Merge remote-tracking branch 'master' into stitch-fix
* 'master' of github.com:itseez/opencv: (82 commits)
moved part of video to contrib/{outflow, bgsegm}; moved matlab to contrib
added some basic functionality needed by the new face module (moved from the old "contrib")
moved to the new opencv_contrib/face module
fixed various warnings and obvious errors reported by clang compiler and the coverity tool.
Fixed review comment from Vadim Pisarevsky
modified farneback sample to use T-API
ECC patch by the author (G. Evangelidis); fixed some OCL Farneback optical flow test failures on Mac
small fix for GaussianBlur ocl test
fix binary package build
small fix for ocl_resize
fix IOS framework
fixed test ocl_MatchTemplate for sparse matrix
Fixed typos
fixing error, wrong template method param.
fixing Mac build
some formal changes (generally adding constness)
Fixed choice of kercn and rowsPerWI for non-Intel device.
fixed nDiffs for CalcBackProject
fixed tests for ocl_filter2d, ocl_matchTemplate, ocl_histogram.cpp
Fixed issue: Mat::copyTo(UMat) if device copy is obsolete. Added test.
...
Conflicts:
modules/core/include/opencv2/core/mat.inl.hpp
This commit is contained in:
mshabunin
@@ -19,9 +19,9 @@ add_subdirectory(native-activity)
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# hello-android sample
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if(HAVE_opencv_highgui)
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ocv_include_modules_recurse(opencv_imgcodecs opencv_videoio opencv_highgui opencv_core)
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add_executable(hello-android hello-android/main.cpp)
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target_link_libraries(hello-android ${OPENCV_LINKER_LIBS} opencv_imgcodecs opencv_videoio opencv_highgui opencv_core)
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ocv_target_include_modules_recurse(hello-android opencv_imgcodecs opencv_videoio opencv_highgui opencv_core)
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ocv_target_link_libraries(hello-android ${OPENCV_LINKER_LIBS} opencv_imgcodecs opencv_videoio opencv_highgui opencv_core)
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set_target_properties(hello-android PROPERTIES OUTPUT_NAME hello-android RUNTIME_OUTPUT_DIRECTORY "${EXECUTABLE_OUTPUT_PATH}")
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add_dependencies(opencv_android_examples hello-android)
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endif()
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@@ -55,14 +55,14 @@ if(BUILD_EXAMPLES AND OCV_DEPENDENCIES_FOUND)
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set(the_target "${sample_kind}_${name}")
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add_executable(${the_target} ${srcs})
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target_link_libraries(${the_target} ${OPENCV_LINKER_LIBS} ${OPENCV_CPP_SAMPLES_REQUIRED_DEPS})
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ocv_target_link_libraries(${the_target} ${OPENCV_LINKER_LIBS} ${OPENCV_CPP_SAMPLES_REQUIRED_DEPS})
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if("${srcs}" MATCHES "gpu/")
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target_link_libraries(${the_target} opencv_cudaarithm opencv_cudafilters)
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ocv_target_link_libraries(${the_target} opencv_cudaarithm opencv_cudafilters)
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endif()
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if(HAVE_opencv_ocl)
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target_link_libraries(${the_target} opencv_ocl)
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ocv_target_link_libraries(${the_target} opencv_ocl)
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endif()
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set_target_properties(${the_target} PROPERTIES
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File diff suppressed because it is too large
Load Diff
@@ -23,6 +23,7 @@
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#define DEBUG_DESC_PROGRESS
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using namespace cv;
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using namespace cv::ml;
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using namespace std;
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const string paramsFile = "params.xml";
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@@ -677,7 +678,7 @@ void VocData::writeClassifierResultsFile( const string& out_dir, const string& o
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result_file.close();
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} else {
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string err_msg = "could not open classifier results file '" + output_file + "' for writing. Before running for the first time, a 'results' subdirectory should be created within the VOC dataset base directory. e.g. if the VOC data is stored in /VOC/VOC2010 then the path /VOC/results must be created.";
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CV_Error(CV_StsError,err_msg.c_str());
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CV_Error(Error::StsError,err_msg.c_str());
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}
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}
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@@ -701,9 +702,9 @@ void VocData::writeClassifierResultsFile( const string& out_dir, const string& o
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string VocData::getResultsFilename(const string& obj_class, const VocTask task, const ObdDatasetType dataset, const int competition, const int number)
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{
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if ((competition < 1) && (competition != -1))
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CV_Error(CV_StsBadArg,"competition argument should be a positive non-zero number or -1 to accept the default");
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CV_Error(Error::StsBadArg,"competition argument should be a positive non-zero number or -1 to accept the default");
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if ((number < 1) && (number != -1))
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CV_Error(CV_StsBadArg,"number argument should be a positive non-zero number or -1 to accept the default");
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CV_Error(Error::StsBadArg,"number argument should be a positive non-zero number or -1 to accept the default");
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string dset, task_type;
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@@ -815,7 +816,7 @@ void VocData::calcClassifierPrecRecall(const string& input_file, vector<float>&
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scoregt_file.close();
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} else {
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string err_msg = "could not open scoregt file '" + scoregt_file_str + "' for writing.";
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CV_Error(CV_StsError,err_msg.c_str());
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CV_Error(Error::StsError,err_msg.c_str());
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}
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}
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@@ -974,7 +975,7 @@ void VocData::calcClassifierConfMatRow(const string& obj_class, const vector<Obd
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if (target_idx_it == output_headers.end())
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{
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string err_msg = "could not find the target object class '" + obj_class + "' in list of valid classes.";
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CV_Error(CV_StsError,err_msg.c_str());
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CV_Error(Error::StsError,err_msg.c_str());
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}
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/* convert iterator to index */
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target_idx = (int)std::distance(output_headers.begin(),target_idx_it);
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@@ -1037,7 +1038,7 @@ void VocData::calcClassifierConfMatRow(const string& obj_class, const vector<Obd
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if (class_idx_it == output_headers.end())
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{
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string err_msg = "could not find object class '" + img_objects[obj_idx].object_class + "' specified in the ground truth file of '" + images[ranking[image_idx]].id +"'in list of valid classes.";
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CV_Error(CV_StsError,err_msg.c_str());
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CV_Error(Error::StsError,err_msg.c_str());
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}
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/* convert iterator to index */
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int class_idx = (int)std::distance(output_headers.begin(),class_idx_it);
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@@ -1189,7 +1190,7 @@ void VocData::calcDetectorConfMatRow(const string& obj_class, const ObdDatasetTy
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if (class_idx_it == output_headers.end())
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{
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string err_msg = "could not find object class '" + img_objects[max_gt_obj_idx].object_class + "' specified in the ground truth file of '" + images[ranking[image_idx]].id +"'in list of valid classes.";
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CV_Error(CV_StsError,err_msg.c_str());
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CV_Error(Error::StsError,err_msg.c_str());
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}
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/* convert iterator to index */
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int class_idx = (int)std::distance(output_headers.begin(),class_idx_it);
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@@ -1282,7 +1283,7 @@ void VocData::savePrecRecallToGnuplot(const string& output_file, const vector<fl
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plot_file.close();
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} else {
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string err_msg = "could not open plot file '" + output_file_std + "' for writing.";
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CV_Error(CV_StsError,err_msg.c_str());
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CV_Error(Error::StsError,err_msg.c_str());
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}
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}
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@@ -1446,7 +1447,7 @@ void VocData::readClassifierGroundTruth(const string& filename, vector<string>&
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if (!gtfile.is_open())
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{
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string err_msg = "could not open VOC ground truth textfile '" + filename + "'.";
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CV_Error(CV_StsError,err_msg.c_str());
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CV_Error(Error::StsError,err_msg.c_str());
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}
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string line;
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@@ -1462,7 +1463,7 @@ void VocData::readClassifierGroundTruth(const string& filename, vector<string>&
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image_codes.push_back(image);
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object_present.push_back(obj_present == 1);
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} else {
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if (!gtfile.eof()) CV_Error(CV_StsParseError,"error parsing VOC ground truth textfile.");
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if (!gtfile.eof()) CV_Error(Error::StsParseError,"error parsing VOC ground truth textfile.");
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}
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}
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gtfile.close();
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@@ -1488,13 +1489,13 @@ void VocData::readClassifierResultsFile(const string& input_file, vector<string>
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image_codes.push_back(image);
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scores.push_back(score);
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} else {
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if(!result_file.eof()) CV_Error(CV_StsParseError,"error parsing VOC classifier results file.");
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if(!result_file.eof()) CV_Error(Error::StsParseError,"error parsing VOC classifier results file.");
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}
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}
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result_file.close();
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} else {
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string err_msg = "could not open classifier results file '" + input_file + "' for reading.";
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CV_Error(CV_StsError,err_msg.c_str());
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CV_Error(Error::StsError,err_msg.c_str());
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}
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}
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@@ -1545,13 +1546,13 @@ void VocData::readDetectorResultsFile(const string& input_file, vector<string>&
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bounding_boxes[image_idx].push_back(bounding_box);
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}
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} else {
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if(!result_file.eof()) CV_Error(CV_StsParseError,"error parsing VOC detector results file.");
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if(!result_file.eof()) CV_Error(Error::StsParseError,"error parsing VOC detector results file.");
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}
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}
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result_file.close();
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} else {
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string err_msg = "could not open detector results file '" + input_file + "' for reading.";
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CV_Error(CV_StsError,err_msg.c_str());
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CV_Error(Error::StsError,err_msg.c_str());
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}
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}
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@@ -1595,23 +1596,23 @@ void VocData::extractVocObjects(const string filename, vector<ObdObject>& object
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//object class -------------
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if (extractXMLBlock(object_contents, "name", 0, tag_contents) == -1) CV_Error(CV_StsError,"missing <name> tag in object definition of '" + filename + "'");
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if (extractXMLBlock(object_contents, "name", 0, tag_contents) == -1) CV_Error(Error::StsError,"missing <name> tag in object definition of '" + filename + "'");
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object.object_class.swap(tag_contents);
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//object bounding box -------------
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int xmax, xmin, ymax, ymin;
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if (extractXMLBlock(object_contents, "xmax", 0, tag_contents) == -1) CV_Error(CV_StsError,"missing <xmax> tag in object definition of '" + filename + "'");
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if (extractXMLBlock(object_contents, "xmax", 0, tag_contents) == -1) CV_Error(Error::StsError,"missing <xmax> tag in object definition of '" + filename + "'");
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xmax = stringToInteger(tag_contents);
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if (extractXMLBlock(object_contents, "xmin", 0, tag_contents) == -1) CV_Error(CV_StsError,"missing <xmin> tag in object definition of '" + filename + "'");
