Chiebot-Mirror
/
opencv
mirror of https://github.com/opencv/opencv.git
8
0
Fork 0
Code Issues Projects Releases Wiki Activity

Updated warning options for GCC; fixed new warnings.

Browse Source
pull/2/head
Andrey Kamaev 13 years ago
parent 779f4e39de
commit d48d7b2b46
50 changed files with 2254 additions and 2007 deletions
Whitespace
Show all changes Ignore whitespace when comparing lines Ignore changes in amount of whitespace Ignore changes in whitespace at EOL
Split View
Diff Options
Show Stats Download Patch File Download Diff File
  1. 2
      3rdparty/libjasper/CMakeLists.txt
  2. 2
      3rdparty/libjpeg/CMakeLists.txt
  3. 7
      3rdparty/libtiff/CMakeLists.txt
  4. 3
      cmake/OpenCVCompilerOptions.cmake
  5. 10
      cmake/OpenCVConfig.cmake
  6. 1
      cmake/OpenCVUtils.cmake
  7. 2
      modules/androidcamera/src/camera_activity.cpp
  8. 4
      modules/contrib/src/basicretinafilter.cpp
  9. 626
      modules/contrib/src/basicretinafilter.hpp
  10. 242
      modules/contrib/src/hybridtracker.cpp
  11. 244
      modules/contrib/src/imagelogpolprojection.hpp
  12. 272
      modules/contrib/src/inputoutput.cpp
  13. 172
      modules/contrib/src/magnoretinafilter.hpp
  14. 4
      modules/contrib/src/parvoretinafilter.hpp
  15. 4
      modules/contrib/src/retinacolor.cpp
  16. 4
      modules/contrib/src/retinacolor.hpp
  17. 10
      modules/contrib/src/retinafilter.cpp
  18. 808
      modules/contrib/src/retinafilter.hpp
  19. 716
      modules/contrib/src/templatebuffer.hpp
  20. 4
      modules/flann/src/precomp.hpp
  21. 36
      modules/highgui/src/grfmt_jpeg.cpp
  22. 34
      modules/highgui/src/grfmt_png.cpp
  23. 19
      modules/highgui/src/window.cpp
  24. 2
      modules/java/CMakeLists.txt
  25. 8
      modules/java/gen_java.py
  26. 336
      modules/java/src/cpp/Mat.cpp
  27. 56
      modules/java/src/cpp/VideoCapture.cpp
  28. 4
      modules/java/src/cpp/jni_part.cpp
  29. 10
      modules/java/src/cpp/utils.cpp
  30. 2
      modules/legacy/src/blobtrackanalysistrackdist.cpp
  31. 3
      modules/legacy/src/calonder.cpp
  32. 8
      modules/legacy/src/lee.cpp
  33. 2
      modules/legacy/test/test_em.cpp
  34. 2
      modules/ml/test/test_emknearestkmeans.cpp
  35. 1
      modules/objdetect/src/datamatrix.cpp
  36. 4
      modules/stitching/perf/perf_stich.cpp
  37. 7
      modules/stitching/src/blenders.cpp
  38. 2
      modules/stitching/src/matchers.cpp
  39. 8
      modules/ts/src/ts_func.cpp
  40. 148
      samples/android/face-detection/jni/DetectionBasedTracker_jni.cpp
  41. 1
      samples/android/hello-android/main.cpp
  42. 2
      samples/android/tutorial-3-native/jni/jni_part.cpp
  43. 2
      samples/android/tutorial-4-mixed/jni/jni_part.cpp
  44. 2
      samples/c/blobtrack_sample.cpp
  45. 2
      samples/c/contours.c
  46. 4
      samples/c/delaunay.c
  47. 2
      samples/c/fback_c.c
  48. 2
      samples/c/morphology.c
  49. 1
      samples/c/pyramid_segmentation.c
  50. 2
      samples/cpp/stitching.cpp

2
3rdparty/libjasper/CMakeLists.txt vendored
Unescape View File

@ -23,7 +23,7 @@ if(MSVC)
add_definitions(-DJAS_WIN_MSVC_BUILD)
endif()
ocv_warnings_disable(CMAKE_C_FLAGS -Wno-implicit-function-declaration -Wno-uninitialized -Wmissing-prototypes -Wmissing-declarations -Wunused -Wshadow
ocv_warnings_disable(CMAKE_C_FLAGS -Wno-implicit-function-declaration -Wno-uninitialized -Wmissing-prototypes -Wmissing-declarations -Wunused -Wshadow -Wsign-compare
/wd4013 /wd4018 /wd4715 /wd4244 /wd4101 /wd4267)
if(UNIX)

2
3rdparty/libjpeg/CMakeLists.txt vendored
Unescape View File

@ -25,7 +25,7 @@ if(CMAKE_COMPILER_IS_GNUCXX)
set_source_files_properties(jcdctmgr.c PROPERTIES COMPILE_FLAGS "-O1")
endif()
ocv_warnings_disable(CMAKE_C_FLAGS -Wcast-align -Wshadow)
ocv_warnings_disable(CMAKE_C_FLAGS -Wcast-align -Wshadow -Wunused)
set_target_properties(${JPEG_LIBRARY}
PROPERTIES OUTPUT_NAME ${JPEG_LIBRARY}

7
3rdparty/libtiff/CMakeLists.txt vendored
Unescape View File

@ -81,18 +81,15 @@ set(lib_srcs
if(UNIX)
list(APPEND lib_srcs tif_unix.c)
if(CMAKE_COMPILER_IS_GNUCXX AND NOT ENABLE_NOISY_WARNINGS)
set_source_files_properties(tif_unix.c PROPERTIES COMPILE_FLAGS "-w")
endif()
endif()
if(WIN32)
list(APPEND lib_srcs tif_win32.c)
endif(WIN32)
ocv_warnings_disable(CMAKE_C_FLAGS -Wno-unused-but-set-variable -Wmissing-prototypes -Wmissing-declarations -Wundef
ocv_warnings_disable(CMAKE_C_FLAGS -Wno-unused-but-set-variable -Wmissing-prototypes -Wmissing-declarations -Wundef -Wunused -Wsign-compare
-Wcast-align -Wshadow -Wno-maybe-uninitialized -Wno-pointer-to-int-cast -Wno-int-to-pointer-cast)
ocv_warnings_disable(CMAKE_CXX_FLAGS -Wmissing-declarations /wd4100 /wd4244 /wd4706 /wd4127 /wd4701 /wd4018 /wd4267 /wd4306 /wd4305 /wd4312 /wd4311)
ocv_warnings_disable(CMAKE_CXX_FLAGS -Wmissing-declarations -Wunused-parameter /wd4100 /wd4244 /wd4706 /wd4127 /wd4701 /wd4018 /wd4267 /wd4306 /wd4305 /wd4312 /wd4311)
if(UNIX AND (CMAKE_COMPILER_IS_GNUCXX OR CV_ICC))
set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS} -fPIC")

3
cmake/OpenCVCompilerOptions.cmake
Unescape View File

@ -58,6 +58,7 @@ endif()
if(CMAKE_COMPILER_IS_GNUCXX)
# High level of warnings.
add_extra_compiler_option(-W)
add_extra_compiler_option(-Wall)
add_extra_compiler_option(-Werror=return-type)
add_extra_compiler_option(-Werror=non-virtual-dtor)
@ -72,6 +73,7 @@ if(CMAKE_COMPILER_IS_GNUCXX)
add_extra_compiler_option(-Winit-self)
add_extra_compiler_option(-Wpointer-arith)
add_extra_compiler_option(-Wshadow)
add_extra_compiler_option(-Wsign-promo)
if(ENABLE_NOISY_WARNINGS)
add_extra_compiler_option(-Wcast-align)
@ -81,6 +83,7 @@ if(CMAKE_COMPILER_IS_GNUCXX)
add_extra_compiler_option(-Wno-delete-non-virtual-dtor)
add_extra_compiler_option(-Wno-unnamed-type-template-args)
endif()
add_extra_compiler_option(-fdiagnostics-show-option)
# The -Wno-long-long is required in 64bit systems when including sytem headers.
if(X86_64)

10
cmake/OpenCVConfig.cmake
Unescape View File

@ -30,10 +30,7 @@
# Advanced variables:
# - OpenCV_SHARED
# - OpenCV_CONFIG_PATH
# - OpenCV_INSTALL_PATH (not set on Windows)
# - OpenCV_LIB_COMPONENTS
# - OpenCV_USE_MANGLED_PATHS
# - OpenCV_HAVE_ANDROID_CAMERA
#
# ===================================================================================
#
@ -41,6 +38,13 @@
# - OpenCV_STATIC
# - OpenCV_CUDA
if(CMAKE_VERSION VERSION_GREATER 2.6)
get_property(OpenCV_LANGUAGES GLOBAL PROPERTY ENABLED_LANGUAGES)
if(NOT ";${OpenCV_LANGUAGES};" MATCHES ";CXX;")
enable_language(CXX)
endif()
endif()
if(NOT DEFINED OpenCV_STATIC)
# look for global setting
if(NOT DEFINED BUILD_SHARED_LIBS OR BUILD_SHARED_LIBS)

1
cmake/OpenCVUtils.cmake
Unescape View File

@ -34,6 +34,7 @@ endmacro()
set(OCV_COMPILER_FAIL_REGEX
"command line option .* is valid for .* but not for C\\+\\+" # GNU
"command line option .* is valid for .* but not for C" # GNU
"unrecognized .*option" # GNU
"unknown .*option" # Clang
"ignoring unknown option" # MSVC

2
modules/androidcamera/src/camera_activity.cpp
Unescape View File

@ -340,7 +340,7 @@ CameraActivity::~CameraActivity()
disconnect();
}
bool CameraActivity::onFrameBuffer(void* buffer, int bufferSize)
bool CameraActivity::onFrameBuffer(void* /*buffer*/, int /*bufferSize*/)
{
LOGD("CameraActivity::onFrameBuffer - empty callback");
return true;

4
modules/contrib/src/basicretinafilter.cpp
Unescape View File

@ -426,7 +426,7 @@ void BasicRetinaFilter::_spatiotemporalLPfilter(const float *inputFrame, float *
}
// run SQUARING LP filter for a new frame input and save result at a specific output adress
const float BasicRetinaFilter::_squaringSpatiotemporalLPfilter(const float *inputFrame, float *outputFrame, const unsigned int filterIndex)
float BasicRetinaFilter::_squaringSpatiotemporalLPfilter(const float *inputFrame, float *outputFrame, const unsigned int filterIndex)
{
unsigned int coefTableOffset=filterIndex*3;
/**********/
@ -597,7 +597,7 @@ void BasicRetinaFilter::_squaringHorizontalCausalFilter(const float *inputFrame,
}
// vertical anticausal filter that returns the mean value of its result
const float BasicRetinaFilter::_verticalAnticausalFilter_returnMeanValue(float *outputFrame, unsigned int IDcolumnStart, unsigned int IDcolumnEnd)
float BasicRetinaFilter::_verticalAnticausalFilter_returnMeanValue(float *outputFrame, unsigned int IDcolumnStart, unsigned int IDcolumnEnd)
{
register float meanValue=0;
float* offset=outputFrame+_filterOutput.getNBpixels()-_filterOutput.getNBcolumns();

626
modules/contrib/src/basicretinafilter.hpp
Unescape View File

@ -119,322 +119,322 @@ class BasicRetinaFilter
{
public:
/**
* constructor of the base bio-inspired toolbox, parameters are only linked to imae input size and number of filtering capabilities of the object
* @param NBrows: number of rows of the input image
* @param NBcolumns: number of columns of the input image
* @param parametersListSize: specifies the number of parameters set (each parameters set represents a specific low pass spatio-temporal filter)
* @param useProgressiveFilter: specifies if the filter has irreguar (progressive) filtering capabilities (this can be activated later using setProgressiveFilterConstants_xxx methods)
*/
BasicRetinaFilter(const unsigned int NBrows, const unsigned int NBcolumns, const unsigned int parametersListSize=1, const bool useProgressiveFilter=false);
/**
* standrad destructore
*/
~BasicRetinaFilter();
/**
* function which clears the output buffer of the object
*/
inline void clearOutputBuffer(){_filterOutput=0;};
/**
* function which clears the secondary buffer of the object
*/
inline void clearSecondaryBuffer(){_localBuffer=0;};
/**
* function which clears the output and the secondary buffer of the object
*/
inline void clearAllBuffers(){clearOutputBuffer();clearSecondaryBuffer();};
/**
* resize basic retina filter object (resize all allocated buffers
* @param NBrows: the new height size
* @param NBcolumns: the new width size
*/
void resize(const unsigned int NBrows, const unsigned int NBcolumns);
/**
* forbiden method inherited from parent std::valarray
* prefer not to use this method since the filter matrix become vectors
*/
void resize(const unsigned int){std::cerr<<"error, not accessible method"<<std::endl;};
/**
* low pass filter call and run (models the homogeneous cells network at the retina level, for example horizontal cells or photoreceptors)
* @param inputFrame: the input image to be processed
* @param filterIndex: the offset which specifies the parameter set that should be used for the filtering
* @return the processed image, the output is reachable later by using function getOutput()
*/
const std::valarray<float> &runFilter_LPfilter(const std::valarray<float> &inputFrame, const unsigned int filterIndex=0); // run the LP filter for a new frame input and save result in _filterOutput
/**
* low pass filter call and run (models the homogeneous cells network at the retina level, for example horizontal cells or photoreceptors)
* @param inputFrame: the input image to be processed
* @param outputFrame: the output buffer in which the result is writed
* @param filterIndex: the offset which specifies the parameter set that should be used for the filtering
*/
void runFilter_LPfilter(const std::valarray<float> &inputFrame, std::valarray<float> &outputFrame, const unsigned int filterIndex=0); // run LP filter on a specific output adress
/**
* low pass filter call and run (models the homogeneous cells network at the retina level, for example horizontal cells or photoreceptors)
* @param inputOutputFrame: the input image to be processed on which the result is rewrited
* @param filterIndex: the offset which specifies the parameter set that should be used for the filtering
*/
void runFilter_LPfilter_Autonomous(std::valarray<float> &inputOutputFrame, const unsigned int filterIndex=0);// run LP filter on the input data and rewrite it
/**
* local luminance adaptation call and run (contrast enhancement property of the photoreceptors)
* @param inputOutputFrame: the input image to be processed
* @param localLuminance: an image which represents the local luminance of the inputFrame parameter, in general, it is its low pass spatial filtering
* @return the processed image, the output is reachable later by using function getOutput()
*/
const std::valarray<float> &runFilter_LocalAdapdation(const std::valarray<float> &inputOutputFrame, const std::valarray<float> &localLuminance);// run local adaptation filter and save result in _filterOutput
/**
* local luminance adaptation call and run (contrast enhancement property of the photoreceptors)
* @param inputFrame: the input image to be processed
* @param localLuminance: an image which represents the local luminance of the inputFrame parameter, in general, it is its low pass spatial filtering
* @param outputFrame: the output buffer in which the result is writed
*/
void runFilter_LocalAdapdation(const std::valarray<float> &inputFrame, const std::valarray<float> &localLuminance, std::valarray<float> &outputFrame); // run local adaptation filter on a specific output adress
/**
* local luminance adaptation call and run (contrast enhancement property of the photoreceptors)
* @param inputFrame: the input image to be processed
* @return the processed image, the output is reachable later by using function getOutput()
*/
const std::valarray<float> &runFilter_LocalAdapdation_autonomous(const std::valarray<float> &inputFrame);// run local adaptation filter and save result in _filterOutput
/**
* local luminance adaptation call and run (contrast enhancement property of the photoreceptors)
* @param inputFrame: the input image to be processed
* @param outputFrame: the output buffer in which the result is writen
*/
void runFilter_LocalAdapdation_autonomous(const std::valarray<float> &inputFrame, std::valarray<float> &outputFrame); // run local adaptation filter on a specific output adress
/**
* run low pass filtering with progressive parameters (models the retina log sampling of the photoreceptors and its low pass filtering effect consequence: more powerfull low pass filtering effect on the corners)
* @param inputFrame: the input image to be processed
* @param filterIndex: the index which specifies the parameter set that should be used for the filtering
* @return the processed image, the output is reachable later by using function getOutput() if outputFrame is NULL
*/
inline void runProgressiveFilter(std::valarray<float> &inputFrame, const unsigned int filterIndex=0){_spatiotemporalLPfilter_Irregular(&inputFrame[0], filterIndex);};
/**
* run low pass filtering with progressive parameters (models the retina log sampling of the photoreceptors and its low pass filtering effect consequence: more powerfull low pass filtering effect on the corners)
* @param inputFrame: the input image to be processed
* @param outputFrame: the output buffer in which the result is writen
* @param filterIndex: the index which specifies the parameter set that should be used for the filtering
*/
inline void runProgressiveFilter(const std::valarray<float> &inputFrame,
std::valarray<float> &outputFrame,
const unsigned int filterIndex=0)
{_spatiotemporalLPfilter_Irregular(get_data(inputFrame), &outputFrame[0], filterIndex);};
/**
* first order spatio-temporal low pass filter setup function
* @param beta: gain of the filter (generally set to zero)
* @param tau: time constant of the filter (unit is frame for video processing)
* @param k: spatial constant of the filter (unit is pixels)
* @param filterIndex: the index which specifies the parameter set that should be used for the filtering
*/
void setLPfilterParameters(const float beta, const float tau, const float k, const unsigned int filterIndex=0); // change the parameters of the filter
/**
* first order spatio-temporal low pass filter setup function
* @param beta: gain of the filter (generally set to zero)
* @param tau: time constant of the filter (unit is frame for video processing)
* @param alpha0: spatial constant of the filter (unit is pixels) on the border of the image
* @param filterIndex: the index which specifies the parameter set that should be used for the filtering
*/
void setProgressiveFilterConstants_CentredAccuracy(const float beta, const float tau, const float alpha0, const unsigned int filterIndex=0);
/**
* first order spatio-temporal low pass filter setup function
* @param beta: gain of the filter (generally set to zero)
* @param tau: time constant of the filter (unit is frame for video processing)
* @param alpha0: spatial constant of the filter (unit is pixels) on the border of the image
* @param accuracyMap an image (float format) which values range is between 0 and 1, where 0 means, apply no filtering and 1 means apply the filtering as specified in the parameters set, intermediate values allow to smooth variations of the filtering strenght
* @param filterIndex: the index which specifies the parameter set that should be used for the filtering
*/
void setProgressiveFilterConstants_CustomAccuracy(const float beta, const float tau, const float alpha0, const std::valarray<float> &accuracyMap, const unsigned int filterIndex=0);
/**
* local luminance adaptation setup, this function should be applied for normal local adaptation (not for tone mapping operation)
* @param v0: compression effect for the local luminance adaptation processing, set a value between 0.6 and 0.9 for best results, a high value yields to a high compression effect
* @param maxInputValue: the maximum amplitude value measured after local adaptation processing (c.f. function runFilter_LocalAdapdation & runFilter_LocalAdapdation_autonomous)
* @param meanLuminance: the a priori meann luminance of the input data (should be 128 for 8bits images but can vary greatly in case of High Dynamic Range Images (HDRI)
*/
void setV0CompressionParameter(const float v0, const float maxInputValue, const float){ _v0=v0*maxInputValue; _localLuminanceFactor=v0; _localLuminanceAddon=maxInputValue*(1.0f-v0); _maxInputValue=maxInputValue;};
/**
* update local luminance adaptation setup, initial maxInputValue is kept. This function should be applied for normal local adaptation (not for tone mapping operation)
* @param v0: compression effect for the local luminance adaptation processing, set a value between 0.6 and 0.9 for best results, a high value yields to a high compression effect
* @param meanLuminance: the a priori meann luminance of the input data (should be 128 for 8bits images but can vary greatly in case of High Dynamic Range Images (HDRI)
*/
void setV0CompressionParameter(const float v0, const float meanLuminance){ this->setV0CompressionParameter(v0, _maxInputValue, meanLuminance);};
/**
* local luminance adaptation setup, this function should be applied for normal local adaptation (not for tone mapping operation)
* @param v0: compression effect for the local luminance adaptation processing, set a value between 0.6 and 0.9 for best results, a high value yields to a high compression effect
*/
void setV0CompressionParameter(const float v0){ _v0=v0*_maxInputValue; _localLuminanceFactor=v0; _localLuminanceAddon=_maxInputValue*(1.0f-v0);};
/**
* local luminance adaptation setup, this function should be applied for local adaptation applied to tone mapping operation
* @param v0: compression effect for the local luminance adaptation processing, set a value between 0.6 and 0.9 for best results, a high value yields to a high compression effect
* @param maxInputValue: the maximum amplitude value measured after local adaptation processing (c.f. function runFilter_LocalAdapdation & runFilter_LocalAdapdation_autonomous)
* @param meanLuminance: the a priori meann luminance of the input data (should be 128 for 8bits images but can vary greatly in case of High Dynamic Range Images (HDRI)
*/
void setV0CompressionParameterToneMapping(const float v0, const float maxInputValue, const float meanLuminance=128.0f){ _v0=v0*maxInputValue; _localLuminanceFactor=1.0f; _localLuminanceAddon=meanLuminance*_v0; _maxInputValue=maxInputValue;};
/**
* update compression parameters while keeping v0 parameter value
* @param meanLuminance the input frame mean luminance
*/
inline void updateCompressionParameter(const float meanLuminance){_localLuminanceFactor=1; _localLuminanceAddon=meanLuminance*_v0;};
/**
* @return the v0 compression parameter used to compute the local adaptation
*/
const float getV0CompressionParameter(){ return _v0/_maxInputValue;};
/**
* @return the output result of the object
*/
inline const std::valarray<float> &getOutput() const {return _filterOutput;};
/**
* @return number of rows of the filter
*/
inline const unsigned int getNBrows(){return _filterOutput.getNBrows();};
/**
* @return number of columns of the filter
*/
inline const unsigned int getNBcolumns(){return _filterOutput.getNBcolumns();};
/**
* @return number of pixels of the filter
*/
inline const unsigned int getNBpixels(){return _filterOutput.getNBpixels();};
/**
* force filter output to be normalized between 0 and maxValue
* @param maxValue: the maximum output value that is required
*/
inline void normalizeGrayOutput_0_maxOutputValue(const float maxValue){_filterOutput.normalizeGrayOutput_0_maxOutputValue(maxValue);};
/**
* force filter output to be normalized around 0 and rescaled with a sigmoide effect (extrem values saturation)
* @param maxValue: the maximum output value that is required
*/
inline void normalizeGrayOutputCentredSigmoide(){_filterOutput.normalizeGrayOutputCentredSigmoide();};
/**
* force filter output to be normalized : data centering and std normalisation
* @param maxValue: the maximum output value that is required
*/
inline void centerReductImageLuminance(){_filterOutput.centerReductImageLuminance();};
/**
* @return the maximum input buffer value
*/
inline const float getMaxInputValue(){return this->_maxInputValue;};
/**
* @return the maximum input buffer value
*/
inline void setMaxInputValue(const float newMaxInputValue){this->_maxInputValue=newMaxInputValue;};
/**
* constructor of the base bio-inspired toolbox, parameters are only linked to imae input size and number of filtering capabilities of the object
* @param NBrows: number of rows of the input image
* @param NBcolumns: number of columns of the input image
* @param parametersListSize: specifies the number of parameters set (each parameters set represents a specific low pass spatio-temporal filter)
* @param useProgressiveFilter: specifies if the filter has irreguar (progressive) filtering capabilities (this can be activated later using setProgressiveFilterConstants_xxx methods)
*/
BasicRetinaFilter(const unsigned int NBrows, const unsigned int NBcolumns, const unsigned int parametersListSize=1, const bool useProgressiveFilter=false);
/**
* standrad destructore
*/
~BasicRetinaFilter();
/**
* function which clears the output buffer of the object
*/
inline void clearOutputBuffer(){_filterOutput=0;};
/**
* function which clears the secondary buffer of the object
*/
inline void clearSecondaryBuffer(){_localBuffer=0;};
/**
* function which clears the output and the secondary buffer of the object
*/
inline void clearAllBuffers(){clearOutputBuffer();clearSecondaryBuffer();};
/**
* resize basic retina filter object (resize all allocated buffers
* @param NBrows: the new height size
* @param NBcolumns: the new width size
*/
void resize(const unsigned int NBrows, const unsigned int NBcolumns);
/**
* forbiden method inherited from parent std::valarray
* prefer not to use this method since the filter matrix become vectors
*/
void resize(const unsigned int){std::cerr<<"error, not accessible method"<<std::endl;};
/**
* low pass filter call and run (models the homogeneous cells network at the retina level, for example horizontal cells or photoreceptors)
* @param inputFrame: the input image to be processed
* @param filterIndex: the offset which specifies the parameter set that should be used for the filtering
* @return the processed image, the output is reachable later by using function getOutput()
*/
const std::valarray<float> &runFilter_LPfilter(const std::valarray<float> &inputFrame, const unsigned int filterIndex=0); // run the LP filter for a new frame input and save result in _filterOutput
/**
* low pass filter call and run (models the homogeneous cells network at the retina level, for example horizontal cells or photoreceptors)
* @param inputFrame: the input image to be processed
* @param outputFrame: the output buffer in which the result is writed
* @param filterIndex: the offset which specifies the parameter set that should be used for the filtering
*/
void runFilter_LPfilter(const std::valarray<float> &inputFrame, std::valarray<float> &outputFrame, const unsigned int filterIndex=0); // run LP filter on a specific output adress
/**
* low pass filter call and run (models the homogeneous cells network at the retina level, for example horizontal cells or photoreceptors)
* @param inputOutputFrame: the input image to be processed on which the result is rewrited
* @param filterIndex: the offset which specifies the parameter set that should be used for the filtering
*/
void runFilter_LPfilter_Autonomous(std::valarray<float> &inputOutputFrame, const unsigned int filterIndex=0);// run LP filter on the input data and rewrite it
/**
* local luminance adaptation call and run (contrast enhancement property of the photoreceptors)
* @param inputOutputFrame: the input image to be processed
* @param localLuminance: an image which represents the local luminance of the inputFrame parameter, in general, it is its low pass spatial filtering
* @return the processed image, the output is reachable later by using function getOutput()
*/
const std::valarray<float> &runFilter_LocalAdapdation(const std::valarray<float> &inputOutputFrame, const std::valarray<float> &localLuminance);// run local adaptation filter and save result in _filterOutput
/**
* local luminance adaptation call and run (contrast enhancement property of the photoreceptors)
* @param inputFrame: the input image to be processed
* @param localLuminance: an image which represents the local luminance of the inputFrame parameter, in general, it is its low pass spatial filtering
* @param outputFrame: the output buffer in which the result is writed
*/
void runFilter_LocalAdapdation(const std::valarray<float> &inputFrame, const std::valarray<float> &localLuminance, std::valarray<float> &outputFrame); // run local adaptation filter on a specific output adress
/**
* local luminance adaptation call and run (contrast enhancement property of the photoreceptors)
* @param inputFrame: the input image to be processed
* @return the processed image, the output is reachable later by using function getOutput()
*/
const std::valarray<float> &runFilter_LocalAdapdation_autonomous(const std::valarray<float> &inputFrame);// run local adaptation filter and save result in _filterOutput
/**
* local luminance adaptation call and run (contrast enhancement property of the photoreceptors)
* @param inputFrame: the input image to be processed
* @param outputFrame: the output buffer in which the result is writen
*/
void runFilter_LocalAdapdation_autonomous(const std::valarray<float> &inputFrame, std::valarray<float> &outputFrame); // run local adaptation filter on a specific output adress
/**
* run low pass filtering with progressive parameters (models the retina log sampling of the photoreceptors and its low pass filtering effect consequence: more powerfull low pass filtering effect on the corners)
* @param inputFrame: the input image to be processed
* @param filterIndex: the index which specifies the parameter set that should be used for the filtering
* @return the processed image, the output is reachable later by using function getOutput() if outputFrame is NULL
*/
inline void runProgressiveFilter(std::valarray<float> &inputFrame, const unsigned int filterIndex=0){_spatiotemporalLPfilter_Irregular(&inputFrame[0], filterIndex);};
/**
* run low pass filtering with progressive parameters (models the retina log sampling of the photoreceptors and its low pass filtering effect consequence: more powerfull low pass filtering effect on the corners)
* @param inputFrame: the input image to be processed
* @param outputFrame: the output buffer in which the result is writen
* @param filterIndex: the index which specifies the parameter set that should be used for the filtering
*/
inline void runProgressiveFilter(const std::valarray<float> &inputFrame,
std::valarray<float> &outputFrame,
const unsigned int filterIndex=0)
{_spatiotemporalLPfilter_Irregular(get_data(inputFrame), &outputFrame[0], filterIndex);};
/**
* first order spatio-temporal low pass filter setup function
* @param beta: gain of the filter (generally set to zero)
* @param tau: time constant of the filter (unit is frame for video processing)
* @param k: spatial constant of the filter (unit is pixels)
* @param filterIndex: the index which specifies the parameter set that should be used for the filtering
*/
void setLPfilterParameters(const float beta, const float tau, const float k, const unsigned int filterIndex=0); // change the parameters of the filter
/**
* first order spatio-temporal low pass filter setup function
* @param beta: gain of the filter (generally set to zero)
* @param tau: time constant of the filter (unit is frame for video processing)
* @param alpha0: spatial constant of the filter (unit is pixels) on the border of the image
* @param filterIndex: the index which specifies the parameter set that should be used for the filtering
*/
void setProgressiveFilterConstants_CentredAccuracy(const float beta, const float tau, const float alpha0, const unsigned int filterIndex=0);
/**
* first order spatio-temporal low pass filter setup function
* @param beta: gain of the filter (generally set to zero)
* @param tau: time constant of the filter (unit is frame for video processing)
* @param alpha0: spatial constant of the filter (unit is pixels) on the border of the image
* @param accuracyMap an image (float format) which values range is between 0 and 1, where 0 means, apply no filtering and 1 means apply the filtering as specified in the parameters set, intermediate values allow to smooth variations of the filtering strenght
* @param filterIndex: the index which specifies the parameter set that should be used for the filtering
*/
void setProgressiveFilterConstants_CustomAccuracy(const float beta, const float tau, const float alpha0, const std::valarray<float> &accuracyMap, const unsigned int filterIndex=0);
/**
* local luminance adaptation setup, this function should be applied for normal local adaptation (not for tone mapping operation)
* @param v0: compression effect for the local luminance adaptation processing, set a value between 0.6 and 0.9 for best results, a high value yields to a high compression effect
* @param maxInputValue: the maximum amplitude value measured after local adaptation processing (c.f. function runFilter_LocalAdapdation & runFilter_LocalAdapdation_autonomous)
* @param meanLuminance: the a priori meann luminance of the input data (should be 128 for 8bits images but can vary greatly in case of High Dynamic Range Images (HDRI)
*/
void setV0CompressionParameter(const float v0, const float maxInputValue, const float){ _v0=v0*maxInputValue; _localLuminanceFactor=v0; _localLuminanceAddon=maxInputValue*(1.0f-v0); _maxInputValue=maxInputValue;};
/**
* update local luminance adaptation setup, initial maxInputValue is kept. This function should be applied for normal local adaptation (not for tone mapping operation)
* @param v0: compression effect for the local luminance adaptation processing, set a value between 0.6 and 0.9 for best results, a high value yields to a high compression effect
* @param meanLuminance: the a priori meann luminance of the input data (should be 128 for 8bits images but can vary greatly in case of High Dynamic Range Images (HDRI)
*/
void setV0CompressionParameter(const float v0, const float meanLuminance){ this->setV0CompressionParameter(v0, _maxInputValue, meanLuminance);};
/**
* local luminance adaptation setup, this function should be applied for normal local adaptation (not for tone mapping operation)
* @param v0: compression effect for the local luminance adaptation processing, set a value between 0.6 and 0.9 for best results, a high value yields to a high compression effect
*/
void setV0CompressionParameter(const float v0){ _v0=v0*_maxInputValue; _localLuminanceFactor=v0; _localLuminanceAddon=_maxInputValue*(1.0f-v0);};
/**
* local luminance adaptation setup, this function should be applied for local adaptation applied to tone mapping operation
* @param v0: compression effect for the local luminance adaptation processing, set a value between 0.6 and 0.9 for best results, a high value yields to a high compression effect
* @param maxInputValue: the maximum amplitude value measured after local adaptation processing (c.f. function runFilter_LocalAdapdation & runFilter_LocalAdapdation_autonomous)
* @param meanLuminance: the a priori meann luminance of the input data (should be 128 for 8bits images but can vary greatly in case of High Dynamic Range Images (HDRI)
*/
void setV0CompressionParameterToneMapping(const float v0, const float maxInputValue, const float meanLuminance=128.0f){ _v0=v0*maxInputValue; _localLuminanceFactor=1.0f; _localLuminanceAddon=meanLuminance*_v0; _maxInputValue=maxInputValue;};
/**
* update compression parameters while keeping v0 parameter value
* @param meanLuminance the input frame mean luminance
*/
inline void updateCompressionParameter(const float meanLuminance){_localLuminanceFactor=1; _localLuminanceAddon=meanLuminance*_v0;};
/**
* @return the v0 compression parameter used to compute the local adaptation
*/
float getV0CompressionParameter(){ return _v0/_maxInputValue;};
/**
* @return the output result of the object
*/
inline const std::valarray<float> &getOutput() const {return _filterOutput;};
/**
* @return number of rows of the filter
*/
inline unsigned int getNBrows(){return _filterOutput.getNBrows();};
/**
* @return number of columns of the filter
*/
inline unsigned int getNBcolumns(){return _filterOutput.getNBcolumns();};
/**
* @return number of pixels of the filter
*/
inline unsigned int getNBpixels(){return _filterOutput.getNBpixels();};
/**
* force filter output to be normalized between 0 and maxValue
* @param maxValue: the maximum output value that is required
*/
inline void normalizeGrayOutput_0_maxOutputValue(const float maxValue){_filterOutput.normalizeGrayOutput_0_maxOutputValue(maxValue);};
/**
* force filter output to be normalized around 0 and rescaled with a sigmoide effect (extrem values saturation)
* @param maxValue: the maximum output value that is required
*/
inline void normalizeGrayOutputCentredSigmoide(){_filterOutput.normalizeGrayOutputCentredSigmoide();};
/**
* force filter output to be normalized : data centering and std normalisation
* @param maxValue: the maximum output value that is required
*/
inline void centerReductImageLuminance(){_filterOutput.centerReductImageLuminance();};
/**
* @return the maximum input buffer value
*/
inline float getMaxInputValue(){return this->_maxInputValue;};
/**
* @return the maximum input buffer value
*/
inline void setMaxInputValue(const float newMaxInputValue){this->_maxInputValue=newMaxInputValue;};
protected:
/////////////////////////
// data buffers
TemplateBuffer<float> _filterOutput; // primary buffer (contains processing outputs)
std::valarray<float> _localBuffer; // local secondary buffer
/////////////////////////
// PARAMETERS
unsigned int _halfNBrows;
unsigned int _halfNBcolumns;
// parameters buffers
std::valarray <float>_filteringCoeficientsTable;
std::valarray <float>_progressiveSpatialConstant;// pointer to a local table containing local spatial constant (allocated with the object)
std::valarray <float>_progressiveGain;// pointer to a local table containing local spatial constant (allocated with the object)
// local adaptation filtering parameters
float _v0; //value used for local luminance adaptation function
float _maxInputValue;
float _meanInputValue;
float _localLuminanceFactor;
float _localLuminanceAddon;
// protected data related to standard low pass filters parameters
float _a;
float _tau;
float _gain;
/////////////////////////
// FILTERS METHODS
// Basic low pass spation temporal low pass filter used by each retina filters
void _spatiotemporalLPfilter(const float *inputFrame, float *LPfilterOutput, const unsigned int coefTableOffset=0);
const float _squaringSpatiotemporalLPfilter(const float *inputFrame, float *outputFrame, const unsigned int filterIndex=0);
// LP filter with an irregular spatial filtering
// -> rewrites the input buffer
void _spatiotemporalLPfilter_Irregular(float *inputOutputFrame, const unsigned int filterIndex=0);
// writes the output on another buffer
void _spatiotemporalLPfilter_Irregular(const float *inputFrame, float *outputFrame, const unsigned int filterIndex=0);
// LP filter that squares the input and computes the output ONLY on the areas where the integrationAreas map are TRUE
void _localSquaringSpatioTemporalLPfilter(const float *inputFrame, float *LPfilterOutput, const unsigned int *integrationAreas, const unsigned int filterIndex=0);
// local luminance adaptation of the input in regard of localLuminance buffer
void _localLuminanceAdaptation(const float *inputFrame, const float *localLuminance, float *outputFrame);
// local luminance adaptation of the input in regard of localLuminance buffer, the input is rewrited and becomes the output
void _localLuminanceAdaptation(float *inputOutputFrame, const float *localLuminance);
// local adaptation applied on a range of values which can be positive and negative
void _localLuminanceAdaptationPosNegValues(const float *inputFrame, const float *localLuminance, float *outputFrame);
//////////////////////////////////////////////////////////////
// 1D directional filters used for the 2D low pass filtering
// 1D filters with image input
void _horizontalCausalFilter_addInput(const float *inputFrame, float *outputFrame, unsigned int IDrowStart, unsigned int IDrowEnd);
// 1D filters with image input that is squared in the function
void _squaringHorizontalCausalFilter(const float *inputFrame, float *outputFrame, unsigned int IDrowStart, unsigned int IDrowEnd);
// vertical anticausal filter that returns the mean value of its result
const float _verticalAnticausalFilter_returnMeanValue(float *outputFrame, unsigned int IDcolumnStart, unsigned int IDcolumnEnd);
// most simple functions: only perform 1D filtering with output=input (no add on)
void _horizontalCausalFilter(float *outputFrame, unsigned int IDrowStart, unsigned int IDrowEnd);
void _horizontalAnticausalFilter(float *outputFrame, unsigned int IDrowStart, unsigned int IDrowEnd);
void _verticalCausalFilter(float *outputFrame, unsigned int IDcolumnStart, unsigned int IDcolumnEnd);
void _verticalAnticausalFilter(float *outputFrame, unsigned int IDcolumnStart, unsigned int IDcolumnEnd);
// perform 1D filtering with output with varrying spatial coefficient
void _horizontalCausalFilter_Irregular(float *outputFrame, unsigned int IDrowStart, unsigned int IDrowEnd);
void _horizontalCausalFilter_Irregular_addInput(const float *inputFrame, float *outputFrame, unsigned int IDrowStart, unsigned int IDrowEnd);
void _horizontalAnticausalFilter_Irregular(float *outputFrame, unsigned int IDrowStart, unsigned int IDrowEnd);
void _verticalCausalFilter_Irregular(float *outputFrame, unsigned int IDcolumnStart, unsigned int IDcolumnEnd);
void _verticalAnticausalFilter_Irregular_multGain(float *outputFrame, unsigned int IDcolumnStart, unsigned int IDcolumnEnd);
// 1D filters in which the output is multiplied by _gain
void _verticalAnticausalFilter_multGain(float *outputFrame, unsigned int IDcolumnStart, unsigned int IDcolumnEnd); // this functions affects _gain at the output
void _horizontalAnticausalFilter_multGain(float *outputFrame, unsigned int IDcolumnStart, unsigned int IDcolumnEnd); // this functions affects _gain at the output
// LP filter on specific parts of the picture instead of all the image
// same functions (some of them) but take a binary flag to allow integration, false flag means, 0 at the output...
void _local_squaringHorizontalCausalFilter(const float *inputFrame, float *outputFrame, unsigned int IDrowStart, unsigned int IDrowEnd, const unsigned int *integrationAreas);
void _local_horizontalAnticausalFilter(float *outputFrame, unsigned int IDrowStart, unsigned int IDrowEnd, const unsigned int *integrationAreas);
void _local_verticalCausalFilter(float *outputFrame, unsigned int IDcolumnStart, unsigned int IDcolumnEnd, const unsigned int *integrationAreas);
void _local_verticalAnticausalFilter_multGain(float *outputFrame, unsigned int IDcolumnStart, unsigned int IDcolumnEnd, const unsigned int *integrationAreas); // this functions affects _gain at the output
/////////////////////////
// data buffers
TemplateBuffer<float> _filterOutput; // primary buffer (contains processing outputs)
std::valarray<float> _localBuffer; // local secondary buffer
/////////////////////////
// PARAMETERS
unsigned int _halfNBrows;
unsigned int _halfNBcolumns;
// parameters buffers
std::valarray <float>_filteringCoeficientsTable;
std::valarray <float>_progressiveSpatialConstant;// pointer to a local table containing local spatial constant (allocated with the object)
std::valarray <float>_progressiveGain;// pointer to a local table containing local spatial constant (allocated with the object)
// local adaptation filtering parameters
float _v0; //value used for local luminance adaptation function
float _maxInputValue;
float _meanInputValue;
float _localLuminanceFactor;
float _localLuminanceAddon;
// protected data related to standard low pass filters parameters
float _a;
float _tau;
float _gain;
/////////////////////////
// FILTERS METHODS
// Basic low pass spation temporal low pass filter used by each retina filters
void _spatiotemporalLPfilter(const float *inputFrame, float *LPfilterOutput, const unsigned int coefTableOffset=0);
float _squaringSpatiotemporalLPfilter(const float *inputFrame, float *outputFrame, const unsigned int filterIndex=0);
// LP filter with an irregular spatial filtering
// -> rewrites the input buffer
void _spatiotemporalLPfilter_Irregular(float *inputOutputFrame, const unsigned int filterIndex=0);
// writes the output on another buffer
void _spatiotemporalLPfilter_Irregular(const float *inputFrame, float *outputFrame, const unsigned int filterIndex=0);
// LP filter that squares the input and computes the output ONLY on the areas where the integrationAreas map are TRUE
void _localSquaringSpatioTemporalLPfilter(const float *inputFrame, float *LPfilterOutput, const unsigned int *integrationAreas, const unsigned int filterIndex=0);
// local luminance adaptation of the input in regard of localLuminance buffer
void _localLuminanceAdaptation(const float *inputFrame, const float *localLuminance, float *outputFrame);
// local luminance adaptation of the input in regard of localLuminance buffer, the input is rewrited and becomes the output
void _localLuminanceAdaptation(float *inputOutputFrame, const float *localLuminance);
// local adaptation applied on a range of values which can be positive and negative
void _localLuminanceAdaptationPosNegValues(const float *inputFrame, const float *localLuminance, float *outputFrame);
//////////////////////////////////////////////////////////////
// 1D directional filters used for the 2D low pass filtering
// 1D filters with image input
void _horizontalCausalFilter_addInput(const float *inputFrame, float *outputFrame, unsigned int IDrowStart, unsigned int IDrowEnd);
// 1D filters with image input that is squared in the function
void _squaringHorizontalCausalFilter(const float *inputFrame, float *outputFrame, unsigned int IDrowStart, unsigned int IDrowEnd);
// vertical anticausal filter that returns the mean value of its result
float _verticalAnticausalFilter_returnMeanValue(float *outputFrame, unsigned int IDcolumnStart, unsigned int IDcolumnEnd);
// most simple functions: only perform 1D filtering with output=input (no add on)
void _horizontalCausalFilter(float *outputFrame, unsigned int IDrowStart, unsigned int IDrowEnd);
void _horizontalAnticausalFilter(float *outputFrame, unsigned int IDrowStart, unsigned int IDrowEnd);
void _verticalCausalFilter(float *outputFrame, unsigned int IDcolumnStart, unsigned int IDcolumnEnd);
void _verticalAnticausalFilter(float *outputFrame, unsigned int IDcolumnStart, unsigned int IDcolumnEnd);
// perform 1D filtering with output with varrying spatial coefficient
void _horizontalCausalFilter_Irregular(float *outputFrame, unsigned int IDrowStart, unsigned int IDrowEnd);
void _horizontalCausalFilter_Irregular_addInput(const float *inputFrame, float *outputFrame, unsigned int IDrowStart, unsigned int IDrowEnd);
void _horizontalAnticausalFilter_Irregular(float *outputFrame, unsigned int IDrowStart, unsigned int IDrowEnd);
void _verticalCausalFilter_Irregular(float *outputFrame, unsigned int IDcolumnStart, unsigned int IDcolumnEnd);
void _verticalAnticausalFilter_Irregular_multGain(float *outputFrame, unsigned int IDcolumnStart, unsigned int IDcolumnEnd);
// 1D filters in which the output is multiplied by _gain
void _verticalAnticausalFilter_multGain(float *outputFrame, unsigned int IDcolumnStart, unsigned int IDcolumnEnd); // this functions affects _gain at the output
void _horizontalAnticausalFilter_multGain(float *outputFrame, unsigned int IDcolumnStart, unsigned int IDcolumnEnd); // this functions affects _gain at the output
// LP filter on specific parts of the picture instead of all the image
// same functions (some of them) but take a binary flag to allow integration, false flag means, 0 at the output...
void _local_squaringHorizontalCausalFilter(const float *inputFrame, float *outputFrame, unsigned int IDrowStart, unsigned int IDrowEnd, const unsigned int *integrationAreas);
void _local_horizontalAnticausalFilter(float *outputFrame, unsigned int IDrowStart, unsigned int IDrowEnd, const unsigned int *integrationAreas);
void _local_verticalCausalFilter(float *outputFrame, unsigned int IDcolumnStart, unsigned int IDcolumnEnd, const unsigned int *integrationAreas);
void _local_verticalAnticausalFilter_multGain(float *outputFrame, unsigned int IDcolumnStart, unsigned int IDcolumnEnd, const unsigned int *integrationAreas); // this functions affects _gain at the output
};

