Mercurial Hosting > traffic-intelligence
changeset 614:5e09583275a4
Merged Nicolas/trafficintelligence into default
| author | Mohamed Gomaa <eng.m.gom3a@gmail.com> |
|---|---|
| date | Fri, 05 Dec 2014 12:13:53 -0500 |
| parents | 11f96bd08552 (current diff) c5406edbcf12 (diff) |
| children | 0954aaf28231 |
| files | Makefile python/calibration-translation.py python/compute-homography.py python/compute-object-from-features.py python/delete-object-tables.py python/display-trajectories.py python/offset-trajectories.py python/play-video.py python/poly-utils.py python/poly_utils.py |
| diffstat | 78 files changed, 5330 insertions(+), 1589 deletions(-) [+] |
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--- a/.hgignore Thu Apr 18 15:29:33 2013 -0400 +++ b/.hgignore Fri Dec 05 12:13:53 2014 -0500 @@ -22,6 +22,7 @@ CMakeCache.txt *.cmake +install_manifest.txt latex html
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/CHANGELOG Fri Dec 05 12:13:53 2014 -0500 @@ -0,0 +1,10 @@ + + +- Changeset 523 +Added functionality to classify tracked road users into cyclist, pedestrian or motorized vehicle, using appearance and speed + +- Changeset 515 +Draw methods have been renamed to plot. + +- Changeset 513 June 5th 2014 +Added functionlities to process video with visible radial distortion (such as GoPro). This means that the configuration file format has changed with several parameters added \ No newline at end of file
--- a/CMakeLists.txt Thu Apr 18 15:29:33 2013 -0400 +++ b/CMakeLists.txt Fri Dec 05 12:13:53 2014 -0500 @@ -14,19 +14,36 @@ CMAKE_CXX_FLAGS "-g -Wall" ) -ADD_EXECUTABLE( - bin/feature-based-tracking - c/feature-based-tracking.cpp - ) +set(CMAKE_RUNTIME_OUTPUT_DIRECTORY bin) + +add_executable(feature-based-tracking + c/cvutils.cpp + c/feature-based-tracking.cpp + c/Motion.cpp + c/Parameters.cpp + c/utils.cpp + c/InputFrameListModule.cpp + c/InputVideoFileModule.cpp + ) + +find_package(Boost REQUIRED program_options filesystem system) +find_library(TrajectoryManagement_LIBRARY TrajectoryManagementAndAnalysis) +find_path(TrajectoryManagement_INCLUDE_DIR src/Trajectory.h) -INCLUDE_DIRECTORIES( -# bin/feature-based-tracking -/home/nicolas/Research/Code/trajectorymanagementandanalysis/trunk/src/TrajectoryManagementAndAnalysis -) +add_definitions( + -DUSE_OPENCV + ) + +include_directories( + ${PROJECT_SOURCE_DIR}/include + ${TrajectoryManagement_INCLUDE_DIR} + ) -#TARGET_LINK_LIBRARIES( -# bin/feature-based-tracking -# ${OpenCV_LIBS} -# $(TrajectoryManagement_DIR) - # ${SQLite3_LIBS} -# ) +target_link_libraries(feature-based-tracking + ${TrajectoryManagement_LIBRARY} + ${SQLite3_LIBS} + ${OpenCV_LIBS} + ${Boost_LIBRARIES} + ) + +install(TARGETS feature-based-tracking DESTINATION bin)
--- a/Makefile Thu Apr 18 15:29:33 2013 -0400 +++ b/Makefile Fri Dec 05 12:13:53 2014 -0500 @@ -1,163 +1,25 @@ -# CMAKE generated file: DO NOT EDIT! -# Generated by "Unix Makefiles" Generator, CMake Version 2.8 -# Default target executed when no arguments are given to make. -default_target: all -.PHONY : default_target - -#============================================================================= -# Special targets provided by cmake. - -# Disable implicit rules so canoncical targets will work. -.SUFFIXES: - -# Remove some rules from gmake that .SUFFIXES does not remove. -SUFFIXES = - -.SUFFIXES: .hpux_make_needs_suffix_list - -# Suppress display of executed commands. -$(VERBOSE).SILENT: - -# A target that is always out of date. -cmake_force: -.PHONY : cmake_force - -#============================================================================= -# Set environment variables for the build. - -# The shell in which to execute make rules. -SHELL = /bin/sh - -# The CMake executable. -CMAKE_COMMAND = /usr/bin/cmake - -# The command to remove a file. -RM = /usr/bin/cmake -E remove -f - -# The program to use to edit the cache. -CMAKE_EDIT_COMMAND = /usr/bin/ccmake +cexe: + @cd c && make feature-based-tracking -# The top-level source directory on which CMake was run. -CMAKE_SOURCE_DIR = /home/nicolas/Research/Code/traffic-intelligence - -# The top-level build directory on which CMake was run. -CMAKE_BINARY_DIR = /home/nicolas/Research/Code/traffic-intelligence - -#============================================================================= -# Targets provided globally by CMake. - -# Special rule for the target edit_cache -edit_cache: - @$(CMAKE_COMMAND) -E cmake_echo_color --switch=$(COLOR) --cyan "Running CMake cache editor..." - /usr/bin/ccmake -H$(CMAKE_SOURCE_DIR) -B$(CMAKE_BINARY_DIR) -.PHONY : edit_cache - -# Special rule for the target edit_cache -edit_cache/fast: edit_cache -.PHONY : edit_cache/fast +doc: + doxygen -# Special rule for the target rebuild_cache -rebuild_cache: - @$(CMAKE_COMMAND) -E cmake_echo_color --switch=$(COLOR) --cyan "Running CMake to regenerate build system..." - /usr/bin/cmake -H$(CMAKE_SOURCE_DIR) -B$(CMAKE_BINARY_DIR) -.PHONY : rebuild_cache - -# Special rule for the target rebuild_cache -rebuild_cache/fast: rebuild_cache -.PHONY : rebuild_cache/fast - -# The main all target -all: cmake_check_build_system - $(CMAKE_COMMAND) -E cmake_progress_start /home/nicolas/Research/Code/traffic-intelligence/CMakeFiles /home/nicolas/Research/Code/traffic-intelligence/CMakeFiles/progress.marks - $(MAKE) -f CMakeFiles/Makefile2 all - $(CMAKE_COMMAND) -E cmake_progress_start /home/nicolas/Research/Code/traffic-intelligence/CMakeFiles 0 -.PHONY : all - -# The main clean target clean: - $(MAKE) -f CMakeFiles/Makefile2 clean -.PHONY : clean + @cd c && make clean + @cd python && rm *.pyc -# The main clean target -clean/fast: clean -.PHONY : clean/fast - -# Prepare targets for installation. -preinstall: all - $(MAKE) -f CMakeFiles/Makefile2 preinstall -.PHONY : preinstall - -# Prepare targets for installation. -preinstall/fast: - $(MAKE) -f CMakeFiles/Makefile2 preinstall -.PHONY : preinstall/fast - -# clear depends -depend: - $(CMAKE_COMMAND) -H$(CMAKE_SOURCE_DIR) -B$(CMAKE_BINARY_DIR) --check-build-system CMakeFiles/Makefile.cmake 1 -.PHONY : depend - -#============================================================================= -# Target rules for targets named bin/feature-based-tracking - -# Build rule for target. -bin/feature-based-tracking: cmake_check_build_system - $(MAKE) -f CMakeFiles/Makefile2 bin/feature-based-tracking -.PHONY : bin/feature-based-tracking - -# fast build rule for target. -bin/feature-based-tracking/fast: - $(MAKE) -f CMakeFiles/bin/feature-based-tracking.dir/build.make CMakeFiles/bin/feature-based-tracking.dir/build -.PHONY : bin/feature-based-tracking/fast - -c/feature-based-tracking.o: c/feature-based-tracking.cpp.o -.PHONY : c/feature-based-tracking.o - -# target to build an object file -c/feature-based-tracking.cpp.o: - $(MAKE) -f CMakeFiles/bin/feature-based-tracking.dir/build.make CMakeFiles/bin/feature-based-tracking.dir/c/feature-based-tracking.cpp.o -.PHONY : c/feature-based-tracking.cpp.o +install: cexe + @echo "=========================================" + @echo "Installing for Linux" + @echo "=========================================" + @echo "Copying feature-based tracking executable" + @cp bin/feature-based-tracking /usr/local/bin + @echo "=========================================" + @echo "Copying Python scripts" + @cp scripts/* /usr/local/bin -c/feature-based-tracking.i: c/feature-based-tracking.cpp.i -.PHONY : c/feature-based-tracking.i - -# target to preprocess a source file -c/feature-based-tracking.cpp.i: - $(MAKE) -f CMakeFiles/bin/feature-based-tracking.dir/build.make CMakeFiles/bin/feature-based-tracking.dir/c/feature-based-tracking.cpp.i -.PHONY : c/feature-based-tracking.cpp.i - -c/feature-based-tracking.s: c/feature-based-tracking.cpp.s -.PHONY : c/feature-based-tracking.s - -# target to generate assembly for a file -c/feature-based-tracking.cpp.s: - $(MAKE) -f CMakeFiles/bin/feature-based-tracking.dir/build.make CMakeFiles/bin/feature-based-tracking.dir/c/feature-based-tracking.cpp.s -.PHONY : c/feature-based-tracking.cpp.s - -# Help Target -help: - @echo "The following are some of the valid targets for this Makefile:" - @echo "... all (the default if no target is provided)" - @echo "... clean" - @echo "... depend" - @echo "... bin/feature-based-tracking" - @echo "... edit_cache" - @echo "... rebuild_cache" - @echo "... c/feature-based-tracking.o" - @echo "... c/feature-based-tracking.i" - @echo "... c/feature-based-tracking.s" -.PHONY : help - - - -#============================================================================= -# Special targets to cleanup operation of make. - -# Special rule to run CMake to check the build system integrity. -# No rule that depends on this can have commands that come from listfiles -# because they might be regenerated. -cmake_check_build_system: - $(CMAKE_COMMAND) -H$(CMAKE_SOURCE_DIR) -B$(CMAKE_BINARY_DIR) --check-build-system CMakeFiles/Makefile.cmake 0 -.PHONY : cmake_check_build_system - +uninstall: + @echo "Uninstalling for Linux" + rm /usr/local/bin/feature-based-tracking + @cd scripts && ./uninstall-scripts.sh \ No newline at end of file
--- a/OpenCV.props Thu Apr 18 15:29:33 2013 -0400 +++ b/OpenCV.props Fri Dec 05 12:13:53 2014 -0500 @@ -13,7 +13,7 @@ </ClCompile> <Link> <AdditionalLibraryDirectories>$(SolutionDir)\win32-depends\opencv\libs\$(PlatformName)\$(ConfigurationName)\;%(AdditionalLibraryDirectories)</AdditionalLibraryDirectories> - <AdditionalDependencies>opencv_video243$(DebugSuffix).lib;opencv_ts243$(DebugSuffix).lib;opencv_objdetect243$(DebugSuffix).lib;opencv_ml243$(DebugSuffix).lib;opencv_legacy243$(DebugSuffix).lib;opencv_imgproc243$(DebugSuffix).lib;opencv_highgui243$(DebugSuffix).lib;opencv_flann243$(DebugSuffix).lib;opencv_features2d243$(DebugSuffix).lib;opencv_core243$(DebugSuffix).lib;opencv_contrib243$(DebugSuffix).lib;opencv_calib3d243$(DebugSuffix).lib;%(AdditionalDependencies)</AdditionalDependencies> + <AdditionalDependencies>opencv_video246$(DebugSuffix).lib;opencv_ts246$(DebugSuffix).lib;opencv_objdetect246$(DebugSuffix).lib;opencv_ml246$(DebugSuffix).lib;opencv_legacy246$(DebugSuffix).lib;opencv_imgproc246$(DebugSuffix).lib;opencv_highgui246$(DebugSuffix).lib;opencv_flann246$(DebugSuffix).lib;opencv_features2d246$(DebugSuffix).lib;opencv_core246$(DebugSuffix).lib;opencv_contrib246$(DebugSuffix).lib;opencv_calib3d246$(DebugSuffix).lib;%(AdditionalDependencies)</AdditionalDependencies> </Link> </ItemDefinitionGroup> <ItemGroup />
--- a/README Thu Apr 18 15:29:33 2013 -0400 +++ b/README Fri Dec 05 12:13:53 2014 -0500 @@ -1,7 +1,15 @@ -This software project provides a set of tools developed by Nicolas Saunier and his collaborators for transportation data processing, in particular road traffic, motorized and non-motorized. The project consists in particular in tools for the most typical transportation data type, trajectories, i.e. temporal series of positions. - -The code is licensed under the MIT open source license (http://www.opensource.org/licenses/mit-license). +This software project provides a set of tools developed by Nicolas +Saunier and his collaborators for transportation data processing, in +particular road traffic, motorized and non-motorized. The project +consists in particular in tools for the most typical transportation +data type, trajectories, i.e. temporal series of positions. -Contact me at nicolas.saunier@polymtl.ca and learn more about my work at http://nicolas.saunier.confins.net. +The code is licensed under the MIT open source license +(http://www.opensource.org/licenses/mit-license). -Please consult the project website on Bitbucket for more information and step-by-step guides https://bitbucket.org/Nicolas/trafficintelligence/wiki/Home \ No newline at end of file +Contact me at nicolas.saunier@polymtl.ca and learn more about my work +at http://nicolas.saunier.confins.net. + +Please consult the project website on Bitbucket for more information +and step-by-step guides +https://bitbucket.org/Nicolas/trafficintelligence/wiki/Home \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/c/InputFrameListModule.cpp Fri Dec 05 12:13:53 2014 -0500 @@ -0,0 +1,61 @@ +#include "InputFrameListModule.h" +#include "utils.hpp" + +#include <fstream> +#include <ostream> +#include <iostream> +#include <algorithm> + +//#include <boost/algorithm/string.hpp> +#include <boost/filesystem.hpp> + +#include "opencv2/core/core.hpp" +#include "opencv2/highgui/highgui.hpp" + +namespace fs = boost::filesystem; + +InputFrameListModule::InputFrameListModule(const std::string& _dirname) + : mCurrentIdx(0), mInit(false), dirname(_dirname){ + loadImageList(); +} + +InputFrameListModule::~InputFrameListModule(void) { } + + +void InputFrameListModule::setFrameNumber(const unsigned int& frameNumber) { + if (frameNumber < filenames.size()) + mCurrentIdx = frameNumber; + else + mCurrentIdx = filenames.size()-1; +} + +bool InputFrameListModule::getNextFrame(cv::Mat& mat) +{ + bool success = false; + if(mCurrentIdx < filenames.size()) { + mat = cv::imread(dirname+filenames[mCurrentIdx++]); + + if(!mat.empty()) + success = true; + } + + return success; +} + +unsigned int InputFrameListModule::getNbFrames(void) { + return filenames.size(); +} + +void InputFrameListModule::loadImageList(void) { + for (fs::directory_iterator iter(dirname); iter!=fs::directory_iterator(); iter++) + filenames.push_back(iter->path().filename().string()); + + sort(filenames.begin(), filenames.end()); + + if(!filenames.empty()) { + std::cout << dirname+filenames[0] << std::endl; + cv::Mat tmpImg = cv::imread(dirname+filenames[0]); + mSize = cv::Size(tmpImg.cols, tmpImg.rows); + mInit = true; + } +}
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/c/InputVideoFileModule.cpp Fri Dec 05 12:13:53 2014 -0500 @@ -0,0 +1,31 @@ +#include "InputVideoFileModule.h" + +InputVideoFileModule::InputVideoFileModule(const std::string& videoPath) + : mInit(false) + , mNumberOfFrame(0) +{ + mInit = mVideoCapture.open(videoPath.c_str()); + double frameCount; + frameCount = mVideoCapture.get(CV_CAP_PROP_FRAME_COUNT); + mSize = cv::Size(mVideoCapture.get(CV_CAP_PROP_FRAME_WIDTH), mVideoCapture.get(CV_CAP_PROP_FRAME_HEIGHT)); + mNumberOfFrame = (unsigned int)frameCount; +} + +InputVideoFileModule::~InputVideoFileModule(void) { } + + +void InputVideoFileModule::setFrameNumber(const unsigned int& frameNumber) { + mVideoCapture.set(CV_CAP_PROP_POS_FRAMES, frameNumber); +} + +bool InputVideoFileModule::getNextFrame(cv::Mat& outputPicture) +{ + bool success = false; + if(mInit) + { + mVideoCapture >> outputPicture; + success = !outputPicture.empty(); + } + return success; +} +
--- a/c/Makefile Thu Apr 18 15:29:33 2013 -0400 +++ b/c/Makefile Fri Dec 05 12:13:53 2014 -0500 @@ -10,8 +10,7 @@ LDFLAGS = -lm LDFLAGS += -lTrajectoryManagementAndAnalysis -lsqlite3 -LDFLAGS += -lboost_program_options -# -lboost_filesystem-mt -lboost_system-mt +LDFLAGS += -lboost_program_options -lboost_filesystem -lboost_system #LDFLAGS += -lfltk CFLAGS = -Wall -W -Wextra @@ -49,7 +48,7 @@ CXXFLAGS = $(INCLUDE) $(CFLAGS) #GUI_OBJS = -CV_OBJS = cvutils.o +CV_OBJS = cvutils.o InputFrameListModule.o InputVideoFileModule.o COMMON_OBJS = utils.o Motion.o Parameters.o utils.o OBJS = $(COMMON_OBJS) $(CV_OBJS) TESTS_OBJS = test_feature.o test_graph.o @@ -72,6 +71,7 @@ $(EXE_DIR)/$@ feature-based-tracking: feature-based-tracking.o $(OBJS) + @$(SCRIPTS_DIR)/createdirectory.sh $(EXE_DIR) $(CXX) $(CFLAGS) $(LIBS) $^ -o $(EXE_DIR)/$@ $(LDFLAGS) track-features.o: track-features.cpp
--- a/c/Motion.cpp Thu Apr 18 15:29:33 2013 -0400 +++ b/c/Motion.cpp Fri Dec 05 12:13:53 2014 -0500 @@ -51,7 +51,7 @@ float disp = 0; for (unsigned int i=0; i<nDisplacements; i++) disp += displacementDistances[nPositions-2-i]; - result = disp < minTotalFeatureDisplacement; + result = disp <= minTotalFeatureDisplacement; } return result; } @@ -66,9 +66,9 @@ else ratio = displacementDistances[nPositions-3] / displacementDistances[nPositions-2]; - float cosine = scalarProduct((*velocities)[nPositions-3],(*velocities)[nPositions-2]) / (displacementDistances[nPositions-3] * displacementDistances[nPositions-2]); + float cosine = (*velocities)[nPositions-3].dot((*velocities)[nPositions-2]) / (displacementDistances[nPositions-3] * displacementDistances[nPositions-2]); - result &= (ratio < accelerationBound) & (cosine > deviationBound); + result = (ratio < accelerationBound) & (cosine > deviationBound); } return result; } @@ -111,6 +111,11 @@ displacementDistances.pop_back(); } +void FeatureTrajectory::movingAverage(const unsigned int& nFramesSmoothing) { + positions->movingAverage(nFramesSmoothing); + velocities->movingAverage(nFramesSmoothing); +} + void FeatureTrajectory::write(TrajectoryDBAccess<Point2f>& trajectoryDB, const string& positionsTableName, const string& velocitiesTableName) const { trajectoryDB.write(*positions, positionsTableName); trajectoryDB.write(*velocities, velocitiesTableName); @@ -164,7 +169,7 @@ if (ft->minMaxSimilarity(*ft2, firstInstant, lastInstant, connectionDistance, segmentationDistance)) { UndirectedGraph::edge_descriptor e; bool unused; - tie(e, unused) = add_edge(newVertex, *vi, graph); + boost::tuples::tie(e, unused) = add_edge(newVertex, *vi, graph); // no need to add measures to graph[e] (edge properties) } } @@ -185,7 +190,7 @@ vector<vector<vertex_descriptor> > tmpobjects(num), objects; // vector of components (component = vector of vertex descriptors) graph_traits<UndirectedGraph>::vertex_iterator vi, vend; - for(tie(vi,vend) = vertices(graph); vi != vend; ++vi) { + for(boost::tuples::tie(vi,vend) = vertices(graph); vi != vend; ++vi) { unsigned int id = components[*vi]; lastInstants[id] = max(lastInstants[id], graph[*vi].feature->getLastInstant()); tmpobjects[id].push_back(*vi); @@ -201,8 +206,8 @@ } } -vector<vector<unsigned int> > FeatureGraph::getFeatureGroups(void) { - vector<vector<unsigned int> > featureGroups; +void FeatureGraph::getFeatureGroups(vector<vector<FeatureTrajectoryPtr> >& featureGroups) { + featureGroups.clear(); for (unsigned int i=0; i<objectHypotheses.size(); ++i) { // check that there is on average at least minNFeaturesPerGroup features at each frame in the group @@ -218,9 +223,9 @@ #if DEBUG cout << "save group " << i << " of " << objectHypotheses[i].size() << " features " << endl; #endif - featureGroups.push_back(vector<unsigned int>()); + featureGroups.push_back(vector<FeatureTrajectoryPtr>()); for (unsigned int j=0; j<objectHypotheses[i].size(); ++j) { - featureGroups.back().push_back(graph[objectHypotheses[i][j]].feature->getId()); + featureGroups.back().push_back(graph[objectHypotheses[i][j]].feature); #if DEBUG cout << featureGroups.size() << " " << objectHypotheses[i][j] << endl; #endif @@ -229,8 +234,6 @@ } } } - - return featureGroups; } string FeatureGraph::informationString(void) const { @@ -246,6 +249,6 @@ void FeatureGraph::computeVertexIndex(void) { graph_traits<FeatureGraph::UndirectedGraph>::vertex_iterator vi, vend; graph_traits<FeatureGraph::UndirectedGraph>::vertices_size_type cnt = 0; - for(tie(vi,vend) = vertices(graph); vi != vend; ++vi) + for(boost::tuples::tie(vi,vend) = vertices(graph); vi != vend; ++vi) graph[*vi].index = cnt++; }
--- a/c/Parameters.cpp Thu Apr 18 15:29:33 2013 -0400 +++ b/c/Parameters.cpp Fri Dec 05 12:13:53 2014 -0500 @@ -18,7 +18,7 @@ ("help,h", "displays this help message") ("tf", "tracks features") ("gf", "groups features") - ("config-file", po::value<string>(&configurationFilename)->default_value("tracking.cfg"), "configuration file") + ("config-file", po::value<string>(&configurationFilename), "configuration file") ; po::positional_options_description p; @@ -29,38 +29,55 @@ ("video-filename", po::value<string>(&videoFilename), "filename of the video to process") ("database-filename", po::value<string>(&databaseFilename), "filename of the database where results are saved") ("homography-filename", po::value<string>(&homographyFilename), "filename of the homography matrix") + ("intrinsic-camera-filename", po::value<string>(&intrinsicCameraFilename), "filename of the homography matrix") + ("distortion-coefficients", po::value<std::vector<float> >(&distortionCoefficients)->multitoken(), "") + ("undistorted-size-multiplication", po::value<float>(&undistortedImageMultiplication), "undistorted image multiplication") + ("interpolation-method", po::value<int>(&interpolationMethod), "Interpolation method for remapping image when correcting for distortion: 0 for INTER_NEAREST - a nearest-neighbor interpolation; 1 for INTER_LINEAR - a bilinear interpolation (used by default); 2 for INTER_CUBIC - a bicubic interpolation over 4x4 pixel neighborhood; 3 for INTER_LANCZOS4") ("mask-filename", po::value<string>(&maskFilename), "filename of the mask image (where features are detected)") + ("undistort", po::value<bool>(&undistort), "undistort the video for feature tracking") ("load-features", po::value<bool>(&loadFeatures), "load features from database") ("display", po::value<bool>(&display), "display trajectories on the video") ("video-fps", po::value<float>(&videoFPS), "original video frame rate") ("frame1", po::value<unsigned int>(&frame1), "first frame to process") ("nframes", po::value<int>(&nFrames), "number of frame to process") // feature tracking - ("max-nfeatures", po::value<int>(&maxNFeatures), "maximum number of features added at each frame") - ("feature-quality", po::value<float>(&featureQuality), "quality level of the good features to track") - ("min-feature-distanceklt", po::value<float>(&minFeatureDistanceKLT), "minimum distance between features") - ("window-size", po::value<int>(&windowSize), "size of the search window at each pyramid level") + ("max-nfeatures", po::value<int>(&maxNFeatures), "maximum number of features added at each frame (1000s)") + ("feature-quality", po::value<float>(&featureQuality), "quality level of the good features to track (]0. 1?])") + ("min-feature-distanceklt", po::value<float>(&minFeatureDistanceKLT), "minimum distance between features (]0. 10?])") + ("block-size", po::value<int>(&blockSize), "size of the block for feature characteristics ([1 ?])") ("use-harris-detector", po::value<bool>(&useHarrisDetector), "use of Harris corner detector") ("k", po::value<float>(&k), "k parameter to detect good features to track (OpenCV)") - ("pyramid-level", po::value<int>(&pyramidLevel), "maximal pyramid level in the feature tracking algorithm") - ("ndisplacements", po::value<unsigned int>(&nDisplacements), "number of displacement to test minimum feature motion") - ("min-feature-displacement", po::value<float>(&minFeatureDisplacement), "minimum displacement to keep features") - ("acceleration-bound", po::value<float>(&accelerationBound), "maximum feature acceleration") - ("deviation-bound", po::value<float>(&deviationBound), "maximum feature deviation") - ("smoothing-halfwidth", po::value<int>(&nFramesSmoothing), "number of frames to smooth positions (half window)") - ("max-number-iterations", po::value<int>(&maxNumberTrackingIterations), "maximum number of iterations to stop feature tracking") - ("min-tracking-error", po::value<float>(&minTrackingError), "minimum error to reach to stop feature tracking") - ("min-feature-time", po::value<unsigned int>(&minFeatureTime), "minimum length of a feature (number of frames) to consider a feature for grouping") - ("mm-connection-distance", po::value<float>(&mmConnectionDistance), "connection distance in feature grouping") - ("mm-segmentation-distance", po::value<float>(&mmSegmentationDistance), "segmentation distance in feature grouping") - ("max-distance", po::value<float>(&maxDistance), "maximum distance between features for grouping") - ("min-velocity-cosine", po::value<float>(&minVelocityCosine), "minimum cosine of the angle between the velocity vectors for grouping") - ("min-nfeatures-group", po::value<float>(&minNFeaturesPerGroup), "minimum average number of features per frame to create a vehicle hypothesis") - ; + ("window-size", po::value<int>(&windowSize), "size of the search window at each pyramid level ([1 ?])") + ("pyramid-level", po::value<int>(&pyramidLevel), "maximal pyramid level in the feature tracking algorithm ([0 maxLevel=5?])") + ("ndisplacements", po::value<unsigned int>(&nDisplacements), "number of displacements to test minimum feature motion ([2 4])") + ("min-feature-displacement", po::value<float>(&minFeatureDisplacement), "minimum displacement per frame (in world space) to keep features (]0. 0.1?])") + ("acceleration-bound", po::value<float>(&accelerationBound), "maximum feature acceleration (]1 3+])") + ("deviation-bound", po::value<float>(&deviationBound), "maximum feature deviation (on cosine) (]0 1])") + ("smoothing-halfwidth", po::value<int>(&nFramesSmoothing), "number of frames to smooth positions (half window) ([0 inf[") + ("max-number-iterations", po::value<int>(&maxNumberTrackingIterations), "maximum number of iterations to stop optical flow (20-30?)") + ("min-tracking-error", po::value<float>(&minTrackingError), "minimum error to reach to stop optical flow (0.3-0.01)") + ("min-feature-eig-threshold", po::value<float>(&minFeatureEigThreshold)->default_value(1e-4), "minimum eigen value of a 2x2 normal matrix of optical flow equations (10^-4)") + ("min-feature-time", po::value<unsigned int>(&minFeatureTime), "minimum length of a feature (number of frames) to consider a feature for grouping [5 20+]") + ("mm-connection-distance", po::value<float>(&mmConnectionDistance), "connection distance in feature grouping (in world space) (ped: [0.5m 2m+], cars: [1.7m 4m+])") + ("mm-segmentation-distance", po::value<float>(&mmSegmentationDistance), "segmentation distance in feature grouping (in world space) (< mm-connection-distance, empirically ~ mm-connection-distance / 2.5)") + ("max-distance", po::value<float>(&maxDistance), "maximum distance between features for grouping (in world space) (unused)") + ("min-velocity-cosine", po::value<float>(&minVelocityCosine), "minimum cosine of the angle between the velocity vectors for grouping (unused)") + ("min-nfeatures-group", po::value<float>(&minNFeaturesPerGroup), "minimum average number of features per frame to create a vehicle hypothesis (]1 3+])") // ("max-uturn-cosine", po::value<float>(&maxUTurnCosine), "maximum cosine value to detect U-turn") // ("nframes-avoid-uturn", po::value<int>(&nFramesAvoidUTurn), "number of frames over which a feature should not make a U-turn") - - + // Safety Analysis + ("max-predicted-speed", po::value<float>(&maxPredictedSpeed)->default_value(50.), "maximum speed when predicting future motion (km/h)") + ("prediction-time-horizon", po::value<float>(&predictionTimeHorizon)->default_value(5.), "time horizon for collision prediction (s)") + ("collision-distance", po::value<float>(&collisionDistance)->default_value(1.8), "collision distance threshold (m)") + ("crossing-zones", po::value<bool>(&crossingZones)->default_value(false), "option to compute crossing zones and predicted PET") + ("prediction-method", po::value<string>(&predictionMethod)->default_value("na"), "prediction method") + ("npredicted-trajectories", po::value<int>(&nPredictedTrajectories)->default_value(1), "number of predicted trajectories (use depends on prediction method)") + ("min-acceleration", po::value<float>(&minAcceleration)->default_value(-9.1), "minimum acceleration for input distribution (m/s2) (used only for evasive action distributions)") + ("max-acceleration", po::value<float>(&maxAcceleration)->default_value(2.), "maximum acceleration for input distribution (m/s2)") + ("max-steering", po::value<float>(&maxSteering)->default_value(0.5), "maximum steering for input distribution (rad/s)") + ("use-features-prediction", po::value<bool>(&useFeaturesForPrediction)->default_value(false), "use feature positions and velocities for prediction") + ; + po::options_description cmdLine; cmdLine.add(onlyCmdLine).add(cmdLineAndFile); try { @@ -69,10 +86,16 @@ options(cmdLine).positional(p).allow_unregistered().run(), vm); notify(vm); + if (vm.count("config-file") == 0) { + cout << "Missing configuration file" << endl; + cout << cmdLine << endl; + exit(0); + } + cout << "Using configuration file " << configurationFilename << endl; ifstream configurationFile(configurationFilename.c_str()); - store(po::parse_config_file(configurationFile, cmdLineAndFile), vm); + store(po::parse_config_file(configurationFile, cmdLineAndFile, true), vm); notify(vm); parameterDescription = getParameterDescription(cmdLineAndFile, vm); @@ -108,7 +131,10 @@ stream << boost::any_cast<float>(value) << separator; else if (value.type() == typeid(string)) stream << boost::any_cast<string>(value) << separator; - else + else if (value.type() == typeid(vector<float>)) { + for (unsigned int j=0; j<boost::any_cast<vector<float> >(value).size(); j++) + stream << boost::any_cast<vector<float> >(value)[j] << separator; + } else cerr << "the type of the option " << optionsVec[i]->long_name() << " (" << i << ") is not int, float or string." << endl; }
--- a/c/cvutils.cpp Thu Apr 18 15:29:33 2013 -0400 +++ b/c/cvutils.cpp Fri Dec 05 12:13:53 2014 -0500 @@ -49,34 +49,34 @@ pts.push_back(kpts[i].pt); } -IplImage* allocateImage(const int& width, const int& height, const int& depth, const int& channels) { return ::allocateImage(cvSize(width, height), depth, channels);} +// IplImage* allocateImage(const int& width, const int& height, const int& depth, const int& channels) { return ::allocateImage(cvSize(width, height), depth, channels);} -IplImage* allocateImage(const CvSize& size, const int& depth, const int& channels) { - IplImage* image = cvCreateImage(size, depth, channels); +// IplImage* allocateImage(const CvSize& size, const int& depth, const int& channels) { +// IplImage* image = cvCreateImage(size, depth, channels); - if (!image) { - cerr << "Error: Couldn't allocate image. Out of memory?\n" << endl; - exit(-1); - } +// if (!image) { +// cerr << "Error: Couldn't allocate image. Out of memory?\n" << endl; +// exit(-1); +// } - return image; -} +// return image; +// } -int goToFrameNum(CvCapture* inputVideo, const int& currentFrameNum, const int& targetFrameNum) { - int frameNum = currentFrameNum; - if (currentFrameNum > targetFrameNum) { - cerr << "Current frame number " << currentFrameNum << " is after the target frame number " << targetFrameNum << "." << endl; - } else if (currentFrameNum < targetFrameNum) { - IplImage* frame = cvQueryFrame(inputVideo); - frameNum++; - while (frame && frameNum<targetFrameNum) { - frame = cvQueryFrame(inputVideo); - frameNum++; - } - } +// int goToFrameNum(CvCapture* inputVideo, const int& currentFrameNum, const int& targetFrameNum) { +// int frameNum = currentFrameNum; +// if (currentFrameNum > targetFrameNum) { +// cerr << "Current frame number " << currentFrameNum << " is after the target frame number " << targetFrameNum << "." << endl; +// } else if (currentFrameNum < targetFrameNum) { +// IplImage* frame = cvQueryFrame(inputVideo); +// frameNum++; +// while (frame && frameNum<targetFrameNum) { +// frame = cvQueryFrame(inputVideo); +// frameNum++; +// } +// } - return frameNum; -} +// return frameNum; +// } const Scalar Colors::color[] = {Colors::red(), Colors::green(), @@ -87,14 +87,15 @@ Colors::white(), Colors::black()}; +// Blue, Green, Red Scalar Colors::black(void) { return Scalar(0,0,0);} -Scalar Colors::red(void) { return Scalar(255,0,0);} +Scalar Colors::blue(void) { return Scalar(255,0,0);} Scalar Colors::green(void) { return Scalar(0,255,0);} -Scalar Colors::blue(void) { return Scalar(0,0,255);} +Scalar Colors::red(void) { return Scalar(0,0,255);} Scalar Colors::white(void) { return Scalar(255,255,255);} Scalar Colors::magenta(void) { return Scalar(255,0,255);} -Scalar Colors::cyan(void) { return Scalar(0,255,255);} -Scalar Colors::yellow(void) { return Scalar(255,255,0);} +Scalar Colors::yellow(void) { return Scalar(0,255,255);} +Scalar Colors::cyan(void) { return Scalar(255,255,0);} Scalar Colors::color3(const int& num) { return Colors::color[num%3];} Scalar Colors::color8(const int& num) { return Colors::color[num%Colors::nColors];}
