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Fix android build warnings

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This commit is contained in:
Andrey Kamaev
2012-09-04 17:44:23 +04:00
parent 8325a28d09
commit 07d92d9e5a
4 changed files with 445 additions and 445 deletions
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22
modules/contrib/src/bowmsctrainer.cpp
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@@ -81,46 +81,46 @@ Mat BOWMSCTrainer::cluster() const {
return cluster(mergedDescriptors); 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. // 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; vector<Mat> initialCentres;
initialCentres.push_back(descriptors.row(0)); initialCentres.push_back(_descriptors.row(0));
for (int i = 1; i < descriptors.rows; i++) { for (int i = 1; i < _descriptors.rows; i++) {
double minDist = DBL_MAX; double minDist = DBL_MAX;
for (size_t j = 0; j < initialCentres.size(); j++) { for (size_t j = 0; j < initialCentres.size(); j++) {
minDist = std::min(minDist, minDist = std::min(minDist,
cv::Mahalanobis(descriptors.row(i),initialCentres[j], cv::Mahalanobis(_descriptors.row(i),initialCentres[j],
icovar)); icovar));
} }
if (minDist > clusterSize) if (minDist > clusterSize)
initialCentres.push_back(descriptors.row(i)); initialCentres.push_back(_descriptors.row(i));
} }
std::vector<std::list<cv::Mat> > clusters; std::vector<std::list<cv::Mat> > clusters;
clusters.resize(initialCentres.size()); 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; int index = 0; double dist = 0, minDist = DBL_MAX;
for (size_t j = 0; j < initialCentres.size(); j++) { 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) { if (dist < minDist) {
minDist = dist; minDist = dist;
index = (int)j; index = (int)j;
} }
} }
clusters[index].push_back(descriptors.row(i)); clusters[index].push_back(_descriptors.row(i));
} }
// TODO: throw away small clusters. // TODO: throw away small clusters.
Mat vocabulary; 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++) { for (size_t i = 0; i < clusters.size(); i++) {
centre.setTo(0); centre.setTo(0);
for (std::list<cv::Mat>::iterator Ci = clusters[i].begin(); Ci != clusters[i].end(); Ci++) { for (std::list<cv::Mat>::iterator Ci = clusters[i].begin(); Ci != clusters[i].end(); Ci++) {

26
modules/contrib/src/openfabmap.cpp
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@@ -445,16 +445,16 @@ FabMap1::~FabMap1() {
} }
void FabMap1::getLikelihoods(const Mat& queryImgDescriptor, 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; bool zq, zpq, Lzq;
double logP = 0; double logP = 0;
for (int q = 0; q < clTree.cols; q++) { for (int q = 0; q < clTree.cols; q++) {
zq = queryImgDescriptor.at<float>(0,q) > 0; zq = queryImgDescriptor.at<float>(0,q) > 0;
zpq = queryImgDescriptor.at<float>(0,pq(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)); logP += log((this->*PzGL)(q, zq, zpq, Lzq));
@@ -490,16 +490,16 @@ FabMapLUT::~FabMapLUT() {
} }
void FabMapLUT::getLikelihoods(const Mat& queryImgDescriptor, 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); 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; unsigned long long int logP = 0;
for (int q = 0; q < clTree.cols; q++) { for (int q = 0; q < clTree.cols; q++) {
logP += table[q][(queryImgDescriptor.at<float>(0,pq(q)) > 0) + logP += table[q][(queryImgDescriptor.at<float>(0,pq(q)) > 0) +
((queryImgDescriptor.at<float>(0, q) > 0) << 1) + ((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)); matches.push_back(IMatch(0,(int)i,-precFactor*(double)logP,0));
} }
@@ -518,7 +518,7 @@ FabMapFBO::~FabMapFBO() {
} }
void FabMapFBO::getLikelihoods(const Mat& queryImgDescriptor, void FabMapFBO::getLikelihoods(const Mat& queryImgDescriptor,
const vector<Mat>& testImgDescriptors, vector<IMatch>& matches) { const vector<Mat>& testImageDescriptors, vector<IMatch>& matches) {
std::multiset<WordStats> wordData; std::multiset<WordStats> wordData;
setWordStatistics(queryImgDescriptor, wordData); setWordStatistics(queryImgDescriptor, wordData);
@@ -526,7 +526,7 @@ void FabMapFBO::getLikelihoods(const Mat& queryImgDescriptor,
vector<int> matchIndices; vector<int> matchIndices;
vector<IMatch> queryMatches; 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)); queryMatches.push_back(IMatch(0,(int)i,0,0));
matchIndices.push_back((int)i); matchIndices.push_back((int)i);
} }
@@ -543,7 +543,7 @@ void FabMapFBO::getLikelihoods(const Mat& queryImgDescriptor,
for (size_t i = 0; i < matchIndices.size(); i++) { for (size_t i = 0; i < matchIndices.size(); i++) {
bool Lzq = bool Lzq =
testImgDescriptors[matchIndices[i]].at<float>(0,wordIter->q) > 0; testImageDescriptors[matchIndices[i]].at<float>(0,wordIter->q) > 0;
queryMatches[matchIndices[i]].likelihood += queryMatches[matchIndices[i]].likelihood +=
log((this->*PzGL)(wordIter->q,zq,zpq,Lzq)); log((this->*PzGL)(wordIter->q,zq,zpq,Lzq));
currBest = currBest =
@@ -689,17 +689,17 @@ void FabMap2::add(const vector<Mat>& queryImgDescriptors) {
} }
void FabMap2::getLikelihoods(const Mat& queryImgDescriptor, 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, getIndexLikelihoods(queryImgDescriptor, testDefaults, testInvertedMap,
matches); matches);
} else { } else {
CV_Assert(!(flags & MOTION_MODEL)); CV_Assert(!(flags & MOTION_MODEL));
vector<double> defaults; vector<double> defaults;
std::map<int, vector<int> > invertedMap; std::map<int, vector<int> > invertedMap;
for (size_t i = 0; i < testImgDescriptors.size(); i++) { for (size_t i = 0; i < testImageDescriptors.size(); i++) {
addToIndex(testImgDescriptors[i],defaults,invertedMap); addToIndex(testImageDescriptors[i],defaults,invertedMap);
} }
getIndexLikelihoods(queryImgDescriptor, defaults, invertedMap, matches); getIndexLikelihoods(queryImgDescriptor, defaults, invertedMap, matches);
} }