242
modules/contrib/src/hybridtracker.cpp
Unescape View File

@ -46,20 +46,20 @@ using namespace cv;
using namespace std;
CvHybridTrackerParams::CvHybridTrackerParams(float _ft_tracker_weight, float _ms_tracker_weight,
CvFeatureTrackerParams _ft_params,
CvMeanShiftTrackerParams _ms_params,
CvMotionModel)
CvFeatureTrackerParams _ft_params,
CvMeanShiftTrackerParams _ms_params,
CvMotionModel)
{
ft_tracker_weight = _ft_tracker_weight;
ms_tracker_weight = _ms_tracker_weight;
ft_params = _ft_params;
ms_params = _ms_params;
ft_tracker_weight = _ft_tracker_weight;
ms_tracker_weight = _ms_tracker_weight;
ft_params = _ft_params;
ms_params = _ms_params;
}
CvMeanShiftTrackerParams::CvMeanShiftTrackerParams(int _tracking_type, CvTermCriteria _term_crit)
{
tracking_type = _tracking_type;
term_crit = _term_crit;
tracking_type = _tracking_type;
term_crit = _term_crit;
}
CvHybridTracker::CvHybridTracker() {
@ -67,148 +67,148 @@ CvHybridTracker::CvHybridTracker() {
}
CvHybridTracker::CvHybridTracker(HybridTrackerParams _params) :
params(_params) {
params.ft_params.feature_type = CvFeatureTrackerParams::SIFT;
mstracker = new CvMeanShiftTracker(params.ms_params);
fttracker = new CvFeatureTracker(params.ft_params);
params(_params) {
params.ft_params.feature_type = CvFeatureTrackerParams::SIFT;
mstracker = new CvMeanShiftTracker(params.ms_params);
fttracker = new CvFeatureTracker(params.ft_params);
}
CvHybridTracker::~CvHybridTracker() {
if (mstracker != NULL)
delete mstracker;
if (fttracker != NULL)
delete fttracker;
if (mstracker != NULL)
delete mstracker;
if (fttracker != NULL)
delete fttracker;
}
inline float CvHybridTracker::getL2Norm(Point2f p1, Point2f p2) {
float distance = (p1.x - p2.x) * (p1.x - p2.x) + (p1.y - p2.y) * (p1.y
- p2.y);
return sqrt(distance);
float distance = (p1.x - p2.x) * (p1.x - p2.x) + (p1.y - p2.y) * (p1.y
- p2.y);
return sqrt(distance);
}
Mat CvHybridTracker::getDistanceProjection(Mat image, Point2f center) {
Mat hist(image.size(), CV_64F);
Mat hist(image.size(), CV_64F);
double lu = getL2Norm(Point(0, 0), center);
double ru = getL2Norm(Point(0, image.size().width), center);
double rd = getL2Norm(Point(image.size().height, image.size().width),
center);
double ld = getL2Norm(Point(image.size().height, 0), center);
double lu = getL2Norm(Point(0, 0), center);
double ru = getL2Norm(Point(0, image.size().width), center);
double rd = getL2Norm(Point(image.size().height, image.size().width),
center);
double ld = getL2Norm(Point(image.size().height, 0), center);
double max = (lu < ru) ? lu : ru;
max = (max < rd) ? max : rd;
max = (max < ld) ? max : ld;
double max = (lu < ru) ? lu : ru;
max = (max < rd) ? max : rd;
max = (max < ld) ? max : ld;
for (int i = 0; i < hist.rows; i++)
for (int j = 0; j < hist.cols; j++)
hist.at<double> (i, j) = 1.0 - (getL2Norm(Point(i, j), center)
/ max);
for (int i = 0; i < hist.rows; i++)
for (int j = 0; j < hist.cols; j++)
hist.at<double> (i, j) = 1.0 - (getL2Norm(Point(i, j), center)
/ max);
return hist;
return hist;
}
Mat CvHybridTracker::getGaussianProjection(Mat image, int ksize, double sigma,
Point2f center) {
Mat kernel = getGaussianKernel(ksize, sigma, CV_64F);
double max = kernel.at<double> (ksize / 2);
Mat hist(image.size(), CV_64F);
for (int i = 0; i < hist.rows; i++)
for (int j = 0; j < hist.cols; j++) {
int pos = cvRound(getL2Norm(Point(i, j), center));
if (pos < ksize / 2.0)
hist.at<double> (i, j) = 1.0 - (kernel.at<double> (pos) / max);
}
return hist;
Point2f center) {
Mat kernel = getGaussianKernel(ksize, sigma, CV_64F);
double max = kernel.at<double> (ksize / 2);
Mat hist(image.size(), CV_64F);
for (int i = 0; i < hist.rows; i++)
for (int j = 0; j < hist.cols; j++) {
int pos = cvRound(getL2Norm(Point(i, j), center));
if (pos < ksize / 2.0)
hist.at<double> (i, j) = 1.0 - (kernel.at<double> (pos) / max);
}
return hist;
}
void CvHybridTracker::newTracker(Mat image, Rect selection) {
prev_proj = Mat::zeros(image.size(), CV_64FC1);
prev_center = Point2f(selection.x + selection.width / 2.0f, selection.y
+ selection.height / 2.0f);
prev_window = selection;
prev_proj = Mat::zeros(image.size(), CV_64FC1);
prev_center = Point2f(selection.x + selection.width / 2.0f, selection.y
+ selection.height / 2.0f);
prev_window = selection;
mstracker->newTrackingWindow(image, selection);
fttracker->newTrackingWindow(image, selection);
mstracker->newTrackingWindow(image, selection);
fttracker->newTrackingWindow(image, selection);
samples = cvCreateMat(2, 1, CV_32FC1);
labels = cvCreateMat(2, 1, CV_32SC1);
samples = cvCreateMat(2, 1, CV_32FC1);
labels = cvCreateMat(2, 1, CV_32SC1);
ittr = 0;
ittr = 0;
}
void CvHybridTracker::updateTracker(Mat image) {
ittr++;
ittr++;
//copy over clean images: TODO
mstracker->updateTrackingWindow(image);
fttracker->updateTrackingWindowWithFlow(image);
//copy over clean images: TODO
mstracker->updateTrackingWindow(image);
fttracker->updateTrackingWindowWithFlow(image);
if (params.motion_model == CvMotionModel::EM)
updateTrackerWithEM(image);
else
updateTrackerWithLowPassFilter(image);
if (params.motion_model == CvMotionModel::EM)
updateTrackerWithEM(image);
else
updateTrackerWithLowPassFilter(image);
// Regression to find new weights
Point2f ms_center = mstracker->getTrackingEllipse().center;
Point2f ft_center = fttracker->getTrackingCenter();
// Regression to find new weights
Point2f ms_center = mstracker->getTrackingEllipse().center;
Point2f ft_center = fttracker->getTrackingCenter();
#ifdef DEBUG_HYTRACKER
circle(image, ms_center, 3, Scalar(0, 0, 255), -1, 8);
circle(image, ft_center, 3, Scalar(255, 0, 0), -1, 8);
putText(image, "ms", Point(ms_center.x+2, ms_center.y), FONT_HERSHEY_PLAIN, 0.75, Scalar(255, 255, 255));
putText(image, "ft", Point(ft_center.x+2, ft_center.y), FONT_HERSHEY_PLAIN, 0.75, Scalar(255, 255, 255));
circle(image, ms_center, 3, Scalar(0, 0, 255), -1, 8);
circle(image, ft_center, 3, Scalar(255, 0, 0), -1, 8);
putText(image, "ms", Point(ms_center.x+2, ms_center.y), FONT_HERSHEY_PLAIN, 0.75, Scalar(255, 255, 255));
putText(image, "ft", Point(ft_center.x+2, ft_center.y), FONT_HERSHEY_PLAIN, 0.75, Scalar(255, 255, 255));
#endif
double ms_len = getL2Norm(ms_center, curr_center);
double ft_len = getL2Norm(ft_center, curr_center);
double total_len = ms_len + ft_len;
double ms_len = getL2Norm(ms_center, curr_center);
double ft_len = getL2Norm(ft_center, curr_center);
double total_len = ms_len + ft_len;
params.ms_tracker_weight *= (ittr - 1);
params.ms_tracker_weight += (float)((ms_len / total_len));
params.ms_tracker_weight /= ittr;
params.ft_tracker_weight *= (ittr - 1);
params.ft_tracker_weight += (float)((ft_len / total_len));
params.ft_tracker_weight /= ittr;
params.ms_tracker_weight *= (ittr - 1);
params.ms_tracker_weight += (float)((ms_len / total_len));
params.ms_tracker_weight /= ittr;
params.ft_tracker_weight *= (ittr - 1);
params.ft_tracker_weight += (float)((ft_len / total_len));
params.ft_tracker_weight /= ittr;
circle(image, prev_center, 3, Scalar(0, 0, 0), -1, 8);
circle(image, curr_center, 3, Scalar(255, 255, 255), -1, 8);
circle(image, prev_center, 3, Scalar(0, 0, 0), -1, 8);
circle(image, curr_center, 3, Scalar(255, 255, 255), -1, 8);
prev_center = curr_center;
prev_window.x = (int)(curr_center.x-prev_window.width/2.0);
prev_window.y = (int)(curr_center.y-prev_window.height/2.0);
prev_center = curr_center;
prev_window.x = (int)(curr_center.x-prev_window.width/2.0);
prev_window.y = (int)(curr_center.y-prev_window.height/2.0);
mstracker->setTrackingWindow(prev_window);
fttracker->setTrackingWindow(prev_window);
mstracker->setTrackingWindow(prev_window);
fttracker->setTrackingWindow(prev_window);
}
void CvHybridTracker::updateTrackerWithEM(Mat image) {
Mat ms_backproj = mstracker->getHistogramProjection(CV_64F);
Mat ms_distproj = getDistanceProjection(image, mstracker->getTrackingCenter());
Mat ms_proj = ms_backproj.mul(ms_distproj);
float dist_err = getL2Norm(mstracker->getTrackingCenter(), fttracker->getTrackingCenter());
Mat ft_gaussproj = getGaussianProjection(image, cvRound(dist_err), -1, fttracker->getTrackingCenter());
Mat ft_distproj = getDistanceProjection(image, fttracker->getTrackingCenter());
Mat ft_proj = ft_gaussproj.mul(ft_distproj);
Mat proj = params.ms_tracker_weight * ms_proj + params.ft_tracker_weight * ft_proj + prev_proj;
int sample_count = countNonZero(proj);
cvReleaseMat(&samples);
cvReleaseMat(&labels);
samples = cvCreateMat(sample_count, 2, CV_32FC1);
labels = cvCreateMat(sample_count, 1, CV_32SC1);
int count = 0;
for (int i = 0; i < proj.rows; i++)
for (int j = 0; j < proj.cols; j++)
if (proj.at<double> (i, j) > 0) {
samples->data.fl[count * 2] = (float)i;
samples->data.fl[count * 2 + 1] = (float)j;
count++;
}
Mat ms_backproj = mstracker->getHistogramProjection(CV_64F);
Mat ms_distproj = getDistanceProjection(image, mstracker->getTrackingCenter());
Mat ms_proj = ms_backproj.mul(ms_distproj);
float dist_err = getL2Norm(mstracker->getTrackingCenter(), fttracker->getTrackingCenter());
Mat ft_gaussproj = getGaussianProjection(image, cvRound(dist_err), -1, fttracker->getTrackingCenter());
Mat ft_distproj = getDistanceProjection(image, fttracker->getTrackingCenter());
Mat ft_proj = ft_gaussproj.mul(ft_distproj);
Mat proj = params.ms_tracker_weight * ms_proj + params.ft_tracker_weight * ft_proj + prev_proj;
int sample_count = countNonZero(proj);
cvReleaseMat(&samples);
cvReleaseMat(&labels);
samples = cvCreateMat(sample_count, 2, CV_32FC1);
labels = cvCreateMat(sample_count, 1, CV_32SC1);
int count = 0;
for (int i = 0; i < proj.rows; i++)
for (int j = 0; j < proj.cols; j++)
if (proj.at<double> (i, j) > 0) {
samples->data.fl[count * 2] = (float)i;
samples->data.fl[count * 2 + 1] = (float)j;
count++;
}
cv::Mat lbls;
@ -218,20 +218,20 @@ void CvHybridTracker::updateTrackerWithEM(Mat image) {
lbls.copyTo(cvarrToMat(labels));
Mat em_means = em_model.get<Mat>("means");
curr_center.x = (float)em_means.at<float>(0, 0);
curr_center.y = (float)em_means.at<float>(0, 1);
curr_center.x = (float)em_means.at<float>(0, 0);
curr_center.y = (float)em_means.at<float>(0, 1);
}
void CvHybridTracker::updateTrackerWithLowPassFilter(Mat image) {
RotatedRect ms_track = mstracker->getTrackingEllipse();
Point2f ft_center = fttracker->getTrackingCenter();
void CvHybridTracker::updateTrackerWithLowPassFilter(Mat) {
RotatedRect ms_track = mstracker->getTrackingEllipse();
Point2f ft_center = fttracker->getTrackingCenter();
float a = params.low_pass_gain;
curr_center.x = (1 - a) * prev_center.x + a * (params.ms_tracker_weight * ms_track.center.x + params.ft_tracker_weight * ft_center.x);
curr_center.y = (1 - a) * prev_center.y + a * (params.ms_tracker_weight * ms_track.center.y + params.ft_tracker_weight * ft_center.y);
float a = params.low_pass_gain;
curr_center.x = (1 - a) * prev_center.x + a * (params.ms_tracker_weight * ms_track.center.x + params.ft_tracker_weight * ft_center.x);
curr_center.y = (1 - a) * prev_center.y + a * (params.ms_tracker_weight * ms_track.center.y + params.ft_tracker_weight * ft_center.y);
}
Rect CvHybridTracker::getTrackingWindow() {
return prev_window;
return prev_window;
}

244
modules/contrib/src/imagelogpolprojection.hpp
Unescape View File

@ -108,131 +108,131 @@ class ImageLogPolProjection:public BasicRetinaFilter
{
public:
enum PROJECTIONTYPE{RETINALOGPROJECTION, CORTEXLOGPOLARPROJECTION};
/**
* constructor, just specifies the image input size and the projection type, no projection initialisation is done
* -> use initLogRetinaSampling() or initLogPolarCortexSampling() for that
* @param nbRows: number of rows of the input image
* @param nbColumns: number of columns of the input image
* @param projection: the type of projection, RETINALOGPROJECTION or CORTEXLOGPOLARPROJECTION
* @param colorMode: specifies if the projection is applied on a grayscale image (false) or color images (3 layers) (true)
*/
ImageLogPolProjection(const unsigned int nbRows, const unsigned int nbColumns, const PROJECTIONTYPE projection, const bool colorMode=false);
/**
* standard destructor
*/
virtual ~ImageLogPolProjection();
/**
* function that clears all buffers of the object
*/
void clearAllBuffers();
/**
* resize retina color filter object (resize all allocated buffers)
* @param NBrows: the new height size
* @param NBcolumns: the new width size
*/
void resize(const unsigned int NBrows, const unsigned int NBcolumns);
/**
* init function depending on the projection type
* @param reductionFactor: the size reduction factor of the ouptup image in regard of the size of the input image, must be superior to 1
* @param samplingStrenght: specifies the strenght of the log compression effect (magnifying coefficient)
* @return true if the init was performed without any errors
*/
bool initProjection(const double reductionFactor, const double samplingStrenght);
/**
* main funtion of the class: run projection function
* @param inputFrame: the input frame to be processed
enum PROJECTIONTYPE{RETINALOGPROJECTION, CORTEXLOGPOLARPROJECTION};
/**
* constructor, just specifies the image input size and the projection type, no projection initialisation is done
* -> use initLogRetinaSampling() or initLogPolarCortexSampling() for that
* @param nbRows: number of rows of the input image
* @param nbColumns: number of columns of the input image
* @param projection: the type of projection, RETINALOGPROJECTION or CORTEXLOGPOLARPROJECTION
* @param colorMode: specifies if the projection is applied on a grayscale image (false) or color images (3 layers) (true)
*/
ImageLogPolProjection(const unsigned int nbRows, const unsigned int nbColumns, const PROJECTIONTYPE projection, const bool colorMode=false);
/**
* standard destructor
*/
virtual ~ImageLogPolProjection();
/**
* function that clears all buffers of the object
*/
void clearAllBuffers();
/**
* resize retina color filter object (resize all allocated buffers)
* @param NBrows: the new height size
* @param NBcolumns: the new width size
*/
void resize(const unsigned int NBrows, const unsigned int NBcolumns);
/**
* init function depending on the projection type
* @param reductionFactor: the size reduction factor of the ouptup image in regard of the size of the input image, must be superior to 1
* @param samplingStrenght: specifies the strenght of the log compression effect (magnifying coefficient)
* @return true if the init was performed without any errors
*/
bool initProjection(const double reductionFactor, const double samplingStrenght);
/**
* main funtion of the class: run projection function
* @param inputFrame: the input frame to be processed
* @param colorMode: the input buffer color mode: false=gray levels, true = 3 color channels mode
* @return the output frame
*/
std::valarray<float> &runProjection(const std::valarray<float> &inputFrame, const bool colorMode=false);
/**
* @return the numbers of rows (height) of the images OUTPUTS of the object
*/
inline const unsigned int getOutputNBrows(){return _outputNBrows;};
/**
* @return the numbers of columns (width) of the images OUTPUTS of the object
*/
inline const unsigned int getOutputNBcolumns(){return _outputNBcolumns;};
/**
* main funtion of the class: run projection function
* @param size: one of the input frame initial dimensions to be processed
* @return the output frame dimension
*/
inline static const unsigned int predictOutputSize(const unsigned int size, const double reductionFactor){return (unsigned int)((double)size/reductionFactor);};
/**
* @return the output of the filter which applies an irregular Low Pass spatial filter to the imag input (see function
*/
inline const std::valarray<float> &getIrregularLPfilteredInputFrame() const {return _irregularLPfilteredFrame;};
/**
* function which allows to retrieve the output frame which was updated after the "runProjection(...) function BasicRetinaFilter::runProgressiveFilter(...)
* @return the projection result
*/
inline const std::valarray<float> &getSampledFrame() const {return _sampledFrame;};
/**
* function which allows gives the tranformation table, its size is (getNBrows()*getNBcolumns()*2)
* @return the transformation matrix [outputPixIndex_i, inputPixIndex_i, outputPixIndex_i+1, inputPixIndex_i+1....]
*/
inline const std::valarray<unsigned int> &getSamplingMap() const {return _transformTable;};
inline const double getOriginalRadiusLength(const double projectedRadiusLength){return _azero/(_alim-projectedRadiusLength*2.0/_minDimension);};
// unsigned int getInputPixelIndex(const unsigned int ){ return _transformTable[index*2+1]};
* @return the output frame
*/
std::valarray<float> &runProjection(const std::valarray<float> &inputFrame, const bool colorMode=false);
/**
* @return the numbers of rows (height) of the images OUTPUTS of the object
*/
inline unsigned int getOutputNBrows(){return _outputNBrows;};
/**
* @return the numbers of columns (width) of the images OUTPUTS of the object
*/
inline unsigned int getOutputNBcolumns(){return _outputNBcolumns;};
/**
* main funtion of the class: run projection function
* @param size: one of the input frame initial dimensions to be processed
* @return the output frame dimension
*/
inline static unsigned int predictOutputSize(const unsigned int size, const double reductionFactor){return (unsigned int)((double)size/reductionFactor);};
/**
* @return the output of the filter which applies an irregular Low Pass spatial filter to the imag input (see function
*/
inline const std::valarray<float> &getIrregularLPfilteredInputFrame() const {return _irregularLPfilteredFrame;};
/**
* function which allows to retrieve the output frame which was updated after the "runProjection(...) function BasicRetinaFilter::runProgressiveFilter(...)
* @return the projection result
*/
inline const std::valarray<float> &getSampledFrame() const {return _sampledFrame;};
/**
* function which allows gives the tranformation table, its size is (getNBrows()*getNBcolumns()*2)
* @return the transformation matrix [outputPixIndex_i, inputPixIndex_i, outputPixIndex_i+1, inputPixIndex_i+1....]
*/
inline const std::valarray<unsigned int> &getSamplingMap() const {return _transformTable;};
inline double getOriginalRadiusLength(const double projectedRadiusLength){return _azero/(_alim-projectedRadiusLength*2.0/_minDimension);};
// unsigned int getInputPixelIndex(const unsigned int ){ return _transformTable[index*2+1]};
private:
PROJECTIONTYPE _selectedProjection;
// size of the image output
unsigned int _outputNBrows;
unsigned int _outputNBcolumns;
unsigned int _outputNBpixels;
unsigned int _outputDoubleNBpixels;
unsigned int _inputDoubleNBpixels;
// is the object able to manage color flag
bool _colorModeCapable;
// sampling strenght factor
double _samplingStrenght;
// sampling reduction factor
double _reductionFactor;
// log sampling parameters
double _azero;
double _alim;
double _minDimension;
// template buffers
std::valarray<float>_sampledFrame;
std::valarray<float>&_tempBuffer;
std::valarray<unsigned int>_transformTable;
std::valarray<float> &_irregularLPfilteredFrame; // just a reference for easier understanding
unsigned int _usefullpixelIndex;
// init transformation tables
bool _computeLogProjection();
bool _computeLogPolarProjection();
// specifies if init was done correctly
bool _initOK;
// private init projections functions called by "initProjection(...)" function
bool _initLogRetinaSampling(const double reductionFactor, const double samplingStrenght);
bool _initLogPolarCortexSampling(const double reductionFactor, const double samplingStrenght);
ImageLogPolProjection(const ImageLogPolProjection&);
ImageLogPolProjection& operator=(const ImageLogPolProjection&);
PROJECTIONTYPE _selectedProjection;
// size of the image output
unsigned int _outputNBrows;
unsigned int _outputNBcolumns;
unsigned int _outputNBpixels;
unsigned int _outputDoubleNBpixels;
unsigned int _inputDoubleNBpixels;
// is the object able to manage color flag
bool _colorModeCapable;
// sampling strenght factor
double _samplingStrenght;
// sampling reduction factor
double _reductionFactor;
// log sampling parameters
double _azero;
double _alim;
double _minDimension;
// template buffers
std::valarray<float>_sampledFrame;
std::valarray<float>&_tempBuffer;
std::valarray<unsigned int>_transformTable;
std::valarray<float> &_irregularLPfilteredFrame; // just a reference for easier understanding
unsigned int _usefullpixelIndex;
// init transformation tables
bool _computeLogProjection();
bool _computeLogPolarProjection();
// specifies if init was done correctly
bool _initOK;
// private init projections functions called by "initProjection(...)" function
bool _initLogRetinaSampling(const double reductionFactor, const double samplingStrenght);
bool _initLogPolarCortexSampling(const double reductionFactor, const double samplingStrenght);
ImageLogPolProjection(const ImageLogPolProjection&);
ImageLogPolProjection& operator=(const ImageLogPolProjection&);
};