--- a/c/feature-based-tracking.cpp Thu Apr 18 15:29:33 2013 -0400 +++ b/c/feature-based-tracking.cpp Fri Dec 05 12:13:53 2014 -0500 @@ -2,6 +2,8 @@ #include "Parameters.hpp" #include "cvutils.hpp" #include "utils.hpp" +#include "InputVideoFileModule.h" +#include "InputFrameListModule.h" #include "src/Trajectory.h" #include "src/TrajectoryDBAccessList.h" @@ -16,13 +18,16 @@ #include <boost/shared_ptr.hpp> #include <boost/foreach.hpp> +#include <boost/filesystem.hpp> #include <iostream> #include <vector> #include <ctime> +#include <cmath> using namespace std; using namespace cv; +namespace fs = boost::filesystem; void drawMatchesRelative(const vector<KeyPoint>& train, const vector<KeyPoint>& query, std::vector<cv::DMatch>& matches, Mat& img) { for (int i = 0; i < (int)matches.size(); i++) @@ -54,12 +59,9 @@ feature(_feature), pointNum(_pointNum) {} }; -inline void saveFeatures(vector<FeatureTrajectoryPtr>& features, TrajectoryDBAccess<Point2f>& db, const string& positionsTableName, const string& velocitiesTableName, const unsigned int& minNFeatures = 0) { - /// \todo smoothing - if (features.size() >= minNFeatures) { - BOOST_FOREACH(FeatureTrajectoryPtr f, features) f->write(db, positionsTableName, velocitiesTableName); - features.clear(); - } +inline void saveFeatures(vector<FeatureTrajectoryPtr>& features, TrajectoryDBAccess<Point2f>& db, const string& positionsTableName, const string& velocitiesTableName) { + BOOST_FOREACH(FeatureTrajectoryPtr f, features) f->write(db, positionsTableName, velocitiesTableName); + features.clear(); } void trackFeatures(const KLTFeatureTrackingParameters& params) { @@ -75,34 +77,59 @@ float minTotalFeatureDisplacement = params.nDisplacements*params.minFeatureDisplacement; Size window = Size(params.windowSize, params.windowSize); + int interpolationMethod = -1; + if (params.interpolationMethod == 0) + interpolationMethod = INTER_NEAREST; + else if (params.interpolationMethod == 1) + interpolationMethod = INTER_LINEAR; + else if (params.interpolationMethod == 2) + interpolationMethod = INTER_CUBIC; + else if (params.interpolationMethod == 3) + interpolationMethod = INTER_LANCZOS4; + else + cout << "Unsupported option " << interpolationMethod << " for interpolation method" << endl; + // BruteForceMatcher<Hamming> descMatcher; // vector<DMatch> matches; - VideoCapture capture; - Size videoSize; - unsigned int nFrames = 0; - capture.open(params.videoFilename); - if(capture.isOpened()) { - videoSize = Size(capture.get(CV_CAP_PROP_FRAME_WIDTH), capture.get(CV_CAP_PROP_FRAME_HEIGHT)); - nFrames = capture.get(CV_CAP_PROP_FRAME_COUNT); - cout << "Video " << params.videoFilename << - ": width=" << videoSize.width << - ", height=" << videoSize.height << - ", nframes=" << nFrames << endl; - } else { + boost::shared_ptr<InputFrameProviderIface> capture; + if (fs::is_directory(fs::path(params.videoFilename))) + capture = boost::shared_ptr<InputFrameListModule>(new InputFrameListModule(params.videoFilename)); + else if(!params.videoFilename.empty()) + capture = boost::shared_ptr<InputVideoFileModule>(new InputVideoFileModule(params.videoFilename)); + else + cout << "No valid input parameters" << endl; + + if(!capture->isOpen()) { cout << "Video filename " << params.videoFilename << " could not be opened. Exiting." << endl; exit(0); } - // if (!capture.isOpened()) - // { - // //help(argv); - // cout << "capture device " << argv[1] << " failed to open!" << endl; - // return 1; - // } + + Size videoSize = capture->getSize(); + unsigned int nFrames = capture->getNbFrames(); + cout << "Video " << params.videoFilename << + ": width=" << videoSize.width << + ", height=" << videoSize.height << + ", nframes=" << nFrames << endl; + Mat map1, map2; + if (params.undistort) { + Mat intrinsicCameraMatrix = ::loadMat(params.intrinsicCameraFilename, " "); + Mat newIntrinsicCameraMatrix = intrinsicCameraMatrix.clone(); + videoSize = Size(static_cast<int>(round(videoSize.width*params.undistortedImageMultiplication)), static_cast<int>(round(videoSize.height*params.undistortedImageMultiplication))); + newIntrinsicCameraMatrix.at<float>(0,2) = videoSize.width/2.; + newIntrinsicCameraMatrix.at<float>(1,2) = videoSize.height/2.; + initUndistortRectifyMap(intrinsicCameraMatrix, params.distortionCoefficients, Mat::eye(3,3, CV_32FC1), newIntrinsicCameraMatrix, videoSize, CV_32FC1, map1, map2); + + cout << "Undistorted width=" << videoSize.width << + ", height=" << videoSize.height << endl; + } + Mat mask = imread(params.maskFilename, 0); - if (mask.empty()) + if (mask.empty()) { + cout << "Mask filename " << params.maskFilename << " could not be opened." << endl; mask = Mat::ones(videoSize, CV_8UC1); + } boost::shared_ptr<TrajectoryDBAccess<Point2f> > trajectoryDB = boost::shared_ptr<TrajectoryDBAccess<Point2f> >(new TrajectoryDBAccessList<Point2f>()); //TrajectoryDBAccess<Point2f>* trajectoryDB = new TrajectoryDBAccessBlob<Point2f>(); @@ -120,46 +147,39 @@ std::vector<FeatureTrajectoryPtr> lostFeatures; std::vector<FeaturePointMatch> featurePointMatches; - HOGDescriptor hog; - hog.setSVMDetector(HOGDescriptor::getDefaultPeopleDetector()); - int key = '?'; unsigned int savedFeatureId=0; - Mat frame = Mat::zeros(1, 1, CV_8UC1), currentFrameBW, previousFrameBW; + Mat frame = Mat::zeros(1, 1, CV_8UC1), currentFrameBW, previousFrameBW, undistortedFrame; unsigned int lastFrameNum = nFrames; if (params.nFrames > 0) lastFrameNum = MIN(params.frame1+static_cast<unsigned int>(params.nFrames), nFrames); - - //capture.set(CV_CAP_PROP_POS_FRAMES, params.frame1); + + capture->setFrameNumber(params.frame1); for (unsigned int frameNum = params.frame1; (frameNum < lastFrameNum) && !::interruptionKey(key); frameNum++) { - capture >> frame; - - if (frame.empty() || frame.size() != videoSize) + bool success = capture->getNextFrame(frame); + if (!success || frame.empty()) { + cout << "Empty frame " << frameNum << ", breaking (" << success << " " << frame.empty() << " [" << frame.size().width << "x" << frame.size().height << "])" << endl; break; - - if (frameNum%50 ==0) + } else if (frameNum%50 ==0) cout << "frame " << frameNum << endl; - //capture.get(CV_CAP_PROP_POS_FRAMES) << " " << prevPts.size() << endl; + if (params.undistort) { + remap(frame, undistortedFrame, map1, map2, interpolationMethod, BORDER_CONSTANT, 0.); + frame = undistortedFrame; - // int emptyFrameNum = 0; - // while (frame.empty()) { - // cerr << "empty frame " << emptyFrameNum << " " << capture.get(CV_CAP_PROP_POS_FRAMES)<< endl; - // capture >> frame;//break; - // emptyFrameNum++; - // if (emptyFrameNum>=3000) - // exit(0); - // } + if (frame.size() != videoSize) { + cout << "Different frame size " << frameNum << ", breaking ([" << frame.size().width << "x" << frame.size().height << "])" << endl; + break; + } + } + cvtColor(frame, currentFrameBW, CV_RGB2GRAY); - // "normal" feature detectors: detect features here - // detector.detect(currentFrameBW, currKpts); // see video_homography c++ sample if (!prevPts.empty()) { - //::keyPoints2Points(prevKpts, prevPts); currPts.clear(); - calcOpticalFlowPyrLK(previousFrameBW, currentFrameBW, prevPts, currPts, status, errors, window, params.pyramidLevel, TermCriteria(3 /*static_cast<int>(TermCriteria::COUNT)+static_cast<int>(TermCriteria::EPS)*/, params.maxNumberTrackingIterations, params.minTrackingError), 0.5 /* unused */, 0); // OPTFLOW_USE_INITIAL_FLOW + calcOpticalFlowPyrLK(previousFrameBW, currentFrameBW, prevPts, currPts, status, errors, window, params.pyramidLevel, TermCriteria(static_cast<int>(TermCriteria::COUNT)+static_cast<int>(TermCriteria::EPS) /* = 3 */, params.maxNumberTrackingIterations, params.minTrackingError), /* int flags = */ 0, params.minFeatureEigThreshold); /// \todo try calcOpticalFlowFarneback std::vector<Point2f> trackedPts; @@ -170,7 +190,7 @@ if (status[iter->pointNum]) { iter->feature->addPoint(frameNum, currPts[iter->pointNum], homography); - deleteFeature |= iter->feature->isDisplacementSmall(params.nDisplacements, minTotalFeatureDisplacement) + deleteFeature = iter->feature->isDisplacementSmall(params.nDisplacements, minTotalFeatureDisplacement) || !iter->feature->isMotionSmooth(params.accelerationBound, params.deviationBound); if (deleteFeature) iter->feature->shorten(); @@ -181,6 +201,7 @@ if (iter->feature->length() >= params.minFeatureTime) { iter->feature->setId(savedFeatureId); savedFeatureId++; + iter->feature->movingAverage(params.nFramesSmoothing); lostFeatures.push_back(iter->feature); } iter = featurePointMatches.erase(iter); @@ -203,12 +224,6 @@ // BOOST_FOREACH(Rect r, locations) // rectangle(frame, r.tl(), r.br(), cv::Scalar(0,255,0), 3); } - //drawOpticalFlow(prevPts, currPts, status, frame); - - // cout << matches.size() << " matches" << endl; - // descMatcher.match(currDesc, prevDesc, matches); - // cout << matches.size() << " matches" << endl; - //drawMatchesRelative(prevKpts, currKpts, matches, frame); } // adding new features, using mask around existing feature positions @@ -217,27 +232,33 @@ for (int j=MAX(0, currPts[n].x-params.minFeatureDistanceKLT); j<MIN(videoSize.width, currPts[n].x+params.minFeatureDistanceKLT+1); j++) for (int i=MAX(0, currPts[n].y-params.minFeatureDistanceKLT); i<MIN(videoSize.height, currPts[n].y+params.minFeatureDistanceKLT+1); i++) featureMask.at<uchar>(i,j)=0; - goodFeaturesToTrack(currentFrameBW, newPts, params.maxNFeatures, params.featureQuality, params.minFeatureDistanceKLT, featureMask, params.windowSize, params.useHarrisDetector, params.k); + goodFeaturesToTrack(currentFrameBW, newPts, params.maxNFeatures, params.featureQuality, params.minFeatureDistanceKLT, featureMask, params.blockSize, params.useHarrisDetector, params.k); BOOST_FOREACH(Point2f p, newPts) { //for (unsigned int i=0; i<newPts.size(); i++) { FeatureTrajectoryPtr f = FeatureTrajectoryPtr(new FeatureTrajectory(frameNum, p, homography)); featurePointMatches.push_back(FeaturePointMatch(f, currPts.size())); currPts.push_back(p); } - // currPts.insert(currPts.end(), newPts.begin(), newPts.end()); - //::keyPoints2Points(currKpts, currPts, false); - - //brief.compute(currentFrameBW, currKpts, currDesc); //Compute brief descriptors at each keypoint location if (params.display) { + imshow("mask", featureMask*256); imshow("frame", frame); - imshow("mask", featureMask*256); key = waitKey(2); } previousFrameBW = currentFrameBW.clone(); prevPts = currPts; - //prevKpts = currKpts; - //currDesc.copyTo(prevDesc); - } + } + + // save the remaining currently tracked features + std::vector<FeaturePointMatch>::iterator iter = featurePointMatches.begin(); + while (iter != featurePointMatches.end()) { + if (iter->feature->length() >= params.minFeatureTime) { + iter->feature->setId(savedFeatureId); + savedFeatureId++; + iter->feature->movingAverage(params.nFramesSmoothing); + iter->feature->write(*trajectoryDB, "positions", "velocities"); + } + iter++; + } trajectoryDB->endTransaction(); trajectoryDB->disconnect(); @@ -312,9 +333,13 @@ int lastInstant = frameNum+params.minFeatureTime-maxTrajectoryLength; if (lastInstant > 0 && frameNum%10==0) { featureGraph.connectedComponents(lastInstant); - vector<vector<unsigned int> > featureGroups = featureGraph.getFeatureGroups(); + vector<vector<FeatureTrajectoryPtr> > featureGroups; + featureGraph.getFeatureGroups(featureGroups); for (unsigned int i=0; i<featureGroups.size(); ++i) { - trajectoryDB->writeObject(savedObjectId, featureGroups[i], -1, 1, string("objects"), string("objects_features")); + vector<unsigned int> featureNumbers; + for (unsigned int j=0; j<featureGroups[i].size(); ++j) + featureNumbers.push_back(featureGroups[i][j]->getId()); + trajectoryDB->writeObject(savedObjectId, featureNumbers, 0 /* unknown */, 1, string("objects"), string("objects_features")); savedObjectId++; } } @@ -325,9 +350,13 @@ // save remaining objects featureGraph.connectedComponents(frameNum+maxTrajectoryLength+1); - vector<vector<unsigned int> > featureGroups = featureGraph.getFeatureGroups(); + vector<vector<FeatureTrajectoryPtr> > featureGroups; + featureGraph.getFeatureGroups(featureGroups); for (unsigned int i=0; i<featureGroups.size(); ++i) { - trajectoryDB->writeObject(savedObjectId, featureGroups[i], -1, 1, string("objects"), string("objects_features")); + vector<unsigned int> featureNumbers; + for (unsigned int j=0; j<featureGroups[i].size(); ++j) + featureNumbers.push_back(featureGroups[i][j]->getId()); + trajectoryDB->writeObject(savedObjectId, featureNumbers, 0 /* unknown */, 1, string("objects"), string("objects_features")); savedObjectId++; } @@ -345,6 +374,8 @@ } else if (params.groupFeatures) { cout << "The program groups features" << endl; groupFeatures(params); + } else { + cout << "Main option missing or misspelt" << endl; } return 0;
--- a/c/test_feature.cpp Thu Apr 18 15:29:33 2013 -0400 +++ b/c/test_feature.cpp Fri Dec 05 12:13:53 2014 -0500 @@ -10,6 +10,38 @@ using namespace std; using namespace cv; +TEST_CASE("trajectory/smoothing", "test trajectory smoothing (from trajectory management library)") { + TrajectoryPoint2f t1; + for(int i=0; i<20;++i) + t1.add(i, cv::Point2f(1+i, 1+0.5*i)); + + TrajectoryPoint2f t2(t1); + t2.movingAverage(3); + for(int i=0; i<20;++i) + REQUIRE(t1.getPoint(i) == t2.getPoint(i)); + t1.clear(); + cv::Point2f p0(1,1); + cv::Point2f p1(2,2); + cv::Point2f p2(2.4,3); + cv::Point2f p3(3.1,3.4); + cv::Point2f p4(3.4,4); + cv::Point2f p5(3.6,4.5); + + t1.add(0, p0); + t1.add(1, p1); + t1.add(2, p2); + t1.add(3, p3); + t1.add(4, p4); + t1.add(5, p5); + t1.movingAverage(2); + REQUIRE(t1.getPoint(0) == p0); + REQUIRE(t1.getPoint(1) == (p0+p1+p2)*(1./3.)); + REQUIRE(t1.getPoint(2) == (p0+p1+p2+p3+p4)*(1./5.)); + REQUIRE(t1.getPoint(3) == (p1+p2+p3+p4+p5)*(1./5.)); + REQUIRE(t1.getPoint(4) == (p3+p4+p5)*(1./3.)); + REQUIRE(t1.getPoint(5) == p5); +} + TEST_CASE("features/similarity", "test feature similarity measure") { FeatureTrajectoryPtr ft1 = createFeatureTrajectory(1, 10, 20, Point2f(1,1), Point2f(0, 1)); FeatureTrajectoryPtr ft2 = createFeatureTrajectory(2, 10, 20, Point2f(2,1), Point2f(0, 1));
--- a/c/test_graph.cpp Thu Apr 18 15:29:33 2013 -0400 +++ b/c/test_graph.cpp Fri Dec 05 12:13:53 2014 -0500 @@ -11,10 +11,14 @@ using namespace cv; TEST_CASE("graph/connected_components", "test graph connected components") { - FeatureGraph featureGraph(5, 1, 5 , 1.); // (float _connectionDistance, float _segmentationDistance, unsigned int _minFeatureTime, float _minNFeaturesPerGroup) - unsigned int lastInstant = 20; - FeatureTrajectoryPtr ft1 = createFeatureTrajectory(1, 10, lastInstant, Point2f(1,1), Point2f(0.5, 0.)); - FeatureTrajectoryPtr ft2 = createFeatureTrajectory(2, 10, lastInstant, Point2f(1.1,1), Point2f(0.5, 0.)); + float connectionDistance = 5.; + float segmentationDistance = 1.; + unsigned int minFeatureTime = 5; + float minNFeaturesPerGroup = 0.99; + FeatureGraph featureGraph(connectionDistance, segmentationDistance, minFeatureTime, minNFeaturesPerGroup); + unsigned int firstInstant = 10, lastInstant = 20; + FeatureTrajectoryPtr ft1 = createFeatureTrajectory(1, firstInstant, lastInstant, Point2f(1,1), Point2f(0.5, 0.)); + FeatureTrajectoryPtr ft2 = createFeatureTrajectory(2, firstInstant, lastInstant, Point2f(1.1,1), Point2f(0.5, 0.)); featureGraph.addFeature(ft1); REQUIRE(featureGraph.getNVertices() == 1); @@ -25,13 +29,14 @@ REQUIRE(featureGraph.getNEdges() == 1); featureGraph.connectedComponents(lastInstant); - vector<vector<unsigned int> > components = featureGraph.getFeatureGroups(); + vector<vector<FeatureTrajectoryPtr> > components; + featureGraph.getFeatureGroups(components); REQUIRE(components.size() == 0); REQUIRE(featureGraph.getNVertices() == 2); REQUIRE(featureGraph.getNEdges() == 1); featureGraph.connectedComponents(lastInstant+1); - components = featureGraph.getFeatureGroups(); + featureGraph.getFeatureGroups(components); REQUIRE(components.size() == 1); REQUIRE(components[0].size() == 2); REQUIRE(featureGraph.getNVertices() == 0); @@ -40,16 +45,23 @@ // test connection distance featureGraph.addFeature(ft1); featureGraph.addFeature(ft2); - FeatureTrajectoryPtr ft3 = createFeatureTrajectory(3, 10, lastInstant, Point2f(6.05,1), Point2f(0.5, 0.)); // connected to ft2 only + FeatureTrajectoryPtr ft3 = createFeatureTrajectory(3, firstInstant, lastInstant, Point2f(6.05,1), Point2f(0.5, 0.)); // connected to ft2 only featureGraph.addFeature(ft3); - FeatureTrajectoryPtr ft4 = createFeatureTrajectory(4, 10, lastInstant, Point2f(11.1,1), Point2f(0.5, 0.)); // not connected + FeatureTrajectoryPtr ft4 = createFeatureTrajectory(4, firstInstant, lastInstant, Point2f(11.1,1), Point2f(0.5, 0.)); // not connected featureGraph.addFeature(ft4); + REQUIRE(ft1->minMaxSimilarity(*ft2, firstInstant, lastInstant, connectionDistance, segmentationDistance)); + REQUIRE(ft2->minMaxSimilarity(*ft3, firstInstant, lastInstant, connectionDistance, segmentationDistance)); + REQUIRE_FALSE(ft1->minMaxSimilarity(*ft3, firstInstant, lastInstant, connectionDistance, segmentationDistance)); + REQUIRE_FALSE(ft1->minMaxSimilarity(*ft4, firstInstant, lastInstant, connectionDistance, segmentationDistance)); + REQUIRE_FALSE(ft2->minMaxSimilarity(*ft4, firstInstant, lastInstant, connectionDistance, segmentationDistance)); + REQUIRE_FALSE(ft3->minMaxSimilarity(*ft4, firstInstant, lastInstant, connectionDistance, segmentationDistance)); + REQUIRE(featureGraph.getNVertices() == 4); REQUIRE(featureGraph.getNEdges() == 2); featureGraph.connectedComponents(lastInstant+1); - components = featureGraph.getFeatureGroups(); + featureGraph.getFeatureGroups(components); REQUIRE(components.size() == 2); REQUIRE(components[0].size() == 3); REQUIRE(components[1].size() == 1);
--- a/c/utils.cpp Thu Apr 18 15:29:33 2013 -0400 +++ b/c/utils.cpp Fri Dec 05 12:13:53 2014 -0500 @@ -41,7 +41,7 @@ getline(f, s); } - if (s[0] == ::commentChar) + if (!s.empty() && s[0] == ::commentChar) s.clear(); return s; }
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/include/InputFrameListModule.h Fri Dec 05 12:13:53 2014 -0500 @@ -0,0 +1,30 @@ +#ifndef INPUT_FRAME_LIST_MODULE_H +#define INPUT_FRAME_LIST_MODULE_H + +#include "InputFrameProviderIface.h" + +#include <string> +#include <vector> + +class InputFrameListModule : public InputFrameProviderIface +{ + public: + InputFrameListModule(const std::string& _dirname); + ~InputFrameListModule(); + + bool getNextFrame(cv::Mat&); + unsigned int getNbFrames(); + bool isOpen() const { return mInit;} + void setFrameNumber(const unsigned int& frameNumber); + + virtual const cv::Size& getSize() const { return mSize;} + private: + void loadImageList(void); + std::vector<std::string> filenames; + unsigned int mCurrentIdx; + bool mInit; + std::string dirname; + cv::Size mSize; +}; + +#endif
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/include/InputFrameProviderIface.h Fri Dec 05 12:13:53 2014 -0500 @@ -0,0 +1,19 @@ +#ifndef INPUT_FRAME_PROVIDER_IFACE_H +#define INPUT_FRAME_PROVIDER_IFACE_H + +#include "opencv2/core/core.hpp" +#include <string> + + +class InputFrameProviderIface +{ +public: + virtual ~InputFrameProviderIface(){} + virtual bool getNextFrame(cv::Mat&)=0; + virtual unsigned int getNbFrames() = 0; + virtual bool isOpen() const = 0; + virtual void setFrameNumber(const unsigned int& frameNumber) = 0; + virtual const cv::Size& getSize() const = 0; +}; + +#endif
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/include/InputVideoFileModule.h Fri Dec 05 12:13:53 2014 -0500 @@ -0,0 +1,32 @@ +#ifndef INPUT_VIDEO_FILE_MODULE_H +#define INPUT_VIDEO_FILE_MODULE_H + +#include "InputFrameProviderIface.h" +#include <string> +#include "opencv2/core/core.hpp" +#include "opencv2/highgui/highgui.hpp" + +class InputVideoFileModule : public InputFrameProviderIface +{ +public: + InputVideoFileModule(const std::string& videoPath); + ~InputVideoFileModule(); + + bool getNextFrame(cv::Mat&); + + unsigned int getNbFrames(){ return mNumberOfFrame;} + + bool isOpen() const { return mInit;} + + void setFrameNumber(const unsigned int& frameNumber); + + const cv::Size& getSize() const { return mSize;} + +private: + cv::Size mSize; + cv::VideoCapture mVideoCapture; + bool mInit; + int mNumberOfFrame; +}; + +#endif
--- a/include/Motion.hpp Thu Apr 18 15:29:33 2013 -0400 +++ b/include/Motion.hpp Fri Dec 05 12:13:53 2014 -0500 @@ -5,6 +5,7 @@ #include <boost/shared_ptr.hpp> #include <boost/graph/adjacency_list.hpp> + template<typename T> class TrajectoryDBAccess; template<typename T> class TrajectoryDBAccessList; @@ -13,7 +14,8 @@ /** Class for feature data positions, velocities and other statistics to evaluate their quality before saving. */ -class FeatureTrajectory { +class FeatureTrajectory +{ public: FeatureTrajectory(const unsigned int& frameNum, const cv::Point2f& p, const cv::Mat& homography); @@ -47,13 +49,15 @@ void shorten(void); + void movingAverage(const unsigned int& nFramesSmoothing); + void write(TrajectoryDBAccess<cv::Point2f>& trajectoryDB, const std::string& positionsTableName, const std::string& velocitiesTableName) const; #ifdef USE_OPENCV void draw(cv::Mat& img, const cv::Mat& homography, const cv::Scalar& color) const; #endif - friend std::stringstream& operator<<(std::stringstream& out, const FeatureTrajectory& ft); + friend std::ostream& operator<<(std::ostream& out, const FeatureTrajectory& ft); protected: /// first frame number @@ -63,7 +67,7 @@ TrajectoryPoint2fPtr positions; /** one fewer velocity than position - v_n = p_n+1 - p_n*/ + v_n = p_n - p_n-1*/ TrajectoryPoint2fPtr velocities; /// norms of velocities for feature constraints, one fewer positions than positions @@ -75,10 +79,12 @@ typedef boost::shared_ptr<FeatureTrajectory> FeatureTrajectoryPtr; // inlined -inline std::stringstream& operator<<(std::stringstream& out, const FeatureTrajectory& ft) { +inline std::ostream& operator<<(std::ostream& out, const FeatureTrajectory& ft) +{ out << *(ft.positions); out << "\n"; out << *(ft.velocities); + return out; } @@ -116,7 +122,7 @@ /** Performs some checks on groups of features and return their lists of ids if correct Removes the vertices from the graph */ - std::vector<std::vector<unsigned int> > getFeatureGroups(void); + void getFeatureGroups(std::vector<std::vector<FeatureTrajectoryPtr> >& featureGroups); std::string informationString(void) const;
--- a/include/Parameters.hpp Thu Apr 18 15:29:33 2013 -0400 +++ b/include/Parameters.hpp Fri Dec 05 12:13:53 2014 -0500 @@ -4,6 +4,7 @@ /// \todo Class for parameters, with utilities to save and load from configuration files #include <string> +#include <vector> namespace boost{ namespace program_options { @@ -19,7 +20,12 @@ std::string videoFilename; std::string databaseFilename; std::string homographyFilename; + std::string intrinsicCameraFilename; + std::vector<float> distortionCoefficients; + float undistortedImageMultiplication; + int interpolationMethod; std::string maskFilename; + bool undistort; bool loadFeatures; bool display; float videoFPS; @@ -27,27 +33,62 @@ unsigned int frame1; int nFrames; // feature tracking + /// "Maximum number of corners to return" (OpenCV goodFeaturesToTrack) (should be large enough not to limit the potential number of features) int maxNFeatures; + /// "Parameter characterizing the minimal accepted quality of image corners" (OpenCV goodFeaturesToTrack ) float featureQuality; + /// "Minimum possible Euclidean distance between the returned corners" (OpenCV goodFeaturesToTrack) float minFeatureDistanceKLT; - int windowSize; + /// "Size of an average block for computing a derivative covariation matrix over each pixel neighborhood" (OpenCV goodFeaturesToTrack) + int blockSize; + /// "Parameter indicating whether to use a Harris detector" (OpenCV goodFeaturesToTrack) bool useHarrisDetector; + /// "Free parameter of the Harris detector" (OpenCV goodFeaturesToTrack) float k; + /// "size of the search window at each pyramid level" (OpenCV calcOpticalFlowPyrLK) + int windowSize; + /// "0-based maximal pyramid level number" (OpenCV calcOpticalFlowPyrLK) higher is higher quality int pyramidLevel; + /// Number of displacements (number of frames-1) over which minimum motion is computed unsigned int nDisplacements; + /// Minimum displacement per frame (in world space) to keep features float minFeatureDisplacement; + /// Maximum feature acceleration float accelerationBound; + /// Maximum feature deviation float deviationBound; + /// Number of frames to smooth positions (half window) int nFramesSmoothing; //int nFramesVelocity; + /// Maximum number of iterations to stop optical flow (OpenCV calcOpticalFlowPyrLK) int maxNumberTrackingIterations; + /// Minimum error to reach to stop optical flow (OpenCV calcOpticalFlowPyrLK) float minTrackingError; + /// Minimum eigen value of a 2x2 normal matrix of optical flow equations (OpenCV calcOpticalFlowPyrLK) + float minFeatureEigThreshold; + /// Minimum length of a feature (number of frames) to consider a feature for grouping unsigned int minFeatureTime; + /// Connection distance in feature grouping (in world space) float mmConnectionDistance; + /// Segmentation distance in feature grouping (in world space) float mmSegmentationDistance; + /// Maximum distance between features for grouping (in world space) (unused) float maxDistance; + /// Minimum cosine of the angle between the velocity vectors for grouping (unused) float minVelocityCosine; + /// Minimum average number of features per frame to create a vehicle hypothesis float minNFeaturesPerGroup; + // safety analysis + float maxPredictedSpeed; + float predictionTimeHorizon; + float collisionDistance; + bool crossingZones; + std::string predictionMethod; + int nPredictedTrajectories; + float minAcceleration; + float maxAcceleration; + float maxSteering; + bool useFeaturesForPrediction; std::string parameterDescription;
--- a/include/cvutils.hpp Thu Apr 18 15:29:33 2013 -0400 +++ b/include/cvutils.hpp Fri Dec 05 12:13:53 2014 -0500 @@ -17,20 +17,20 @@ /** Loads a cv mat from a text file where the numbers are saved line by line separated by separator */ cv::Mat loadMat(const std::string& filename, const std::string& separator); -template<typename T> -float scalarProduct(const cv::Point_<T>& v1, const cv::Point_<T>& v2) { return v1.x*v2.x+v1.y*v2.y;} +//template<typename T> +//float scalarProduct(const cv::Point_<T>& v1, const cv::Point_<T>& v2) { return v1.x*v2.x+v1.y*v2.y;} void keyPoints2Points(const std::vector<cv::KeyPoint>& kpts, std::vector<cv::Point2f>& pts, const bool& clearPts = true); /** Allocates a new IplImage. */ -IplImage* allocateImage(const int& width, const int& height, const int& depth, const int& channels); +// IplImage* allocateImage(const int& width, const int& height, const int& depth, const int& channels); -IplImage* allocateImage(const CvSize& size, const int& depth, const int& channels); +// IplImage* allocateImage(const CvSize& size, const int& depth, const int& channels); /** Goes to the target frame number, by querying frame, supposing the video input is currently at current frame number. Returns the frame number that was reached.*/ -int goToFrameNum(CvCapture* inputVideo, const int& currentFrameNum, const int& targetFrameNum); +// int goToFrameNum(CvCapture* inputVideo, const int& currentFrameNum, const int& targetFrameNum); /// Pre-defined colors class Colors {
--- a/python/calibration-translation.py Thu Apr 18 15:29:33 2013 -0400 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,116 +0,0 @@ -#!/usr/bin/env python - -import sys -import os - -import matplotlib.mlab as pylab -import matplotlib.pyplot as plt -import numpy as np - -import cv2 -import utils -import cvutils - -# development for the data collected and stabilized by Paul in Summer 2011 -# todo write help, add options to control the parameters for matching (n points and distance) - -options = utils.parseCLIOptions('Program to re-calibrate an initial calibration based on point correspondences by adjusting the points to slightly different viewpoints, where all the points are still visible\n\nUsage: ', ['ref_video=', 'ref_points='], sys.argv, ['mask_img=']) - -referenceVideoFilename=options['--ref_video'] -wldPts, imgPts = cvutils.loadPointCorrespondences(options['--ref_points']) - -def translatePoints(points, t): - 'points is Nx2, t is [x,y]' - translated = points.copy() - for i in xrange(2): - translated[i] += t[i] - return translated - -filenames = [f for f in utils.listfiles('.','avi')] # directory to examine should be current directory - -referenceVideoIndex = filenames.index(referenceVideoFilename) -indices = set(range(len(filenames))) -indices.discard(referenceVideoIndex) - -images = {} -captures = {} - -captures[referenceVideoFilename] = cv2.VideoCapture(referenceVideoFilename) -(ret, img) = captures[referenceVideoFilename].read() -images[referenceVideoFilename] = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY) - -# load a mask image to compute the translation -if '--mask_img' in options.keys(): - maskImg = cv2.imread('mask.png', cv2.CV_LOAD_IMAGE_GRAYSCALE) # todo add possibility to look in the whole image if not providing mask -else: - maskImg = np.ones(images[referenceVideoFilename].shape, dtype=np.uint8) - -referenceFeatures = cv2.goodFeaturesToTrack(images[referenceVideoFilename], 1000, 0.02, 2, useHarrisDetector = True, mask=maskImg) -displayRef = cv2.cvtColor(images[referenceVideoFilename], cv2.COLOR_GRAY2RGB) -for j,p in enumerate(imgPts): - cv2.circle(displayRef, tuple(p), 3, (255,0,0)) - cv2.putText(displayRef, str(j+1), tuple(p), cv2.FONT_HERSHEY_PLAIN, 1, (255,0,0)) -cv2.imshow('Reference',displayRef) - -# get suitable image references for each video -for f in filenames: - captures[f] = cv2.VideoCapture(f) - frameFilename = utils.removeExtension(f)+'-frame.png' # TODO if frame image already exists, no need to search for it again - if not os.path.exists(frameFilename): - key = -1 - while chr(key&255) != 'y': - (ret, img) = captures[f].read() - cv2.imshow('Image',img) - print('Can one see the reference points in the image? (y/n)') - key = cv2.waitKey(0) - - images[f] = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY) - cv2.imwrite(frameFilename, img) - else: - images[f] = cv2.imread(frameFilename, cv2.CV_LOAD_IMAGE_GRAYSCALE) - #features[f] = cv2.goodFeaturesToTrack(images[f], 1000, 0.02, 2, useHarrisDetector = True, mask=maskImg) # todo put parameters on the command line ? - # goodFeaturesToTrack(image, maxCorners, qualityLevel, minDistance[, corners[, mask[, blockSize[, useHarrisDetector[, k]]]]]) - # display features - # if False: - # display = img.copy()#cv2.cvtColor(images[f], cv2.COLOR_GRAY2RGB) #.copy() - # for p in features[f]: - # cv2.circle(display, tuple(p[0]), 3, (255,0,0)) - # cv2.imshow('Reference',display) - # cv2.waitKey() - -plt.close('all') - -# validate or input point correspondences and compute homography -for i in indices: - t = cvutils.computeTranslation(images[filenames[referenceVideoIndex]], images[filenames[i]], referenceFeatures, 100, 10) - print filenames[i],t - key = -1 - if t != None: # show translated points and ask if ok - displayImg = cv2.cvtColor(images[filenames[i]], cv2.COLOR_GRAY2RGB) #.copy() - for p in imgPts: - cv2.circle(displayImg, tuple(p+t[0]), 3, (255,0,0)) - cv2.imshow('Image',displayImg) - - while not(chr(key&255) == 'y' or chr(key&255) == 'n'): - print('Are the translated points rightly located (y/n)?') - key = cv2.waitKey(0) - if chr(key&255) == 'y': # compute homography with translated numbers - newImgPts = np.array([p+t[0] for p in imgPts]) - else: - print('No translation could be found automatically. You will have to manually input world reference points.') - - if t==None or chr(key&255) != 'y':# if no translation could computed or it is not satisfactory - print('Select the corresponding points in the same order as in the reference image') - plt.figure(1) - plt.imshow(displayRef) - plt.figure(2) - plt.imshow(img) - plt.show() - newImgPts = np.array([list(p) for p in plt.ginput(n=wldPts.shape[0], timeout=-1)], dtype = np.float32) - - homography, mask = cv2.findHomography(newImgPts, wldPts) # method=0, ransacReprojThreshold=3 - print homography - np.savetxt(utils.removeExtension(filenames[i])+'-homography.txt',homography) - np.savetxt(utils.removeExtension(filenames[i])+'-point-correspondences.txt', np.append(wldPts.T, newImgPts.T, axis=0)) - -cv2.destroyAllWindows()
--- a/python/compute-homography.py Thu Apr 18 15:29:33 2013 -0400 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,122 +0,0 @@ -#! /usr/bin/env python - -import sys,getopt - -import matplotlib.pyplot as plt -import numpy as np -import cv2 - -import cvutils -import utils - -options, args = getopt.getopt(sys.argv[1:], 'hp:i:w:n:u:',['help']) -options = dict(options) - -# TODO process camera intrinsic and extrinsic parameters to obtain image to world homography, taking example from Work/src/python/generate-homography.py script -# cameraMat = load(videoFilenamePrefix+'-camera.txt'); -# T1 = cameraMat[3:6,:].copy(); -# A = cameraMat[0:3,0:3].copy(); - -# # pay attention, rotation may be the transpose -# # R = T1[:,0:3].T; -# R = T1[:,0:3]; -# rT = dot(R, T1[:,3]/1000); -# T = zeros((3,4),'f'); -# T[:,0:3] = R[:]; -# T[:,3] = rT; - -# AT = dot(A,T); - -# nPoints = 4; -# worldPoints = cvCreateMat(nPoints, 3, CV_64FC1); -# imagePoints = cvCreateMat(nPoints, 3, CV_64FC1); - -# # extract homography from the camera calibration -# worldPoints = cvCreateMat(4, 3, CV_64FC1); -# imagePoints = cvCreateMat(4, 3, CV_64FC1); - -# worldPoints[0,:] = [[1, 1, 0]]; -# worldPoints[1,:] = [[1, 2, 0]]; -# worldPoints[2,:] = [[2, 1, 0]]; -# worldPoints[3,:] = [[2, 2, 0]]; - -# wPoints = [[1,1,2,2], -# [1,2,1,2], -# [0,0,0,0]]; -# iPoints = utils.worldToImage(AT, wPoints); - -# for i in range(nPoints): -# imagePoints[i,:] = [iPoints[:,i].tolist()]; - -# H = cvCreateMat(3, 3, CV_64FC1); - -# cvFindHomography(imagePoints, worldPoints, H); - -if '--help' in options.keys() or '-h' in options.keys() or len(options) == 0: - print('Usage: {0} --help|-h [-p point-correspondences.txt] [ -i video-frame] [ -w world-frame] [n number-points] [-u unit-per-pixel=1]'.format(sys.argv[0])) - print('''The input data can be provided either as point correspondences already saved - in a text file or inputed by clicking a certain number of points (>=4) - in a video frame and a world image. - -The point correspondence file contains at least 4 non-colinear point coordinates -with the following format: - - the first two lines are the x and y coordinates in the projected space (usually world space) - - the last two lines are the x and y coordinates in the origin space (usually image space) - -If providing video and world images, with a number of points to input -and a ration to convert pixels to world distance unit (eg meters per pixel), -the images will be shown in turn and the user should click -in the same order the corresponding points in world and image spaces. ''') - sys.exit() - -homography = np.array([]) -if '-p' in options.keys(): - worldPts, videoPts = cvutils.loadPointCorrespondences(options['-p']) - homography, mask = cv2.findHomography(videoPts, worldPts) # method=0, ransacReprojThreshold=3 -elif '-i' in options.keys() and '-w' in options.keys(): - nPoints = 4 - if '-n' in options.keys(): - nPoints = int(options['-n']) - unitsPerPixel = 1 - if '-u' in options.keys(): - unitsPerPixel = float(options['-u']) - worldImg = plt.imread(options['-w']) - videoImg = plt.imread(options['-i']) - print('Click on {0} points in the video frame'.format(nPoints)) - plt.figure() - plt.imshow(videoImg) - videoPts = np.array(plt.ginput(nPoints)) - print('Click on {0} points in the world image'.format(nPoints)) - plt.figure() - plt.imshow(worldImg) - worldPts = unitsPerPixel*np.array(plt.ginput(nPoints)) - plt.close('all') - homography, mask = cv2.findHomography(videoPts, worldPts) - # save the points in file - f = open('point-correspondences.txt', 'a') - np.savetxt(f, worldPts.T) - np.savetxt(f, videoPts.T) - f.close() - -if homography.size>0: - np.savetxt('homography.txt',homography) - -if '-i' in options.keys() and homography.size>0: - videoImg = cv2.imread(options['-i']) - worldImg = cv2.imread(options['-w']) - invHomography = np.linalg.inv(homography) - projectedWorldPts = cvutils.projectArray(invHomography, worldPts.T).T - if '-u' in options.keys(): - unitsPerPixel = float(options['-u']) - projectedVideoPts = cvutils.projectArray(invHomography, videoPts.T).T - for i in range(worldPts.shape[0]): - cv2.circle(videoImg,tuple(np.int32(np.round(videoPts[i]))),2,cvutils.cvRed) - cv2.circle(videoImg,tuple(np.int32(np.round(projectedWorldPts[i]))),2,cvutils.cvBlue) - if '-u' in options.keys(): - cv2.circle(worldImg,tuple(np.int32(np.round(worldPts[i]/unitsPerPixel))),2,cvutils.cvRed) - cv2.circle(worldImg,tuple(np.int32(np.round(projectedVideoPts[i]/unitsPerPixel))),2,cvutils.cvRed) - #print('img: {0} / projected: {1}'.format(videoPts[i], p)) - cv2.imshow('video frame',videoImg) - if '-u' in options.keys(): - cv2.imshow('world image',worldImg) - cv2.waitKey()
