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https://github.com/opencv/opencv.git
synced 2024-11-29 22:00:25 +08:00
Merge pull request #79 from taka-no-me:warnings
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commit
781d89829f
@ -182,10 +182,20 @@ MACRO(ADD_PRECOMPILED_HEADER_TO_TARGET _targetName _input _pch_output_to_use )
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_PCH_GET_TARGET_COMPILE_FLAGS(_target_cflags ${_name} ${_pch_output_to_use} ${_dowarn})
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#MESSAGE("Add flags ${_target_cflags} to ${_targetName} " )
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SET_TARGET_PROPERTIES(${_targetName}
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PROPERTIES
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COMPILE_FLAGS ${_target_cflags}
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)
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GET_TARGET_PROPERTY(_sources ${_targetName} SOURCES)
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FOREACH(src ${_sources})
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if(NOT "${src}" MATCHES "\\.mm$")
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get_source_file_property(_flags "${src}" COMPILE_FLAGS)
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if(_flags)
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set(_flags "${_flags} ${_target_cflags}")
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else()
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set(_flags "${_target_cflags}")
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endif()
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set_source_files_properties("${src}" PROPERTIES COMPILE_FLAGS "${_flags}")
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endif()
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ENDFOREACH()
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ADD_CUSTOM_TARGET(pch_Generate_${_targetName}
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DEPENDS ${_pch_output_to_use}
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@ -59,6 +59,9 @@
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# ifdef ANDROID
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template <typename Scalar> Scalar log2(Scalar v) { using std::log; return log(v)/log(Scalar(2)); }
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# endif
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# if defined __GNUC__ && defined __APPLE__
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# pragma GCC diagnostic ignored "-Wshadow"
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# endif
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# include <unsupported/Eigen/MatrixFunctions>
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# include <Eigen/Dense>
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#endif
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@ -86,6 +86,11 @@
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#include <limits>
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#ifdef _MSC_VER
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# pragma warning(push)
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# pragma warning(disable:4127) //conditional expression is constant
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#endif
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namespace cv
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{
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@ -3950,5 +3955,9 @@ template<typename _Tp> inline void AlgorithmInfo::addParam(Algorithm& algo, cons
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}
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#ifdef _MSC_VER
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# pragma warning(pop)
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#endif
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#endif // __cplusplus
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#endif
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@ -2813,7 +2813,7 @@ PCA::PCA(InputArray data, InputArray _mean, int flags, int maxComponents)
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PCA::PCA(InputArray data, InputArray _mean, int flags, double retainedVariance)
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{
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operator()(data, _mean, flags, retainedVariance);
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computeVar(data, _mean, flags, retainedVariance);
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}
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PCA& PCA::operator()(InputArray _data, InputArray __mean, int flags, int maxComponents)
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@ -3077,7 +3077,7 @@ void cv::PCAComputeVar(InputArray data, InputOutputArray mean,
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OutputArray eigenvectors, double retainedVariance)
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{
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PCA pca;
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pca(data, mean, 0, retainedVariance);
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pca.computeVar(data, mean, 0, retainedVariance);
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pca.mean.copyTo(mean);
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pca.eigenvectors.copyTo(eigenvectors);
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}
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@ -362,8 +362,8 @@ void cv::setNumThreads( int threads )
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else if (pplScheduler == 0 || 1 + pplScheduler->GetNumberOfVirtualProcessors() != (unsigned int)threads)
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{
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pplScheduler = Concurrency::Scheduler::Create(Concurrency::SchedulerPolicy(2,
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Concurrency::PolicyElementKey::MinConcurrency, threads-1,
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Concurrency::PolicyElementKey::MaxConcurrency, threads-1));
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Concurrency::MinConcurrency, threads-1,
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Concurrency::MaxConcurrency, threads-1));
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}
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#endif
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@ -426,7 +426,7 @@ protected:
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}
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// 3. check C++ PCA w/retainedVariance
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cPCA( rPoints.t(), Mat(), CV_PCA_DATA_AS_COL, retainedVariance );
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cPCA.computeVar( rPoints.t(), Mat(), CV_PCA_DATA_AS_COL, retainedVariance );
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diffPrjEps = 1, diffBackPrjEps = 1;
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Mat rvPrjTestPoints = cPCA.project(rTestPoints.t());
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@ -500,7 +500,7 @@ int runRadiusSearch_(void* index, const Mat& query, Mat& indices, Mat& dists,
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::cvflann::Matrix<DistanceType> _dists((DistanceType*)dists.data, dists.rows, dists.cols);
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return ((IndexType*)index)->radiusSearch(_query, _indices, _dists,
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saturate_cast<DistanceType>(radius),
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saturate_cast<float>(radius),
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(const ::cvflann::SearchParams&)get_params(params));
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}
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@ -503,6 +503,9 @@ bool CvCaptureCAM::setProperty(int property_id, double value) {
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didOutputVideoFrame:(CVImageBufferRef)videoFrame
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withSampleBuffer:(QTSampleBuffer *)sampleBuffer
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fromConnection:(QTCaptureConnection *)connection {