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if (extractXMLBlock(object_contents, "xmin", 0, tag_contents) == -1) CV_Error(Error::StsError,"missing <xmin> tag in object definition of '" + filename + "'");
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xmin = stringToInteger(tag_contents);
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if (extractXMLBlock(object_contents, "ymax", 0, tag_contents) == -1) CV_Error(CV_StsError,"missing <ymax> tag in object definition of '" + filename + "'");
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if (extractXMLBlock(object_contents, "ymax", 0, tag_contents) == -1) CV_Error(Error::StsError,"missing <ymax> tag in object definition of '" + filename + "'");
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ymax = stringToInteger(tag_contents);
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if (extractXMLBlock(object_contents, "ymin", 0, tag_contents) == -1) CV_Error(CV_StsError,"missing <ymin> tag in object definition of '" + filename + "'");
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if (extractXMLBlock(object_contents, "ymin", 0, tag_contents) == -1) CV_Error(Error::StsError,"missing <ymin> tag in object definition of '" + filename + "'");
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ymin = stringToInteger(tag_contents);
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object.boundingBox.x = xmin-1; //convert to 0-based indexing
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@@ -1714,11 +1715,11 @@ void VocData::extractDataFromResultsFilename(const string& input_file, string& c
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size_t fnameend = input_file_std.rfind(".txt");
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if ((fnamestart == input_file_std.npos) || (fnameend == input_file_std.npos))
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CV_Error(CV_StsError,"Could not extract filename of results file.");
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CV_Error(Error::StsError,"Could not extract filename of results file.");
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++fnamestart;
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if (fnamestart >= fnameend)
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CV_Error(CV_StsError,"Could not extract filename of results file.");
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CV_Error(Error::StsError,"Could not extract filename of results file.");
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//extract dataset and class names, triggering exception if the filename format is not correct
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string filename = input_file_std.substr(fnamestart, fnameend-fnamestart);
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@@ -1729,11 +1730,11 @@ void VocData::extractDataFromResultsFilename(const string& input_file, string& c
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size_t classend = filename.find("_",classstart+1);
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if (classend == filename.npos) classend = filename.size();
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if ((datasetstart == filename.npos) || (classstart == filename.npos))
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CV_Error(CV_StsError,"Error parsing results filename. Is it in standard format of 'comp<n>_{cls/det}_<dataset>_<objclass>.txt'?");
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CV_Error(Error::StsError,"Error parsing results filename. Is it in standard format of 'comp<n>_{cls/det}_<dataset>_<objclass>.txt'?");
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++datasetstart;
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++classstart;
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if (((datasetstart-classstart) < 1) || ((classend-datasetstart) < 1))
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CV_Error(CV_StsError,"Error parsing results filename. Is it in standard format of 'comp<n>_{cls/det}_<dataset>_<objclass>.txt'?");
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CV_Error(Error::StsError,"Error parsing results filename. Is it in standard format of 'comp<n>_{cls/det}_<dataset>_<objclass>.txt'?");
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dataset_name = filename.substr(datasetstart,classstart-datasetstart-1);
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class_name = filename.substr(classstart,classend-classstart);
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@@ -1781,7 +1782,7 @@ bool VocData::getClassifierGroundTruthImage(const string& obj_class, const strin
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return m_classifier_gt_all_present[std::distance(m_classifier_gt_all_ids.begin(),it)] != 0;
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} else {
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string err_msg = "could not find classifier ground truth for image '" + id + "' and class '" + obj_class + "'";
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CV_Error(CV_StsError,err_msg.c_str());
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CV_Error(Error::StsError,err_msg.c_str());
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}
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return true;
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@@ -1814,7 +1815,7 @@ void VocData::getSortOrder(const vector<float>& values, vector<size_t>& order, b
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void VocData::readFileToString(const string filename, string& file_contents)
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{
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std::ifstream ifs(filename.c_str());
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if (!ifs.is_open()) CV_Error(CV_StsError,"could not open text file");
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if (!ifs.is_open()) CV_Error(Error::StsError,"could not open text file");
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stringstream oss;
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oss << ifs.rdbuf();
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@@ -1829,7 +1830,7 @@ int VocData::stringToInteger(const string input_str)
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stringstream ss(input_str);
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if ((ss >> result).fail())
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{
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CV_Error(CV_StsBadArg,"could not perform string to integer conversion");
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CV_Error(Error::StsBadArg,"could not perform string to integer conversion");
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}
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return result;
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}
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@@ -1841,7 +1842,7 @@ string VocData::integerToString(const int input_int)
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stringstream ss;
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if ((ss << input_int).fail())
|
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{
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CV_Error(CV_StsBadArg,"could not perform integer to string conversion");
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CV_Error(Error::StsBadArg,"could not perform integer to string conversion");
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}
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result = ss.str();
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return result;
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@@ -2325,14 +2326,14 @@ static void removeBowImageDescriptorsByCount( vector<ObdImage>& images, vector<M
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CV_Assert( bowImageDescriptors.size() == objectPresent.size() );
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}
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|
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static void setSVMParams( CvSVMParams& svmParams, CvMat& class_wts_cv, const Mat& responses, bool balanceClasses )
|
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static void setSVMParams( SVM::Params& svmParams, Mat& class_wts_cv, const Mat& responses, bool balanceClasses )
|
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{
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int pos_ex = countNonZero(responses == 1);
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int neg_ex = countNonZero(responses == -1);
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cout << pos_ex << " positive training samples; " << neg_ex << " negative training samples" << endl;
|
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|
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svmParams.svm_type = CvSVM::C_SVC;
|
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svmParams.kernel_type = CvSVM::RBF;
|
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svmParams.svmType = SVM::C_SVC;
|
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svmParams.kernelType = SVM::RBF;
|
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if( balanceClasses )
|
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{
|
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Mat class_wts( 2, 1, CV_32FC1 );
|
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@@ -2350,43 +2351,44 @@ static void setSVMParams( CvSVMParams& svmParams, CvMat& class_wts_cv, const Mat
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class_wts.at<float>(1) = static_cast<float>(pos_ex)/static_cast<float>(pos_ex+neg_ex);
|
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}
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class_wts_cv = class_wts;
|
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svmParams.class_weights = &class_wts_cv;
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svmParams.classWeights = class_wts_cv;
|
||||
}
|
||||
}
|
||||
|
||||
static void setSVMTrainAutoParams( CvParamGrid& c_grid, CvParamGrid& gamma_grid,
|
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CvParamGrid& p_grid, CvParamGrid& nu_grid,
|
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CvParamGrid& coef_grid, CvParamGrid& degree_grid )
|
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static void setSVMTrainAutoParams( ParamGrid& c_grid, ParamGrid& gamma_grid,
|
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ParamGrid& p_grid, ParamGrid& nu_grid,
|
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ParamGrid& coef_grid, ParamGrid& degree_grid )
|
||||
{
|
||||
c_grid = CvSVM::get_default_grid(CvSVM::C);
|
||||
c_grid = SVM::getDefaultGrid(SVM::C);
|
||||
|
||||
gamma_grid = CvSVM::get_default_grid(CvSVM::GAMMA);
|
||||
gamma_grid = SVM::getDefaultGrid(SVM::GAMMA);
|
||||
|
||||
p_grid = CvSVM::get_default_grid(CvSVM::P);
|
||||
p_grid.step = 0;
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||||
p_grid = SVM::getDefaultGrid(SVM::P);
|
||||
p_grid.logStep = 0;
|
||||
|
||||
nu_grid = CvSVM::get_default_grid(CvSVM::NU);
|
||||
nu_grid.step = 0;
|
||||
nu_grid = SVM::getDefaultGrid(SVM::NU);
|
||||
nu_grid.logStep = 0;
|
||||
|
||||
coef_grid = CvSVM::get_default_grid(CvSVM::COEF);
|
||||
coef_grid.step = 0;
|
||||
coef_grid = SVM::getDefaultGrid(SVM::COEF);
|
||||
coef_grid.logStep = 0;
|
||||
|
||||
degree_grid = CvSVM::get_default_grid(CvSVM::DEGREE);
|
||||
degree_grid.step = 0;
|
||||
degree_grid = SVM::getDefaultGrid(SVM::DEGREE);
|
||||
degree_grid.logStep = 0;
|
||||
}
|
||||
|
||||
static void trainSVMClassifier( CvSVM& svm, const SVMTrainParamsExt& svmParamsExt, const string& objClassName, VocData& vocData,
|
||||
static Ptr<SVM> trainSVMClassifier( const SVMTrainParamsExt& svmParamsExt, const string& objClassName, VocData& vocData,
|
||||
Ptr<BOWImgDescriptorExtractor>& bowExtractor, const Ptr<FeatureDetector>& fdetector,
|
||||
const string& resPath )
|
||||
{
|
||||
/* first check if a previously trained svm for the current class has been saved to file */
|
||||
string svmFilename = resPath + svmsDir + "/" + objClassName + ".xml.gz";
|
||||
Ptr<SVM> svm;
|
||||
|
||||
FileStorage fs( svmFilename, FileStorage::READ);
|
||||
if( fs.isOpened() )
|
||||
{
|
||||
cout << "*** LOADING SVM CLASSIFIER FOR CLASS " << objClassName << " ***" << endl;
|
||||
svm.load( svmFilename.c_str() );
|
||||
svm = StatModel::load<SVM>( svmFilename );
|
||||
}
|
||||
else
|
||||
{
|
||||
@@ -2437,20 +2439,24 @@ static void trainSVMClassifier( CvSVM& svm, const SVMTrainParamsExt& svmParamsEx
|
||||
}
|
||||
|
||||
cout << "TRAINING SVM FOR CLASS ..." << objClassName << "..." << endl;
|
||||
CvSVMParams svmParams;
|
||||
CvMat class_wts_cv;
|
||||
SVM::Params svmParams;
|
||||
Mat class_wts_cv;
|
||||
setSVMParams( svmParams, class_wts_cv, responses, svmParamsExt.balanceClasses );
|
||||
CvParamGrid c_grid, gamma_grid, p_grid, nu_grid, coef_grid, degree_grid;
|
||||
svm = SVM::create(svmParams);
|
||||
ParamGrid c_grid, gamma_grid, p_grid, nu_grid, coef_grid, degree_grid;
|
||||
setSVMTrainAutoParams( c_grid, gamma_grid, p_grid, nu_grid, coef_grid, degree_grid );
|
||||
svm.train_auto( trainData, responses, Mat(), Mat(), svmParams, 10, c_grid, gamma_grid, p_grid, nu_grid, coef_grid, degree_grid );
|
||||
|
||||
svm->trainAuto(TrainData::create(trainData, ROW_SAMPLE, responses), 10,
|
||||
c_grid, gamma_grid, p_grid, nu_grid, coef_grid, degree_grid);
|
||||
cout << "SVM TRAINING FOR CLASS " << objClassName << " COMPLETED" << endl;
|
||||
|
||||
svm.save( svmFilename.c_str() );
|
||||
svm->save( svmFilename );
|
||||
cout << "SAVED CLASSIFIER TO FILE" << endl;
|
||||
}
|
||||
return svm;
|
||||
}
|
||||
|
||||
static void computeConfidences( CvSVM& svm, const string& objClassName, VocData& vocData,
|
||||
static void computeConfidences( const Ptr<SVM>& svm, const string& objClassName, VocData& vocData,
|
||||
Ptr<BOWImgDescriptorExtractor>& bowExtractor, const Ptr<FeatureDetector>& fdetector,
|
||||
const string& resPath )
|
||||
{
|
||||
@@ -2476,12 +2482,12 @@ static void computeConfidences( CvSVM& svm, const string& objClassName, VocData&
|
||||
if( imageIdx == 0 )
|
||||
{
|
||||
// In the first iteration, determine the sign of the positive class
|
||||
float classVal = confidences[imageIdx] = svm.predict( bowImageDescriptors[imageIdx], false );
|
||||
float scoreVal = confidences[imageIdx] = svm.predict( bowImageDescriptors[imageIdx], true );
|
||||
float classVal = confidences[imageIdx] = svm->predict( bowImageDescriptors[imageIdx], noArray(), 0 );
|
||||
float scoreVal = confidences[imageIdx] = svm->predict( bowImageDescriptors[imageIdx], noArray(), StatModel::RAW_OUTPUT );
|
||||
signMul = (classVal < 0) == (scoreVal < 0) ? 1.f : -1.f;
|
||||
}
|
||||
// svm output of decision function
|
||||
confidences[imageIdx] = signMul * svm.predict( bowImageDescriptors[imageIdx], true );
|
||||
confidences[imageIdx] = signMul * svm->predict( bowImageDescriptors[imageIdx], noArray(), StatModel::RAW_OUTPUT );
|
||||
}
|
||||
|
||||
cout << "WRITING QUERY RESULTS TO VOC RESULTS FILE FOR CLASS " << objClassName << "..." << endl;
|
||||
@@ -2591,9 +2597,8 @@ int main(int argc, char** argv)
|
||||
for( size_t classIdx = 0; classIdx < objClasses.size(); ++classIdx )
|
||||
{
|
||||
// Train a classifier on train dataset
|
||||
CvSVM svm;
|
||||
trainSVMClassifier( svm, svmTrainParamsExt, objClasses[classIdx], vocData,
|
||||
bowExtractor, featureDetector, resPath );
|
||||
Ptr<SVM> svm = trainSVMClassifier( svmTrainParamsExt, objClasses[classIdx], vocData,
|
||||
bowExtractor, featureDetector, resPath );
|
||||
|
||||
// Now use the classifier over all images on the test dataset and rank according to score order
|
||||
// also calculating precision-recall etc.
|
||||
|
||||
@@ -1,81 +0,0 @@
|
||||
/*
|
||||
* FGBGTest.cpp
|
||||
*
|
||||
* Created on: May 7, 2012
|
||||
* Author: Andrew B. Godbehere
|
||||
*/
|
||||
|
||||
#include "opencv2/video.hpp"
|
||||
#include "opencv2/videoio.hpp"
|
||||
#include "opencv2/highgui.hpp"
|
||||
#include <opencv2/core/utility.hpp>
|
||||
#include <iostream>
|
||||
|
||||
using namespace cv;
|
||||
|
||||
static void help()
|
||||
{
|
||||
std::cout <<
|
||||
"\nA program demonstrating the use and capabilities of a particular BackgroundSubtraction\n"
|
||||
"algorithm described in A. Godbehere, A. Matsukawa, K. Goldberg, \n"
|
||||
"\"Visual Tracking of Human Visitors under Variable-Lighting Conditions for a Responsive\n"
|
||||
"Audio Art Installation\", American Control Conference, 2012, used in an interactive\n"
|
||||
"installation at the Contemporary Jewish Museum in San Francisco, CA from March 31 through\n"
|
||||
"July 31, 2011.\n"
|
||||
"Call:\n"
|
||||