--- a/python/compute-object-from-features.py Thu Apr 18 15:29:33 2013 -0400 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,141 +0,0 @@ -#!/usr/bin/env python - -import sys - -import matplotlib.mlab as pylab -import matplotlib.pyplot as plt -import numpy as np - -import cv -import utils -import cvutils -import ubc_utils -import moving - -# use something like getopt to manage arguments if necessary - -if len(sys.argv) < 3: - print('Usage: {0} <video-filename> <n-objects>'.format(sys.argv[0])) - sys.exit() - -if sys.argv[1].endswith('.avi'): - videoFilenamePrefix = utils.removeExtension(sys.argv[1],'.') -else: - videoFilenamePrefix = sys.argv[1] - -objectNum = int(sys.argv[2]) - -objects = ubc_utils.loadTrajectories(videoFilenamePrefix+'-objects.txt', objectNum+1) -obj = objects[objectNum] -features = ubc_utils.loadTrajectories(videoFilenamePrefix+'-features.txt', max(obj.featureNumbers)+1) -h = np.loadtxt(videoFilenamePrefix+'-homography.txt') - -invh = cvutils.invertHomography(h) - -def computeGroundTrajectory(features, homography, timeInterval = None): - '''Computes a trajectory for the set of features as the closes point to the ground - using the homography in image space''' - if not timeInterval: - raise Exception('not implemented') # compute from the features - - yCoordinates = -np.ones((len(features),int(timeInterval.length()))) - for i,f in enumerate(features): - traj = f.getPositions().asArray() - imgTraj = cvutils.projectArray(homography, traj) - yCoordinates[i,f.getFirstInstant()-timeInterval.first:f.getLastInstant()+1-timeInterval.first] = imgTraj[1,:] - - indices = np.argmax(yCoordinates,0) - newTraj = moving.Trajectory() - for j,idx in enumerate(indices): - newTraj.addPosition(features[idx].getPositionAtInstant(j+timeInterval.first)) - #newVelocities.addPosition(features[obj.featureNumbers[idx]].getVelocityAtInstant(j+obj.getFirstInstant())) - - return newTraj - -def computeMedianTrajectory(features, timeInterval = None): - if not timeInterval: - raise Exception('not implemented') # compute from the features - - newTraj = moving.Trajectory() - for t in timeInterval: - points = [] - for f in features: - if f.existsAtInstant(t): - points.append(f.getPositionAtInstant(t).aslist()) - med = np.median(np.array(points), 0) - newTraj.addPositionXY(med[0], med[1]) - - return newTraj - -# TODO version median: conversion to large matrix will not work, have to do it frame by frame - -def kalmanFilter(positions, velocities, processNoiseCov, measurementNoiseCov): - kalman=cv.CreateKalman(6, 4) - kalman.transition_matrix[0,2]=1 - kalman.transition_matrix[0,4]=1./2 - kalman.transition_matrix[1,3]=1 - kalman.transition_matrix[1,5]=1./2 - kalman.transition_matrix[2,4]=1 - kalman.transition_matrix[3,5]=1 - - cv.SetIdentity(kalman.measurement_matrix, 1.) - cv.SetIdentity(kalman.process_noise_cov, processNoiseCov) - cv.SetIdentity(kalman.measurement_noise_cov, measurementNoiseCov) - cv.SetIdentity(kalman.error_cov_post, 1.) - - p = positions[0] - v = velocities[0] - v2 = velocities[2] - a = (v2-v).multiply(0.5) - kalman.state_post[0,0]=p.x - kalman.state_post[1,0]=p.y - kalman.state_post[2,0]=v.x - kalman.state_post[3,0]=v.y - kalman.state_post[4,0]=a.x - kalman.state_post[5,0]=a.y - - filteredPositions = moving.Trajectory() - filteredVelocities = moving.Trajectory() - measurement = cv.CreateMat(4,1,cv.CV_32FC1) - for i in xrange(positions.length()): - cv.KalmanPredict(kalman) # no control - p = positions[i] - v = velocities[i] - measurement[0,0] = p.x - measurement[1,0] = p.y - measurement[2,0] = v.x - measurement[3,0] = v.y - cv.KalmanCorrect(kalman, measurement) - filteredPositions.addPositionXY(kalman.state_post[0,0], kalman.state_post[1,0]) - filteredVelocities.addPositionXY(kalman.state_post[2,0], kalman.state_post[3,0]) - - return (filteredPositions, filteredVelocities) - -groundTrajectory = computeGroundTrajectory([features[i] for i in obj.featureNumbers], invh, obj.getTimeInterval()) -(filteredPositions, filteredVelocities) = kalmanFilter(groundTrajectory, obj.getVelocities(), 0.1, 0.1) - -#medianTrajectory = computeMedianTrajectory([features[i] for i in obj.featureNumbers], obj.getTimeInterval()) - -delta = [] -for t in obj.getTimeInterval(): - p1 = obj.getPositionAtInstant(t) - p2 = groundTrajectory[t-obj.getFirstInstant()] - delta.append((p1-p2).aslist()) - -delta = np.median(delta, 0) - -translated = moving.Trajectory() -for t in obj.getTimeInterval(): - p1 = obj.getPositionAtInstant(t) - p1.x -= delta[0] - p1.y -= delta[1] - translated.addPosition(p1) - -plt.clf() -obj.draw('rx-') -for fnum in obj.featureNumbers: features[fnum].draw() -groundTrajectory.draw('bx-') -filteredPositions.draw('gx-') -translated.draw('kx-') -#medianTrajectory.draw('kx-') -plt.axis('equal')
--- a/python/cvutils.py Thu Apr 18 15:29:33 2013 -0400 +++ b/python/cvutils.py Fri Dec 05 12:13:53 2014 -0500 @@ -1,13 +1,19 @@ #! /usr/bin/env python '''Image/Video utilities''' -import Image, ImageDraw # PIL try: import cv2 - opencvExists = True + opencvAvailable = True except ImportError: - print('OpenCV library could not be loaded') - opencvExists = False + print('OpenCV library could not be loaded (video replay functions will not be available)') # TODO change to logging module + opencvAvailable = False +try: + import skimage + skimageAvailable = True +except ImportError: + print('Scikit-image library could not be loaded (HoG-based classification methods will not be available)') + skimageAvailable = False + from sys import stdout import utils @@ -30,8 +36,9 @@ def saveKey(key): return chr(key&255) == 's' -def drawLines(filename, origins, destinations, w = 1, resultFilename='image.png'): +def plotLines(filename, origins, destinations, w = 1, resultFilename='image.png'): '''Draws lines over the image ''' + import Image, ImageDraw # PIL img = Image.open(filename) @@ -41,7 +48,7 @@ for p1, p2 in zip(origins, destinations): draw.line([p1.x, p1.y, p2.x, p2.y], width = w, fill = (256,0,0)) #draw.line([p1.x, p1.y, p2.x, p2.y], pen) - del draw + del draw #out = utils.openCheck(resultFilename) img.save(resultFilename) @@ -63,6 +70,7 @@ def cvMatToArray(cvmat): '''Converts an OpenCV CvMat to numpy array.''' + print('Deprecated, use new interface') from numpy.core.multiarray import zeros a = zeros((cvmat.rows, cvmat.cols))#array([[0.0]*cvmat.width]*cvmat.height) for i in xrange(cvmat.rows): @@ -70,32 +78,56 @@ a[i,j] = cvmat[i,j] return a -if opencvExists: - def computeHomography(srcPoints, dstPoints, method=0, ransacReprojThreshold=0.0): +if opencvAvailable: + def computeHomography(srcPoints, dstPoints, method=0, ransacReprojThreshold=3.0): '''Returns the homography matrix mapping from srcPoints to dstPoints (dimension Nx2)''' H, mask = cv2.findHomography(srcPoints, dstPoints, method, ransacReprojThreshold) return H - def arrayToCvMat(a, t = cv2.cv.CV_64FC1): + def arrayToCvMat(a, t = cv2.CV_64FC1): '''Converts a numpy array to an OpenCV CvMat, with default type CV_64FC1.''' + print('Deprecated, use new interface') cvmat = cv2.cv.CreateMat(a.shape[0], a.shape[1], t) for i in range(cvmat.rows): for j in range(cvmat.cols): cvmat[i,j] = a[i,j] return cvmat - def draw(img, positions, color, lastCoordinate = None): + def cvPlot(img, positions, color, lastCoordinate = None): last = lastCoordinate+1 if lastCoordinate != None and lastCoordinate >=0: last = min(positions.length()-1, lastCoordinate) for i in range(0, last-1): cv2.line(img, positions[i].asint().astuple(), positions[i+1].asint().astuple(), color) - def playVideo(filename, firstFrameNum = 0, frameRate = -1): + def cvImshow(windowName, img, rescale = 1.0): + 'Rescales the image (in particular if too large)' + from cv2 import resize + if rescale != 1.: + size = (int(round(img.shape[1]*rescale)), int(round(img.shape[0]*rescale))) + resizedImg = resize(img, size) + cv2.imshow(windowName, resizedImg) + else: + cv2.imshow(windowName, img) + + def computeUndistortMaps(width, height, undistortedImageMultiplication, intrinsicCameraMatrix, distortionCoefficients): + from copy import deepcopy + from numpy import identity, array + newImgSize = (int(round(width*undistortedImageMultiplication)), int(round(height*undistortedImageMultiplication))) + newCameraMatrix = deepcopy(intrinsicCameraMatrix) + newCameraMatrix[0,2] = newImgSize[0]/2. + newCameraMatrix[1,2] = newImgSize[1]/2. + return cv2.initUndistortRectifyMap(intrinsicCameraMatrix, array(distortionCoefficients), identity(3), newCameraMatrix, newImgSize, cv2.CV_32FC1) + + def playVideo(filename, firstFrameNum = 0, frameRate = -1, interactive = False, printFrames = True, text = None, rescale = 1.): '''Plays the video''' + windowName = 'frame' + cv2.namedWindow(windowName, cv2.WINDOW_NORMAL) wait = 5 if frameRate > 0: wait = int(round(1000./frameRate)) + if interactive: + wait = 0 capture = cv2.VideoCapture(filename) if capture.isOpened(): key = -1 @@ -105,49 +137,114 @@ while ret and not quitKey(key): ret, img = capture.read() if ret: - print('frame {0}'.format(frameNum)) + if printFrames: + print('frame {0}'.format(frameNum)) frameNum+=1 - cv2.imshow('frame', img) + if text != None: + cv2.putText(img, text, (10,50), cv2.cv.CV_FONT_HERSHEY_PLAIN, 1, cvRed) + cvImshow(windowName, img, rescale) key = cv2.waitKey(wait) + cv2.destroyAllWindows() + else: + print('Video capture for {} failed'.format(filename)) - def getImagesFromVideo(filename, nImages = 1, saveImage = False): - '''Returns nImages images from the video sequence''' + def getImagesFromVideo(videoFilename, firstFrameNum = 0, nFrames = 1, saveImage = False, outputPrefix = 'image'): + '''Returns nFrames images from the video sequence''' + from math import floor, log10 images = [] - capture = cv2.VideoCapture(filename) - if capture.isOpened(): + capture = cv2.VideoCapture(videoFilename) + if capture.isOpened(): + nDigits = int(floor(log10(capture.get(cv2.cv.CV_CAP_PROP_FRAME_COUNT))))+1 ret = False - numImg = 0 - while numImg<nImages: + capture.set(cv2.cv.CV_CAP_PROP_POS_FRAMES, firstFrameNum) + imgNum = 0 + while imgNum<nFrames: ret, img = capture.read() i = 0 while not ret and i<10: ret, img = capture.read() i += 1 if img.size>0: - numImg +=1 if saveImage: - cv2.imwrite('image{0:04d}.png'.format(numImg), img) + imgNumStr = format(firstFrameNum+imgNum, '0{}d'.format(nDigits)) + cv2.imwrite(outputPrefix+imgNumStr+'.png', img) else: images.append(img) + imgNum +=1 + capture.release() + else: + print('Video capture for {} failed'.format(videoFilename)) return images + + def getFPS(videoFilename): + capture = cv2.VideoCapture(videoFilename) + if capture.isOpened(): + fps = capture.get(cv2.cv.CV_CAP_PROP_FPS) + capture.release() + return fps + else: + print('Video capture for {} failed'.format(videoFilename)) + return None - def displayTrajectories(videoFilename, objects, homography = None, firstFrameNum = 0, lastFrameNumArg = None): + def imageBox(img, obj, frameNum, homography, width, height, px = 0.2, py = 0.2, pixelThreshold = 800): + 'Computes the bounding box of object at frameNum' + x = [] + y = [] + for f in obj.features: + if f.existsAtInstant(frameNum): + projectedPosition = f.getPositionAtInstant(frameNum).project(homography) + x.append(projectedPosition.x) + y.append(projectedPosition.y) + xmin = min(x) + xmax = max(x) + ymin = min(y) + ymax = max(y) + xMm = px * (xmax - xmin) + yMm = py * (ymax - ymin) + a = max(ymax - ymin + (2 * yMm), xmax - (xmin + 2 * xMm)) + yCropMin = int(max(0, .5 * (ymin + ymax - a))) + yCropMax = int(min(height - 1, .5 * (ymin + ymax + a))) + xCropMin = int(max(0, .5 * (xmin + xmax - a))) + xCropMax = int(min(width - 1, .5 * (xmin + xmax + a))) + if yCropMax != yCropMin and xCropMax != xCropMin and (yCropMax - yCropMin) * (xCropMax - xCropMin) > pixelThreshold: + croppedImg = img[yCropMin : yCropMax, xCropMin : xCropMax] + else: + croppedImg = [] + return croppedImg, yCropMin, yCropMax, xCropMin, xCropMax + + + def displayTrajectories(videoFilename, objects, boundingBoxes = {}, homography = None, firstFrameNum = 0, lastFrameNumArg = None, printFrames = True, rescale = 1., nFramesStep = 1, saveAllImages = False, undistort = False, intrinsicCameraMatrix = None, distortionCoefficients = None, undistortedImageMultiplication = 1.): '''Displays the objects overlaid frame by frame over the video ''' + from moving import userTypeNames + from math import ceil, log10 + capture = cv2.VideoCapture(videoFilename) + width = int(capture.get(cv2.cv.CV_CAP_PROP_FRAME_WIDTH)) + height = int(capture.get(cv2.cv.CV_CAP_PROP_FRAME_HEIGHT)) + + windowName = 'frame' + #cv2.namedWindow(windowName, cv2.WINDOW_NORMAL) + + if undistort: # setup undistortion + [map1, map2] = computeUndistortMaps(width, height, undistortedImageMultiplication, intrinsicCameraMatrix, distortionCoefficients) if capture.isOpened(): key = -1 ret = True frameNum = firstFrameNum capture.set(cv2.cv.CV_CAP_PROP_POS_FRAMES, firstFrameNum) - if not lastFrameNumArg: + if lastFrameNumArg == None: from sys import maxint lastFrameNum = maxint else: lastFrameNum = lastFrameNumArg + nZerosFilename = int(ceil(log10(lastFrameNum))) while ret and not quitKey(key) and frameNum < lastFrameNum: ret, img = capture.read() if ret: - print('frame {0}'.format(frameNum)) + if undistort: + img = cv2.remap(img, map1, map2, interpolation=cv2.INTER_LINEAR) + if printFrames: + print('frame {0}'.format(frameNum)) for obj in objects: if obj.existsAtInstant(frameNum): if not hasattr(obj, 'projectedPositions'): @@ -155,25 +252,168 @@ obj.projectedPositions = obj.positions.project(homography) else: obj.projectedPositions = obj.positions - draw(img, obj.projectedPositions, cvRed, frameNum-obj.getFirstInstant()) - cv2.putText(img, '{0}'.format(obj.num), obj.projectedPositions[frameNum-obj.getFirstInstant()].asint().astuple(), cv2.FONT_HERSHEY_PLAIN, 1, cvRed) - cv2.imshow('frame', img) - key = cv2.waitKey() - if saveKey(key): - cv2.imwrite('image.png', img) - frameNum += 1 - + cvPlot(img, obj.projectedPositions, cvRed, frameNum-obj.getFirstInstant()) + if frameNum in boundingBoxes.keys(): + for rect in boundingBoxes[frameNum]: + cv2.rectangle(img, rect[0].asint().astuple(), rect[1].asint().astuple(), cvRed) + elif len(obj.features) != 0: + imgcrop, yCropMin, yCropMax, xCropMin, xCropMax = imageBox(img, obj, frameNum, homography, width, height) + cv2.rectangle(img, (xCropMin, yCropMin), (xCropMax, yCropMax), cvBlue, 1) + objDescription = '{} '.format(obj.num) + if userTypeNames[obj.userType] != 'unknown': + objDescription += userTypeNames[obj.userType][0].upper() + cv2.putText(img, objDescription, obj.projectedPositions[frameNum-obj.getFirstInstant()].asint().astuple(), cv2.cv.CV_FONT_HERSHEY_PLAIN, 1, cvRed) + if not saveAllImages: + cvImshow(windowName, img, rescale) + key = cv2.waitKey() + if saveAllImages or saveKey(key): + cv2.imwrite('image-{{:0{}}}.png'.format(nZerosFilename).format(frameNum), img) + frameNum += nFramesStep + if nFramesStep > 1: + capture.set(cv2.cv.CV_CAP_PROP_POS_FRAMES, frameNum) + cv2.destroyAllWindows() + else: + print 'Cannot load file ' + videoFilename + + def computeHomographyFromPDTV(cameraFilename, method=0, ransacReprojThreshold=3.0): + '''Returns the homography matrix at ground level from PDTV format + https://bitbucket.org/hakanardo/pdtv''' + import pdtv + from numpy import array + camera = pdtv.load(cameraFilename) + srcPoints = [[x,y] for x, y in zip([1.,2.,2.,1.],[1.,1.,2.,2.])] # need floats!! + dstPoints = [] + for srcPoint in srcPoints: + projected = camera.image_to_world(tuple(srcPoint)) + dstPoints.append([projected[0], projected[1]]) + H, mask = cv2.findHomography(array(srcPoints), array(dstPoints), method, ransacReprojThreshold) + return H + + def undistortedCoordinates(map1, map2, x, y, maxDistance = 1.): + '''Returns the coordinates of a point in undistorted image + map1 and map2 are the mapping functions from undistorted image + to distorted (original image) + map1(x,y) = originalx, originaly''' + from numpy import abs, logical_and, unravel_index, dot, sum + from matplotlib.mlab import find + distx = abs(map1-x) + disty = abs(map2-y) + indices = logical_and(distx<maxDistance, disty<maxDistance) + closeCoordinates = unravel_index(find(indices), distx.shape) # returns i,j, ie y,x + xWeights = 1-distx[indices] + yWeights = 1-disty[indices] + return dot(xWeights, closeCoordinates[1])/sum(xWeights), dot(yWeights, closeCoordinates[0])/sum(yWeights) + + def undistortTrajectoryFromCVMapping(map1, map2, t): + '''test 'perfect' inversion''' + from moving import Trajectory + from numpy import isnan + undistortedTrajectory = Trajectory() + for i,p in enumerate(t): + res = undistortedCoordinates(map1, map2, p.x,p.y) + if not isnan(res).any(): + undistortedTrajectory.addPositionXY(res[0], res[1]) + else: + print i,p,res + return undistortedTrajectory + + def computeInverseMapping(originalImageSize, map1, map2): + 'Computes inverse mapping from maps provided by cv2.initUndistortRectifyMap' + from numpy import ones, isnan + invMap1 = -ones(originalImageSize) + invMap2 = -ones(originalImageSize) + for x in range(0,originalImageSize[1]): + for y in range(0,originalImageSize[0]): + res = undistortedCoordinates(x,y, map1, map2) + if not isnan(res).any(): + invMap1[y,x] = res[0] + invMap2[y,x] = res[1] + return invMap1, invMap2 + + def cameraIntrinsicCalibration(path, checkerBoardSize=[6,7], secondPassSearch=False, display=False): + ''' Camera calibration searches through all the images (jpg or png) located + in _path_ for matches to a checkerboard pattern of size checkboardSize. + These images should all be of the same camera with the same resolution. + + For best results, use an asymetric board and ensure that the image has + very high contrast, including the background. Suitable checkerboard: + http://ftp.isr.ist.utl.pt/pub/roswiki/attachments/camera_calibration(2f)Tutorials(2f)StereoCalibration/check-108.png + + The code below is based off of: + https://opencv-python-tutroals.readthedocs.org/en/latest/py_tutorials/py_calib3d/py_calibration/py_calibration.html + Modified by Paul St-Aubin + ''' + from numpy import zeros, mgrid, float32, savetxt + import glob, os + + # termination criteria + criteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 30, 0.001) + + # prepare object points, like (0,0,0), (1,0,0), (2,0,0) ....,(6,5,0) + objp = zeros((checkerBoardSize[0]*checkerBoardSize[1],3), float32) + objp[:,:2] = mgrid[0:checkerBoardSize[1],0:checkerBoardSize[0]].T.reshape(-1,2) + + # Arrays to store object points and image points from all the images. + objpoints = [] # 3d point in real world space + imgpoints = [] # 2d points in image plane. + + ## Loop throuhg all images in _path_ + images = glob.glob(os.path.join(path,'*.[jJ][pP][gG]'))+glob.glob(os.path.join(path,'*.[jJ][pP][eE][gG]'))+glob.glob(os.path.join(path,'*.[pP][nN][gG]')) + for fname in images: + img = cv2.imread(fname) + gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) + + # Find the chess board corners + ret, corners = cv2.findChessboardCorners(gray, (checkerBoardSize[1],checkerBoardSize[0]), None) + + # If found, add object points, image points (after refining them) + if ret: + print 'Found pattern in '+fname + + if(secondPassSearch): + corners = cv2.cornerSubPix(gray, corners, (11,11), (-1,-1), criteria) + + objpoints.append(objp) + imgpoints.append(corners) + + # Draw and display the corners + if(display): + img = cv2.drawChessboardCorners(img, (checkerBoardSize[1],checkerBoardSize[0]), corners, ret) + if(img): + cv2.imshow('img',img) + cv2.waitKey(0) + + ## Close up image loading and calibrate + cv2.destroyAllWindows() + if len(objpoints) == 0 or len(imgpoints) == 0: + return False + try: + ret, camera_matrix, dist_coeffs, rvecs, tvecs = cv2.calibrateCamera(objpoints, imgpoints, gray.shape[::-1], None, None) + except NameError: + return False + savetxt('intrinsic-camera.txt', camera_matrix) + return camera_matrix, dist_coeffs + + def undistortImage(img, intrinsicCameraMatrix = None, distortionCoefficients = None, undistortedImageMultiplication = 1., interpolation=cv2.INTER_LINEAR): + '''Undistorts the image passed in argument''' + width = img.shape[1] + height = img.shape[0] + [map1, map2] = computeUndistortMaps(width, height, undistortedImageMultiplication, intrinsicCameraMatrix, distortionCoefficients) + return cv2.remap(img, map1, map2, interpolation=interpolation) + + def printCvMat(cvmat, out = stdout): '''Prints the cvmat to out''' + print('Deprecated, use new interface') for i in xrange(cvmat.rows): for j in xrange(cvmat.cols): out.write('{0} '.format(cvmat[i,j])) out.write('\n') def projectArray(homography, points): - '''Returns the coordinates of the projected points (format 2xN points) - through homography''' - from numpy.core._dotblas import dot + '''Returns the coordinates of the projected points through homography + (format: array 2xN points)''' + from numpy.core import dot from numpy.core.multiarray import array from numpy.lib.function_base import append @@ -185,10 +425,10 @@ prod = dot(homography, augmentedPoints) return prod[0:2]/prod[2] else: - return p + return points def project(homography, p): - '''Returns the coordinates of the projection of the point p + '''Returns the coordinates of the projection of the point p with coordinates p[0], p[1] through homography''' from numpy import array return projectArray(homography, array([[p[0]],[p[1]]])) @@ -201,13 +441,39 @@ return projectArray(homography, array(trajectory)) def invertHomography(homography): - 'Returns an inverted homography' + '''Returns an inverted homography + Unnecessary for reprojection over camera image''' from numpy.linalg.linalg import inv invH = inv(homography) invH /= invH[2,2] return invH -if opencvExists: +def undistortTrajectory(invMap1, invMap2, positions): + from numpy import floor, ceil + floorPositions = floor(positions) + #ceilPositions = ceil(positions) + undistortedTrajectory = [[],[]] + for i in xrange(len(positions[0])): + x,y = None, None + if positions[0][i]+1 < invMap1.shape[1] and positions[1][i]+1 < invMap1.shape[0]: + floorX = invMap1[floorPositions[1][i], floorPositions[0][i]] + floorY = invMap2[floorPositions[1][i], floorPositions[0][i]] + ceilX = invMap1[floorPositions[1][i]+1, floorPositions[0][i]+1] + ceilY = invMap2[floorPositions[1][i]+1, floorPositions[0][i]+1] + #ceilX = invMap1[ceilPositions[1][i], ceilPositions[0][i]] + #ceilY = invMap2[ceilPositions[1][i], ceilPositions[0][i]] + if floorX >=0 and floorY >=0 and ceilX >=0 and ceilY >=0: + x = floorX+(positions[0][i]-floorPositions[0][i])*(ceilX-floorX) + y = floorY+(positions[1][i]-floorPositions[1][i])*(ceilY-floorY) + undistortedTrajectory[0].append(x) + undistortedTrajectory[1].append(y) + return undistortedTrajectory + +def projectGInputPoints(homography, points): + from numpy import array + return projectTrajectory(homography, array(points+[points[0]]).T) + +if opencvAvailable: def computeTranslation(img1, img2, img1Points, maxTranslation2, minNMatches, windowSize = (5,5), level = 5, criteria = (cv2.TERM_CRITERIA_EPS, 0, 0.01)): '''Computes the translation of img2 with respect to img1 (loaded using OpenCV as numpy arrays) @@ -233,3 +499,38 @@ else: print(dp) return None + +if skimageAvailable: + def HOG(image, rescaleSize = (64, 64), orientations=9, pixelsPerCell=(8, 8), cellsPerBlock=(2, 2), visualize=False, normalize=False): + from skimage.feature import hog + from skimage import color, transform + + bwImg = color.rgb2gray(image) + inputImg = transform.resize(bwImg, rescaleSize) + features = hog(inputImg, orientations, pixelsPerCell, cellsPerBlock, visualize, normalize) + if visualize: + from matplotlib.pyplot import imshow, figure, subplot + hogViz = features[1] + features = features[0] + figure() + subplot(1,2,1) + imshow(img) + subplot(1,2,2) + imshow(hogViz) + return features + + def createHOGTrainingSet(imageDirectory, classLabel, rescaleSize = (64, 64), orientations=9, pixelsPerCell=(8, 8), cellsPerBlock=(2, 2), visualize=False, normalize=False): + from os import listdir + from numpy import array, float32 + from matplotlib.pyplot import imread + + inputData = [] + for filename in listdir(imageDirectory): + img = imread(imageDirectory+filename) + features = HOG(img, rescaleSize, orientations, pixelsPerCell, cellsPerBlock, visualize, normalize) + inputData.append(features) + + nImages = len(inputData) + return array(inputData, dtype = float32), array([classLabel]*nImages, dtype = float32) + +
--- a/python/delete-object-tables.py Thu Apr 18 15:29:33 2013 -0400 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,15 +0,0 @@ -#! /usr/bin/env python - -import sys,getopt - -import utils -import storage - -options, args = getopt.getopt(sys.argv[1:], 'h',['help']) -options = dict(options) - -if '--help' in options.keys() or '-h' in options.keys() or len(args) == 0: - print('Usage: {0} --help|-h <database-filename.sqlite>'.format(sys.argv[0])) - sys.exit() - -storage.removeObjectsFromSqlite(args[0])
--- a/python/display-trajectories.py Thu Apr 18 15:29:33 2013 -0400 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,50 +0,0 @@ -#! /usr/bin/env python - -import sys,getopt - -import storage -import cvutils -from utils import FakeSecHead - -from numpy.linalg.linalg import inv -from numpy import loadtxt -from ConfigParser import ConfigParser - -options, args = getopt.getopt(sys.argv[1:], 'hi:d:t:o:f:',['help']) -# alternative long names are a pain to support ,'video-filename=','database-filename=', 'type=' - -options = dict(options) - -print options, args - -if '--help' in options.keys() or '-h' in options.keys() or len(sys.argv) == 1: - print('Usage: '+sys.argv[0]+' --help|-h -i video-filename -d database-filename [-t object_type] [-o image2world_homography] [-f first_frame]\n' - +'Or : '+sys.argv[0]+' [-t object_type] config_file.cfg\n\n' - 'Order matters between positional and named arguments\n' - 'object_type can be feature or object') - sys.exit() - -objectType = 'feature' -if '-t' in options.keys(): - objectType = options['-t'] - -if len(args)>0: # consider there is a configuration file - config = ConfigParser() - config.readfp(FakeSecHead(open(args[0]))) - sectionHeader = config.sections()[0] - videoFilename = config.get(sectionHeader, 'video-filename') - databaseFilename = config.get(sectionHeader, 'database-filename') - homography = inv(loadtxt(config.get(sectionHeader, 'homography-filename'))) - firstFrameNum = config.getint(sectionHeader, 'frame1') -else: - videoFilename = options['-i'] - databaseFilename = options['-d'] - homography = None - if '-o' in options.keys(): - homography = inv(loadtxt(options['-o'])) - firstFrameNum = 0 - if '-f' in options.keys(): - firstFrameNum = int(options['-f']) - -objects = storage.loadTrajectoriesFromSqlite(databaseFilename, objectType) -cvutils.displayTrajectories(videoFilename, objects, homography, firstFrameNum)
--- a/python/events.py Thu Apr 18 15:29:33 2013 -0400 +++ b/python/events.py Fri Dec 05 12:13:53 2014 -0500 @@ -8,9 +8,7 @@ import multiprocessing import itertools -import moving -import prediction -import indicators +import moving, prediction, indicators, utils __metaclass__ = type @@ -22,75 +20,205 @@ contains the indicators in a dictionary with the names as keys ''' - categories = {'headon': 0, + categories = {'Head On': 0, 'rearend': 1, 'side': 2, 'parallel': 3} - def __init__(self, num = None, timeInterval = None, roaduserNum1 = None, roaduserNum2 = None, movingObject1 = None, movingObject2 = None, categoryNum = None): + indicatorNames = ['Collision Course Dot Product', + 'Collision Course Angle', + 'Distance', + 'Minimum Distance', + 'Velocity Angle', + 'Speed Differential', + 'Collision Probability', + 'Time to Collision', + 'Probability of Successful Evasive Action', + 'predicted Post Encroachment Time'] + + indicatorNameToIndices = utils.inverseEnumeration(indicatorNames) + + indicatorShortNames = ['CCDP', + 'CCA', + 'Dist', + 'MinDist', + 'VA', + 'SD', + 'PoC', + 'TTC', + 'P(SEA)', + 'pPET'] + + indicatorUnits = ['', + 'rad', + 'm', + 'm', + 'rad', + 'm/s', + '', + 's', + '', + ''] + + def __init__(self, num = None, timeInterval = None, roaduserNum1 = None, roaduserNum2 = None, roadUser1 = None, roadUser2 = None, categoryNum = None): moving.STObject.