272
modules/contrib/src/inputoutput.cpp
Unescape View File

@ -2,145 +2,147 @@
#include "opencv2/contrib/contrib.hpp"
#ifdef WIN32
#include <windows.h>
#include <tchar.h>
#include <windows.h>
#include <tchar.h>
#else
#include <dirent.h>
#include <dirent.h>
#endif
namespace cv
{
std::vector<std::string> Directory::GetListFiles( const std::string& path, const std::string & exten, bool addPath )
{
std::vector<std::string> list;
list.clear();
std::string path_f = path + "/" + exten;
#ifdef WIN32
WIN32_FIND_DATA FindFileData;
HANDLE hFind;
hFind = FindFirstFile((LPCSTR)path_f.c_str(), &FindFileData);
if (hFind == INVALID_HANDLE_VALUE)
{
return list;
}
else
{
do
{
if (FindFileData.dwFileAttributes == FILE_ATTRIBUTE_NORMAL ||
FindFileData.dwFileAttributes == FILE_ATTRIBUTE_ARCHIVE ||
FindFileData.dwFileAttributes == FILE_ATTRIBUTE_HIDDEN ||
FindFileData.dwFileAttributes == FILE_ATTRIBUTE_SYSTEM ||
FindFileData.dwFileAttributes == FILE_ATTRIBUTE_READONLY)
{
if (addPath)
list.push_back(path + "/" + FindFileData.cFileName);
else
list.push_back(FindFileData.cFileName);
}
}
while(FindNextFile(hFind, &FindFileData));
FindClose(hFind);
}
#else
DIR *dp;
struct dirent *dirp;
if((dp = opendir(path.c_str())) == NULL)
{
return list;
}
while ((dirp = readdir(dp)) != NULL)
{
if (dirp->d_type == DT_REG)
{
if (exten.compare("*") == 0)
list.push_back(static_cast<std::string>(dirp->d_name));
else
if (std::string(dirp->d_name).find(exten) != std::string::npos)
list.push_back(static_cast<std::string>(dirp->d_name));
}
}
closedir(dp);
#endif
return list;
}
std::vector<std::string> Directory::GetListFolders( const std::string& path, const std::string & exten, bool addPath )
{
std::vector<std::string> list;
std::string path_f = path + "/" + exten;
list.clear();
#ifdef WIN32
WIN32_FIND_DATA FindFileData;
HANDLE hFind;
hFind = FindFirstFile((LPCSTR)path_f.c_str(), &FindFileData);
if (hFind == INVALID_HANDLE_VALUE)
{
return list;
}
else
{
do
{
if (FindFileData.dwFileAttributes == FILE_ATTRIBUTE_DIRECTORY &&
strcmp(FindFileData.cFileName, ".") != 0 &&
strcmp(FindFileData.cFileName, "..") != 0)
{
if (addPath)
list.push_back(path + "/" + FindFileData.cFileName);
else
list.push_back(FindFileData.cFileName);
}
}
while(FindNextFile(hFind, &FindFileData));
FindClose(hFind);
}
#else
DIR *dp;
struct dirent *dirp;
if((dp = opendir(path_f.c_str())) == NULL)
{
return list;
}
while ((dirp = readdir(dp)) != NULL)
{
if (dirp->d_type == DT_DIR &&
strcmp(dirp->d_name, ".") != 0 &&
strcmp(dirp->d_name, "..") != 0 )
{
if (exten.compare("*") == 0)
list.push_back(static_cast<std::string>(dirp->d_name));
else
if (std::string(dirp->d_name).find(exten) != std::string::npos)
list.push_back(static_cast<std::string>(dirp->d_name));
}
}
closedir(dp);
#endif
return list;
}
std::vector<std::string> Directory::GetListFilesR ( const std::string& path, const std::string & exten, bool addPath )
{
std::vector<std::string> list = Directory::GetListFiles(path, exten, addPath);
std::vector<std::string> dirs = Directory::GetListFolders(path, exten, addPath);
std::vector<std::string>::const_iterator it;
for (it = dirs.begin(); it != dirs.end(); ++it)
{
std::vector<std::string> cl = Directory::GetListFiles(*it, exten, addPath);
list.insert(list.end(), cl.begin(), cl.end());
}
return list;
}
std::vector<std::string> Directory::GetListFiles( const std::string& path, const std::string & exten, bool addPath )
{
std::vector<std::string> list;
list.clear();
std::string path_f = path + "/" + exten;
#ifdef WIN32
WIN32_FIND_DATA FindFileData;
HANDLE hFind;
hFind = FindFirstFile((LPCSTR)path_f.c_str(), &FindFileData);
if (hFind == INVALID_HANDLE_VALUE)
{
return list;
}
else
{
do
{
if (FindFileData.dwFileAttributes == FILE_ATTRIBUTE_NORMAL ||
FindFileData.dwFileAttributes == FILE_ATTRIBUTE_ARCHIVE ||
FindFileData.dwFileAttributes == FILE_ATTRIBUTE_HIDDEN ||
FindFileData.dwFileAttributes == FILE_ATTRIBUTE_SYSTEM ||
FindFileData.dwFileAttributes == FILE_ATTRIBUTE_READONLY)
{
if (addPath)
list.push_back(path + "/" + FindFileData.cFileName);
else
list.push_back(FindFileData.cFileName);
}
}
while(FindNextFile(hFind, &FindFileData));
FindClose(hFind);
}
#else
(void)addPath;
DIR *dp;
struct dirent *dirp;
if((dp = opendir(path.c_str())) == NULL)
{
return list;
}
while ((dirp = readdir(dp)) != NULL)
{
if (dirp->d_type == DT_REG)
{
if (exten.compare("*") == 0)
list.push_back(static_cast<std::string>(dirp->d_name));
else
if (std::string(dirp->d_name).find(exten) != std::string::npos)
list.push_back(static_cast<std::string>(dirp->d_name));
}
}
closedir(dp);
#endif
return list;
}
std::vector<std::string> Directory::GetListFolders( const std::string& path, const std::string & exten, bool addPath )
{
std::vector<std::string> list;
std::string path_f = path + "/" + exten;
list.clear();
#ifdef WIN32
WIN32_FIND_DATA FindFileData;
HANDLE hFind;
hFind = FindFirstFile((LPCSTR)path_f.c_str(), &FindFileData);
if (hFind == INVALID_HANDLE_VALUE)
{
return list;
}
else
{
do
{
if (FindFileData.dwFileAttributes == FILE_ATTRIBUTE_DIRECTORY &&
strcmp(FindFileData.cFileName, ".") != 0 &&
strcmp(FindFileData.cFileName, "..") != 0)
{
if (addPath)
list.push_back(path + "/" + FindFileData.cFileName);
else
list.push_back(FindFileData.cFileName);
}
}
while(FindNextFile(hFind, &FindFileData));
FindClose(hFind);
}
#else
(void)addPath;
DIR *dp;
struct dirent *dirp;
if((dp = opendir(path_f.c_str())) == NULL)
{
return list;
}
while ((dirp = readdir(dp)) != NULL)
{
if (dirp->d_type == DT_DIR &&
strcmp(dirp->d_name, ".") != 0 &&
strcmp(dirp->d_name, "..") != 0 )
{
if (exten.compare("*") == 0)
list.push_back(static_cast<std::string>(dirp->d_name));
else
if (std::string(dirp->d_name).find(exten) != std::string::npos)
list.push_back(static_cast<std::string>(dirp->d_name));
}
}
closedir(dp);
#endif
return list;
}
std::vector<std::string> Directory::GetListFilesR ( const std::string& path, const std::string & exten, bool addPath )
{
std::vector<std::string> list = Directory::GetListFiles(path, exten, addPath);
std::vector<std::string> dirs = Directory::GetListFolders(path, exten, addPath);
std::vector<std::string>::const_iterator it;
for (it = dirs.begin(); it != dirs.end(); ++it)
{
std::vector<std::string> cl = Directory::GetListFiles(*it, exten, addPath);
list.insert(list.end(), cl.begin(), cl.end());
}
return list;
}
}

172
modules/contrib/src/magnoretinafilter.hpp
Unescape View File

@ -103,95 +103,95 @@ namespace cv
class MagnoRetinaFilter: public BasicRetinaFilter
{
public:
/**
* constructor parameters are only linked to image input size
* @param NBrows: number of rows of the input image
* @param NBcolumns: number of columns of the input image
*/
MagnoRetinaFilter(const unsigned int NBrows, const unsigned int NBcolumns);
/**
* destructor
*/
virtual ~MagnoRetinaFilter();
/**
* function that clears all buffers of the object
*/
void clearAllBuffers();
/**
* resize retina magno filter object (resize all allocated buffers)
* @param NBrows: the new height size
* @param NBcolumns: the new width size
*/
void resize(const unsigned int NBrows, const unsigned int NBcolumns);
/**
* set parameters values
* @param parasolCells_beta: the low pass filter gain used for local contrast adaptation at the IPL level of the retina (for ganglion cells local adaptation), typical value is 0
* @param parasolCells_tau: the low pass filter time constant used for local contrast adaptation at the IPL level of the retina (for ganglion cells local adaptation), unit is frame, typical value is 0 (immediate response)
* @param parasolCells_k: the low pass filter spatial constant used for local contrast adaptation at the IPL level of the retina (for ganglion cells local adaptation), unit is pixels, typical value is 5
* @param amacrinCellsTemporalCutFrequency: the time constant of the first order high pass fiter of the magnocellular way (motion information channel), unit is frames, tipicall value is 5
* @param localAdaptIntegration_tau: specifies the temporal constant of the low pas filter involved in the computation of the local "motion mean" for the local adaptation computation
* @param localAdaptIntegration_k: specifies the spatial constant of the low pas filter involved in the computation of the local "motion mean" for the local adaptation computation
*/
void setCoefficientsTable(const float parasolCells_beta, const float parasolCells_tau, const float parasolCells_k, const float amacrinCellsTemporalCutFrequency, const float localAdaptIntegration_tau, const float localAdaptIntegration_k);
/**
* launch filter that runs all the IPL magno filter (model of the magnocellular channel of the Inner Plexiform Layer of the retina)
* @param OPL_ON: the output of the bipolar ON cells of the retina (available from the ParvoRetinaFilter class (getBipolarCellsON() function)
* @param OPL_OFF: the output of the bipolar OFF cells of the retina (available from the ParvoRetinaFilter class (getBipolarCellsOFF() function)
* @return the processed result without post-processing
*/
const std::valarray<float> &runFilter(const std::valarray<float> &OPL_ON, const std::valarray<float> &OPL_OFF);
/**
* @return the Magnocellular ON channel filtering output
*/
inline const std::valarray<float> &getMagnoON() const {return _magnoXOutputON;};
/**
* @return the Magnocellular OFF channel filtering output
*/
inline const std::valarray<float> &getMagnoOFF() const {return _magnoXOutputOFF;};
/**
* @return the Magnocellular Y (sum of the ON and OFF magno channels) filtering output
*/
inline const std::valarray<float> &getMagnoYsaturated() const {return *_magnoYsaturated;};
/**
* applies an image normalization which saturates the high output values by the use of an assymetric sigmoide
*/
inline void normalizeGrayOutputNearZeroCentreredSigmoide(){_filterOutput.normalizeGrayOutputNearZeroCentreredSigmoide(&(*_magnoYOutput)[0], &(*_magnoYsaturated)[0]);};
/**
* @return the horizontal cells' temporal constant
*/
inline const float getTemporalConstant(){return this->_filteringCoeficientsTable[2];};
/**
* constructor parameters are only linked to image input size
* @param NBrows: number of rows of the input image
* @param NBcolumns: number of columns of the input image
*/
MagnoRetinaFilter(const unsigned int NBrows, const unsigned int NBcolumns);
/**
* destructor
*/
virtual ~MagnoRetinaFilter();
/**
* function that clears all buffers of the object
*/
void clearAllBuffers();
/**
* resize retina magno filter object (resize all allocated buffers)
* @param NBrows: the new height size
* @param NBcolumns: the new width size
*/
void resize(const unsigned int NBrows, const unsigned int NBcolumns);
/**
* set parameters values
* @param parasolCells_beta: the low pass filter gain used for local contrast adaptation at the IPL level of the retina (for ganglion cells local adaptation), typical value is 0
* @param parasolCells_tau: the low pass filter time constant used for local contrast adaptation at the IPL level of the retina (for ganglion cells local adaptation), unit is frame, typical value is 0 (immediate response)
* @param parasolCells_k: the low pass filter spatial constant used for local contrast adaptation at the IPL level of the retina (for ganglion cells local adaptation), unit is pixels, typical value is 5
* @param amacrinCellsTemporalCutFrequency: the time constant of the first order high pass fiter of the magnocellular way (motion information channel), unit is frames, tipicall value is 5
* @param localAdaptIntegration_tau: specifies the temporal constant of the low pas filter involved in the computation of the local "motion mean" for the local adaptation computation
* @param localAdaptIntegration_k: specifies the spatial constant of the low pas filter involved in the computation of the local "motion mean" for the local adaptation computation
*/
void setCoefficientsTable(const float parasolCells_beta, const float parasolCells_tau, const float parasolCells_k, const float amacrinCellsTemporalCutFrequency, const float localAdaptIntegration_tau, const float localAdaptIntegration_k);
/**
* launch filter that runs all the IPL magno filter (model of the magnocellular channel of the Inner Plexiform Layer of the retina)
* @param OPL_ON: the output of the bipolar ON cells of the retina (available from the ParvoRetinaFilter class (getBipolarCellsON() function)
* @param OPL_OFF: the output of the bipolar OFF cells of the retina (available from the ParvoRetinaFilter class (getBipolarCellsOFF() function)
* @return the processed result without post-processing
*/
const std::valarray<float> &runFilter(const std::valarray<float> &OPL_ON, const std::valarray<float> &OPL_OFF);
/**
* @return the Magnocellular ON channel filtering output
*/
inline const std::valarray<float> &getMagnoON() const {return _magnoXOutputON;};
/**
* @return the Magnocellular OFF channel filtering output
*/
inline const std::valarray<float> &getMagnoOFF() const {return _magnoXOutputOFF;};
/**
* @return the Magnocellular Y (sum of the ON and OFF magno channels) filtering output
*/
inline const std::valarray<float> &getMagnoYsaturated() const {return *_magnoYsaturated;};
/**
* applies an image normalization which saturates the high output values by the use of an assymetric sigmoide
*/
inline void normalizeGrayOutputNearZeroCentreredSigmoide(){_filterOutput.normalizeGrayOutputNearZeroCentreredSigmoide(&(*_magnoYOutput)[0], &(*_magnoYsaturated)[0]);};
/**
* @return the horizontal cells' temporal constant
*/
inline float getTemporalConstant(){return this->_filteringCoeficientsTable[2];};
private:
// related pointers to these buffers
std::valarray<float> _previousInput_ON;
std::valarray<float> _previousInput_OFF;
std::valarray<float> _amacrinCellsTempOutput_ON;
std::valarray<float> _amacrinCellsTempOutput_OFF;
std::valarray<float> _magnoXOutputON;
std::valarray<float> _magnoXOutputOFF;
std::valarray<float> _localProcessBufferON;
std::valarray<float> _localProcessBufferOFF;
// reference to parent buffers and allow better readability
TemplateBuffer<float> *_magnoYOutput;
std::valarray<float> *_magnoYsaturated;
// varialbles
float _temporalCoefficient;
// amacrine cells filter : high pass temporal filter
void _amacrineCellsComputing(const float *ONinput, const float *OFFinput);
// related pointers to these buffers
std::valarray<float> _previousInput_ON;
std::valarray<float> _previousInput_OFF;
std::valarray<float> _amacrinCellsTempOutput_ON;
std::valarray<float> _amacrinCellsTempOutput_OFF;
std::valarray<float> _magnoXOutputON;
std::valarray<float> _magnoXOutputOFF;
std::valarray<float> _localProcessBufferON;
std::valarray<float> _localProcessBufferOFF;
// reference to parent buffers and allow better readability
TemplateBuffer<float> *_magnoYOutput;
std::valarray<float> *_magnoYsaturated;
// varialbles
float _temporalCoefficient;
// amacrine cells filter : high pass temporal filter
void _amacrineCellsComputing(const float *ONinput, const float *OFFinput);
};

4
modules/contrib/src/parvoretinafilter.hpp
Unescape View File

@ -195,12 +195,12 @@ public:
/**
* @return the photoreceptors's temporal constant
*/
inline const float getPhotoreceptorsTemporalConstant(){return this->_filteringCoeficientsTable[2];};
inline float getPhotoreceptorsTemporalConstant(){return this->_filteringCoeficientsTable[2];};
/**
* @return the horizontal cells' temporal constant
*/
inline const float getHcellsTemporalConstant(){return this->_filteringCoeficientsTable[5];};
inline float getHcellsTemporalConstant(){return this->_filteringCoeficientsTable[5];};
private:
// template buffers

4
modules/contrib/src/retinacolor.cpp
Unescape View File

@ -687,7 +687,7 @@ void RetinaColor::_computeGradient(const float *luminance)
}
}
const bool RetinaColor::applyKrauskopfLMS2Acr1cr2Transform(std::valarray<float> &result)
bool RetinaColor::applyKrauskopfLMS2Acr1cr2Transform(std::valarray<float> &result)
{
bool processSuccess=true;
// basic preliminary error check
@ -703,7 +703,7 @@ const bool RetinaColor::applyKrauskopfLMS2Acr1cr2Transform(std::valarray<float>
return processSuccess;
}
const bool RetinaColor::applyLMS2LabTransform(std::valarray<float> &result)
bool RetinaColor::applyLMS2LabTransform(std::valarray<float> &result)
{
bool processSuccess=true;
// basic preliminary error check

4
modules/contrib/src/retinacolor.hpp
Unescape View File

@ -163,14 +163,14 @@ public:
* @param result: the input buffer to fill with the transformed colorspace retina output
* @return true if process ended successfully
*/
const bool applyKrauskopfLMS2Acr1cr2Transform(std::valarray<float> &result);
bool applyKrauskopfLMS2Acr1cr2Transform(std::valarray<float> &result);
/**
* apply to the retina color output the CIE Lab color transformation
* @param result: the input buffer to fill with the transformed colorspace retina output
* @return true if process ended successfully
*/
const bool applyLMS2LabTransform(std::valarray<float> &result);
bool applyLMS2LabTransform(std::valarray<float> &result);
/**
* @return the multiplexed frame result (use this after function runColorMultiplexing)

10
modules/contrib/src/retinafilter.cpp
Unescape View File

@ -126,7 +126,7 @@ namespace cv
clearAllBuffers();
// std::cout<<"RetinaFilter::size( "<<this->getNBrows()<<", "<<this->getNBcolumns()<<")"<<_filterOutput.size()<<" =? "<<_filterOutput.getNBpixels()<<std::endl;
// std::cout<<"RetinaFilter::size( "<<this->getNBrows()<<", "<<this->getNBcolumns()<<")"<<_filterOutput.size()<<" =? "<<_filterOutput.getNBpixels()<<std::endl;
}
@ -240,7 +240,7 @@ namespace cv
_setInitPeriodCount();
}
const bool RetinaFilter::checkInput(const std::valarray<float> &input, const bool)
bool RetinaFilter::checkInput(const std::valarray<float> &input, const bool)
{
BasicRetinaFilter *inputTarget=&_photoreceptorsPrefilter;
@ -259,7 +259,7 @@ namespace cv
}
// main function that runs the filter for a given input frame
const bool RetinaFilter::runFilter(const std::valarray<float> &imageInput, const bool useAdaptiveFiltering, const bool processRetinaParvoMagnoMapping, const bool useColorMode, const bool inputIsColorMultiplexed)
bool RetinaFilter::runFilter(const std::valarray<float> &imageInput, const bool useAdaptiveFiltering, const bool processRetinaParvoMagnoMapping, const bool useColorMode, const bool inputIsColorMultiplexed)
{
// preliminary check
bool processSuccess=true;
@ -488,7 +488,7 @@ namespace cv
}
const bool RetinaFilter::getParvoFoveaResponse(std::valarray<float> &parvoFovealResponse)
bool RetinaFilter::getParvoFoveaResponse(std::valarray<float> &parvoFovealResponse)
{
if (!_useParvoOutput)
return false;
@ -508,7 +508,7 @@ namespace cv
}
// method to retrieve the parafoveal magnocellular pathway response (no energy motion in fovea)
const bool RetinaFilter::getMagnoParaFoveaResponse(std::valarray<float> &magnoParafovealResponse)
bool RetinaFilter::getMagnoParaFoveaResponse(std::valarray<float> &magnoParafovealResponse)
{
if (!_useMagnoOutput)
return false;