"./BackgroundSubtractorGMG_sample\n"
|
||||
"Using OpenCV version " << CV_VERSION << "\n"<<std::endl;
|
||||
}
|
||||
|
||||
int main(int argc, char** argv)
|
||||
{
|
||||
help();
|
||||
|
||||
initModule_video();
|
||||
setUseOptimized(true);
|
||||
setNumThreads(8);
|
||||
|
||||
Ptr<BackgroundSubtractor> fgbg = createBackgroundSubtractorGMG(20, 0.7);
|
||||
if (!fgbg)
|
||||
{
|
||||
std::cerr << "Failed to create BackgroundSubtractor.GMG Algorithm." << std::endl;
|
||||
return -1;
|
||||
}
|
||||
|
||||
VideoCapture cap;
|
||||
if (argc > 1)
|
||||
cap.open(argv[1]);
|
||||
else
|
||||
cap.open(0);
|
||||
|
||||
if (!cap.isOpened())
|
||||
{
|
||||
std::cerr << "Cannot read video. Try moving video file to sample directory." << std::endl;
|
||||
return -1;
|
||||
}
|
||||
|
||||
Mat frame, fgmask, segm;
|
||||
|
||||
namedWindow("FG Segmentation", WINDOW_NORMAL);
|
||||
|
||||
for (;;)
|
||||
{
|
||||
cap >> frame;
|
||||
|
||||
if (frame.empty())
|
||||
break;
|
||||
|
||||
fgbg->apply(frame, fgmask);
|
||||
|
||||
frame.convertTo(segm, CV_8U, 0.5);
|
||||
add(frame, Scalar(100, 100, 0), segm, fgmask);
|
||||
|
||||
imshow("FG Segmentation", segm);
|
||||
|
||||
int c = waitKey(30);
|
||||
if (c == 'q' || c == 'Q' || (c & 255) == 27)
|
||||
break;
|
||||
}
|
||||
|
||||
return 0;
|
||||
}
|
||||
@@ -21,6 +21,8 @@ static void help()
|
||||
const char* keys =
|
||||
{
|
||||
"{c camera | | use camera or not}"
|
||||
"{m method |mog2 | method (knn or mog2) }"
|
||||
"{s smooth | | smooth the mask }"
|
||||
"{fn file_name|tree.avi | movie file }"
|
||||
};
|
||||
|
||||
@@ -31,7 +33,9 @@ int main(int argc, const char** argv)
|
||||
|
||||
CommandLineParser parser(argc, argv, keys);
|
||||
bool useCamera = parser.has("camera");
|
||||
bool smoothMask = parser.has("smooth");
|
||||
string file = parser.get<string>("file_name");
|
||||
string method = parser.get<string>("method");
|
||||
VideoCapture cap;
|
||||
bool update_bg_model = true;
|
||||
|
||||
@@ -53,24 +57,31 @@ int main(int argc, const char** argv)
|
||||
namedWindow("foreground image", WINDOW_NORMAL);
|
||||
namedWindow("mean background image", WINDOW_NORMAL);
|
||||
|
||||
Ptr<BackgroundSubtractor> bg_model = createBackgroundSubtractorMOG2();
|
||||
Ptr<BackgroundSubtractor> bg_model = method == "knn" ?
|
||||
createBackgroundSubtractorKNN().dynamicCast<BackgroundSubtractor>() :
|
||||
createBackgroundSubtractorMOG2().dynamicCast<BackgroundSubtractor>();
|
||||
|
||||
Mat img, fgmask, fgimg;
|
||||
Mat img0, img, fgmask, fgimg;
|
||||
|
||||
for(;;)
|
||||
{
|
||||
cap >> img;
|
||||
cap >> img0;
|
||||
|
||||
if( img.empty() )
|
||||
if( img0.empty() )
|
||||
break;
|
||||
|
||||
//cvtColor(_img, img, COLOR_BGR2GRAY);
|
||||
resize(img0, img, Size(640, 640*img0.rows/img0.cols), INTER_LINEAR);
|
||||
|
||||
if( fgimg.empty() )
|
||||
fgimg.create(img.size(), img.type());
|
||||
|
||||
//update the model
|
||||
bg_model->apply(img, fgmask, update_bg_model ? -1 : 0);
|
||||
if( smoothMask )
|
||||
{
|
||||
GaussianBlur(fgmask, fgmask, Size(11, 11), 3.5, 3.5);
|
||||
threshold(fgmask, fgmask, 10, 255, THRESH_BINARY);
|
||||
}
|
||||
|
||||
fgimg = Scalar::all(0);
|
||||
img.copyTo(fgimg, fgmask);
|
||||
|
||||
+5
-3
@@ -2,6 +2,7 @@
|
||||
#include "opencv2/ml.hpp"
|
||||
|
||||
using namespace cv;
|
||||
using namespace cv::ml;
|
||||
|
||||
int main( int /*argc*/, char** /*argv*/ )
|
||||
{
|
||||
@@ -34,8 +35,9 @@ int main( int /*argc*/, char** /*argv*/ )
|
||||
samples = samples.reshape(1, 0);
|
||||
|
||||
// cluster the data
|
||||
EM em_model(N, EM::COV_MAT_SPHERICAL, TermCriteria(TermCriteria::COUNT+TermCriteria::EPS, 300, 0.1));
|
||||
em_model.train( samples, noArray(), labels, noArray() );
|
||||
Ptr<EM> em_model = EM::train( samples, noArray(), labels, noArray(),
|
||||
EM::Params(N, EM::COV_MAT_SPHERICAL,
|
||||
TermCriteria(TermCriteria::COUNT+TermCriteria::EPS, 300, 0.1)));
|
||||
|
||||
// classify every image pixel
|
||||
for( i = 0; i < img.rows; i++ )
|
||||
@@ -44,7 +46,7 @@ int main( int /*argc*/, char** /*argv*/ )
|
||||
{
|
||||
sample.at<float>(0) = (float)j;
|
||||
sample.at<float>(1) = (float)i;
|
||||
int response = cvRound(em_model.predict( sample )[1]);
|
||||
int response = cvRound(em_model->predict2( sample, noArray() )[1]);
|
||||
Scalar c = colors[response];
|
||||
|
||||
circle( img, Point(j, i), 1, c*0.75, FILLED );
|
||||
|
||||
@@ -37,7 +37,8 @@ int main(int, char**)
|
||||
if( !cap.isOpened() )
|
||||
return -1;
|
||||
|
||||
Mat prevgray, gray, flow, cflow, frame;
|
||||
Mat flow, cflow, frame;
|
||||
UMat gray, prevgray, uflow;
|
||||
namedWindow("flow", 1);
|
||||
|
||||
for(;;)
|
||||
@@ -45,10 +46,11 @@ int main(int, char**)
|
||||
cap >> frame;
|
||||
cvtColor(frame, gray, COLOR_BGR2GRAY);
|
||||
|
||||
if( prevgray.data )
|
||||
if( !prevgray.empty() )
|
||||
{
|
||||
calcOpticalFlowFarneback(prevgray, gray, flow, 0.5, 3, 15, 3, 5, 1.2, 0);
|
||||
calcOpticalFlowFarneback(prevgray, gray, uflow, 0.5, 3, 15, 3, 5, 1.2, 0);
|
||||
cvtColor(prevgray, cflow, COLOR_GRAY2BGR);
|
||||
uflow.copyTo(flow);
|
||||
drawOptFlowMap(flow, cflow, 16, 1.5, Scalar(0, 255, 0));
|
||||
imshow("flow", cflow);
|
||||
}
|
||||
|
||||
+285
-534
File diff suppressed because it is too large
Load Diff
@@ -1,204 +0,0 @@
|
||||
#include "opencv2/video/tracking_c.h"
|
||||
#include "opencv2/imgproc/imgproc_c.h"
|
||||
#include "opencv2/videoio/videoio_c.h"
|
||||
#include "opencv2/highgui/highgui_c.h"
|
||||
#include <time.h>
|
||||
#include <stdio.h>
|
||||
#include <ctype.h>
|
||||
|
||||
static void help(void)
|
||||
{
|
||||
printf(
|
||||
"\nThis program demonstrated the use of motion templates -- basically using the gradients\n"
|
||||
"of thresholded layers of decaying frame differencing. New movements are stamped on top with floating system\n"
|
||||
"time code and motions too old are thresholded away. This is the 'motion history file'. The program reads from the camera of your choice or from\n"
|
||||
"a file. Gradients of motion history are used to detect direction of motoin etc\n"
|
||||
"Usage :\n"
|
||||
"./motempl [camera number 0-n or file name, default is camera 0]\n"
|
||||
);
|
||||
}
|
||||
// various tracking parameters (in seconds)
|
||||
const double MHI_DURATION = 1;
|
||||
const double MAX_TIME_DELTA = 0.5;
|
||||
const double MIN_TIME_DELTA = 0.05;
|
||||
// number of cyclic frame buffer used for motion detection
|
||||
// (should, probably, depend on FPS)
|
||||
const int N = 4;
|
||||
|
||||
// ring image buffer
|
||||
IplImage **buf = 0;
|
||||
int last = 0;
|
||||
|
||||
// temporary images
|
||||
IplImage *mhi = 0; // MHI
|
||||
IplImage *orient = 0; // orientation
|
||||
IplImage *mask = 0; // valid orientation mask
|
||||
IplImage *segmask = 0; // motion segmentation map
|
||||
CvMemStorage* storage = 0; // temporary storage
|
||||
|
||||
// parameters:
|
||||
// img - input video frame
|
||||
// dst - resultant motion picture
|
||||
// args - optional parameters
|
||||
static void update_mhi( IplImage* img, IplImage* dst, int diff_threshold )
|
||||
{
|
||||
double timestamp = (double)clock()/CLOCKS_PER_SEC; // get current time in seconds
|
||||
CvSize size = cvSize(img->width,img->height); // get current frame size
|
||||
int i, idx1 = last, idx2;
|
||||
IplImage* silh;
|
||||
CvSeq* seq;
|
||||
CvRect comp_rect;
|
||||
double count;
|
||||
double angle;
|
||||
CvPoint center;
|
||||
double magnitude;
|
||||
CvScalar color;
|
||||
|
||||
// allocate images at the beginning or
|
||||
// reallocate them if the frame size is changed
|
||||
if( !mhi || mhi->width != size.width || mhi->height != size.height ) {
|
||||
if( buf == 0 ) {
|
||||
buf = (IplImage**)malloc(N*sizeof(buf[0]));
|
||||
memset( buf, 0, N*sizeof(buf[0]));
|
||||
}
|
||||
|
||||
for( i = 0; i < N; i++ ) {
|
||||
cvReleaseImage( &buf[i] );
|
||||
buf[i] = cvCreateImage( size, IPL_DEPTH_8U, 1 );
|
||||
cvZero( buf[i] );
|
||||
}
|
||||
cvReleaseImage( &mhi );
|
||||
cvReleaseImage( &orient );
|
||||
cvReleaseImage( &segmask );
|
||||
cvReleaseImage( &mask );
|
||||
|
||||
mhi = cvCreateImage( size, IPL_DEPTH_32F, 1 );
|
||||
cvZero( mhi ); // clear MHI at the beginning
|
||||
orient = cvCreateImage( size, IPL_DEPTH_32F, 1 );
|
||||
segmask = cvCreateImage( size, IPL_DEPTH_32F, 1 );
|
||||
mask = cvCreateImage( size, IPL_DEPTH_8U, 1 );
|
||||
}
|
||||
|
||||
cvCvtColor( img, buf[last], CV_BGR2GRAY ); // convert frame to grayscale
|
||||
|
||||
idx2 = (last + 1) % N; // index of (last - (N-1))th frame
|
||||
last = idx2;
|
||||
|
||||
silh = buf[idx2];
|
||||
cvAbsDiff( buf[idx1], buf[idx2], silh ); // get difference between frames
|
||||
|
||||
cvThreshold( silh, silh, diff_threshold, 1, CV_THRESH_BINARY ); // and threshold it
|
||||
cvUpdateMotionHistory( silh, mhi, timestamp, MHI_DURATION ); // update MHI
|
||||
|
||||
// convert MHI to blue 8u image
|
||||
cvCvtScale( mhi, mask, 255./MHI_DURATION,
|
||||
(MHI_DURATION - timestamp)*255./MHI_DURATION );
|
||||
cvZero( dst );
|
||||
cvMerge( mask, 0, 0, 0, dst );
|
||||
|
||||
// calculate motion gradient orientation and valid orientation mask
|
||||
cvCalcMotionGradient( mhi, mask, orient, MAX_TIME_DELTA, MIN_TIME_DELTA, 3 );
|
||||
|
||||
if( !storage )
|
||||
storage = cvCreateMemStorage(0);
|
||||
else
|
||||
cvClearMemStorage(storage);
|
||||
|
||||
// segment motion: get sequence of motion components
|
||||
// segmask is marked motion components map. It is not used further
|
||||
seq = cvSegmentMotion( mhi, segmask, storage, timestamp, MAX_TIME_DELTA );
|
||||
|
||||
// iterate through the motion components,
|
||||
// One more iteration (i == -1) corresponds to the whole image (global motion)
|
||||
for( i = -1; i < seq->total; i++ ) {
|
||||
|
||||
if( i < 0 ) { // case of the whole image
|
||||
comp_rect = cvRect( 0, 0, size.width, size.height );
|
||||
color = CV_RGB(255,255,255);
|
||||
magnitude = 100;
|
||||
}
|
||||
else { // i-th motion component
|
||||
comp_rect = ((CvConnectedComp*)cvGetSeqElem( seq, i ))->rect;
|
||||
if( comp_rect.width + comp_rect.height < 100 ) // reject very small components
|
||||
continue;
|
||||
color = CV_RGB(255,0,0);
|
||||
magnitude = 30;
|
||||
}
|
||||
|
||||
// select component ROI
|
||||
cvSetImageROI( silh, comp_rect );
|
||||
cvSetImageROI( mhi, comp_rect );
|
||||
cvSetImageROI( orient, comp_rect );
|
||||
cvSetImageROI( mask, comp_rect );
|
||||
|
||||
// calculate orientation
|
||||
angle = cvCalcGlobalOrientation( orient, mask, mhi, timestamp, MHI_DURATION);
|
||||
angle = 360.0 - angle; // adjust for images with top-left origin
|
||||
|
||||
count = cvNorm( silh, 0, CV_L1, 0 ); // calculate number of points within silhouette ROI
|
||||
|
||||
cvResetImageROI( mhi );
|
||||
cvResetImageROI( orient );
|
||||
cvResetImageROI( mask );
|
||||
cvResetImageROI( silh );
|
||||
|
||||
// check for the case of little motion
|
||||
if( count < comp_rect.width*comp_rect.height * 0.05 )
|
||||
continue;
|
||||
|
||||
// draw a clock with arrow indicating the direction
|
||||
center = cvPoint( (comp_rect.x + comp_rect.width/2),
|
||||
(comp_rect.y + comp_rect.height/2) );
|
||||
|
||||
cvCircle( dst, center, cvRound(magnitude*1.2), color, 3, CV_AA, 0 );
|
||||
cvLine( dst, center, cvPoint( cvRound( center.x + magnitude*cos(angle*CV_PI/180)),
|
||||
cvRound( center.y - magnitude*sin(angle*CV_PI/180))), color, 3, CV_AA, 0 );
|
||||
}
|
||||
}
|
||||
|
||||
|
||||
int main(int argc, char** argv)
|
||||
{
|
||||
IplImage* motion = 0;
|
||||
CvCapture* capture = 0;
|
||||
|
||||
help();
|
||||
|
||||
if( argc == 1 || (argc == 2 && strlen(argv[1]) == 1 && isdigit(argv[1][0])))
|
||||
capture = cvCaptureFromCAM( argc == 2 ? argv[1][0] - '0' : 0 );
|
||||
else if( argc == 2 )
|
||||
capture = cvCaptureFromFile( argv[1] );
|
||||
|
||||
if( capture )
|
||||
{
|
||||
cvNamedWindow( "Motion", 1 );
|
||||
|
||||
for(;;)
|
||||
{
|
||||
IplImage* image = cvQueryFrame( capture );
|
||||
if( !image )
|
||||
break;
|
||||
|
||||
if( !motion )
|
||||
{
|
||||
motion = cvCreateImage( cvSize(image->width,image->height), 8, 3 );
|
||||
cvZero( motion );
|
||||
motion->origin = image->origin;
|
||||
}
|
||||
|
||||
update_mhi( image, motion, 30 );
|
||||
cvShowImage( "Motion", motion );
|
||||
|
||||
if( cvWaitKey(10) >= 0 )
|
||||
break;
|
||||
}
|
||||
cvReleaseCapture( &capture );
|
||||
cvDestroyWindow( "Motion" );
|
||||
}
|
||||
|
||||
return 0;
|
||||
}
|
||||
|
||||
#ifdef _EiC
|
||||
main(1,"motempl.c");
|
||||
#endif
|
||||
@@ -1,322 +0,0 @@
|
||||
#include "opencv2/core/core_c.h"
|
||||
#include "opencv2/ml/ml.hpp"
|
||||
#include <stdio.h>
|
||||
|
||||
static void help()
|
||||
{
|
||||
printf("\nThis program demonstrated the use of OpenCV's decision tree function for learning and predicting data\n"
|
||||
"Usage :\n"
|
||||
"./mushroom <path to agaricus-lepiota.data>\n"
|
||||
"\n"
|
||||
"The sample demonstrates how to build a decision tree for classifying mushrooms.\n"
|
||||
"It uses the sample base agaricus-lepiota.data from UCI Repository, here is the link:\n"
|
||||
"\n"
|
||||
"Newman, D.J. & Hettich, S. & Blake, C.L. & Merz, C.J. (1998).\n"
|
||||
"UCI Repository of machine learning databases\n"
|
||||
"[http://www.ics.uci.edu/~mlearn/MLRepository.html].\n"
|
||||
"Irvine, CA: University of California, Department of Information and Computer Science.\n"
|
||||
"\n"
|
||||
"// loads the mushroom database, which is a text file, containing\n"
|
||||
"// one training sample per row, all the input variables and the output variable are categorical,\n"
|
||||
"// the values are encoded by characters.\n\n");
|
||||
}
|
||||
|
||||
static int mushroom_read_database( const char* filename, CvMat** data, CvMat** missing, CvMat** responses )
|
||||
{
|
||||
const int M = 1024;
|
||||
FILE* f = fopen( filename, "rt" );
|
||||
CvMemStorage* storage;
|
||||
CvSeq* seq;
|
||||
char buf[M+2], *ptr;
|
||||
float* el_ptr;
|
||||
CvSeqReader reader;
|
||||
int i, j, var_count = 0;
|
||||
|
||||
if( !f )
|
||||
return 0;
|
||||
|
||||
// read the first line and determine the number of variables
|
||||
if( !fgets( buf, M, f ))
|
||||
{
|
||||
fclose(f);
|
||||
return 0;
|
||||
}
|
||||
|
||||
for( ptr = buf; *ptr != '\0'; ptr++ )
|
||||
var_count += *ptr == ',';
|
||||
assert( ptr - buf == (var_count+1)*2 );
|
||||
|
||||
// create temporary memory storage to store the whole database
|
||||
el_ptr = new float[var_count+1];
|
||||
storage = cvCreateMemStorage();
|
||||
seq = cvCreateSeq( 0, sizeof(*seq), (var_count+1)*sizeof(float), storage );
|
||||
|
||||
for(;;)
|
||||
{
|
||||
for( i = 0; i <= var_count; i++ )
|
||||
{
|
||||
int c = buf[i*2];
|
||||
el_ptr[i] = c == '?' ? -1.f : (float)c;
|
||||
}
|
||||
if( i != var_count+1 )
|
||||
break;
|
||||
cvSeqPush( seq, el_ptr );
|
||||
if( !fgets( buf, M, f ) || !strchr( buf, ',' ) )
|
||||
break;
|
||||
}
|
||||
fclose(f);
|
||||
|
||||
// allocate the output matrices and copy the base there
|
||||
*data = cvCreateMat( seq->total, var_count, CV_32F );
|
||||
*missing = cvCreateMat( seq->total, var_count, CV_8U );
|
||||
*responses = cvCreateMat( seq->total, 1, CV_32F );
|
||||
|
||||
cvStartReadSeq( seq, &reader );
|
||||
|
||||
for( i = 0; i < seq->total; i++ )
|
||||
{
|
||||
const float* sdata = (float*)reader.ptr + 1;
|
||||
float* ddata = data[0]->data.fl + var_count*i;
|
||||
float* dr = responses[0]->data.fl + i;
|
||||
uchar* dm = missing[0]->data.ptr + var_count*i;
|
||||
|
||||
for( j = 0; j < var_count; j++ )
|
||||
{
|
||||
ddata[j] = sdata[j];
|
||||
dm[j] = sdata[j] < 0;
|
||||
}
|
||||
*dr = sdata[-1];
|
||||
CV_NEXT_SEQ_ELEM( seq->elem_size, reader );
|
||||
}
|
||||
|
||||
cvReleaseMemStorage( &storage );
|
||||
delete [] el_ptr;
|
||||
return 1;
|
||||
}
|
||||
|
||||
|
||||
static CvDTree* mushroom_create_dtree( const CvMat* data, const CvMat* missing,
|
||||
const CvMat* responses, float p_weight )
|
||||
{
|
||||
CvDTree* dtree;
|
||||
CvMat* var_type;
|
||||
int i, hr1 = 0, hr2 = 0, p_total = 0;
|
||||
float priors[] = { 1, p_weight };
|
||||
|
||||
var_type = cvCreateMat( data->cols + 1, 1, CV_8U );
|
||||
cvSet( var_type, cvScalarAll(CV_VAR_CATEGORICAL) ); // all the variables are categorical
|
||||
|
||||
dtree = new CvDTree;
|
||||
|
||||
dtree->train( data, CV_ROW_SAMPLE, responses, 0, 0, var_type, missing,
|
||||
CvDTreeParams( 8, // max depth
|
||||
10, // min sample count
|
||||
0, // regression accuracy: N/A here
|
||||
true, // compute surrogate split, as we have missing data
|
||||
15, // max number of categories (use sub-optimal algorithm for larger numbers)
|
||||
10, // the number of cross-validation folds
|
||||
true, // use 1SE rule => smaller tree
|
||||
true, // throw away the pruned tree branches
|
||||
priors // the array of priors, the bigger p_weight, the more attention
|
||||
// to the poisonous mushrooms
|
||||
// (a mushroom will be judjed to be poisonous with bigger chance)
|
||||
));
|
||||
|
||||
// compute hit-rate on the training database, demonstrates predict usage.