__init__(self, num, timeInterval) - self.roaduserNumbers = set([roaduserNum1, roaduserNum2]) - self.movingObject1 = movingObject1 - self.movingObject2 = movingObject2 + if timeInterval == None and roadUser1 != None and roadUser2 != None: + self.timeInterval = roadUser1.commonTimeInterval(roadUser2) + self.roadUser1 = roadUser1 + self.roadUser2 = roadUser2 + if roaduserNum1 != None and roaduserNum2 != None: + self.roadUserNumbers = set([roaduserNum1, roaduserNum2]) + elif roadUser1 != None and roadUser2 != None: + self.roadUserNumbers = set(roadUser1.getNum(), roadUser2.getNum()) + else: + self.roadUserNumbers = None self.categoryNum = categoryNum self.indicators = {} + self.interactionInterval = None + + def getRoadUserNumbers(self): + return self.roadUserNumbers def getIndicator(self, indicatorName): - return self.indicators[indicatorName] + return self.indicators.get(indicatorName, None) def addIndicator(self, indicator): - self.indicators[indicator.name] = indicator + if indicator: + self.indicators[indicator.name] = indicator def computeIndicators(self): '''Computes the collision course cosine only if the cosine is positive''' collisionCourseDotProducts = {}#[0]*int(self.timeInterval.length()) - collisionCourseCosines = {} + collisionCourseAngles = {} + velocityAngles = {} distances = {}#[0]*int(self.timeInterval.length()) speedDifferentials = {} + interactionInstants = [] for instant in self.timeInterval: - deltap = self.movingObject1.getPositionAtInstant(instant)-self.movingObject2.getPositionAtInstant(instant) - deltav = self.movingObject2.getVelocityAtInstant(instant)-self.movingObject1.getVelocityAtInstant(instant) + deltap = self.roadUser1.getPositionAtInstant(instant)-self.roadUser2.getPositionAtInstant(instant) + v1 = self.roadUser1.getVelocityAtInstant(instant) + v2 = self.roadUser2.getVelocityAtInstant(instant) + deltav = v2-v1 + velocityAngles[instant] = arccos(moving.Point.dot(v1, v2)/(v1.norm2()*v2.norm2())) collisionCourseDotProducts[instant] = moving.Point.dot(deltap, deltav) - distances[instant] = deltap.norm2() # todo compute closest feature distance, if features + distances[instant] = deltap.norm2() speedDifferentials[instant] = deltav.norm2() if collisionCourseDotProducts[instant] > 0: - collisionCourseCosines[instant] = arccos(collisionCourseDotProducts[instant]/(distances[instant]*speedDifferentials[instant])) + interactionInstants.append(instant) + collisionCourseAngles[instant] = arccos(collisionCourseDotProducts[instant]/(distances[instant]*speedDifferentials[instant])) + + if len(interactionInstants) >= 2: + self.interactionInterval = moving.TimeInterval(interactionInstants[0], interactionInstants[-1]) + else: + self.interactionInterval = moving.TimeInterval() + self.addIndicator(indicators.SeverityIndicator(Interaction.indicatorNames[0], collisionCourseDotProducts)) + self.addIndicator(indicators.SeverityIndicator(Interaction.indicatorNames[1], collisionCourseAngles)) + self.addIndicator(indicators.SeverityIndicator(Interaction.indicatorNames[2], distances)) + self.addIndicator(indicators.SeverityIndicator(Interaction.indicatorNames[4], velocityAngles)) + self.addIndicator(indicators.SeverityIndicator(Interaction.indicatorNames[5], speedDifferentials)) + + # if we have features, compute other indicators + if len(self.roadUser1.features) != 0 and len(self.roadUser2.features) != 0: + minDistance={} + for instant in self.timeInterval: + minDistance[instant] = moving.MovingObject.minDistance(self.roadUser1, self.roadUser2, instant) + self.addIndicator(indicators.SeverityIndicator(Interaction.indicatorNames[3], minDistance)) - # todo shorten the time intervals based on the interaction definition - self.addIndicator(indicators.SeverityIndicator('Collision Course Dot Product', collisionCourseDotProducts)) - self.addIndicator(indicators.SeverityIndicator('Distance', distances)) - self.addIndicator(indicators.SeverityIndicator('Speed Differential', speedDifferentials)) - self.addIndicator(indicators.SeverityIndicator('Collision Course Cosine', collisionCourseCosines)) + def computeCrossingsCollisions(self, predictionParameters, collisionDistanceThreshold, timeHorizon, computeCZ = False, debug = False, timeInterval = None, nProcesses = 1): + '''Computes all crossing and collision points at each common instant for two road users. ''' + self.collisionPoints={} + self.crossingZones={} + TTCs = {} - # todo test for interaction instants and interval, compute indicators + if timeInterval: + commonTimeInterval = timeInterval + else: + commonTimeInterval = self.timeInterval + self.collisionPoints, self.crossingZones = prediction.computeCrossingsCollisions(predictionParameters, self.roadUser1, self.roadUser2, collisionDistanceThreshold, timeHorizon, computeCZ, debug, commonTimeInterval, nProcesses) + for i, cp in self.collisionPoints.iteritems(): + TTCs[i] = prediction.SafetyPoint.computeExpectedIndicator(cp) + # add probability of collision, and probability of successful evasive action + self.addIndicator(indicators.SeverityIndicator(Interaction.indicatorNames[7], TTCs)) + + if computeCZ: + pPETs = {} + for i in list(commonTimeInterval)[:-1]: + if len(self.crossingZones[i]) > 0: + pPETs[i] = prediction.SafetyPoint.computeExpectedIndicator(self.crossingZones[i]) + self.addIndicator(indicators.SeverityIndicator(Interaction.indicatorNames[9], pPETs)) def addVideoFilename(self,videoFilename): self.videoFilename= videoFilename def addInteractionType(self,interactionType): - ''' interaction types: conflict or collision if they are known''' - self.interactionType= interactionType + ''' interaction types: conflict or collision if they are known''' + self.interactionType= interactionType -def createInteractions(objects): - '''Create all interactions of two co-existing road users +def createInteractions(objects, _others = None): + '''Create all interactions of two co-existing road users''' + if _others != None: + others = _others - todo add test to compute categories?''' interactions = [] num = 0 for i in xrange(len(objects)): - for j in xrange(i): - commonTimeInterval = objects[i].commonTimeInterval(objects[j]) + if _others == None: + others = objects[:i] + for j in xrange(len(others)): + commonTimeInterval = objects[i].commonTimeInterval(others[j]) if not commonTimeInterval.empty(): - interactions.append(Interaction(num, commonTimeInterval, objects[i].num, objects[j].num, objects[i], objects[j])) + interactions.append(Interaction(num, commonTimeInterval, objects[i].num, others[j].num, objects[i], others[j])) num += 1 return interactions +def prototypeCluster(interactions, similarityMatrix, alignmentMatrix, indicatorName, minSimilarity): + '''Finds exemplar indicator time series for all interactions + Returns the prototype indices (in the interaction list) and the label of each indicator (interaction) + + if an indicator profile (time series) is different enough (<minSimilarity), + it will become a new prototype. + Non-prototype interactions will be assigned to an existing prototype''' + + # sort indicators based on length + indices = range(similarityMatrix.shape[0]) + def compare(i, j): + if len(interactions[i].getIndicator(indicatorName)) > len(interactions[j].getIndicator(indicatorName)): + return -1 + elif len(interactions[i].getIndicator(indicatorName)) == len(interactions[j].getIndicator(indicatorName)): + return 0 + else: + return 1 + indices.sort(compare) + # go through all indicators + prototypeIndices = [indices[0]] + for i in indices[1:]: + if similarityMatrix[i][prototypeIndices].max() < minSimilarity: + prototypeIndices.append(i) + + # assignment + labels = [-1]*similarityMatrix.shape[0] + indices = [i for i in range(similarityMatrix.shape[0]) if i not in prototypeIndices] + for i in prototypeIndices: + labels[i] = i + for i in indices: + prototypeIndex = similarityMatrix[i][prototypeIndices].argmax() + labels[i] = prototypeIndices[prototypeIndex] + + return prototypeIndices, labels + +def prototypeMultivariateCluster(interactions, similarityMatrics, indicatorNames, minSimilarities, minClusterSize): + '''Finds exmaple indicator time series (several indicators) for all interactions + + if any interaction indicator time series is different enough (<minSimilarity), + it will become a new prototype. + Non-prototype interactions will be assigned to an existing prototype if all indicators are similar enough''' + pass # TODO: #http://stackoverflow.com/questions/3288595/multiprocessing-using-pool-map-on-a-function-defined-in-a-class #http://www.rueckstiess.net/research/snippets/show/ca1d7d90 def calculateIndicatorPipe(pairs, predParam, timeHorizon=75,collisionDistanceThreshold=1.8): - collisionPoints, crossingZones = prediction.computeCrossingsCollisions(pairs.movingObject1, pairs.movingObject2, predParam, collisionDistanceThreshold, timeHorizon) + collisionPoints, crossingZones = prediction.computeCrossingsCollisions(pairs.roadUser1, pairs.roadUser2, predParam, collisionDistanceThreshold, timeHorizon) #print pairs.num # Ignore empty collision points empty = 1 @@ -137,7 +265,7 @@ #prog = Tools.ProgressBar(0, len(self.pairs), 77) #Removed in traffic-intelligenc port for j in xrange(len(self.pairs)): #prog.updateAmount(j) #Removed in traffic-intelligenc port - collisionPoints, crossingZones = prediction.computeCrossingsCollisions(self.pairs[j].movingObject1, self.pairs[j].movingObject2, predParam, collisionDistanceThreshold, timeHorizon) + collisionPoints, crossingZones = prediction.computeCrossingsCollisions(self.pairs[j].roadUser1, self.pairs[j].roadUser2, predParam, collisionDistanceThreshold, timeHorizon) # Ignore empty collision points empty = 1 @@ -181,7 +309,7 @@ lists.append(j.num) return lists - def getCPlist(self,indicatorThreshold=99999): + def getCPlist(self,indicatorThreshold=float('Inf')): lists = [] for j in self.pairs: if(j.hasCP): @@ -190,7 +318,7 @@ lists.append([k,j.CP[k][0]]) return lists - def getCZlist(self,indicatorThreshold=99999): + def getCZlist(self,indicatorThreshold=float('Inf')): lists = [] for j in self.pairs: if(j.hasCZ): @@ -229,7 +357,7 @@ if __name__ == "__main__": import doctest import unittest - #suite = doctest.DocFileSuite('tests/moving.txt') - suite = doctest.DocTestSuite() + suite = doctest.DocFileSuite('tests/events.txt') + #suite = doctest.DocTestSuite() unittest.TextTestRunner().run(suite)
--- a/python/indicators.py Thu Apr 18 15:29:33 2013 -0400 +++ b/python/indicators.py Fri Dec 05 12:13:53 2014 -0500 @@ -18,7 +18,6 @@ def __init__(self, name, values, timeInterval=None, maxValue = None): self.name = name - self.isCosine = (name.find('Cosine') >= 0) if timeInterval: assert len(values) == timeInterval.length() self.timeInterval = timeInterval @@ -69,52 +68,70 @@ def getTimeInterval(self): return self.timeInterval + def getName(self): + return self.name + def getValues(self): return [self.__getitem__(t) for t in self.timeInterval] - def getAngleValues(self): - '''if the indicator is a function of an angle, - transform it to an angle (eg cos) - (no transformation otherwise)''' - from numpy import arccos - values = self.getValues() - if self.isCosine: - return [arccos(c) for c in values] - else: - return values - - def plot(self, options = '', xfactor = 1., **kwargs): + def plot(self, options = '', xfactor = 1., yfactor = 1., timeShift = 0, **kwargs): from matplotlib.pylab import plot,ylim if self.getTimeInterval().length() == 1: marker = 'o' else: marker = '' time = sorted(self.values.keys()) - plot([x/xfactor for x in time], [self.values[i] for i in time], options+marker, **kwargs) + plot([(x+timeShift)/xfactor for x in time], [self.values[i]/yfactor for i in time], options+marker, **kwargs) if self.maxValue: ylim(ymax = self.maxValue) + + def valueSorted(self): + ''' return the values after sort the keys in the indicator + This should probably not be used: to delete''' + print('Deprecated: values should not be accessed in this way') + values=[] + keys = self.values.keys() + keys.sort() + for key in keys: + values.append(self.values[key]) + return values -def computeDLCSS(indicator1, indicator2, threshold, delta = float('inf'), method= 'min'): - ''' compute the distance between two indicators using LCSS - two common methods are used: min or mean of the indicators length''' - from utils import LCSS + +def l1Distance(x, y): # lambda x,y:abs(x-y) + if x == None or y == None: + return float('inf') + else: + return abs(x-y) + +from utils import LCSS as utilsLCSS - def distance(x, y): # lambda x,y:abs(x-y) - if x == None or y == None: - return float('inf') - else: - return abs(x-y) +class LCSS(utilsLCSS): + '''Adapted LCSS class for indicators, same pattern''' + def __init__(self, similarityFunc, delta = float('inf'), minLength = 0, aligned = False, lengthFunc = min): + utilsLCSS.__init__(self, similarityFunc, delta, aligned, lengthFunc) + self.minLength = minLength + + def checkIndicator(self, indicator): + return indicator != None and len(indicator) >= self.minLength - lcss = LCSS(indicator1.getValues(), indicator2.getValues(), threshold, distance, delta) - if method == 'min': - denominator = min(len(indicator1), len(indicator2)) - elif method == 'mean': - denominator = float(len(indicator1) + len(indicator2))/2 - else: - print('Unknown denominator method name') - denominator = 1. - return 1-float(lcss)/denominator + def compute(self, indicator1, indicator2, computeSubSequence = False): + if self.checkIndicator(indicator1) and self.checkIndicator(indicator2): + return self._compute(indicator1.getValues(), indicator2.getValues(), computeSubSequence) + else: + return 0 + def computeNormalized(self, indicator1, indicator2, computeSubSequence = False): + if self.checkIndicator(indicator1) and self.checkIndicator(indicator2): + return self._computeNormalized(indicator1.getValues(), indicator2.getValues(), computeSubSequence) + else: + return 0. + + def computeDistance(self, indicator1, indicator2, computeSubSequence = False): + if self.checkIndicator(indicator1) and self.checkIndicator(indicator2): + return self._computeDistance(indicator1.getValues(), indicator2.getValues(), computeSubSequence) + else: + return 1. + class SeverityIndicator(TemporalIndicator): '''Class for severity indicators field mostSevereIsMax is True
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/python/metadata.py Fri Dec 05 12:13:53 2014 -0500 @@ -0,0 +1,170 @@ +# from moving import Point + +from datetime import datetime +from os import path + +from sqlalchemy import create_engine, Column, Integer, Float, DateTime, String, ForeignKey +from sqlalchemy.orm import relationship, backref, sessionmaker +from sqlalchemy.ext.declarative import declarative_base + +from utils import datetimeFormat + +Base = declarative_base() + +class Site(Base): + __tablename__ = 'sites' + idx = Column(Integer, primary_key=True) + name = Column(String) # same as path, relative to the database position + description = Column(String) # longer names, eg intersection of road1 and road2 + xcoordinate = Column(Float) # ideally moving.Point, but needs to be + ycoordinate = Column(Float) + + def __init__(self, name, description = "", xcoordinate = None, ycoordinate = None): + self.name = name + self.description = description + self.xcoordinate = xcoordinate + self.ycoordinate = ycoordinate + + def getFilename(self): + return self.name + +class EnvironementalFactors(Base): + '''Represents any environmental factors that may affect the results, in particular + * changing weather conditions + * changing road configuration, geometry, signalization, etc. + ex: sunny, rainy, before counter-measure, after counter-measure''' + __tablename__ = 'environmental_factors' + idx = Column(Integer, primary_key=True) + startTime = Column(DateTime) + endTime = Column(DateTime) + description = Column(String) # eg sunny, before, after + siteIdx = Column(Integer, ForeignKey('sites.idx')) + + site = relationship("Site", backref=backref('environmental_factors', order_by = idx)) + + def __init__(self, startTime, endTime, description, site): + 'startTime is passed as string in utils.datetimeFormat, eg 2011-06-22 10:00:39' + self.startTime = datetime.strptime(startTime, datetimeFormat) + self.endTime = datetime.strptime(endTime, datetimeFormat) + self.description = description + self.site = site + +class CameraView(Base): + __tablename__ = 'camera_views' + idx = Column(Integer, primary_key=True) + frameRate = Column(Float) + homographyFilename = Column(String) # path to homograph filename, relative to the site name + cameraCalibrationFilename = Column(String) # path to full camera calibration, relative to the site name + siteIdx = Column(Integer, ForeignKey('sites.idx')) + homographyDistanceUnit = Column(String, default = 'm') # make sure it is default in the database + configurationFilename = Column(String) # path to configuration .cfg file, relative to site name + + site = relationship("Site", backref=backref('camera_views', order_by = idx)) + + def __init__(self, frameRate, homographyFilename, cameraCalibrationFilename, site, configurationFilename): + self.frameRate = frameRate + self.homographyFilename = homographyFilename + self.site = site + self.configurationFilename = configurationFilename + + def getHomographyFilename(self, relativeToSiteFilename = True): + if relativeToSiteFilename: + return self.site.getFilename()+path.sep+self.homographyFilename + else: + return self.homographyFilename + +class Alignment(Base): + __tablename__ = 'alignments' + idx = Column(Integer, primary_key=True) + cameraViewIdx = Column(Integer, ForeignKey('camera_views.idx')) + + cameraView = relationship("CameraView", backref=backref('alignments', order_by = idx)) + + def __init__(self, cameraView): + self.cameraView = cameraView + +class Point(Base): + __tablename__ = 'points' + alignmentIdx = Column(Integer, ForeignKey('alignments.idx'), primary_key=True) + index = Column(Integer, primary_key=True) # order of points in this alignment + x = Column(Float) + y = Column(Float) + + alignment = relationship("Alignment", backref=backref('points', order_by = index)) + + def __init__(self, alignmentIdx, index, x, y): + self.alignmentIdx = alignmentIdx + self.index = index + self.x = x + self.y = y + +class VideoSequence(Base): + __tablename__ = 'video_sequences' + idx = Column(Integer, primary_key=True) + name = Column(String) # path relative to the the site name + startTime = Column(DateTime) + duration = Column(Float) # video sequence duration + durationUnit = Column(String, default = 's') + siteIdx = Column(Integer, ForeignKey('sites.idx')) + cameraViewIdx = Column(Integer, ForeignKey('camera_views.idx')) + configurationFilename = Column(String) + + site = relationship("Site", backref=backref('video_sequences', order_by = idx)) + cameraView = relationship("CameraView", backref=backref('video_sequences', order_by = idx)) + + def __init__(self, name, startTime, duration, site, cameraView, configurationFilename = None): + 'startTime is passed as string in utils.datetimeFormat, eg 2011-06-22 10:00:39' + self.name = name + self.startTime = datetime.strptime(startTime, datetimeFormat) + self.duration = duration + self.site = site + self.cameraView = cameraView + self.configurationFilename = configurationFilename + + def getVideoSequenceFilename(self, relativeToSiteFilename = True): + if relativeToSiteFilename: + return self.site.getFilename()+path.sep+self.name + else: + return self.name + + #def getConfigurationFilename(self): + #'returns the local configuration filename, or the one of the camera view otherwise' + +# add class for Analysis: foreign key VideoSequenceId, dataFilename, configFilename (get the one from camera view by default), mask? (no, can be referenced in the tracking cfg file) + +# class SiteDescription(Base): # list of lines and polygons describing the site, eg for sidewalks, center lines + +# class Analysis(Base): # parameters necessary for processing the data: free form +# eg bounding box depends on camera view, tracking configuration depends on camera view +# results: sqlite + +def createDatabase(filename): + 'creates a session to query the filename' + engine = create_engine('sqlite:///'+filename) + Base.metadata.create_all(engine) + Session = sessionmaker(bind=engine) + return Session() + +def connectDatabase(filename): + 'creates a session to query the filename' + engine = create_engine('sqlite:///'+filename) + Session = sessionmaker(bind=engine) + return Session() + +def initializeSites(session, directoryName): + '''Initializes default site objects and Camera Views + + eg somedirectory/montreal/ contains intersection1, intersection2, etc. + The site names would be somedirectory/montreal/intersection1, somedirectory/montreal/intersection2, etc.''' + from os import listdir, path + sites = [] + cameraViews = [] + names = listdir(directoryName) + for name in names: + if path.isdir(directoryName+'/'+name): + sites.append(Site(directoryName+'/'+name, None)) + cameraViews.append(CameraView(-1, None, None, sites[-1], None)) + session.add_all(sites) + session.add_all(cameraViews) + session.commit() +# TODO crawler for video files?
--- a/python/ml.py Thu Apr 18 15:29:33 2013 -0400 +++ b/python/ml.py Fri Dec 05 12:13:53 2014 -0500 @@ -5,6 +5,29 @@ __metaclass__ = type +class Model(object): + '''Abstract class for loading/saving model''' + def load(self, fn): + self.model.load(fn) + + def save(self, fn): + self.model.save(fn) + +class SVM(Model): + '''wrapper for OpenCV SimpleVectorMachine algorithm''' + + def __init__(self, svm_type, kernel_type, degree = 0, gamma = 1, coef0 = 0, Cvalue = 1, nu = 0, p = 0): + import cv2 + self.model = cv2.SVM() + self.params = dict(svm_type = svm_type, kernel_type = kernel_type, degree = degree, gamma = gamma, coef0 = coef0, Cvalue = Cvalue, nu = nu, p = p) + + def train(self, samples, responses): + self.model.train(samples, responses, params = self.params) + + def predict(self, samples): + return np.float32([self.model.predict(s) for s in samples]) + + class Centroid: 'Wrapper around instances to add a counter' @@ -25,13 +48,17 @@ inst.multiply(1/(self.nInstances+instance.nInstances)) return Centroid(inst, self.nInstances+instance.nInstances) - def draw(self, options = ''): + def plot(self, options = ''): from matplotlib.pylab import text - self.instance.draw(options) + self.instance.plot(options) text(self.instance.position.x+1, self.instance.position.y+1, str(self.nInstances)) +def kMedoids(similarityMatrix, initialCentroids = None, k = None): + '''Algorithm that clusters any dataset based on a similarity matrix + Either the initialCentroids or k are passed''' + pass -def clustering(data, similar, initialCentroids = []): +def assignCluster(data, similarFunc, initialCentroids = None, shuffleData = True): '''k-means algorithm with similarity function Two instances should be in the same cluster if the sameCluster function returns true for two instances. It is supposed that the average centroid of a set of instances can be computed, using the function. The number of clusters will be determined accordingly @@ -42,14 +69,15 @@ from random import shuffle from copy import copy, deepcopy localdata = copy(data) # shallow copy to avoid modifying data - shuffle(localdata) - if initialCentroids: + if shuffleData: + shuffle(localdata) + if initialCentroids == None: + centroids = [Centroid(localdata[0])] + else: centroids = deepcopy(initialCentroids) - else: - centroids = [Centroid(localdata[0])] for instance in localdata[1:]: i = 0 - while i<len(centroids) and not similar(centroids[i].instance, instance): + while i<len(centroids) and not similarFunc(centroids[i].instance, instance): i += 1 if i == len(centroids): centroids.append(Centroid(instance)) @@ -58,6 +86,8 @@ return centroids +# TODO recompute centroids for each cluster: instance that minimizes some measure to all other elements + def spectralClustering(similarityMatrix, k, iter=20): '''Spectral Clustering algorithm''' n = len(similarityMatrix) @@ -77,3 +107,14 @@ centroids,distortion = kmeans(features,k, iter) code,distance = vq(features,centroids) # code starting from 0 (represent first cluster) to k-1 (last cluster) return code,sigma + +def motionPatterLearning(objects, maxDistance): + ''' + Option to use only the (n?) longest features per object instead of all for speed up + TODO''' + pass + +def prototypeCluster(): + ''' + TODO''' + pass
--- a/python/moving.py Thu Apr 18 15:29:33 2013 -0400 +++ b/python/moving.py Fri Dec 05 12:13:53 2014 -0500 @@ -5,8 +5,15 @@ import cvutils from math import sqrt +from numpy import median -#from shapely.geometry import Polygon +try: + from shapely.geometry import Polygon, Point as shapelyPoint + from shapely.prepared import prep + shapelyAvailable = True +except ImportError: + print('Shapely library could not be loaded') + shapelyAvailable = False __metaclass__ = type @@ -29,6 +36,9 @@ def empty(self): return self.first > self.last + def center(self): + return (self.first+self.last)/2. + def length(self): '''Returns the length of the interval''' return float(max(0,self.last-self.first)) @@ -90,7 +100,11 @@ def __getitem__(self, i): if not self.empty(): - return self.first+i + if isinstance(i, int): + return self.first+i + else: + raise TypeError, "Invalid argument type." + #elif isinstance( key, slice ): def __iter__(self): self.iterInstantNum = -1 @@ -128,7 +142,7 @@ def empty(self): return self.timeInterval.empty() or not self.boudingPolygon - def getId(self): + def getNum(self): return self.num def getFirstInstant(self): @@ -163,16 +177,31 @@ def __sub__(self, other): return Point(self.x-other.x, self.y-other.y) + def __neg__(self): + return Point(-self.x, -self.y) + + def __getitem__(self, i): + if i == 0: + return self.x + elif i == 1: + return self.y + else: + raise IndexError() + + def orthogonal(self): + return Point(self.y, -self.x) + def multiply(self, alpha): + 'Warning, returns a new Point' return Point(self.x*alpha, self.y*alpha) - def draw(self, options = 'o', **kwargs): + def plot(self, options = 'o', **kwargs): from matplotlib.pylab import plot plot([self.x], [self.y], options, **kwargs) def norm2Squared(self): '''2-norm distance (Euclidean distance)''' - return self.x*self.x+self.y*self.y + return self.x**2+self.y**2 def norm2(self): '''2-norm distance (Euclidean distance)''' @@ -193,36 +222,44 @@ def asint(self): return Point(int(self.x), int(self.y)) + if shapelyAvailable: + def asShapely(self): + return shapelyPoint(self.x, self.y) + def project(self, homography): from numpy.core.multiarray import array projected = cvutils.projectArray(homography, array([[self.x], [self.y]])) return Point(projected[0], projected[1]) - def inPolygon(self, poly): - '''Returns if the point x, y is inside the polygon. - The polygon is defined by the ordered list of points in poly - + def inPolygonNoShapely(self, polygon): + '''Indicates if the point x, y is inside the polygon + (array of Nx2 coordinates of the polygon vertices) + taken from http://www.ariel.com.au/a/python-point-int-poly.html - Use points_inside_poly from matplotlib.nxutils''' + Use Polygon.contains if Shapely is installed''' - n = len(poly); + n = polygon.shape[0]; counter = 0; - p1 = poly[0]; + p1 = polygon[0,:]; for i in range(n+1): - p2 = poly[i % n]; - if self.y > min(p1.y,p2.y): - if self.y <= max(p1.y,p2.y): - if self.x <= max(p1.x,p2.x): - if p1.y != p2.y: - xinters = (self.y-p1.y)*(p2.x-p1.x)/(p2.y-p1.y)+p1.x; - if p1.x == p2.x or self.x <= xinters: + p2 = polygon[i % n,:]; + if self.y > min(p1[1],p2[1]): + if self.y <= max(p1[1],p2[1]): + if self.x <= max(p1[0],p2[0]): + if p1[1] != p2[1]: + xinters = (self.y-p1[1])*(p2[0]-p1[0])/(p2[1]-p1[1])+p1[0]; + if p1[0] == p2[0] or self.x <= xinters: counter+=1; p1=p2 return (counter%2 == 1); @staticmethod + def fromList(p): + return Point(p[0], p[1]) + + @staticmethod def dot(p1, p2): 'Scalar product' return p1.x*p2.x+p1.y*p2.y @@ -233,13 +270,201 @@ return p1.x*p2.y-p1.y*p2.x @staticmethod + def cosine(p1, p2): + return Point.dot(p1,p2)/(p1.norm2()*p2.norm2()) + + @staticmethod def distanceNorm2(p1, p2): return (p1-p2).norm2() @staticmethod - def plotAll(points, color='r'): + def plotAll(points, **kwargs): from matplotlib.pyplot import scatter - scatter([p.x for p in points],[p.y for p in points], c=color) + scatter([p.x for p in points],[p.y for p in points], **kwargs) + + def similarOrientation(self, refDirection, cosineThreshold): + 'Indicates whether the cosine of the vector and refDirection is smaller than cosineThreshold' + return Point.cosine(self, refDirection) >= cosineThreshold + + @staticmethod + def timeToCollision(p1, p2, v1, v2, collisionThreshold): + '''Computes exact time to collision with a distance threshold + The unknown of the equation is the time to reach the intersection + between the relative trajectory of one road user + and the circle of radius collisionThreshold around the other road user''' + from math import sqrt + dv = v1-v2 + dp = p1-p2 + a = dv.norm2Squared()#(v1.x-v2.x)**2 + (v1.y-v2.y)**2 + b = 2*Point.dot(dv, dp)#2 * ((p1.x-p2.x) * (v1.x-v2.x) + (p1.y-p2.y) * (v1.y-v2.y)) + c = dp.norm2Squared() - collisionThreshold**2#(p1.x-p2.x)**2 + (p1.y-p2.y)**2 - collisionThreshold**2 + + delta = b**2 - 4*a*c + if delta >= 0: + deltaRoot = sqrt(delta) + ttc1 = (-b + deltaRoot)/(2*a) + ttc2 = (-b - deltaRoot)/(2*a) + if ttc1 >= 0 and ttc2 >= 0: + ttc = min(ttc1,ttc2) + elif ttc1 >= 0: + ttc = ttc1 + elif ttc2 >= 0: + ttc = ttc2 + else: # ttc1 < 0 and ttc2 < 0: + ttc = None + else: + ttc = None + return ttc + + @staticmethod + def midPoint(p1, p2): + 'Returns the middle of the segment [p1, p2]' + return Point(0.5*p1.x+0.5*p2.x, 0.5*p1.y+0.5*p2.y) + +if shapelyAvailable: + def pointsInPolygon(points, polygon): + '''Optimized tests of a series of points within (Shapely) polygon ''' + prepared_polygon = prep(polygon) + return filter(prepared_polygon.contains, points) + +# Functions for coordinate transformation +# From Paul St-Aubin's PVA tools +def subsec_spline_dist(splines): + ''' Prepare list of spline subsegments from a spline list. + + Output: + ======= + ss_spline_d[spline #][mode][station] + + where: + mode=0: incremental distance + mode=1: cumulative distance + mode=2: cumulative distance with trailing distance + ''' + + from numpy import zeros + ss_spline_d = [] + #Prepare subsegment distances + for spline in range(len(splines)): + ss_spline_d.append([[],[],[]]) + ss_spline_d[spline][0] = zeros(len(splines[spline])-1) #Incremental distance + ss_spline_d[spline][1] = zeros(len(splines[spline])-1) #Cumulative distance + ss_spline_d[spline][2] = zeros(len(splines[spline])) #Cumulative distance with trailing distance + for spline_p in range(len(splines[spline])): + if spline_p > (len(splines[spline]) - 2): + break + ss_spline_d[spline][0][spline_p] = utils.pointDistanceL2(splines[spline][spline_p][0],splines[spline][spline_p][1],splines[spline][(spline_p+1)][0],splines[spline][(spline_p+1)][1]) + ss_spline_d[spline][1][spline_p] = sum(ss_spline_d[spline][0][0:spline_p]) + ss_spline_d[spline][2][spline_p] = ss_spline_d[spline][1][spline_p]#sum(ss_spline_d[spline][0][0:spline_p]) + + ss_spline_d[spline][2][-1] = ss_spline_d[spline][2][-2] + ss_spline_d[spline][0][-1] + + return ss_spline_d + +def ppldb2p(qx,qy, p0x,p0y, p1x,p1y): + ''' Point-projection (Q) on line defined by 2 points (P0,P1). + http://cs.nyu.edu/~yap/classes/visual/03s/hw/h2/math.pdf + ''' + if(p0x == p1x and p0y == p1y): + return None + try: + #Approximate slope singularity by giving some slope roundoff; account for roundoff error + if(round(p0x, 10) == round(p1x, 10)): + p1x += 0.0000000001 + if(round(p0y, 10) == round(p1y, 10)): + p1y += 0.0000000001 + #make the calculation + Y = (-(qx)*(p0y-p1y)-(qy*(p0y-p1y)**2)/(p0x-p1x)+p0x**2*(p0y-p1y)/(p0x-p1x)-p0x*p1x*(p0y-p1y)/(p0x-p1x)-p0y*(p0x-p1x))/(p1x-p0x-(p0y-p1y)**2/(p0x-p1x)) + X = (-Y*(p1y-p0y)+qx*(p1x-p0x)+qy*(p1y-p0y))/(p1x-p0x) + except ZeroDivisionError: + print('Error: Division by zero in ppldb2p. Please report this error with the full traceback:') + print('qx={0}, qy={1}, p0x={2}, p0y={3}, p1x={4}, p1y={5}...'.format(qx, qy, p0x, p0y, p1x, p1y)) + import pdb; pdb.set_trace() + return Point(X,Y) + +def getSYfromXY(p, splines, goodEnoughSplineDistance = 0.5): + ''' Snap a point p to it's nearest subsegment of it's nearest spline (from the list splines). A spline is a list of points (class Point), most likely a trajectory. + + Output: + ======= + [spline index, + subsegment leading point index, + snapped point, + subsegment distance, + spline distance, + orthogonal point offset] + ''' + minOffsetY = float('inf') + #For each spline + for spline in range(len(splines)): + #For each spline point index + for spline_p in range(len(splines[spline])-1): + #Get closest point on spline + closestPoint = ppldb2p(p.x,p.y,splines[spline][spline_p][0],splines[spline][spline_p][1],splines[spline][spline_p+1][0],splines[spline][spline_p+1][1]) + if closestPoint == None: + print('Error: Spline {0}, segment {1} has identical bounds and therefore is not a vector. Projection cannot continue.'.format(spline, spline_p)) + return None + # check if the + if utils.inBetween(splines[spline][spline_p][0], splines[spline][spline_p+1][0], closestPoint.x) and utils.inBetween(splines[spline][spline_p][1], splines[spline][spline_p+1][1], closestPoint.y): + offsetY = Point.distanceNorm2(closestPoint, p) + if offsetY < minOffsetY: + minOffsetY = offsetY + snappedSpline = spline + snappedSplineLeadingPoint = spline_p + snappedPoint = Point(closestPoint.x, closestPoint.y) + #Jump loop if significantly close + if offsetY < goodEnoughSplineDistance: + break + #Get sub-segment distance + if minOffsetY != float('inf'): + subsegmentDistance = Point.distanceNorm2(snappedPoint, splines[snappedSpline][snappedSplineLeadingPoint]) + #Get cumulative alignment distance (total segment distance) + splineDistanceS = splines[snappedSpline].getCumulativeDistance(snappedSplineLeadingPoint) + subsegmentDistance + orthogonalSplineVector = (splines[snappedSpline][snappedSplineLeadingPoint+1]-splines[snappedSpline][snappedSplineLeadingPoint]).orthogonal() + offsetVector = p-snappedPoint + if Point.dot(orthogonalSplineVector, offsetVector) < 0: + minOffsetY = -minOffsetY + return [snappedSpline, snappedSplineLeadingPoint, snappedPoint, subsegmentDistance, splineDistanceS, minOffsetY] + else: + return None + +def getXYfromSY(s, y, splineNum, splines, mode = 0): + ''' Find X,Y coordinate from S,Y data. + if mode = 0 : return Snapped X,Y + if mode !=0 : return Real X,Y + ''' + + #(buckle in, it gets ugly from here on out) + ss_spline_d = subsec_spline_dist(splines) + + #Find subsegment + snapped_x = None + snapped_y = None + for spline_ss_index in range(len(ss_spline_d[splineNum][1])): + if(s < ss_spline_d[splineNum][1][spline_ss_index]): + ss_value = s - ss_spline_d[splineNum][1][spline_ss_index-1] + #Get normal vector and then snap + vector_l_x = (splines[splineNum][spline_ss_index][0] - splines[splineNum][spline_ss_index-1][0]) + vector_l_y = (splines[splineNum][spline_ss_index][1] - splines[splineNum][spline_ss_index-1][1]) + magnitude = sqrt(vector_l_x**2 + vector_l_y**2) + n_vector_x = vector_l_x/magnitude + n_vector_y = vector_l_y/magnitude + snapped_x = splines[splineNum][spline_ss_index-1][0] + ss_value*n_vector_x + snapped_y = splines[splineNum][spline_ss_index-1][1] + ss_value*n_vector_y + + #Real values (including orthogonal projection of y)) + real_x = snapped_x - y*n_vector_y + real_y = snapped_y + y*n_vector_x + break + + if mode == 0 or (not snapped_x): + if(not snapped_x): + snapped_x = splines[splineNum][-1][0] + snapped_y = splines[splineNum][-1][1] + return [snapped_x,snapped_y] + else: + return [real_x,real_y] + class NormAngle(object): '''Alternate encoding of a point, by its norm and orientation''' @@ -254,6 +479,8 @@ norm = p.norm2() if norm > 0: angle = atan2(p.y, p.x) + else: + angle = 0. return NormAngle(norm, angle) def __add__(self, other): @@ -294,44 +521,59 @@ def multiply(self, alpha): return FlowVector(self.position.multiply(alpha), self.velocity.multiply(alpha)) - def draw(self, options = '', **kwargs): + def plot(self, options = '', **kwargs): from matplotlib.pylab import plot plot([self.position.x, self.position.x+self.velocity.x], [self.position.y, self.position.y+self.velocity.y], options, **kwargs) - self.position.draw(options+'x', **kwargs) + self.position.plot(options+'x', **kwargs) @staticmethod def similar(f1, f2, maxDistance2, maxDeltavelocity2): return (f1.position-f2.position).norm2Squared()<maxDistance2 and (f1.velocity-f2.velocity).norm2Squared()<maxDeltavelocity2 +def intersection(p1, p2, p3, p4): + ''' Intersection point (x,y) of lines formed by the vectors p1-p2 and p3-p4 + http://paulbourke.net/geometry/pointlineplane/''' + dp12 = p2-p1 + dp34 = p4-p3 + #det = (p4.y-p3.y)*(p2.x-p1.x)-(p4.x-p3.x)*(p2.y-p1.y) + det = dp34.y*dp12.x-dp34.x*dp12.y + if det == 0: + return None + else: + ua = (dp34.x*(p1.y-p3.y)-dp34.y*(p1.x-p3.x))/det + return p1+dp12.multiply(ua) + +# def intersection(p1, p2, dp1, dp2): +# '''Returns the intersection point between the two lines +# defined by the respective vectors (dp) and origin points (p)''' +# from numpy import matrix +# from numpy.linalg import linalg +# A = matrix([[dp1.y, -dp1.x], +# [dp2.y, -dp2.x]]) +# B = matrix([[dp1.y*p1.x-dp1.x*p1.y], +# [dp2.y*p2.x-dp2.x*p2.y]]) + +# if linalg.det(A) == 0: +# return None +# else: +# intersection = linalg.solve(A,B) +# return Point(intersection[0,0], intersection[1,0]) + def segmentIntersection(p1, p2, p3, p4): '''Returns the intersecting point of the segments [p1, p2] and [p3, p4], None otherwise''' - from numpy import matrix - from numpy.linalg import linalg, det if (Interval.intersection(Interval(p1.x,p2.x,True), Interval(p3.x,p4.x,True)).empty()) or (Interval.intersection(Interval(p1.y,p2.y,True), Interval(p3.y,p4.y,True)).empty()): return None else: - dp1 = p2-p1#[s1[0][1]-s1[0][0], s1[1][1]-s1[1][0]] - dp2 = p4-p3#[s2[0][1]-s2[0][0], s2[1][1]-s2[1][0]] - - A = matrix([[dp1.y, -dp1.x], - [dp2.y, -dp2.x]]) - B = matrix([[dp1.y*p1.x-dp1.x*p1.y], - [dp2.y*p3.x-dp2.x*p3.y]]) - - if linalg.det(A) == 0:#crossProduct(ds1, ds2) == 0: - return None + inter = intersection(p1, p2, p3, p4) + if (inter != None + and utils.inBetween(p1.x, p2.x, inter.x) + and utils.inBetween(p3.x, p4.x, inter.x) + and utils.inBetween(p1.y, p2.y, inter.y) + and utils.inBetween(p3.y, p4.y, inter.y)): + return inter else: - intersection = linalg.solve(A,B) - if (utils.inBetween(p1.x, p2.x, intersection[0,0]) - and utils.inBetween(p3.x, p4.x, intersection[0,0]) - and utils.inBetween(p1.y, p2.y, intersection[1,0]) - and utils.inBetween(p3.y, p4.y, intersection[1,0])): - return Point(intersection[0,0], intersection[1,0]) - else: - return None - -# TODO: implement a better algorithm for intersections of sets of segments http://en.wikipedia.org/wiki/Line_segment_intersection + return None class Trajectory(object): '''Class for trajectories: temporal sequence of positions @@ -345,6 +587,16 @@ self.positions = [[],[]] @staticmethod + def generate(p, v, nPoints): + t = Trajectory() + p0 = Point(p.x, p.y) + t.addPosition(p0) + for i in xrange(nPoints-1): + p0 += v + t.addPosition(p0) + return t, Trajectory([[v.x]*nPoints, [v.y]*nPoints]) + + @staticmethod def load(line1, line2): return Trajectory([[float(n) for n in line1.split(' ')], [float(n) for n in line2.split(' ')]]) @@ -352,18 +604,36 @@ @staticmethod def fromPointList(points): t = Trajectory() - for p in points: - t.addPosition(p) + if isinstance(points[0], list) or isinstance(points[0], tuple): + for p in points: + t.addPositionXY(p[0],p[1]) + else: + for p in points: + t.addPosition(p) return t + def __len__(self): + return len(self.positions[0]) + + def length(self): + return self.