808
modules/contrib/src/retinafilter.hpp
Unescape View File

@ -116,427 +116,427 @@ class RetinaFilter//: public BasicRetinaFilter
{
public:
/**
* constructor of the retina filter model with log sampling of the input frame (models the photoreceptors log sampling (central high resolution fovea and lower precision borders))
* @param sizeRows: number of rows of the input image
* @param sizeColumns: number of columns of the input image
* @param colorMode: specifies if the retina works with color (true) of stays in grayscale processing (false), can be adjusted online by the use of setColorMode method
* @param samplingMethod: specifies which kind of color sampling will be used
* @param useRetinaLogSampling: activate retina log sampling, if true, the 2 following parameters can be used
* @param reductionFactor: only usefull if param useRetinaLogSampling=true, specifies the reduction factor of the output frame (as the center (fovea) is high resolution and corners can be underscaled, then a reduction of the output is allowed without precision leak
* @param samplingStrenght: only usefull if param useRetinaLogSampling=true, specifies the strenght of the log scale that is applied
*/
RetinaFilter(const unsigned int sizeRows, const unsigned int sizeColumns, const bool colorMode=false, const RETINA_COLORSAMPLINGMETHOD samplingMethod=RETINA_COLOR_BAYER, const bool useRetinaLogSampling=false, const double reductionFactor=1.0, const double samplingStrenght=10.0);
/**
* standard destructor
*/
~RetinaFilter();
/**
* function that clears all buffers of the object
*/
void clearAllBuffers();
/**
* resize retina parvo filter object (resize all allocated buffers)
* @param NBrows: the new height size
* @param NBcolumns: the new width size
*/
void resize(const unsigned int NBrows, const unsigned int NBcolumns);
/**
* Input buffer checker: allows to check if the passed image buffer corresponds to retina filter expectations
* @param input: the input image buffer
* @param colorMode: specifiy if the input should be considered by the retina as colored of not
* @return false if not compatible or it returns true if OK
*/
const bool checkInput(const std::valarray<float> &input, const bool colorMode);
/**
* run the initilized retina filter, after this call all retina outputs are updated
* @param imageInput: image input buffer, can be grayscale or RGB image respecting the size specified at the constructor level
* @param useAdaptiveFiltering: set true if you want to use adaptive color demultilexing (solve some color artefact problems), see RetinaColor for citation references
* @param processRetinaParvoMagnoMapping: tels if the main outputs takes into account the mapping of the Parvo and Magno channels on the retina (centred parvo (fovea) and magno outside (parafovea))
* @param useColorMode: color information is used if true, warning, if input is only gray level, a buffer overflow error will occur
-> note that if color mode is activated and processRetinaParvoMagnoMapping==true, then the demultiplexed color frame (accessible throw getColorOutput() will be a color contours frame in the fovea and gray level moving contours outside
@param inputIsColorMultiplexed: set trus if the input data is a multiplexed color image (using Bayer sampling for example), the color sampling method must correspond to the RETINA_COLORSAMPLINGMETHOD passed at constructor!
* @return true if process ran well, false in case of failure
*/
const bool runFilter(const std::valarray<float> &imageInput, const bool useAdaptiveFiltering=true, const bool processRetinaParvoMagnoMapping=false, const bool useColorMode=false, const bool inputIsColorMultiplexed=false);
/**
* run the initilized retina filter in order to perform color tone mapping applied on an RGB image, after this call the color output of the retina is updated (use function getColorOutput() to grab it)
* the algorithm is based on David Alleyson, Sabine Susstruck and Laurence Meylan's work, please cite:
* -> Meylan L., Alleysson D., and S<EFBFBD>sstrunk S., A Model of Retinal Local Adaptation for the Tone Mapping of Color Filter Array Images, Journal of Optical Society of America, A, Vol. 24, N<EFBFBD> 9, September, 1st, 2007, pp. 2807-2816
* get the resulting gray frame by calling function getParvoColor()
* @param grayImageInput: RGB image input buffer respecting the size specified at the constructor level
* @param PhotoreceptorsCompression: sets the log compression parameters applied at the photoreceptors level (enhance luminance in dark areas)
* @param ganglionCellsCompression: sets the log compression applied at the gnaglion cells output (enhance contrast)
*/
void runGrayToneMapping(const std::valarray<float> &grayImageInput, std::valarray<float> &grayImageOutput, const float PhotoreceptorsCompression=0.6, const float ganglionCellsCompression=0.6);
/**
* run the initilized retina filter in order to perform color tone mapping applied on an RGB image, after this call the color output of the retina is updated (use function getColorOutput() to grab it)
* the algorithm is based on David Alleyson, Sabine Susstruck and Laurence Meylan's work, please cite:
* -> Meylan L., Alleysson D., and S<EFBFBD>sstrunk S., A Model of Retinal Local Adaptation for the Tone Mapping of Color Filter Array Images, Journal of Optical Society of America, A, Vol. 24, N<EFBFBD> 9, September, 1st, 2007, pp. 2807-2816
* get the resulting RGB frame by calling function getParvoColor()
* @param RGBimageInput: RGB image input buffer respecting the size specified at the constructor level
* @param useAdaptiveFiltering: set true if you want to use adaptive color demultilexing (solve some color artefact problems), see RetinaColor for citation references
* @param PhotoreceptorsCompression: sets the log compression parameters applied at the photoreceptors level (enhance luminance in dark areas)
* @param ganglionCellsCompression: sets the log compression applied at the ganglion cells output (enhance contrast)
*/
void runRGBToneMapping(const std::valarray<float> &RGBimageInput, std::valarray<float> &imageOutput, const bool useAdaptiveFiltering, const float PhotoreceptorsCompression=0.6, const float ganglionCellsCompression=0.6);
/**
* run the initilized retina filter in order to perform color tone mapping applied on an RGB image, after this call the color output of the retina is updated (use function getColorOutput() to grab it)
* get the resulting RGB frame by calling function getParvoColor()
* @param LMSimageInput: RGB image input buffer respecting the size specified at the constructor level
* @param useAdaptiveFiltering: set true if you want to use adaptive color demultilexing (solve some color artefact problems), see RetinaColor for citation references
* @param PhotoreceptorsCompression: sets the log compression parameters applied at the photoreceptors level (enhance luminance in dark areas)
* @param ganglionCellsCompression: sets the log compression applied at the gnaglion cells output (enhance contrast)
*/
void runLMSToneMapping(const std::valarray<float> &LMSimageInput, std::valarray<float> &imageOutput, const bool useAdaptiveFiltering, const float PhotoreceptorsCompression=0.6, const float ganglionCellsCompression=0.6);
/**
* set up function of the retina filter: all the retina is initialized at this step, some specific parameters are set by default, use setOPLandParvoCoefficientsTable() and setMagnoCoefficientsTable in order to setup the retina with more options
* @param OPLspatialResponse1: (equal to k1 in setOPLandParvoCoefficientsTable() function) the spatial constant of the first order low pass filter of the photoreceptors, use it to cut high spatial frequencies (noise or thick contours), unit is pixels, typical value is 1 pixel
* @param OPLtemporalresponse1: (equal to tau1 in setOPLandParvoCoefficientsTable() function) the time constant of the first order low pass filter of the photoreceptors, use it to cut high temporal frequencies (noise or fast motion), unit is frames, typical value is 1 frame
* @param OPLassymetryGain: (equal to beta2 in setOPLandParvoCoefficientsTable() function) gain of the horizontal cells network, if 0, then the mean value of the output is zero, if the parameter is near 1, then, the luminance is not filtered and is still reachable at the output, typicall value is 0
* @param OPLspatialResponse2: (equal to k2 in setOPLandParvoCoefficientsTable() function) the spatial constant of the first order low pass filter of the horizontal cells, use it to cut low spatial frequencies (local luminance), unit is pixels, typical value is 5 pixel
* @param OPLtemporalresponse2: (equal to tau2 in setOPLandParvoCoefficientsTable() function) the time constant of the first order low pass filter of the horizontal cells, use it to cut low temporal frequencies (local luminance variations), unit is frames, typical value is 1 frame, as the photoreceptors
* @param LPfilterSpatialResponse: (equal to parasolCells_k in setMagnoCoefficientsTable() function) the low pass filter spatial constant used for local contrast adaptation at the IPL level of the retina (for ganglion cells local adaptation), unit is pixels, typical value is 5
* @param LPfilterGain: (equal to parasolCells_beta in setMagnoCoefficientsTable() function) the low pass filter gain used for local contrast adaptation at the IPL level of the retina (for ganglion cells local adaptation), typical value is 0
* @param LPfilterTemporalresponse: (equal to parasolCells_tau in setMagnoCoefficientsTable() function) the low pass filter time constant used for local contrast adaptation at the IPL level of the retina (for ganglion cells local adaptation), unit is frame, typical value is 0 (immediate response)
* @param MovingContoursExtractorCoefficient: (equal to amacrinCellsTemporalCutFrequency in setMagnoCoefficientsTable() function)the time constant of the first order high pass fiter of the magnocellular way (motion information channel), unit is frames, tipicall value is 5
* @param normalizeParvoOutput_0_maxOutputValue: specifies if the Parvo cellular output should be normalized between 0 and maxOutputValue (true) or not (false) in order to remain at a null mean value, true value is recommended for visualisation
* @param normalizeMagnoOutput_0_maxOutputValue: specifies if the Magno cellular output should be normalized between 0 and maxOutputValue (true) or not (false), setting true may be hazardous because it can enhace the noise response when nothing is moving
* @param maxOutputValue: the maximum amplitude value of the normalized outputs (generally 255 for 8bit per channel pictures)
* @param maxInputValue: the maximum pixel value of the input picture (generally 255 for 8bit per channel pictures), specify it in other case (for example High Dynamic Range Images)
* @param meanValue: the global mean value of the input data usefull for local adaptation setup
*/
void setGlobalParameters(const float OPLspatialResponse1=0.7, const float OPLtemporalresponse1=1, const float OPLassymetryGain=0, const float OPLspatialResponse2=5, const float OPLtemporalresponse2=1, const float LPfilterSpatialResponse=5, const float LPfilterGain=0, const float LPfilterTemporalresponse=0, const float MovingContoursExtractorCoefficient=5, const bool normalizeParvoOutput_0_maxOutputValue=false, const bool normalizeMagnoOutput_0_maxOutputValue=false, const float maxOutputValue=255.0, const float maxInputValue=255.0, const float meanValue=128.0);
/**
* setup the local luminance adaptation capability
* @param V0CompressionParameter: the compression strengh of the photoreceptors local adaptation output, set a value between 160 and 250 for best results, a high value increases more the low value sensitivity... and the output saturates faster, recommended value: 160
*/
inline void setPhotoreceptorsLocalAdaptationSensitivity(const float V0CompressionParameter){_photoreceptorsPrefilter.setV0CompressionParameter(1-V0CompressionParameter);_setInitPeriodCount();};
/**
* setup the local luminance adaptation capability
* @param V0CompressionParameter: the compression strengh of the parvocellular pathway (details) local adaptation output, set a value between 160 and 250 for best results, a high value increases more the low value sensitivity... and the output saturates faster, recommended value: 160
*/
inline void setParvoGanglionCellsLocalAdaptationSensitivity(const float V0CompressionParameter){_ParvoRetinaFilter.setV0CompressionParameter(V0CompressionParameter);_setInitPeriodCount();};
/**
* setup the local luminance adaptation area of integration
* @param spatialResponse: the spatial constant of the low pass filter applied on the bipolar cells output in order to compute local contrast mean values
* @param temporalResponse: the spatial constant of the low pass filter applied on the bipolar cells output in order to compute local contrast mean values (generally set to zero: immediate response)
*/
inline void setGanglionCellsLocalAdaptationLPfilterParameters(const float spatialResponse, const float temporalResponse){_ParvoRetinaFilter.setGanglionCellsLocalAdaptationLPfilterParameters(temporalResponse, spatialResponse);_setInitPeriodCount();};
/**
* setup the local luminance adaptation capability
* @param V0CompressionParameter: the compression strengh of the magnocellular pathway (motion) local adaptation output, set a value between 160 and 250 for best results, a high value increases more the low value sensitivity... and the output saturates faster, recommended value: 160
*/
inline void setMagnoGanglionCellsLocalAdaptationSensitivity(const float V0CompressionParameter){_MagnoRetinaFilter.setV0CompressionParameter(V0CompressionParameter);_setInitPeriodCount();};
/**
* setup the OPL and IPL parvo channels
* @param beta1: gain of the horizontal cells network, if 0, then the mean value of the output is zero (default value), if the parameter is near 1, the amplitude is boosted but it should only be used for values rescaling... if needed
* @param tau1: the time constant of the first order low pass filter of the photoreceptors, use it to cut high temporal frequencies (noise or fast motion), unit is frames, typical value is 1 frame
* @param k1: the spatial constant of the first order low pass filter of the photoreceptors, use it to cut high spatial frequencies (noise or thick contours), unit is pixels, typical value is 1 pixel
* @param beta2: gain of the horizontal cells network, if 0, then the mean value of the output is zero, if the parameter is near 1, then, the luminance is not filtered and is still reachable at the output, typicall value is 0
* @param tau2: the time constant of the first order low pass filter of the horizontal cells, use it to cut low temporal frequencies (local luminance variations), unit is frames, typical value is 1 frame, as the photoreceptors
* @param k2: the spatial constant of the first order low pass filter of the horizontal cells, use it to cut low spatial frequencies (local luminance), unit is pixels, typical value is 5 pixel, this value is also used for local contrast computing when computing the local contrast adaptation at the ganglion cells level (Inner Plexiform Layer parvocellular channel model)
* @param V0CompressionParameter: the compression strengh of the ganglion cells local adaptation output, set a value between 160 and 250 for best results, a high value increases more the low value sensitivity... and the output saturates faster, recommended value: 230
*/
void setOPLandParvoParameters(const float beta1, const float tau1, const float k1, const float beta2, const float tau2, const float k2, const float V0CompressionParameter){_ParvoRetinaFilter.setOPLandParvoFiltersParameters(beta1, tau1, k1, beta2, tau2, k2);_ParvoRetinaFilter.setV0CompressionParameter(V0CompressionParameter);_setInitPeriodCount();};
/**
* set parameters values for the Inner Plexiform Layer (IPL) magnocellular channel
* @param parasolCells_beta: the low pass filter gain used for local contrast adaptation at the IPL level of the retina (for ganglion cells local adaptation), typical value is 0
* @param parasolCells_tau: the low pass filter time constant used for local contrast adaptation at the IPL level of the retina (for ganglion cells local adaptation), unit is frame, typical value is 0 (immediate response)
* @param parasolCells_k: the low pass filter spatial constant used for local contrast adaptation at the IPL level of the retina (for ganglion cells local adaptation), unit is pixels, typical value is 5
* @param amacrinCellsTemporalCutFrequency: the time constant of the first order high pass fiter of the magnocellular way (motion information channel), unit is frames, tipicall value is 5
* @param V0CompressionParameter: the compression strengh of the ganglion cells local adaptation output, set a value between 160 and 250 for best results, a high value increases more the low value sensitivity... and the output saturates faster, recommended value: 200
* @param localAdaptintegration_tau: specifies the temporal constant of the low pas filter involved in the computation of the local "motion mean" for the local adaptation computation
* @param localAdaptintegration_k: specifies the spatial constant of the low pas filter involved in the computation of the local "motion mean" for the local adaptation computation
*/
void setMagnoCoefficientsTable(const float parasolCells_beta, const float parasolCells_tau, const float parasolCells_k, const float amacrinCellsTemporalCutFrequency, const float V0CompressionParameter, const float localAdaptintegration_tau, const float localAdaptintegration_k){_MagnoRetinaFilter.setCoefficientsTable(parasolCells_beta, parasolCells_tau, parasolCells_k, amacrinCellsTemporalCutFrequency, localAdaptintegration_tau, localAdaptintegration_k);_MagnoRetinaFilter.setV0CompressionParameter(V0CompressionParameter);_setInitPeriodCount();};
/**
* set if the parvo output should be or not normalized between 0 and 255 (for display purpose generally)
* @param normalizeParvoOutput_0_maxOutputValue: true if normalization should be done
*/
inline void activateNormalizeParvoOutput_0_maxOutputValue(const bool normalizeParvoOutput_0_maxOutputValue){_normalizeParvoOutput_0_maxOutputValue=normalizeParvoOutput_0_maxOutputValue;};
/**
* set if the magno output should be or not normalized between 0 and 255 (for display purpose generally), take care, if nothing is moving, then, the noise will be enanced !!!
* @param normalizeMagnoOutput_0_maxOutputValue: true if normalization should be done
*/
inline void activateNormalizeMagnoOutput_0_maxOutputValue(const bool normalizeMagnoOutput_0_maxOutputValue){_normalizeMagnoOutput_0_maxOutputValue=normalizeMagnoOutput_0_maxOutputValue;};
/**
* setup the maximum amplitude value of the normalized outputs (generally 255 for 8bit per channel pictures)
* @param maxOutputValue: maximum amplitude value of the normalized outputs (generally 255 for 8bit per channel pictures)
*/
inline void setMaxOutputValue(const float maxOutputValue){_maxOutputValue=maxOutputValue;};
/**
* sets the color mode of the frame grabber
* @param desiredColorMode: true if the user needs color information, false for graylevels
*/
void setColorMode(const bool desiredColorMode){_useColorMode=desiredColorMode;};
/**
* activate color saturation as the final step of the color demultiplexing process
* -> this saturation is a sigmoide function applied to each channel of the demultiplexed image.
* @param saturateColors: boolean that activates color saturation (if true) or desactivate (if false)
* @param colorSaturationValue: the saturation factor
* */
inline void setColorSaturation(const bool saturateColors=true, const float colorSaturationValue=4.0){_colorEngine.setColorSaturation(saturateColors, colorSaturationValue);};
/////////////////////////////////////////////////////////////////
// function that retrieve the main retina outputs, one by one, or all in a structure
/**
* @return the input image sampled by the photoreceptors spatial sampling
*/
inline const std::valarray<float> &getPhotoreceptorsSampledFrame() const
/**
* constructor of the retina filter model with log sampling of the input frame (models the photoreceptors log sampling (central high resolution fovea and lower precision borders))
* @param sizeRows: number of rows of the input image
* @param sizeColumns: number of columns of the input image
* @param colorMode: specifies if the retina works with color (true) of stays in grayscale processing (false), can be adjusted online by the use of setColorMode method
* @param samplingMethod: specifies which kind of color sampling will be used
* @param useRetinaLogSampling: activate retina log sampling, if true, the 2 following parameters can be used
* @param reductionFactor: only usefull if param useRetinaLogSampling=true, specifies the reduction factor of the output frame (as the center (fovea) is high resolution and corners can be underscaled, then a reduction of the output is allowed without precision leak
* @param samplingStrenght: only usefull if param useRetinaLogSampling=true, specifies the strenght of the log scale that is applied
*/
RetinaFilter(const unsigned int sizeRows, const unsigned int sizeColumns, const bool colorMode=false, const RETINA_COLORSAMPLINGMETHOD samplingMethod=RETINA_COLOR_BAYER, const bool useRetinaLogSampling=false, const double reductionFactor=1.0, const double samplingStrenght=10.0);
/**
* standard destructor
*/
~RetinaFilter();
/**
* function that clears all buffers of the object
*/
void clearAllBuffers();
/**
* resize retina parvo filter object (resize all allocated buffers)
* @param NBrows: the new height size
* @param NBcolumns: the new width size
*/
void resize(const unsigned int NBrows, const unsigned int NBcolumns);
/**
* Input buffer checker: allows to check if the passed image buffer corresponds to retina filter expectations
* @param input: the input image buffer
* @param colorMode: specifiy if the input should be considered by the retina as colored of not
* @return false if not compatible or it returns true if OK
*/
bool checkInput(const std::valarray<float> &input, const bool colorMode);
/**
* run the initilized retina filter, after this call all retina outputs are updated
* @param imageInput: image input buffer, can be grayscale or RGB image respecting the size specified at the constructor level
* @param useAdaptiveFiltering: set true if you want to use adaptive color demultilexing (solve some color artefact problems), see RetinaColor for citation references
* @param processRetinaParvoMagnoMapping: tels if the main outputs takes into account the mapping of the Parvo and Magno channels on the retina (centred parvo (fovea) and magno outside (parafovea))
* @param useColorMode: color information is used if true, warning, if input is only gray level, a buffer overflow error will occur
-> note that if color mode is activated and processRetinaParvoMagnoMapping==true, then the demultiplexed color frame (accessible throw getColorOutput() will be a color contours frame in the fovea and gray level moving contours outside
@param inputIsColorMultiplexed: set trus if the input data is a multiplexed color image (using Bayer sampling for example), the color sampling method must correspond to the RETINA_COLORSAMPLINGMETHOD passed at constructor!
* @return true if process ran well, false in case of failure
*/
bool runFilter(const std::valarray<float> &imageInput, const bool useAdaptiveFiltering=true, const bool processRetinaParvoMagnoMapping=false, const bool useColorMode=false, const bool inputIsColorMultiplexed=false);
/**
* run the initilized retina filter in order to perform color tone mapping applied on an RGB image, after this call the color output of the retina is updated (use function getColorOutput() to grab it)
* the algorithm is based on David Alleyson, Sabine Susstruck and Laurence Meylan's work, please cite:
* -> Meylan L., Alleysson D., and S<EFBFBD>sstrunk S., A Model of Retinal Local Adaptation for the Tone Mapping of Color Filter Array Images, Journal of Optical Society of America, A, Vol. 24, N<EFBFBD> 9, September, 1st, 2007, pp. 2807-2816
* get the resulting gray frame by calling function getParvoColor()
* @param grayImageInput: RGB image input buffer respecting the size specified at the constructor level
* @param PhotoreceptorsCompression: sets the log compression parameters applied at the photoreceptors level (enhance luminance in dark areas)
* @param ganglionCellsCompression: sets the log compression applied at the gnaglion cells output (enhance contrast)
*/
void runGrayToneMapping(const std::valarray<float> &grayImageInput, std::valarray<float> &grayImageOutput, const float PhotoreceptorsCompression=0.6, const float ganglionCellsCompression=0.6);
/**
* run the initilized retina filter in order to perform color tone mapping applied on an RGB image, after this call the color output of the retina is updated (use function getColorOutput() to grab it)
* the algorithm is based on David Alleyson, Sabine Susstruck and Laurence Meylan's work, please cite:
* -> Meylan L., Alleysson D., and S<EFBFBD>sstrunk S., A Model of Retinal Local Adaptation for the Tone Mapping of Color Filter Array Images, Journal of Optical Society of America, A, Vol. 24, N<EFBFBD> 9, September, 1st, 2007, pp. 2807-2816
* get the resulting RGB frame by calling function getParvoColor()
* @param RGBimageInput: RGB image input buffer respecting the size specified at the constructor level
* @param useAdaptiveFiltering: set true if you want to use adaptive color demultilexing (solve some color artefact problems), see RetinaColor for citation references
* @param PhotoreceptorsCompression: sets the log compression parameters applied at the photoreceptors level (enhance luminance in dark areas)
* @param ganglionCellsCompression: sets the log compression applied at the ganglion cells output (enhance contrast)
*/
void runRGBToneMapping(const std::valarray<float> &RGBimageInput, std::valarray<float> &imageOutput, const bool useAdaptiveFiltering, const float PhotoreceptorsCompression=0.6, const float ganglionCellsCompression=0.6);
/**
* run the initilized retina filter in order to perform color tone mapping applied on an RGB image, after this call the color output of the retina is updated (use function getColorOutput() to grab it)
* get the resulting RGB frame by calling function getParvoColor()
* @param LMSimageInput: RGB image input buffer respecting the size specified at the constructor level
* @param useAdaptiveFiltering: set true if you want to use adaptive color demultilexing (solve some color artefact problems), see RetinaColor for citation references
* @param PhotoreceptorsCompression: sets the log compression parameters applied at the photoreceptors level (enhance luminance in dark areas)
* @param ganglionCellsCompression: sets the log compression applied at the gnaglion cells output (enhance contrast)
*/
void runLMSToneMapping(const std::valarray<float> &LMSimageInput, std::valarray<float> &imageOutput, const bool useAdaptiveFiltering, const float PhotoreceptorsCompression=0.6, const float ganglionCellsCompression=0.6);
/**
* set up function of the retina filter: all the retina is initialized at this step, some specific parameters are set by default, use setOPLandParvoCoefficientsTable() and setMagnoCoefficientsTable in order to setup the retina with more options
* @param OPLspatialResponse1: (equal to k1 in setOPLandParvoCoefficientsTable() function) the spatial constant of the first order low pass filter of the photoreceptors, use it to cut high spatial frequencies (noise or thick contours), unit is pixels, typical value is 1 pixel
* @param OPLtemporalresponse1: (equal to tau1 in setOPLandParvoCoefficientsTable() function) the time constant of the first order low pass filter of the photoreceptors, use it to cut high temporal frequencies (noise or fast motion), unit is frames, typical value is 1 frame
* @param OPLassymetryGain: (equal to beta2 in setOPLandParvoCoefficientsTable() function) gain of the horizontal cells network, if 0, then the mean value of the output is zero, if the parameter is near 1, then, the luminance is not filtered and is still reachable at the output, typicall value is 0
* @param OPLspatialResponse2: (equal to k2 in setOPLandParvoCoefficientsTable() function) the spatial constant of the first order low pass filter of the horizontal cells, use it to cut low spatial frequencies (local luminance), unit is pixels, typical value is 5 pixel
* @param OPLtemporalresponse2: (equal to tau2 in setOPLandParvoCoefficientsTable() function) the time constant of the first order low pass filter of the horizontal cells, use it to cut low temporal frequencies (local luminance variations), unit is frames, typical value is 1 frame, as the photoreceptors
* @param LPfilterSpatialResponse: (equal to parasolCells_k in setMagnoCoefficientsTable() function) the low pass filter spatial constant used for local contrast adaptation at the IPL level of the retina (for ganglion cells local adaptation), unit is pixels, typical value is 5
* @param LPfilterGain: (equal to parasolCells_beta in setMagnoCoefficientsTable() function) the low pass filter gain used for local contrast adaptation at the IPL level of the retina (for ganglion cells local adaptation), typical value is 0
* @param LPfilterTemporalresponse: (equal to parasolCells_tau in setMagnoCoefficientsTable() function) the low pass filter time constant used for local contrast adaptation at the IPL level of the retina (for ganglion cells local adaptation), unit is frame, typical value is 0 (immediate response)
* @param MovingContoursExtractorCoefficient: (equal to amacrinCellsTemporalCutFrequency in setMagnoCoefficientsTable() function)the time constant of the first order high pass fiter of the magnocellular way (motion information channel), unit is frames, tipicall value is 5
* @param normalizeParvoOutput_0_maxOutputValue: specifies if the Parvo cellular output should be normalized between 0 and maxOutputValue (true) or not (false) in order to remain at a null mean value, true value is recommended for visualisation
* @param normalizeMagnoOutput_0_maxOutputValue: specifies if the Magno cellular output should be normalized between 0 and maxOutputValue (true) or not (false), setting true may be hazardous because it can enhace the noise response when nothing is moving
* @param maxOutputValue: the maximum amplitude value of the normalized outputs (generally 255 for 8bit per channel pictures)
* @param maxInputValue: the maximum pixel value of the input picture (generally 255 for 8bit per channel pictures), specify it in other case (for example High Dynamic Range Images)
* @param meanValue: the global mean value of the input data usefull for local adaptation setup
*/
void setGlobalParameters(const float OPLspatialResponse1=0.7, const float OPLtemporalresponse1=1, const float OPLassymetryGain=0, const float OPLspatialResponse2=5, const float OPLtemporalresponse2=1, const float LPfilterSpatialResponse=5, const float LPfilterGain=0, const float LPfilterTemporalresponse=0, const float MovingContoursExtractorCoefficient=5, const bool normalizeParvoOutput_0_maxOutputValue=false, const bool normalizeMagnoOutput_0_maxOutputValue=false, const float maxOutputValue=255.0, const float maxInputValue=255.0, const float meanValue=128.0);
/**
* setup the local luminance adaptation capability
* @param V0CompressionParameter: the compression strengh of the photoreceptors local adaptation output, set a value between 160 and 250 for best results, a high value increases more the low value sensitivity... and the output saturates faster, recommended value: 160
*/
inline void setPhotoreceptorsLocalAdaptationSensitivity(const float V0CompressionParameter){_photoreceptorsPrefilter.setV0CompressionParameter(1-V0CompressionParameter);_setInitPeriodCount();};
/**
* setup the local luminance adaptation capability
* @param V0CompressionParameter: the compression strengh of the parvocellular pathway (details) local adaptation output, set a value between 160 and 250 for best results, a high value increases more the low value sensitivity... and the output saturates faster, recommended value: 160
*/
inline void setParvoGanglionCellsLocalAdaptationSensitivity(const float V0CompressionParameter){_ParvoRetinaFilter.setV0CompressionParameter(V0CompressionParameter);_setInitPeriodCount();};
/**
* setup the local luminance adaptation area of integration
* @param spatialResponse: the spatial constant of the low pass filter applied on the bipolar cells output in order to compute local contrast mean values
* @param temporalResponse: the spatial constant of the low pass filter applied on the bipolar cells output in order to compute local contrast mean values (generally set to zero: immediate response)
*/
inline void setGanglionCellsLocalAdaptationLPfilterParameters(const float spatialResponse, const float temporalResponse){_ParvoRetinaFilter.setGanglionCellsLocalAdaptationLPfilterParameters(temporalResponse, spatialResponse);_setInitPeriodCount();};
/**
* setup the local luminance adaptation capability
* @param V0CompressionParameter: the compression strengh of the magnocellular pathway (motion) local adaptation output, set a value between 160 and 250 for best results, a high value increases more the low value sensitivity... and the output saturates faster, recommended value: 160
*/
inline void setMagnoGanglionCellsLocalAdaptationSensitivity(const float V0CompressionParameter){_MagnoRetinaFilter.setV0CompressionParameter(V0CompressionParameter);_setInitPeriodCount();};
/**
* setup the OPL and IPL parvo channels
* @param beta1: gain of the horizontal cells network, if 0, then the mean value of the output is zero (default value), if the parameter is near 1, the amplitude is boosted but it should only be used for values rescaling... if needed
* @param tau1: the time constant of the first order low pass filter of the photoreceptors, use it to cut high temporal frequencies (noise or fast motion), unit is frames, typical value is 1 frame
* @param k1: the spatial constant of the first order low pass filter of the photoreceptors, use it to cut high spatial frequencies (noise or thick contours), unit is pixels, typical value is 1 pixel
* @param beta2: gain of the horizontal cells network, if 0, then the mean value of the output is zero, if the parameter is near 1, then, the luminance is not filtered and is still reachable at the output, typicall value is 0
* @param tau2: the time constant of the first order low pass filter of the horizontal cells, use it to cut low temporal frequencies (local luminance variations), unit is frames, typical value is 1 frame, as the photoreceptors
* @param k2: the spatial constant of the first order low pass filter of the horizontal cells, use it to cut low spatial frequencies (local luminance), unit is pixels, typical value is 5 pixel, this value is also used for local contrast computing when computing the local contrast adaptation at the ganglion cells level (Inner Plexiform Layer parvocellular channel model)
* @param V0CompressionParameter: the compression strengh of the ganglion cells local adaptation output, set a value between 160 and 250 for best results, a high value increases more the low value sensitivity... and the output saturates faster, recommended value: 230
*/
void setOPLandParvoParameters(const float beta1, const float tau1, const float k1, const float beta2, const float tau2, const float k2, const float V0CompressionParameter){_ParvoRetinaFilter.setOPLandParvoFiltersParameters(beta1, tau1, k1, beta2, tau2, k2);_ParvoRetinaFilter.setV0CompressionParameter(V0CompressionParameter);_setInitPeriodCount();};
/**
* set parameters values for the Inner Plexiform Layer (IPL) magnocellular channel
* @param parasolCells_beta: the low pass filter gain used for local contrast adaptation at the IPL level of the retina (for ganglion cells local adaptation), typical value is 0
* @param parasolCells_tau: the low pass filter time constant used for local contrast adaptation at the IPL level of the retina (for ganglion cells local adaptation), unit is frame, typical value is 0 (immediate response)
* @param parasolCells_k: the low pass filter spatial constant used for local contrast adaptation at the IPL level of the retina (for ganglion cells local adaptation), unit is pixels, typical value is 5
* @param amacrinCellsTemporalCutFrequency: the time constant of the first order high pass fiter of the magnocellular way (motion information channel), unit is frames, tipicall value is 5
* @param V0CompressionParameter: the compression strengh of the ganglion cells local adaptation output, set a value between 160 and 250 for best results, a high value increases more the low value sensitivity... and the output saturates faster, recommended value: 200
* @param localAdaptintegration_tau: specifies the temporal constant of the low pas filter involved in the computation of the local "motion mean" for the local adaptation computation
* @param localAdaptintegration_k: specifies the spatial constant of the low pas filter involved in the computation of the local "motion mean" for the local adaptation computation
*/
void setMagnoCoefficientsTable(const float parasolCells_beta, const float parasolCells_tau, const float parasolCells_k, const float amacrinCellsTemporalCutFrequency, const float V0CompressionParameter, const float localAdaptintegration_tau, const float localAdaptintegration_k){_MagnoRetinaFilter.setCoefficientsTable(parasolCells_beta, parasolCells_tau, parasolCells_k, amacrinCellsTemporalCutFrequency, localAdaptintegration_tau, localAdaptintegration_k);_MagnoRetinaFilter.setV0CompressionParameter(V0CompressionParameter);_setInitPeriodCount();};
/**
* set if the parvo output should be or not normalized between 0 and 255 (for display purpose generally)
* @param normalizeParvoOutput_0_maxOutputValue: true if normalization should be done
*/
inline void activateNormalizeParvoOutput_0_maxOutputValue(const bool normalizeParvoOutput_0_maxOutputValue){_normalizeParvoOutput_0_maxOutputValue=normalizeParvoOutput_0_maxOutputValue;};
/**
* set if the magno output should be or not normalized between 0 and 255 (for display purpose generally), take care, if nothing is moving, then, the noise will be enanced !!!
* @param normalizeMagnoOutput_0_maxOutputValue: true if normalization should be done
*/
inline void activateNormalizeMagnoOutput_0_maxOutputValue(const bool normalizeMagnoOutput_0_maxOutputValue){_normalizeMagnoOutput_0_maxOutputValue=normalizeMagnoOutput_0_maxOutputValue;};
/**
* setup the maximum amplitude value of the normalized outputs (generally 255 for 8bit per channel pictures)
* @param maxOutputValue: maximum amplitude value of the normalized outputs (generally 255 for 8bit per channel pictures)
*/
inline void setMaxOutputValue(const float maxOutputValue){_maxOutputValue=maxOutputValue;};
/**
* sets the color mode of the frame grabber
* @param desiredColorMode: true if the user needs color information, false for graylevels
*/
void setColorMode(const bool desiredColorMode){_useColorMode=desiredColorMode;};
/**
* activate color saturation as the final step of the color demultiplexing process
* -> this saturation is a sigmoide function applied to each channel of the demultiplexed image.
* @param saturateColors: boolean that activates color saturation (if true) or desactivate (if false)
* @param colorSaturationValue: the saturation factor
* */
inline void setColorSaturation(const bool saturateColors=true, const float colorSaturationValue=4.0){_colorEngine.setColorSaturation(saturateColors, colorSaturationValue);};
/////////////////////////////////////////////////////////////////
// function that retrieve the main retina outputs, one by one, or all in a structure
/**
* @return the input image sampled by the photoreceptors spatial sampling
*/
inline const std::valarray<float> &getPhotoreceptorsSampledFrame() const
{
CV_Assert(_photoreceptorsLogSampling);
return _photoreceptorsLogSampling->getSampledFrame();
};
/**
* @return photoreceptors output, locally adapted luminance only, no high frequency spatio-temporal noise reduction at the next retina processing stages, use getPhotoreceptors method to get complete photoreceptors output
*/
inline const std::valarray<float> &getLocalAdaptation() const {return _photoreceptorsPrefilter.getOutput();};
/**
* @return photoreceptors output: locally adapted luminance and high frequency spatio-temporal noise reduction, high luminance is a little saturated at this stage, but this is corrected naturally at the next retina processing stages
*/
inline const std::valarray<float> &getPhotoreceptors() const {return _ParvoRetinaFilter.getPhotoreceptorsLPfilteringOutput();};
/**
* @return the local luminance of the processed frame (it is the horizontal cells output)
*/
inline const std::valarray<float> &getHorizontalCells() const {return _ParvoRetinaFilter.getHorizontalCellsOutput();};
///////// CONTOURS part, PARVOCELLULAR RETINA PATHWAY
/**
* @return true if Parvocellular output is activated, false if not
*/
inline const bool areContoursProcessed(){return _useParvoOutput;};
/**
* method to retrieve the foveal parvocellular pathway response (no details energy in parafovea)
* @param parvoParafovealResponse: buffer that will be filled with the response of the magnocellular pathway in the parafoveal area
* @return true if process succeeded (if buffer exists, is its size matches retina size, if magno channel is activated and if mapping is initialized
*/
const bool getParvoFoveaResponse(std::valarray<float> &parvoFovealResponse);
/**
* @param useParvoOutput: true if Parvocellular output should be activated, false if not
*/
inline void activateContoursProcessing(const bool useParvoOutput){_useParvoOutput=useParvoOutput;};
/**
* @return the parvocellular contours information (details), should be used at the fovea level
*/
const std::valarray<float> &getContours(); // Parvocellular output
/**
* @return the parvocellular contours ON information (details), should be used at the fovea level
*/
inline const std::valarray<float> &getContoursON() const {return _ParvoRetinaFilter.getParvoON();};// Parvocellular ON output
/**
* @return the parvocellular contours OFF information (details), should be used at the fovea level
*/
inline const std::valarray<float> &getContoursOFF() const {return _ParvoRetinaFilter.getParvoOFF();};// Parvocellular OFF output
///////// MOVING CONTOURS part, MAGNOCELLULAR RETINA PATHWAY
/**
* @return true if Magnocellular output is activated, false if not
*/
inline const bool areMovingContoursProcessed(){return _useMagnoOutput;};
/**
* method to retrieve the parafoveal magnocellular pathway response (no motion energy in fovea)
* @param magnoParafovealResponse: buffer that will be filled with the response of the magnocellular pathway in the parafoveal area
* @return true if process succeeded (if buffer exists, is its size matches retina size, if magno channel is activated and if mapping is initialized
*/
const bool getMagnoParaFoveaResponse(std::valarray<float> &magnoParafovealResponse);
/**
* @param useMagnoOutput: true if Magnoocellular output should be activated, false if not
*/
inline void activateMovingContoursProcessing(const bool useMagnoOutput){_useMagnoOutput=useMagnoOutput;};
/**
* @return the magnocellular moving contours information (motion), should be used at the parafovea level without post-processing
*/
inline const std::valarray<float> &getMovingContours() const {return _MagnoRetinaFilter.getOutput();};// Magnocellular output
/**
* @return the magnocellular moving contours information (motion), should be used at the parafovea level with assymetric sigmoide post-processing which saturates motion information
*/
inline const std::valarray<float> &getMovingContoursSaturated() const {return _MagnoRetinaFilter.getMagnoYsaturated();};// Saturated Magnocellular output
/**
* @return the magnocellular moving contours ON information (motion), should be used at the parafovea level without post-processing
*/
inline const std::valarray<float> &getMovingContoursON() const {return _MagnoRetinaFilter.getMagnoON();};// Magnocellular ON output
/**
* @return the magnocellular moving contours OFF information (motion), should be used at the parafovea level without post-processing
*/
inline const std::valarray<float> &getMovingContoursOFF() const {return _MagnoRetinaFilter.getMagnoOFF();};// Magnocellular OFF output
/**
* @return a gray level image with center Parvo and peripheral Magno X channels, WARNING, the result will be ok if you called previously fucntion runFilter(imageInput, processRetinaParvoMagnoMapping=true);
* -> will be accessible even if color mode is activated (but the image is color sampled so quality is poor), but get the same thing but in color by the use of function getParvoColor()
*/
inline const std::valarray<float> &getRetinaParvoMagnoMappedOutput() const {return _retinaParvoMagnoMappedFrame;};// return image with center Parvo and peripheral Magno channels
/**
* color processing dedicated functions
* @return the parvo channel (contours, details) of the processed frame, grayscale output
*/
inline const std::valarray<float> &getParvoContoursChannel() const {return _colorEngine.getLuminance();};
/**
* color processing dedicated functions
* @return the chrominance of the processed frame (same colorspace as the input output, usually RGB)
*/
inline const std::valarray<float> &getParvoChrominance() const {return _colorEngine.getChrominance();}; // only retreive chrominance
/**
* color processing dedicated functions
* @return the parvo + chrominance channels of the processed frame (same colorspace as the input output, usually RGB)
*/
inline const std::valarray<float> &getColorOutput() const {return _colorEngine.getDemultiplexedColorFrame();};// retrieve luminance+chrominance
/**
* apply to the retina color output the Krauskopf transformation which leads to an opponent color system: output colorspace if Acr1cr2 if input of the retina was LMS color space
* @param result: the input buffer to fill with the transformed colorspace retina output
* @return true if process ended successfully
*/
inline const bool applyKrauskopfLMS2Acr1cr2Transform(std::valarray<float> &result){return _colorEngine.applyKrauskopfLMS2Acr1cr2Transform(result);};
/**
* apply to the retina color output the Krauskopf transformation which leads to an opponent color system: output colorspace if Acr1cr2 if input of the retina was LMS color space
* @param result: the input buffer to fill with the transformed colorspace retina output
* @return true if process ended successfully
*/
inline const bool applyLMS2LabTransform(std::valarray<float> &result){return _colorEngine.applyLMS2LabTransform(result);};
/**
* color processing dedicated functions
* @return the retina initialized mode, true if color mode (RGB), false if grayscale
*/
inline const bool isColorMode(){return _useColorMode;}; // return true if RGB mode, false if gray level mode
/**
* @return the irregular low pass filter ouput at the photoreceptors level
*/
inline const std::valarray<float> &getIrregularLPfilteredInputFrame() const {return _photoreceptorsLogSampling->getIrregularLPfilteredInputFrame();};
/**
* @return true if color mode is activated, false if gray levels processing
*/
const bool getColorMode(){return _useColorMode;};
/**
*
* @return true if a sufficient number of processed frames has been done since the last parameters update in order to get the stable state (r<EFBFBD>gime permanent)
*/
inline const bool isInitTransitionDone(){if (_ellapsedFramesSinceLastReset<_globalTemporalConstant)return false; return true;};
/**
* find a distance in the image input space when the distance is known in the retina log sampled space...read again if it is not clear enough....sorry, i should sleep
* @param projectedRadiusLength: the distance to image center in the retina log sampled space
* @return the distance to image center in the input image space
*/
inline const float getRetinaSamplingBackProjection(const float projectedRadiusLength)
{
if (_photoreceptorsLogSampling)
return (float)_photoreceptorsLogSampling->getOriginalRadiusLength(projectedRadiusLength);
return projectedRadiusLength;
};
/////////////////:
// retina dimensions getters
/**
* @return number of rows of the filter
*/
inline const unsigned int getInputNBrows(){if (_photoreceptorsLogSampling) return _photoreceptorsLogSampling->getNBrows();else return _photoreceptorsPrefilter.getNBrows();};
/**
* @return number of columns of the filter
*/
inline const unsigned int getInputNBcolumns(){if (_photoreceptorsLogSampling) return _photoreceptorsLogSampling->getNBcolumns();else return _photoreceptorsPrefilter.getNBcolumns();};
/**
* @return number of pixels of the filter
*/
inline const unsigned int getInputNBpixels(){if (_photoreceptorsLogSampling) return _photoreceptorsLogSampling->getNBpixels();else return _photoreceptorsPrefilter.getNBpixels();};
/**
* @return the height of the frame output
*/
inline const unsigned int getOutputNBrows(){return _photoreceptorsPrefilter.getNBrows();};
/**
* @return the width of the frame output
*/
inline const unsigned int getOutputNBcolumns(){return _photoreceptorsPrefilter.getNBcolumns();};
/**
* @return the numbers of output pixels (width*height) of the images used by the object
*/
inline const unsigned int getOutputNBpixels(){return _photoreceptorsPrefilter.getNBpixels();};
/**
* @return photoreceptors output, locally adapted luminance only, no high frequency spatio-temporal noise reduction at the next retina processing stages, use getPhotoreceptors method to get complete photoreceptors output
*/
inline const std::valarray<float> &getLocalAdaptation() const {return _photoreceptorsPrefilter.getOutput();};
/**
* @return photoreceptors output: locally adapted luminance and high frequency spatio-temporal noise reduction, high luminance is a little saturated at this stage, but this is corrected naturally at the next retina processing stages
*/
inline const std::valarray<float> &getPhotoreceptors() const {return _ParvoRetinaFilter.getPhotoreceptorsLPfilteringOutput();};
/**
* @return the local luminance of the processed frame (it is the horizontal cells output)
*/
inline const std::valarray<float> &getHorizontalCells() const {return _ParvoRetinaFilter.getHorizontalCellsOutput();};
///////// CONTOURS part, PARVOCELLULAR RETINA PATHWAY
/**
* @return true if Parvocellular output is activated, false if not
*/
inline bool areContoursProcessed(){return _useParvoOutput;};
/**
* method to retrieve the foveal parvocellular pathway response (no details energy in parafovea)
* @param parvoParafovealResponse: buffer that will be filled with the response of the magnocellular pathway in the parafoveal area
* @return true if process succeeded (if buffer exists, is its size matches retina size, if magno channel is activated and if mapping is initialized
*/
bool getParvoFoveaResponse(std::valarray<float> &parvoFovealResponse);
/**
* @param useParvoOutput: true if Parvocellular output should be activated, false if not
*/
inline void activateContoursProcessing(const bool useParvoOutput){_useParvoOutput=useParvoOutput;};
/**
* @return the parvocellular contours information (details), should be used at the fovea level
*/
const std::valarray<float> &getContours(); // Parvocellular output
/**
* @return the parvocellular contours ON information (details), should be used at the fovea level
*/
inline const std::valarray<float> &getContoursON() const {return _ParvoRetinaFilter.getParvoON();};// Parvocellular ON output
/**
* @return the parvocellular contours OFF information (details), should be used at the fovea level
*/
inline const std::valarray<float> &getContoursOFF() const {return _ParvoRetinaFilter.getParvoOFF();};// Parvocellular OFF output
///////// MOVING CONTOURS part, MAGNOCELLULAR RETINA PATHWAY
/**
* @return true if Magnocellular output is activated, false if not
*/
inline bool areMovingContoursProcessed(){return _useMagnoOutput;};
/**
* method to retrieve the parafoveal magnocellular pathway response (no motion energy in fovea)
* @param magnoParafovealResponse: buffer that will be filled with the response of the magnocellular pathway in the parafoveal area
* @return true if process succeeded (if buffer exists, is its size matches retina size, if magno channel is activated and if mapping is initialized
*/
bool getMagnoParaFoveaResponse(std::valarray<float> &magnoParafovealResponse);
/**
* @param useMagnoOutput: true if Magnoocellular output should be activated, false if not
*/
inline void activateMovingContoursProcessing(const bool useMagnoOutput){_useMagnoOutput=useMagnoOutput;};
/**
* @return the magnocellular moving contours information (motion), should be used at the parafovea level without post-processing
*/
inline const std::valarray<float> &getMovingContours() const {return _MagnoRetinaFilter.getOutput();};// Magnocellular output
/**
* @return the magnocellular moving contours information (motion), should be used at the parafovea level with assymetric sigmoide post-processing which saturates motion information
*/
inline const std::valarray<float> &getMovingContoursSaturated() const {return _MagnoRetinaFilter.getMagnoYsaturated();};// Saturated Magnocellular output
/**
* @return the magnocellular moving contours ON information (motion), should be used at the parafovea level without post-processing
*/
inline const std::valarray<float> &getMovingContoursON() const {return _MagnoRetinaFilter.getMagnoON();};// Magnocellular ON output
/**
* @return the magnocellular moving contours OFF information (motion), should be used at the parafovea level without post-processing
*/
inline const std::valarray<float> &getMovingContoursOFF() const {return _MagnoRetinaFilter.getMagnoOFF();};// Magnocellular OFF output
/**
* @return a gray level image with center Parvo and peripheral Magno X channels, WARNING, the result will be ok if you called previously fucntion runFilter(imageInput, processRetinaParvoMagnoMapping=true);
* -> will be accessible even if color mode is activated (but the image is color sampled so quality is poor), but get the same thing but in color by the use of function getParvoColor()
*/
inline const std::valarray<float> &getRetinaParvoMagnoMappedOutput() const {return _retinaParvoMagnoMappedFrame;};// return image with center Parvo and peripheral Magno channels
/**
* color processing dedicated functions
* @return the parvo channel (contours, details) of the processed frame, grayscale output
*/
inline const std::valarray<float> &getParvoContoursChannel() const {return _colorEngine.getLuminance();};
/**
* color processing dedicated functions
* @return the chrominance of the processed frame (same colorspace as the input output, usually RGB)
*/
inline const std::valarray<float> &getParvoChrominance() const {return _colorEngine.getChrominance();}; // only retreive chrominance
/**
* color processing dedicated functions
* @return the parvo + chrominance channels of the processed frame (same colorspace as the input output, usually RGB)
*/
inline const std::valarray<float> &getColorOutput() const {return _colorEngine.getDemultiplexedColorFrame();};// retrieve luminance+chrominance
/**
* apply to the retina color output the Krauskopf transformation which leads to an opponent color system: output colorspace if Acr1cr2 if input of the retina was LMS color space
* @param result: the input buffer to fill with the transformed colorspace retina output
* @return true if process ended successfully
*/
inline bool applyKrauskopfLMS2Acr1cr2Transform(std::valarray<float> &result){return _colorEngine.applyKrauskopfLMS2Acr1cr2Transform(result);};
/**
* apply to the retina color output the Krauskopf transformation which leads to an opponent color system: output colorspace if Acr1cr2 if input of the retina was LMS color space
* @param result: the input buffer to fill with the transformed colorspace retina output
* @return true if process ended successfully
*/
inline bool applyLMS2LabTransform(std::valarray<float> &result){return _colorEngine.applyLMS2LabTransform(result);};
/**
* color processing dedicated functions
* @return the retina initialized mode, true if color mode (RGB), false if grayscale
*/
inline bool isColorMode(){return _useColorMode;}; // return true if RGB mode, false if gray level mode
/**
* @return the irregular low pass filter ouput at the photoreceptors level
*/
inline const std::valarray<float> &getIrregularLPfilteredInputFrame() const {return _photoreceptorsLogSampling->getIrregularLPfilteredInputFrame();};
/**
* @return true if color mode is activated, false if gray levels processing
*/
bool getColorMode(){return _useColorMode;};
/**
*
* @return true if a sufficient number of processed frames has been done since the last parameters update in order to get the stable state (r<EFBFBD>gime permanent)
*/
inline bool isInitTransitionDone(){if (_ellapsedFramesSinceLastReset<_globalTemporalConstant)return false; return true;};
/**
* find a distance in the image input space when the distance is known in the retina log sampled space...read again if it is not clear enough....sorry, i should sleep
* @param projectedRadiusLength: the distance to image center in the retina log sampled space
* @return the distance to image center in the input image space
*/
inline float getRetinaSamplingBackProjection(const float projectedRadiusLength)
{
if (_photoreceptorsLogSampling)
return (float)_photoreceptorsLogSampling->getOriginalRadiusLength(projectedRadiusLength);
return projectedRadiusLength;
};
/////////////////:
// retina dimensions getters
/**
* @return number of rows of the filter
*/
inline unsigned int getInputNBrows(){if (_photoreceptorsLogSampling) return _photoreceptorsLogSampling->getNBrows();else return _photoreceptorsPrefilter.getNBrows();};
/**
* @return number of columns of the filter
*/
inline unsigned int getInputNBcolumns(){if (_photoreceptorsLogSampling) return _photoreceptorsLogSampling->getNBcolumns();else return _photoreceptorsPrefilter.getNBcolumns();};
/**
* @return number of pixels of the filter
*/
inline unsigned int getInputNBpixels(){if (_photoreceptorsLogSampling) return _photoreceptorsLogSampling->getNBpixels();else return _photoreceptorsPrefilter.getNBpixels();};
/**
* @return the height of the frame output
*/
inline unsigned int getOutputNBrows(){return _photoreceptorsPrefilter.getNBrows();};
/**
* @return the width of the frame output
*/
inline unsigned int getOutputNBcolumns(){return _photoreceptorsPrefilter.getNBcolumns();};
/**
* @return the numbers of output pixels (width*height) of the images used by the object
*/
inline unsigned int getOutputNBpixels(){return _photoreceptorsPrefilter.getNBpixels();};
private:
// processing activation flags
bool _useParvoOutput;
bool _useMagnoOutput;
// processing activation flags
bool _useParvoOutput;
bool _useMagnoOutput;
// filter stability controls
unsigned int _ellapsedFramesSinceLastReset;
unsigned int _globalTemporalConstant;
// filter stability controls
unsigned int _ellapsedFramesSinceLastReset;
unsigned int _globalTemporalConstant;
// private template buffers and related access pointers
std::valarray<float> _retinaParvoMagnoMappedFrame;
std::valarray<float> _retinaParvoMagnoMapCoefTable;
// private objects of the class
BasicRetinaFilter _photoreceptorsPrefilter;
ParvoRetinaFilter _ParvoRetinaFilter;
MagnoRetinaFilter _MagnoRetinaFilter;
RetinaColor _colorEngine;
ImageLogPolProjection *_photoreceptorsLogSampling;
// private template buffers and related access pointers
std::valarray<float> _retinaParvoMagnoMappedFrame;
std::valarray<float> _retinaParvoMagnoMapCoefTable;
// private objects of the class
BasicRetinaFilter _photoreceptorsPrefilter;
ParvoRetinaFilter _ParvoRetinaFilter;
MagnoRetinaFilter _MagnoRetinaFilter;
RetinaColor _colorEngine;
ImageLogPolProjection *_photoreceptorsLogSampling;
bool _useMinimalMemoryForToneMappingONLY;
bool _useMinimalMemoryForToneMappingONLY;
bool _normalizeParvoOutput_0_maxOutputValue;
bool _normalizeMagnoOutput_0_maxOutputValue;
float _maxOutputValue;
bool _useColorMode;
bool _normalizeParvoOutput_0_maxOutputValue;
bool _normalizeMagnoOutput_0_maxOutputValue;
float _maxOutputValue;
bool _useColorMode;
// private functions
void _setInitPeriodCount();
void _createHybridTable();
void _processRetinaParvoMagnoMapping();
void _runGrayToneMapping(const std::valarray<float> &grayImageInput, std::valarray<float> &grayImageOutput ,const float PhotoreceptorsCompression=0.6, const float ganglionCellsCompression=0.6);
// private functions
void _setInitPeriodCount();
void _createHybridTable();
void _processRetinaParvoMagnoMapping();
void _runGrayToneMapping(const std::valarray<float> &grayImageInput, std::valarray<float> &grayImageOutput ,const float PhotoreceptorsCompression=0.6, const float ganglionCellsCompression=0.6);
};