|
||||
for( i = 0; i < data->rows; i++ )
|
||||
{
|
||||
CvMat sample, mask;
|
||||
cvGetRow( data, &sample, i );
|
||||
cvGetRow( missing, &mask, i );
|
||||
double r = dtree->predict( &sample, &mask )->value;
|
||||
int d = fabs(r - responses->data.fl[i]) >= FLT_EPSILON;
|
||||
if( d )
|
||||
{
|
||||
if( r != 'p' )
|
||||
hr1++;
|
||||
else
|
||||
hr2++;
|
||||
}
|
||||
p_total += responses->data.fl[i] == 'p';
|
||||
}
|
||||
|
||||
printf( "Results on the training database:\n"
|
||||
"\tPoisonous mushrooms mis-predicted: %d (%g%%)\n"
|
||||
"\tFalse-alarms: %d (%g%%)\n", hr1, (double)hr1*100/p_total,
|
||||
hr2, (double)hr2*100/(data->rows - p_total) );
|
||||
|
||||
cvReleaseMat( &var_type );
|
||||
|
||||
return dtree;
|
||||
}
|
||||
|
||||
|
||||
static const char* var_desc[] =
|
||||
{
|
||||
"cap shape (bell=b,conical=c,convex=x,flat=f)",
|
||||
"cap surface (fibrous=f,grooves=g,scaly=y,smooth=s)",
|
||||
"cap color (brown=n,buff=b,cinnamon=c,gray=g,green=r,\n\tpink=p,purple=u,red=e,white=w,yellow=y)",
|
||||
"bruises? (bruises=t,no=f)",
|
||||
"odor (almond=a,anise=l,creosote=c,fishy=y,foul=f,\n\tmusty=m,none=n,pungent=p,spicy=s)",
|
||||
"gill attachment (attached=a,descending=d,free=f,notched=n)",
|
||||
"gill spacing (close=c,crowded=w,distant=d)",
|
||||
"gill size (broad=b,narrow=n)",
|
||||
"gill color (black=k,brown=n,buff=b,chocolate=h,gray=g,\n\tgreen=r,orange=o,pink=p,purple=u,red=e,white=w,yellow=y)",
|
||||
"stalk shape (enlarging=e,tapering=t)",
|
||||
"stalk root (bulbous=b,club=c,cup=u,equal=e,rhizomorphs=z,rooted=r)",
|
||||
"stalk surface above ring (ibrous=f,scaly=y,silky=k,smooth=s)",
|
||||
"stalk surface below ring (ibrous=f,scaly=y,silky=k,smooth=s)",
|
||||
"stalk color above ring (brown=n,buff=b,cinnamon=c,gray=g,orange=o,\n\tpink=p,red=e,white=w,yellow=y)",
|
||||
"stalk color below ring (brown=n,buff=b,cinnamon=c,gray=g,orange=o,\n\tpink=p,red=e,white=w,yellow=y)",
|
||||
"veil type (partial=p,universal=u)",
|
||||
"veil color (brown=n,orange=o,white=w,yellow=y)",
|
||||
"ring number (none=n,one=o,two=t)",
|
||||
"ring type (cobwebby=c,evanescent=e,flaring=f,large=l,\n\tnone=n,pendant=p,sheathing=s,zone=z)",
|
||||
"spore print color (black=k,brown=n,buff=b,chocolate=h,green=r,\n\torange=o,purple=u,white=w,yellow=y)",
|
||||
"population (abundant=a,clustered=c,numerous=n,\n\tscattered=s,several=v,solitary=y)",
|
||||
"habitat (grasses=g,leaves=l,meadows=m,paths=p\n\turban=u,waste=w,woods=d)",
|
||||
0
|
||||
};
|
||||
|
||||
|
||||
static void print_variable_importance( CvDTree* dtree )
|
||||
{
|
||||
const CvMat* var_importance = dtree->get_var_importance();
|
||||
int i;
|
||||
char input[1000];
|
||||
|
||||
if( !var_importance )
|
||||
{
|
||||
printf( "Error: Variable importance can not be retrieved\n" );
|
||||
return;
|
||||
}
|
||||
|
||||
printf( "Print variable importance information? (y/n) " );
|
||||
int values_read = scanf( "%1s", input );
|
||||
CV_Assert(values_read == 1);
|
||||
|
||||
if( input[0] != 'y' && input[0] != 'Y' )
|
||||
return;
|
||||
|
||||
for( i = 0; i < var_importance->cols*var_importance->rows; i++ )
|
||||
{
|
||||
double val = var_importance->data.db[i];
|
||||
char buf[100];
|
||||
int len = (int)(strchr( var_desc[i], '(' ) - var_desc[i] - 1);
|
||||
strncpy( buf, var_desc[i], len );
|
||||
buf[len] = '\0';
|
||||
printf( "%s", buf );
|
||||
printf( ": %g%%\n", val*100. );
|
||||
}
|
||||
}
|
||||
|
||||
static void interactive_classification( CvDTree* dtree )
|
||||
{
|
||||
char input[1000];
|
||||
const CvDTreeNode* root;
|
||||
CvDTreeTrainData* data;
|
||||
|
||||
if( !dtree )
|
||||
return;
|
||||
|
||||
root = dtree->get_root();
|
||||
data = dtree->get_data();
|
||||
|
||||
for(;;)
|
||||
{
|
||||
const CvDTreeNode* node;
|
||||
|
||||
printf( "Start/Proceed with interactive mushroom classification (y/n): " );
|
||||
int values_read = scanf( "%1s", input );
|
||||
CV_Assert(values_read == 1);
|
||||
|
||||
if( input[0] != 'y' && input[0] != 'Y' )
|
||||
break;
|
||||
printf( "Enter 1-letter answers, '?' for missing/unknown value...\n" );
|
||||
|
||||
// custom version of predict
|
||||
node = root;
|
||||
for(;;)
|
||||
{
|
||||
CvDTreeSplit* split = node->split;
|
||||
int dir = 0;
|
||||
|
||||
if( !node->left || node->Tn <= dtree->get_pruned_tree_idx() || !node->split )
|
||||
break;
|
||||
|
||||
for( ; split != 0; )
|
||||
{
|
||||
int vi = split->var_idx, j;
|
||||
int count = data->cat_count->data.i[vi];
|
||||
const int* map = data->cat_map->data.i + data->cat_ofs->data.i[vi];
|
||||
|
||||
printf( "%s: ", var_desc[vi] );
|
||||
values_read = scanf( "%1s", input );
|
||||
CV_Assert(values_read == 1);
|
||||
|
||||
if( input[0] == '?' )
|
||||
{
|
||||
split = split->next;
|
||||
continue;
|
||||
}
|
||||
|
||||
// convert the input character to the normalized value of the variable
|
||||
for( j = 0; j < count; j++ )
|
||||
if( map[j] == input[0] )
|
||||
break;
|
||||
if( j < count )
|
||||
{
|
||||
dir = (split->subset[j>>5] & (1 << (j&31))) ? -1 : 1;
|
||||
if( split->inversed )
|
||||
dir = -dir;
|
||||
break;
|
||||
}
|
||||
else
|
||||
printf( "Error: unrecognized value\n" );
|
||||
}
|
||||
|
||||
if( !dir )
|
||||
{
|
||||
printf( "Impossible to classify the sample\n");
|
||||
node = 0;
|
||||
break;
|
||||
}
|
||||
node = dir < 0 ? node->left : node->right;
|
||||
}
|
||||
|
||||
if( node )
|
||||
printf( "Prediction result: the mushroom is %s\n",
|
||||
node->class_idx == 0 ? "EDIBLE" : "POISONOUS" );
|
||||
printf( "\n-----------------------------\n" );
|
||||
}
|
||||
}
|
||||
|
||||
|
||||
int main( int argc, char** argv )
|
||||
{
|
||||
CvMat *data = 0, *missing = 0, *responses = 0;
|
||||
CvDTree* dtree;
|
||||
const char* base_path = argc >= 2 ? argv[1] : "agaricus-lepiota.data";
|
||||
|
||||
help();
|
||||
|
||||
if( !mushroom_read_database( base_path, &data, &missing, &responses ) )
|
||||
{
|
||||
printf( "\nUnable to load the training database\n\n");
|
||||
help();
|
||||
return -1;
|
||||
}
|
||||
|
||||
dtree = mushroom_create_dtree( data, missing, responses,
|
||||
10 // poisonous mushrooms will have 10x higher weight in the decision tree
|
||||
);
|
||||
cvReleaseMat( &data );
|
||||
cvReleaseMat( &missing );
|
||||
cvReleaseMat( &responses );
|
||||
|
||||
print_variable_importance( dtree );
|
||||
interactive_classification( dtree );
|
||||
delete dtree;
|
||||
|
||||
return 0;
|
||||
}
|
||||
+137
-378
@@ -12,6 +12,7 @@
|
||||
|
||||
using namespace std;
|
||||
using namespace cv;
|
||||
using namespace cv::ml;
|
||||
|
||||
const Scalar WHITE_COLOR = Scalar(255,255,255);
|
||||
const string winName = "points";
|
||||
@@ -22,18 +23,20 @@ RNG rng;
|
||||
|
||||
vector<Point> trainedPoints;
|
||||
vector<int> trainedPointsMarkers;
|
||||
vector<Scalar> classColors;
|
||||
const int MAX_CLASSES = 2;
|
||||
vector<Vec3b> classColors(MAX_CLASSES);
|
||||
int currentClass = 0;
|
||||
vector<int> classCounters(MAX_CLASSES);
|
||||
|
||||
#define _NBC_ 0 // normal Bayessian classifier
|
||||
#define _KNN_ 0 // k nearest neighbors classifier
|
||||
#define _SVM_ 0 // support vectors machine
|
||||
#define _NBC_ 1 // normal Bayessian classifier
|
||||
#define _KNN_ 1 // k nearest neighbors classifier
|
||||
#define _SVM_ 1 // support vectors machine
|
||||
#define _DT_ 1 // decision tree
|
||||
#define _BT_ 0 // ADA Boost
|
||||
#define _BT_ 1 // ADA Boost
|
||||
#define _GBT_ 0 // gradient boosted trees
|
||||
#define _RF_ 0 // random forest
|
||||
#define _ERT_ 0 // extremely randomized trees
|
||||
#define _ANN_ 0 // artificial neural networks
|
||||
#define _EM_ 0 // expectation-maximization
|
||||
#define _RF_ 1 // random forest
|
||||
#define _ANN_ 1 // artificial neural networks
|
||||
#define _EM_ 1 // expectation-maximization
|
||||
|
||||
static void on_mouse( int event, int x, int y, int /*flags*/, void* )
|
||||
{
|
||||
@@ -44,76 +47,43 @@ static void on_mouse( int event, int x, int y, int /*flags*/, void* )
|
||||
|
||||
if( event == EVENT_LBUTTONUP )
|
||||
{
|
||||
if( classColors.empty() )
|
||||
return;
|
||||
|
||||
trainedPoints.push_back( Point(x,y) );
|
||||
trainedPointsMarkers.push_back( (int)(classColors.size()-1) );
|
||||
trainedPointsMarkers.push_back( currentClass );
|
||||
classCounters[currentClass]++;
|
||||
updateFlag = true;
|
||||
}
|
||||
else if( event == EVENT_RBUTTONUP )
|
||||
{
|
||||
#if _BT_
|
||||
if( classColors.size() < 2 )
|
||||
{
|
||||
#endif
|
||||
classColors.push_back( Scalar((uchar)rng(256), (uchar)rng(256), (uchar)rng(256)) );
|
||||
updateFlag = true;
|
||||
#if _BT_
|
||||
}
|
||||
else
|
||||
cout << "New class can not be added, because CvBoost can only be used for 2-class classification" << endl;
|
||||
#endif
|
||||
|
||||
}
|
||||
|
||||
//draw
|
||||
if( updateFlag )
|
||||
{
|
||||
img = Scalar::all(0);
|
||||
|
||||
// put the text
|
||||
stringstream text;
|
||||
text << "current class " << classColors.size()-1;
|
||||
putText( img, text.str(), Point(10,25), FONT_HERSHEY_SIMPLEX, 0.8f, WHITE_COLOR, 2 );
|
||||
|
||||
text.str("");
|
||||
text << "total classes " << classColors.size();
|
||||
putText( img, text.str(), Point(10,50), FONT_HERSHEY_SIMPLEX, 0.8f, WHITE_COLOR, 2 );
|
||||
|
||||
text.str("");
|
||||
text << "total points " << trainedPoints.size();
|
||||
putText(img, text.str(), Point(10,75), FONT_HERSHEY_SIMPLEX, 0.8f, WHITE_COLOR, 2 );
|
||||
|
||||
// draw points
|
||||
for( size_t i = 0; i < trainedPoints.size(); i++ )
|
||||
circle( img, trainedPoints[i], 5, classColors[trainedPointsMarkers[i]], -1 );
|
||||
{
|
||||
Vec3b c = classColors[trainedPointsMarkers[i]];
|
||||
circle( img, trainedPoints[i], 5, Scalar(c), -1 );
|
||||
}
|
||||
|
||||
imshow( winName, img );
|
||||
}
|
||||
}
|
||||
|
||||
static void prepare_train_data( Mat& samples, Mat& classes )
|
||||
static Mat prepare_train_samples(const vector<Point>& pts)
|
||||
{
|
||||
Mat( trainedPoints ).copyTo( samples );
|
||||
Mat( trainedPointsMarkers ).copyTo( classes );
|
||||
|
||||
// reshape trainData and change its type
|
||||
samples = samples.reshape( 1, samples.rows );
|
||||
samples.convertTo( samples, CV_32FC1 );
|
||||
Mat samples;
|
||||
Mat(pts).reshape(1, (int)pts.size()).convertTo(samples, CV_32F);
|
||||
return samples;
|
||||
}
|
||||
|
||||
#if _NBC_
|
||||
static void find_decision_boundary_NBC()
|
||||
static Ptr<TrainData> prepare_train_data()
|
||||
{
|
||||
img.copyTo( imgDst );
|
||||
|
||||
Mat trainSamples, trainClasses;
|
||||
prepare_train_data( trainSamples, trainClasses );
|
||||
|
||||
// learn classifier
|
||||
CvNormalBayesClassifier normalBayesClassifier( trainSamples, trainClasses );
|
||||
Mat samples = prepare_train_samples(trainedPoints);
|
||||
return TrainData::create(samples, ROW_SAMPLE, Mat(trainedPointsMarkers));
|
||||
}
|
||||
|
||||
static void predict_and_paint(const Ptr<StatModel>& model, Mat& dst)
|
||||
{
|
||||
Mat testSample( 1, 2, CV_32FC1 );
|
||||
for( int y = 0; y < img.rows; y += testStep )
|
||||
{
|
||||
@@ -122,378 +92,171 @@ static void find_decision_boundary_NBC()
|
||||
testSample.at<float>(0) = (float)x;
|
||||
testSample.at<float>(1) = (float)y;
|
||||
|
||||
int response = (int)normalBayesClassifier.predict( testSample );
|
||||
circle( imgDst, Point(x,y), 1, classColors[response] );
|
||||
int response = (int)model->predict( testSample );
|
||||
dst.at<Vec3b>(y, x) = classColors[response];
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
#if _NBC_
|
||||
static void find_decision_boundary_NBC()
|
||||
{
|
||||
// learn classifier
|
||||
Ptr<NormalBayesClassifier> normalBayesClassifier = StatModel::train<NormalBayesClassifier>(prepare_train_data(), NormalBayesClassifier::Params());
|
||||
|
||||
predict_and_paint(normalBayesClassifier, imgDst);
|
||||
}
|
||||
#endif
|
||||
|
||||
|
||||
#if _KNN_
|
||||
static void find_decision_boundary_KNN( int K )
|
||||
{
|
||||
img.copyTo( imgDst );
|
||||
|
||||
Mat trainSamples, trainClasses;
|
||||
prepare_train_data( trainSamples, trainClasses );
|
||||
|
||||
// learn classifier
|
||||
#if defined HAVE_OPENCV_OCL && _OCL_KNN_
|
||||
cv::ocl::KNearestNeighbour knnClassifier;
|
||||
Mat temp, result;
|
||||
knnClassifier.train(trainSamples, trainClasses, temp, false, K);
|
||||
cv::ocl::oclMat testSample_ocl, reslut_ocl;
|
||||
#else
|
||||
CvKNearest knnClassifier( trainSamples, trainClasses, Mat(), false, K );
|
||||
#endif
|
||||
|
||||
Mat testSample( 1, 2, CV_32FC1 );
|
||||
for( int y = 0; y < img.rows; y += testStep )
|
||||
{
|
||||
for( int x = 0; x < img.cols; x += testStep )
|
||||
{
|
||||
testSample.at<float>(0) = (float)x;
|
||||
testSample.at<float>(1) = (float)y;
|
||||
#if defined HAVE_OPENCV_OCL && _OCL_KNN_
|
||||
testSample_ocl.upload(testSample);
|
||||
|
||||
knnClassifier.find_nearest(testSample_ocl, K, reslut_ocl);
|
||||
|
||||
reslut_ocl.download(result);
|
||||
int response = saturate_cast<int>(result.at<float>(0));
|
||||
circle(imgDst, Point(x, y), 1, classColors[response]);
|
||||
#else
|
||||
|
||||
int response = (int)knnClassifier.find_nearest( testSample, K );
|
||||
circle( imgDst, Point(x,y), 1, classColors[response] );
|
||||
#endif
|
||||
}
|
||||
}
|
||||
Ptr<KNearest> knn = StatModel::train<KNearest>(prepare_train_data(), KNearest::Params(K, true));
|
||||