__len__() + + def empty(self): + return self.__len__() == 0 + + def __getitem__(self, i): + if isinstance(i, int): + return Point(self.positions[0][i], self.positions[1][i]) + else: + raise TypeError, "Invalid argument type." + #elif isinstance( key, slice ): + def __str__(self): return ' '.join([self.__getitem__(i).__str__() for i in xrange(self.length())]) def __repr__(self): - return str(self) + return self.__str__() - def __getitem__(self, i): - return Point(self.positions[0][i], self.positions[1][i]) def __iter__(self): self.iterInstantNum = 0 @@ -376,11 +646,13 @@ self.iterInstantNum += 1 return self[self.iterInstantNum-1] - def length(self): - return len(self.positions[0]) + def setPositionXY(self, i, x, y): + if i < self.__len__(): + self.positions[0][i] = x + self.positions[1][i] = y - def __len__(self): - return self.length() + def setPosition(self, i, p): + self.setPositionXY(i, p.x, p.y) def addPositionXY(self, x, y): self.positions[0].append(x) @@ -389,8 +661,12 @@ def addPosition(self, p): self.addPositionXY(p.x, p.y) + def duplicateLastPosition(self): + self.positions[0].append(self.positions[0][-1]) + self.positions[1].append(self.positions[1][-1]) + @staticmethod - def _draw(positions, options = '', withOrigin = False, lastCoordinate = None, timeStep = 1, **kwargs): + def _plot(positions, options = '', withOrigin = False, lastCoordinate = None, timeStep = 1, **kwargs): from matplotlib.pylab import plot if lastCoordinate == None: plot(positions[0][::timeStep], positions[1][::timeStep], options, **kwargs) @@ -400,21 +676,19 @@ plot([positions[0][0]], [positions[1][0]], 'ro', **kwargs) def project(self, homography): - from numpy.core.multiarray import array - projected = cvutils.projectArray(homography, array(self.positions)) - return Trajectory(projected) + return Trajectory(cvutils.projectTrajectory(homography, self.positions)) + + def plot(self, options = '', withOrigin = False, timeStep = 1, **kwargs): + Trajectory._plot(self.positions, options, withOrigin, None, timeStep, **kwargs) - def draw(self, options = '', withOrigin = False, timeStep = 1, **kwargs): - Trajectory._draw(self.positions, options, withOrigin, None, timeStep, **kwargs) + def plotAt(self, lastCoordinate, options = '', withOrigin = False, timeStep = 1, **kwargs): + Trajectory._plot(self.positions, options, withOrigin, lastCoordinate, timeStep, **kwargs) - def drawAt(self, lastCoordinate, options = '', withOrigin = False, timeStep = 1, **kwargs): - Trajectory._draw(self.positions, options, withOrigin, lastCoordinate, timeStep, **kwargs) - - def drawOnWorldImage(self, nPixelsPerUnitDistance, imageHeight, options = '', withOrigin = False, timeStep = 1, **kwargs): + def plotOnWorldImage(self, nPixelsPerUnitDistance, imageHeight, options = '', withOrigin = False, timeStep = 1, **kwargs): from matplotlib.pylab import plot imgPositions = [[x*nPixelsPerUnitDistance for x in self.positions[0]], [-x*nPixelsPerUnitDistance+imageHeight for x in self.positions[1]]] - Trajectory._draw(imgPositions, options, withOrigin, timeStep, **kwargs) + Trajectory._plot(imgPositions, options, withOrigin, timeStep, **kwargs) def getXCoordinates(self): return self.positions[0] @@ -452,6 +726,14 @@ return Trajectory([[a-b for a,b in zip(self.getXCoordinates(),traj2.getXCoordinates())], [a-b for a,b in zip(self.getYCoordinates(),traj2.getYCoordinates())]]) + def differentiate(self, doubleLastPosition = False): + diff = Trajectory() + for i in xrange(1, self.length()): + diff.addPosition(self[i]-self[i-1]) + if doubleLastPosition: + diff.addPosition(diff[-1]) + return diff + def norm(self): '''Returns the list of the norms at each instant''' # def add(x, y): return x+y @@ -460,12 +742,52 @@ from numpy import hypot return hypot(self.positions[0], self.positions[1]) - def cumulatedDisplacement(self): - 'Returns the sum of the distances between each successive point' - displacement = 0 + # def cumulatedDisplacement(self): + # 'Returns the sum of the distances between each successive point' + # displacement = 0 + # for i in xrange(self.length()-1): + # displacement += Point.distanceNorm2(self.__getitem__(i),self.__getitem__(i+1)) + # return displacement + + def computeCumulativeDistances(self): + '''Computes the distance from each point to the next and the cumulative distance up to the point + Can be accessed through getDistance(idx) and getCumulativeDistance(idx)''' + self.distances = [] + self.cumulativeDistances = [0.] + p1 = self[0] + cumulativeDistance = 0. for i in xrange(self.length()-1): - displacement += Point.distanceNorm2(self.__getitem__(i),self.__getitem__(i+1)) - return displacement + p2 = self[i+1] + self.distances.append(Point.distanceNorm2(p1,p2)) + cumulativeDistance += self.distances[-1] + self.cumulativeDistances.append(cumulativeDistance) + p1 = p2 + + def getDistance(self,i): + '''Return the distance between points i and i+1''' + if i < self.length()-1: + return self.distances[i] + else: + print('Index {} beyond trajectory length {}-1'.format(i, self.length())) + + def getCumulativeDistance(self, i): + '''Return the cumulative distance between the beginning and point i''' + if i < self.length(): + return self.cumulativeDistances[i] + else: + print('Index {} beyond trajectory length {}'.format(i, self.length())) + + def similarOrientation(self, refDirection, cosineThreshold, minProportion = 0.5): + '''Indicates whether the minProportion (<=1.) (eg half) of the trajectory elements (vectors for velocity) + have a cosine with refDirection is smaller than cosineThreshold''' + count = 0 + lengthThreshold = float(self.length())*minProportion + for p in self: + if p.similarOrientation(refDirection, cosineThreshold): + count += 1 + if count > lengthThreshold: + return True + return False def wiggliness(self): return self.cumulatedDisplacement()/float(Point.distanceNorm2(self.__getitem__(0),self.__getitem__(self.length()-1))) @@ -497,34 +819,104 @@ else: return None - def getTrajectoryInPolygon(self, polygon): - '''Returns the set of points inside the polygon + def getTrajectoryInPolygonNoShapely(self, polygon): + '''Returns the trajectory built with the set of points inside the polygon (array of Nx2 coordinates of the polygon vertices)''' - import matplotlib.nxutils as nx traj = Trajectory() - result = nx.points_inside_poly(self.asArray().T, polygon) - for i in xrange(self.length()): - if result[i]: - traj.addPositionXY(self.positions[0][i], self.positions[1][i]) + for p in self: + if p.inPolygonNoShapely(polygon): + traj.addPosition(p) return traj - # version 2: use shapely polygon contains - + if shapelyAvailable: + def getTrajectoryInPolygon(self, polygon): + '''Returns the trajectory built with the set of points inside the (shapely) polygon''' + traj = Trajectory() + points = [p.asShapely() for p in self] + for p in pointsInPolygon(points, polygon): + traj.addPositionXY(p.x, p.y) + return traj + @staticmethod - def norm2LCSS(t1, t2, threshold): - return utils.LCSS(t1, t2, threshold, Point.distanceNorm2) + def lcss(t1, t2, lcss): + return lcss.compute(t1, t2) + +class CurvilinearTrajectory(Trajectory): + '''Sub class of trajectory for trajectories with curvilinear coordinates and lane assignements + longitudinal coordinate is stored as first coordinate (exterior name S) + lateral coordiante is stored as second coordinate''' + + def __init__(self, S = None, Y = None, lanes = None): + if S == None or Y == None or len(S) != len(Y): + self.positions = [[],[]] + if S != None and Y != None and len(S) != len(Y): + print("S and Y coordinates of different lengths\nInitializing to empty lists") + else: + self.positions = [S,Y] + if lanes == None or len(lanes) != self.length(): + self.lanes = [] + else: + self.lanes = lanes + + def __getitem__(self,i): + if isinstance(i, int): + return [self.positions[0][i], self.positions[1][i], self.lanes[i]] + else: + raise TypeError, "Invalid argument type." + #elif isinstance( key, slice ): + + def getSCoordinates(self): + return self.getXCoordinates() + + def getLanes(self): + return self.lanes - @staticmethod - def normMaxLCSS(t1, t2, threshold): - return utils.LCSS(t1, t2, threshold, lambda p1, p2: (p1-p2).normMax()) + def addPositionSYL(self, s, y, lane): + self.addPositionXY(s,y) + self.lanes.append(lane) + + def addPosition(self, p): + 'Adds position in the point format for curvilinear of list with 3 values' + self.addPositionSYL(p[0], p[1], p[2]) + + def setPosition(self, i, s, y, lane): + self.setPositionXY(i, s, y) + if i < self.__len__(): + self.lanes[i] = lane + + def differentiate(self, doubleLastPosition = False): + diff = CurvilinearTrajectory() + p1 = self[0] + for i in xrange(1, self.length()): + p2 = self[i] + diff.addPositionSYL(p2[0]-p1[0], p2[1]-p1[1], p1[2]) + p1=p2 + if doubleLastPosition and self.length() > 1: + diff.addPosition(diff[-1]) + return diff + + def getIntersections(self, S1, lane = None): + '''Returns a list of the indices at which the trajectory + goes past the curvilinear coordinate S1 + (in provided lane if lane != None) + the list is empty if there is no crossing''' + indices = [] + for i in xrange(self.length()-1): + q1=self.__getitem__(i) + q2=self.__getitem__(i+1) + if q1[0] <= S1 < q2[0] and (lane == None or (self.lanes[i] == lane and self.lanes[i+1] == lane)): + indices.append(i+(S1-q1[0])/(q2[0]-q1[0])) + return indices ################## # Moving Objects ################## -userTypeNames = ['car', +userTypeNames = ['unknown', + 'car', 'pedestrian', - 'twowheels', + 'motorcycle', + 'bicycle', 'bus', 'truck'] @@ -532,16 +924,24 @@ class MovingObject(STObject): '''Class for moving objects: a spatio-temporal object - with a trajectory and a geometry (constant volume over time) and a usertype (e.g. road user) + with a trajectory and a geometry (constant volume over time) + and a usertype (e.g. road user) coded as a number (see userTypeNames) ''' - def __init__(self, num = None, timeInterval = None, positions = None, geometry = None, userType = None): + def __init__(self, num = None, timeInterval = None, positions = None, velocities = None, geometry = None, userType = userType2Num['unknown']): super(MovingObject, self).__init__(num, timeInterval) self.positions = positions + self.velocities = velocities self.geometry = geometry self.userType = userType + self.features = [] # compute bounding polygon from trajectory + @staticmethod + def generate(p, v, timeInterval): + positions, velocities = Trajectory.generate(p, v, int(timeInterval.length())) + return MovingObject(timeInterval = timeInterval, positions = positions, velocities = velocities) + def getObjectInTimeInterval(self, inter): '''Returns a new object extracted from self, restricted to time interval inter''' @@ -565,6 +965,18 @@ def getVelocities(self): return self.velocities + def getUserType(self): + return self.userType + + def getCurvilinearPositions(self): + if hasattr(self, 'curvilinearPositions'): + return self.curvilinearPositions + else: + return None + + def setUserType(self, userType): + self.userType = userType + def setFeatures(self, features): self.features = [features[i] for i in self.featureNumbers] @@ -589,15 +1001,73 @@ def getYCoordinates(self): return self.positions.getYCoordinates() - def draw(self, options = '', withOrigin = False, timeStep = 1, **kwargs): - self.positions.draw(options, withOrigin, timeStep, **kwargs) + def plot(self, options = '', withOrigin = False, timeStep = 1, withFeatures = False, **kwargs): + if withFeatures: + for f in self.features: + f.positions.plot('r', True, timeStep, **kwargs) + self.positions.plot('bx-', True, timeStep, **kwargs) + else: + self.positions.plot(options, withOrigin, timeStep, **kwargs) - def drawOnWorldImage(self, nPixelsPerUnitDistance, imageHeight, options = '', withOrigin = False, timeStep = 1, **kwargs): - self.positions.drawOnWorldImage(nPixelsPerUnitDistance, imageHeight, options, withOrigin, timeStep, **kwargs) + def plotOnWorldImage(self, nPixelsPerUnitDistance, imageHeight, options = '', withOrigin = False, timeStep = 1, **kwargs): + self.positions.plotOnWorldImage(nPixelsPerUnitDistance, imageHeight, options, withOrigin, timeStep, **kwargs) def play(self, videoFilename, homography = None): cvutils.displayTrajectories(videoFilename, [self], homography, self.getFirstInstant(), self.getLastInstant()) + def speedDiagnostics(self, framerate = 1., display = False): + from numpy import std + from scipy.stats import scoreatpercentile + speeds = framerate*self.getSpeeds() + coef = utils.linearRegression(range(len(speeds)), speeds) + print('min/5th perc speed: {} / {}\nspeed diff: {}\nspeed stdev: {}\nregression: {}'.format(min(speeds), scoreatpercentile(speeds, 5), speeds[-2]-speeds[1], std(speeds), coef[0])) + if display: + from matplotlib.pyplot import figure, plot, axis + figure(1) + self.plot() + axis('equal') + figure(2) + plot(list(self.getTimeInterval()), speeds) + + @staticmethod + def distances(obj1, obj2, instant1, _instant2 = None): + from scipy.spatial.distance import cdist + if _instant2 == None: + instant2 = instant1 + else: + instant2 = _instant2 + positions1 = [f.getPositionAtInstant(instant1).astuple() for f in obj1.features if f.existsAtInstant(instant1)] + positions2 = [f.getPositionAtInstant(instant2).astuple() for f in obj2.features if f.existsAtInstant(instant2)] + return cdist(positions1, positions2, metric = 'euclidean') + + @staticmethod + def minDistance(obj1, obj2, instant1, instant2 = None): + return MovingObject.distances(obj1, obj2, instant1, instant2).min() + + @staticmethod + def maxDistance(obj1, obj2, instant, instant2 = None): + return MovingObject.distances(obj1, obj2, instant1, instant2).max() + + def maxSize(self): + '''Returns the max distance between features + at instant there are the most features''' + if hasattr(self, 'features'): + nFeatures = -1 + tMaxFeatures = 0 + for t in self.getTimeInterval(): + n = len([f for f in self.features if f.existsAtInstant(t)]) + if n > nFeatures: + nFeatures = n + tMaxFeatures = t + return MovingObject.maxDistance(self, self, tMaxFeatures) + else: + print('Load features to compute a maximum size') + return None + + def setRoutes(self, startRouteID, endRouteID): + self.startRouteID = startRouteID + self.endRouteID = endRouteID + def getInstantsCrossingLane(self, p1, p2): '''Returns the instant(s) at which the object passes from one side of the segment to the other @@ -610,6 +1080,190 @@ at constant speed''' return predictPositionNoLimit(nTimeSteps, self.getPositionAtInstant(instant), self.getVelocityAtInstant(instant), externalAcceleration) + def projectCurvilinear(self, alignments, ln_mv_av_win=3): + ''' Add, for every object position, the class 'moving.CurvilinearTrajectory()' + (curvilinearPositions instance) which holds information about the + curvilinear coordinates using alignment metadata. + From Paul St-Aubin's PVA tools + ====== + + Input: + ====== + alignments = a list of alignments, where each alignment is a list of + points (class Point). + ln_mv_av_win = moving average window (in points) in which to smooth + lane changes. As per tools_math.cat_mvgavg(), this term + is a search *radius* around the center of the window. + + ''' + + self.curvilinearPositions = CurvilinearTrajectory() + + #For each point + for i in xrange(int(self.length())): + result = getSYfromXY(self.getPositionAt(i), alignments) + + # Error handling + if(result == None): + print('Warning: trajectory {} at point {} {} has alignment errors (spline snapping)\nCurvilinear trajectory could not be computed'.format(self.getNum(), i, self.getPositionAt(i))) + else: + [align, alignPoint, snappedPoint, subsegmentDistance, S, Y] = result + self.curvilinearPositions.addPositionSYL(S, Y, align) + + ## Go back through points and correct lane + #Run through objects looking for outlier point + smoothed_lanes = utils.cat_mvgavg(self.curvilinearPositions.getLanes(),ln_mv_av_win) + ## Recalculate projected point to new lane + lanes = self.curvilinearPositions.getLanes() + if(lanes != smoothed_lanes): + for i in xrange(len(lanes)): + if(lanes[i] != smoothed_lanes[i]): + result = getSYfromXY(self.getPositionAt(i),[alignments[smoothed_lanes[i]]]) + + # Error handling + if(result == None): + ## This can be triggered by tracking errors when the trajectory jumps around passed another alignment. + print(' Warning: trajectory {} at point {} {} has alignment errors during trajectory smoothing and will not be corrected.'.format(self.getNum(), i, self.getPositionAt(i))) + else: + [align, alignPoint, snappedPoint, subsegmentDistance, S, Y] = result + self.curvilinearPositions.setPosition(i, S, Y, align) + + def computeSmoothTrajectory(self, minCommonIntervalLength): + '''Computes the trajectory as the mean of all features + if a feature exists, its position is + + Warning work in progress + TODO? not use the first/last 1-.. positions''' + from numpy import array, median + nFeatures = len(self.features) + if nFeatures == 0: + print('Empty object features\nCannot compute smooth trajectory') + else: + # compute the relative position vectors + relativePositions = {} # relativePositions[(i,j)] is the position of j relative to i + for i in xrange(nFeatures): + for j in xrange(i): + fi = self.features[i] + fj = self.features[j] + inter = fi.commonTimeInterval(fj) + if inter.length() >= minCommonIntervalLength: + xi = array(fi.getXCoordinates()[inter.first-fi.getFirstInstant():int(fi.length())-(fi.getLastInstant()-inter.last)]) + yi = array(fi.getYCoordinates()[inter.first-fi.getFirstInstant():int(fi.length())-(fi.getLastInstant()-inter.last)]) + xj = array(fj.getXCoordinates()[inter.first-fj.getFirstInstant():int(fj.length())-(fj.getLastInstant()-inter.last)]) + yj = array(fj.getYCoordinates()[inter.first-fj.getFirstInstant():int(fj.length())-(fj.getLastInstant()-inter.last)]) + relativePositions[(i,j)] = Point(median(xj-xi), median(yj-yi)) + relativePositions[(j,i)] = -relativePositions[(i,j)] + + ### + # User Type Classification + ### + def classifyUserTypeSpeedMotorized(self, threshold, aggregationFunc = median, ignoreNInstantsAtEnds = 0): + '''Classifies slow and fast road users + slow: non-motorized -> pedestrians + fast: motorized -> cars''' + if ignoreNInstantsAtEnds > 0: + speeds = self.getSpeeds()[ignoreNInstantsAtEnds:-ignoreNInstantsAtEnds] + else: + speeds = self.getSpeeds() + if aggregationFunc(speeds) >= threshold: + self.setUserType(userType2Num['car']) + else: + self.setUserType(userType2Num['pedestrian']) + + def classifyUserTypeSpeed(self, speedProbabilities, aggregationFunc = median): + '''Classifies road user per road user type + speedProbabilities are functions return P(speed|class) + in a dictionary indexed by user type names + Returns probabilities for each class + + for simple threshold classification, simply pass non-overlapping indicator functions (membership) + e.g. def indic(x): + if abs(x-mu) < sigma: + return 1 + else: + return x''' + if not hasattr(self, aggregatedSpeed): + self.aggregatedSpeed = aggregationFunc(self.getSpeeds()) + userTypeProbabilities = {} + for userTypename in speedProbabilities: + userTypeProbabilities[userType2Num[userTypename]] = speedProbabilities[userTypename](self.aggregatedSpeed) + self.setUserType(utils.argmaxDict(userTypeProbabilities)) + return userTypeProbabilities + + def initClassifyUserTypeHoGSVM(self, aggregationFunc = median): + '''Initializes the data structures for classification + + TODO? compute speed for longest feature? + Skip beginning and end of feature for speed? Offer options instead of median''' + self.aggregatedSpeed = aggregationFunc(self.getSpeeds()) + self.userTypes = {} + + def classifyUserTypeHoGSVMAtInstant(self, img, pedBikeCarSVM, instant, homography, width, height, bikeCarSVM = None, pedBikeSpeedTreshold = float('Inf'), bikeCarSpeedThreshold = float('Inf'), px = 0.2, py = 0.2, pixelThreshold = 800): + '''Extract the image box around the object and + applies the SVM model on it''' + from numpy import array + croppedImg, yCropMin, yCropMax, xCropMin, xCropMax = imageBox(img, self, instant, homography, width, height, px, py, pixelThreshold) + if len(croppedImg) > 0: # != [] + hog = array([cvutils.HOG(croppedImg)], dtype = np.float32) + if self.aggregatedSpeed < pedBikeSpeedTreshold or bikeCarSVM == None: + self.userTypes[instant] = int(pedBikeCarSVM.predict(hog)) + elif self.aggregatedSpeed < bikeCarSpeedTreshold: + self.userTypes[instant] = int(bikeCarSVM.predict(hog)) + else: + self.userTypes[instant] = userType2Num['car'] + else: + self.userTypes[instant] = userType2Num['unknown'] + + def classifyUserTypeHoGSVM(self, images, pedBikeCarSVM, homography, width, height, bikeCarSVM = None, pedBikeSpeedTreshold = float('Inf'), bikeCarSpeedThreshold = float('Inf'), speedProbabilities = None, aggregationFunc = median, px = 0.2, py = 0.2, pixelThreshold = 800): + '''Agregates SVM detections in each image and returns probability + (proportion of instants with classification in each category) + + iamges is a dictionary of images indexed by instant + With default parameters, the general (ped-bike-car) classifier will be used + TODO? consider all categories?''' + if not hasattr(self, aggregatedSpeed) or not hasattr(self, userTypes): + print('Initilize the data structures for classification by HoG-SVM') + self.initClassifyUserTypeHoGSVM(aggregationFunc) + + if len(self.userTypes) != self.length(): # if classification has not been done previously + for t in self.getTimeInterval(): + if t not in self.userTypes: + self.classifyUserTypeHoGSVMAtInstant(images[t], pedBikeCarSVM, t, homography, width, height, bikeCarSVM, pedBikeSpeedTreshold, bikeCarSpeedThreshold, px, py, pixelThreshold) + # compute P(Speed|Class) + if speedProbabilities == None: # equiprobable information from speed + userTypeProbabilities = {userType2Num['car']: 1., userType2Num['pedestrian']: 1., userType2Num['bicycle']: 1.} + else: + userTypeProbabilities = {userType2Num[userTypename]: speedProbabilities[userTypename](self.aggregatedSpeed) for userTypename in speedProbabilities} + # result is P(Class|Appearance) x P(Speed|Class) + nInstantsUserType = {userType2Num[userTypename]: 0 for userTypename in userTypeProbabilities}# number of instants the object is classified as userTypename + for t in self.userTypes: + nInstantsUserType[self.userTypes[t]] += 1 + for userTypename in userTypeProbabilities: + userTypeProbabilities[userTypename] *= nInstantsUserType[userTypename] + # class is the user type that maximizes usertype probabilities + self.setUserType(utils.argmaxDict(userTypeProbabilities)) + + def classifyUserTypeArea(self, areas, homography): + '''Classifies the object based on its location (projected to image space) + areas is a dictionary of matrix of the size of the image space + for different road users possible locations, indexed by road user type names + + TODO: areas could be a wrapper object with a contains method that would work for polygons and images (with wrapper class) + skip frames at beginning/end?''' + print('not implemented/tested yet') + if not hasattr(self, projectedPositions): + if homography != None: + self.projectedPositions = obj.positions.project(homography) + else: + self.projectedPositions = obj.positions + possibleUserTypes = {userType: 0 for userType in range(len(userTypenames))} + for p in self.projectedPositions: + for userTypename in areas: + if areas[userTypename][p.x, p.y] != 0: + possibleUserTypes[userType2Enum[userTypename]] += 1 + # what to do: threshold for most common type? self.setUserType() + return possibleUserTypes + @staticmethod def collisionCourseDotProduct(movingObject1, movingObject2, instant): 'A positive result indicates that the road users are getting closer' @@ -620,16 +1274,33 @@ @staticmethod def collisionCourseCosine(movingObject1, movingObject2, instant): 'A positive result indicates that the road users are getting closer' - deltap = movingObject1.getPositionAtInstant(instant)-movingObject2.getPositionAtInstant(instant) - deltav = movingObject2.getVelocityAtInstant(instant)-movingObject1.getVelocityAtInstant(instant) - return Point.dot(deltap, deltav)/(deltap.norm2()*deltav.norm2()) + return Point.cosine(movingObject1.getPositionAtInstant(instant)-movingObject2.getPositionAtInstant(instant), #deltap + movingObject2.getVelocityAtInstant(instant)-movingObject1.getVelocityAtInstant(instant)) #deltav + + +################## +# Annotations +################## + +class BBAnnotation(MovingObject): + '''Class for : a series of ground truth annotations using bounding boxes + Its center is the center of the containing shape + ''' + + def __init__(self, num = None, timeInterval = None, topPositions = None, bottomPositions = None, userType = userType2Num['unknown']): + super(BBAnnotation, self).__init__(num, timeInterval, Trajectory(), userType = userType) + self.topPositions = topPositions.getPositions() + self.bottomPositions = bottomPositions.getPositions() + for i in xrange(int(topPositions.length())): + self.positions.addPosition((topPositions.getPositionAt(i) + bottomPositions.getPositionAt(i)).multiply(0.5)) + def plotRoadUsers(objects, colors): '''Colors is a PlottingPropertyValues instance''' from matplotlib.pyplot import figure, axis figure() for obj in objects: - obj.draw(colors.get(obj.userType)) + obj.plot(colors.get(obj.userType)) axis('equal')
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/python/objectsmoothing.py Fri Dec 05 12:13:53 2014 -0500 @@ -0,0 +1,131 @@ +import storage, moving, utils +from math import * #atan2,asin,degrees,sin,cos,pi +import numpy as np + +import matplotlib.pyplot as plt + +def findNearest(feat, featureSet,t,reverse=True): + dist={} + for f in featureSet: + if reverse: + dist[f]= moving.Point.distanceNorm2(feat.getPositionAtInstant(t+1),f.getPositionAtInstant(t)) + else: + dist[f]= moving.Point.distanceNorm2(feat.getPositionAtInstant(t-1),f.getPositionAtInstant(t)) + return min(dist, key=dist.get) # = utils.argmaxDict(dist) + +def getFeatures(obj): + longestFeature = utils.argmaxDict({f:f.length() for i,f in enumerate(obj.features)}) + t1,t3 = longestFeature.getFirstInstant(), longestFeature.getLastInstant() + listFeatures=[[longestFeature,t1,t3,moving.Point(0,0)]] + # find the features to fill in the beginning of the object existence + currentFeature = longestFeature + while t1!=obj.getFirstInstant(): + delta=listFeatures[-1][3] + featureSet = [f for f in obj.features if f.existsAtInstant(t1-1)] + feat = findNearest(currentFeature,featureSet,t1-1,reverse=True) + if feat.existsAtInstant(t1): + listFeatures.append([feat,feat.getFirstInstant(),t1-1,(currentFeature.getPositionAtInstant(t1)-feat.getPositionAtInstant(t1))+delta]) + else: + listFeatures.append([feat,feat.getFirstInstant(),t1-1,(currentFeature.getPositionAtInstant(t1)-feat.getPositionAtInstant(t1-1))+delta]) + currentFeature = feat + t1= feat.getFirstInstant() + # find the features to fill in the end of the object existence + delta=moving.Point(0,0) + currentFeature = longestFeature + while t3!= obj.getLastInstant(): + featureSet = [f for f in obj.features if f.existsAtInstant(t3+1)] + feat = findNearest(currentFeature,featureSet,t3+1,reverse=False) + if feat.existsAtInstant(t3): + listFeatures.append([feat,t3+1,feat.getLastInstant(),(currentFeature.getPositionAtInstant(t3)-feat.getPositionAtInstant(t3))+delta]) + else: + listFeatures.append([feat,t3+1,feat.getLastInstant(),(currentFeature.getPositionAtInstant(t3)-feat.getPositionAtInstant(t3+1))+delta]) + currentFeature = feat + t3= feat.getLastInstant() + delta=listFeatures[-1][3] + return listFeatures + +def buildFeature(obj,num=1): + listFeatures= getFeatures(obj) + tmp={} + delta={} + for i in listFeatures: + for t in xrange(i[1],i[2]+1): + tmp[t]=[i[0],i[3]] + newTraj = moving.Trajectory() + + for instant in obj.getTimeInterval(): + newTraj.addPosition(tmp[instant][0].getPositionAtInstant(instant)+tmp[instant][1]) + newFeature= moving.MovingObject(num,timeInterval=obj.getTimeInterval(),positions=newTraj) + return newFeature + +def getBearing(p1,p2,p3): + angle = degrees(atan2(p3.y -p1.y, p3.x -p1.x)) + bearing1 = (90 - angle) % 360 + angle2 = degrees(atan2(p2.y -p1.y, p2.x -p1.x)) + bearing2 = (90 - angle2) % 360 + dist= moving.Point.distanceNorm2(p1, p2) + return [dist,bearing1,bearing2,bearing2-bearing1] + +def smoothObjectTrajectory(obj,newNum,smoothing=False,plotResults=True,halfWidth=3): + results=[] + bearing={} + feature= buildFeature(obj,1) + for t in feature.getTimeInterval(): + p1= feature.getPositionAtInstant(t) + p2= obj.getPositionAtInstant(t) + if t!=feature.getLastInstant(): + p3= feature.getPositionAtInstant(t+1) + else: + p1= feature.getPositionAtInstant(t-1) + p3= feature.getPositionAtInstant(t) + bearing[t]= getBearing(p1,p2,p3)[1] + results.append(getBearing(p1,p2,p3)) + + medianResults=np.median(results,0) + dist= medianResults[0] + angle= medianResults[3] + + for i in sorted(bearing.keys()): + bearing[i]= bearing[i]+angle + + if smoothing: + bearingInput=[] + for i in sorted(bearing.keys()): + bearingInput.append(bearing[i]) + bearingOut=utils.filterMovingWindow(bearingInput, halfWidth) + for t,i in enumerate(sorted(bearing.keys())): + bearing[i]=bearingOut[t] + + #solve a smoothing problem in case of big drop in computing bearing (0,360) + for t,i in enumerate(sorted(bearing.keys())): + if i!= max(bearing.keys()) and abs(bearingInput[t] - bearingInput[t+1])>=340: + for x in xrange(max(i-halfWidth,min(bearing.keys())),min(i+halfWidth,max(bearing.keys()))+1): + bearing[x]=bearingInput[t-i+x] + + translated = moving.Trajectory() + for t in feature.getTimeInterval(): + p1= feature.getPositionAtInstant(t) + p1.x = p1.x + dist*sin(bearing[t]*pi/180) + p1.y = p1.y + dist*cos(bearing[t]*pi/180) + translated.addPosition(p1) + + #modify first and last un-smoothed positions (half width) + if smoothing: + d1= translated[halfWidth]- feature.positions[halfWidth] + d2= translated[-halfWidth-1]- feature.positions[-halfWidth-1] + for i in xrange(halfWidth): + p1 = feature.getPositionAt(i)+d1 + p2 = feature.getPositionAt(-i-1)+d2 + translated.setPosition(i,p1) + translated.setPosition(-i-1,p2) + + newObj= moving.MovingObject(newNum,timeInterval=feature.getTimeInterval(),positions=translated,velocities=obj.getVelocities()) + newObj.features=obj.features + newObj.userType=obj.userType + if plotResults: + plt.figure() + plt.title('objects_id = {}'.format(obj.num)) + newObj.plot('gx-') + feature.plot('cx-') + obj.plot('rx-') + return newObj
--- a/python/offset-trajectories.py Thu Apr 18 15:29:33 2013 -0400 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,23 +0,0 @@ -#! /usr/bin/env python -import sys - -import ubc_utils -import utils - -if len(sys.argv) < 3: - print("Usage: %s filename offsetframes") - sys.exit() - -nFrames = int(sys.argv[2]) - -def modifyLines(objectNum, lines): - result = lines - tmp = lines[0].split(" ") - firstInstant = int(tmp[1])+nFrames - lastInstant = int(tmp[2])+nFrames - tmp[1] = str(firstInstant) - tmp[2] = str(lastInstant) - result[0] = " ".join(tmp) - return result - -ubc_utils.modifyTrajectoryFile(modifyLines, sys.argv[1], sys.argv[1]+".new")