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(void)captureOutput;
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(void)sampleBuffer;
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(void)connection;
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CVBufferRetain(videoFrame);
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CVImageBufferRef imageBufferToRelease = mCurrentImageBuffer;
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@ -519,6 +522,9 @@ bool CvCaptureCAM::setProperty(int property_id, double value) {
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- (void)captureOutput:(QTCaptureOutput *)captureOutput
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didDropVideoFrameWithSampleBuffer:(QTSampleBuffer *)sampleBuffer
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fromConnection:(QTCaptureConnection *)connection {
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(void)captureOutput;
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(void)sampleBuffer;
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(void)connection;
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cout << "Camera dropped frame!" << endl;
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}
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@ -48,6 +48,10 @@
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# pragma warning( disable: 4100 4244 4267 )
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#endif
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#if defined __GNUC__ && defined __APPLE__
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# pragma GCC diagnostic ignored "-Wshadow"
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#endif
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#include <ImfHeader.h>
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#include <ImfInputFile.h>
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#include <ImfOutputFile.h>
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@ -45,6 +45,10 @@
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#ifdef HAVE_OPENEXR
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#if defined __GNUC__ && defined __APPLE__
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# pragma GCC diagnostic ignored "-Wshadow"
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#endif
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#include <ImfChromaticities.h>
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#include <ImfInputFile.h>
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#include <ImfChannelList.h>
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@ -136,7 +136,7 @@ static bool wasInitialized = false;
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}
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}*/
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CV_IMPL int cvInitSystem( int argc, char** argv)
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CV_IMPL int cvInitSystem( int , char** )
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{
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//cout << "cvInitSystem" << endl;
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wasInitialized = true;
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@ -159,7 +159,7 @@ CV_IMPL int cvInitSystem( int argc, char** argv)
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return 0;
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}
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CVWindow *cvGetWindow(const char *name) {
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static CVWindow *cvGetWindow(const char *name) {
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//cout << "cvGetWindow" << endl;
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NSAutoreleasePool* localpool = [[NSAutoreleasePool alloc] init];
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NSString *cvname = [NSString stringWithFormat:@"%s", name];
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@ -614,6 +614,7 @@ void cvSetModeWindow_COCOA( const char* name, double prop_value )
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@synthesize status;
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- (void)cvSendMouseEvent:(NSEvent *)event type:(int)type flags:(int)flags {
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(void)event;
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//cout << "cvSendMouseEvent" << endl;
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NSPoint mp = [NSEvent mouseLocation];
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//NSRect visible = [[self contentView] frame];
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@ -924,6 +925,7 @@ void cvSetModeWindow_COCOA( const char* name, double prop_value )
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}
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- (void)sliderChanged:(NSNotification *)notification {
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(void)notification;
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int pos = [slider intValue];
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if(value)
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*value = pos;
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@ -1387,8 +1387,8 @@ struct RGB2Lab_f
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C6 = coeffs[6], C7 = coeffs[7], C8 = coeffs[8];
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n *= 3;
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static const float _1_3 = 1.0 / 3.0;
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static const double _a = 16.0 / 116;
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static const float _1_3 = 1.0f / 3.0f;
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static const float _a = 16.0f / 116.0f;
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for (i = 0; i < n; i += 3, src += scn )
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{
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float R = clip(src[0]);
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@ -1409,11 +1409,11 @@ struct RGB2Lab_f
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float Y = R*C3 + G*C4 + B*C5;
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float Z = R*C6 + G*C7 + B*C8;
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float FX = X > 0.008856 ? pow(X, _1_3) : (7.787f * X + _a);
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float FY = Y > 0.008856 ? pow(Y, _1_3) : (7.787f * Y + _a);
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float FZ = Z > 0.008856 ? pow(Z, _1_3) : (7.787f * Z + _a);
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float FX = X > 0.008856f ? pow(X, _1_3) : (7.787f * X + _a);
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float FY = Y > 0.008856f ? pow(Y, _1_3) : (7.787f * Y + _a);
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float FZ = Z > 0.008856f ? pow(Z, _1_3) : (7.787f * Z + _a);
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float L = Y > 0.008856 ? (116.f * FY - 16.f) : (903.3 * Y);
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float L = Y > 0.008856f ? (116.f * FY - 16.f) : (903.3f * Y);
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float a = 500.f * (FX - FY);
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float b = 200.f * (FY - FZ);
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@ -169,7 +169,7 @@ private:
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__instype *split = median;
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for (; split != last && deref(ctor(*split), dim) ==
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deref(ctor(*median), dim); ++split);
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deref(ctor(*median), dim); ++split) {}
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if (split == last) { // leaf
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int nexti = -1;
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@ -387,9 +387,8 @@ public:
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// ret_nn_pq is an array containing the (at most) k nearest neighbors
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// (see bbf_nn structure def above).