predict_and_paint(knn, imgDst);
|
||||
}
|
||||
#endif
|
||||
|
||||
#if _SVM_
|
||||
static void find_decision_boundary_SVM( CvSVMParams params )
|
||||
static void find_decision_boundary_SVM( SVM::Params params )
|
||||
{
|
||||
img.copyTo( imgDst );
|
||||
Ptr<SVM> svm = StatModel::train<SVM>(prepare_train_data(), params);
|
||||
predict_and_paint(svm, imgDst);
|
||||
|
||||
Mat trainSamples, trainClasses;
|
||||
prepare_train_data( trainSamples, trainClasses );
|
||||
|
||||
// learn classifier
|
||||
#if defined HAVE_OPENCV_OCL && _OCL_SVM_
|
||||
cv::ocl::CvSVM_OCL svmClassifier(trainSamples, trainClasses, Mat(), Mat(), params);
|
||||
#else
|
||||
CvSVM svmClassifier( trainSamples, trainClasses, Mat(), Mat(), params );
|
||||
#endif
|
||||
|
||||
Mat testSample( 1, 2, CV_32FC1 );
|
||||
for( int y = 0; y < img.rows; y += testStep )
|
||||
Mat sv = svm->getSupportVectors();
|
||||
for( int i = 0; i < sv.rows; i++ )
|
||||
{
|
||||
for( int x = 0; x < img.cols; x += testStep )
|
||||
{
|
||||
testSample.at<float>(0) = (float)x;
|
||||
testSample.at<float>(1) = (float)y;
|
||||
|
||||
int response = (int)svmClassifier.predict( testSample );
|
||||
circle( imgDst, Point(x,y), 2, classColors[response], 1 );
|
||||
}
|
||||
const float* supportVector = sv.ptr<float>(i);
|
||||
circle( imgDst, Point(saturate_cast<int>(supportVector[0]),saturate_cast<int>(supportVector[1])), 5, Scalar(255,255,255), -1 );
|
||||
}
|
||||
|
||||
|
||||
for( int i = 0; i < svmClassifier.get_support_vector_count(); i++ )
|
||||
{
|
||||
const float* supportVector = svmClassifier.get_support_vector(i);
|
||||
circle( imgDst, Point(saturate_cast<int>(supportVector[0]),saturate_cast<int>(supportVector[1])), 5, CV_RGB(255,255,255), -1 );
|
||||
}
|
||||
|
||||
}
|
||||
#endif
|
||||
|
||||
#if _DT_
|
||||
static void find_decision_boundary_DT()
|
||||
{
|
||||
img.copyTo( imgDst );
|
||||
DTrees::Params params;
|
||||
params.maxDepth = 8;
|
||||
params.minSampleCount = 2;
|
||||
params.useSurrogates = false;
|
||||
params.CVFolds = 0; // the number of cross-validation folds
|
||||
params.use1SERule = false;
|
||||
params.truncatePrunedTree = false;
|
||||
|
||||
Mat trainSamples, trainClasses;
|
||||
prepare_train_data( trainSamples, trainClasses );
|
||||
Ptr<DTrees> dtree = StatModel::train<DTrees>(prepare_train_data(), params);
|
||||
|
||||
// learn classifier
|
||||
CvDTree dtree;
|
||||
|
||||
Mat var_types( 1, trainSamples.cols + 1, CV_8UC1, Scalar(CV_VAR_ORDERED) );
|
||||
var_types.at<uchar>( trainSamples.cols ) = CV_VAR_CATEGORICAL;
|
||||
|
||||
CvDTreeParams params;
|
||||
params.max_depth = 8;
|
||||
params.min_sample_count = 2;
|
||||
params.use_surrogates = false;
|
||||
params.cv_folds = 0; // the number of cross-validation folds
|
||||
params.use_1se_rule = false;
|
||||
params.truncate_pruned_tree = false;
|
||||
|
||||
dtree.train( trainSamples, CV_ROW_SAMPLE, trainClasses,
|
||||
Mat(), Mat(), var_types, Mat(), params );
|
||||
|
||||
Mat testSample(1, 2, CV_32FC1 );
|
||||
for( int y = 0; y < img.rows; y += testStep )
|
||||
{
|
||||
for( int x = 0; x < img.cols; x += testStep )
|
||||
{
|
||||
testSample.at<float>(0) = (float)x;
|
||||
testSample.at<float>(1) = (float)y;
|
||||
|
||||
int response = (int)dtree.predict( testSample )->value;
|
||||
circle( imgDst, Point(x,y), 2, classColors[response], 1 );
|
||||
}
|
||||
}
|
||||
predict_and_paint(dtree, imgDst);
|
||||
}
|
||||
#endif
|
||||
|
||||
#if _BT_
|
||||
void find_decision_boundary_BT()
|
||||
static void find_decision_boundary_BT()
|
||||
{
|
||||
img.copyTo( imgDst );
|
||||
Boost::Params params( Boost::DISCRETE, // boost_type
|
||||
100, // weak_count
|
||||
0.95, // weight_trim_rate
|
||||
2, // max_depth
|
||||
false, //use_surrogates
|
||||
Mat() // priors
|
||||
);
|
||||
|
||||
Mat trainSamples, trainClasses;
|
||||
prepare_train_data( trainSamples, trainClasses );
|
||||
|
||||
// learn classifier
|
||||
CvBoost boost;
|
||||
|
||||
Mat var_types( 1, trainSamples.cols + 1, CV_8UC1, Scalar(CV_VAR_ORDERED) );
|
||||
var_types.at<uchar>( trainSamples.cols ) = CV_VAR_CATEGORICAL;
|
||||
|
||||
CvBoostParams params( CvBoost::DISCRETE, // boost_type
|
||||
100, // weak_count
|
||||
0.95, // weight_trim_rate
|
||||
2, // max_depth
|
||||
false, //use_surrogates
|
||||
0 // priors
|
||||
);
|
||||
|
||||
boost.train( trainSamples, CV_ROW_SAMPLE, trainClasses, Mat(), Mat(), var_types, Mat(), params );
|
||||
|
||||
Mat testSample(1, 2, CV_32FC1 );
|
||||
for( int y = 0; y < img.rows; y += testStep )
|
||||
{
|
||||
for( int x = 0; x < img.cols; x += testStep )
|
||||
{
|
||||
testSample.at<float>(0) = (float)x;
|
||||
testSample.at<float>(1) = (float)y;
|
||||
|
||||
int response = (int)boost.predict( testSample );
|
||||
circle( imgDst, Point(x,y), 2, classColors[response], 1 );
|
||||
}
|
||||
}
|
||||
Ptr<Boost> boost = StatModel::train<Boost>(prepare_train_data(), params);
|
||||
predict_and_paint(boost, imgDst);
|
||||
}
|
||||
|
||||
#endif
|
||||
|
||||
#if _GBT_
|
||||
void find_decision_boundary_GBT()
|
||||
static void find_decision_boundary_GBT()
|
||||
{
|
||||
img.copyTo( imgDst );
|
||||
GBTrees::Params params( GBTrees::DEVIANCE_LOSS, // loss_function_type
|
||||
100, // weak_count
|
||||
0.1f, // shrinkage
|
||||
1.0f, // subsample_portion
|
||||
2, // max_depth
|
||||
false // use_surrogates )
|
||||
);
|
||||
|
||||
Mat trainSamples, trainClasses;
|
||||
prepare_train_data( trainSamples, trainClasses );
|
||||
|
||||
// learn classifier
|
||||
CvGBTrees gbtrees;
|
||||
|
||||
Mat var_types( 1, trainSamples.cols + 1, CV_8UC1, Scalar(CV_VAR_ORDERED) );
|
||||
var_types.at<uchar>( trainSamples.cols ) = CV_VAR_CATEGORICAL;
|
||||
|
||||
CvGBTreesParams params( CvGBTrees::DEVIANCE_LOSS, // loss_function_type
|
||||
100, // weak_count
|
||||
0.1f, // shrinkage
|
||||
1.0f, // subsample_portion
|
||||
2, // max_depth
|
||||
false // use_surrogates )
|
||||
);
|
||||
|
||||
gbtrees.train( trainSamples, CV_ROW_SAMPLE, trainClasses, Mat(), Mat(), var_types, Mat(), params );
|
||||
|
||||
Mat testSample(1, 2, CV_32FC1 );
|
||||
for( int y = 0; y < img.rows; y += testStep )
|
||||
{
|
||||
for( int x = 0; x < img.cols; x += testStep )
|
||||
{
|
||||
testSample.at<float>(0) = (float)x;
|
||||
testSample.at<float>(1) = (float)y;
|
||||
|
||||
int response = (int)gbtrees.predict( testSample );
|
||||
circle( imgDst, Point(x,y), 2, classColors[response], 1 );
|
||||
}
|
||||
}
|
||||
Ptr<GBTrees> gbtrees = StatModel::train<GBTrees>(prepare_train_data(), params);
|
||||
predict_and_paint(gbtrees, imgDst);
|
||||
}
|
||||
|
||||
#endif
|
||||
|
||||
#if _RF_
|
||||
void find_decision_boundary_RF()
|
||||
static void find_decision_boundary_RF()
|
||||
{
|
||||
img.copyTo( imgDst );
|
||||
|
||||
Mat trainSamples, trainClasses;
|
||||
prepare_train_data( trainSamples, trainClasses );
|
||||
|
||||
// learn classifier
|
||||
CvRTrees rtrees;
|
||||
CvRTParams params( 4, // max_depth,
|
||||
RTrees::Params params( 4, // max_depth,
|
||||
2, // min_sample_count,
|
||||
0.f, // regression_accuracy,
|
||||
false, // use_surrogates,
|
||||
16, // max_categories,
|
||||
0, // priors,
|
||||
Mat(), // priors,
|
||||
false, // calc_var_importance,
|
||||
1, // nactive_vars,
|
||||
5, // max_num_of_trees_in_the_forest,
|
||||
0, // forest_accuracy,
|
||||
CV_TERMCRIT_ITER // termcrit_type
|
||||
TermCriteria(TermCriteria::MAX_ITER, 5, 0) // max_num_of_trees_in_the_forest,
|
||||
);
|
||||
|
||||
rtrees.train( trainSamples, CV_ROW_SAMPLE, trainClasses, Mat(), Mat(), Mat(), Mat(), params );
|
||||
|
||||
Mat testSample(1, 2, CV_32FC1 );
|
||||
for( int y = 0; y < img.rows; y += testStep )
|
||||
{
|
||||
for( int x = 0; x < img.cols; x += testStep )
|
||||
{
|
||||
testSample.at<float>(0) = (float)x;
|
||||
testSample.at<float>(1) = (float)y;
|
||||
|
||||
int response = (int)rtrees.predict( testSample );
|
||||
circle( imgDst, Point(x,y), 2, classColors[response], 1 );
|
||||
}
|
||||
}
|
||||
Ptr<RTrees> rtrees = StatModel::train<RTrees>(prepare_train_data(), params);
|
||||
predict_and_paint(rtrees, imgDst);
|
||||
}
|
||||
|
||||
#endif
|
||||
|
||||
#if _ERT_
|
||||
void find_decision_boundary_ERT()
|
||||
{
|
||||
img.copyTo( imgDst );
|
||||
|
||||
Mat trainSamples, trainClasses;
|
||||
prepare_train_data( trainSamples, trainClasses );
|
||||
|
||||
// learn classifier
|
||||
CvERTrees ertrees;
|
||||
|
||||
Mat var_types( 1, trainSamples.cols + 1, CV_8UC1, Scalar(CV_VAR_ORDERED) );
|
||||
var_types.at<uchar>( trainSamples.cols ) = CV_VAR_CATEGORICAL;
|
||||
|
||||
CvRTParams params( 4, // max_depth,
|
||||
2, // min_sample_count,
|
||||
0.f, // regression_accuracy,
|
||||
false, // use_surrogates,
|
||||
16, // max_categories,
|
||||
0, // priors,
|
||||
false, // calc_var_importance,
|
||||
1, // nactive_vars,
|
||||
5, // max_num_of_trees_in_the_forest,
|
||||
0, // forest_accuracy,
|
||||
CV_TERMCRIT_ITER // termcrit_type
|
||||
);
|
||||
|
||||
ertrees.train( trainSamples, CV_ROW_SAMPLE, trainClasses, Mat(), Mat(), var_types, Mat(), params );
|
||||
|
||||
Mat testSample(1, 2, CV_32FC1 );
|
||||
for( int y = 0; y < img.rows; y += testStep )
|
||||
{
|
||||
for( int x = 0; x < img.cols; x += testStep )
|
||||
{
|
||||
testSample.at<float>(0) = (float)x;
|
||||
testSample.at<float>(1) = (float)y;
|
||||
|
||||
int response = (int)ertrees.predict( testSample );
|
||||
circle( imgDst, Point(x,y), 2, classColors[response], 1 );
|
||||
}
|
||||
}
|
||||
}
|
||||
#endif
|
||||
|
||||
#if _ANN_
|
||||
void find_decision_boundary_ANN( const Mat& layer_sizes )
|
||||
static void find_decision_boundary_ANN( const Mat& layer_sizes )
|
||||
{
|
||||
img.copyTo( imgDst );
|
||||
ANN_MLP::Params params(layer_sizes, ANN_MLP::SIGMOID_SYM, 1, 1, TermCriteria(TermCriteria::MAX_ITER+TermCriteria::EPS, 300, FLT_EPSILON),
|
||||
ANN_MLP::Params::BACKPROP, 0.001);
|
||||
|
||||
Mat trainSamples, trainClasses;
|
||||
prepare_train_data( trainSamples, trainClasses );
|
||||
|
||||
// prerare trainClasses
|
||||
trainClasses.create( trainedPoints.size(), classColors.size(), CV_32FC1 );
|
||||
for( int i = 0; i < trainClasses.rows; i++ )
|
||||
Mat trainClasses = Mat::zeros( (int)trainedPoints.size(), (int)classColors.size(), CV_32FC1 );
|
||||
for( int i = 0; i < trainClasses.rows; i++ )
|
||||
{
|
||||
for( int k = 0; k < trainClasses.cols; k++ )
|
||||
{
|
||||
if( k == trainedPointsMarkers[i] )
|
||||
trainClasses.at<float>(i,k) = 1;
|
||||
else
|
||||
trainClasses.at<float>(i,k) = 0;
|
||||
}
|
||||
trainClasses.at<float>(i, trainedPointsMarkers[i]) = 1.f;
|
||||
}
|
||||
|
||||
Mat weights( 1, trainedPoints.size(), CV_32FC1, Scalar::all(1) );
|
||||
Mat samples = prepare_train_samples(trainedPoints);
|
||||
Ptr<TrainData> tdata = TrainData::create(samples, ROW_SAMPLE, trainClasses);
|
||||
|
||||
// learn classifier
|
||||
CvANN_MLP ann( layer_sizes, CvANN_MLP::SIGMOID_SYM, 1, 1 );
|
||||
ann.train( trainSamples, trainClasses, weights );
|
||||
|
||||
Mat testSample( 1, 2, CV_32FC1 );
|
||||
for( int y = 0; y < img.rows; y += testStep )
|
||||
{
|
||||
for( int x = 0; x < img.cols; x += testStep )
|
||||
{
|
||||
testSample.at<float>(0) = (float)x;
|
||||
testSample.at<float>(1) = (float)y;
|
||||
|
||||
Mat outputs( 1, classColors.size(), CV_32FC1, testSample.data );
|
||||
ann.predict( testSample, outputs );
|
||||
Point maxLoc;
|
||||
minMaxLoc( outputs, 0, 0, 0, &maxLoc );
|
||||
circle( imgDst, Point(x,y), 2, classColors[maxLoc.x], 1 );
|
||||
}
|
||||
}
|
||||
Ptr<ANN_MLP> ann = StatModel::train<ANN_MLP>(tdata, params);
|
||||
predict_and_paint(ann, imgDst);
|
||||
}
|
||||
#endif
|
||||
|
||||
#if _EM_
|
||||
void find_decision_boundary_EM()
|
||||
static void find_decision_boundary_EM()
|
||||
{
|
||||
img.copyTo( imgDst );
|
||||
|
||||
Mat trainSamples, trainClasses;
|
||||
prepare_train_data( trainSamples, trainClasses );
|
||||
Mat samples = prepare_train_samples(trainedPoints);
|
||||
|
||||
vector<cv::EM> em_models(classColors.size());
|
||||
int i, j, nmodels = (int)classColors.size();
|
||||
vector<Ptr<EM> > em_models(nmodels);
|
||||
Mat modelSamples;
|
||||
|
||||
CV_Assert((int)trainClasses.total() == trainSamples.rows);
|
||||
CV_Assert((int)trainClasses.type() == CV_32SC1);
|
||||
|
||||
for(size_t modelIndex = 0; modelIndex < em_models.size(); modelIndex++)
|
||||
for( i = 0; i < nmodels; i++ )
|
||||
{
|
||||
const int componentCount = 3;
|
||||
em_models[modelIndex] = EM(componentCount, cv::EM::COV_MAT_DIAGONAL);
|
||||
|
||||
Mat modelSamples;
|
||||
for(int sampleIndex = 0; sampleIndex < trainSamples.rows; sampleIndex++)
|
||||
modelSamples.release();
|
||||
for( j = 0; j < samples.rows; j++ )
|
||||
{
|
||||
if(trainClasses.at<int>(sampleIndex) == (int)modelIndex)
|
||||
modelSamples.push_back(trainSamples.row(sampleIndex));
|
||||
if( trainedPointsMarkers[j] == i )
|
||||
modelSamples.push_back(samples.row(j));
|
||||
}
|
||||
|
||||
// learn models
|
||||
if(!modelSamples.empty())
|
||||
em_models[modelIndex].train(modelSamples);
|
||||
if( !modelSamples.empty() )
|
||||
{
|
||||
em_models[i] = EM::train(modelSamples, noArray(), noArray(), noArray(),
|
||||
EM::Params(componentCount, EM::COV_MAT_DIAGONAL));
|
||||
}
|
||||
}
|
||||
|
||||
// classify coordinate plane points using the bayes classifier, i.e.