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/python/pavement.py Fri Dec 05 12:13:53 2014 -0500 @@ -0,0 +1,314 @@ +#! /usr/bin/env python +'''Tools for processing and analyzing pavement marking data''' + +import utils + +import numpy as np + +__metaclass__ = type + +paintTypes = {0: "Non-existant", + 1: "Eau", + 2: "Epoxy", + 3: "Alkyde", + 4: "Autre"} + +durabilities = {1: 98, #96 to 100 + 2: 85, #75 to 96 + 3: 62, #50 to 75 + 4: 32, #15 to 50 + 5: 7 #0 to 15 + } + +roadFunctionalClasses = {40: "Collectrice", + 20: "Nationale", + 30: "Regionale", + 10: "Autoroute", + 60: "Acces ressources", + 51: "Local 1", + 52: "Local 2", + 53: "Local 3", + 15: "Aut (PRN)", + 25: "Nat (PRN)", + 70: "Acces isolees", + 99: "Autres"} + +def caracteristiques(rtss, maintenanceLevel, rtssWeatherStation, fmr, paintType): + '''Computes characteristic data for the RTSS (class rtss) + maintenanceLevel = pylab.csv2rec('C:\Users\Alexandre\Desktop\Projet_maitrise_recherche\BDD_access\\analyse_donnees_deneigement\\exigence_circuits.txt', delimiter = ';') + rtssWeatherStation = pylab.csv2rec('C:\Users\Alexandre\Desktop\Projet_maitrise_recherche\stations_environnement_canada\\rtssWeatherStation\juste_pour_rtss_avec_donnees_entretien_hiv\\rtssWeatherStation_EC3.txt', delimiter = ',') + fmr = pylab.csv2rec('C:\Users\Alexandre\Desktop\Projet_maitrise_recherche\BDD_access\\analyse_donnees_deneigement\\fmr.txt', delimiter = ';') + paintType = pylab.csv2rec('C:\Users\Alexandre\Desktop\Projet_maitrise_recherche\BDD_access\\analyse_donnees_deneigement\\type_peinture.txt', delimiter = ';') + ''' + # determination exigence deneigement + if rtss.id in maintenanceLevel['rtss_debut']: + for i in range(len(maintenanceLevel)): + if maintenanceLevel['rtss_debut'][i] == rtss.id: + exigence = maintenanceLevel['exigence'][i] + else: + exigence = '' + + # determination x/y + if rtss.id in rtssWeatherStation['rtss']: + for i in range(len(rtssWeatherStation)): + if rtssWeatherStation['rtss'][i] == rtss.id: + x_moy = rtssWeatherStation['x_moy'][i] + y_moy = rtssWeatherStation['y_moy'][i] + else: + x_moy, y_moy = '','' + + # determination info fmr + age_revtm, classe_fonct, type_revtm, milieu, djma, pourc_camions, vit_max = [], [], [], [], [], [], [] + if rtss.id in fmr['rtss_debut']: + for i in range(len(fmr)): + if fmr['rtss_debut'][i] == rtss.id: + age_revtm.append(fmr['age_revtm'][i]) + classe_fonct.append(fmr['des_clasf_fonct'][i]) + type_revtm.append(fmr['des_type_revtm'][i]) + milieu.append(fmr['des_cod_mil'][i]) + djma.append(fmr['val_djma'][i]) + pourc_camions.append(fmr['val_pourc_camns'][i]) + vit_max.append(fmr['val_limt_vitss'][i]) + age_revtm = utils.mostCommon(age_revtm) + classe_fonct = utils.mostCommon(classe_fonct) + type_revtm = utils.mostCommon(type_revtm) + milieu = utils.mostCommon(milieu) + djma = utils.mostCommon(djma) + vit_max = utils.mostCommon(vit_max) + if vit_max < 0: + vit_max = '' + pourc_camions = utils.mostCommon(pourc_camions) + if pourc_camions == "" or pourc_camions < 0: + djma_camions = "" + else: + djma_camions = pourc_camions*djma/100 + else: + age_revtm, classe_fonct, type_revtm, milieu, djma, djma_camions, vit_max = '','','','','','','' + + # determination type peinture + peinture_rd, peinture_rg, peinture_cl = [], [], [] + peinture_lrd, peinture_lrg, peinture_lc = 0,0,0 + if rtss.id in paintType['rtss_debut_orig']: + for i in range(len(paintType)): + if paintType['rtss_debut_orig'][i] == rtss.id: + peinture_rd.append((paintType['peinture_rd'][i])) + peinture_rg.append((paintType['peinture_rg'][i])) + peinture_cl.append((paintType['peinture_cl'][i])) + peinture_lrd = utils.mostCommon(peinture_rd) + peinture_lrg = utils.mostCommon(peinture_rg) + peinture_lc = utils.mostCommon(peinture_cl) + else: + peinture_lrd, peinture_lrg, peinture_lc = '','','' + + return (exigence, x_moy, y_moy, age_revtm, classe_fonct, type_revtm, milieu, djma, djma_camions, vit_max, peinture_lrd, peinture_lrg, peinture_lc) + +def winterMaintenanceIndicators(data, startDate, endDate, circuitReference, snowThreshold): + '''Computes several winter maintenance indicators + data = entretien_hivernal = pylab.csv2rec('C:\Users\Alexandre\Documents\Cours\Poly\Projet\mesures_entretien_hivernal\mesures_deneigement.txt', delimiter = ',')''' + import datetime + somme_eau, somme_neige, somme_abrasif, somme_sel, somme_lc, somme_lrg, somme_lrd, compteur_premiere_neige, compteur_somme_abrasif = 0,0,0,0,0,0,0,0,0 + + if circuitReference in data['ref_circuit']: + for i in range(len(data)): + if data['ref_circuit'][i] == circuitReference and (data['date'][i] + datetime.timedelta(days = 6)) <= endDate and (data['date'][i] + datetime.timedelta(days = 6)) > startDate: + compteur_premiere_neige += float(data['premiere_neige'][i]) + somme_neige += float(data['neige'][i]) + somme_eau += float(data['eau'][i]) + somme_abrasif += float(data['abrasif'][i]) + somme_sel += float(data['sel'][i]) + somme_lc += float(data['lc'][i]) + somme_lrg += float(data['lrg'][i]) + somme_lrd += float(data['lrd'][i]) + compteur_somme_abrasif += float(data['autre_abrasif_binaire'][i]) + if compteur_premiere_neige >= 1: + premiere_neige = 1 + else: + premiere_neige = 0 + if compteur_somme_abrasif >= 1: + autres_abrasifs = 1 + else: + autres_abrasifs = 0 + if somme_neige < snowThreshold: + neigeMTQ_sup_seuil = 0 + else: + neigeMTQ_sup_seuil = 1 + else: + somme_eau, somme_neige, somme_abrasif, somme_sel, somme_lc, somme_lrg, somme_lrd, premiere_neige, autres_abrasifs, neigeMTQ_sup_seuil = '','','','','','','','','','' + + return (somme_eau, somme_neige, neigeMTQ_sup_seuil, somme_abrasif, somme_sel, somme_lc, somme_lrg, somme_lrd, premiere_neige, autres_abrasifs) + +def weatherIndicators(data, startDate, endDate, snowThreshold, weatherDatatype, minProportionMeasures = 0.): + '''Computes the indicators from Environment Canada files + (loaded as a recarray using csv2rec in data), + between start and end dates (datetime.datetime objects) + + weatherDataType is to indicate Environnement Canada data ('ec') or else MTQ + minProportionMeasures is proportion of measures necessary to consider the indicators''' + from matplotlib.mlab import find + nbre_jours_T_negatif,nbre_jours_gel_degel,pluie_tot,neige_tot,ecart_type_T = 0,0,0,0,0 + compteur,nbre_jours_gel_consecutifs=0,0 + tmoys = [] + seuils_T = [20,15,10,5] + deltas_T = [0,0,0,0] + startIndex = find(data['date'] == startDate) + nDays = int((endDate - startDate).days)+1 + if len(startIndex) > 0 and startIndex+nDays <= len(data): + startIndex = startIndex[0] + for i in range(startIndex, startIndex+nDays): + if not np.isnan(data['tmax'][i]): + tmax = data['tmax'][i] + else: + tmax = None + if not np.isnan(data['tmin'][i]): + tmin = data['tmin'][i] + else: + tmin = None + if weatherDatatype == 'ec': + if data['pluie_tot'][i] != None and not np.isnan(data['pluie_tot'][i]): + pluie_tot += data['pluie_tot'][i] + if data['neige_tot'][i] != None and not np.isnan(data['neige_tot'][i]): + neige_tot += data['neige_tot'][i] + if tmax != None: + if tmax < 0: + nbre_jours_T_negatif += 1 + if tmax != None and tmin != None: + if tmax > 0 and tmin < 0: + nbre_jours_gel_degel += 1 + for l in range(len(seuils_T)): + if tmax - tmin >=seuils_T[l]: + deltas_T[l] += 1 + if not np.isnan(data['tmoy'][i]): + tmoys.append(data['tmoy'][i]) + if tmax != None: + if tmax < 0: + compteur += 1 + elif tmax >= 0 and compteur >= nbre_jours_gel_consecutifs: + nbre_jours_gel_consecutifs = compteur + compteur = 0 + else: + compteur = 0 + nbre_jours_gel_consecutifs = max(nbre_jours_gel_consecutifs,compteur) + if len(tmoys) > 0 and float(len(tmoys))/nDays >= minProportionMeasures: + if tmoys != []: + ecart_type_T = np.std(tmoys) + else: + ecart_type = None + if neige_tot < snowThreshold: + neigeEC_sup_seuil = 0 + else: + neigeEC_sup_seuil = 1 + return (nbre_jours_T_negatif,nbre_jours_gel_degel, deltas_T, nbre_jours_gel_consecutifs, pluie_tot, neige_tot, neigeEC_sup_seuil, ecart_type_T) + else: + return [None]*2+[[None]*len(seuils_T)]+[None]*5 + +def mtqWeatherIndicators(data, startDate, endDate,tmax,tmin,tmoy): + print("Deprecated, use weatherIndicators") + from matplotlib.mlab import find + nbre_jours_T_negatif,nbre_jours_gel_degel,ecart_type_T = 0,0,0 + compteur,nbre_jours_gel_consecutifs=0,0 + tmoys = [] + seuils_T = [20,15,10,5] + deltas_T = [0,0,0,0] + startIndex = find(data['date'] == startDate) + nDays = (endDate - startDate).days+1 + for i in range(startIndex, startIndex+nDays): + if tmax[i] < 0: + nbre_jours_T_negatif += 1 + if tmax[i] > 0 and tmin[i] < 0: + nbre_jours_gel_degel += 1 + for l in range(len(seuils_T)): + if tmax[i] - tmin[i] >=seuils_T[l]: + deltas_T[l] += 1 + tmoys.append(tmoy[i]) + if tmax[i] < 0: + compteur += 1 + elif tmax[i] >= 0 and compteur >= nbre_jours_gel_consecutifs: + nbre_jours_gel_consecutifs = compteur + compteur = 0 + else: + compteur = 0 + nbre_jours_gel_consecutifs = max(nbre_jours_gel_consecutifs,compteur) + if tmoys != []: + ecart_type_T = np.std(tmoys) + else: + ecart_type = None + + return (nbre_jours_T_negatif,nbre_jours_gel_degel, deltas_T, nbre_jours_gel_consecutifs, ecart_type_T) + +class RTSS: + '''class for data related to a RTSS: + - agregating pavement marking measurements + - RTSS characteristics from FMR: pavement type, age, AADT, truck AADT + - winter maintenance level from V155 + + If divided highway, the RTSS ends with G or D and are distinct: there is no ambiguity + - retroreflectivity types: there are CB, RJ and RB + If undivided, ending with C + - durability is fine: ETAT_MARQG_RG ETAT_MARQG_CL ETAT_MARQG_RD (+SG/SD, but recent) + - retroreflectivity: CJ is center line, RB and SB are left/right if DEBUT-FIN>0 or <0 + ''' + + def __init__(self, _id, name, data): + self.id = _id + self.name = name + self.data = data + +class MarkingTest: + '''class for a test site for a given product + + including the series of measurements over the years''' + + def __init__(self, _id, paintingDate, paintingType, color, data): + self.id = _id + self.paintingDate = paintingDate + self.paintingType = paintingType + self.color = color + self.data = data + self.nMeasures = len(data) + + def getSite(self): + return int(self.id[:2]) + + def getTestAttributes(self): + return [self.paintingType, self.color, self.paintingDate.year] + + def plot(self, measure, options = 'o', dayRatio = 1., **kwargs): + from matplotlib.pyplot import plot + plot(self.data['jours']/float(dayRatio), + self.data[measure], options, **kwargs) + + def getMarkingMeasures(self, dataLabel): + nonZeroIndices = ~np.isnan(self.data[dataLabel]) + return self.data[nonZeroIndices]['jours'], self.data[nonZeroIndices][dataLabel] + + def plotMarkingMeasures(self, measure, options = 'o', dayRatio = 1., **kwargs): + for i in range(1,7): + self.plot('{}_{}'.format(measure, i), options, dayRatio, **kwargs) + + def computeMarkingMeasureVariations(self, dataLabel, lanePositions, weatherData, snowThreshold, weatherDataType = 'ec', minProportionMeasures = 0.): + '''Computes for each successive measurement + lanePositions = None + measure variation, initial measure, time duration, weather indicators + + TODO if measurements per lane, add a variable for lane position (position1 to 6) + lanePositions = list of integers (range(1,7)) + measure variation, initial measure, time duration, lane position1, weather indicators + measure variation, initial measure, time duration, lane position2, weather indicators + ...''' + variationData = [] + if lanePositions == None: + nonZeroIndices = ~np.isnan(self.data[dataLabel]) + days = self.data[nonZeroIndices]['jours'] + dates = self.data[nonZeroIndices]['date_mesure'] + measures = self.data[nonZeroIndices][dataLabel] + for i in range(1, len(dates)): + nDaysTNegative, nDaysThawFreeze, deltaTemp, nConsecutiveFrozenDays, totalRain, totalSnow, snowAboveThreshold, stdevTemp = weatherIndicators(weatherData, dates[i-1], dates[i], snowThreshold, weatherDataType, minProportionMeasures) + if dates[i-1].year+1 == dates[i].year: + winter = 1 + if days[i-1]<365: + firstWinter = 1 + else: + winter = 0 + firstWinter = 0 + variationData.append([measures[i-1]-measures[i], measures[i-1], days[i]-days[i-1], days[i-1], winter, firstWinter, nDaysTNegative, nDaysThawFreeze] + deltaTemp + [nConsecutiveFrozenDays, totalRain, totalSnow, snowAboveThreshold, stdevTemp]) + return variationData
--- a/python/play-video.py Thu Apr 18 15:29:33 2013 -0400 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,22 +0,0 @@ -#! /usr/bin/env python - -import sys,getopt -import cvutils - -options, args = getopt.getopt(sys.argv[1:], 'hi:f:',['help', 'fps=']) -options = dict(options) -print options - -if '--help' in options.keys() or '-h' in options.keys() or len(sys.argv) == 1: - print('Usage: '+sys.argv[0]+' --help|-h -i video-filename [-f first_frame] [--fps frame_rate]') - sys.exit() - -firstFrameNum = 0 -if '-f' in options.keys(): - firstFrameNum = int(options['-f']) - -frameRate = -1 -if '--fps' in options.keys(): - frameRate = int(options['--fps']) - -cvutils.playVideo(options['-i'], firstFrameNum, frameRate)
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/python/poly-utils.py Fri Dec 05 12:13:53 2014 -0500 @@ -0,0 +1,126 @@ +#! /usr/bin/env python +'''Various utilities to load data saved by the POLY new output(s)''' +import sys +import utils +from moving import TimeInterval +import numpy as np + +__metaclass__ = type +from indicators import SeverityIndicator + + +def loadNewInteractions(videoFilename,interactionType,dirname, extension, indicatorsNames, roaduserNum1,roaduserNum2, selectedIndicators=[]): + '''Loads interactions from the POLY traffic event format''' + from events import Interaction + filename= dirname + videoFilename + extension + #filename= dirname + interactionType+ '-' + videoFilename + extension # case of min distance todo: change the saving format to be matched with all outputs + file = utils.openCheck(filename) + if (not file): + return [] + #interactions = [] + interactionNum = 0 + data= np.loadtxt(filename) + indicatorFrameNums= data[:,0] + inter = Interaction(interactionNum, TimeInterval(indicatorFrameNums[0],indicatorFrameNums[-1]), roaduserNum1, roaduserNum2) + inter.addVideoFilename(videoFilename) + inter.addInteractionType(interactionType) + for key in indicatorsNames.keys(): + values= {} + for i,t in enumerate(indicatorFrameNums): + values[t] = data[i,key] + inter.addIndicator(SeverityIndicator(indicatorsNames[key], values)) + if selectedIndicators !=[]: + values= {} + for i,t in enumerate(indicatorFrameNums): + values[t] = [data[i,index] for index in selectedIndicators] + inter.addIndicator(SeverityIndicator('selectedIndicators', values)) + + #interactions.append(inter) + file.close() + #return interactions + return inter + +# Plotting results + +frameRate = 15. + +# To run in directory that contains the directories that contain the results (Miss-xx and Incident-xx) +#dirname = '/home/nicolas/Research/Data/kentucky-db/' + +interactingRoadUsers = {'Miss/0404052336': [(0,3)] # 0,2 and 1 vs 3 + #, + #'Incident/0306022035': [(1,3)] + #, + #'Miss/0208030956': [(4,5),(5,7)] + } + + +def getIndicatorName(filename, withUnit = False): + if withUnit: + unit = ' (s)' + else: + unit = '' + if 'collision-point' in filename: + return 'TTC'+unit + elif 'crossing' in filename: + return 'pPET'+unit + elif 'probability' in filename: + return 'P(UEA)' + +def getMethodName(fileprefix): + if fileprefix == 'constant-velocity': + return 'Con. Vel.' + elif fileprefix == 'normal-adaptation': + return 'Norm. Ad.' + elif fileprefix == 'point-set': + return 'Pos. Set' + elif fileprefix == 'evasive-action': + return 'Ev. Act.' + elif fileprefix == 'point-set-evasive-action': + return 'Pos. Set' + +indicator2TimeIdx = {'TTC':2,'pPET':2, 'P(UEA)':3} + +def getDataAtInstant(data, i): + return data[data[:,2] == i] + +def getPointsAtInstant(data, i): + return getDataAtInstant(i)[3:5] + +def getIndicator(data, roadUserNumbers, indicatorName): + if data.ndim ==1: + data.shape = (1,data.shape[0]) + + # find the order for the roadUserNumbers + uniqueObj1 = np.unique(data[:,0]) + uniqueObj2 = np.unique(data[:,1]) + found = False + if roadUserNumbers[0] in uniqueObj1 and roadUserNumbers[1] in uniqueObj2: + objNum1 = roadUserNumbers[0] + objNum2 = roadUserNumbers[1] + found = True + if roadUserNumbers[1] in uniqueObj1 and roadUserNumbers[0] in uniqueObj2: + objNum1 = roadUserNumbers[1] + objNum2 = roadUserNumbers[0] + found = True + + # get subset of data for road user numbers + if found: + roadUserData = data[np.logical_and(data[:,0] == objNum1, data[:,1] == objNum2),:] + if roadUserData.size > 0: + time = np.unique(roadUserData[:,indicator2TimeIdx[indicatorName]]) + values = {} + if indicatorName == 'P(UEA)': + tmp = roadUserData[:,4] + for k,v in zip(time, tmp): + values[k]=v + return SeverityIndicator(indicatorName, values, mostSevereIsMax = False, maxValue = 1.), roadUserData + else: + for i in xrange(time[0],time[-1]+1): + try: + tmp = getDataAtInstant(roadUserData, i) + values[i] = np.sum(tmp[:,5]*tmp[:,6])/np.sum(tmp[:,5])/frameRate + except IOError: + values[i] = np.inf + return SeverityIndicator(indicatorName, values, mostSevereIsMax = False), roadUserData + return None, None \ No newline at end of file
--- a/python/poly_utils.py Thu Apr 18 15:29:33 2013 -0400 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,126 +0,0 @@ -#! /usr/bin/env python -'''Various utilities to load data saved by the POLY new output(s)''' -import sys -import utils -from moving import TimeInterval -import numpy as np - -__metaclass__ = type -from indicators import SeverityIndicator - - -def loadNewInteractions(videoFilename,interactionType,dirname, extension, indicatorsNames, roaduserNum1,roaduserNum2, selectedIndicators=[]): - '''Loads interactions from the POLY traffic event format''' - from events import Interaction - filename= dirname + videoFilename + extension - #filename= dirname + interactionType+ '-' + videoFilename + extension # case of min distance todo: change the saving format to be matched with all outputs - file = utils.openCheck(filename) - if (not file): - return [] - #interactions = [] - interactionNum = 0 - data= np.loadtxt(filename) - indicatorFrameNums= data[:,0] - inter = Interaction(interactionNum, TimeInterval(indicatorFrameNums[0],indicatorFrameNums[-1]), roaduserNum1, roaduserNum2) - inter.addVideoFilename(videoFilename) - inter.addInteractionType(interactionType) - for key in indicatorsNames.keys(): - values= {} - for i,t in enumerate(indicatorFrameNums): - values[t] = data[i,key] - inter.addIndicator(SeverityIndicator(indicatorsNames[key], values)) - if selectedIndicators !=[]: - values= {} - for i,t in enumerate(indicatorFrameNums): - values[t] = [data[i,index] for index in selectedIndicators] - inter.addIndicator(SeverityIndicator('selectedIndicators', values)) - - #interactions.append(inter) - file.close() - #return interactions - return inter - -# Plotting results - -frameRate = 15. - -# To run in directory that contains the directories that contain the results (Miss-xx and Incident-xx) -#dirname = '/home/nicolas/Research/Data/kentucky-db/' - -interactingRoadUsers = {'Miss/0404052336': [(0,3)] # 0,2 and 1 vs 3 - #, - #'Incident/0306022035': [(1,3)] - #, - #'Miss/0208030956': [(4,5),(5,7)] - } - - -def getIndicatorName(filename, withUnit = False): - if withUnit: - unit = ' (s)' - else: - unit = '' - if 'collision-point' in filename: - return 'TTC'+unit - elif 'crossing' in filename: - return 'pPET'+unit - elif 'probability' in filename: - return 'P(UEA)' - -def getMethodName(fileprefix): - if fileprefix == 'constant-velocity': - return 'Con. Vel.' - elif fileprefix == 'normal-adaptation': - return 'Norm. Ad.' - elif fileprefix == 'point-set': - return 'Pos. Set' - elif fileprefix == 'evasive-action': - return 'Ev. Act.' - elif fileprefix == 'point-set-evasive-action': - return 'Pos. Set' - -indicator2TimeIdx = {'TTC':2,'pPET':2, 'P(UEA)':3} - -def getDataAtInstant(data, i): - return data[data[:,2] == i] - -def getPointsAtInstant(data, i): - return getDataAtInstant(i)[3:5] - -def getIndicator(data, roadUserNumbers, indicatorName): - if data.ndim ==1: - data.shape = (1,data.shape[0]) - - # find the order for the roadUserNumbers - uniqueObj1 = np.unique(data[:,0]) - uniqueObj2 = np.unique(data[:,1]) - found = False - if roadUserNumbers[0] in uniqueObj1 and roadUserNumbers[1] in uniqueObj2: - objNum1 = roadUserNumbers[0] - objNum2 = roadUserNumbers[1] - found = True - if roadUserNumbers[1] in uniqueObj1 and roadUserNumbers[0] in uniqueObj2: - objNum1 = roadUserNumbers[1] - objNum2 = roadUserNumbers[0] - found = True - - # get subset of data for road user numbers - if found: - roadUserData = data[np.logical_and(data[:,0] == objNum1, data[:,1] == objNum2),:] - if roadUserData.size > 0: - time = np.unique(roadUserData[:,indicator2TimeIdx[indicatorName]]) - values = {} - if indicatorName == 'P(UEA)': - tmp = roadUserData[:,4] - for k,v in zip(time, tmp): - values[k]=v - return SeverityIndicator(indicatorName, values, mostSevereIsMax = False, maxValue = 1.), roadUserData - else: - for i in xrange(time[0],time[-1]+1): - try: - tmp = getDataAtInstant(roadUserData, i) - values[i] = np.sum(tmp[:,5]*tmp[:,6])/np.sum(tmp[:,5])/frameRate - except IOError: - values[i] = np.inf - return SeverityIndicator(indicatorName, values, mostSevereIsMax = False), roadUserData - return None, None \ No newline at end of file
--- a/python/prediction.py Thu Apr 18 15:29:33 2013 -0400 +++ b/python/prediction.py Fri Dec 05 12:13:53 2014 -0500 @@ -30,14 +30,14 @@ def getPredictedSpeeds(self): return [so.norm for so in self.predictedSpeedOrientations.values()] - def draw(self, options = '', withOrigin = False, timeStep = 1, **kwargs): - self.getPredictedTrajectory().draw(options, withOrigin, timeStep, **kwargs) + def plot(self, options = '', withOrigin = False, timeStep = 1, **kwargs): + self.getPredictedTrajectory().plot(options, withOrigin, timeStep, **kwargs) class PredictedTrajectoryConstant(PredictedTrajectory): '''Predicted trajectory at constant speed or acceleration TODO generalize by passing a series of velocities/accelerations''' - def __init__(self, initialPosition, initialVelocity, control = moving.NormAngle(0,0), probability = 1, maxSpeed = None): + def __init__(self, initialPosition, initialVelocity, control = moving.NormAngle(0,0), probability = 1., maxSpeed = None): self.control = control self.maxSpeed = maxSpeed self.probability = probability @@ -47,9 +47,37 @@ def getControl(self): return self.control -class PredictedTrajectoryNormalAdaptation(PredictedTrajectory): - '''Random small adaptation of vehicle control ''' - def __init__(self, initialPosition, initialVelocity, accelerationDistribution, steeringDistribution, probability = 1, maxSpeed = None): +class PredictedTrajectoryPrototype(PredictedTrajectory): + '''Predicted trajectory that follows a prototype trajectory + The prototype is in the format of a moving.Trajectory: it could be + 1. an observed trajectory (extracted from video) + 2. a generic polyline (eg the road centerline) that a vehicle is supposed to follow + + Prediction can be done + 1. at constant speed (the instantaneous user speed) + 2. following the trajectory path, at the speed of the user + (applying a constant ratio equal + to the ratio of the user instantaneous speed and the trajectory closest speed)''' + + def __init__(self, initialPosition, initialVelocity, prototypeTrajectory, constantSpeed = True, probability = 1.): + self.prototypeTrajectory = prototypeTrajectory + self.constantSpeed = constantSpeed + self.probability = probability + self.predictedPositions = {0: initialPosition} + self.predictedSpeedOrientations = {0: moving.NormAngle.fromPoint(initialVelocity)} + + def predictPosition(self, nTimeSteps): + if nTimeSteps > 0 and not nTimeSteps in self.predictedPositions.keys(): + if constantSpeed: + # calculate cumulative distance + pass + else: # see c++ code, calculate ratio + pass + return self.predictedPositions[nTimeSteps] + +class PredictedTrajectoryRandomControl(PredictedTrajectory): + '''Random vehicle control: suitable for normal adaptation''' + def __init__(self, initialPosition, initialVelocity, accelerationDistribution, steeringDistribution, probability = 1., maxSpeed = None): '''Constructor accelerationDistribution and steeringDistribution are distributions that return random numbers drawn from them''' @@ -63,101 +91,6 @@ def getControl(self): return moving.NormAngle(self.accelerationDistribution(),self.steeringDistribution()) -class PredictionParameters: - def __init__(self, name, maxSpeed): - self.name = name - self.maxSpeed = maxSpeed - - def __str__(self): - return '{0} {1}'.format(self.name, self.maxSpeed) - -class ConstantPredictionParameters(PredictionParameters): - def __init__(self, maxSpeed): - PredictionParameters.__init__(self, 'constant velocity', maxSpeed) - - def generatePredictedTrajectories(self, obj, instant): - return [PredictedTrajectoryConstant(obj.getPositionAtInstant(instant), obj.getVelocityAtInstant(instant), - maxSpeed = self.maxSpeed)] - -class NormalAdaptationPredictionParameters(PredictionParameters): - def __init__(self, maxSpeed, nPredictedTrajectories, maxAcceleration, maxSteering): - PredictionParameters.__init__(self, 'normal adaptation', maxSpeed) - self.nPredictedTrajectories = nPredictedTrajectories - self.maxAcceleration = maxAcceleration - self.maxSteering = maxSteering - self.accelerationDistribution = lambda: random.triangular(-self.maxAcceleration, - self.maxAcceleration, 0.) - self.steeringDistribution = lambda: random.triangular(-self.maxSteering, - self.maxSteering, 0.) - - def __str__(self): - return PredictionParameters.__str__(self)+' {0} {1} {2}'.format(self.nPredictedTrajectories, - self.maxAcceleration, - self.maxSteering) - - def generatePredictedTrajectories(self, obj, instant): - predictedTrajectories = [] - for i in xrange(self.nPredictedTrajectories): - predictedTrajectories.append(PredictedTrajectoryNormalAdaptation(obj.getPositionAtInstant(instant), - obj.getVelocityAtInstant(instant), - self.accelerationDistribution, - self.steeringDistribution, - maxSpeed = self.maxSpeed)) - return predictedTrajectories - -class PointSetPredictionParameters(PredictionParameters): - # todo generate several trajectories with normal adaptatoins from each position (feature) - def __init__(self, maxSpeed): - PredictionParameters.__init__(self, 'point set', maxSpeed) - - def generatePredictedTrajectories(self, obj, instant): - predictedTrajectories = [] - features = [f for f in obj.features if f.existsAtInstant(instant)] - positions = [f.getPositionAtInstant(instant) for f in features] - velocities = [f.getVelocityAtInstant(instant) for f in features] - for initialPosition,initialVelocity in zip(positions, velocities): - predictedTrajectories.append(PredictedTrajectoryConstant(initialPosition, initialVelocity, - maxSpeed = self.maxSpeed)) - return predictedTrajectories - -class EvasiveActionPredictionParameters(PredictionParameters): - def __init__(self, maxSpeed, nPredictedTrajectories, minAcceleration, maxAcceleration, maxSteering, useFeatures = False): - if useFeatures: - name = 'point set evasive action' - else: - name = 'evasive action' - PredictionParameters.__init__(self, name, maxSpeed) - self.nPredictedTrajectories = nPredictedTrajectories - self.minAcceleration = minAcceleration - self.maxAcceleration = maxAcceleration - self.maxSteering = maxSteering - self.useFeatures = useFeatures - self.accelerationDistribution = lambda: random.triangular(self.minAcceleration, - self.maxAcceleration, 0.) - self.steeringDistribution = lambda: random.triangular(-self.maxSteering, - self.maxSteering, 0.) - - def __str__(self): - return PredictionParameters.__str__(self)+' {0} {1} {2} {3}'.format(self.nPredictedTrajectories, self.minAcceleration, self.maxAcceleration, self.maxSteering) - - def generatePredictedTrajectories(self, obj, instant): - predictedTrajectories = [] - if self.useFeatures: - features = [f for f in obj.features if f.existsAtInstant(instant)] - positions = [f.getPositionAtInstant(instant) for f in features] - velocities = [f.getVelocityAtInstant(instant) for f in features] - else: - positions = [obj.getPositionAtInstant(instant)] - velocities = [obj.getVelocityAtInstant(instant)] - for i in xrange(self.nPredictedTrajectories): - for initialPosition,initialVelocity in zip(positions, velocities): - predictedTrajectories.append(PredictedTrajectoryConstant(initialPosition, - initialVelocity, - moving.NormAngle(self.accelerationDistribution(), - self.steeringDistribution()), - maxSpeed = self.maxSpeed)) - return predictedTrajectories - class SafetyPoint(moving.Point): '''Can represent a collision point or crossing zone with respective safety indicator, TTC or pPET''' @@ -175,9 +108,10 @@ for p in points: out.write('{0} {1} {2} {3}\n'.format(objNum1, objNum2, predictionInstant, p)) -def computeExpectedIndicator(points): - from numpy import sum - return sum([p.indicator*p.probability for p in points])/sum([p.probability for p in points]) + @staticmethod + def computeExpectedIndicator(points): + from numpy import sum + return sum([p.indicator*p.probability for p in points])/sum([p.probability for p in points]) def computeCollisionTime(predictedTrajectory1, predictedTrajectory2, collisionDistanceThreshold, timeHorizon): '''Computes the first instant at which two predicted trajectories are within some distance threshold''' @@ -190,19 +124,34 @@ t += 1 return t, p1, p2 -def computeCrossingsCollisionsAtInstant(currentInstant, obj1, obj2, predictionParameters, collisionDistanceThreshold, timeHorizon, computeCZ = False, debug = False): - '''returns the lists of collision points and crossing zones - - Check: Predicting all the points together, as if they represent the whole vehicle''' - predictedTrajectories1 = predictionParameters.generatePredictedTrajectories(obj1, currentInstant) - predictedTrajectories2 = predictionParameters.generatePredictedTrajectories(obj2, currentInstant) +def savePredictedTrajectoriesFigure(currentInstant, obj1, obj2, predictedTrajectories1, predictedTrajectories2, timeHorizon): + from matplotlib.pyplot import figure, axis, title, close, savefig + figure() + for et in predictedTrajectories1: + et.predictPosition(timeHorizon) + et.plot('rx') + + for et in predictedTrajectories2: + et.predictPosition(timeHorizon) + et.plot('bx') + obj1.plot('r') + obj2.plot('b') + title('instant {0}'.format(currentInstant)) + axis('equal') + savefig('predicted-trajectories-t-{0}.png'.format(currentInstant)) + close() + +def computeCrossingsCollisionsAtInstant(predictionParams, currentInstant, obj1, obj2, collisionDistanceThreshold, timeHorizon, computeCZ = False, debug = False): + '''returns the lists of collision points and crossing zones''' + predictedTrajectories1 = predictionParams.generatePredictedTrajectories(obj1, currentInstant) + predictedTrajectories2 = predictionParams.generatePredictedTrajectories(obj2, currentInstant) collisionPoints = [] crossingZones = [] for et1 in predictedTrajectories1: for et2 in predictedTrajectories2: t, p1, p2 = computeCollisionTime(et1, et2, collisionDistanceThreshold, timeHorizon) - + if t <= timeHorizon: collisionPoints.append(SafetyPoint((p1+p2).multiply(0.5), et1.probability*et2.probability, t)) elif computeCZ: # check if there is a crossing zone @@ -217,29 +166,17 @@ #if (et1.predictPosition(t1)-et2.predictPosition(t2)).norm2() < collisionDistanceThreshold: # cz = (et1.predictPosition(t1)+et2.predictPosition(t2)).multiply(0.5) cz = moving.segmentIntersection(et1.predictPosition(t1), et1.predictPosition(t1+1), et2.predictPosition(t2), et2.predictPosition(t2+1)) - if cz: + if cz != None: crossingZones.append(SafetyPoint(cz, et1.probability*et2.probability, abs(t1-t2))) t2 += 1 t1 += 1 if debug: - from matplotlib.pyplot import figure, axis, title - figure() - for et in predictedTrajectories1: - et.predictPosition(timeHorizon) - et.draw('rx') + savePredictedTrajectoriesFigure(currentInstant, obj1, obj2, predictedTrajectories1, predictedTrajectories2, timeHorizon) - for et in predictedTrajectories2: - et.predictPosition(timeHorizon) - et.draw('bx') - obj1.draw('r') - obj2.draw('b') - title('instant {0}'.format(i)) - axis('equal') + return currentInstant, collisionPoints, crossingZones - return collisionPoints, crossingZones - -def computeCrossingsCollisions(obj1, obj2, predictionParameters, collisionDistanceThreshold, timeHorizon, computeCZ = False, debug = False, timeInterval = None): +def computeCrossingsCollisions(predictionParams, obj1, obj2, collisionDistanceThreshold, timeHorizon, computeCZ = False, debug = False, timeInterval = None, nProcesses = 1): '''Computes all crossing and collision points at each common instant for two road users. ''' collisionPoints={} crossingZones={} @@ -247,49 +184,253 @@ commonTimeInterval = timeInterval else: commonTimeInterval = obj1.commonTimeInterval(obj2) - for i in list(commonTimeInterval)[:-1]: # do not look at the 1 last position/velocities, often with errors - collisionPoints[i], crossingZones[i] = computeCrossingsCollisionsAtInstant(i, obj1, obj2, predictionParameters, collisionDistanceThreshold, timeHorizon, computeCZ, debug) - - return collisionPoints, crossingZones - -def computeCollisionProbability(obj1, obj2, predictionParameters, collisionDistanceThreshold, timeHorizon, debug = False, timeInterval = None): - '''Computes only collision probabilities - Returns for each instant the collision probability and number of samples drawn''' - collisionProbabilities = {} - if timeInterval: - commonTimeInterval = timeInterval + if nProcesses == 1: + for i in list(commonTimeInterval)[:-1]: # do not look at the 1 last position/velocities, often with errors + i, cp, cz = computeCrossingsCollisionsAtInstant(predictionParams, i, obj1, obj2, collisionDistanceThreshold, timeHorizon, computeCZ, debug) + if len(cp) != 0: + collisionPoints[i] = cp + if len(cz) != 0: + crossingZones[i] = cz else: - commonTimeInterval = obj1.commonTimeInterval(obj2) - for i in list(commonTimeInterval)[:-1]: - nCollisions = 0 - print(obj1.num, obj2.num, i) - predictedTrajectories1 = predictionParameters.generatePredictedTrajectories(obj1, i) - predictedTrajectories2 = predictionParameters.generatePredictedTrajectories(obj2, i) - for et1 in predictedTrajectories1: - for et2 in predictedTrajectories2: - t, p1, p2 = computeCollisionTime(et1, et2, collisionDistanceThreshold, timeHorizon) - if t <= timeHorizon: - nCollisions += 1 - # take into account probabilities ?? - nSamples = float(len(predictedTrajectories1)*len(predictedTrajectories2)) - collisionProbabilities[i] = [nSamples, float(nCollisions)/nSamples] + from multiprocessing import Pool + pool = Pool(processes = nProcesses) + jobs = [pool.apply_async(computeCrossingsCollisionsAtInstant, args = (predictionParams, i, obj1, obj2, collisionDistanceThreshold, timeHorizon, computeCZ, debug)) for i in list(commonTimeInterval)[:-1]] + #results = [j.get() for j in jobs] + #results.sort() + for j in jobs: + i, cp, cz = j.get() + #if len(cp) != 0 or len(cz) != 0: + if len(cp) != 0: + collisionPoints[i] = cp + if len(cz) != 0: + crossingZones[i] = cz + pool.close() + return collisionPoints, crossingZones + +class PredictionParameters: + def __init__(self, name, maxSpeed): + self.name = name + self.maxSpeed = maxSpeed + + def __str__(self): + return '{0} {1}'.format(self.name, self.maxSpeed) + + def generatePredictedTrajectories(self, obj, instant): + return [] + + def computeCrossingsCollisionsAtInstant(self, currentInstant, obj1, obj2, collisionDistanceThreshold, timeHorizon, computeCZ = False, debug = False): + return computeCrossingsCollisionsAtInstant(self, currentInstant, obj1, obj2, collisionDistanceThreshold, timeHorizon, computeCZ, debug) + + def computeCrossingsCollisions(self, obj1, obj2, collisionDistanceThreshold, timeHorizon, computeCZ = False, debug = False, timeInterval = None, nProcesses = 1): + return computeCrossingsCollisions(self, obj1, obj2, collisionDistanceThreshold, timeHorizon, computeCZ, debug, timeInterval, nProcesses) + + def computeCollisionProbability(self, obj1, obj2, collisionDistanceThreshold, timeHorizon, debug = False, timeInterval = None): + '''Computes only collision probabilities + Returns for each instant the collision probability and number of samples drawn''' + collisionProbabilities = {} + if timeInterval: + commonTimeInterval = timeInterval + else: + commonTimeInterval = obj1.commonTimeInterval(obj2) + for i in list(commonTimeInterval)[:-1]: + nCollisions = 0 + predictedTrajectories1 = self.generatePredictedTrajectories(obj1, i) + predictedTrajectories2 = self.generatePredictedTrajectories(obj2, i) + for et1 in predictedTrajectories1: + for et2 in predictedTrajectories2: + t, p1, p2 = computeCollisionTime(et1, et2, collisionDistanceThreshold, timeHorizon) + if t <= timeHorizon: + nCollisions += 1 + # take into account probabilities ?? + nSamples = float(len(predictedTrajectories1)*len(predictedTrajectories2)) + collisionProbabilities[i] = [nSamples, float(nCollisions)/nSamples] + + if debug: + savePredictedTrajectoriesFigure(i, obj1, obj2, predictedTrajectories1, predictedTrajectories2, timeHorizon) + + return collisionProbabilities + +class ConstantPredictionParameters(PredictionParameters): + def __init__(self, maxSpeed): + PredictionParameters.