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template < class __desctype >
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int find_nn_bbf(const __desctype * d,
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int k, int emax,
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bbf_nn_pqueue & ret_nn_pq) const {
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int find_nn_bbf(const __desctype * d, int k, int emax, bbf_nn_pqueue & ret_nn_pq) const
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{
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assert(k > 0);
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ret_nn_pq.clear();
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@ -400,7 +399,8 @@ public:
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// iterate while queue non-empty and emax>0
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tmp_pq.clear();
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tmp_pq.push_back(bbf_node(root_node, 0));
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while (tmp_pq.size() && emax > 0) {
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while (tmp_pq.size() && emax > 0)
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{
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// from node nearest query point d, run to leaf
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std::pop_heap(tmp_pq.begin(), tmp_pq.end());
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@ -408,18 +408,17 @@ public:
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tmp_pq.erase(tmp_pq.end() - 1);
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int i;
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for (i = bbf.node;
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i != -1 && nodes[i].dim >= 0;
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i = bbf_branch(i, d, tmp_pq));
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for (i = bbf.node; i != -1 && nodes[i].dim >= 0; i = bbf_branch(i, d, tmp_pq)) {}
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if (i != -1) {
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if (i != -1)
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{
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// add points in leaf/bin to ret_nn_pq
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do {
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bbf_new_nn(ret_nn_pq, k, d, nodes[i].value);
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} while (-1 != (i = nodes[i].right));
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// add points in leaf/bin to ret_nn_pq
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do {
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bbf_new_nn(ret_nn_pq, k, d, nodes[i].value);
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} while (-1 != (i = nodes[i].right));
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--emax;
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--emax;
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}
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}
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@ -414,7 +414,7 @@ static void icvFindStereoCorrespondenceByBirchfieldDP( uchar* src1, uchar* src2,
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for( x = 1; x < imgW; x++ )
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{
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i = x - 1;
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for( ; x < imgW && dest[y*widthStep+x] == dest[y*widthStep+x-1]; x++ );
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for( ; x < imgW && dest[y*widthStep+x] == dest[y*widthStep+x-1]; x++ ) {}
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s = x - i;
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for( ; i < x; i++ )
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{
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@ -633,7 +633,7 @@ static CvTestSeqElem* icvTestSeqReadElemOne(CvTestSeq_* pTS, CvFileStorage* fs,
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for(i0=0, i1=1; i1<KeyFrameNum;)
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{
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for(i1=i0+1; i1<KeyFrameNum && KeyFrames[i1]<0; i1++);
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for(i1=i0+1; i1<KeyFrameNum && KeyFrames[i1]<0; i1++) {}
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assert(i1<KeyFrameNum);
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assert(i1>i0);
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@ -779,7 +779,7 @@ static CvTestSeqElem* icvTestSeqReadElemAll(CvTestSeq_* pTS, CvFileStorage* fs,
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}
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/* Find last element: */
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for(pElemLast=pElemNew;pElemLast && pElemLast->next;pElemLast= pElemLast->next);
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for(pElemLast=pElemNew;pElemLast && pElemLast->next;pElemLast= pElemLast->next) {}
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} /* Next element. */
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} /* Read all element in sequence. */
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@ -842,7 +842,7 @@ CvTestSeq* cvCreateTestSeq(char* pConfigfile, char** videos, int numvideo, float
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else
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{
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CvTestSeqElem* p = NULL;
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for(p=pTS->pElemList;p->next;p=p->next);
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for(p=pTS->pElemList;p->next;p=p->next) {}
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p->next = pElemNew;
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}
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} /* Read all videos. */
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@ -3829,8 +3829,9 @@ static int zero = 0;
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coeff_usage = CV_VALUE; \
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else if ((length == alpha.count) && (alpha.count == beta.count) && (beta.count == gamma.count)) \
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coeff_usage = CV_ARRAY; \
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else \
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return (PyObject*)failmsg("SnakeImage weights invalid"); \
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else { \
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failmsg("SnakeImage weights invalid"); \
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return (PyObject*)0; } \
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cvSnakeImage(image, points, length, a, b, g, coeff_usage, win, criteria, calc_gradient); \
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} while (0)
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