|
||||
// y(x) = arg max_i=1_modelsCount likelihoods_i(x)
|
||||
Mat testSample(1, 2, CV_32FC1 );
|
||||
Mat logLikelihoods(1, nmodels, CV_64FC1, Scalar(-DBL_MAX));
|
||||
|
||||
for( int y = 0; y < img.rows; y += testStep )
|
||||
{
|
||||
for( int x = 0; x < img.cols; x += testStep )
|
||||
@@ -501,17 +264,14 @@ void find_decision_boundary_EM()
|
||||
testSample.at<float>(0) = (float)x;
|
||||
testSample.at<float>(1) = (float)y;
|
||||
|
||||
Mat logLikelihoods(1, em_models.size(), CV_64FC1, Scalar(-DBL_MAX));
|
||||
for(size_t modelIndex = 0; modelIndex < em_models.size(); modelIndex++)
|
||||
for( i = 0; i < nmodels; i++ )
|
||||
{
|
||||
if(em_models[modelIndex].isTrained())
|
||||
logLikelihoods.at<double>(modelIndex) = em_models[modelIndex].predict(testSample)[0];
|
||||
if( !em_models[i].empty() )
|
||||
logLikelihoods.at<double>(i) = em_models[i]->predict2(testSample, noArray())[0];
|
||||
}
|
||||
Point maxLoc;
|
||||
minMaxLoc(logLikelihoods, 0, 0, 0, &maxLoc);
|
||||
|
||||
int response = maxLoc.x;
|
||||
circle( imgDst, Point(x,y), 2, classColors[response], 1 );
|
||||
imgDst.at<Vec3b>(y, x) = classColors[maxLoc.x];
|
||||
}
|
||||
}
|
||||
}
|
||||
@@ -520,7 +280,7 @@ void find_decision_boundary_EM()
|
||||
int main()
|
||||
{
|
||||
cout << "Use:" << endl
|
||||
<< " right mouse button - to add new class;" << endl
|
||||
<< " key '0' .. '1' - switch to class #n" << endl
|
||||
<< " left mouse button - to add new point;" << endl
|
||||
<< " key 'r' - to run the ML model;" << endl
|
||||
<< " key 'i' - to init (clear) the data." << endl << endl;
|
||||
@@ -532,6 +292,9 @@ int main()
|
||||
imshow( "points", img );
|
||||
setMouseCallback( "points", on_mouse );
|
||||
|
||||
classColors[0] = Vec3b(0, 255, 0);
|
||||
classColors[1] = Vec3b(0, 0, 255);
|
||||
|
||||
for(;;)
|
||||
{
|
||||
uchar key = (uchar)waitKey();
|
||||
@@ -542,98 +305,94 @@ int main()
|
||||
{
|
||||
img = Scalar::all(0);
|
||||
|
||||
classColors.clear();
|
||||
trainedPoints.clear();
|
||||
trainedPointsMarkers.clear();
|
||||
classCounters.assign(MAX_CLASSES, 0);
|
||||
|
||||
imshow( winName, img );
|
||||
}
|
||||
|
||||
if( key == '0' || key == '1' )
|
||||
{
|
||||
currentClass = key - '0';
|
||||
}
|
||||
|
||||
if( key == 'r' ) // run
|
||||
{
|
||||
double minVal = 0;
|
||||
minMaxLoc(classCounters, &minVal, 0, 0, 0);
|
||||
if( minVal == 0 )
|
||||
{
|
||||
printf("each class should have at least 1 point\n");
|
||||
continue;
|
||||
}
|
||||
img.copyTo( imgDst );
|
||||
#if _NBC_
|
||||
find_decision_boundary_NBC();
|
||||
namedWindow( "NormalBayesClassifier", WINDOW_AUTOSIZE );
|
||||
imshow( "NormalBayesClassifier", imgDst );
|
||||
#endif
|
||||
#if _KNN_
|
||||
int K = 3;
|
||||
find_decision_boundary_KNN( K );
|
||||
namedWindow( "kNN", WINDOW_AUTOSIZE );
|
||||
imshow( "kNN", imgDst );
|
||||
|
||||
K = 15;
|
||||
find_decision_boundary_KNN( K );
|
||||
namedWindow( "kNN2", WINDOW_AUTOSIZE );
|
||||
imshow( "kNN2", imgDst );
|
||||
#endif
|
||||
|
||||
#if _SVM_
|
||||
//(1)-(2)separable and not sets
|
||||
CvSVMParams params;
|
||||
params.svm_type = CvSVM::C_SVC;
|
||||
params.kernel_type = CvSVM::POLY; //CvSVM::LINEAR;
|
||||
SVM::Params params;
|
||||
params.svmType = SVM::C_SVC;
|
||||
params.kernelType = SVM::POLY; //CvSVM::LINEAR;
|
||||
params.degree = 0.5;
|
||||
params.gamma = 1;
|
||||
params.coef0 = 1;
|
||||
params.C = 1;
|
||||
params.nu = 0.5;
|
||||
params.p = 0;
|
||||
params.term_crit = cvTermCriteria(CV_TERMCRIT_ITER, 1000, 0.01);
|
||||
params.termCrit = TermCriteria(TermCriteria::MAX_ITER+TermCriteria::EPS, 1000, 0.01);
|
||||
|
||||
find_decision_boundary_SVM( params );
|
||||
namedWindow( "classificationSVM1", WINDOW_AUTOSIZE );
|
||||
imshow( "classificationSVM1", imgDst );
|
||||
|
||||
params.C = 10;
|
||||
find_decision_boundary_SVM( params );
|
||||
namedWindow( "classificationSVM2", WINDOW_AUTOSIZE );
|
||||
imshow( "classificationSVM2", imgDst );
|
||||
#endif
|
||||
|
||||
#if _DT_
|
||||
find_decision_boundary_DT();
|
||||
namedWindow( "DT", WINDOW_AUTOSIZE );
|
||||
imshow( "DT", imgDst );
|
||||
#endif
|
||||
|
||||
#if _BT_
|
||||
find_decision_boundary_BT();
|
||||
namedWindow( "BT", WINDOW_AUTOSIZE );
|
||||
imshow( "BT", imgDst);
|
||||
#endif
|
||||
|
||||
#if _GBT_
|
||||
find_decision_boundary_GBT();
|
||||
namedWindow( "GBT", WINDOW_AUTOSIZE );
|
||||
imshow( "GBT", imgDst);
|
||||
#endif
|
||||
|
||||
#if _RF_
|
||||
find_decision_boundary_RF();
|
||||
namedWindow( "RF", WINDOW_AUTOSIZE );
|
||||
imshow( "RF", imgDst);
|
||||
#endif
|
||||
|
||||
#if _ERT_
|
||||
find_decision_boundary_ERT();
|
||||
namedWindow( "ERT", WINDOW_AUTOSIZE );
|
||||
imshow( "ERT", imgDst);
|
||||
#endif
|
||||
|
||||
#if _ANN_
|
||||
Mat layer_sizes1( 1, 3, CV_32SC1 );
|
||||
layer_sizes1.at<int>(0) = 2;
|
||||
layer_sizes1.at<int>(1) = 5;
|
||||
layer_sizes1.at<int>(2) = classColors.size();
|
||||
layer_sizes1.at<int>(2) = (int)classColors.size();
|
||||
find_decision_boundary_ANN( layer_sizes1 );
|
||||
namedWindow( "ANN", WINDOW_AUTOSIZE );
|
||||
imshow( "ANN", imgDst );
|
||||
#endif
|
||||
|
||||
#if _EM_
|
||||
find_decision_boundary_EM();
|
||||
namedWindow( "EM", WINDOW_AUTOSIZE );
|
||||
imshow( "EM", imgDst );
|
||||
#endif
|
||||
}
|
||||
|
||||
@@ -88,8 +88,8 @@ int main(int argc, char** argv)
|
||||
namedWindow("video", 1);
|
||||
namedWindow("segmented", 1);
|
||||
|
||||
Ptr<BackgroundSubtractorMOG> bgsubtractor=createBackgroundSubtractorMOG();
|
||||
bgsubtractor->setNoiseSigma(10);
|
||||
Ptr<BackgroundSubtractorMOG2> bgsubtractor=createBackgroundSubtractorMOG2();
|
||||
bgsubtractor->setVarThreshold(10);
|
||||
|
||||
for(;;)
|
||||
{
|
||||
|
||||
@@ -1,221 +0,0 @@
|
||||
#include <opencv2/core/utility.hpp>
|
||||
#include "opencv2/video/tracking.hpp"
|
||||
#include "opencv2/imgproc.hpp"
|
||||
#include "opencv2/imgcodecs.hpp"
|
||||
#include "opencv2/highgui.hpp"
|
||||
|
||||
#include <cstdio>
|
||||
#include <iostream>
|
||||
|
||||
using namespace cv;
|
||||
using namespace std;
|
||||
|
||||
#define APP_NAME "simpleflow_demo : "
|
||||
|
||||
static void help()
|
||||
{
|
||||
// print a welcome message, and the OpenCV version
|
||||
printf("This is a demo of SimpleFlow optical flow algorithm,\n"
|
||||
"Using OpenCV version %s\n\n", CV_VERSION);
|
||||
|
||||
printf("Usage: simpleflow_demo frame1 frame2 output_flow"
|
||||
"\nApplication will write estimated flow "
|
||||
"\nbetween 'frame1' and 'frame2' in binary format"
|
||||
"\ninto file 'output_flow'"
|
||||
"\nThen one can use code from http://vision.middlebury.edu/flow/data/"
|
||||
"\nto convert flow in binary file to image\n");
|
||||
}
|
||||
|
||||
// binary file format for flow data specified here:
|
||||
// http://vision.middlebury.edu/flow/data/
|
||||
static void writeOpticalFlowToFile(const Mat& flow, FILE* file) {
|
||||
int cols = flow.cols;
|
||||
int rows = flow.rows;
|
||||
|
||||
fprintf(file, "PIEH");
|
||||
|
||||
if (fwrite(&cols, sizeof(int), 1, file) != 1 ||
|
||||
fwrite(&rows, sizeof(int), 1, file) != 1) {
|
||||
printf(APP_NAME "writeOpticalFlowToFile : problem writing header\n");
|
||||
exit(1);
|
||||
}
|
||||
|
||||
for (int i= 0; i < rows; ++i) {
|
||||
for (int j = 0; j < cols; ++j) {
|
||||
Vec2f flow_at_point = flow.at<Vec2f>(i, j);
|
||||
|
||||
if (fwrite(&(flow_at_point[0]), sizeof(float), 1, file) != 1 ||
|
||||
fwrite(&(flow_at_point[1]), sizeof(float), 1, file) != 1) {
|
||||
printf(APP_NAME "writeOpticalFlowToFile : problem writing data\n");
|
||||
exit(1);
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
static void run(int argc, char** argv) {
|
||||
if (argc < 3) {
|
||||
printf(APP_NAME "Wrong number of command line arguments for mode `run`: %d (expected %d)\n",
|
||||
argc, 3);
|
||||
exit(1);
|
||||
}
|
||||
|
||||
Mat frame1 = imread(argv[0]);
|
||||
Mat frame2 = imread(argv[1]);
|
||||
|
||||
if (frame1.empty()) {
|
||||
printf(APP_NAME "Image #1 : %s cannot be read\n", argv[0]);
|
||||
exit(1);
|
||||
}
|
||||
|
||||
if (frame2.empty()) {
|
||||
printf(APP_NAME "Image #2 : %s cannot be read\n", argv[1]);
|
||||
exit(1);
|
||||
}
|
||||
|
||||
if (frame1.rows != frame2.rows && frame1.cols != frame2.cols) {
|
||||
printf(APP_NAME "Images should be of equal sizes\n");
|
||||
exit(1);
|
||||
}
|
||||
|
||||
if (frame1.type() != 16 || frame2.type() != 16) {
|
||||
printf(APP_NAME "Images should be of equal type CV_8UC3\n");
|
||||
exit(1);
|
||||
}
|
||||
|
||||
printf(APP_NAME "Read two images of size [rows = %d, cols = %d]\n",
|
||||
frame1.rows, frame1.cols);
|
||||
|
||||
Mat flow;
|
||||
|
||||
float start = (float)getTickCount();
|
||||
calcOpticalFlowSF(frame1, frame2,
|
||||
flow,
|
||||
3, 2, 4, 4.1, 25.5, 18, 55.0, 25.5, 0.35, 18, 55.0, 25.5, 10);
|
||||
printf(APP_NAME "calcOpticalFlowSF : %lf sec\n", (getTickCount() - start) / getTickFrequency());
|
||||
|
||||
FILE* file = fopen(argv[2], "wb");
|
||||
if (file == NULL) {
|
||||
printf(APP_NAME "Unable to open file '%s' for writing\n", argv[2]);
|
||||
exit(1);
|
||||
}
|
||||
printf(APP_NAME "Writing to file\n");
|
||||
writeOpticalFlowToFile(flow, file);
|
||||
fclose(file);
|
||||
}
|
||||
|
||||
static bool readOpticalFlowFromFile(FILE* file, Mat& flow) {
|
||||
char header[5];
|
||||
if (fread(header, 1, 4, file) < 4 && (string)header != "PIEH") {
|
||||
return false;
|
||||
}
|
||||
|
||||
int cols, rows;
|
||||
if (fread(&cols, sizeof(int), 1, file) != 1||
|
||||
fread(&rows, sizeof(int), 1, file) != 1) {
|
||||
return false;
|
||||
}
|
||||
|
||||
flow = Mat::zeros(rows, cols, CV_32FC2);
|
||||
|
||||
for (int i = 0; i < rows; ++i) {
|
||||
for (int j = 0; j < cols; ++j) {
|
||||
Vec2f flow_at_point;
|
||||
if (fread(&(flow_at_point[0]), sizeof(float), 1, file) != 1 ||
|
||||
fread(&(flow_at_point[1]), sizeof(float), 1, file) != 1) {
|
||||
return false;
|
||||
}
|
||||
flow.at<Vec2f>(i, j) = flow_at_point;
|
||||
}
|
||||
}
|
||||
|
||||
return true;
|
||||
}
|
||||
|
||||
static bool isFlowCorrect(float u) {
|
||||
return !cvIsNaN(u) && (fabs(u) < 1e9);
|
||||
}
|
||||
|
||||
static float calc_rmse(Mat flow1, Mat flow2) {
|
||||
float sum = 0;
|
||||
int counter = 0;
|
||||
const int rows = flow1.rows;
|
||||
const int cols = flow1.cols;
|
||||
|
||||
for (int y = 0; y < rows; ++y) {
|
||||
for (int x = 0; x < cols; ++x) {
|
||||
Vec2f flow1_at_point = flow1.at<Vec2f>(y, x);
|
||||
Vec2f flow2_at_point = flow2.at<Vec2f>(y, x);
|
||||
|
||||
float u1 = flow1_at_point[0];
|
||||
float v1 = flow1_at_point[1];
|
||||
float u2 = flow2_at_point[0];
|
||||
float v2 = flow2_at_point[1];
|
||||
|
||||
if (isFlowCorrect(u1) && isFlowCorrect(u2) && isFlowCorrect(v1) && isFlowCorrect(v2)) {
|
||||
sum += (u1-u2)*(u1-u2) + (v1-v2)*(v1-v2);
|
||||
counter++;
|
||||
}
|
||||
}
|
||||
}
|
||||
return (float)sqrt(sum / (1e-9 + counter));
|
||||
}
|
||||
|
||||
static void eval(int argc, char** argv) {
|
||||
if (argc < 2) {
|
||||
printf(APP_NAME "Wrong number of command line arguments for mode `eval` : %d (expected %d)\n",
|
||||
argc, 2);
|
||||
exit(1);
|
||||
}
|
||||
|
||||
Mat flow1, flow2;
|
||||
|
||||
FILE* flow_file_1 = fopen(argv[0], "rb");
|
||||
if (flow_file_1 == NULL) {
|
||||
printf(APP_NAME "Cannot open file with first flow : %s\n", argv[0]);
|
||||
exit(1);
|
||||
}
|
||||
if (!readOpticalFlowFromFile(flow_file_1, flow1)) {
|
||||
printf(APP_NAME "Cannot read flow data from file %s\n", argv[0]);
|
||||
exit(1);
|
||||
}
|
||||
fclose(flow_file_1);
|
||||
|
||||
FILE* flow_file_2 = fopen(argv[1], "rb");
|
||||
if (flow_file_2 == NULL) {
|
||||
printf(APP_NAME "Cannot open file with first flow : %s\n", argv[1]);
|
||||
exit(1);
|
||||
}
|
||||
if (!readOpticalFlowFromFile(flow_file_2, flow2)) {
|
||||
printf(APP_NAME "Cannot read flow data from file %s\n", argv[1]);
|
||||
exit(1);
|
||||
}
|
||||
fclose(flow_file_2);
|
||||
|
||||
float rmse = calc_rmse(flow1, flow2);
|
||||
printf("%lf\n", rmse);
|
||||
}
|
||||
|
||||
int main(int argc, char** argv) {
|
||||
if (argc < 2) {
|
||||
printf(APP_NAME "Mode is not specified\n");
|
||||
help();
|
||||
exit(1);
|
||||
}
|
||||
string mode = (string)argv[1];
|
||||
int new_argc = argc - 2;
|
||||
char** new_argv = &argv[2];
|
||||
|
||||
if ("run" == mode) {
|
||||
run(new_argc, new_argv);
|
||||
} else if ("eval" == mode) {
|
||||
eval(new_argc, new_argv);
|
||||
} else if ("help" == mode)
|
||||
help();
|
||||
else {
|
||||
printf(APP_NAME "Unknown mode : %s\n", argv[1]);
|
||||
help();
|
||||
}
|
||||
|
||||
return 0;
|
||||
}
|
||||
+26
-52
@@ -8,9 +8,10 @@
|
||||
#include <time.h>
|
||||
|
||||
using namespace cv;
|
||||
using namespace cv::ml;
|
||||
using namespace std;
|
||||
|
||||
void get_svm_detector(const SVM& svm, vector< float > & hog_detector );
|
||||
void get_svm_detector(const Ptr<SVM>& svm, vector< float > & hog_detector );
|
||||
void convert_to_ml(const std::vector< cv::Mat > & train_samples, cv::Mat& trainData );
|
||||
void load_images( const string & prefix, const string & filename, vector< Mat > & img_lst );
|
||||
void sample_neg( const vector< Mat > & full_neg_lst, vector< Mat > & neg_lst, const Size & size );
|
||||
@@ -20,49 +21,24 @@ void train_svm( const vector< Mat > & gradient_lst, const vector< int > & labels
|
||||
void draw_locations( Mat & img, const vector< Rect > & locations, const Scalar & color );
|
||||
void test_it( const Size & size );
|
||||
|
||||
void get_svm_detector(const SVM& svm, vector< float > & hog_detector )
|
||||
void get_svm_detector(const Ptr<SVM>& svm, vector< float > & hog_detector )
|
||||
{
|
||||
// get the number of variables
|
||||
const int var_all = svm.get_var_count();
|
||||
// get the number of support vectors
|
||||
const int sv_total = svm.get_support_vector_count();
|
||||
// get the decision function
|
||||
const CvSVMDecisionFunc* decision_func = svm.get_decision_function();
|
||||
// get the support vectors
|
||||
const float** sv = new const float*[ sv_total ];
|
||||
for( int i = 0 ; i < sv_total ; ++i )
|
||||
sv[ i ] = svm.get_support_vector(i);
|
||||
Mat sv = svm->getSupportVectors();
|
||||
const int sv_total = sv.rows;
|
||||
// get the decision function
|
||||
Mat alpha, svidx;
|
||||
double rho = svm->getDecisionFunction(0, alpha, svidx);
|
||||
|
||||
CV_Assert( var_all > 0 &&
|
||||
sv_total > 0 &&
|
||||
decision_func != 0 &&
|
||||
decision_func->alpha != 0 &&
|
||||
decision_func->sv_count == sv_total );
|
||||
CV_Assert( alpha.total() == 1 && svidx.total() == 1 && sv_total == 1 );
|
||||
CV_Assert( (alpha.type() == CV_64F && alpha.at<double>(0) == 1.) ||
|
||||
(alpha.type() == CV_32F && alpha.at<float>(0) == 1.f) );
|
||||
CV_Assert( sv.type() == CV_32F );
|
||||
hog_detector.clear();
|
||||
|
||||
float svi = 0.f;
|
||||
|
||||
hog_detector.clear(); //clear stuff in vector.
|
||||
hog_detector.reserve( var_all + 1 ); //reserve place for memory efficiency.
|
||||
|
||||
/**
|
||||
* hog_detector^i = \sum_j support_vector_j^i * \alpha_j
|
||||
* hog_detector^dim = -\rho
|
||||
*/
|
||||
for( int i = 0 ; i < var_all ; ++i )
|
||||
{
|
||||
svi = 0.f;
|
||||
for( int j = 0 ; j < sv_total ; ++j )
|
||||
{
|
||||
if( decision_func->sv_index != NULL ) // sometime the sv_index isn't store on YML/XML.