__init__(self, 'constant velocity', maxSpeed) + + def generatePredictedTrajectories(self, obj, instant): + return [PredictedTrajectoryConstant(obj.getPositionAtInstant(instant), obj.getVelocityAtInstant(instant), + maxSpeed = self.maxSpeed)] + +class NormalAdaptationPredictionParameters(PredictionParameters): + def __init__(self, maxSpeed, nPredictedTrajectories, accelerationDistribution, steeringDistribution, useFeatures = False): + '''An example of acceleration and steering distributions is + lambda: random.triangular(-self.maxAcceleration, self.maxAcceleration, 0.) + ''' + if useFeatures: + name = 'point set normal adaptation' + else: + name = 'normal adaptation' + PredictionParameters.__init__(self, name, maxSpeed) + self.nPredictedTrajectories = nPredictedTrajectories + self.useFeatures = useFeatures + self.accelerationDistribution = accelerationDistribution + self.steeringDistribution = steeringDistribution + + def __str__(self): + return PredictionParameters.__str__(self)+' {0} {1} {2}'.format(self.nPredictedTrajectories, + self.maxAcceleration, + self.maxSteering) - if debug: - from matplotlib.pyplot import figure, axis, title + def generatePredictedTrajectories(self, obj, instant): + predictedTrajectories = [] + if self.useFeatures: + features = [f for f in obj.features if f.existsAtInstant(instant)] + positions = [f.getPositionAtInstant(instant) for f in features] + velocities = [f.getVelocityAtInstant(instant) for f in features] + else: + positions = [obj.getPositionAtInstant(instant)] + velocities = [obj.getVelocityAtInstant(instant)] + for i in xrange(self.nPredictedTrajectories): + for initialPosition,initialVelocity in zip(positions, velocities): + predictedTrajectories.append(PredictedTrajectoryRandomControl(initialPosition, + initialVelocity, + self.accelerationDistribution, + self.steeringDistribution, + maxSpeed = self.maxSpeed)) + return predictedTrajectories + +class PointSetPredictionParameters(PredictionParameters): + # todo generate several trajectories with normal adaptatoins from each position (feature) + def __init__(self, maxSpeed): + PredictionParameters.__init__(self, 'point set', maxSpeed) + #self.nPredictedTrajectories = nPredictedTrajectories + + def generatePredictedTrajectories(self, obj, instant): + predictedTrajectories = [] + features = [f for f in obj.features if f.existsAtInstant(instant)] + positions = [f.getPositionAtInstant(instant) for f in features] + velocities = [f.getVelocityAtInstant(instant) for f in features] + #for i in xrange(self.nPredictedTrajectories): + for initialPosition,initialVelocity in zip(positions, velocities): + predictedTrajectories.append(PredictedTrajectoryConstant(initialPosition, initialVelocity, + maxSpeed = self.maxSpeed)) + return predictedTrajectories + +class EvasiveActionPredictionParameters(PredictionParameters): + def __init__(self, maxSpeed, nPredictedTrajectories, accelerationDistribution, steeringDistribution, useFeatures = False): + '''Suggested acceleration distribution may not be symmetric, eg + lambda: random.triangular(self.minAcceleration, self.maxAcceleration, 0.)''' + + if useFeatures: + name = 'point set evasive action' + else: + name = 'evasive action' + PredictionParameters.__init__(self, name, maxSpeed) + self.nPredictedTrajectories = nPredictedTrajectories + self.useFeatures = useFeatures + self.accelerationDistribution = accelerationDistribution + self.steeringDistribution = steeringDistribution + + def __str__(self): + return PredictionParameters.__str__(self)+' {0} {1} {2} {3}'.format(self.nPredictedTrajectories, self.minAcceleration, self.maxAcceleration, self.maxSteering) + + def generatePredictedTrajectories(self, obj, instant): + predictedTrajectories = [] + if self.useFeatures: + features = [f for f in obj.features if f.existsAtInstant(instant)] + positions = [f.getPositionAtInstant(instant) for f in features] + velocities = [f.getVelocityAtInstant(instant) for f in features] + else: + positions = [obj.getPositionAtInstant(instant)] + velocities = [obj.getVelocityAtInstant(instant)] + for i in xrange(self.nPredictedTrajectories): + for initialPosition,initialVelocity in zip(positions, velocities): + predictedTrajectories.append(PredictedTrajectoryConstant(initialPosition, + initialVelocity, + moving.NormAngle(self.accelerationDistribution(), + self.steeringDistribution()), + maxSpeed = self.maxSpeed)) + return predictedTrajectories + + +class CVDirectPredictionParameters(PredictionParameters): + '''Prediction parameters of prediction at constant velocity + using direct computation of the intersecting point''' + + def __init__(self): + PredictionParameters.__init__(self, 'constant velocity (direct computation)', None) + + def computeCrossingsCollisionsAtInstant(self, currentInstant, obj1, obj2, collisionDistanceThreshold, timeHorizon, computeCZ = False, debug = False): + collisionPoints = [] + crossingZones = [] + + p1 = obj1.getPositionAtInstant(currentInstant) + p2 = obj2.getPositionAtInstant(currentInstant) + if (p1-p2).norm2() <= collisionDistanceThreshold: + collisionPoints = [SafetyPoint((p1+p1).multiply(0.5), 1., 0.)] + else: + v1 = obj1.getVelocityAtInstant(currentInstant) + v2 = obj2.getVelocityAtInstant(currentInstant) + intersection = moving.intersection(p1, p2, v1, v2) + + if intersection != None: + dp1 = intersection-p1 + dp2 = intersection-p2 + if moving.Point.dot(dp1, v1) > 0 and moving.Point.dot(dp2, v2) > 0: # if the road users are moving towards the intersection + dist1 = dp1.norm2() + dist2 = dp2.norm2() + s1 = v1.norm2() + s2 = v2.norm2() + halfCollisionDistanceThreshold = collisionDistanceThreshold/2. + timeInterval1 = moving.TimeInterval(max(0,dist1-halfCollisionDistanceThreshold)/s1, (dist1+halfCollisionDistanceThreshold)/s1) + timeInterval2 = moving.TimeInterval(max(0,dist2-halfCollisionDistanceThreshold)/s2, (dist2+halfCollisionDistanceThreshold)/s2) + collisionTimeInterval = moving.TimeInterval.intersection(timeInterval1, timeInterval2) + if computeCZ and collisionTimeInterval.empty(): + crossingZones = [SafetyPoint(intersection, 1., timeInterval1.distance(timeInterval2))] + else: + collisionPoints = [SafetyPoint(intersection, 1., collisionTimeInterval.center())] + + if debug and intersection!= None: + from matplotlib.pyplot import plot, figure, axis, title figure() - for et in predictedTrajectories1: - et.predictPosition(timeHorizon) - et.draw('rx') - - for et in predictedTrajectories2: - et.predictPosition(timeHorizon) - et.draw('bx') - obj1.draw('r') - obj2.draw('b') - title('instant {0}'.format(i)) + plot([p1.x, intersection.x], [p1.y, intersection.y], 'r') + plot([p2.x, intersection.x], [p2.y, intersection.y], 'b') + intersection.plot() + obj1.plot('r') + obj2.plot('b') + title('instant {0}'.format(currentInstant)) axis('equal') - return collisionProbabilities + return collisionPoints, crossingZones + +class CVExactPredictionParameters(PredictionParameters): + '''Prediction parameters of prediction at constant velocity + using direct computation of the intersecting point (solving for the equation''' + + def __init__(self): + PredictionParameters.__init__(self, 'constant velocity (direct exact computation)', None) + + def computeCrossingsCollisionsAtInstant(self, currentInstant, obj1, obj2, collisionDistanceThreshold, timeHorizon, computeCZ = False, debug = False): + 'TODO add collision point coordinates, compute pPET' + #collisionPoints = [] + #crossingZones = [] + + p1 = obj1.getPositionAtInstant(currentInstant) + p2 = obj2.getPositionAtInstant(currentInstant) + v1 = obj1.getVelocityAtInstant(currentInstant) + v2 = obj2.getVelocityAtInstant(currentInstant) + intersection = moving.intersection(p1, p2, v1, v2) + + if intersection != None: + ttc = moving.Point.timeToCollision(p1, p2, v1, v2, collisionDistanceThreshold) + if ttc: + return [SafetyPoint(intersection, 1., ttc)], [] # (p1+v1.multiply(ttc)+p2+v2.multiply(ttc)).multiply(0.5) + else: + return [],[] + +#### +# Other Methods +#### + + + if __name__ == "__main__":
--- a/python/requirements.txt Thu Apr 18 15:29:33 2013 -0400 +++ b/python/requirements.txt Fri Dec 05 12:13:53 2014 -0500 @@ -4,7 +4,7 @@ The following libraries are necessary for (sometimes very) specific classes/functions. CV functionalities (cvutils.py): opencv -Image functionalities (cvutils.py): Python Image Library +Image functionalities (cvutils.py): Python Image Library (new version is called Pillow) Machine learning (ml.py): scipy
--- a/python/run-tests.sh Thu Apr 18 15:29:33 2013 -0400 +++ b/python/run-tests.sh Fri Dec 05 12:13:53 2014 -0500 @@ -1,8 +1,7 @@ #!/bin/sh # for file in tests/*... basename -python moving.py -python storage.py +for f in ./*.py +do + python $f +done rm nonexistent -python indicators.py -python utils.py -python prediction.py \ No newline at end of file
--- a/python/storage.py Thu Apr 18 15:29:33 2013 -0400 +++ b/python/storage.py Fri Dec 05 12:13:53 2014 -0500 @@ -2,40 +2,52 @@ # -*- coding: utf-8 -*- '''Various utilities to save and load data''' -import utils -import moving +import utils, moving, events, indicators + +import sqlite3, logging __metaclass__ = type +commentChar = '#' + +delimiterChar = '%'; + ngsimUserTypes = {'twowheels':1, 'car':2, 'truck':3} ######################### -# txt files -######################### - - - -######################### # Sqlite ######################### -def writeTrajectoriesToSqlite(objects, outFile, trajectoryType, objectNumbers = -1): +# utils +def printDBError(error): + print('DB Error: {}'.format(error)) + +def dropTables(connection, tableNames): + 'deletes the table with names in tableNames' + try: + cursor = connection.cursor() + for tableName in tableNames: + cursor.execute('DROP TABLE IF EXISTS '+tableName) + except sqlite3.OperationalError as error: + printDBError(error) + +# TODO: add test if database connection is open +# IO to sqlite +def writeTrajectoriesToSqlite(objects, outputFilename, trajectoryType, objectNumbers = -1): """ This function writers trajectories to a specified sqlite file @param[in] objects -> a list of trajectories @param[in] trajectoryType - - @param[out] outFile -> the .sqlite file containting the written objects + @param[out] outputFilename -> the .sqlite file containting the written objects @param[in] objectNumber : number of objects loaded """ - - import sqlite3 - connection = sqlite3.connect(outFile) + connection = sqlite3.connect(outputFilename) cursor = connection.cursor() - schema = "CREATE TABLE \"positions\"(trajectory_id INTEGER,frame_number INTEGER, x_coordinate REAL, y_coordinate REAL, PRIMARY KEY(trajectory_id, frame_number))" + schema = "CREATE TABLE IF NOT EXISTS \"positions\"(trajectory_id INTEGER,frame_number INTEGER, x_coordinate REAL, y_coordinate REAL, PRIMARY KEY(trajectory_id, frame_number))" cursor.execute(schema) trajectory_id = 0 @@ -50,7 +62,166 @@ query = "insert into positions (trajectory_id, frame_number, x_coordinate, y_coordinate) values (?,?,?,?)" cursor.execute(query,(trajectory_id,frame_number,position.x,position.y)) - connection.commit() + connection.commit() + connection.close() + +def writeFeaturesToSqlite(objects, outputFilename, trajectoryType, objectNumbers = -1): + '''write features trajectories maintain trajectory ID,velocities dataset ''' + connection = sqlite3.connect(outputFilename) + cursor = connection.cursor() + + cursor.execute("CREATE TABLE IF NOT EXISTS \"positions\"(trajectory_id INTEGER,frame_number INTEGER, x_coordinate REAL, y_coordinate REAL, PRIMARY KEY(trajectory_id, frame_number))") + cursor.execute("CREATE TABLE IF NOT EXISTS \"velocities\"(trajectory_id INTEGER,frame_number INTEGER, x_coordinate REAL, y_coordinate REAL, PRIMARY KEY(trajectory_id, frame_number))") + + if trajectoryType == 'feature': + if type(objectNumbers) == int and objectNumbers == -1: + for trajectory in objects: + trajectory_id = trajectory.num + frame_number = trajectory.timeInterval.first + for position,velocity in zip(trajectory.getPositions(),trajectory.getVelocities()): + cursor.execute("insert into positions (trajectory_id, frame_number, x_coordinate, y_coordinate) values (?,?,?,?)",(trajectory_id,frame_number,position.x,position.y)) + cursor.execute("insert into velocities (trajectory_id, frame_number, x_coordinate, y_coordinate) values (?,?,?,?)",(trajectory_id,frame_number,velocity.x,velocity.y)) + frame_number += 1 + + connection.commit() + connection.close() + +def writePrototypesToSqlite(prototypes,nMatching, outputFilename): + """ prototype dataset is a dictionary with keys== routes, values== prototypes Ids """ + connection = sqlite3.connect(outputFilename) + cursor = connection.cursor() + + cursor.execute("CREATE TABLE IF NOT EXISTS \"prototypes\"(prototype_id INTEGER,routeIDstart INTEGER,routeIDend INTEGER, nMatching INTEGER, PRIMARY KEY(prototype_id))") + + for route in prototypes.keys(): + if prototypes[route]!=[]: + for i in prototypes[route]: + cursor.execute("insert into prototypes (prototype_id, routeIDstart,routeIDend, nMatching) values (?,?,?,?)",(i,route[0],route[1],nMatching[route][i])) + + connection.commit() + connection.close() + +def loadPrototypesFromSqlite(filename): + """ + This function loads the prototype file in the database + It returns a dictionary for prototypes for each route and nMatching + """ + prototypes = {} + nMatching={} + + connection = sqlite3.connect(filename) + cursor = connection.cursor() + + try: + cursor.execute('SELECT * from prototypes order by prototype_id, routeIDstart,routeIDend, nMatching') + except sqlite3.OperationalError as error: + utils.printDBError(error) + return [] + + for row in cursor: + route=(row[1],row[2]) + if route not in prototypes.keys(): + prototypes[route]=[] + prototypes[route].append(row[0]) + nMatching[row[0]]=row[3] + + connection.close() + return prototypes,nMatching + +def writeLabelsToSqlite(labels, outputFilename): + """ labels is a dictionary with keys: routes, values: prototypes Ids + """ + connection = sqlite3.connect(outputFilename) + cursor = connection.cursor() + + cursor.execute("CREATE TABLE IF NOT EXISTS \"labels\"(object_id INTEGER,routeIDstart INTEGER,routeIDend INTEGER, prototype_id INTEGER, PRIMARY KEY(object_id))") + + for route in labels.keys(): + if labels[route]!=[]: + for i in labels[route]: + for j in labels[route][i]: + cursor.execute("insert into labels (object_id, routeIDstart,routeIDend, prototype_id) values (?,?,?,?)",(j,route[0],route[1],i)) + + connection.commit() + connection.close() + +def loadLabelsFromSqlite(filename): + labels = {} + + connection = sqlite3.connect(filename) + cursor = connection.cursor() + + try: + cursor.execute('SELECT * from labels order by object_id, routeIDstart,routeIDend, prototype_id') + except sqlite3.OperationalError as error: + utils.printDBError(error) + return [] + + for row in cursor: + route=(row[1],row[2]) + p=row[3] + if route not in labels.keys(): + labels[route]={} + if p not in labels[route].keys(): + labels[route][p]=[] + labels[route][p].append(row[0]) + + connection.close() + return labels + +def writeRoutesToSqlite(Routes, outputFilename): + """ This function writes the activity path define by start and end IDs""" + connection = sqlite3.connect(outputFilename) + cursor = connection.cursor() + + cursor.execute("CREATE TABLE IF NOT EXISTS \"routes\"(object_id INTEGER,routeIDstart INTEGER,routeIDend INTEGER, PRIMARY KEY(object_id))") + + for route in Routes.keys(): + if Routes[route]!=[]: + for i in Routes[route]: + cursor.execute("insert into routes (object_id, routeIDstart,routeIDend) values (?,?,?)",(i,route[0],route[1])) + + connection.commit() + connection.close() + +def loadRoutesFromSqlite(filename): + Routes = {} + + connection = sqlite3.connect(filename) + cursor = connection.cursor() + + try: + cursor.execute('SELECT * from routes order by object_id, routeIDstart,routeIDend') + except sqlite3.OperationalError as error: + utils.printDBError(error) + return [] + + for row in cursor: + route=(row[1],row[2]) + if route not in Routes.keys(): + Routes[route]=[] + Routes[route].append(row[0]) + + connection.close() + return Routes + +def setRoutes(filename, objects): + connection = sqlite3.connect(filename) + cursor = connection.cursor() + for obj in objects: + cursor.execute('update objects set startRouteID = {} where object_id = {}'.format(obj.startRouteID, obj.getNum())) + cursor.execute('update objects set endRouteID = {} where object_id = {}'.format(obj.endRouteID, obj.getNum())) + connection.commit() + connection.close() + +def setRoadUserTypes(filename, objects): + '''Saves the user types of the objects in the sqlite database stored in filename + The objects should exist in the objects table''' + connection = sqlite3.connect(filename) + cursor = connection.cursor() + for obj in objects: + cursor.execute('update objects set road_user_type = {} where object_id = {}'.format(obj.getUserType(), obj.getNum())) + connection.commit() connection.close() def loadPrototypeMatchIndexesFromSqlite(filename): @@ -60,14 +231,13 @@ """ matched_indexes = [] - import sqlite3 connection = sqlite3.connect(filename) cursor = connection.cursor() try: cursor.execute('SELECT * from prototypes order by prototype_id, trajectory_id_matched') - except sqlite3.OperationalError as err: - print('DB Error: {0}'.format(err)) + except sqlite3.OperationalError as error: + printDBError(error) return [] for row in cursor: @@ -78,41 +248,51 @@ def getTrajectoryIdQuery(objectNumbers, trajectoryType): if trajectoryType == 'feature': - statementBeginning = ' where trajectory_id' + statementBeginning = 'where trajectory_id ' elif trajectoryType == 'object': - statementBeginning = ' and OF.object_id' + statementBeginning = 'and OF.object_id ' + elif trajectoryType == 'bbtop' or 'bbbottom': + statementBeginning = 'where object_id ' else: print('no trajectory type was chosen') - if type(objectNumbers) == int: - if objectNumbers == -1: - query = '' - else: - query = statementBeginning+' between 0 and {0}'.format(objectNumbers) + if objectNumbers is None: + query = '' + elif type(objectNumbers) == int: + query = statementBeginning+'between 0 and {0} '.format(objectNumbers) elif type(objectNumbers) == list: - query = statementBeginning+' in ('+', '.join([str(n) for n in objectNumbers])+')' + query = statementBeginning+'in ('+', '.join([str(n) for n in objectNumbers])+') ' return query -def loadTrajectoriesFromTable(connection, tableName, trajectoryType, objectNumbers = -1): +def loadTrajectoriesFromTable(connection, tableName, trajectoryType, objectNumbers = None): '''Loads trajectories (in the general sense) from the given table can be positions or velocities returns a moving object''' - import sqlite3 - cursor = connection.cursor() try: + idQuery = getTrajectoryIdQuery(objectNumbers, trajectoryType) if trajectoryType == 'feature': - trajectoryIdQuery = getTrajectoryIdQuery(objectNumbers, trajectoryType) - cursor.execute('SELECT * from '+tableName+trajectoryIdQuery+' order by trajectory_id, frame_number') + queryStatement = 'SELECT * from '+tableName+' '+idQuery+'ORDER BY trajectory_id, frame_number' + cursor.execute(queryStatement) + logging.debug(queryStatement) elif trajectoryType == 'object': - objectIdQuery = getTrajectoryIdQuery(objectNumbers, trajectoryType) - cursor.execute('SELECT OF.object_id, P.frame_number, avg(P.x_coordinate), avg(P.y_coordinate) from '+tableName+' P, objects_features OF where P.trajectory_id = OF.trajectory_id '+objectIdQuery+' group by object_id, frame_number') + queryStatement = 'SELECT OF.object_id, P.frame_number, avg(P.x_coordinate), avg(P.y_coordinate) from '+tableName+' P, objects_features OF where P.trajectory_id = OF.trajectory_id '+idQuery+'group by OF.object_id, P.frame_number ORDER BY OF.object_id, P.frame_number' + cursor.execute(queryStatement) + logging.debug(queryStatement) + elif trajectoryType in ['bbtop', 'bbbottom']: + if trajectoryType == 'bbtop': + corner = 'top_left' + elif trajectoryType == 'bbbottom': + corner = 'bottom_right' + queryStatement = 'SELECT object_id, frame_number, x_'+corner+', y_'+corner+' FROM '+tableName+' '+trajectoryIdQuery+'ORDER BY object_id, frame_number' + cursor.execute(queryStatement) + logging.debug(queryStatement) else: print('no trajectory type was chosen') - except sqlite3.OperationalError as err: - print('DB Error: {0}'.format(err)) + except sqlite3.OperationalError as error: + printDBError(error) return [] objId = -1 @@ -121,78 +301,365 @@ for row in cursor: if row[0] != objId: objId = row[0] - if obj: + if obj != None and obj.length() == obj.positions.length(): objects.append(obj) + elif obj != None: + print('Object {} is missing {} positions'.format(obj.getNum(), int(obj.length())-obj.positions.length())) obj = moving.MovingObject(row[0], timeInterval = moving.TimeInterval(row[1], row[1]), positions = moving.Trajectory([[row[2]],[row[3]]])) else: obj.timeInterval.last = row[1] obj.positions.addPositionXY(row[2],row[3]) - if obj: + if obj != None and obj.length() == obj.positions.length(): objects.append(obj) + elif obj != None: + print('Object {} is missing {} positions'.format(obj.getNum(), int(obj.length())-obj.positions.length())) return objects -def loadTrajectoriesFromSqlite(filename, trajectoryType, objectNumbers = -1): - '''Loads nObjects or the indices in objectNumbers from the database - TODO: load feature numbers and not average feature trajectories - TODO: other ways of averaging trajectories (load all points, sorted by frame_number and leave the agregation to be done in python) - ''' - import sqlite3 - - connection = sqlite3.connect(filename) # add test if it open +def loadUserTypesFromTable(cursor, trajectoryType, objectNumbers): + objectIdQuery = getTrajectoryIdQuery(objectNumbers, trajectoryType) + if objectIdQuery == '': + cursor.execute('SELECT object_id, road_user_type from objects') + else: + cursor.execute('SELECT object_id, road_user_type from objects where '+objectIdQuery[7:]) + userTypes = {} + for row in cursor: + userTypes[row[0]] = row[1] + return userTypes + +def loadTrajectoriesFromSqlite(filename, trajectoryType, objectNumbers = None): + '''Loads the first objectNumbers objects or the indices in objectNumbers from the database''' + connection = sqlite3.connect(filename) objects = loadTrajectoriesFromTable(connection, 'positions', trajectoryType, objectNumbers) objectVelocities = loadTrajectoriesFromTable(connection, 'velocities', trajectoryType, objectNumbers) if len(objectVelocities) > 0: for o,v in zip(objects, objectVelocities): - if o.num == v.num: + if o.getNum() == v.getNum(): o.velocities = v.positions + o.velocities.duplicateLastPosition() # avoid having velocity shorter by one position than positions else: - print('Could not match positions {0} with velocities {1}'.format(o.num, v.num)) + print('Could not match positions {0} with velocities {1}'.format(o.getNum(), v.getNum())) if trajectoryType == 'object': cursor = connection.cursor() try: + # attribute feature numbers to objects objectIdQuery = getTrajectoryIdQuery(objectNumbers, trajectoryType) - cursor.execute('SELECT P.trajectory_id, OF.object_id from positions P, objects_features OF where P.trajectory_id = OF.trajectory_id '+objectIdQuery+' group by P.trajectory_id order by OF.object_id') + queryStatement = 'SELECT P.trajectory_id, OF.object_id from positions P, objects_features OF where P.trajectory_id = OF.trajectory_id '+objectIdQuery+'group by P.trajectory_id order by OF.object_id' # order is important to group all features per object + cursor.execute(queryStatement) + logging.debug(queryStatement) - # attribute feature numbers to objects - objId = -1 featureNumbers = {} for row in cursor: - if row[1] != objId: - objId = row[1] + objId = row[1] + if objId not in featureNumbers: featureNumbers[objId] = [row[0]] else: featureNumbers[objId].append(row[0]) for obj in objects: - obj.featureNumbers = featureNumbers[obj.num] - except sqlite3.OperationalError as err: - print('DB Error: {0}'.format(err)) - return [] + obj.featureNumbers = featureNumbers[obj.getNum()] + + # load userType + userTypes = loadUserTypesFromTable(cursor, trajectoryType, objectNumbers) + for obj in objects: + obj.userType = userTypes[obj.getNum()] + + except sqlite3.OperationalError as error: + printDBError(error) + objects = [] connection.close() return objects -def removeObjectsFromSqlite(filename): - 'Removes the objects and object_features tables in the filename' - import sqlite3 +def loadGroundTruthFromSqlite(filename, gtType, gtNumbers = None): + 'Loads bounding box annotations (ground truth) from an SQLite ' connection = sqlite3.connect(filename) - utils.dropTables(connection, ['objects', 'objects_features']) + gt = [] + + if gtType == 'bb': + topCorners = loadTrajectoriesFromTable(connection, 'bounding_boxes', 'bbtop', gtNumbers) + bottomCorners = loadTrajectoriesFromTable(connection, 'bounding_boxes', 'bbbottom', gtNumbers) + userTypes = loadUserTypesFromTable(connection.cursor(), 'object', gtNumbers) # string format is same as object + + for t, b in zip(topCorners, bottomCorners): + num = t.getNum() + if t.getNum() == b.getNum(): + annotation = moving.BBAnnotation(num, t.getTimeInterval(), t, b, userTypes[num]) + gt.append(annotation) + else: + print ('Unknown type of annotation {}'.format(gtType)) + + connection.close() + return gt + +def deleteFromSqlite(filename, dataType): + 'Deletes (drops) some tables in the filename depending on type of data' + import os + if os.path.isfile(filename): + connection = sqlite3.connect(filename) + if dataType == 'object': + dropTables(connection, ['objects', 'objects_features']) + elif dataType == 'interaction': + dropTables(connection, ['interactions', 'indicators']) + elif dataType == 'bb': + dropTables(connection, ['bounding_boxes']) + else: + print('Unknown data type {} to delete from database'.format(dataType)) + connection.close() + else: + print('{} does not exist'.format(filename)) + +def createInteractionTable(cursor): + cursor.execute('CREATE TABLE IF NOT EXISTS interactions (id INTEGER PRIMARY KEY, object_id1 INTEGER, object_id2 INTEGER, first_frame_number INTEGER, last_frame_number INTEGER, FOREIGN KEY(object_id1) REFERENCES objects(id), FOREIGN KEY(object_id2) REFERENCES objects(id))') + +def createIndicatorTables(cursor): + # cursor.execute('CREATE TABLE IF NOT EXISTS indicators (id INTEGER PRIMARY KEY, interaction_id INTEGER, indicator_type INTEGER, FOREIGN KEY(interaction_id) REFERENCES interactions(id))') + # cursor.execute('CREATE TABLE IF NOT EXISTS indicator_values (indicator_id INTEGER, frame_number INTEGER, value REAL, FOREIGN KEY(indicator_id) REFERENCES indicators(id), PRIMARY KEY(indicator_id, frame_number))') + cursor.execute('CREATE TABLE IF NOT EXISTS indicators (interaction_id INTEGER, indicator_type INTEGER, frame_number INTEGER, value REAL, FOREIGN KEY(interaction_id) REFERENCES interactions(id), PRIMARY KEY(interaction_id, indicator_type, frame_number))') + +def saveInteraction(cursor, interaction): + roadUserNumbers = list(interaction.getRoadUserNumbers()) + cursor.execute('INSERT INTO interactions VALUES({}, {}, {}, {}, {})'.format(interaction.getNum(), roadUserNumbers[0], roadUserNumbers[1], interaction.getFirstInstant(), interaction.getLastInstant())) + +def saveInteractions(filename, interactions): + 'Saves the interactions in the table' + connection = sqlite3.connect(filename) + cursor = connection.cursor() + try: + createInteractionTable(cursor) + for inter in interactions: + saveInteraction(cursor, inter) + except sqlite3.OperationalError as error: + printDBError(error) + connection.commit() + connection.close() + +def saveIndicator(cursor, interactionNum, indicator): + for instant in indicator.getTimeInterval(): + if indicator[instant]: + cursor.execute('INSERT INTO indicators VALUES({}, {}, {}, {})'.format(interactionNum, events.Interaction.indicatorNameToIndices[indicator.getName()], instant, indicator[instant])) + +def saveIndicators(filename, interactions, indicatorNames = events.Interaction.indicatorNames): + 'Saves the indicator values in the table' + connection = sqlite3.connect(filename) + cursor = connection.cursor() + try: + createInteractionTable(cursor) + createIndicatorTables(cursor) + for inter in interactions: + saveInteraction(cursor, inter) + for indicatorName in indicatorNames: + indicator = inter.getIndicator(indicatorName) + if indicator != None: + saveIndicator(cursor, inter.getNum(), indicator) + except sqlite3.OperationalError as error: + printDBError(error) + connection.commit() connection.close() +def loadInteractions(filename): + '''Loads interaction and their indicators + + TODO choose the interactions to load''' + interactions = [] + connection = sqlite3.connect(filename) + cursor = connection.cursor() + try: + cursor.execute('select INT.id, INT.object_id1, INT.object_id2, INT.first_frame_number, INT.last_frame_number, IND.indicator_type, IND.frame_number, IND.value from interactions INT, indicators IND where INT.id = IND.interaction_id ORDER BY INT.id, IND.indicator_type') + interactionNum = -1 + indicatorTypeNum = -1 + tmpIndicators = {} + for row in cursor: + if row[0] != interactionNum: # save interaction and create new interaction + if interactionNum >= 0: + interactions.append(events.Interaction(interactionNum, moving.TimeInterval(row[3],row[4]), roadUserNumbers[0], roadUserNumbers[1])) + interactions[-1].indicators = tmpIndicators + tmpIndicators = {} + interactionNum = row[0] + roadUserNumbers = row[1:3] + if indicatorTypeNum != row[5]: + if indicatorTypeNum >= 0: + indicatorName = events.Interaction.indicatorNames[indicatorTypeNum] + tmpIndicators[indicatorName] = indicators.SeverityIndicator(indicatorName, indicatorValues) + indicatorTypeNum = row[5] + indicatorValues = {row[6]:row[7]} + else: + indicatorValues[row[6]] = row[7] + if interactionNum >= 0: + if indicatorTypeNum >= 0: + indicatorName = events.Interaction.indicatorNames[indicatorTypeNum] + tmpIndicators[indicatorName] = indicators.SeverityIndicator(indicatorName, indicatorValues) + interactions.append(events.Interaction(interactionNum, moving.TimeInterval(row[3],row[4]), roadUserNumbers[0], roadUserNumbers[1])) + interactions[-1].indicators = tmpIndicators + except sqlite3.OperationalError as error: + printDBError(error) + return [] + connection.close() + return interactions +# load first and last object instants +# CREATE TEMP TABLE IF NOT EXISTS object_instants AS SELECT OF.object_id, min(frame_number) as first_instant, max(frame_number) as last_instant from positions P, objects_features OF where P.trajectory_id = OF.trajectory_id group by OF.object_id order by OF.object_id + +def createBoundingBoxTable(filename, invHomography = None): + '''Create the table