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Commit 07d92d9e authored by Andrey Kamaev's avatar Andrey Kamaev
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Fix android build warnings

parent 8325a28d
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Showing with 34 additions and 34 deletions
modules/contrib/src/bowmsctrainer.cpp
View file @ 07d92d9e
......@@ -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
View file @ 07d92d9e
......@@ -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);
}
......
modules/objdetect/src/haar.cpp
View file @ 07d92d9e
......@@ -1020,7 +1020,7 @@ cvRunHaarClassifierCascadeSum( const CvHaarClassifierCascade* _cascade,
}
else
#endif
#ifdef CV_HAAR_USE_SSE && !CV_HAAR_USE_AVX //old SSE optimization
#if defined CV_HAAR_USE_SSE && CV_HAAR_USE_SSE && !CV_HAAR_USE_AVX //old SSE optimization
if(haveSSE2)
{
for( i = start_stage; i < cascade->count; i++ )
......@@ -1111,23 +1111,23 @@ cvRunHaarClassifierCascadeSum( const CvHaarClassifierCascade* _cascade,
for( i = start_stage; i < cascade->count; i++ )
{
stage_sum = 0.0;
int j = 0;
int k = 0;
#ifdef CV_HAAR_USE_AVX
if(haveAVX)
{
for( ; j < cascade->stage_classifier[i].count-8; j+=8 )
for( ; k < cascade->stage_classifier[i].count-8; k+=8 )
{
stage_sum += icvEvalHidHaarClassifierAVX(
cascade->stage_classifier[i].classifier+j,
cascade->stage_classifier[i].classifier+k,
variance_norm_factor, p_offset );
}
}
#endif
for(; j < cascade->stage_classifier[i].count; j++ )
for(; k < cascade->stage_classifier[i].count; k++ )
{
stage_sum += icvEvalHidHaarClassifier(
cascade->stage_classifier[i].classifier + j,
cascade->stage_classifier[i].classifier + k,
variance_norm_factor, p_offset );
}
......
samples/cpp/pca.cpp
View file @ 07d92d9e
......@@ -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.";
......@@ -62,7 +62,7 @@ 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++)
......@@ -74,7 +74,7 @@ Mat formatImagesForPCA(const vector<Mat> &data)
return dst;
}
Mat toGrayscale(InputArray _src) {
static Mat toGrayscale(InputArray _src) {
Mat src = _src.getMat();
// only allow one channel
if(src.channels() != 1) {
......@@ -95,7 +95,7 @@ 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;
......
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