|
||||
svi += (float)( sv[decision_func->sv_index[j]][i] * decision_func->alpha[ j ] );
|
||||
else
|
||||
svi += (float)( sv[j][i] * decision_func->alpha[ j ] );
|
||||
}
|
||||
hog_detector.push_back( svi );
|
||||
}
|
||||
hog_detector.push_back( (float)-decision_func->rho );
|
||||
|
||||
delete[] sv;
|
||||
hog_detector.resize(sv.cols + 1);
|
||||
memcpy(&hog_detector[0], sv.data, sv.cols*sizeof(hog_detector[0]));
|
||||
hog_detector[sv.cols] = (float)-rho;
|
||||
}
|
||||
|
||||
|
||||
@@ -263,7 +239,7 @@ Mat get_hogdescriptor_visu(const Mat& color_origImg, vector<float>& descriptorVa
|
||||
int mx = drawX + cellSize/2;
|
||||
int my = drawY + cellSize/2;
|
||||
|
||||
rectangle(visu, Point((int)(drawX*zoomFac), (int)(drawY*zoomFac)), Point((int)((drawX+cellSize)*zoomFac), (int)((drawY+cellSize)*zoomFac)), CV_RGB(100,100,100), 1);
|
||||
rectangle(visu, Point((int)(drawX*zoomFac), (int)(drawY*zoomFac)), Point((int)((drawX+cellSize)*zoomFac), (int)((drawY+cellSize)*zoomFac)), Scalar(100,100,100), 1);
|
||||
|
||||
// draw in each cell all 9 gradient strengths
|
||||
for (int bin=0; bin<gradientBinSize; bin++)
|
||||
@@ -288,7 +264,7 @@ Mat get_hogdescriptor_visu(const Mat& color_origImg, vector<float>& descriptorVa
|
||||
float y2 = my + dirVecY * currentGradStrength * maxVecLen * scale;
|
||||
|
||||
// draw gradient visualization
|
||||
line(visu, Point((int)(x1*zoomFac),(int)(y1*zoomFac)), Point((int)(x2*zoomFac),(int)(y2*zoomFac)), CV_RGB(0,255,0), 1);
|
||||
line(visu, Point((int)(x1*zoomFac),(int)(y1*zoomFac)), Point((int)(x2*zoomFac),(int)(y2*zoomFac)), Scalar(0,255,0), 1);
|
||||
|
||||
} // for (all bins)
|
||||
|
||||
@@ -337,28 +313,26 @@ void compute_hog( const vector< Mat > & img_lst, vector< Mat > & gradient_lst, c
|
||||
|
||||
void train_svm( const vector< Mat > & gradient_lst, const vector< int > & labels )
|
||||
{
|
||||
SVM svm;
|
||||
|
||||
/* Default values to train SVM */
|
||||
SVMParams params;
|
||||
SVM::Params params;
|
||||
params.coef0 = 0.0;
|
||||
params.degree = 3;
|
||||
params.term_crit.epsilon = 1e-3;
|
||||
params.termCrit.epsilon = 1e-3;
|
||||
params.gamma = 0;
|
||||
params.kernel_type = SVM::LINEAR;
|
||||
params.kernelType = SVM::LINEAR;
|
||||
params.nu = 0.5;
|
||||
params.p = 0.1; // for EPSILON_SVR, epsilon in loss function?
|
||||
params.C = 0.01; // From paper, soft classifier
|
||||
params.svm_type = SVM::EPS_SVR; // C_SVC; // EPSILON_SVR; // may be also NU_SVR; // do regression task
|
||||
params.svmType = SVM::EPS_SVR; // C_SVC; // EPSILON_SVR; // may be also NU_SVR; // do regression task
|
||||
|
||||
Mat train_data;
|
||||
convert_to_ml( gradient_lst, train_data );
|
||||
|
||||
clog << "Start training...";
|
||||
svm.train( train_data, Mat( labels ), Mat(), Mat(), params );
|
||||
Ptr<SVM> svm = StatModel::train<SVM>(train_data, ROW_SAMPLE, Mat(labels), params);
|
||||
clog << "...[done]" << endl;
|
||||
|
||||
svm.save( "my_people_detector.yml" );
|
||||
svm->save( "my_people_detector.yml" );
|
||||
}
|
||||
|
||||
void draw_locations( Mat & img, const vector< Rect > & locations, const Scalar & color )
|
||||
@@ -380,7 +354,7 @@ void test_it( const Size & size )
|
||||
Scalar reference( 0, 255, 0 );
|
||||
Scalar trained( 0, 0, 255 );
|
||||
Mat img, draw;
|
||||
SVM svm;
|
||||
Ptr<SVM> svm;
|
||||
HOGDescriptor hog;
|
||||
HOGDescriptor my_hog;
|
||||
my_hog.winSize = size;
|
||||
@@ -388,7 +362,7 @@ void test_it( const Size & size )
|
||||
vector< Rect > locations;
|
||||
|
||||
// Load the trained SVM.
|
||||
svm.load( "my_people_detector.yml" );
|
||||
svm = StatModel::load<SVM>( "my_people_detector.yml" );
|
||||
// Set the trained svm to my_hog
|
||||
vector< float > hog_detector;
|
||||
get_svm_detector( svm, hog_detector );
|
||||
|
||||
File diff suppressed because it is too large
Load Diff
File diff suppressed because it is too large
Load Diff
File diff suppressed because it is too large
Load Diff
+40
-88
@@ -1,63 +1,35 @@
|
||||
#include "opencv2/ml/ml.hpp"
|
||||
#include "opencv2/core/core_c.h"
|
||||
#include "opencv2/core/core.hpp"
|
||||
#include "opencv2/core/utility.hpp"
|
||||
#include <stdio.h>
|
||||
#include <string>
|
||||
#include <map>
|
||||
|
||||
using namespace cv;
|
||||
using namespace cv::ml;
|
||||
|
||||
static void help()
|
||||
{
|
||||
printf(
|
||||
"\nThis sample demonstrates how to use different decision trees and forests including boosting and random trees:\n"
|
||||
"CvDTree dtree;\n"
|
||||
"CvBoost boost;\n"
|
||||
"CvRTrees rtrees;\n"
|
||||
"CvERTrees ertrees;\n"
|
||||
"CvGBTrees gbtrees;\n"
|
||||
"Call:\n\t./tree_engine [-r <response_column>] [-c] <csv filename>\n"
|
||||
"\nThis sample demonstrates how to use different decision trees and forests including boosting and random trees.\n"
|
||||
"Usage:\n\t./tree_engine [-r <response_column>] [-ts type_spec] <csv filename>\n"
|
||||
"where -r <response_column> specified the 0-based index of the response (0 by default)\n"
|
||||
"-c specifies that the response is categorical (it's ordered by default) and\n"
|
||||
"-ts specifies the var type spec in the form ord[n1,n2-n3,n4-n5,...]cat[m1-m2,m3,m4-m5,...]\n"
|
||||
"<csv filename> is the name of training data file in comma-separated value format\n\n");
|
||||
}
|
||||
|
||||
|
||||
static int count_classes(CvMLData& data)
|
||||
static void train_and_print_errs(Ptr<StatModel> model, const Ptr<TrainData>& data)
|
||||
{
|
||||
cv::Mat r = cv::cvarrToMat(data.get_responses());
|
||||
std::map<int, int> rmap;
|
||||
int i, n = (int)r.total();
|
||||
for( i = 0; i < n; i++ )
|
||||
bool ok = model->train(data);
|
||||
if( !ok )
|
||||
{
|
||||
float val = r.at<float>(i);
|
||||
int ival = cvRound(val);
|
||||
if( ival != val )
|
||||
return -1;
|
||||
rmap[ival] = 1;
|
||||
printf("Training failed\n");
|
||||
}
|
||||
return (int)rmap.size();
|
||||
}
|
||||
|
||||
static void print_result(float train_err, float test_err, const CvMat* _var_imp)
|
||||
{
|
||||
printf( "train error %f\n", train_err );
|
||||
printf( "test error %f\n\n", test_err );
|
||||
|
||||
if (_var_imp)
|
||||
else
|
||||
{
|
||||
cv::Mat var_imp = cv::cvarrToMat(_var_imp), sorted_idx;
|
||||
cv::sortIdx(var_imp, sorted_idx, CV_SORT_EVERY_ROW + CV_SORT_DESCENDING);
|
||||
|
||||
printf( "variable importance:\n" );
|
||||
int i, n = (int)var_imp.total();
|
||||
int type = var_imp.type();
|
||||
CV_Assert(type == CV_32F || type == CV_64F);
|
||||
|
||||
for( i = 0; i < n; i++)
|
||||
{
|
||||
int k = sorted_idx.at<int>(i);
|
||||
printf( "%d\t%f\n", k, type == CV_32F ? var_imp.at<float>(k) : var_imp.at<double>(k));
|
||||
}
|
||||
printf( "train error: %f\n", model->calcError(data, false, noArray()) );
|
||||
printf( "test error: %f\n\n", model->calcError(data, true, noArray()) );
|
||||
}
|
||||
printf("\n");
|
||||
}
|
||||
|
||||
int main(int argc, char** argv)
|
||||
@@ -69,14 +41,14 @@ int main(int argc, char** argv)
|
||||
}
|
||||
const char* filename = 0;
|
||||
int response_idx = 0;
|
||||
bool categorical_response = false;
|
||||
std::string typespec;
|
||||
|
||||
for(int i = 1; i < argc; i++)
|
||||
{
|
||||
if(strcmp(argv[i], "-r") == 0)
|
||||
sscanf(argv[++i], "%d", &response_idx);
|
||||
else if(strcmp(argv[i], "-c") == 0)
|
||||
categorical_response = true;
|
||||
else if(strcmp(argv[i], "-ts") == 0)
|
||||
typespec = argv[++i];
|
||||
else if(argv[i][0] != '-' )
|
||||
filename = argv[i];
|
||||
else
|
||||
@@ -88,52 +60,32 @@ int main(int argc, char** argv)
|
||||
}
|
||||
|
||||
printf("\nReading in %s...\n\n",filename);
|
||||
CvDTree dtree;
|
||||
CvBoost boost;
|
||||
CvRTrees rtrees;
|
||||
CvERTrees ertrees;
|
||||
CvGBTrees gbtrees;
|
||||
const double train_test_split_ratio = 0.5;
|
||||
|
||||
CvMLData data;
|
||||
Ptr<TrainData> data = TrainData::loadFromCSV(filename, 0, response_idx, response_idx+1, typespec);
|
||||
|
||||
|
||||
CvTrainTestSplit spl( 0.5f );
|
||||
|
||||
if ( data.read_csv( filename ) == 0)
|
||||
if( data.empty() )
|
||||
{
|
||||
data.set_response_idx( response_idx );
|
||||
if(categorical_response)
|
||||
data.change_var_type( response_idx, CV_VAR_CATEGORICAL );
|
||||
data.set_train_test_split( &spl );
|
||||
|
||||
printf("======DTREE=====\n");
|
||||
dtree.train( &data, CvDTreeParams( 10, 2, 0, false, 16, 0, false, false, 0 ));
|
||||
print_result( dtree.calc_error( &data, CV_TRAIN_ERROR), dtree.calc_error( &data, CV_TEST_ERROR ), dtree.get_var_importance() );
|
||||
|
||||
if( categorical_response && count_classes(data) == 2 )
|
||||
{
|
||||
printf("======BOOST=====\n");
|
||||
boost.train( &data, CvBoostParams(CvBoost::DISCRETE, 100, 0.95, 2, false, 0));
|
||||
print_result( boost.calc_error( &data, CV_TRAIN_ERROR ), boost.calc_error( &data, CV_TEST_ERROR ), 0 ); //doesn't compute importance
|
||||
}
|
||||
|
||||
printf("======RTREES=====\n");
|
||||
rtrees.train( &data, CvRTParams( 10, 2, 0, false, 16, 0, true, 0, 100, 0, CV_TERMCRIT_ITER ));
|
||||
print_result( rtrees.calc_error( &data, CV_TRAIN_ERROR), rtrees.calc_error( &data, CV_TEST_ERROR ), rtrees.get_var_importance() );
|
||||
|
||||
printf("======ERTREES=====\n");
|
||||
ertrees.train( &data, CvRTParams( 18, 2, 0, false, 16, 0, true, 0, 100, 0, CV_TERMCRIT_ITER ));
|
||||
print_result( ertrees.calc_error( &data, CV_TRAIN_ERROR), ertrees.calc_error( &data, CV_TEST_ERROR ), ertrees.get_var_importance() );
|
||||
|
||||
printf("======GBTREES=====\n");
|
||||
if (categorical_response)
|
||||
gbtrees.train( &data, CvGBTreesParams(CvGBTrees::DEVIANCE_LOSS, 100, 0.1f, 0.8f, 5, false));
|
||||
else
|
||||
gbtrees.train( &data, CvGBTreesParams(CvGBTrees::SQUARED_LOSS, 100, 0.1f, 0.8f, 5, false));
|
||||
print_result( gbtrees.calc_error( &data, CV_TRAIN_ERROR), gbtrees.calc_error( &data, CV_TEST_ERROR ), 0 ); //doesn't compute importance
|
||||
printf("ERROR: File %s can not be read\n", filename);
|
||||
return 0;
|
||||
}
|
||||
else
|
||||
printf("File can not be read");
|
||||
|
||||
data->setTrainTestSplitRatio(train_test_split_ratio);
|
||||
|
||||
printf("======DTREE=====\n");
|
||||
Ptr<DTrees> dtree = DTrees::create(DTrees::Params( 10, 2, 0, false, 16, 0, false, false, Mat() ));
|
||||
train_and_print_errs(dtree, data);
|
||||
|
||||
if( (int)data->getClassLabels().total() <= 2 ) // regression or 2-class classification problem
|
||||
{
|
||||
printf("======BOOST=====\n");
|
||||
Ptr<Boost> boost = Boost::create(Boost::Params(Boost::GENTLE, 100, 0.95, 2, false, Mat()));
|
||||
train_and_print_errs(boost, data);
|
||||
}
|
||||
|
||||
printf("======RTREES=====\n");
|
||||
Ptr<RTrees> rtrees = RTrees::create(RTrees::Params(10, 2, 0, false, 16, Mat(), false, 0, TermCriteria(TermCriteria::MAX_ITER, 100, 0)));
|
||||
train_and_print_errs(rtrees, data);
|
||||
|
||||
return 0;
|
||||
}
|
||||
|
||||
@@ -4,29 +4,29 @@
|
||||
#include <opencv2/ml/ml.hpp>
|
||||
|
||||
using namespace cv;
|
||||
using namespace cv::ml;
|
||||
|
||||
int main()
|
||||
int main(int, char**)
|
||||
{
|
||||
// Data for visual representation
|
||||
int width = 512, height = 512;
|
||||
Mat image = Mat::zeros(height, width, CV_8UC3);
|
||||
|
||||
// Set up training data
|
||||
float labels[4] = {1.0, -1.0, -1.0, -1.0};
|
||||
Mat labelsMat(4, 1, CV_32FC1, labels);
|
||||
int labels[4] = {1, -1, -1, -1};
|
||||
Mat labelsMat(4, 1, CV_32SC1, labels);
|
||||
|
||||
float trainingData[4][2] = { {501, 10}, {255, 10}, {501, 255}, {10, 501} };
|
||||
Mat trainingDataMat(4, 2, CV_32FC1, trainingData);
|
||||
|
||||
// Set up SVM's parameters
|
||||
CvSVMParams params;
|
||||
params.svm_type = CvSVM::C_SVC;
|
||||
params.kernel_type = CvSVM::LINEAR;
|
||||
params.term_crit = cvTermCriteria(CV_TERMCRIT_ITER, 100, 1e-6);
|
||||
SVM::Params params;
|
||||
params.svmType = SVM::C_SVC;
|
||||
params.kernelType = SVM::LINEAR;
|
||||
params.termCrit = TermCriteria(TermCriteria::MAX_ITER, 100, 1e-6);
|
||||
|
||||
// Train the SVM
|
||||
CvSVM SVM;
|
||||
SVM.train(trainingDataMat, labelsMat, Mat(), Mat(), params);
|
||||
Ptr<SVM> svm = StatModel::train<SVM>(trainingDataMat, ROW_SAMPLE, labelsMat, params);
|
||||
|
||||
Vec3b green(0,255,0), blue (255,0,0);
|
||||
// Show the decision regions given by the SVM
|
||||
@@ -34,30 +34,30 @@ int main()
|
||||
for (int j = 0; j < image.cols; ++j)
|
||||
{
|
||||
Mat sampleMat = (Mat_<float>(1,2) << j,i);
|
||||
float response = SVM.predict(sampleMat);
|
||||
float response = svm->predict(sampleMat);
|
||||
|
||||
if (response == 1)
|
||||
image.at<Vec3b>(i,j) = green;
|
||||
else if (response == -1)
|
||||
image.at<Vec3b>(i,j) = blue;
|
||||
image.at<Vec3b>(i,j) = blue;
|
||||
}
|
||||
|
||||
// Show the training data
|
||||
int thickness = -1;
|
||||
int lineType = 8;
|
||||
circle( image, Point(501, 10), 5, Scalar( 0, 0, 0), thickness, lineType);
|
||||
circle( image, Point(255, 10), 5, Scalar(255, 255, 255), thickness, lineType);
|
||||
circle( image, Point(501, 255), 5, Scalar(255, 255, 255), thickness, lineType);
|
||||
circle( image, Point( 10, 501), 5, Scalar(255, 255, 255), thickness, lineType);
|
||||
circle( image, Point(501, 10), 5, Scalar( 0, 0, 0), thickness, lineType );
|
||||
circle( image, Point(255, 10), 5, Scalar(255, 255, 255), thickness, lineType );
|
||||
circle( image, Point(501, 255), 5, Scalar(255, 255, 255), thickness, lineType );
|
||||