716
modules/contrib/src/templatebuffer.hpp
Unescape View File

@ -85,303 +85,303 @@ template <class type> class TemplateBuffer : public std::valarray<type>
{
public:
/**
* constructor for monodimensional array
* @param dim: the size of the vector
*/
TemplateBuffer(const size_t dim=0)
: std::valarray<type>((type)0, dim)
{
_NBrows=1;
_NBcolumns=dim;
_NBdepths=1;
_NBpixels=dim;
_doubleNBpixels=2*dim;
}
/**
* constructor by copy for monodimensional array
* @param pVal: the pointer to a buffer to copy
* @param dim: the size of the vector
*/
TemplateBuffer(const type* pVal, const size_t dim)
: std::valarray<type>(pVal, dim)
{
_NBrows=1;
_NBcolumns=dim;
_NBdepths=1;
_NBpixels=dim;
_doubleNBpixels=2*dim;
}
/**
* constructor for bidimensional array
* @param dimRows: the size of the vector
* @param dimColumns: the size of the vector
* @param depth: the number of layers of the buffer in its third dimension (3 of color images, 1 for gray images.
*/
TemplateBuffer(const size_t dimRows, const size_t dimColumns, const size_t depth=1)
: std::valarray<type>((type)0, dimRows*dimColumns*depth)
{
/**
* constructor for monodimensional array
* @param dim: the size of the vector
*/
TemplateBuffer(const size_t dim=0)
: std::valarray<type>((type)0, dim)
{
_NBrows=1;
_NBcolumns=dim;
_NBdepths=1;
_NBpixels=dim;
_doubleNBpixels=2*dim;
}
/**
* constructor by copy for monodimensional array
* @param pVal: the pointer to a buffer to copy
* @param dim: the size of the vector
*/
TemplateBuffer(const type* pVal, const size_t dim)
: std::valarray<type>(pVal, dim)
{
_NBrows=1;
_NBcolumns=dim;
_NBdepths=1;
_NBpixels=dim;
_doubleNBpixels=2*dim;
}
/**
* constructor for bidimensional array
* @param dimRows: the size of the vector
* @param dimColumns: the size of the vector
* @param depth: the number of layers of the buffer in its third dimension (3 of color images, 1 for gray images.
*/
TemplateBuffer(const size_t dimRows, const size_t dimColumns, const size_t depth=1)
: std::valarray<type>((type)0, dimRows*dimColumns*depth)
{
#ifdef TEMPLATEBUFFERDEBUG
std::cout<<"TemplateBuffer::TemplateBuffer: new buffer, size="<<dimRows<<", "<<dimColumns<<", "<<depth<<"valarraySize="<<this->size()<<std::endl;
std::cout<<"TemplateBuffer::TemplateBuffer: new buffer, size="<<dimRows<<", "<<dimColumns<<", "<<depth<<"valarraySize="<<this->size()<<std::endl;
#endif
_NBrows=dimRows;
_NBcolumns=dimColumns;
_NBdepths=depth;
_NBpixels=dimRows*dimColumns;
_doubleNBpixels=2*dimRows*dimColumns;
//_createTableIndex();
_NBrows=dimRows;
_NBcolumns=dimColumns;
_NBdepths=depth;
_NBpixels=dimRows*dimColumns;
_doubleNBpixels=2*dimRows*dimColumns;
//_createTableIndex();
#ifdef TEMPLATEBUFFERDEBUG
std::cout<<"TemplateBuffer::TemplateBuffer: construction successful"<<std::endl;
std::cout<<"TemplateBuffer::TemplateBuffer: construction successful"<<std::endl;
#endif
}
/**
* copy constructor
* @param toCopy
* @return thenconstructed instance
*emplateBuffer(const TemplateBuffer &toCopy)
:_NBrows(toCopy.getNBrows()),_NBcolumns(toCopy.getNBcolumns()),_NBdepths(toCopy.getNBdephs()), _NBpixels(toCopy.getNBpixels()), _doubleNBpixels(toCopy.getNBpixels()*2)
//std::valarray<type>(toCopy)
{
memcpy(Buffer(), toCopy.Buffer(), this->size());
}*/
/**
* destructor
*/
virtual ~TemplateBuffer()
{
}
/**
* copy constructor
* @param toCopy
* @return thenconstructed instance
*emplateBuffer(const TemplateBuffer &toCopy)
:_NBrows(toCopy.getNBrows()),_NBcolumns(toCopy.getNBcolumns()),_NBdepths(toCopy.getNBdephs()), _NBpixels(toCopy.getNBpixels()), _doubleNBpixels(toCopy.getNBpixels()*2)
//std::valarray<type>(toCopy)
{
memcpy(Buffer(), toCopy.Buffer(), this->size());
}*/
/**
* destructor
*/
virtual ~TemplateBuffer()
{
#ifdef TEMPLATEBUFFERDEBUG
std::cout<<"~TemplateBuffer"<<std::endl;
std::cout<<"~TemplateBuffer"<<std::endl;
#endif
}
/**
* delete the buffer content (set zeros)
*/
inline void setZero(){std::valarray<type>::operator=(0);};//memset(Buffer(), 0, sizeof(type)*_NBpixels);};
/**
* @return the numbers of rows (height) of the images used by the object
*/
inline unsigned int getNBrows(){return (unsigned int)_NBrows;};
/**
* @return the numbers of columns (width) of the images used by the object
*/
inline unsigned int getNBcolumns(){return (unsigned int)_NBcolumns;};
/**
* @return the numbers of pixels (width*height) of the images used by the object
*/
inline unsigned int getNBpixels(){return (unsigned int)_NBpixels;};
/**
* @return the numbers of pixels (width*height) of the images used by the object
*/
inline unsigned int getDoubleNBpixels(){return (unsigned int)_doubleNBpixels;};
/**
* @return the numbers of depths (3rd dimension: 1 for gray images, 3 for rgb images) of the images used by the object
*/
inline unsigned int getDepthSize(){return (unsigned int)_NBdepths;};
/**
* resize the buffer and recompute table index etc.
*/
void resizeBuffer(const size_t dimRows, const size_t dimColumns, const size_t depth=1)
{
this->resize(dimRows*dimColumns*depth);
_NBrows=dimRows;
_NBcolumns=dimColumns;
_NBdepths=depth;
_NBpixels=dimRows*dimColumns;
_doubleNBpixels=2*dimRows*dimColumns;
}
inline TemplateBuffer<type> & operator=(const std::valarray<type> &b)
{
//std::cout<<"TemplateBuffer<type> & operator= affect vector: "<<std::endl;
std::valarray<type>::operator=(b);
return *this;
}
inline TemplateBuffer<type> & operator=(const type &b)
{
//std::cout<<"TemplateBuffer<type> & operator= affect value: "<<b<<std::endl;
std::valarray<type>::operator=(b);
return *this;
}
/* inline const type &operator[](const unsigned int &b)
}
/**
* delete the buffer content (set zeros)
*/
inline void setZero(){std::valarray<type>::operator=(0);};//memset(Buffer(), 0, sizeof(type)*_NBpixels);};
/**
* @return the numbers of rows (height) of the images used by the object
*/
inline unsigned int getNBrows(){return (unsigned int)_NBrows;};
/**
* @return the numbers of columns (width) of the images used by the object
*/
inline unsigned int getNBcolumns(){return (unsigned int)_NBcolumns;};
/**
* @return the numbers of pixels (width*height) of the images used by the object
*/
inline unsigned int getNBpixels(){return (unsigned int)_NBpixels;};
/**
* @return the numbers of pixels (width*height) of the images used by the object
*/
inline unsigned int getDoubleNBpixels(){return (unsigned int)_doubleNBpixels;};
/**
* @return the numbers of depths (3rd dimension: 1 for gray images, 3 for rgb images) of the images used by the object
*/
inline unsigned int getDepthSize(){return (unsigned int)_NBdepths;};
/**
* resize the buffer and recompute table index etc.
*/
void resizeBuffer(const size_t dimRows, const size_t dimColumns, const size_t depth=1)
{
this->resize(dimRows*dimColumns*depth);
_NBrows=dimRows;
_NBcolumns=dimColumns;
_NBdepths=depth;
_NBpixels=dimRows*dimColumns;
_doubleNBpixels=2*dimRows*dimColumns;
}
inline TemplateBuffer<type> & operator=(const std::valarray<type> &b)
{
//std::cout<<"TemplateBuffer<type> & operator= affect vector: "<<std::endl;
std::valarray<type>::operator=(b);
return *this;
}
inline TemplateBuffer<type> & operator=(const type &b)
{
//std::cout<<"TemplateBuffer<type> & operator= affect value: "<<b<<std::endl;
std::valarray<type>::operator=(b);
return *this;
}
/* inline const type &operator[](const unsigned int &b)
{
return (*this)[b];
return (*this)[b];
}
*/
/**
* @return the buffer adress in non const mode
*/
inline type* Buffer() { return &(*this)[0]; }
///////////////////////////////////////////////////////
// Standard Image manipulation functions
/**
* standard 0 to 255 image normalization function
* @param inputOutputBuffer: the image to be normalized (rewrites the input), if no parameter, then, the built in buffer reachable by getOutput() function is normalized
* @param nbPixels: specifies the number of pixel on which the normalization should be performed, if 0, then all pixels specified in the constructor are processed
* @param maxOutputValue: the maximum output value
*/
static void normalizeGrayOutput_0_maxOutputValue(type *inputOutputBuffer, const size_t nbPixels, const type maxOutputValue=(type)255.0);
/**
* standard 0 to 255 image normalization function
* @param inputOutputBuffer: the image to be normalized (rewrites the input), if no parameter, then, the built in buffer reachable by getOutput() function is normalized
* @param nbPixels: specifies the number of pixel on which the normalization should be performed, if 0, then all pixels specified in the constructor are processed
* @param maxOutputValue: the maximum output value
*/
void normalizeGrayOutput_0_maxOutputValue(const type maxOutputValue=(type)255.0){normalizeGrayOutput_0_maxOutputValue(this->Buffer(), this->size(), maxOutputValue);};
/**
* sigmoide image normalization function (saturates min and max values)
* @param meanValue: specifies the mean value of th pixels to be processed
* @param sensitivity: strenght of the sigmoide
* @param inputPicture: the image to be normalized if no parameter, then, the built in buffer reachable by getOutput() function is normalized
* @param outputBuffer: the ouput buffer on which the result is writed, if no parameter, then, the built in buffer reachable by getOutput() function is normalized
* @param maxOutputValue: the maximum output value
*/
static void normalizeGrayOutputCentredSigmoide(const type meanValue, const type sensitivity, const type maxOutputValue, type *inputPicture, type *outputBuffer, const unsigned int nbPixels);
/**
* sigmoide image normalization function on the current buffer (saturates min and max values)
* @param meanValue: specifies the mean value of th pixels to be processed
* @param sensitivity: strenght of the sigmoide
* @param maxOutputValue: the maximum output value
*/
inline void normalizeGrayOutputCentredSigmoide(const type meanValue=(type)0.0, const type sensitivity=(type)2.0, const type maxOutputValue=(type)255.0){ (void)maxOutputValue; normalizeGrayOutputCentredSigmoide(meanValue, sensitivity, 255.0, this->Buffer(), this->Buffer(), this->getNBpixels());};
/**
* sigmoide image normalization function (saturates min and max values), in this function, the sigmoide is centered on low values (high saturation of the medium and high values
* @param inputPicture: the image to be normalized if no parameter, then, the built in buffer reachable by getOutput() function is normalized
* @param outputBuffer: the ouput buffer on which the result is writed, if no parameter, then, the built in buffer reachable by getOutput() function is normalized
* @param sensitivity: strenght of the sigmoide
* @param maxOutputValue: the maximum output value
*/
void normalizeGrayOutputNearZeroCentreredSigmoide(type *inputPicture=(type*)NULL, type *outputBuffer=(type*)NULL, const type sensitivity=(type)40, const type maxOutputValue=(type)255.0);
/**
* center and reduct the image (image-mean)/std
* @param inputOutputBuffer: the image to be normalized if no parameter, the result is rewrited on it
*/
void centerReductImageLuminance(type *inputOutputBuffer=(type*)NULL);
/**
* @return standard deviation of the buffer
*/
const double getStandardDeviation()
{
double standardDeviation=0;
double meanValue=getMean();
type *bufferPTR=Buffer();
for (unsigned int i=0;i<this->size();++i)
{
double diff=(*(bufferPTR++)-meanValue);
standardDeviation+=diff*diff;
}
return sqrt(standardDeviation/this->size());
};
/**
* Clip buffer histogram
* @param minRatio: the minimum ratio of the lower pixel values, range=[0,1] and lower than maxRatio
* @param maxRatio: the aximum ratio of the higher pixel values, range=[0,1] and higher than minRatio
*/
void clipHistogram(double minRatio, double maxRatio, double maxOutputValue)
{
if (minRatio>=maxRatio)
{
std::cerr<<"TemplateBuffer::clipHistogram: minRatio must be inferior to maxRatio, buffer unchanged"<<std::endl;
return;
}
/* minRatio=min(max(minRatio, 1.0),0.0);
*/
/**
* @return the buffer adress in non const mode
*/
inline type* Buffer() { return &(*this)[0]; }
///////////////////////////////////////////////////////
// Standard Image manipulation functions
/**
* standard 0 to 255 image normalization function
* @param inputOutputBuffer: the image to be normalized (rewrites the input), if no parameter, then, the built in buffer reachable by getOutput() function is normalized
* @param nbPixels: specifies the number of pixel on which the normalization should be performed, if 0, then all pixels specified in the constructor are processed
* @param maxOutputValue: the maximum output value
*/
static void normalizeGrayOutput_0_maxOutputValue(type *inputOutputBuffer, const size_t nbPixels, const type maxOutputValue=(type)255.0);
/**
* standard 0 to 255 image normalization function
* @param inputOutputBuffer: the image to be normalized (rewrites the input), if no parameter, then, the built in buffer reachable by getOutput() function is normalized
* @param nbPixels: specifies the number of pixel on which the normalization should be performed, if 0, then all pixels specified in the constructor are processed
* @param maxOutputValue: the maximum output value
*/
void normalizeGrayOutput_0_maxOutputValue(const type maxOutputValue=(type)255.0){normalizeGrayOutput_0_maxOutputValue(this->Buffer(), this->size(), maxOutputValue);};
/**
* sigmoide image normalization function (saturates min and max values)
* @param meanValue: specifies the mean value of th pixels to be processed
* @param sensitivity: strenght of the sigmoide
* @param inputPicture: the image to be normalized if no parameter, then, the built in buffer reachable by getOutput() function is normalized
* @param outputBuffer: the ouput buffer on which the result is writed, if no parameter, then, the built in buffer reachable by getOutput() function is normalized
* @param maxOutputValue: the maximum output value
*/
static void normalizeGrayOutputCentredSigmoide(const type meanValue, const type sensitivity, const type maxOutputValue, type *inputPicture, type *outputBuffer, const unsigned int nbPixels);
/**
* sigmoide image normalization function on the current buffer (saturates min and max values)
* @param meanValue: specifies the mean value of th pixels to be processed
* @param sensitivity: strenght of the sigmoide
* @param maxOutputValue: the maximum output value
*/
inline void normalizeGrayOutputCentredSigmoide(const type meanValue=(type)0.0, const type sensitivity=(type)2.0, const type maxOutputValue=(type)255.0){ (void)maxOutputValue; normalizeGrayOutputCentredSigmoide(meanValue, sensitivity, 255.0, this->Buffer(), this->Buffer(), this->getNBpixels());};
/**
* sigmoide image normalization function (saturates min and max values), in this function, the sigmoide is centered on low values (high saturation of the medium and high values
* @param inputPicture: the image to be normalized if no parameter, then, the built in buffer reachable by getOutput() function is normalized
* @param outputBuffer: the ouput buffer on which the result is writed, if no parameter, then, the built in buffer reachable by getOutput() function is normalized
* @param sensitivity: strenght of the sigmoide
* @param maxOutputValue: the maximum output value
*/
void normalizeGrayOutputNearZeroCentreredSigmoide(type *inputPicture=(type*)NULL, type *outputBuffer=(type*)NULL, const type sensitivity=(type)40, const type maxOutputValue=(type)255.0);
/**
* center and reduct the image (image-mean)/std
* @param inputOutputBuffer: the image to be normalized if no parameter, the result is rewrited on it
*/
void centerReductImageLuminance(type *inputOutputBuffer=(type*)NULL);
/**
* @return standard deviation of the buffer
*/
double getStandardDeviation()
{
double standardDeviation=0;
double meanValue=getMean();
type *bufferPTR=Buffer();
for (unsigned int i=0;i<this->size();++i)
{
double diff=(*(bufferPTR++)-meanValue);
standardDeviation+=diff*diff;
}
return sqrt(standardDeviation/this->size());
};
/**
* Clip buffer histogram
* @param minRatio: the minimum ratio of the lower pixel values, range=[0,1] and lower than maxRatio
* @param maxRatio: the aximum ratio of the higher pixel values, range=[0,1] and higher than minRatio
*/
void clipHistogram(double minRatio, double maxRatio, double maxOutputValue)
{
if (minRatio>=maxRatio)
{
std::cerr<<"TemplateBuffer::clipHistogram: minRatio must be inferior to maxRatio, buffer unchanged"<<std::endl;
return;
}
/* minRatio=min(max(minRatio, 1.0),0.0);
maxRatio=max(max(maxRatio, 0.0),1.0);
*/
// find the pixel value just above the threshold
const double maxThreshold=this->max()*maxRatio;
const double minThreshold=(this->max()-this->min())*minRatio+this->min();
type *bufferPTR=this->Buffer();
double deltaH=maxThreshold;
double deltaL=maxThreshold;
double updatedHighValue=maxThreshold;
double updatedLowValue=maxThreshold;
for (unsigned int i=0;i<this->size();++i)
{
double curentValue=(double)*(bufferPTR++);
// updating "closest to the high threshold" pixel value
double highValueTest=maxThreshold-curentValue;
if (highValueTest>0)
{
if (deltaH>highValueTest)
{
deltaH=highValueTest;
updatedHighValue=curentValue;
}
}
// updating "closest to the low threshold" pixel value
double lowValueTest=curentValue-minThreshold;
if (lowValueTest>0)
{
if (deltaL>lowValueTest)
{
deltaL=lowValueTest;
updatedLowValue=curentValue;
}
}
}
std::cout<<"Tdebug"<<std::endl;
std::cout<<"deltaL="<<deltaL<<", deltaH="<<deltaH<<std::endl;
std::cout<<"this->max()"<<this->max()<<"maxThreshold="<<maxThreshold<<"updatedHighValue="<<updatedHighValue<<std::endl;
std::cout<<"this->min()"<<this->min()<<"minThreshold="<<minThreshold<<"updatedLowValue="<<updatedLowValue<<std::endl;
// clipping values outside than the updated thresholds
bufferPTR=this->Buffer();
for (unsigned int i=0;i<this->size();++i, ++bufferPTR)
{
if (*bufferPTR<updatedLowValue)
*bufferPTR=updatedLowValue;
else if (*bufferPTR>updatedHighValue)
*bufferPTR=updatedHighValue;
}
normalizeGrayOutput_0_maxOutputValue(this->Buffer(), this->size(), maxOutputValue);
}
/**
* @return the mean value of the vector
*/
inline const double getMean(){return this->sum()/this->size();};
*/
// find the pixel value just above the threshold
const double maxThreshold=this->max()*maxRatio;
const double minThreshold=(this->max()-this->min())*minRatio+this->min();
type *bufferPTR=this->Buffer();
double deltaH=maxThreshold;
double deltaL=maxThreshold;
double updatedHighValue=maxThreshold;
double updatedLowValue=maxThreshold;
for (unsigned int i=0;i<this->size();++i)
{
double curentValue=(double)*(bufferPTR++);
// updating "closest to the high threshold" pixel value
double highValueTest=maxThreshold-curentValue;
if (highValueTest>0)
{
if (deltaH>highValueTest)
{
deltaH=highValueTest;
updatedHighValue=curentValue;
}
}
// updating "closest to the low threshold" pixel value
double lowValueTest=curentValue-minThreshold;
if (lowValueTest>0)
{
if (deltaL>lowValueTest)
{
deltaL=lowValueTest;
updatedLowValue=curentValue;
}
}
}
std::cout<<"Tdebug"<<std::endl;
std::cout<<"deltaL="<<deltaL<<", deltaH="<<deltaH<<std::endl;
std::cout<<"this->max()"<<this->max()<<"maxThreshold="<<maxThreshold<<"updatedHighValue="<<updatedHighValue<<std::endl;
std::cout<<"this->min()"<<this->min()<<"minThreshold="<<minThreshold<<"updatedLowValue="<<updatedLowValue<<std::endl;
// clipping values outside than the updated thresholds
bufferPTR=this->Buffer();
for (unsigned int i=0;i<this->size();++i, ++bufferPTR)
{
if (*bufferPTR<updatedLowValue)
*bufferPTR=updatedLowValue;
else if (*bufferPTR>updatedHighValue)
*bufferPTR=updatedHighValue;
}
normalizeGrayOutput_0_maxOutputValue(this->Buffer(), this->size(), maxOutputValue);
}
/**
* @return the mean value of the vector
*/
inline double getMean(){return this->sum()/this->size();};
protected:
size_t _NBrows;
size_t _NBcolumns;
size_t _NBdepths;
size_t _NBpixels;
size_t _doubleNBpixels;
// utilities
static type _abs(const type x);
size_t _NBrows;
size_t _NBcolumns;
size_t _NBdepths;
size_t _NBpixels;
size_t _doubleNBpixels;
// utilities
static type _abs(const type x);
};
@ -390,48 +390,48 @@ protected:
template <class type>
void TemplateBuffer<type>::normalizeGrayOutput_0_maxOutputValue(type *inputOutputBuffer, const size_t processedPixels, const type maxOutputValue)
{
type maxValue=inputOutputBuffer[0], minValue=inputOutputBuffer[0];
// get the min and max value
register type *inputOutputBufferPTR=inputOutputBuffer;
for (register size_t j = 0; j<processedPixels; ++j)
{
type pixValue = *(inputOutputBufferPTR++);
if (maxValue < pixValue)
maxValue = pixValue;
else if (minValue > pixValue)
minValue = pixValue;
}
// change the range of the data to 0->255
type factor = maxOutputValue/(maxValue-minValue);
type offset = (type)(-minValue*factor);
inputOutputBufferPTR=inputOutputBuffer;
for (register size_t j = 0; j < processedPixels; ++j, ++inputOutputBufferPTR)
*inputOutputBufferPTR=*(inputOutputBufferPTR)*factor+offset;
type maxValue=inputOutputBuffer[0], minValue=inputOutputBuffer[0];
// get the min and max value
register type *inputOutputBufferPTR=inputOutputBuffer;
for (register size_t j = 0; j<processedPixels; ++j)
{
type pixValue = *(inputOutputBufferPTR++);
if (maxValue < pixValue)
maxValue = pixValue;
else if (minValue > pixValue)
minValue = pixValue;
}
// change the range of the data to 0->255
type factor = maxOutputValue/(maxValue-minValue);
type offset = (type)(-minValue*factor);
inputOutputBufferPTR=inputOutputBuffer;
for (register size_t j = 0; j < processedPixels; ++j, ++inputOutputBufferPTR)
*inputOutputBufferPTR=*(inputOutputBufferPTR)*factor+offset;
}
// normalize data with a sigmoide close to 0 (saturates values for those superior to 0)
template <class type>
void TemplateBuffer<type>::normalizeGrayOutputNearZeroCentreredSigmoide(type *inputBuffer, type *outputBuffer, const type sensitivity, const type maxOutputValue)
{
if (inputBuffer==NULL)
inputBuffer=Buffer();
if (outputBuffer==NULL)
outputBuffer=Buffer();
if (inputBuffer==NULL)
inputBuffer=Buffer();
if (outputBuffer==NULL)
outputBuffer=Buffer();
type X0cube=sensitivity*sensitivity*sensitivity;
type X0cube=sensitivity*sensitivity*sensitivity;
register type *inputBufferPTR=inputBuffer;
register type *outputBufferPTR=outputBuffer;
register type *inputBufferPTR=inputBuffer;
register type *outputBufferPTR=outputBuffer;
for (register size_t j = 0; j < _NBpixels; ++j, ++inputBufferPTR)
{
for (register size_t j = 0; j < _NBpixels; ++j, ++inputBufferPTR)
{
type currentCubeLuminance=*inputBufferPTR**inputBufferPTR**inputBufferPTR;
*(outputBufferPTR++)=maxOutputValue*currentCubeLuminance/(currentCubeLuminance+X0cube);
}
type currentCubeLuminance=*inputBufferPTR**inputBufferPTR**inputBufferPTR;
*(outputBufferPTR++)=maxOutputValue*currentCubeLuminance/(currentCubeLuminance+X0cube);
}
}
// normalize and adjust luminance with a centered to 128 sigmode
@ -439,20 +439,20 @@ template <class type>
void TemplateBuffer<type>::normalizeGrayOutputCentredSigmoide(const type meanValue, const type sensitivity, const type maxOutputValue, type *inputBuffer, type *outputBuffer, const unsigned int nbPixels)
{
if (sensitivity==1.0)
{
std::cerr<<"TemplateBuffer::TemplateBuffer<type>::normalizeGrayOutputCentredSigmoide error: 2nd parameter (sensitivity) must not equal 0, copying original data..."<<std::endl;
memcpy(outputBuffer, inputBuffer, sizeof(type)*nbPixels);
return;
}
if (sensitivity==1.0)
{
std::cerr<<"TemplateBuffer::TemplateBuffer<type>::normalizeGrayOutputCentredSigmoide error: 2nd parameter (sensitivity) must not equal 0, copying original data..."<<std::endl;
memcpy(outputBuffer, inputBuffer, sizeof(type)*nbPixels);
return;
}
type X0=maxOutputValue/(sensitivity-(type)1.0);
type X0=maxOutputValue/(sensitivity-(type)1.0);
register type *inputBufferPTR=inputBuffer;
register type *outputBufferPTR=outputBuffer;
register type *inputBufferPTR=inputBuffer;
register type *outputBufferPTR=outputBuffer;
for (register size_t j = 0; j < nbPixels; ++j, ++inputBufferPTR)
*(outputBufferPTR++)=(meanValue+(meanValue+X0)*(*(inputBufferPTR)-meanValue)/(_abs(*(inputBufferPTR)-meanValue)+X0));
for (register size_t j = 0; j < nbPixels; ++j, ++inputBufferPTR)
*(outputBufferPTR++)=(meanValue+(meanValue+X0)*(*(inputBufferPTR)-meanValue)/(_abs(*(inputBufferPTR)-meanValue)+X0));
}
@ -460,29 +460,29 @@ void TemplateBuffer<type>::normalizeGrayOutputCentredSigmoide(const type meanVal
template <class type>
void TemplateBuffer<type>::centerReductImageLuminance(type *inputOutputBuffer)
{
// if outputBuffer unsassigned, the rewrite the buffer
if (inputOutputBuffer==NULL)
inputOutputBuffer=Buffer();
type meanValue=0, stdValue=0;
// compute mean value
for (register size_t j = 0; j < _NBpixels; ++j)
meanValue+=inputOutputBuffer[j];
meanValue/=((type)_NBpixels);
// compute std value
register type *inputOutputBufferPTR=inputOutputBuffer;
for (size_t index=0;index<_NBpixels;++index)
{
type inputMinusMean=*(inputOutputBufferPTR++)-meanValue;
stdValue+=inputMinusMean*inputMinusMean;
}
stdValue=sqrt(stdValue/((type)_NBpixels));
// adjust luminance in regard of mean and std value;
inputOutputBufferPTR=inputOutputBuffer;
for (size_t index=0;index<_NBpixels;++index, ++inputOutputBufferPTR)
*inputOutputBufferPTR=(*(inputOutputBufferPTR)-meanValue)/stdValue;
// if outputBuffer unsassigned, the rewrite the buffer
if (inputOutputBuffer==NULL)
inputOutputBuffer=Buffer();
type meanValue=0, stdValue=0;
// compute mean value
for (register size_t j = 0; j < _NBpixels; ++j)
meanValue+=inputOutputBuffer[j];
meanValue/=((type)_NBpixels);
// compute std value
register type *inputOutputBufferPTR=inputOutputBuffer;
for (size_t index=0;index<_NBpixels;++index)
{
type inputMinusMean=*(inputOutputBufferPTR++)-meanValue;
stdValue+=inputMinusMean*inputMinusMean;
}
stdValue=sqrt(stdValue/((type)_NBpixels));
// adjust luminance in regard of mean and std value;
inputOutputBufferPTR=inputOutputBuffer;
for (size_t index=0;index<_NBpixels;++index, ++inputOutputBufferPTR)
*inputOutputBufferPTR=(*(inputOutputBufferPTR)-meanValue)/stdValue;
}
@ -490,27 +490,27 @@ template <class type>
type TemplateBuffer<type>::_abs(const type x)
{
if (x>0)
return x;
else
return -x;
if (x>0)
return x;
else
return -x;
}
template < >
inline int TemplateBuffer<int>::_abs(const int x)
{
return std::abs(x);
return std::abs(x);
}
template < >
inline double TemplateBuffer<double>::_abs(const double x)
{
return std::fabs(x);
return std::fabs(x);
}
template < >
inline float TemplateBuffer<float>::_abs(const float x)
{
return std::fabs(x);
return std::fabs(x);
}
}

