>& matches) {
 
-    if (&testImgDescriptors== &(this->testImgDescriptors)) {
+    if (&testImageDescriptors == &testImgDescriptors) {
         getIndexLikelihoods(queryImgDescriptor, testDefaults, testInvertedMap,
             matches);
     } else {
         CV_Assert(!(flags & MOTION_MODEL));
         vector<double> defaults;
         std::map<int, vector<int> > invertedMap;
-        for (size_t i = 0; i < testImgDescriptors.size(); i++) {
-            addToIndex(testImgDescriptors[i],defaults,invertedMap);
+        for (size_t i = 0; i < testImageDescriptors.size(); i++) {
+            addToIndex(testImageDescriptors[i],defaults,invertedMap);
         }
         getIndexLikelihoods(queryImgDescriptor, defaults, invertedMap, matches);
     }

samples/cpp/pca.cpp

 /*
 * pca.cpp
 *
-*  Author: 
+*  Author:
 *  Kevin Hughes <kevinhughes27[at]gmail[dot]com>
 *
 *  Special Thanks to:
 *  Philipp Wagner <bytefish[at]gmx[dot]de>
 *
-* This program demonstrates how to use OpenCV PCA with a 
+* This program demonstrates how to use OpenCV PCA with a
 * specified amount of variance to retain. The effect
 * is illustrated further by using a trackbar to
 * change the value for retained varaince.
@@ -17,9 +17,9 @@
 * on this list of images. The author recommends using
 * the first 15 faces of the AT&T face data set:
 * http://www.cl.cam.ac.uk/research/dtg/attarchive/facedatabase.html
-* 
+*
 * so for example your input text file would look like this:
-* 
+*
 *        <path_to_at&t_faces>/orl_faces/s1/1.pgm
 *        <path_to_at&t_faces>/orl_faces/s2/1.pgm
 *        <path_to_at&t_faces>/orl_faces/s3/1.pgm
@@ -50,7 +50,7 @@ using namespace std;
 
 ///////////////////////
 // Functions
-void read_imgList(const string& filename, vector<Mat>& images) {
+static void read_imgList(const string& filename, vector<Mat>& images) {
     std::ifstream file(filename.c_str(), ifstream::in);
     if (!file) {
         string error_message = "No valid input file was given, please check the given filename.";
@@ -62,19 +62,19 @@ void read_imgList(const string& filename, vector<Mat>& images) {
     }
 }
 
-Mat formatImagesForPCA(const vector<Mat> &data)
+static  Mat formatImagesForPCA(const vector<Mat> &data)
 {
     Mat dst(data.size(), data[0].rows*data[0].cols, CV_32F);
     for(unsigned int i = 0; i < data.size(); i++)
     {
         Mat image_row = data[i].clone().reshape(1,1);
         Mat row_i = dst.row(i);
-        image_row.convertTo(row_i,CV_32F);    
+        image_row.convertTo(row_i,CV_32F);
     }
     return dst;
 }
 
-Mat toGrayscale(InputArray _src) {
+static Mat toGrayscale(InputArray _src) {
     Mat src = _src.getMat();
     // only allow one channel
     if(src.channels() != 1) {
@@ -95,22 +95,22 @@ struct params
     string winName;
 };
 
-void onTrackbar(int pos, void* ptr) 
-{    
+static void onTrackbar(int pos, void* ptr)
+{
     cout << "Retained Variance = " << pos << "%   ";
     cout << "re-calculating PCA..." << std::flush;
-    
+
     double var = pos / 100.0;
-    
+
     struct params *p = (struct params *)ptr;
-    
+
     p->pca = PCA(p->data, cv::Mat(), CV_PCA_DATA_AS_ROW, var);
-    
+
     Mat point = p->pca.project(p->data.row(0));
     Mat reconstruction = p->pca.backProject(point);
     reconstruction = reconstruction.reshape(p->ch, p->rows);
     reconstruction = toGrayscale(reconstruction);
-    
+
     imshow(p->winName, reconstruction);
     cout << "done!   # of principal components: " << p->pca.eigenvectors.rows << endl;
 }
@@ -118,19 +118,19 @@ void onTrackbar(int pos, void* ptr)
 
 ///////////////////////
 // Main
-int main(int argc, char** argv) 
+int main(int argc, char** argv)
 {
     if (argc != 2) {
         cout << "usage: " << argv[0] << " <image_list.txt>" << endl;
         exit(1);
     }
-    
+
     // Get the path to your CSV.
     string imgList = string(argv[1]);
-    
+
     // vector to hold the images
     vector<Mat> images;
-    
+
     // Read in the data. This can fail if not valid
     try {
         read_imgList(imgList, images);
@@ -138,29 +138,29 @@ int main(int argc, char** argv)
         cerr << "Error opening file \"" << imgList << "\". Reason: " << e.msg << endl;
         exit(1);
     }
-    
+
     // Quit if there are not enough images for this demo.
     if(images.size() <= 1) {
         string error_message = "This demo needs at least 2 images to work. Please add more images to your data set!";
         CV_Error(CV_StsError, error_message);
     }
-        
+
     // Reshape and stack images into a rowMatrix
     Mat data = formatImagesForPCA(images);
-    
+
     // perform PCA
     PCA pca(data, cv::Mat(), CV_PCA_DATA_AS_ROW, 0.95); // trackbar is initially set here, also this is a common value for retainedVariance
-    
-    // Demonstration of the effect of retainedVariance on the first image 
+
+    // Demonstration of the effect of retainedVariance on the first image
     Mat point = pca.project(data.row(0)); // project into the eigenspace, thus the image becomes a "point"
     Mat reconstruction = pca.backProject(point); // re-create the image from the "point"
     reconstruction = reconstruction.reshape(images[0].channels(), images[0].rows); // reshape from a row vector into image shape
     reconstruction = toGrayscale(reconstruction); // re-scale for displaying purposes
-    
+
     // init highgui window
     string winName = "Reconstruction | press 'q' to quit";
     namedWindow(winName, CV_WINDOW_NORMAL);
-    
+
     // params struct to pass to the trackbar handler
     params p;
     p.data = data;
@@ -168,17 +168,17 @@ int main(int argc, char** argv)
     p.rows = images[0].rows;
     p.pca = pca;
     p.winName = winName;
-    
+
     // create the tracbar
     int pos = 95;
-    createTrackbar("Retained Variance (%)", winName, &pos, 100, onTrackbar, (void*)&p); 
-    
+    createTrackbar("Retained Variance (%)", winName, &pos, 100, onTrackbar, (void*)&p);
+
     // display until user presses q
     imshow(winName, reconstruction);
-    
+
     char key = 0;
     while(key != 'q')
         key = waitKey();
-   
-   return 0; 
+
+   return 0;
 }