circle( image, Point( 10, 501), 5, Scalar(255, 255, 255), thickness, lineType );
|
||||
|
||||
// Show support vectors
|
||||
thickness = 2;
|
||||
lineType = 8;
|
||||
int c = SVM.get_support_vector_count();
|
||||
Mat sv = svm->getSupportVectors();
|
||||
|
||||
for (int i = 0; i < c; ++i)
|
||||
for (int i = 0; i < sv.rows; ++i)
|
||||
{
|
||||
const float* v = SVM.get_support_vector(i);
|
||||
const float* v = sv.ptr<float>(i);
|
||||
circle( image, Point( (int) v[0], (int) v[1]), 6, Scalar(128, 128, 128), thickness, lineType);
|
||||
}
|
||||
|
||||
|
||||
@@ -8,6 +8,7 @@
|
||||
#define FRAC_LINEAR_SEP 0.9f // Fraction of samples which compose the linear separable part
|
||||
|
||||
using namespace cv;
|
||||
using namespace cv::ml;
|
||||
using namespace std;
|
||||
|
||||
static void help()
|
||||
@@ -30,7 +31,7 @@ int main()
|
||||
|
||||
//--------------------- 1. Set up training data randomly ---------------------------------------
|
||||
Mat trainData(2*NTRAINING_SAMPLES, 2, CV_32FC1);
|
||||
Mat labels (2*NTRAINING_SAMPLES, 1, CV_32FC1);
|
||||
Mat labels (2*NTRAINING_SAMPLES, 1, CV_32SC1);
|
||||
|
||||
RNG rng(100); // Random value generation class
|
||||
|
||||
@@ -71,16 +72,15 @@ int main()
|
||||
labels.rowRange(NTRAINING_SAMPLES, 2*NTRAINING_SAMPLES).setTo(2); // Class 2
|
||||
|
||||
//------------------------ 2. Set up the support vector machines parameters --------------------
|
||||
CvSVMParams params;
|
||||
params.svm_type = SVM::C_SVC;
|
||||
SVM::Params params;
|
||||
params.svmType = SVM::C_SVC;
|
||||
params.C = 0.1;
|
||||
params.kernel_type = SVM::LINEAR;
|
||||
params.term_crit = TermCriteria(CV_TERMCRIT_ITER, (int)1e7, 1e-6);
|
||||
params.kernelType = SVM::LINEAR;
|
||||
params.termCrit = TermCriteria(TermCriteria::MAX_ITER, (int)1e7, 1e-6);
|
||||
|
||||
//------------------------ 3. Train the svm ----------------------------------------------------
|
||||
cout << "Starting training process" << endl;
|
||||
CvSVM svm;
|
||||
svm.train(trainData, labels, Mat(), Mat(), params);
|
||||
Ptr<SVM> svm = StatModel::train<SVM>(trainData, ROW_SAMPLE, labels, params);
|
||||
cout << "Finished training process" << endl;
|
||||
|
||||
//------------------------ 4. Show the decision regions ----------------------------------------
|
||||
@@ -89,7 +89,7 @@ int main()
|
||||
for (int j = 0; j < I.cols; ++j)
|
||||
{
|
||||
Mat sampleMat = (Mat_<float>(1,2) << i, j);
|
||||
float response = svm.predict(sampleMat);
|
||||
float response = svm->predict(sampleMat);
|
||||
|
||||
if (response == 1) I.at<Vec3b>(j, i) = green;
|
||||
else if (response == 2) I.at<Vec3b>(j, i) = blue;
|
||||
@@ -117,11 +117,11 @@ int main()
|
||||
//------------------------- 6. Show support vectors --------------------------------------------
|
||||
thick = 2;
|
||||
lineType = 8;
|
||||
int x = svm.get_support_vector_count();
|
||||
Mat sv = svm->getSupportVectors();
|
||||
|
||||
for (int i = 0; i < x; ++i)
|
||||
for (int i = 0; i < sv.rows; ++i)
|
||||
{
|
||||
const float* v = svm.get_support_vector(i);
|
||||
const float* v = sv.ptr<float>(i);
|
||||
circle( I, Point( (int) v[0], (int) v[1]), 6, Scalar(128, 128, 128), thick, lineType);
|
||||
}
|
||||
|
||||
|
||||
@@ -20,9 +20,7 @@ using namespace std;
|
||||
|
||||
// Global variables
|
||||
Mat frame; //current frame
|
||||
Mat fgMaskMOG; //fg mask generated by MOG method
|
||||
Mat fgMaskMOG2; //fg mask fg mask generated by MOG2 method
|
||||
Ptr<BackgroundSubtractor> pMOG; //MOG Background subtractor
|
||||
Ptr<BackgroundSubtractor> pMOG2; //MOG2 Background subtractor
|
||||
int keyboard; //input from keyboard
|
||||
|
||||
@@ -63,11 +61,9 @@ int main(int argc, char* argv[])
|
||||
|
||||
//create GUI windows
|
||||
namedWindow("Frame");
|
||||
namedWindow("FG Mask MOG");
|
||||
namedWindow("FG Mask MOG 2");
|
||||
|
||||
//create Background Subtractor objects
|
||||
pMOG = createBackgroundSubtractorMOG(); //MOG approach
|
||||
pMOG2 = createBackgroundSubtractorMOG2(); //MOG2 approach
|
||||
|
||||
if(strcmp(argv[1], "-vid") == 0) {
|
||||
@@ -109,7 +105,6 @@ void processVideo(char* videoFilename) {
|
||||
exit(EXIT_FAILURE);
|
||||
}
|
||||
//update the background model
|
||||
pMOG->apply(frame, fgMaskMOG);
|
||||
pMOG2->apply(frame, fgMaskMOG2);
|
||||
//get the frame number and write it on the current frame
|
||||
stringstream ss;
|
||||
@@ -121,7 +116,6 @@ void processVideo(char* videoFilename) {
|
||||
FONT_HERSHEY_SIMPLEX, 0.5 , cv::Scalar(0,0,0));
|
||||
//show the current frame and the fg masks
|
||||
imshow("Frame", frame);
|
||||
imshow("FG Mask MOG", fgMaskMOG);
|
||||
imshow("FG Mask MOG 2", fgMaskMOG2);
|
||||
//get the input from the keyboard
|
||||
keyboard = waitKey( 30 );
|
||||
@@ -146,7 +140,6 @@ void processImages(char* fistFrameFilename) {
|
||||
//read input data. ESC or 'q' for quitting
|
||||
while( (char)keyboard != 'q' && (char)keyboard != 27 ){
|
||||
//update the background model
|
||||
pMOG->apply(frame, fgMaskMOG);
|
||||
pMOG2->apply(frame, fgMaskMOG2);
|
||||
//get the frame number and write it on the current frame
|
||||
size_t index = fn.find_last_of("/");
|
||||
@@ -166,7 +159,6 @@ void processImages(char* fistFrameFilename) {
|
||||
FONT_HERSHEY_SIMPLEX, 0.5 , cv::Scalar(0,0,0));
|
||||
//show the current frame and the fg masks
|
||||
imshow("Frame", frame);
|
||||
imshow("FG Mask MOG", fgMaskMOG);
|
||||
imshow("FG Mask MOG 2", fgMaskMOG2);
|
||||
//get the input from the keyboard
|
||||
keyboard = waitKey( 30 );
|
||||
|
||||
@@ -17,7 +17,7 @@ if(BUILD_EXAMPLES AND OCV_DEPENDENCIES_FOUND)
|
||||
set(the_target "example_${project}_${name}")
|
||||
add_executable(${the_target} ${srcs})
|
||||
|
||||
target_link_libraries(${the_target} ${OPENCV_LINKER_LIBS} ${OPENCV_DIRECTX_SAMPLES_REQUIRED_DEPS})
|
||||
ocv_target_link_libraries(${the_target} ${OPENCV_LINKER_LIBS} ${OPENCV_DIRECTX_SAMPLES_REQUIRED_DEPS})
|
||||
|
||||
set_target_properties(${the_target} PROPERTIES
|
||||
OUTPUT_NAME "${project}-example-${name}"
|
||||
|
||||
@@ -47,21 +47,21 @@ if(BUILD_EXAMPLES AND OCV_DEPENDENCIES_FOUND)
|
||||
set(the_target "example_${project}_${name}")
|
||||
add_executable(${the_target} ${srcs})
|
||||
|
||||
target_link_libraries(${the_target} ${OPENCV_LINKER_LIBS} ${OPENCV_CUDA_SAMPLES_REQUIRED_DEPS})
|
||||
ocv_target_link_libraries(${the_target} ${OPENCV_LINKER_LIBS} ${OPENCV_CUDA_SAMPLES_REQUIRED_DEPS})
|
||||
|
||||
if(HAVE_CUDA AND NOT ANDROID)
|
||||
target_link_libraries(${the_target} ${CUDA_CUDA_LIBRARY})
|
||||
ocv_target_link_libraries(${the_target} ${CUDA_CUDA_LIBRARY})
|
||||
endif()
|
||||
|
||||
if(HAVE_opencv_nonfree)
|
||||
target_link_libraries(${the_target} opencv_nonfree)
|
||||
ocv_target_link_libraries(${the_target} opencv_nonfree)
|
||||
endif()
|
||||
if(HAVE_opencv_cudacodec)
|
||||
target_link_libraries(${the_target} opencv_cudacodec)
|
||||
ocv_target_link_libraries(${the_target} opencv_cudacodec)
|
||||
endif()
|
||||
|
||||
if(HAVE_opencv_ocl)
|
||||
target_link_libraries(${the_target} opencv_ocl)
|
||||
ocv_target_link_libraries(${the_target} opencv_ocl)
|
||||
endif()
|
||||
|
||||
set_target_properties(${the_target} PROPERTIES
|
||||
|
||||
@@ -8,10 +8,10 @@ if(HAVE_opencv_nonfree)
|
||||
endif()
|
||||
|
||||
add_executable(${the_target} ${sources} ${headers})
|
||||
target_link_libraries(${the_target} ${OPENCV_LINKER_LIBS} ${OPENCV_CUDA_SAMPLES_REQUIRED_DEPS})
|
||||
ocv_target_link_libraries(${the_target} ${OPENCV_LINKER_LIBS} ${OPENCV_CUDA_SAMPLES_REQUIRED_DEPS})
|
||||
|
||||
if(HAVE_opencv_nonfree)
|
||||
target_link_libraries(${the_target} opencv_nonfree)
|
||||
ocv_target_link_libraries(${the_target} opencv_nonfree)
|
||||
endif()
|
||||
|
||||
set_target_properties(${the_target} PROPERTIES
|
||||
|
||||
@@ -1,85 +0,0 @@
|
||||
#!/usr/bin/env python
|
||||
|
||||
import numpy as np
|
||||
import cv2
|
||||
import video
|
||||
from common import nothing, clock, draw_str
|
||||
|
||||
MHI_DURATION = 0.5
|
||||
DEFAULT_THRESHOLD = 32
|
||||
MAX_TIME_DELTA = 0.25
|
||||
MIN_TIME_DELTA = 0.05
|
||||
|
||||
def draw_motion_comp(vis, (x, y, w, h), angle, color):
|
||||
cv2.rectangle(vis, (x, y), (x+w, y+h), (0, 255, 0))
|
||||
r = min(w/2, h/2)
|
||||
cx, cy = x+w/2, y+h/2
|
||||
angle = angle*np.pi/180
|
||||
cv2.circle(vis, (cx, cy), r, color, 3)
|
||||
cv2.line(vis, (cx, cy), (int(cx+np.cos(angle)*r), int(cy+np.sin(angle)*r)), color, 3)
|
||||
|
||||
if __name__ == '__main__':
|
||||
import sys
|
||||
try:
|
||||
video_src = sys.argv[1]
|
||||
except:
|
||||
video_src = 0
|
||||
|
||||
cv2.namedWindow('motempl')
|
||||
visuals = ['input', 'frame_diff', 'motion_hist', 'grad_orient']
|
||||
cv2.createTrackbar('visual', 'motempl', 2, len(visuals)-1, nothing)
|
||||
cv2.createTrackbar('threshold', 'motempl', DEFAULT_THRESHOLD, 255, nothing)
|
||||
|
||||
cam = video.create_capture(video_src, fallback='synth:class=chess:bg=../cpp/lena.jpg:noise=0.01')
|
||||
ret, frame = cam.read()
|
||||
h, w = frame.shape[:2]
|
||||
prev_frame = frame.copy()
|
||||
motion_history = np.zeros((h, w), np.float32)
|
||||
hsv = np.zeros((h, w, 3), np.uint8)
|
||||
hsv[:,:,1] = 255
|
||||
while True:
|
||||
ret, frame = cam.read()
|
||||
frame_diff = cv2.absdiff(frame, prev_frame)
|
||||
gray_diff = cv2.cvtColor(frame_diff, cv2.COLOR_BGR2GRAY)
|
||||
thrs = cv2.getTrackbarPos('threshold', 'motempl')
|
||||
ret, motion_mask = cv2.threshold(gray_diff, thrs, 1, cv2.THRESH_BINARY)
|
||||
timestamp = clock()
|
||||
cv2.updateMotionHistory(motion_mask, motion_history, timestamp, MHI_DURATION)
|
||||
mg_mask, mg_orient = cv2.calcMotionGradient( motion_history, MAX_TIME_DELTA, MIN_TIME_DELTA, apertureSize=5 )
|
||||
seg_mask, seg_bounds = cv2.segmentMotion(motion_history, timestamp, MAX_TIME_DELTA)
|
||||
|
||||
visual_name = visuals[cv2.getTrackbarPos('visual', 'motempl')]
|
||||
if visual_name == 'input':
|
||||
vis = frame.copy()
|
||||
elif visual_name == 'frame_diff':
|
||||
vis = frame_diff.copy()
|
||||
elif visual_name == 'motion_hist':
|
||||
vis = np.uint8(np.clip((motion_history-(timestamp-MHI_DURATION)) / MHI_DURATION, 0, 1)*255)
|
||||
vis = cv2.cvtColor(vis, cv2.COLOR_GRAY2BGR)
|
||||
elif visual_name == 'grad_orient':
|
||||
hsv[:,:,0] = mg_orient/2
|
||||
hsv[:,:,2] = mg_mask*255
|
||||
vis = cv2.cvtColor(hsv, cv2.COLOR_HSV2BGR)
|
||||
|
||||
for i, rect in enumerate([(0, 0, w, h)] + list(seg_bounds)):
|
||||
x, y, rw, rh = rect
|
||||
area = rw*rh
|
||||
if area < 64**2:
|
||||
continue
|
||||
silh_roi = motion_mask [y:y+rh,x:x+rw]
|
||||
orient_roi = mg_orient [y:y+rh,x:x+rw]
|
||||
mask_roi = mg_mask [y:y+rh,x:x+rw]
|
||||
mhi_roi = motion_history[y:y+rh,x:x+rw]
|
||||
if cv2.norm(silh_roi, cv2.NORM_L1) < area*0.05:
|
||||
continue
|
||||
angle = cv2.calcGlobalOrientation(orient_roi, mask_roi, mhi_roi, timestamp, MHI_DURATION)
|
||||
color = ((255, 0, 0), (0, 0, 255))[i == 0]
|
||||
draw_motion_comp(vis, rect, angle, color)
|
||||
|
||||
draw_str(vis, (20, 20), visual_name)
|
||||
cv2.imshow('motempl', vis)
|
||||
|
||||
prev_frame = frame.copy()
|
||||
if 0xFF & cv2.waitKey(5) == 27:
|
||||
break
|
||||
cv2.destroyAllWindows()
|
||||
@@ -17,7 +17,7 @@ if(BUILD_EXAMPLES AND OCV_DEPENDENCIES_FOUND)
|
||||
set(the_target "example_${project}_${name}")
|
||||
add_executable(${the_target} ${srcs})
|
||||
|
||||
target_link_libraries(${the_target} ${OPENCV_LINKER_LIBS} ${OPENCV_TAPI_SAMPLES_REQUIRED_DEPS})
|
||||
ocv_target_link_libraries(${the_target} ${OPENCV_LINKER_LIBS} ${OPENCV_TAPI_SAMPLES_REQUIRED_DEPS})
|
||||
|
||||
set_target_properties(${the_target} PROPERTIES
|
||||
OUTPUT_NAME "${project}-example-${name}"
|
||||
|
||||
@@ -11,15 +11,15 @@
|
||||
using namespace std;
|
||||
using namespace cv;
|
||||
|
||||
#define M_MOG 1
|
||||
#define M_MOG2 2
|
||||
#define M_KNN 3
|
||||
|
||||
int main(int argc, const char** argv)
|
||||
{
|
||||
CommandLineParser cmd(argc, argv,
|
||||
"{ c camera | false | use camera }"
|
||||
"{ f file | 768x576.avi | input video file }"
|
||||
"{ t type | mog | method's type (mog, mog2) }"
|
||||
"{ t type | mog2 | method's type (knn, mog2) }"
|
||||
"{ h help | false | print help message }"
|
||||
"{ m cpu_mode | false | press 'm' to switch OpenCL<->CPU}");
|
||||
|
||||
@@ -41,7 +41,7 @@ int main(int argc, const char** argv)
|
||||
return EXIT_FAILURE;
|
||||
}
|
||||
|
||||
int m = method == "mog" ? M_MOG : M_MOG2;
|
||||
int m = method == "mog2" ? M_MOG2 : M_KNN;
|
||||
|
||||
VideoCapture cap;
|
||||
if (useCamera)
|
||||
@@ -59,13 +59,13 @@ int main(int argc, const char** argv)
|
||||
cap >> frame;
|
||||
fgimg.create(frame.size(), frame.type());
|
||||
|
||||
Ptr<BackgroundSubtractorMOG> mog = createBackgroundSubtractorMOG();
|
||||
Ptr<BackgroundSubtractorKNN> knn = createBackgroundSubtractorKNN();
|
||||
Ptr<BackgroundSubtractorMOG2> mog2 = createBackgroundSubtractorMOG2();
|
||||
|
||||
switch (m)
|
||||
{
|
||||
case M_MOG:
|
||||
mog->apply(frame, fgmask, 0.01f);
|
||||
case M_KNN:
|
||||
knn->apply(frame, fgmask);
|
||||
break;
|
||||
|
||||
case M_MOG2:
|
||||
@@ -86,8 +86,8 @@ int main(int argc, const char** argv)
|
||||
//update the model
|
||||
switch (m)
|
||||
{
|
||||
case M_MOG:
|
||||
mog->apply(frame, fgmask, 0.01f);
|
||||
case M_KNN:
|
||||
knn->apply(frame, fgmask);
|
||||
break;
|
||||
|
||||
case M_MOG2:
|
||||
|
||||
Reference in New Issue
Block a user