4
modules/flann/src/precomp.hpp
Unescape View File

@ -1,3 +1,7 @@
#ifdef __GNUC__
# pragma GCC diagnostic ignored "-Wsign-promo"
#endif
#ifndef _OPENCV_FLANN_PRECOMP_HPP_
#define _OPENCV_FLANN_PRECOMP_HPP_

36
modules/highgui/src/grfmt_jpeg.cpp
Unescape View File

@ -380,7 +380,6 @@ int my_jpeg_load_dht (struct jpeg_decompress_struct *info, unsigned char *dht,
bool JpegDecoder::readData( Mat& img )
{
bool result = false;
uchar* data = img.data;
int step = (int)img.step;
bool color = img.channels() > 1;
JpegState* state = (JpegState*)m_state;
@ -439,6 +438,7 @@ bool JpegDecoder::readData( Mat& img )
buffer = (*cinfo->mem->alloc_sarray)((j_common_ptr)cinfo,
JPOOL_IMAGE, m_width*4, 1 );
uchar* data = img.data;
for( ; m_height--; data += step )
{
jpeg_read_scanlines( cinfo, buffer, 1 );
@ -533,19 +533,15 @@ ImageEncoder JpegEncoder::newEncoder() const
bool JpegEncoder::write( const Mat& img, const vector<int>& params )
{
int quality = 95;
for( size_t i = 0; i < params.size(); i += 2 )
struct fileWrapper
{
if( params[i] == CV_IMWRITE_JPEG_QUALITY )
{
quality = params[i+1];
quality = MIN(MAX(quality, 0), 100);
}
}
FILE* f;
fileWrapper() : f(0) {}
~fileWrapper() { if(f) fclose(f); }
};
bool result = false;
FILE* f = 0;
fileWrapper fw;
int _channels = img.channels();
int channels = _channels > 1 ? 3 : 1;
int width = img.cols, height = img.rows;
@ -564,10 +560,10 @@ bool JpegEncoder::write( const Mat& img, const vector<int>& params )
if( !m_buf )
{
f = fopen( m_filename.c_str(), "wb" );
if( !f )
fw.f = fopen( m_filename.c_str(), "wb" );
if( !fw.f )
goto _exit_;
jpeg_stdio_dest( &cinfo, f );
jpeg_stdio_dest( &cinfo, fw.f );
}
else
{
@ -587,6 +583,17 @@ bool JpegEncoder::write( const Mat& img, const vector<int>& params )
cinfo.input_components = channels;
cinfo.in_color_space = channels > 1 ? JCS_RGB : JCS_GRAYSCALE;
int quality = 95;
for( size_t i = 0; i < params.size(); i += 2 )
{
if( params[i] == CV_IMWRITE_JPEG_QUALITY )
{
quality = params[i+1];
quality = MIN(MAX(quality, 0), 100);
}
}
jpeg_set_defaults( &cinfo );
jpeg_set_quality( &cinfo, quality,
TRUE /* limit to baseline-JPEG values */ );
@ -619,7 +626,6 @@ bool JpegEncoder::write( const Mat& img, const vector<int>& params )
}
_exit_:
if(f) fclose(f);
jpeg_destroy_compress( &cinfo );
return result;

34
modules/highgui/src/grfmt_png.cpp
Unescape View File

@ -317,23 +317,6 @@ void PngEncoder::flushBuf(void*)
bool PngEncoder::write( const Mat& img, const vector<int>& params )
{
int compression_level = 0;
int compression_strategy = Z_RLE;
for( size_t i = 0; i < params.size(); i += 2 )
{
if( params[i] == CV_IMWRITE_PNG_COMPRESSION )
{
compression_level = params[i+1];
compression_level = MIN(MAX(compression_level, 0), MAX_MEM_LEVEL);
}
if( params[i] == CV_IMWRITE_PNG_STRATEGY )
{
compression_strategy = params[i+1];
compression_strategy = MIN(MAX(compression_strategy, 0), Z_FIXED);
}
}
png_structp png_ptr = png_create_write_struct( PNG_LIBPNG_VER_STRING, 0, 0, 0 );
png_infop info_ptr = 0;
FILE* f = 0;
@ -365,6 +348,23 @@ bool PngEncoder::write( const Mat& img, const vector<int>& params )
png_init_io( png_ptr, f );
}
int compression_level = 0;
int compression_strategy = Z_RLE;
for( size_t i = 0; i < params.size(); i += 2 )
{
if( params[i] == CV_IMWRITE_PNG_COMPRESSION )
{
compression_level = params[i+1];
compression_level = MIN(MAX(compression_level, 0), MAX_MEM_LEVEL);
}
if( params[i] == CV_IMWRITE_PNG_STRATEGY )
{
compression_strategy = params[i+1];
compression_strategy = MIN(MAX(compression_strategy, 0), Z_FIXED);
}
}
if( m_buf || f )
{
if( compression_level > 0 )

19
modules/highgui/src/window.cpp
Unescape View File

@ -635,9 +635,9 @@ cvCreateTrackbar( const char*, const char*,
}
CV_IMPL int
cvCreateTrackbar2( const char* trackbar_name, const char* window_name,
int* val, int count, CvTrackbarCallback2 on_notify2,
void* userdata )
cvCreateTrackbar2( const char* /*trackbar_name*/, const char* /*window_name*/,
int* /*val*/, int /*count*/, CvTrackbarCallback2 /*on_notify2*/,
void* /*userdata*/ )
{
CV_NO_GUI_ERROR( "cvCreateTrackbar2" );
return -1;
@ -678,7 +678,7 @@ CV_IMPL int cvWaitKey( int )
return -1;
}
CV_IMPL int cvInitSystem( int argc, char** argv )
CV_IMPL int cvInitSystem( int , char** )
{
CV_NO_GUI_ERROR( "cvInitSystem" );
@ -693,23 +693,24 @@ CV_IMPL int cvStartWindowThread()
}
//-------- Qt ---------
CV_IMPL void cvAddText( const CvArr*, const char*, CvPoint org, CvFont* font)
CV_IMPL void cvAddText( const CvArr*, const char*, CvPoint , CvFont* )
{
CV_NO_GUI_ERROR("cvAddText");
}
CV_IMPL void cvDisplayStatusBar(const char* name, const char* arg2, int arg3)
CV_IMPL void cvDisplayStatusBar(const char* , const char* , int )
{
CV_NO_GUI_ERROR("cvDisplayStatusBar");
}
CV_IMPL void cvDisplayOverlay(const char* name, const char* text, int delayms)
CV_IMPL void cvDisplayOverlay(const char* , const char* , int )
{
CV_NO_GUI_ERROR("cvNamedWindow");
}
CV_IMPL int cvStartLoop(int (*pt2Func)(int argc, char *argv[]), int argc, char* argv[])
CV_IMPL int cvStartLoop(int (*)(int argc, char *argv[]), int , char* argv[])
{
(void)argv;
CV_NO_GUI_ERROR("cvStartLoop");
return -1;
}
@ -719,7 +720,7 @@ CV_IMPL void cvStopLoop()
CV_NO_GUI_ERROR("cvStopLoop");
}
CV_IMPL void cvSaveWindowParameters(const char* name)
CV_IMPL void cvSaveWindowParameters(const char* )
{
CV_NO_GUI_ERROR("cvSaveWindowParameters");
}

2
modules/java/CMakeLists.txt
Unescape View File

@ -162,8 +162,6 @@ else()
endif()
add_dependencies(${the_module} ${api_target})
ocv_warnings_disable(CMAKE_CXX_FLAGS -Wmissing-declarations)
# Additional target properties
set_target_properties(${the_module} PROPERTIES
OUTPUT_NAME "${the_module}"

8
modules/java/gen_java.py
Unescape View File

@ -362,7 +362,7 @@ ManualFuncs = {
// C++: minMaxLoc(Mat src, double* minVal, double* maxVal=0, Point* minLoc=0, Point* maxLoc=0, InputArray mask=noArray())
JNIEXPORT jdoubleArray JNICALL Java_org_opencv_core_Core_n_1minMaxLocManual
(JNIEnv* env, jclass cls, jlong src_nativeObj, jlong mask_nativeObj)
(JNIEnv* env, jclass, jlong src_nativeObj, jlong mask_nativeObj)
{
try {
LOGD("Core::n_1minMaxLoc()");
@ -432,7 +432,7 @@ JNIEXPORT jdoubleArray JNICALL Java_org_opencv_core_Core_n_1minMaxLocManual
// C++: Size getTextSize(const string& text, int fontFace, double fontScale, int thickness, int* baseLine);
JNIEXPORT jdoubleArray JNICALL Java_org_opencv_core_Core_n_1getTextSize
(JNIEnv* env, jclass cls, jstring text, jint fontFace, jdouble fontScale, jint thickness, jintArray baseLine)
(JNIEnv* env, jclass, jstring text, jint fontFace, jdouble fontScale, jint thickness, jintArray baseLine)
{
try {
LOGD("Core::n_1getTextSize()");
@ -1010,7 +1010,7 @@ extern "C" {
# java native method args
jn_args = []
# jni (cpp) function args
jni_args = [ArgInfo([ "env", "env", "", [], "" ]), ArgInfo([ "cls", "cls", "", [], "" ])]
jni_args = [ArgInfo([ "env", "env", "", [], "" ]), ArgInfo([ "cls", "", "", [], "" ])]
j_prologue = []
j_epilogue = []
c_prologue = []
@ -1375,7 +1375,7 @@ JNIEXPORT $rtype JNICALL Java_org_opencv_${module}_${clazz}_$fname
JNIEXPORT void JNICALL Java_org_opencv_%(module)s_%(j_cls)s_delete(JNIEnv*, jclass, jlong);
JNIEXPORT void JNICALL Java_org_opencv_%(module)s_%(j_cls)s_delete
(JNIEnv* env, jclass cls, jlong self)
(JNIEnv*, jclass, jlong self)
{
delete (%(cls)s*) self;
}