12
modules/objdetect/src/haar.cpp
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@@ -1020,7 +1020,7 @@ cvRunHaarClassifierCascadeSum( const CvHaarClassifierCascade* _cascade,
} }
else else
#endif #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) if(haveSSE2)
{ {
for( i = start_stage; i < cascade->count; i++ ) for( i = start_stage; i < cascade->count; i++ )
@@ -1111,23 +1111,23 @@ cvRunHaarClassifierCascadeSum( const CvHaarClassifierCascade* _cascade,
for( i = start_stage; i < cascade->count; i++ ) for( i = start_stage; i < cascade->count; i++ )
{ {
stage_sum = 0.0; stage_sum = 0.0;
int j = 0; int k = 0;
#ifdef CV_HAAR_USE_AVX #ifdef CV_HAAR_USE_AVX
if(haveAVX) 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( stage_sum += icvEvalHidHaarClassifierAVX(
cascade->stage_classifier[i].classifier+j, cascade->stage_classifier[i].classifier+k,
variance_norm_factor, p_offset ); variance_norm_factor, p_offset );
} }
} }
#endif #endif
for(; j < cascade->stage_classifier[i].count; j++ ) for(; k < cascade->stage_classifier[i].count; k++ )
{ {
stage_sum += icvEvalHidHaarClassifier( stage_sum += icvEvalHidHaarClassifier(
cascade->stage_classifier[i].classifier + j, cascade->stage_classifier[i].classifier + k,
variance_norm_factor, p_offset ); variance_norm_factor, p_offset );
} }

8
samples/cpp/pca.cpp
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@@ -50,7 +50,7 @@ using namespace std;
/////////////////////// ///////////////////////
// Functions // 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); std::ifstream file(filename.c_str(), ifstream::in);
if (!file) { if (!file) {
string error_message = "No valid input file was given, please check the given filename."; 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); Mat dst(data.size(), data[0].rows*data[0].cols, CV_32F);
for(unsigned int i = 0; i < data.size(); i++) for(unsigned int i = 0; i < data.size(); i++)
@@ -74,7 +74,7 @@ Mat formatImagesForPCA(const vector<Mat> &data)
return dst; return dst;
} }
Mat toGrayscale(InputArray _src) { static Mat toGrayscale(InputArray _src) {
Mat src = _src.getMat(); Mat src = _src.getMat();
// only allow one channel // only allow one channel
if(src.channels() != 1) { if(src.channels() != 1) {
@@ -95,7 +95,7 @@ struct params
string winName; string winName;
}; };
void onTrackbar(int pos, void* ptr) static void onTrackbar(int pos, void* ptr)
{ {
cout << "Retained Variance = " << pos << "% "; cout << "Retained Variance = " << pos << "% ";
cout << "re-calculating PCA..." << std::flush; cout << "re-calculating PCA..." << std::flush;