336
modules/java/src/cpp/Mat.cpp
Unescape View File

@ -24,7 +24,10 @@ extern "C" {
JNIEXPORT jlong JNICALL Java_org_opencv_core_Mat_n_1Mat__
(JNIEnv* env, jclass cls)
(JNIEnv*, jclass);
JNIEXPORT jlong JNICALL Java_org_opencv_core_Mat_n_1Mat__
(JNIEnv*, jclass)
{
LOGD("Mat::n_1Mat__()");
return (jlong) new cv::Mat();
@ -36,9 +39,11 @@ JNIEXPORT jlong JNICALL Java_org_opencv_core_Mat_n_1Mat__
// Mat::Mat(int rows, int cols, int type)
//
JNIEXPORT jlong JNICALL Java_org_opencv_core_Mat_n_1Mat__III
(JNIEnv* env, jclass, jint rows, jint cols, jint type);
JNIEXPORT jlong JNICALL Java_org_opencv_core_Mat_n_1Mat__III
(JNIEnv* env, jclass cls, jint rows, jint cols, jint type)
(JNIEnv* env, jclass, jint rows, jint cols, jint type)
{
try {
LOGD("Mat::n_1Mat__III()");
@ -66,9 +71,11 @@ JNIEXPORT jlong JNICALL Java_org_opencv_core_Mat_n_1Mat__III
// Mat::Mat(Size size, int type)
//
JNIEXPORT jlong JNICALL Java_org_opencv_core_Mat_n_1Mat__DDI
(JNIEnv* env, jclass, jdouble size_width, jdouble size_height, jint type);
JNIEXPORT jlong JNICALL Java_org_opencv_core_Mat_n_1Mat__DDI
(JNIEnv* env, jclass cls, jdouble size_width, jdouble size_height, jint type)
(JNIEnv* env, jclass, jdouble size_width, jdouble size_height, jint type)
{
try {
LOGD("Mat::n_1Mat__DDI()");
@ -96,9 +103,12 @@ JNIEXPORT jlong JNICALL Java_org_opencv_core_Mat_n_1Mat__DDI
// Mat::Mat(int rows, int cols, int type, Scalar s)
//
JNIEXPORT jlong JNICALL Java_org_opencv_core_Mat_n_1Mat__IIIDDDD
(JNIEnv* env, jclass, jint rows, jint cols, jint type, jdouble s_val0, jdouble s_val1, jdouble s_val2, jdouble s_val3);
JNIEXPORT jlong JNICALL Java_org_opencv_core_Mat_n_1Mat__IIIDDDD
(JNIEnv* env, jclass cls, jint rows, jint cols, jint type, jdouble s_val0, jdouble s_val1, jdouble s_val2, jdouble s_val3)
(JNIEnv* env, jclass, jint rows, jint cols, jint type, jdouble s_val0, jdouble s_val1, jdouble s_val2, jdouble s_val3)
{
try {
LOGD("Mat::n_1Mat__IIIDDDD()");
@ -126,9 +136,11 @@ JNIEXPORT jlong JNICALL Java_org_opencv_core_Mat_n_1Mat__IIIDDDD
// Mat::Mat(Size size, int type, Scalar s)
//
JNIEXPORT jlong JNICALL Java_org_opencv_core_Mat_n_1Mat__DDIDDDD
(JNIEnv* env, jclass, jdouble size_width, jdouble size_height, jint type, jdouble s_val0, jdouble s_val1, jdouble s_val2, jdouble s_val3);
JNIEXPORT jlong JNICALL Java_org_opencv_core_Mat_n_1Mat__DDIDDDD
(JNIEnv* env, jclass cls, jdouble size_width, jdouble size_height, jint type, jdouble s_val0, jdouble s_val1, jdouble s_val2, jdouble s_val3)
(JNIEnv* env, jclass, jdouble size_width, jdouble size_height, jint type, jdouble s_val0, jdouble s_val1, jdouble s_val2, jdouble s_val3)
{
try {
LOGD("Mat::n_1Mat__DDIDDDD()");
@ -157,9 +169,11 @@ JNIEXPORT jlong JNICALL Java_org_opencv_core_Mat_n_1Mat__DDIDDDD
// Mat::Mat(Mat m, Range rowRange, Range colRange = Range::all())
//
JNIEXPORT jlong JNICALL Java_org_opencv_core_Mat_n_1Mat__JIIII
(JNIEnv* env, jclass, jlong m_nativeObj, jint rowRange_start, jint rowRange_end, jint colRange_start, jint colRange_end);
JNIEXPORT jlong JNICALL Java_org_opencv_core_Mat_n_1Mat__JIIII
(JNIEnv* env, jclass cls, jlong m_nativeObj, jint rowRange_start, jint rowRange_end, jint colRange_start, jint colRange_end)
(JNIEnv* env, jclass, jlong m_nativeObj, jint rowRange_start, jint rowRange_end, jint colRange_start, jint colRange_end)
{
try {
LOGD("Mat::n_1Mat__JIIII()");
@ -183,10 +197,12 @@ JNIEXPORT jlong JNICALL Java_org_opencv_core_Mat_n_1Mat__JIIII
}
JNIEXPORT jlong JNICALL Java_org_opencv_core_Mat_n_1Mat__JII
(JNIEnv* env, jclass, jlong m_nativeObj, jint rowRange_start, jint rowRange_end);
JNIEXPORT jlong JNICALL Java_org_opencv_core_Mat_n_1Mat__JII
(JNIEnv* env, jclass cls, jlong m_nativeObj, jint rowRange_start, jint rowRange_end)
(JNIEnv* env, jclass, jlong m_nativeObj, jint rowRange_start, jint rowRange_end)
{
try {
LOGD("Mat::n_1Mat__JII()");
@ -213,9 +229,11 @@ JNIEXPORT jlong JNICALL Java_org_opencv_core_Mat_n_1Mat__JII
// Mat Mat::adjustROI(int dtop, int dbottom, int dleft, int dright)
//
JNIEXPORT jlong JNICALL Java_org_opencv_core_Mat_n_1adjustROI
(JNIEnv* env, jclass, jlong self, jint dtop, jint dbottom, jint dleft, jint dright);
JNIEXPORT jlong JNICALL Java_org_opencv_core_Mat_n_1adjustROI
(JNIEnv* env, jclass cls, jlong self, jint dtop, jint dbottom, jint dleft, jint dright)
(JNIEnv* env, jclass, jlong self, jint dtop, jint dbottom, jint dleft, jint dright)
{
try {
LOGD("Mat::n_1adjustROI()");
@ -243,9 +261,11 @@ JNIEXPORT jlong JNICALL Java_org_opencv_core_Mat_n_1adjustROI
// void Mat::assignTo(Mat m, int type = -1)
//
JNIEXPORT void JNICALL Java_org_opencv_core_Mat_n_1assignTo__JJI
(JNIEnv* env, jclass, jlong self, jlong m_nativeObj, jint type);
JNIEXPORT void JNICALL Java_org_opencv_core_Mat_n_1assignTo__JJI
(JNIEnv* env, jclass cls, jlong self, jlong m_nativeObj, jint type)
(JNIEnv* env, jclass, jlong self, jlong m_nativeObj, jint type)
{
try {
LOGD("Mat::n_1assignTo__JJI()");
@ -268,10 +288,11 @@ JNIEXPORT void JNICALL Java_org_opencv_core_Mat_n_1assignTo__JJI
}
JNIEXPORT void JNICALL Java_org_opencv_core_Mat_n_1assignTo__JJ
(JNIEnv* env, jclass, jlong self, jlong m_nativeObj);
JNIEXPORT void JNICALL Java_org_opencv_core_Mat_n_1assignTo__JJ
(JNIEnv* env, jclass cls, jlong self, jlong m_nativeObj)
(JNIEnv* env, jclass, jlong self, jlong m_nativeObj)
{
try {
LOGD("Mat::n_1assignTo__JJ()");
@ -299,9 +320,11 @@ JNIEXPORT void JNICALL Java_org_opencv_core_Mat_n_1assignTo__JJ
// int Mat::channels()
//
JNIEXPORT jint JNICALL Java_org_opencv_core_Mat_n_1channels
(JNIEnv* env, jclass, jlong self);
JNIEXPORT jint JNICALL Java_org_opencv_core_Mat_n_1channels
(JNIEnv* env, jclass cls, jlong self)
(JNIEnv* env, jclass, jlong self)
{
try {
LOGD("Mat::n_1channels()");
@ -329,9 +352,11 @@ JNIEXPORT jint JNICALL Java_org_opencv_core_Mat_n_1channels
// int Mat::checkVector(int elemChannels, int depth = -1, bool requireContinuous = true)
//
JNIEXPORT jint JNICALL Java_org_opencv_core_Mat_n_1checkVector__JIIZ
(JNIEnv* env, jclass, jlong self, jint elemChannels, jint depth, jboolean requireContinuous);
JNIEXPORT jint JNICALL Java_org_opencv_core_Mat_n_1checkVector__JIIZ
(JNIEnv* env, jclass cls, jlong self, jint elemChannels, jint depth, jboolean requireContinuous)
(JNIEnv* env, jclass, jlong self, jint elemChannels, jint depth, jboolean requireContinuous)
{
try {
LOGD("Mat::n_1checkVector__JIIZ()");
@ -355,9 +380,11 @@ JNIEXPORT jint JNICALL Java_org_opencv_core_Mat_n_1checkVector__JIIZ
JNIEXPORT jint JNICALL Java_org_opencv_core_Mat_n_1checkVector__JII
(JNIEnv* env, jclass, jlong self, jint elemChannels, jint depth);
JNIEXPORT jint JNICALL Java_org_opencv_core_Mat_n_1checkVector__JII
(JNIEnv* env, jclass cls, jlong self, jint elemChannels, jint depth)
(JNIEnv* env, jclass, jlong self, jint elemChannels, jint depth)
{
try {
LOGD("Mat::n_1checkVector__JII()");
@ -380,10 +407,12 @@ JNIEXPORT jint JNICALL Java_org_opencv_core_Mat_n_1checkVector__JII
}
JNIEXPORT jint JNICALL Java_org_opencv_core_Mat_n_1checkVector__JI
(JNIEnv* env, jclass, jlong self, jint elemChannels);
JNIEXPORT jint JNICALL Java_org_opencv_core_Mat_n_1checkVector__JI
(JNIEnv* env, jclass cls, jlong self, jint elemChannels)
(JNIEnv* env, jclass, jlong self, jint elemChannels)
{
try {
LOGD("Mat::n_1checkVector__JI()");
@ -411,9 +440,12 @@ JNIEXPORT jint JNICALL Java_org_opencv_core_Mat_n_1checkVector__JI
// Mat Mat::clone()
//
JNIEXPORT jlong JNICALL Java_org_opencv_core_Mat_n_1clone
(JNIEnv* env, jclass, jlong self);
JNIEXPORT jlong JNICALL Java_org_opencv_core_Mat_n_1clone
(JNIEnv* env, jclass cls, jlong self)
(JNIEnv* env, jclass, jlong self)
{
try {
LOGD("Mat::n_1clone()");
@ -441,9 +473,11 @@ JNIEXPORT jlong JNICALL Java_org_opencv_core_Mat_n_1clone
// Mat Mat::col(int x)
//
JNIEXPORT jlong JNICALL Java_org_opencv_core_Mat_n_1col
(JNIEnv* env, jclass, jlong self, jint x);
JNIEXPORT jlong JNICALL Java_org_opencv_core_Mat_n_1col
(JNIEnv* env, jclass cls, jlong self, jint x)
(JNIEnv* env, jclass, jlong self, jint x)
{
try {
LOGD("Mat::n_1col()");
@ -471,9 +505,11 @@ JNIEXPORT jlong JNICALL Java_org_opencv_core_Mat_n_1col
// Mat Mat::colRange(int startcol, int endcol)
//
JNIEXPORT jlong JNICALL Java_org_opencv_core_Mat_n_1colRange
(JNIEnv* env, jclass, jlong self, jint startcol, jint endcol);
JNIEXPORT jlong JNICALL Java_org_opencv_core_Mat_n_1colRange
(JNIEnv* env, jclass cls, jlong self, jint startcol, jint endcol)
(JNIEnv* env, jclass, jlong self, jint startcol, jint endcol)
{
try {
LOGD("Mat::n_1colRange()");
@ -501,9 +537,11 @@ JNIEXPORT jlong JNICALL Java_org_opencv_core_Mat_n_1colRange
// int Mat::cols()
//
JNIEXPORT jint JNICALL Java_org_opencv_core_Mat_n_1cols
(JNIEnv* env, jclass, jlong self);
JNIEXPORT jint JNICALL Java_org_opencv_core_Mat_n_1cols
(JNIEnv* env, jclass cls, jlong self)
(JNIEnv* env, jclass, jlong self)
{
try {
LOGD("Mat::n_1cols()");
@ -531,9 +569,11 @@ JNIEXPORT jint JNICALL Java_org_opencv_core_Mat_n_1cols
// void Mat::convertTo(Mat& m, int rtype, double alpha = 1, double beta = 0)
//
JNIEXPORT void JNICALL Java_org_opencv_core_Mat_n_1convertTo__JJIDD
(JNIEnv* env, jclass, jlong self, jlong m_nativeObj, jint rtype, jdouble alpha, jdouble beta);
JNIEXPORT void JNICALL Java_org_opencv_core_Mat_n_1convertTo__JJIDD
(JNIEnv* env, jclass cls, jlong self, jlong m_nativeObj, jint rtype, jdouble alpha, jdouble beta)
(JNIEnv* env, jclass, jlong self, jlong m_nativeObj, jint rtype, jdouble alpha, jdouble beta)
{
try {
LOGD("Mat::n_1convertTo__JJIDD()");
@ -557,10 +597,11 @@ JNIEXPORT void JNICALL Java_org_opencv_core_Mat_n_1convertTo__JJIDD
}
JNIEXPORT void JNICALL Java_org_opencv_core_Mat_n_1convertTo__JJID
(JNIEnv* env, jclass, jlong self, jlong m_nativeObj, jint rtype, jdouble alpha);
JNIEXPORT void JNICALL Java_org_opencv_core_Mat_n_1convertTo__JJID
(JNIEnv* env, jclass cls, jlong self, jlong m_nativeObj, jint rtype, jdouble alpha)
(JNIEnv* env, jclass, jlong self, jlong m_nativeObj, jint rtype, jdouble alpha)
{
try {
LOGD("Mat::n_1convertTo__JJID()");
@ -584,10 +625,11 @@ JNIEXPORT void JNICALL Java_org_opencv_core_Mat_n_1convertTo__JJID
}
JNIEXPORT void JNICALL Java_org_opencv_core_Mat_n_1convertTo__JJI
(JNIEnv* env, jclass, jlong self, jlong m_nativeObj, jint rtype);
JNIEXPORT void JNICALL Java_org_opencv_core_Mat_n_1convertTo__JJI
(JNIEnv* env, jclass cls, jlong self, jlong m_nativeObj, jint rtype)
(JNIEnv* env, jclass, jlong self, jlong m_nativeObj, jint rtype)
{
try {
LOGD("Mat::n_1convertTo__JJI()");
@ -616,9 +658,11 @@ JNIEXPORT void JNICALL Java_org_opencv_core_Mat_n_1convertTo__JJI
// void Mat::copyTo(Mat& m)
//
JNIEXPORT void JNICALL Java_org_opencv_core_Mat_n_1copyTo__JJ
(JNIEnv* env, jclass, jlong self, jlong m_nativeObj);
JNIEXPORT void JNICALL Java_org_opencv_core_Mat_n_1copyTo__JJ
(JNIEnv* env, jclass cls, jlong self, jlong m_nativeObj)
(JNIEnv* env, jclass, jlong self, jlong m_nativeObj)
{
try {
LOGD("Mat::n_1copyTo__JJ()");
@ -647,9 +691,11 @@ JNIEXPORT void JNICALL Java_org_opencv_core_Mat_n_1copyTo__JJ
// void Mat::copyTo(Mat& m, Mat mask)
//
JNIEXPORT void JNICALL Java_org_opencv_core_Mat_n_1copyTo__JJJ
(JNIEnv* env, jclass, jlong self, jlong m_nativeObj, jlong mask_nativeObj);
JNIEXPORT void JNICALL Java_org_opencv_core_Mat_n_1copyTo__JJJ
(JNIEnv* env, jclass cls, jlong self, jlong m_nativeObj, jlong mask_nativeObj)
(JNIEnv* env, jclass, jlong self, jlong m_nativeObj, jlong mask_nativeObj)
{
try {
LOGD("Mat::n_1copyTo__JJJ()");
@ -679,9 +725,11 @@ JNIEXPORT void JNICALL Java_org_opencv_core_Mat_n_1copyTo__JJJ
// void Mat::create(int rows, int cols, int type)
//
JNIEXPORT void JNICALL Java_org_opencv_core_Mat_n_1create__JIII
(JNIEnv* env, jclass, jlong self, jint rows, jint cols, jint type);
JNIEXPORT void JNICALL Java_org_opencv_core_Mat_n_1create__JIII
(JNIEnv* env, jclass cls, jlong self, jint rows, jint cols, jint type)
(JNIEnv* env, jclass, jlong self, jint rows, jint cols, jint type)
{
try {
LOGD("Mat::n_1create__JIII()");
@ -709,9 +757,11 @@ JNIEXPORT void JNICALL Java_org_opencv_core_Mat_n_1create__JIII
// void Mat::create(Size size, int type)
//
JNIEXPORT void JNICALL Java_org_opencv_core_Mat_n_1create__JDDI
(JNIEnv* env, jclass, jlong self, jdouble size_width, jdouble size_height, jint type);
JNIEXPORT void JNICALL Java_org_opencv_core_Mat_n_1create__JDDI
(JNIEnv* env, jclass cls, jlong self, jdouble size_width, jdouble size_height, jint type)
(JNIEnv* env, jclass, jlong self, jdouble size_width, jdouble size_height, jint type)
{
try {
LOGD("Mat::n_1create__JDDI()");
@ -740,9 +790,11 @@ JNIEXPORT void JNICALL Java_org_opencv_core_Mat_n_1create__JDDI
// Mat Mat::cross(Mat m)
//
JNIEXPORT jlong JNICALL Java_org_opencv_core_Mat_n_1cross
(JNIEnv* env, jclass, jlong self, jlong m_nativeObj);
JNIEXPORT jlong JNICALL Java_org_opencv_core_Mat_n_1cross
(JNIEnv* env, jclass cls, jlong self, jlong m_nativeObj)
(JNIEnv* env, jclass, jlong self, jlong m_nativeObj)
{
try {
LOGD("Mat::n_1cross()");
@ -771,9 +823,11 @@ JNIEXPORT jlong JNICALL Java_org_opencv_core_Mat_n_1cross
// long Mat::dataAddr()
//
JNIEXPORT jlong JNICALL Java_org_opencv_core_Mat_n_1dataAddr
(JNIEnv*, jclass, jlong self);
JNIEXPORT jlong JNICALL Java_org_opencv_core_Mat_n_1dataAddr
(JNIEnv* env, jclass cls, jlong self)
(JNIEnv*, jclass, jlong self)
{
LOGD("Mat::n_1dataAddr()");
Mat* me = (Mat*) self; //TODO: check for NULL
@ -786,9 +840,11 @@ JNIEXPORT jlong JNICALL Java_org_opencv_core_Mat_n_1dataAddr
// int Mat::depth()
//
JNIEXPORT jint JNICALL Java_org_opencv_core_Mat_n_1depth
(JNIEnv* env, jclass, jlong self);
JNIEXPORT jint JNICALL Java_org_opencv_core_Mat_n_1depth
(JNIEnv* env, jclass cls, jlong self)
(JNIEnv* env, jclass, jlong self)
{
try {
LOGD("Mat::n_1depth()");
@ -816,9 +872,11 @@ JNIEXPORT jint JNICALL Java_org_opencv_core_Mat_n_1depth
// Mat Mat::diag(int d = 0)
//
JNIEXPORT jlong JNICALL Java_org_opencv_core_Mat_n_1diag__JI
(JNIEnv* env, jclass, jlong self, jint d);
JNIEXPORT jlong JNICALL Java_org_opencv_core_Mat_n_1diag__JI
(JNIEnv* env, jclass cls, jlong self, jint d)
(JNIEnv* env, jclass, jlong self, jint d)
{
try {
LOGD("Mat::n_1diag__JI()");
@ -847,9 +905,11 @@ JNIEXPORT jlong JNICALL Java_org_opencv_core_Mat_n_1diag__JI
// static Mat Mat::diag(Mat d)
//
JNIEXPORT jlong JNICALL Java_org_opencv_core_Mat_n_1diag__J
(JNIEnv* env, jclass, jlong d_nativeObj);
JNIEXPORT jlong JNICALL Java_org_opencv_core_Mat_n_1diag__J
(JNIEnv* env, jclass cls, jlong d_nativeObj)
(JNIEnv* env, jclass, jlong d_nativeObj)
{
try {
LOGD("Mat::n_1diag__J()");
@ -877,9 +937,11 @@ JNIEXPORT jlong JNICALL Java_org_opencv_core_Mat_n_1diag__J
// double Mat::dot(Mat m)
//
JNIEXPORT jdouble JNICALL Java_org_opencv_core_Mat_n_1dot
(JNIEnv* env, jclass, jlong self, jlong m_nativeObj);
JNIEXPORT jdouble JNICALL Java_org_opencv_core_Mat_n_1dot
(JNIEnv* env, jclass cls, jlong self, jlong m_nativeObj)
(JNIEnv* env, jclass, jlong self, jlong m_nativeObj)
{
try {
LOGD("Mat::n_1dot()");
@ -908,9 +970,11 @@ JNIEXPORT jdouble JNICALL Java_org_opencv_core_Mat_n_1dot
// size_t Mat::elemSize()
//
JNIEXPORT jlong JNICALL Java_org_opencv_core_Mat_n_1elemSize
(JNIEnv* env, jclass, jlong self);
JNIEXPORT jlong JNICALL Java_org_opencv_core_Mat_n_1elemSize
(JNIEnv* env, jclass cls, jlong self)
(JNIEnv* env, jclass, jlong self)
{
try {
LOGD("Mat::n_1elemSize()");
@ -938,9 +1002,11 @@ JNIEXPORT jlong JNICALL Java_org_opencv_core_Mat_n_1elemSize
// size_t Mat::elemSize1()
//
JNIEXPORT jlong JNICALL Java_org_opencv_core_Mat_n_1elemSize1
(JNIEnv* env, jclass, jlong self);
JNIEXPORT jlong JNICALL Java_org_opencv_core_Mat_n_1elemSize1
(JNIEnv* env, jclass cls, jlong self)
(JNIEnv* env, jclass, jlong self)
{
try {
LOGD("Mat::n_1elemSize1()");
@ -968,9 +1034,11 @@ JNIEXPORT jlong JNICALL Java_org_opencv_core_Mat_n_1elemSize1
// bool Mat::empty()
//
JNIEXPORT jboolean JNICALL Java_org_opencv_core_Mat_n_1empty
(JNIEnv* env, jclass, jlong self);
JNIEXPORT jboolean JNICALL Java_org_opencv_core_Mat_n_1empty
(JNIEnv* env, jclass cls, jlong self)
(JNIEnv* env, jclass, jlong self)
{
try {
LOGD("Mat::n_1empty()");
@ -998,9 +1066,11 @@ JNIEXPORT jboolean JNICALL Java_org_opencv_core_Mat_n_1empty
// static Mat Mat::eye(int rows, int cols, int type)
//
JNIEXPORT jlong JNICALL Java_org_opencv_core_Mat_n_1eye__III
(JNIEnv* env, jclass, jint rows, jint cols, jint type);
JNIEXPORT jlong JNICALL Java_org_opencv_core_Mat_n_1eye__III
(JNIEnv* env, jclass cls, jint rows, jint cols, jint type)
(JNIEnv* env, jclass, jint rows, jint cols, jint type)
{
try {
LOGD("Mat::n_1eye__III()");
@ -1028,9 +1098,11 @@ JNIEXPORT jlong JNICALL Java_org_opencv_core_Mat_n_1eye__III
// static Mat Mat::eye(Size size, int type)
//
JNIEXPORT jlong JNICALL Java_org_opencv_core_Mat_n_1eye__DDI
(JNIEnv* env, jclass, jdouble size_width, jdouble size_height, jint type);
JNIEXPORT jlong JNICALL Java_org_opencv_core_Mat_n_1eye__DDI
(JNIEnv* env, jclass cls, jdouble size_width, jdouble size_height, jint type)
(JNIEnv* env, jclass, jdouble size_width, jdouble size_height, jint type)
{
try {
LOGD("Mat::n_1eye__DDI()");
@ -1058,9 +1130,11 @@ JNIEXPORT jlong JNICALL Java_org_opencv_core_Mat_n_1eye__DDI
// Mat Mat::inv(int method = DECOMP_LU)
//
JNIEXPORT jlong JNICALL Java_org_opencv_core_Mat_n_1inv__JI
(JNIEnv* env, jclass, jlong self, jint method);
JNIEXPORT jlong JNICALL Java_org_opencv_core_Mat_n_1inv__JI
(JNIEnv* env, jclass cls, jlong self, jint method)
(JNIEnv* env, jclass, jlong self, jint method)
{
try {
LOGD("Mat::n_1inv__JI()");
@ -1083,10 +1157,11 @@ JNIEXPORT jlong JNICALL Java_org_opencv_core_Mat_n_1inv__JI
}
JNIEXPORT jlong JNICALL Java_org_opencv_core_Mat_n_1inv__J
(JNIEnv* env, jclass, jlong self);
JNIEXPORT jlong JNICALL Java_org_opencv_core_Mat_n_1inv__J
(JNIEnv* env, jclass cls, jlong self)
(JNIEnv* env, jclass, jlong self)
{
try {
LOGD("Mat::n_1inv__J()");
@ -1114,9 +1189,11 @@ JNIEXPORT jlong JNICALL Java_org_opencv_core_Mat_n_1inv__J
// bool Mat::isContinuous()
//
JNIEXPORT jboolean JNICALL Java_org_opencv_core_Mat_n_1isContinuous
(JNIEnv* env, jclass, jlong self);
JNIEXPORT jboolean JNICALL Java_org_opencv_core_Mat_n_1isContinuous
(JNIEnv* env, jclass cls, jlong self)
(JNIEnv* env, jclass, jlong self)
{
try {
LOGD("Mat::n_1isContinuous()");
@ -1144,9 +1221,11 @@ JNIEXPORT jboolean JNICALL Java_org_opencv_core_Mat_n_1isContinuous
// bool Mat::isSubmatrix()
//
JNIEXPORT jboolean JNICALL Java_org_opencv_core_Mat_n_1isSubmatrix
(JNIEnv* env, jclass, jlong self);
JNIEXPORT jboolean JNICALL Java_org_opencv_core_Mat_n_1isSubmatrix
(JNIEnv* env, jclass cls, jlong self)
(JNIEnv* env, jclass, jlong self)
{
try {
LOGD("Mat::n_1isSubmatrix()");
@ -1174,9 +1253,11 @@ JNIEXPORT jboolean JNICALL Java_org_opencv_core_Mat_n_1isSubmatrix
// void Mat::locateROI(Size wholeSize, Point ofs)
//
JNIEXPORT void JNICALL Java_org_opencv_core_Mat_locateROI_10
(JNIEnv* env, jclass, jlong self, jdoubleArray wholeSize_out, jdoubleArray ofs_out);
JNIEXPORT void JNICALL Java_org_opencv_core_Mat_locateROI_10
(JNIEnv* env, jclass cls, jlong self, jdoubleArray wholeSize_out, jdoubleArray ofs_out)
(JNIEnv* env, jclass, jlong self, jdoubleArray wholeSize_out, jdoubleArray ofs_out)
{
try {
LOGD("core::locateROI_10()");
@ -1206,9 +1287,11 @@ JNIEXPORT void JNICALL Java_org_opencv_core_Mat_locateROI_10
// Mat Mat::mul(Mat m, double scale = 1)
//
JNIEXPORT jlong JNICALL Java_org_opencv_core_Mat_n_1mul__JJD
(JNIEnv* env, jclass, jlong self, jlong m_nativeObj, jdouble scale);
JNIEXPORT jlong JNICALL Java_org_opencv_core_Mat_n_1mul__JJD
(JNIEnv* env, jclass cls, jlong self, jlong m_nativeObj, jdouble scale)
(JNIEnv* env, jclass, jlong self, jlong m_nativeObj, jdouble scale)
{
try {
LOGD("Mat::n_1mul__JJD()");
@ -1233,9 +1316,11 @@ JNIEXPORT jlong JNICALL Java_org_opencv_core_Mat_n_1mul__JJD
JNIEXPORT jlong JNICALL Java_org_opencv_core_Mat_n_1mul__JJ
(JNIEnv* env, jclass, jlong self, jlong m_nativeObj);
JNIEXPORT jlong JNICALL Java_org_opencv_core_Mat_n_1mul__JJ
(JNIEnv* env, jclass cls, jlong self, jlong m_nativeObj)
(JNIEnv* env, jclass, jlong self, jlong m_nativeObj)
{
try {
LOGD("Mat::n_1mul__JJ()");
@ -1264,9 +1349,11 @@ JNIEXPORT jlong JNICALL Java_org_opencv_core_Mat_n_1mul__JJ
// static Mat Mat::ones(int rows, int cols, int type)
//
JNIEXPORT jlong JNICALL Java_org_opencv_core_Mat_n_1ones__III
(JNIEnv* env, jclass, jint rows, jint cols, jint type);
JNIEXPORT jlong JNICALL Java_org_opencv_core_Mat_n_1ones__III
(JNIEnv* env, jclass cls, jint rows, jint cols, jint type)
(JNIEnv* env, jclass, jint rows, jint cols, jint type)
{
try {
LOGD("Mat::n_1ones__III()");
@ -1294,9 +1381,11 @@ JNIEXPORT jlong JNICALL Java_org_opencv_core_Mat_n_1ones__III
// static Mat Mat::ones(Size size, int type)
//
JNIEXPORT jlong JNICALL Java_org_opencv_core_Mat_n_1ones__DDI
(JNIEnv* env, jclass, jdouble size_width, jdouble size_height, jint type);
JNIEXPORT jlong JNICALL Java_org_opencv_core_Mat_n_1ones__DDI
(JNIEnv* env, jclass cls, jdouble size_width, jdouble size_height, jint type)
(JNIEnv* env, jclass, jdouble size_width, jdouble size_height, jint type)
{
try {
LOGD("Mat::n_1ones__DDI()");
@ -1324,9 +1413,11 @@ JNIEXPORT jlong JNICALL Java_org_opencv_core_Mat_n_1ones__DDI
// void Mat::push_back(Mat m)
//
JNIEXPORT void JNICALL Java_org_opencv_core_Mat_n_1push_1back
(JNIEnv* env, jclass, jlong self, jlong m_nativeObj);
JNIEXPORT void JNICALL Java_org_opencv_core_Mat_n_1push_1back
(JNIEnv* env, jclass cls, jlong self, jlong m_nativeObj)
(JNIEnv* env, jclass, jlong self, jlong m_nativeObj)
{
try {
LOGD("Mat::n_1push_1back()");
@ -1354,9 +1445,11 @@ JNIEXPORT void JNICALL Java_org_opencv_core_Mat_n_1push_1back
// void Mat::release()
//
JNIEXPORT void JNICALL Java_org_opencv_core_Mat_n_1release
(JNIEnv* env, jclass, jlong self);
JNIEXPORT void JNICALL Java_org_opencv_core_Mat_n_1release
(JNIEnv* env, jclass cls, jlong self)
(JNIEnv* env, jclass, jlong self)
{
try {
LOGD("Mat::n_1release()");
@ -1384,9 +1477,11 @@ JNIEXPORT void JNICALL Java_org_opencv_core_Mat_n_1release
// Mat Mat::reshape(int cn, int rows = 0)
//
JNIEXPORT jlong JNICALL Java_org_opencv_core_Mat_n_1reshape__JII
(JNIEnv* env, jclass, jlong self, jint cn, jint rows);
JNIEXPORT jlong JNICALL Java_org_opencv_core_Mat_n_1reshape__JII
(JNIEnv* env, jclass cls, jlong self, jint cn, jint rows)
(JNIEnv* env, jclass, jlong self, jint cn, jint rows)
{
try {
LOGD("Mat::n_1reshape__JII()");
@ -1410,9 +1505,11 @@ JNIEXPORT jlong JNICALL Java_org_opencv_core_Mat_n_1reshape__JII
JNIEXPORT jlong JNICALL Java_org_opencv_core_Mat_n_1reshape__JI
(JNIEnv* env, jclass, jlong self, jint cn);
JNIEXPORT jlong JNICALL Java_org_opencv_core_Mat_n_1reshape__JI
(JNIEnv* env, jclass cls, jlong self, jint cn)
(JNIEnv* env, jclass, jlong self, jint cn)
{
try {
LOGD("Mat::n_1reshape__JI()");
@ -1440,9 +1537,11 @@ JNIEXPORT jlong JNICALL Java_org_opencv_core_Mat_n_1reshape__JI
// Mat Mat::row(int y)
//
JNIEXPORT jlong JNICALL Java_org_opencv_core_Mat_n_1row
(JNIEnv* env, jclass, jlong self, jint y);
JNIEXPORT jlong JNICALL Java_org_opencv_core_Mat_n_1row
(JNIEnv* env, jclass cls, jlong self, jint y)
(JNIEnv* env, jclass, jlong self, jint y)
{
try {
LOGD("Mat::n_1row()");
@ -1470,9 +1569,11 @@ JNIEXPORT jlong JNICALL Java_org_opencv_core_Mat_n_1row
// Mat Mat::rowRange(int startrow, int endrow)
//
JNIEXPORT jlong JNICALL Java_org_opencv_core_Mat_n_1rowRange
(JNIEnv* env, jclass, jlong self, jint startrow, jint endrow);
JNIEXPORT jlong JNICALL Java_org_opencv_core_Mat_n_1rowRange
(JNIEnv* env, jclass cls, jlong self, jint startrow, jint endrow)
(JNIEnv* env, jclass, jlong self, jint startrow, jint endrow)
{
try {
LOGD("Mat::n_1rowRange()");
@ -1500,9 +1601,11 @@ JNIEXPORT jlong JNICALL Java_org_opencv_core_Mat_n_1rowRange
// int Mat::rows()
//
JNIEXPORT jint JNICALL Java_org_opencv_core_Mat_n_1rows
(JNIEnv* env, jclass, jlong self);
JNIEXPORT jint JNICALL Java_org_opencv_core_Mat_n_1rows
(JNIEnv* env, jclass cls, jlong self)
(JNIEnv* env, jclass, jlong self)
{
try {
LOGD("Mat::n_1rows()");
@ -1530,9 +1633,11 @@ JNIEXPORT jint JNICALL Java_org_opencv_core_Mat_n_1rows
// Mat Mat::operator =(Scalar s)
//
JNIEXPORT jlong JNICALL Java_org_opencv_core_Mat_n_1setTo__JDDDD
(JNIEnv* env, jclass, jlong self, jdouble s_val0, jdouble s_val1, jdouble s_val2, jdouble s_val3);
JNIEXPORT jlong JNICALL Java_org_opencv_core_Mat_n_1setTo__JDDDD
(JNIEnv* env, jclass cls, jlong self, jdouble s_val0, jdouble s_val1, jdouble s_val2, jdouble s_val3)
(JNIEnv* env, jclass, jlong self, jdouble s_val0, jdouble s_val1, jdouble s_val2, jdouble s_val3)
{
try {
LOGD("Mat::n_1setTo__JDDDD()");
@ -1561,9 +1666,11 @@ JNIEXPORT jlong JNICALL Java_org_opencv_core_Mat_n_1setTo__JDDDD
// Mat Mat::setTo(Scalar value, Mat mask = Mat())
//
JNIEXPORT jlong JNICALL Java_org_opencv_core_Mat_n_1setTo__JDDDDJ
(JNIEnv* env, jclass, jlong self, jdouble s_val0, jdouble s_val1, jdouble s_val2, jdouble s_val3, jlong mask_nativeObj);
JNIEXPORT jlong JNICALL Java_org_opencv_core_Mat_n_1setTo__JDDDDJ
(JNIEnv* env, jclass cls, jlong self, jdouble s_val0, jdouble s_val1, jdouble s_val2, jdouble s_val3, jlong mask_nativeObj)
(JNIEnv* env, jclass, jlong self, jdouble s_val0, jdouble s_val1, jdouble s_val2, jdouble s_val3, jlong mask_nativeObj)
{
try {
LOGD("Mat::n_1setTo__JDDDDJ()");
@ -1593,9 +1700,11 @@ JNIEXPORT jlong JNICALL Java_org_opencv_core_Mat_n_1setTo__JDDDDJ
// Mat Mat::setTo(Mat value, Mat mask = Mat())
//
JNIEXPORT jlong JNICALL Java_org_opencv_core_Mat_n_1setTo__JJJ
(JNIEnv* env, jclass, jlong self, jlong value_nativeObj, jlong mask_nativeObj);
JNIEXPORT jlong JNICALL Java_org_opencv_core_Mat_n_1setTo__JJJ
(JNIEnv* env, jclass cls, jlong self, jlong value_nativeObj, jlong mask_nativeObj)
(JNIEnv* env, jclass, jlong self, jlong value_nativeObj, jlong mask_nativeObj)
{
try {
LOGD("Mat::n_1setTo__JJJ()");
@ -1621,9 +1730,11 @@ JNIEXPORT jlong JNICALL Java_org_opencv_core_Mat_n_1setTo__JJJ
JNIEXPORT jlong JNICALL Java_org_opencv_core_Mat_n_1setTo__JJ
(JNIEnv* env, jclass, jlong self, jlong value_nativeObj);
JNIEXPORT jlong JNICALL Java_org_opencv_core_Mat_n_1setTo__JJ
(JNIEnv* env, jclass cls, jlong self, jlong value_nativeObj)
(JNIEnv* env, jclass, jlong self, jlong value_nativeObj)
{
try {
LOGD("Mat::n_1setTo__JJ()");
@ -1652,9 +1763,11 @@ JNIEXPORT jlong JNICALL Java_org_opencv_core_Mat_n_1setTo__JJ
// Size Mat::size()
//
JNIEXPORT jdoubleArray JNICALL Java_org_opencv_core_Mat_n_1size
(JNIEnv* env, jclass, jlong self);
JNIEXPORT jdoubleArray JNICALL Java_org_opencv_core_Mat_n_1size
(JNIEnv* env, jclass cls, jlong self)
(JNIEnv* env, jclass, jlong self)
{
try {
LOGD("Mat::n_1size()");
@ -1682,9 +1795,11 @@ JNIEXPORT jdoubleArray JNICALL Java_org_opencv_core_Mat_n_1size
// size_t Mat::step1(int i = 0)
//
JNIEXPORT jlong JNICALL Java_org_opencv_core_Mat_n_1step1__JI
(JNIEnv* env, jclass, jlong self, jint i);
JNIEXPORT jlong JNICALL Java_org_opencv_core_Mat_n_1step1__JI
(JNIEnv* env, jclass cls, jlong self, jint i)
(JNIEnv* env, jclass, jlong self, jint i)
{
try {
LOGD("Mat::n_1step1__JI()");
@ -1708,9 +1823,11 @@ JNIEXPORT jlong JNICALL Java_org_opencv_core_Mat_n_1step1__JI
JNIEXPORT jlong JNICALL Java_org_opencv_core_Mat_n_1step1__J
(JNIEnv* env, jclass, jlong self);
JNIEXPORT jlong JNICALL Java_org_opencv_core_Mat_n_1step1__J
(JNIEnv* env, jclass cls, jlong self)
(JNIEnv* env, jclass, jlong self)
{
try {
LOGD("Mat::n_1step1__J()");
@ -1736,9 +1853,11 @@ JNIEXPORT jlong JNICALL Java_org_opencv_core_Mat_n_1step1__J
// Mat Mat::operator()(Range rowRange, Range colRange)
//
JNIEXPORT jlong JNICALL Java_org_opencv_core_Mat_n_1submat_1rr
(JNIEnv* env, jclass, jlong self, jint rowRange_start, jint rowRange_end, jint colRange_start, jint colRange_end);
JNIEXPORT jlong JNICALL Java_org_opencv_core_Mat_n_1submat_1rr
(JNIEnv* env, jclass cls, jlong self, jint rowRange_start, jint rowRange_end, jint colRange_start, jint colRange_end)
(JNIEnv* env, jclass, jlong self, jint rowRange_start, jint rowRange_end, jint colRange_start, jint colRange_end)
{
try {
LOGD("Mat::n_1submat_1rr()");
@ -1768,9 +1887,11 @@ JNIEXPORT jlong JNICALL Java_org_opencv_core_Mat_n_1submat_1rr
// Mat Mat::operator()(Rect roi)
//
JNIEXPORT jlong JNICALL Java_org_opencv_core_Mat_n_1submat
(JNIEnv* env, jclass, jlong self, jint roi_x, jint roi_y, jint roi_width, jint roi_height);
JNIEXPORT jlong JNICALL Java_org_opencv_core_Mat_n_1submat
(JNIEnv* env, jclass cls, jlong self, jint roi_x, jint roi_y, jint roi_width, jint roi_height)
(JNIEnv* env, jclass, jlong self, jint roi_x, jint roi_y, jint roi_width, jint roi_height)
{
try {
LOGD("Mat::n_1submat()");
@ -1799,9 +1920,11 @@ JNIEXPORT jlong JNICALL Java_org_opencv_core_Mat_n_1submat
// Mat Mat::t()
//
JNIEXPORT jlong JNICALL Java_org_opencv_core_Mat_n_1t
(JNIEnv* env, jclass, jlong self);
JNIEXPORT jlong JNICALL Java_org_opencv_core_Mat_n_1t
(JNIEnv* env, jclass cls, jlong self)
(JNIEnv* env, jclass, jlong self)
{
try {
LOGD("Mat::n_1t()");
@ -1829,9 +1952,11 @@ JNIEXPORT jlong JNICALL Java_org_opencv_core_Mat_n_1t
// size_t Mat::total()
//
JNIEXPORT jlong JNICALL Java_org_opencv_core_Mat_n_1total
(JNIEnv* env, jclass, jlong self);
JNIEXPORT jlong JNICALL Java_org_opencv_core_Mat_n_1total
(JNIEnv* env, jclass cls, jlong self)
(JNIEnv* env, jclass, jlong self)
{
try {
LOGD("Mat::n_1total()");
@ -1859,9 +1984,11 @@ JNIEXPORT jlong JNICALL Java_org_opencv_core_Mat_n_1total
// int Mat::type()
//
JNIEXPORT jint JNICALL Java_org_opencv_core_Mat_n_1type
(JNIEnv* env, jclass, jlong self);
JNIEXPORT jint JNICALL Java_org_opencv_core_Mat_n_1type
(JNIEnv* env, jclass cls, jlong self)
(JNIEnv* env, jclass, jlong self)
{
try {
LOGD("Mat::n_1type()");
@ -1889,9 +2016,11 @@ JNIEXPORT jint JNICALL Java_org_opencv_core_Mat_n_1type
// static Mat Mat::zeros(int rows, int cols, int type)
//
JNIEXPORT jlong JNICALL Java_org_opencv_core_Mat_n_1zeros__III
(JNIEnv* env, jclass, jint rows, jint cols, jint type);
JNIEXPORT jlong JNICALL Java_org_opencv_core_Mat_n_1zeros__III
(JNIEnv* env, jclass cls, jint rows, jint cols, jint type)
(JNIEnv* env, jclass, jint rows, jint cols, jint type)
{
try {
LOGD("Mat::n_1zeros__III()");
@ -1919,9 +2048,11 @@ JNIEXPORT jlong JNICALL Java_org_opencv_core_Mat_n_1zeros__III
// static Mat Mat::zeros(Size size, int type)
//
JNIEXPORT jlong JNICALL Java_org_opencv_core_Mat_n_1zeros__DDI
(JNIEnv* env, jclass, jdouble size_width, jdouble size_height, jint type);
JNIEXPORT jlong JNICALL Java_org_opencv_core_Mat_n_1zeros__DDI
(JNIEnv* env, jclass cls, jdouble size_width, jdouble size_height, jint type)
(JNIEnv* env, jclass, jdouble size_width, jdouble size_height, jint type)
{
try {
LOGD("Mat::n_1zeros__DDI()");
@ -1951,7 +2082,10 @@ JNIEXPORT jlong JNICALL Java_org_opencv_core_Mat_n_1zeros__DDI
//
JNIEXPORT void JNICALL Java_org_opencv_core_Mat_n_1delete
(JNIEnv* env, jclass cls, jlong self)
(JNIEnv*, jclass, jlong self);
JNIEXPORT void JNICALL Java_org_opencv_core_Mat_n_1delete
(JNIEnv*, jclass, jlong self)
{
delete (Mat*) self;
}
@ -1960,7 +2094,10 @@ JNIEXPORT void JNICALL Java_org_opencv_core_Mat_n_1delete
#define PUT_ITEM(T, R, C) { T*dst = (T*)me->ptr(R, C); for(int ch=0; ch<me->channels() && count>0; count--,ch++,src++,dst++) *dst = cv::saturate_cast<T>(*src); }
JNIEXPORT jint JNICALL Java_org_opencv_core_Mat_nPutD
(JNIEnv* env, jclass cls, jlong self, jint row, jint col, jint count, jdoubleArray vals)
(JNIEnv* env, jclass, jlong self, jint row, jint col, jint count, jdoubleArray vals);
JNIEXPORT jint JNICALL Java_org_opencv_core_Mat_nPutD
(JNIEnv* env, jclass, jlong self, jint row, jint col, jint count, jdoubleArray vals)
{
try {
LOGD("Mat::nPutD()");
@ -2054,7 +2191,10 @@ template<typename T> static int mat_put(cv::Mat* m, int row, int col, int count,
extern "C" {
JNIEXPORT jint JNICALL Java_org_opencv_core_Mat_nPutB
(JNIEnv* env, jclass cls, jlong self, jint row, jint col, jint count, jbyteArray vals)
(JNIEnv* env, jclass, jlong self, jint row, jint col, jint count, jbyteArray vals);
JNIEXPORT jint JNICALL Java_org_opencv_core_Mat_nPutB
(JNIEnv* env, jclass, jlong self, jint row, jint col, jint count, jbyteArray vals)
{
try {
LOGD("Mat::nPutB()");
@ -2082,7 +2222,10 @@ JNIEXPORT jint JNICALL Java_org_opencv_core_Mat_nPutB
}
JNIEXPORT jint JNICALL Java_org_opencv_core_Mat_nPutS
(JNIEnv* env, jclass cls, jlong self, jint row, jint col, jint count, jshortArray vals)
(JNIEnv* env, jclass, jlong self, jint row, jint col, jint count, jshortArray vals);
JNIEXPORT jint JNICALL Java_org_opencv_core_Mat_nPutS
(JNIEnv* env, jclass, jlong self, jint row, jint col, jint count, jshortArray vals)
{
try {
LOGD("Mat::nPutS()");
@ -2110,7 +2253,10 @@ JNIEXPORT jint JNICALL Java_org_opencv_core_Mat_nPutS
}
JNIEXPORT jint JNICALL Java_org_opencv_core_Mat_nPutI
(JNIEnv* env, jclass cls, jlong self, jint row, jint col, jint count, jintArray vals)
(JNIEnv* env, jclass, jlong self, jint row, jint col, jint count, jintArray vals);
JNIEXPORT jint JNICALL Java_org_opencv_core_Mat_nPutI
(JNIEnv* env, jclass, jlong self, jint row, jint col, jint count, jintArray vals)
{
try {
LOGD("Mat::nPutI()");
@ -2138,7 +2284,10 @@ JNIEXPORT jint JNICALL Java_org_opencv_core_Mat_nPutI
}
JNIEXPORT jint JNICALL Java_org_opencv_core_Mat_nPutF
(JNIEnv* env, jclass cls, jlong self, jint row, jint col, jint count, jfloatArray vals)
(JNIEnv* env, jclass, jlong self, jint row, jint col, jint count, jfloatArray vals);
JNIEXPORT jint JNICALL Java_org_opencv_core_Mat_nPutF
(JNIEnv* env, jclass, jlong self, jint row, jint col, jint count, jfloatArray vals)
{
try {
LOGD("Mat::nPutF()");
@ -2201,7 +2350,10 @@ template<typename T> int mat_get(cv::Mat* m, int row, int col, int count, char*
extern "C" {
JNIEXPORT jint JNICALL Java_org_opencv_core_Mat_nGetB
(JNIEnv* env, jclass cls, jlong self, jint row, jint col, jint count, jbyteArray vals)
(JNIEnv* env, jclass, jlong self, jint row, jint col, jint count, jbyteArray vals);
JNIEXPORT jint JNICALL Java_org_opencv_core_Mat_nGetB
(JNIEnv* env, jclass, jlong self, jint row, jint col, jint count, jbyteArray vals)
{
try {
LOGD("Mat::nGetB()");
@ -2229,7 +2381,10 @@ JNIEXPORT jint JNICALL Java_org_opencv_core_Mat_nGetB
}
JNIEXPORT jint JNICALL Java_org_opencv_core_Mat_nGetS
(JNIEnv* env, jclass cls, jlong self, jint row, jint col, jint count, jshortArray vals)
(JNIEnv* env, jclass, jlong self, jint row, jint col, jint count, jshortArray vals);
JNIEXPORT jint JNICALL Java_org_opencv_core_Mat_nGetS
(JNIEnv* env, jclass, jlong self, jint row, jint col, jint count, jshortArray vals)
{
try {
LOGD("Mat::nGetS()");
@ -2257,7 +2412,10 @@ JNIEXPORT jint JNICALL Java_org_opencv_core_Mat_nGetS
}
JNIEXPORT jint JNICALL Java_org_opencv_core_Mat_nGetI
(JNIEnv* env, jclass cls, jlong self, jint row, jint col, jint count, jintArray vals)
(JNIEnv* env, jclass, jlong self, jint row, jint col, jint count, jintArray vals);
JNIEXPORT jint JNICALL Java_org_opencv_core_Mat_nGetI
(JNIEnv* env, jclass, jlong self, jint row, jint col, jint count, jintArray vals)
{
try {
LOGD("Mat::nGetI()");
@ -2285,7 +2443,10 @@ JNIEXPORT jint JNICALL Java_org_opencv_core_Mat_nGetI
}
JNIEXPORT jint JNICALL Java_org_opencv_core_Mat_nGetF
(JNIEnv* env, jclass cls, jlong self, jint row, jint col, jint count, jfloatArray vals)
(JNIEnv* env, jclass, jlong self, jint row, jint col, jint count, jfloatArray vals);
JNIEXPORT jint JNICALL Java_org_opencv_core_Mat_nGetF
(JNIEnv* env, jclass, jlong self, jint row, jint col, jint count, jfloatArray vals)
{
try {
LOGD("Mat::nGetF()");
@ -2313,7 +2474,10 @@ JNIEXPORT jint JNICALL Java_org_opencv_core_Mat_nGetF
}
JNIEXPORT jint JNICALL Java_org_opencv_core_Mat_nGetD
(JNIEnv* env, jclass cls, jlong self, jint row, jint col, jint count, jdoubleArray vals)
(JNIEnv* env, jclass, jlong self, jint row, jint col, jint count, jdoubleArray vals);
JNIEXPORT jint JNICALL Java_org_opencv_core_Mat_nGetD
(JNIEnv* env, jclass, jlong self, jint row, jint col, jint count, jdoubleArray vals)
{
try {
LOGD("Mat::nGetD()");
@ -2341,7 +2505,10 @@ JNIEXPORT jint JNICALL Java_org_opencv_core_Mat_nGetD
}
JNIEXPORT jdoubleArray JNICALL Java_org_opencv_core_Mat_nGet
(JNIEnv* env, jclass cls, jlong self, jint row, jint col, jint count)
(JNIEnv* env, jclass, jlong self, jint row, jint col, jint count);
JNIEXPORT jdoubleArray JNICALL Java_org_opencv_core_Mat_nGet
(JNIEnv* env, jclass, jlong self, jint row, jint col, jint count)
{
try {
LOGD("Mat::nGet()");
@ -2380,7 +2547,10 @@ JNIEXPORT jdoubleArray JNICALL Java_org_opencv_core_Mat_nGet
}
JNIEXPORT jstring JNICALL Java_org_opencv_core_Mat_nDump
(JNIEnv *env, jclass cls, jlong self)
(JNIEnv *env, jclass, jlong self);
JNIEXPORT jstring JNICALL Java_org_opencv_core_Mat_nDump
(JNIEnv *env, jclass, jlong self)
{
cv::Mat* me = (cv::Mat*) self; //TODO: check for NULL
std::stringstream s;

56
modules/java/src/cpp/VideoCapture.cpp
Unescape View File

@ -20,9 +20,11 @@ extern "C" {
// VideoCapture::VideoCapture()
//
JNIEXPORT jlong JNICALL Java_org_opencv_highgui_VideoCapture_n_1VideoCapture__
(JNIEnv* env, jclass);
JNIEXPORT jlong JNICALL Java_org_opencv_highgui_VideoCapture_n_1VideoCapture__
(JNIEnv* env, jclass cls)
(JNIEnv* env, jclass)
{
try {
#ifdef DEBUG
@ -55,9 +57,11 @@ JNIEXPORT jlong JNICALL Java_org_opencv_highgui_VideoCapture_n_1VideoCapture__
// VideoCapture::VideoCapture(int device)
//
JNIEXPORT jlong JNICALL Java_org_opencv_highgui_VideoCapture_n_1VideoCapture__I
(JNIEnv* env, jclass, jint device);
JNIEXPORT jlong JNICALL Java_org_opencv_highgui_VideoCapture_n_1VideoCapture__I
(JNIEnv* env, jclass cls, jint device)
(JNIEnv* env, jclass, jint device)
{
try {
#ifdef DEBUG
@ -91,9 +95,11 @@ JNIEXPORT jlong JNICALL Java_org_opencv_highgui_VideoCapture_n_1VideoCapture__I
// double VideoCapture::get(int propId)
//
JNIEXPORT jdouble JNICALL Java_org_opencv_highgui_VideoCapture_n_1get
(JNIEnv* env, jclass, jlong self, jint propId);
JNIEXPORT jdouble JNICALL Java_org_opencv_highgui_VideoCapture_n_1get
(JNIEnv* env, jclass cls, jlong self, jint propId)
(JNIEnv* env, jclass, jlong self, jint propId)
{
try {
#ifdef DEBUG
@ -127,9 +133,11 @@ JNIEXPORT jdouble JNICALL Java_org_opencv_highgui_VideoCapture_n_1get
// bool VideoCapture::grab()
//
JNIEXPORT jboolean JNICALL Java_org_opencv_highgui_VideoCapture_n_1grab
(JNIEnv* env, jclass, jlong self);
JNIEXPORT jboolean JNICALL Java_org_opencv_highgui_VideoCapture_n_1grab
(JNIEnv* env, jclass cls, jlong self)
(JNIEnv* env, jclass, jlong self)
{
try {
#ifdef DEBUG
@ -163,9 +171,11 @@ JNIEXPORT jboolean JNICALL Java_org_opencv_highgui_VideoCapture_n_1grab
// bool VideoCapture::isOpened()
//
JNIEXPORT jboolean JNICALL Java_org_opencv_highgui_VideoCapture_n_1isOpened
(JNIEnv* env, jclass, jlong self);
JNIEXPORT jboolean JNICALL Java_org_opencv_highgui_VideoCapture_n_1isOpened
(JNIEnv* env, jclass cls, jlong self)
(JNIEnv* env, jclass, jlong self)
{
try {
#ifdef DEBUG
@ -198,9 +208,11 @@ JNIEXPORT jboolean JNICALL Java_org_opencv_highgui_VideoCapture_n_1isOpened
// bool VideoCapture::open(int device)
//
JNIEXPORT jboolean JNICALL Java_org_opencv_highgui_VideoCapture_n_1open__JI
(JNIEnv* env, jclass, jlong self, jint device);
JNIEXPORT jboolean JNICALL Java_org_opencv_highgui_VideoCapture_n_1open__JI
(JNIEnv* env, jclass cls, jlong self, jint device)
(JNIEnv* env, jclass, jlong self, jint device)
{
try {
#ifdef DEBUG
@ -234,9 +246,11 @@ JNIEXPORT jboolean JNICALL Java_org_opencv_highgui_VideoCapture_n_1open__JI
// bool VideoCapture::read(Mat image)
//
JNIEXPORT jboolean JNICALL Java_org_opencv_highgui_VideoCapture_n_1read
(JNIEnv* env, jclass, jlong self, jlong image_nativeObj);
JNIEXPORT jboolean JNICALL Java_org_opencv_highgui_VideoCapture_n_1read
(JNIEnv* env, jclass cls, jlong self, jlong image_nativeObj)
(JNIEnv* env, jclass, jlong self, jlong image_nativeObj)
{
try {
#ifdef DEBUG
@ -271,9 +285,11 @@ JNIEXPORT jboolean JNICALL Java_org_opencv_highgui_VideoCapture_n_1read
// void VideoCapture::release()
//
JNIEXPORT void JNICALL Java_org_opencv_highgui_VideoCapture_n_1release
(JNIEnv* env, jclass, jlong self);
JNIEXPORT void JNICALL Java_org_opencv_highgui_VideoCapture_n_1release
(JNIEnv* env, jclass cls, jlong self)
(JNIEnv* env, jclass, jlong self)
{
try {
#ifdef DEBUG
@ -307,9 +323,11 @@ JNIEXPORT void JNICALL Java_org_opencv_highgui_VideoCapture_n_1release
// bool VideoCapture::retrieve(Mat image, int channel = 0)
//
JNIEXPORT jboolean JNICALL Java_org_opencv_highgui_VideoCapture_n_1retrieve__JJI
(JNIEnv* env, jclass, jlong self, jlong image_nativeObj, jint channel);
JNIEXPORT jboolean JNICALL Java_org_opencv_highgui_VideoCapture_n_1retrieve__JJI
(JNIEnv* env, jclass cls, jlong self, jlong image_nativeObj, jint channel)
(JNIEnv* env, jclass, jlong self, jlong image_nativeObj, jint channel)
{
try {
#ifdef DEBUG
@ -340,9 +358,11 @@ JNIEXPORT jboolean JNICALL Java_org_opencv_highgui_VideoCapture_n_1retrieve__JJI
JNIEXPORT jboolean JNICALL Java_org_opencv_highgui_VideoCapture_n_1retrieve__JJ
(JNIEnv* env, jclass, jlong self, jlong image_nativeObj);
JNIEXPORT jboolean JNICALL Java_org_opencv_highgui_VideoCapture_n_1retrieve__JJ
(JNIEnv* env, jclass cls, jlong self, jlong image_nativeObj)
(JNIEnv* env, jclass, jlong self, jlong image_nativeObj)
{
try {
#ifdef DEBUG
@ -377,9 +397,11 @@ JNIEXPORT jboolean JNICALL Java_org_opencv_highgui_VideoCapture_n_1retrieve__JJ
// bool VideoCapture::set(int propId, double value)
//
JNIEXPORT jboolean JNICALL Java_org_opencv_highgui_VideoCapture_n_1set
(JNIEnv* env, jclass, jlong self, jint propId, jdouble value);
JNIEXPORT jboolean JNICALL Java_org_opencv_highgui_VideoCapture_n_1set
(JNIEnv* env, jclass cls, jlong self, jint propId, jdouble value)
(JNIEnv* env, jclass, jlong self, jint propId, jdouble value)
{
try {
#ifdef DEBUG
@ -408,14 +430,17 @@ JNIEXPORT jboolean JNICALL Java_org_opencv_highgui_VideoCapture_n_1set
}
JNIEXPORT jstring JNICALL Java_org_opencv_highgui_VideoCapture_n_1getSupportedPreviewSizes
(JNIEnv *env, jclass cls, jlong self)
(JNIEnv *env, jclass, jlong self);
JNIEXPORT jstring JNICALL Java_org_opencv_highgui_VideoCapture_n_1getSupportedPreviewSizes
(JNIEnv *env, jclass, jlong self)
{
try {
#ifdef DEBUG
LOGD("highgui::VideoCapture_n_1set()");
#endif // DEBUG
VideoCapture* me = (VideoCapture*) self; //TODO: check for NULL
union {double prop; const char* name;} u;
union {double prop; const char* name;} u;
u.prop = me->get(CV_CAP_PROP_SUPPORTED_PREVIEW_SIZES_STRING);
return env->NewStringUTF(u.name);
} catch(cv::Exception e) {
@ -444,7 +469,10 @@ JNIEXPORT jstring JNICALL Java_org_opencv_highgui_VideoCapture_n_1getSupportedPr
//
JNIEXPORT void JNICALL Java_org_opencv_highgui_VideoCapture_n_1delete
(JNIEnv* env, jclass cls, jlong self)
(JNIEnv*, jclass, jlong self);
JNIEXPORT void JNICALL Java_org_opencv_highgui_VideoCapture_n_1delete
(JNIEnv*, jclass, jlong self)
{
delete (VideoCapture*) self;
}

4
modules/java/src/cpp/jni_part.cpp
Unescape View File

@ -21,7 +21,7 @@
extern "C" {
JNIEXPORT jint JNICALL
JNI_OnLoad(JavaVM* vm, void* reserved)
JNI_OnLoad(JavaVM* vm, void* )
{
JNIEnv* env;
if (vm->GetEnv((void**) &env, JNI_VERSION_1_6) != JNI_OK)
@ -51,7 +51,7 @@ JNI_OnLoad(JavaVM* vm, void* reserved)
}
JNIEXPORT void JNICALL
JNI_OnUnload(JavaVM *vm, void *reserved)
JNI_OnUnload(JavaVM*, void*)
{
//do nothing
}

10
modules/java/src/cpp/utils.cpp
Unescape View File

@ -25,7 +25,10 @@ extern "C" {
*/
JNIEXPORT void JNICALL Java_org_opencv_android_Utils_nBitmapToMat
(JNIEnv * env, jclass cls, jobject bitmap, jlong m_addr)
(JNIEnv * env, jclass, jobject bitmap, jlong m_addr);
JNIEXPORT void JNICALL Java_org_opencv_android_Utils_nBitmapToMat
(JNIEnv * env, jclass, jobject bitmap, jlong m_addr)
{
AndroidBitmapInfo info;
void* pixels = 0;
@ -73,7 +76,10 @@ JNIEXPORT void JNICALL Java_org_opencv_android_Utils_nBitmapToMat
* Method: void nMatToBitmap(long m_addr, Bitmap b)
*/
JNIEXPORT void JNICALL Java_org_opencv_android_Utils_nMatToBitmap
(JNIEnv * env, jclass cls, jlong m_addr, jobject bitmap)
(JNIEnv * env, jclass, jlong m_addr, jobject bitmap);
JNIEXPORT void JNICALL Java_org_opencv_android_Utils_nMatToBitmap
(JNIEnv * env, jclass, jlong m_addr, jobject bitmap)
{
AndroidBitmapInfo info;
void* pixels = 0;

2
modules/legacy/src/blobtrackanalysistrackdist.cpp
Unescape View File

@ -75,7 +75,7 @@ public:
};
inline void AddPoint(float x, float y, float r)
{
DefTrackPoint p = {x,y,r,0};
DefTrackPoint p = {x,y,r,0,0,0};
int Num = GetPointNum();
if(Num > 0)

3
modules/legacy/src/calonder.cpp
Unescape View File

@ -273,6 +273,9 @@ inline void sum_50t_176c(uchar **pp, uchar *sig, unsigned short *temp)
ssig[9] =_mm_packus_epi16(_mm_srai_epi16(ttemp[18],2),_mm_srai_epi16(ttemp[19],2));
ssig[10] =_mm_packus_epi16(_mm_srai_epi16(ttemp[20],2),_mm_srai_epi16(ttemp[21],2));
#else
(void)pp;
(void)sig;
(void)temp;
CV_Error( CV_StsNotImplemented, "Not supported without SSE2" );
#endif
}

8
modules/legacy/src/lee.cpp
Unescape View File

@ -3078,8 +3078,8 @@ static int _cvFindOppositSiteCW(pCvVoronoiHole pHole, CvVoronoiDiagramInt* pVoro
pCvVoronoiEdge pEdge_left = pSite_left->edge2->next_edge;
pCvVoronoiEdge pEdge_right = &Edge_right;
CvVoronoiEdgeInt Edge = {NULL,pNode,pSite_right,NULL,NULL,NULL,NULL,NULL};
CvVoronoiEdgeInt Edge_cur = {NULL,NULL,NULL,NULL,NULL,NULL,NULL};
CvVoronoiEdgeInt Edge = {NULL,pNode,pSite_right,NULL,NULL,NULL,NULL,NULL};
CvVoronoiEdgeInt Edge_cur = {NULL,NULL, NULL, NULL,NULL,NULL,NULL,NULL};
pCvVoronoiEdge pEdge = &Edge;
float radius1, radius2,dist1, dist2;
@ -3133,8 +3133,8 @@ static int _cvFindOppositSiteCCW(pCvVoronoiHole pHole,CvVoronoiDiagramInt* pVoro
pCvVoronoiEdge pEdge_left = &Edge_left;
pCvVoronoiEdge pEdge_right = pSite_right->edge1->prev_edge;
CvVoronoiEdgeInt Edge = {NULL,pNode,pSite_left,NULL,NULL,NULL,NULL};
CvVoronoiEdgeInt Edge_cur = {NULL,NULL,NULL,NULL,NULL,NULL,NULL};
CvVoronoiEdgeInt Edge = {NULL,pNode,pSite_left,NULL,NULL,NULL,NULL,NULL};
CvVoronoiEdgeInt Edge_cur = {NULL,NULL, NULL, NULL,NULL,NULL,NULL,NULL};
pCvVoronoiEdge pEdge = &Edge;
double dist1, dist2;

2
modules/legacy/test/test_em.cpp
Unescape View File

@ -105,7 +105,9 @@ void generateData( Mat& data, Mat& labels, const vector<int>& sizes, const Mat&
else if( labelType == CV_32SC1 )
labels.at<int>(p, 0) = l;
else
{
CV_DbgAssert(0);
}
}
}
}

2
modules/ml/test/test_emknearestkmeans.cpp
Unescape View File

@ -105,7 +105,9 @@ void generateData( Mat& data, Mat& labels, const vector<int>& sizes, const Mat&
else if( labelType == CV_32SC1 )
labels.at<int>(p, 0) = l;
else
{
CV_DbgAssert(0);
}
}
}
}

1
modules/objdetect/src/datamatrix.cpp
Unescape View File

@ -486,6 +486,7 @@ endo: ; // end search for this o
}
return rc;
#else
(void)im;
deque <CvDataMatrixCode> rc;
return rc;
#endif

4
modules/stitching/perf/perf_stich.cpp
Unescape View File

@ -1,5 +1,9 @@
#include "perf_precomp.hpp"
#ifdef __GNUC__
# pragma GCC diagnostic ignored "-Wsign-promo"
#endif
#include "opencv2/highgui/highgui.hpp"
#include "opencv2/core/internal.hpp"
#include "opencv2/flann/flann.hpp"

7
modules/stitching/src/blenders.cpp
Unescape View File

@ -192,6 +192,7 @@ MultiBandBlender::MultiBandBlender(int try_gpu, int num_bands, int weight_type)
#ifdef HAVE_OPENCV_GPU
can_use_gpu_ = try_gpu && gpu::getCudaEnabledDeviceCount();
#else
(void)try_gpu;
can_use_gpu_ = false;
#endif
CV_Assert(weight_type == CV_32F || weight_type == CV_16S);
@ -507,6 +508,10 @@ void createLaplacePyrGpu(const Mat &img, int num_levels, vector<Mat> &pyr)
}
gpu_pyr[num_levels].download(pyr[num_levels]);
#else
(void)img;
(void)num_levels;
(void)pyr;
#endif
}
@ -542,6 +547,8 @@ void restoreImageFromLaplacePyrGpu(vector<Mat> &pyr)
}
gpu_pyr[0].download(pyr[0]);
#else
(void)pyr;
#endif
}

2
modules/stitching/src/matchers.cpp
Unescape View File

@ -534,6 +534,8 @@ BestOf2NearestMatcher::BestOf2NearestMatcher(bool try_use_gpu, float match_conf,
if (try_use_gpu && getCudaEnabledDeviceCount() > 0)
impl_ = new GpuMatcher(match_conf);
else
#else
(void)try_use_gpu;
#endif
impl_ = new CpuMatcher(match_conf);

8
modules/ts/src/ts_func.cpp
Unescape View File

@ -1129,26 +1129,26 @@ norm_(const _Tp* src, size_t total, int cn, int normType, double startval, const
{
if( !mask )
for( i = 0; i < total; i++ )
result = std::max(result, (double)std::abs(src[i]));
result = std::max(result, (double)std::abs(int(src[i])));
else
for( int c = 0; c < cn; c++ )
{
for( i = 0; i < total; i++ )
if( mask[i] )
result = std::max(result, (double)std::abs(src[i*cn + c]));
result = std::max(result, (double)std::abs(int(src[i*cn + c])));
}
}
else if( normType == NORM_L1 )
{
if( !mask )
for( i = 0; i < total; i++ )
result += std::abs(src[i]);
result += std::abs(int(src[i]));
else
for( int c = 0; c < cn; c++ )
{
for( i = 0; i < total; i++ )
if( mask[i] )
result += std::abs(src[i*cn + c]);
result += std::abs(int(src[i*cn + c]));
}
}
else

148
samples/android/face-detection/jni/DetectionBasedTracker_jni.cpp
Unescape View File

@ -19,7 +19,7 @@ inline void vector_Rect_to_Mat(vector<Rect>& v_rect, Mat& mat)
}
JNIEXPORT jlong JNICALL Java_org_opencv_samples_fd_DetectionBasedTracker_nativeCreateObject
(JNIEnv * jenv, jclass jobj, jstring jFileName, jint faceSize)
(JNIEnv * jenv, jclass, jstring jFileName, jint faceSize)
{
const char* jnamestr = jenv->GetStringUTFChars(jFileName, NULL);
string stdFileName(jnamestr);
@ -27,153 +27,153 @@ JNIEXPORT jlong JNICALL Java_org_opencv_samples_fd_DetectionBasedTracker_nativeC
try
{
DetectionBasedTracker::Parameters DetectorParams;
if (faceSize > 0)
DetectorParams.minObjectSize = faceSize;
result = (jlong)new DetectionBasedTracker(stdFileName, DetectorParams);
DetectionBasedTracker::Parameters DetectorParams;
if (faceSize > 0)
DetectorParams.minObjectSize = faceSize;
result = (jlong)new DetectionBasedTracker(stdFileName, DetectorParams);
}
catch(cv::Exception e)
{
LOGD("nativeCreateObject catched cv::Exception: %s", e.what());
jclass je = jenv->FindClass("org/opencv/core/CvException");
if(!je)
je = jenv->FindClass("java/lang/Exception");
jenv->ThrowNew(je, e.what());
LOGD("nativeCreateObject catched cv::Exception: %s", e.what());
jclass je = jenv->FindClass("org/opencv/core/CvException");
if(!je)
je = jenv->FindClass("java/lang/Exception");
jenv->ThrowNew(je, e.what());
}
catch (...)
{
LOGD("nativeCreateObject catched unknown exception");
jclass je = jenv->FindClass("java/lang/Exception");
jenv->ThrowNew(je, "Unknown exception in JNI code {highgui::VideoCapture_n_1VideoCapture__()}");
return 0;
LOGD("nativeCreateObject catched unknown exception");
jclass je = jenv->FindClass("java/lang/Exception");
jenv->ThrowNew(je, "Unknown exception in JNI code {highgui::VideoCapture_n_1VideoCapture__()}");
return 0;
}
return result;
}
JNIEXPORT void JNICALL Java_org_opencv_samples_fd_DetectionBasedTracker_nativeDestroyObject
(JNIEnv * jenv, jclass jobj, jlong thiz)
(JNIEnv * jenv, jclass, jlong thiz)
{
try
{
((DetectionBasedTracker*)thiz)->stop();
delete (DetectionBasedTracker*)thiz;
((DetectionBasedTracker*)thiz)->stop();
delete (DetectionBasedTracker*)thiz;
}
catch(cv::Exception e)
{
LOGD("nativeestroyObject catched cv::Exception: %s", e.what());
jclass je = jenv->FindClass("org/opencv/core/CvException");
if(!je)
je = jenv->FindClass("java/lang/Exception");
jenv->ThrowNew(je, e.what());
LOGD("nativeestroyObject catched cv::Exception: %s", e.what());
jclass je = jenv->FindClass("org/opencv/core/CvException");
if(!je)
je = jenv->FindClass("java/lang/Exception");
jenv->ThrowNew(je, e.what());
}
catch (...)
{
LOGD("nativeDestroyObject catched unknown exception");
jclass je = jenv->FindClass("java/lang/Exception");
jenv->ThrowNew(je, "Unknown exception in JNI code {highgui::VideoCapture_n_1VideoCapture__()}");
LOGD("nativeDestroyObject catched unknown exception");
jclass je = jenv->FindClass("java/lang/Exception");
jenv->ThrowNew(je, "Unknown exception in JNI code {highgui::VideoCapture_n_1VideoCapture__()}");
}
}
JNIEXPORT void JNICALL Java_org_opencv_samples_fd_DetectionBasedTracker_nativeStart
(JNIEnv * jenv, jclass jobj, jlong thiz)
(JNIEnv * jenv, jclass, jlong thiz)
{
try
{
((DetectionBasedTracker*)thiz)->run();
((DetectionBasedTracker*)thiz)->run();
}
catch(cv::Exception e)
{
LOGD("nativeStart catched cv::Exception: %s", e.what());
jclass je = jenv->FindClass("org/opencv/core/CvException");
if(!je)
je = jenv->FindClass("java/lang/Exception");
jenv->ThrowNew(je, e.what());
LOGD("nativeStart catched cv::Exception: %s", e.what());
jclass je = jenv->FindClass("org/opencv/core/CvException");
if(!je)
je = jenv->FindClass("java/lang/Exception");
jenv->ThrowNew(je, e.what());
}
catch (...)
{
LOGD("nativeStart catched unknown exception");
jclass je = jenv->FindClass("java/lang/Exception");
jenv->ThrowNew(je, "Unknown exception in JNI code {highgui::VideoCapture_n_1VideoCapture__()}");
LOGD("nativeStart catched unknown exception");
jclass je = jenv->FindClass("java/lang/Exception");
jenv->ThrowNew(je, "Unknown exception in JNI code {highgui::VideoCapture_n_1VideoCapture__()}");
}
}
JNIEXPORT void JNICALL Java_org_opencv_samples_fd_DetectionBasedTracker_nativeStop
(JNIEnv * jenv, jclass jobj, jlong thiz)
(JNIEnv * jenv, jclass, jlong thiz)
{
try
{
((DetectionBasedTracker*)thiz)->stop();
((DetectionBasedTracker*)thiz)->stop();
}
catch(cv::Exception e)
{
LOGD("nativeStop catched cv::Exception: %s", e.what());
jclass je = jenv->FindClass("org/opencv/core/CvException");
if(!je)
je = jenv->FindClass("java/lang/Exception");
jenv->ThrowNew(je, e.what());
LOGD("nativeStop catched cv::Exception: %s", e.what());
jclass je = jenv->FindClass("org/opencv/core/CvException");
if(!je)
je = jenv->FindClass("java/lang/Exception");
jenv->ThrowNew(je, e.what());
}
catch (...)
{
LOGD("nativeStop catched unknown exception");
jclass je = jenv->FindClass("java/lang/Exception");
jenv->ThrowNew(je, "Unknown exception in JNI code {highgui::VideoCapture_n_1VideoCapture__()}");
LOGD("nativeStop catched unknown exception");
jclass je = jenv->FindClass("java/lang/Exception");
jenv->ThrowNew(je, "Unknown exception in JNI code {highgui::VideoCapture_n_1VideoCapture__()}");
}
}
JNIEXPORT void JNICALL Java_org_opencv_samples_fd_DetectionBasedTracker_nativeSetFaceSize
(JNIEnv * jenv, jclass jobj, jlong thiz, jint faceSize)
(JNIEnv * jenv, jclass, jlong thiz, jint faceSize)
{
try
{
if (faceSize > 0)
{
DetectionBasedTracker::Parameters DetectorParams = \
((DetectionBasedTracker*)thiz)->getParameters();
DetectorParams.minObjectSize = faceSize;
((DetectionBasedTracker*)thiz)->setParameters(DetectorParams);
}
if (faceSize > 0)
{
DetectionBasedTracker::Parameters DetectorParams = \
((DetectionBasedTracker*)thiz)->getParameters();
DetectorParams.minObjectSize = faceSize;
((DetectionBasedTracker*)thiz)->setParameters(DetectorParams);
}
}
catch(cv::Exception e)
{
LOGD("nativeStop catched cv::Exception: %s", e.what());
jclass je = jenv->FindClass("org/opencv/core/CvException");
if(!je)
je = jenv->FindClass("java/lang/Exception");
jenv->ThrowNew(je, e.what());
LOGD("nativeStop catched cv::Exception: %s", e.what());
jclass je = jenv->FindClass("org/opencv/core/CvException");
if(!je)
je = jenv->FindClass("java/lang/Exception");
jenv->ThrowNew(je, e.what());
}
catch (...)
{
LOGD("nativeSetFaceSize catched unknown exception");
jclass je = jenv->FindClass("java/lang/Exception");
jenv->ThrowNew(je, "Unknown exception in JNI code {highgui::VideoCapture_n_1VideoCapture__()}");
LOGD("nativeSetFaceSize catched unknown exception");
jclass je = jenv->FindClass("java/lang/Exception");
jenv->ThrowNew(je, "Unknown exception in JNI code {highgui::VideoCapture_n_1VideoCapture__()}");
}
}
JNIEXPORT void JNICALL Java_org_opencv_samples_fd_DetectionBasedTracker_nativeDetect
(JNIEnv * jenv, jclass jobj, jlong thiz, jlong imageGray, jlong faces)
(JNIEnv * jenv, jclass, jlong thiz, jlong imageGray, jlong faces)
{
try
{
vector<Rect> RectFaces;
((DetectionBasedTracker*)thiz)->process(*((Mat*)imageGray));
((DetectionBasedTracker*)thiz)->getObjects(RectFaces);
vector_Rect_to_Mat(RectFaces, *((Mat*)faces));
vector<Rect> RectFaces;
((DetectionBasedTracker*)thiz)->process(*((Mat*)imageGray));
((DetectionBasedTracker*)thiz)->getObjects(RectFaces);
vector_Rect_to_Mat(RectFaces, *((Mat*)faces));
}
catch(cv::Exception e)
{
LOGD("nativeCreateObject catched cv::Exception: %s", e.what());
jclass je = jenv->FindClass("org/opencv/core/CvException");
if(!je)
je = jenv->FindClass("java/lang/Exception");
jenv->ThrowNew(je, e.what());
LOGD("nativeCreateObject catched cv::Exception: %s", e.what());
jclass je = jenv->FindClass("org/opencv/core/CvException");
if(!je)
je = jenv->FindClass("java/lang/Exception");
jenv->ThrowNew(je, e.what());
}
catch (...)
{
LOGD("nativeDetect catched unknown exception");
jclass je = jenv->FindClass("java/lang/Exception");
jenv->ThrowNew(je, "Unknown exception in JNI code {highgui::VideoCapture_n_1VideoCapture__()}");
LOGD("nativeDetect catched unknown exception");
jclass je = jenv->FindClass("java/lang/Exception");
jenv->ThrowNew(je, "Unknown exception in JNI code {highgui::VideoCapture_n_1VideoCapture__()}");
}
}

1
samples/android/hello-android/main.cpp
Unescape View File

@ -6,6 +6,7 @@ const char* message = "Hello Android!";
int main(int argc, char* argv[])
{
(void)argc; (void)argv;
// print message to console
printf("%s\n", message);

2
samples/android/tutorial-3-native/jni/jni_part.cpp
Unescape View File

@ -8,7 +8,7 @@ using namespace std;
using namespace cv;
extern "C" {
JNIEXPORT void JNICALL Java_org_opencv_samples_tutorial3_Sample3View_FindFeatures(JNIEnv* env, jobject thiz, jint width, jint height, jbyteArray yuv, jintArray bgra)
JNIEXPORT void JNICALL Java_org_opencv_samples_tutorial3_Sample3View_FindFeatures(JNIEnv* env, jobject, jint width, jint height, jbyteArray yuv, jintArray bgra)
{
jbyte* _yuv = env->GetByteArrayElements(yuv, 0);
jint* _bgra = env->GetIntArrayElements(bgra, 0);

2
samples/android/tutorial-4-mixed/jni/jni_part.cpp
Unescape View File

@ -8,7 +8,7 @@ using namespace std;
using namespace cv;
extern "C" {
JNIEXPORT void JNICALL Java_org_opencv_samples_tutorial4_Sample4View_FindFeatures(JNIEnv* env, jobject thiz, jlong addrGray, jlong addrRgba)
JNIEXPORT void JNICALL Java_org_opencv_samples_tutorial4_Sample4View_FindFeatures(JNIEnv*, jobject, jlong addrGray, jlong addrRgba)
{
Mat* pMatGr=(Mat*)addrGray;
Mat* pMatRgb=(Mat*)addrRgba;

2
samples/c/blobtrack_sample.cpp
Unescape View File

@ -368,7 +368,7 @@ static void print_params(CvVSModule* pM, const char* module, const char* log_nam
int main(int argc, char* argv[])
{ /* Main function: */
CvCapture* pCap = NULL;
CvBlobTrackerAutoParam1 param = {0};
CvBlobTrackerAutoParam1 param = {0,0,0,0,0,0,0,0};
CvBlobTrackerAuto* pTracker = NULL;
//float scale = 1;

2
samples/c/contours.c
Unescape View File

@ -19,6 +19,7 @@ CvSeq* contours = 0;
static void on_trackbar(int pos)
{
(void)pos;
IplImage* cnt_img = cvCreateImage( cvSize(w,w), 8, 3 );
CvSeq* _contours = contours;
int _levels = levels - 3;
@ -52,6 +53,7 @@ static void findCComp( IplImage* img )
int main(int argc, char* argv[])
{
(void)argc; (void)argv;
int i, j;
CvMemStorage* storage = cvCreateMemStorage(0);
IplImage* img = cvCreateImage( cvSize(w,w), 8, 1 );

4
samples/c/delaunay.c
Unescape View File

@ -231,9 +231,7 @@ static void run(void)
int main( int argc, char** argv )
{
#ifdef _MSC_VER
argc, argv;
#endif
(void)argc; (void)argv;
help();
run();
return 0;

2
samples/c/fback_c.c
Unescape View File

@ -16,6 +16,7 @@ static void help(void)
static void drawOptFlowMap(const CvMat* flow, CvMat* cflowmap, int step,
double scale, CvScalar color)
{
(void)scale;
int x, y;
for( y = 0; y < cflowmap->rows; y += step)
for( x = 0; x < cflowmap->cols; x += step)
@ -29,6 +30,7 @@ static void drawOptFlowMap(const CvMat* flow, CvMat* cflowmap, int step,
int main( int argc, char** argv )
{
(void)argc; (void)argv;
CvCapture* capture = cvCreateCameraCapture(0);
CvMat* prevgray = 0, *gray = 0, *flow = 0, *cflow = 0;

2
samples/c/morphology.c
Unescape View File

@ -17,6 +17,7 @@ int erode_dilate_pos = 0;
// callback function for open/close trackbar
static void OpenClose(int pos)
{
(void)pos;
int n = open_close_pos - max_iters;
int an = n > 0 ? n : -n;
element = cvCreateStructuringElementEx( an*2+1, an*2+1, an, an, element_shape, 0 );
@ -37,6 +38,7 @@ static void OpenClose(int pos)
// callback function for erode/dilate trackbar
static void ErodeDilate(int pos)
{
(void)pos;
int n = erode_dilate_pos - max_iters;
int an = n > 0 ? n : -n;
element = cvCreateStructuringElementEx( an*2+1, an*2+1, an, an, element_shape, 0 );

1
samples/c/pyramid_segmentation.c
Unescape View File

@ -34,6 +34,7 @@ CvPoint pt1, pt2;
static void ON_SEGMENT(int a)
{
(void)a;
cvPyrSegmentation(image0, image1, storage, &comp,
level, threshold1+1, threshold2+1);

2
samples/cpp/stitching.cpp
Unescape View File

@ -66,7 +66,7 @@ int main(int argc, char* argv[])
if (status != Stitcher::OK)
{
cout << "Can't stitch images, error code = " << status << endl;
cout << "Can't stitch images, error code = " << int(status) << endl;
return -1;
}

Write Preview
Loading…
Cancel
Save