to store the object bounding boxes in image space + ''' + connection = sqlite3.connect(filename) + cursor = connection.cursor() + try: + cursor.execute('CREATE TABLE IF NOT EXISTS bounding_boxes (object_id INTEGER, frame_number INTEGER, x_top_left REAL, y_top_left REAL, x_bottom_right REAL, y_bottom_right REAL, PRIMARY KEY(object_id, frame_number))') + cursor.execute('INSERT INTO bounding_boxes SELECT object_id, frame_number, min(x), min(y), max(x), max(y) from ' + '(SELECT object_id, frame_number, (x*{}+y*{}+{})/w as x, (x*{}+y*{}+{})/w as y from ' + '(SELECT OF.object_id, P.frame_number, P.x_coordinate as x, P.y_coordinate as y, P.x_coordinate*{}+P.y_coordinate*{}+{} as w from positions P, objects_features OF where P.trajectory_id = OF.trajectory_id)) '.format(invHomography[0,0], invHomography[0,1], invHomography[0,2], invHomography[1,0], invHomography[1,1], invHomography[1,2], invHomography[2,0], invHomography[2,1], invHomography[2,2])+ + 'GROUP BY object_id, frame_number') + except sqlite3.OperationalError as error: + printDBError(error) + connection.commit() + connection.close() + +def loadBoundingBoxTableForDisplay(filename): + connection = sqlite3.connect(filename) + cursor = connection.cursor() + boundingBoxes = {} # list of bounding boxes for each instant + try: + cursor.execute('SELECT name FROM sqlite_master WHERE type=\'table\' AND name=\'bounding_boxes\'') + result = [row for row in cursor] + if len(result) > 0: + cursor.execute('SELECT * FROM bounding_boxes') + for row in cursor: + boundingBoxes.setdefault(row[1], []).append([moving.Point(row[2], row[3]), moving.Point(row[4], row[5])]) + except sqlite3.OperationalError as error: + printDBError(error) + return boundingBoxes + connection.close() + return boundingBoxes + +def loadBoundingBoxTable(filename): + connection = sqlite3.connect(filename) + cursor = connection.cursor() + boundingBoxes = [] + + try: + pass + except sqlite3.OperationalError as error: + printDBError(error) + return boundingBoxes + connection.close() + return boundingBoxes + + +######################### +# txt files +######################### + +def openCheck(filename, option = 'r', quitting = False): + '''Open file filename in read mode by default + and checks it is open''' + try: + return open(filename, option) + except IOError: + print 'File %s could not be opened.' % filename + if quitting: + from sys import exit + exit() + return None + +def readline(f, commentCharacters = commentChar): + '''Modified readline function to skip comments + Can take a list of characters or a string (in will work in both)''' + s = f.readline() + while (len(s) > 0) and s[0] in commentCharacters: + s = f.readline() + return s.strip() + +def getLines(f, commentCharacters = commentChar): + '''Gets a complete entry (all the lines) in between delimiterChar.''' + dataStrings = [] + s = readline(f, commentCharacters) + while len(s) > 0: + dataStrings += [s.strip()] + s = readline(f, commentCharacters) + return dataStrings + +def writeList(filename, l): + f = openCheck(filename, 'w') + for x in l: + f.write('{}\n'.format(x)) + f.close() + +def loadListStrings(filename, commentCharacters = commentChar): + f = openCheck(filename, 'r') + result = getLines(f, commentCharacters) + f.close() + return result + +def getValuesFromINIFile(filename, option, delimiterChar = '=', commentCharacters = commentChar): + values = [] + for l in loadListStrings(filename, commentCharacters): + if l.startswith(option): + values.append(l.split(delimiterChar)[1].strip()) + return values + +class FakeSecHead(object): + '''Add fake section header [asection] + + from http://stackoverflow.com/questions/2819696/parsing-properties-file-in-python/2819788#2819788 + use read_file in Python 3.2+ + ''' + def __init__(self, fp): + self.fp = fp + self.sechead = '[main]\n' + + def readline(self): + if self.sechead: + try: return self.sechead + finally: self.sechead = None + else: return self.fp.readline() + +def loadTrajectoriesFromVissimFile(filename, simulationStepsPerTimeUnit, nObjects = -1, warmUpLastInstant = None): + '''Reads data from VISSIM .fzp trajectory file + simulationStepsPerTimeUnit is the number of simulation steps per unit of time used by VISSIM + for example, there seems to be 5 simulation steps per simulated second in VISSIM, + so simulationStepsPerTimeUnit should be 5, + so that all times correspond to the number of the simulation step (and can be stored as integers) + + Assumed to be sorted over time''' + objects = {} # dictionary of objects index by their id + firstInstants = {} + + inputfile = openCheck(filename, quitting = True) + + # data = pd.read_csv(filename, skiprows=15, delimiter=';') + # skip header: 15 lines + 1 + line = readline(inputfile, '*$') + while len(line) > 0:#for line in inputfile: + data = line.strip().split(';') + objNum = int(data[1]) + instant = int(float(data[0])*simulationStepsPerTimeUnit) + s = float(data[4]) + y = float(data[5]) + lane = data[2]+'_'+data[3] + if objNum not in firstInstants: + firstInstants[objNum] = instant + if warmUpLastInstant == None or firstInstants[objNum] >= warmUpLastInstant: + if nObjects < 0 or len(objects) < nObjects: + objects[objNum] = moving.MovingObject(num = objNum, timeInterval = moving.TimeInterval(instant, instant)) + objects[objNum].curvilinearPositions = moving.CurvilinearTrajectory() + if (warmUpLastInstant == None or firstInstants[objNum] >= warmUpLastInstant) and objNum in objects: + objects[objNum].timeInterval.last = instant + objects[objNum].curvilinearPositions.addPositionSYL(s, y, lane) + line = readline(inputfile, '*$') + + return objects.values() + def loadTrajectoriesFromNgsimFile(filename, nObjects = -1, sequenceNum = -1): '''Reads data from the trajectory data provided by NGSIM project and returns the list of Feature objects''' objects = [] - input = utils.openCheck(filename) - if not input: - import sys - sys.exit() + inputfile = openCheck(filename, quitting = True) def createObject(numbers): firstFrameNum = int(numbers[1]) @@ -211,21 +678,21 @@ obj.followingVehicles = [int(numbers[15])] # following vehicle (after) obj.spaceHeadways = [float(numbers[16])] # feet obj.timeHeadways = [float(numbers[17])] # seconds - obj.curvilinearPositions = moving.Trajectory([[float(numbers[5])],[float(numbers[4])]]) # X is the longitudinal coordinate + obj.curvilinearPositions = moving.CurvilinearTrajectory([float(numbers[5])],[float(numbers[4])], obj.laneNums) # X is the longitudinal coordinate obj.speeds = [float(numbers[11])] obj.size = [float(numbers[8]), float(numbers[9])] # 8 lengh, 9 width # TODO: temporary, should use a geometry object return obj - numbers = input.readline().strip().split() + numbers = readline(inputfile).strip().split() if (len(numbers) > 0): obj = createObject(numbers) - for line in input: + for line in inputfile: numbers = line.strip().split() - if obj.num != int(numbers[0]): + if obj.getNum() != int(numbers[0]): # check and adapt the length to deal with issues in NGSIM data if (obj.length() != obj.positions.length()): - print 'length pb with object %s (%d,%d)' % (obj.num,obj.length(),obj.positions.length()) + print 'length pb with object %s (%d,%d)' % (obj.getNum(),obj.length(),obj.positions.length()) obj.last = obj.getFirstInstant()+obj.positions.length()-1 #obj.velocities = utils.computeVelocities(f.positions) # compare norm to speeds ? objects.append(obj) @@ -233,33 +700,33 @@ break obj = createObject(numbers) else: + obj.laneNums.append(int(numbers[13])) obj.positions.addPositionXY(float(numbers[6]), float(numbers[7])) - obj.curvilinearPositions.addPositionXY(float(numbers[5]), float(numbers[4])) + obj.curvilinearPositions.addPositionSYL(float(numbers[5]), float(numbers[4]), obj.laneNums[-1]) obj.speeds.append(float(numbers[11])) - obj.laneNums.append(int(numbers[13])) obj.precedingVehicles.append(int(numbers[14])) obj.followingVehicles.append(int(numbers[15])) obj.spaceHeadways.append(float(numbers[16])) obj.timeHeadways.append(float(numbers[17])) if (obj.size[0] != float(numbers[8])): - print 'changed length obj %d' % (f.num) + print 'changed length obj %d' % (obj.getNum()) if (obj.size[1] != float(numbers[9])): - print 'changed width obj %d' % (f.num) + print 'changed width obj %d' % (obj.getNum()) - input.close() + inputfile.close() return objects -def convertNgsimFile(inFile, outFile, append = False, nObjects = -1, sequenceNum = 0): +def convertNgsimFile(inputfile, outputfile, append = False, nObjects = -1, sequenceNum = 0): '''Reads data from the trajectory data provided by NGSIM project and converts to our current format.''' if append: - out = open(outFile,'a') + out = openCheck(outputfile,'a') else: - out = open(outFile,'w') + out = openCheck(outputfile,'w') nObjectsPerType = [0,0,0] - features = loadNgsimFile(inFile, sequenceNum) + features = loadNgsimFile(inputfile, sequenceNum) for f in features: nObjectsPerType[f.userType-1] += 1 f.write(out) @@ -268,6 +735,114 @@ out.close() +def writePositionsToCsv(f, obj): + timeInterval = obj.getTimeInterval() + positions = obj.getPositions() + curvilinearPositions = obj.getCurvilinearPositions() + for i in xrange(int(obj.length())): + p1 = positions[i] + s = '{},{},{},{}'.format(obj.num,timeInterval[i],p1.x,p1.y) + if curvilinearPositions != None: + p2 = curvilinearPositions[i] + s += ',{},{}'.format(p2[0],p2[1]) + f.write(s+'\n') + +def writeTrajectoriesToCsv(filename, objects): + f = openCheck(filename, 'w') + for i,obj in enumerate(objects): + writePositionsToCsv(f, obj) + f.close() + + +######################### +# Utils to read .ini type text files for configuration, meta data... +######################### + +class ProcessParameters: + '''Class for all parameters controlling data processing: input, + method parameters, etc. for tracking, classification and safety + + Note: framerate is already taken into account''' + + def loadConfigFile(self, filename): + from ConfigParser import ConfigParser + from numpy import loadtxt + from os import path + + config = ConfigParser() + config.readfp(FakeSecHead(openCheck(filename))) + self.sectionHeader = config.sections()[0] + # Tracking/display parameters + self.videoFilename = config.get(self.sectionHeader, 'video-filename') + self.databaseFilename = config.get(self.sectionHeader, 'database-filename') + self.homographyFilename = config.get(self.sectionHeader, 'homography-filename') + if (path.exists(self.homographyFilename)): + self.homography = loadtxt(self.homographyFilename) + else: + self.homography = None + self.intrinsicCameraFilename = config.get(self.sectionHeader, 'intrinsic-camera-filename') + if (path.exists(self.intrinsicCameraFilename)): + self.intrinsicCameraMatrix = loadtxt(self.intrinsicCameraFilename) + else: + self.intrinsicCameraMatrix = None + distortionCoefficients = getValuesFromINIFile(filename, 'distortion-coefficients', '=') + self.distortionCoefficients = [float(x) for x in distortionCoefficients] + self.undistortedImageMultiplication = config.getfloat(self.sectionHeader, 'undistorted-size-multiplication') + self.undistort = config.getboolean(self.sectionHeader, 'undistort') + self.firstFrameNum = config.getint(self.sectionHeader, 'frame1') + self.videoFrameRate = config.getfloat(self.sectionHeader, 'video-fps') + + # Classification parameters + + + # Safety parameters + self.maxPredictedSpeed = config.getfloat(self.sectionHeader, 'max-predicted-speed')/3.6/self.videoFrameRate + self.predictionTimeHorizon = config.getfloat(self.sectionHeader, 'prediction-time-horizon')*self.videoFrameRate + self.collisionDistance = config.getfloat(self.sectionHeader, 'collision-distance') + self.crossingZones = config.getboolean(self.sectionHeader, 'crossing-zones') + self.predictionMethod = config.get(self.sectionHeader, 'prediction-method') + self.nPredictedTrajectories = config.getint(self.sectionHeader, 'npredicted-trajectories') + self.maxNormalAcceleration = config.getfloat(self.sectionHeader, 'max-normal-acceleration')/self.videoFrameRate**2 + self.maxNormalSteering = config.getfloat(self.sectionHeader, 'max-normal-steering')/self.videoFrameRate + self.minExtremeAcceleration = config.getfloat(self.sectionHeader, 'min-extreme-acceleration')/self.videoFrameRate**2 + self.maxExtremeAcceleration = config.getfloat(self.sectionHeader, 'max-extreme-acceleration')/self.videoFrameRate**2 + self.maxExtremeSteering = config.getfloat(self.sectionHeader, 'max-extreme-steering')/self.videoFrameRate + self.useFeaturesForPrediction = config.getboolean(self.sectionHeader, 'use-features-prediction') + + def __init__(self, filename = None): + if filename != None: + self.loadConfigFile(filename) + +class SceneParameters: + def __init__(self, config, sectionName): + from ConfigParser import NoOptionError + from ast import literal_eval + try: + self.sitename = config.get(sectionName, 'sitename') + self.databaseFilename = config.get(sectionName, 'data-filename') + self.homographyFilename = config.get(sectionName, 'homography-filename') + self.calibrationFilename = config.get(sectionName, 'calibration-filename') + self.videoFilename = config.get(sectionName, 'video-filename') + self.frameRate = config.getfloat(sectionName, 'framerate') + self.date = datetime.strptime(config.get(sectionName, 'date'), datetimeFormat) # 2011-06-22 11:00:39 + self.translation = literal_eval(config.get(sectionName, 'translation')) # = [0.0, 0.0] + self.rotation = config.getfloat(sectionName, 'rotation') + self.duration = config.getint(sectionName, 'duration') + except NoOptionError as e: + print(e) + print('Not a section for scene meta-data') + + @staticmethod + def loadConfigFile(filename): + from ConfigParser import ConfigParser + config = ConfigParser() + config.readfp(openCheck(filename)) + configDict = dict() + for sectionName in config.sections(): + configDict[sectionName] = SceneParameters(config, sectionName) + return configDict + + if __name__ == "__main__": import doctest import unittest
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/python/tests/events.txt Fri Dec 05 12:13:53 2014 -0500 @@ -0,0 +1,11 @@ +>>> from events import * +>>> from moving import MovingObject, TimeInterval + +>>> objects = [MovingObject(num = i, timeInterval = TimeInterval(0,10)) for i in range(10)] +>>> interactions = createInteractions(objects) +>>> len([i for i in interactions if len(i.roadUserNumbers) == 1]) +0 +>>> objects2 = [MovingObject(num = i, timeInterval = TimeInterval(0,10)) for i in range(100, 110)] +>>> interactions = createInteractions(objects, objects2) +>>> len([i for i in interactions if len(i.roadUserNumbers) == 1]) +0
--- a/python/tests/moving.txt Thu Apr 18 15:29:33 2013 -0400 +++ b/python/tests/moving.txt Fri Dec 05 12:13:53 2014 -0500 @@ -42,6 +42,10 @@ >>> Point(3,4)-Point(1,7) (2.000000,-3.000000) +>>> -Point(1,2) +(-1.000000,-2.000000) +>>> Point(1,2).multiply(0.5) +(0.500000,1.000000) >>> Point(3,2).norm2Squared() 13 @@ -49,19 +53,29 @@ >>> Point.distanceNorm2(Point(3,4),Point(1,7)) 3.605551275463989 ->>> Point(3,2).inPolygon([Point(0,0),Point(1,0),Point(1,1),Point(0,1)]) +>>> Point(3,2).inPolygonNoShapely(np.array([[0,0],[1,0],[1,1],[0,1]])) False ->>> Point(3,2).inPolygon([Point(0,0),Point(4,0),Point(4,3),Point(0,3)]) +>>> Point(3,2).inPolygonNoShapely(np.array([[0,0],[4,0],[4,3],[0,3]])) True >>> predictPositionNoLimit(10, Point(0,0), Point(1,1)) # doctest:+ELLIPSIS ((1.0...,1.0...), (10.0...,10.0...)) ->>> segmentIntersection(Point(0,0),Point(1,1), Point(0,1), Point(1,2)) ->>> segmentIntersection(Point(0,1),Point(1,0), Point(0,2), Point(2,1)) ->>> segmentIntersection(Point(0,0),Point(2,0), Point(1,-1),Point(1,1)) -(1.000000,0.000000) ->>> segmentIntersection(Point(0,1),Point(2,0),Point(1,1),Point(1,2)) +>>> segmentIntersection(Point(0,0), Point(0,1), Point(1,1), Point(2,3)) +>>> segmentIntersection(Point(0,1), Point(0,3), Point(1,0), Point(3,1)) +>>> segmentIntersection(Point(0.,0.), Point(2.,2.), Point(0.,2.), Point(2.,0.)) +(1.000000,1.000000) +>>> segmentIntersection(Point(0,1), Point(1,2), Point(2,0), Point(3,2)) + +>>> left = Trajectory.fromPointList([(92.291666666666686, 102.99239033124439), (56.774193548387103, 69.688898836168306)]) +>>> middle = Trajectory.fromPointList([(87.211021505376351, 93.390778871978512), (59.032258064516128, 67.540286481647257)]) +>>> right = Trajectory.fromPointList([(118.82392473118281, 115.68263205013426), (63.172043010752688, 66.600268576544309)]) +>>> alignments = [left, middle, right] +>>> for a in alignments: a.computeCumulativeDistances() +>>> getSYfromXY(Point(73, 82), alignments) +[1, 0, (73.819977,81.106170), 18.172277808821125, 18.172277808821125, 1.2129694042343868] +>>> getSYfromXY(Point(78, 83), alignments, 0.5) +[1, 0, (77.033188,84.053889), 13.811799123113715, 13.811799123113715, -1.4301775140225983] >>> Trajectory().length() 0 @@ -70,12 +84,79 @@ True >>> t1[1] (1.500000,3.500000) ->>> t1.getTrajectoryInPolygon(np.array([[0,0],[4,0],[4,3],[0,3]])) +>>> t1.getTrajectoryInPolygonNoShapely(np.array([[0,0],[4,0],[4,3],[0,3]])) (0.500000,0.500000) ->>> t1.getTrajectoryInPolygon(np.array([[10,10],[14,10],[14,13],[10,13]])).length() +>>> t1.getTrajectoryInPolygonNoShapely(np.array([[10,10],[14,10],[14,13],[10,13]])).length() 0 ->>> Trajectory.norm2LCSS(t1, t1, 0.1) +>>> t1.differentiate() +(1.000000,3.000000) (1.000000,3.000000) +>>> t1.differentiate(True) +(1.000000,3.000000) (1.000000,3.000000) (1.000000,3.000000) +>>> t1 = Trajectory([[0.5,1.5,3.5],[0.5,2.5,7.5]]) +>>> t1.differentiate() +(1.000000,2.000000) (2.000000,5.000000) + +>>> t1.computeCumulativeDistances() +>>> t1.getDistance(0) +2.23606797749979 +>>> t1.getDistance(1) +5.385164807134504 +>>> t1.getDistance(2) +Index 2 beyond trajectory length 3-1 +>>> t1.getCumulativeDistance(0) +0.0 +>>> t1.getCumulativeDistance(1) +2.23606797749979 +>>> t1.getCumulativeDistance(2) +7.6212327846342935 +>>> t1.getCumulativeDistance(3) +Index 3 beyond trajectory length 3 + + +>>> from utils import LCSS +>>> lcss = LCSS(lambda x,y: Point.distanceNorm2(x,y) <= 0.1) +>>> Trajectory.lcss(t1, t1, lcss) +3 +>>> lcss = LCSS(lambda p1, p2: (p1-p2).normMax() <= 0.1) +>>> Trajectory.lcss(t1, t1, lcss) 3 ->>> Trajectory.normMaxLCSS(t1, t1, 0.1) -3 + +>>> p1=Point(0,0) +>>> p2=Point(1,0) +>>> v1 = Point(0.1,0.1) +>>> v2 = Point(-0.1, 0.1) +>>> abs(Point.timeToCollision(p1, p2, v1, v2, 0.)-5.0) < 0.00001 +True +>>> abs(Point.timeToCollision(p1, p2, v1, v2, 0.1)-4.5) < 0.00001 +True +>>> p1=Point(0,1) +>>> p2=Point(1,0) +>>> v1 = Point(0,0.1) +>>> v2 = Point(0.1, 0) +>>> Point.timeToCollision(p1, p2, v1, v2, 0.) == None +True +>>> Point.timeToCollision(p2, p1, v2, v1, 0.) == None +True +>>> Point.midPoint(p1, p2) +(0.500000,0.500000) + +>>> t = CurvilinearTrajectory(S = [1., 2., 3., 5.], Y = [0.5, 0.5, 0.6, 0.7], lanes = ['1']*4) +>>> t.differentiate() # doctest:+ELLIPSIS +[1.0, 0.0, '1'] [1.0, 0.099..., '1'] [2.0, 0.099..., '1'] +>>> t.differentiate(True) # doctest:+ELLIPSIS +[1.0, 0.0, '1'] [1.0, 0.099..., '1'] [2.0, 0.099..., '1'] [2.0, 0.099..., '1'] +>>> t = CurvilinearTrajectory(S = [1.], Y = [0.5], lanes = ['1']) +>>> t.differentiate().empty() +True + +>>> o1 = MovingObject(positions = Trajectory([[0]*3,[2]*3]), velocities = Trajectory([[0]*3,[1]*3])) +>>> o1.classifyUserTypeSpeedMotorized(0.5, np.median) +>>> userTypeNames[o1.getUserType()] +'car' +>>> o1.classifyUserTypeSpeedMotorized(0.5, np.mean) +>>> userTypeNames[o1.getUserType()] +'car' +>>> o1.classifyUserTypeSpeedMotorized(1.5, np.median) +>>> userTypeNames[o1.getUserType()] +'pedestrian'
--- a/python/tests/prediction.txt Thu Apr 18 15:29:33 2013 -0400 +++ b/python/tests/prediction.txt Fri Dec 05 12:13:53 2014 -0500 @@ -18,7 +18,7 @@ >>> import random >>> acceleration = lambda: random.uniform(-0.5,0.5) >>> steering = lambda: random.uniform(-0.1,0.1) ->>> et = prediction.PredictedTrajectoryNormalAdaptation(moving.Point(0,0),moving.Point(1,1), acceleration, steering, maxSpeed = 2) +>>> et = prediction.PredictedTrajectoryRandomControl(moving.Point(0,0),moving.Point(1,1), acceleration, steering, maxSpeed = 2) >>> p = et.predictPosition(500) >>> from numpy import max >>> max(et.getPredictedSpeeds()) <= 2.
--- a/python/tests/storage.txt Thu Apr 18 15:29:33 2013 -0400 +++ b/python/tests/storage.txt Fri Dec 05 12:13:53 2014 -0500 @@ -1,9 +1,31 @@ >>> from storage import * +>>> from StringIO import StringIO ->>> loadPrototypeMatchIndexesFromSqlite("nonexistent") +>>> f = openCheck('non_existant_file.txt') +File non_existant_file.txt could not be opened. + +>>> nonexistentFilename = "nonexistent" +>>> loadPrototypeMatchIndexesFromSqlite(nonexistentFilename) DB Error: no such table: prototypes [] ->>> loadTrajectoriesFromSqlite("nonexistent", 'feature') +>>> loadTrajectoriesFromSqlite(nonexistentFilename, 'feature') DB Error: no such table: positions DB Error: no such table: velocities [] +>>> from os import remove +>>> remove(nonexistentFilename) + +>>> strio = StringIO('# asdlfjasdlkj0\nsadlkfjsdlakjf') +>>> readline(strio) +'sadlkfjsdlakjf' +>>> strio = StringIO('# asdlfjasdlkj0\nsadlkfjsdlakjf') +>>> readline(strio, ['#']) +'sadlkfjsdlakjf' +>>> strio = StringIO('# asdlfjasdlkj0\nsadlkfjsdlakjf') +>>> readline(strio, ['%']) +'# asdlfjasdlkj0' +>>> strio = StringIO('# asdlfjasdlkj0\nsadlkfjsdlakjf') +>>> readline(strio, '%*$') +'# asdlfjasdlkj0' +>>> readline(strio, '%#') +'sadlkfjsdlakjf'
--- a/python/tests/utils.txt Thu Apr 18 15:29:33 2013 -0400 +++ b/python/tests/utils.txt Fri Dec 05 12:13:53 2014 -0500 @@ -20,9 +20,6 @@ >>> inBetween(1,2,0) False ->>> f = openCheck('non_existant_file.txt') -File non_existant_file.txt could not be opened. - >>> removeExtension('test-adfasdf.asdfa.txt') 'test-adfasdf.asdfa' >>> removeExtension('test-adfasdf') @@ -44,10 +41,79 @@ >>> stepPlot([3, 5, 7, 8], 1, 10, 0) ([1, 3, 3, 5, 5, 7, 7, 8, 8, 10], [0, 0, 1, 1, 2, 2, 3, 3, 4, 4]) ->>> LCSS(range(5), range(5), 0.1, lambda x,y:abs(x-y)) +>>> mostCommon(['a','b','c','b']) +'b' +>>> mostCommon(['a','b','c','b', 'c']) +'b' +>>> mostCommon(range(10)+[1]) +1 +>>> mostCommon([range(2), range(4), range(2)]) +[0, 1] + +>>> lcss = LCSS(lambda x,y: abs(x-y) <= 0.1) +>>> lcss.compute(range(5), range(5)) 5 ->>> LCSS(range(1,5), range(5), 0.1, lambda x,y:abs(x-y)) +>>> lcss.compute(range(1,5), range(5)) +4 +>>> lcss.compute(range(5,10), range(5)) +0 +>>> lcss.compute(range(5), range(10)) +5 +>>> lcss.compute(range(5), range(10), 2) +5 +>>> lcss.similarityFunc = lambda x,y: x == y +>>> lcss.compute(['a','b','c'], ['a','b','c', 'd']) +3 +>>> lcss.computeNormalized(['a','b','c'], ['a','b','c', 'd']) #doctest: +ELLIPSIS +1.0 +>>> lcss.computeNormalized(['a','b','c','x'], ['a','b','c', 'd']) #doctest: +ELLIPSIS +0.75 +>>> lcss.compute(['a','b','c'], ['a','b','c', 'd']) +3 +>>> lcss.compute(['a','x','b','c'], ['a','b','c','d','x']) +3 +>>> lcss.compute(['a','b','c','x','d'], ['a','b','c','d','x']) 4 ->>> LCSS(range(5,10), range(5), 0.1, lambda x,y:abs(x-y)) +>>> lcss.delta = 1 +>>> lcss.compute(['a','b','c'], ['a','b','x','x','c']) +2 + +>>> lcss.delta = float('inf') +>>> lcss.compute(['a','b','c'], ['a','b','c', 'd'], computeSubSequence = True) +3 +>>> lcss.subSequenceIndices +[(0, 0), (1, 1), (2, 2)] +>>> lcss.compute(['a','b','c'], ['x','a','b','c'], computeSubSequence = True) +3 +>>> lcss.subSequenceIndices +[(0, 1), (1, 2), (2, 3)] +>>> lcss.compute(['a','g','b','c'], ['a','b','c', 'd'], computeSubSequence = True) +3 +>>> lcss.subSequenceIndices +[(0, 0), (2, 1), (3, 2)] + +>>> alignedLcss = LCSS(lambda x,y:(abs(x-y) <= 0.1), delta = 2, aligned = True) +>>> alignedLcss.compute(range(5), range(5)) +5 +>>> alignedLcss.compute(range(1,5), range(5)) +4 + +>>> alignedLcss.compute(range(5,10), range(10)) +5 + +>>> lcss.delta = 2 +>>> lcss.compute(range(5,10), range(10)) 0 - +>>> alignedLcss.delta = 6 +>>> alignedLcss.compute(range(5), range(5)) +5 +>>> alignedLcss.compute(range(5), range(6)) +5 +>>> lcss.delta = 10 +>>> alignedLcss.compute(range(1,7), range(6)) +5 +>>> lcss = LCSS(lambda x,y: x == y, delta = 2, aligned = True) +>>> lcss.compute(range(20), [2,4,6,7,8,9,11,13], True) +8 +>>> lcss.subSequenceIndices +[(2, 0), (4, 1), (6, 2), (7, 3), (8, 4), (9, 5), (11, 6), (13, 7)]
--- a/python/traffic_engineering.py Thu Apr 18 15:29:33 2013 -0400 +++ b/python/traffic_engineering.py Fri Dec 05 12:13:53 2014 -0500 @@ -12,6 +12,20 @@ # Simulation ######################### +def generateTimeHeadways(meanTimeHeadway, simulationTime): + '''Generates the time headways between arrivals + given the meanTimeHeadway and the negative exponential distribution + over a time interval of length simulationTime (assumed to be in same time unit as headway''' + from random import expovariate + headways = [] + totalTime = 0 + flow = 1/meanTimeHeadway + while totalTime < simulationTime: + h = expovariate(flow) + headways.append(h) + totalTime += h + return headways + class Vehicle: '''Generic vehicle class 1D coordinates for now''' @@ -159,11 +173,16 @@ self.types = types self.proportions = proportions self.equivalents = equivalents - self.nLanes = nLanes # unused + self.nLanes = nLanes else: print('Proportions do not sum to 1') pass + def checkProtected(self, opposedThroughMvt): + '''Checks if this left movement should be protected, + ie if one of the main two conditions on left turn is verified''' + return self.volume >= 200 or self.volume*opposedThroughMvt.volume/opposedThroughMvt.nLanes > 50000 + def getPCUVolume(self): '''Returns the passenger-car equivalent for the input volume''' v = 0 @@ -182,15 +201,6 @@ def getTVUVolume(self): return self.mvtEquivalent*self.volume.getPCUVolume() -class IntersectionApproach: # should probably not be used - def __init__(self, leftTurnVolume, throughVolume, rightTurnVolume): - self.leftTurnVolume = leftTurnVolume - self.throughVolume = throughVolume - self.rightTurnVolume = rightTurnVolume - - def getTVUVolume(self, leftTurnEquivalent = 1, throughEquivalent = 1, rightTurnEquivalent = 1): - return self.leftTurnVolume.getPCUVolume()*leftTurnEquivalent+self.throughVolume.getPCUVolume()*throughEquivalent+self.rightTurnVolume.getPCUVolume()*rightTurnEquivalent - class LaneGroup: '''Class that represents a group of mouvements''' @@ -204,11 +214,6 @@ def getCharge(self, saturationVolume): return self.getTVUVolume()/(self.nLanes*saturationVolume) -def checkProtectedLeftTurn(leftMvt, opposedThroughMvt): - '''Checks if one of the main two conditions on left turn is verified - The lane groups should contain left and through movement''' - return leftMvt.volume >= 200 or leftMvt.volume*opposedThroughMvt.volume/opposedThroughMvt.nLanes > 50000 - def optimalCycle(lostTime, criticalCharge): return (1.5*lostTime+5)/(1-criticalCharge) @@ -226,9 +231,7 @@ self.saturationVolume = saturationVolume def computeCriticalCharges(self): - self.criticalCharges = [] - for phase in self.phases: - self.criticalCharges.append(max([lg.getCharge(self.saturationVolume) for lg in phase])) + self.criticalCharges = [max([lg.getCharge(self.saturationVolume) for lg in phase]) for phase in self.phases] self.criticalCharge = sum(self.criticalCharges) def computeOptimalCycle(self): @@ -242,7 +245,7 @@ return self.C def computeEffectiveGreen(self): - from numpy import round + #from numpy import round #self.computeCycle() # in case it was not done before effectiveGreenTime = self.C-self.lostTime self.effectiveGreens = [round(c*effectiveGreenTime/self.criticalCharge,1) for c in self.criticalCharges] @@ -263,6 +266,15 @@ '''Computes the uniform delay''' return 0.5*cycleLength*(1-float(effectiveGreen)/cycleLength)/(1-float(effectiveGreen*saturationDegree)/cycleLength) +def overflowDelay(T, X, c, k=0.5, I=1): + '''Computes the overflow delay (HCM) + T in hours + c capacity of the lane group + k default for fixed time signal + I=1 for isolated intersection (Poisson arrival)''' + from math import sqrt + return 900*T*(X - 1 + sqrt((X - 1)**2 + 8*k*I*X/(c*T))) + ######################### # misc #########################
--- a/python/ubc_utils.py Thu Apr 18 15:29:33 2013 -0400 +++ b/python/ubc_utils.py Fri Dec 05 12:13:53 2014 -0500 @@ -1,7 +1,7 @@ #! /usr/bin/env python '''Various utilities to load data saved by the UBC tool(s)''' -import utils +import utils, events from moving import MovingObject, TimeInterval, Trajectory __metaclass__ = type @@ -12,12 +12,18 @@ 'contoursequence'] severityIndicatorNames = ['Distance', - 'Cosine', + 'Collision Course Cosine', 'Velocity Cosine', 'Speed Differential', 'Collision Probability', 'Severity Index', - 'TTC'] + 'Time to Collision'] + +userTypeNames = ['car', + 'pedestrian', + 'twowheels', + 'bus', + 'truck'] # severityIndicator = {'Distance': 0, # 'Cosine': 1,
--- a/python/utils.py Thu Apr 18 15:29:33 2013 -0400 +++ b/python/utils.py Fri Dec 05 12:13:53 2014 -0500 @@ -3,12 +3,12 @@ #from numpy import * #from pylab import * +from datetime import time, datetime +from math import sqrt __metaclass__ = type -commentChar = '#' - -delimiterChar = '%'; +datetimeFormat = "%Y-%m-%d %H:%M:%S" ######################### # Enumerations @@ -22,46 +22,29 @@ return result ######################### -# CLI utils -######################### - -def parseCLIOptions(helpMessage, options, cliArgs, optionalOptions=[]): - ''' Simple function to handle similar argument parsing - Returns the dictionary of options and their values - - * cliArgs are most likely directly sys.argv - (only the elements after the first one are considered) - - * options should be a list of strings for getopt options, - eg ['frame=','correspondences=','video='] - A value must be provided for each option, or the program quits''' - import sys, getopt - from numpy.core.fromnumeric import all - optionValues, args = getopt.getopt(cliArgs[1:], 'h', ['help']+options+optionalOptions) - optionValues = dict(optionValues) - - if '--help' in optionValues.keys() or '-h' in optionValues.keys(): - print(helpMessage+ - '\n - Compulsory options: '+' '.join([opt.replace('=','') for opt in options])+ - '\n - Non-compulsory options: '+' '.join([opt.replace('=','') for opt in optionalOptions])) - sys.exit() - - missingArgument = [('--'+opt.replace('=','') in optionValues.keys()) for opt in options] - if not all(missingArgument): - print('Missing argument') - print(optionValues) - sys.exit() - - return optionValues - -######################### # simple statistics ######################### -def confidenceInterval(mean, stdev, nSamples, percentConfidence, printLatex = False): - from math import sqrt +def sampleSize(stdev, tolerance, percentConfidence, printLatex = False): from scipy.stats.distributions import norm k = round(norm.ppf(0.5+percentConfidence/200., 0, 1)*100)/100. # 1.-(100-percentConfidence)/200. + if printLatex: + print('${0}^2\\frac{{{1}^2}}{{{2}^2}}$'.format(k, stdev, tolerance)) + return (k*stdev/tolerance)**2 + +def confidenceInterval(mean, stdev, nSamples, percentConfidence, trueStd = True, printLatex = False): + '''if trueStd, use normal distribution, otherwise, Student + + Use otherwise t.interval or norm.interval + ex: norm.interval(0.95, loc = 0., scale = 2.3/sqrt(11)) + t.interval(0.95, 10, loc=1.2, scale = 2.3/sqrt(nSamples)) + loc is mean, scale is sigma/sqrt(n) (for Student, 10 is df)''' + from math import sqrt + from scipy.stats.distributions import norm, t + if trueStd: + k = round(norm.ppf(0.5+percentConfidence/200., 0, 1)*100)/100. # 1.-(100-percentConfidence)/200. + else: # use Student + k = round(t.ppf(0.5+percentConfidence/200., nSamples-1)*100)/100. e = k*stdev/sqrt(nSamples) if printLatex: print('${0} \pm {1}\\frac{{{2}}}{{\sqrt{{{3}}}}}$'.format(mean, k, stdev, nSamples)) @@ -78,15 +61,13 @@ def nSamples(self): return sum(self.counts) -def cumulativeDensityFunction(sample): +def cumulativeDensityFunction(sample, normalized = False): '''Returns the cumulative density function of the sample of a random variable''' - from numpy.core.multiarray import array - from numpy.lib.function_base import unique - from numpy.core.fromnumeric import sum - a = array(sample) - a.sort() - xaxis = unique(a) - counts = [sum(a <= x) for x in xaxis] + from numpy import arange, cumsum + xaxis = sorted(sample) + counts = arange(1,len(sample)+1) # dtype = float + if normalized: + counts /= float(len(sample)) return xaxis, counts class EmpiricalDiscreteDistribution(EmpiricalDistribution): @@ -174,33 +155,75 @@ # maths section ######################### -def LCSS(l1, l2, threshold, distance, delta = float('inf')): - '''returns the longest common subsequence similarity - based on the threshold on distance between two elements of lists l1, l2 - ''' - from numpy import zeros, int as npint - m = len(l1) - n = len(l2) - similarity = zeros((m+1,n+1), dtype = npint) - for i in xrange(1,m+1): - for j in xrange(max(1,i-delta),min(n+1,i+delta)): - if distance(l1[i-1], l2[j-1])<=threshold: - similarity[i][j] = similarity[i-1][j-1]+1 - else: - similarity[i][j] = max(similarity[i-1][j], similarity[i][j-1]) - return similarity[-1][-1] +# def kernelSmoothing(sampleX, X, Y, weightFunc, halfwidth): +# '''Returns a smoothed weighted version of Y at the predefined values of sampleX +# Sum_x weight(sample_x,x) * y(x)''' +# from numpy import zeros, array +# smoothed = zeros(len(sampleX)) +# for i,x in enumerate(sampleX): +# weights = array([weightFunc(x,xx, halfwidth) for xx in X]) +# if sum(weights)>0: +# smoothed[i] = sum(weights*Y)/sum(weights) +# else: +# smoothed[i] = 0 +# return smoothed + +def kernelSmoothing(x, X, Y, weightFunc, halfwidth): + '''Returns the smoothed estimate of (X,Y) at x + Sum_x weight(sample_x,x) * y(x)''' + from numpy import zeros, array + weights = array([weightFunc(x,observedx, halfwidth) for observedx in X]) + if sum(weights)>0: + return sum(weights*Y)/sum(weights) + else: + return 0 + +def uniform(center, x, halfwidth): + if abs(center-x)<halfwidth: + return 1. + else: + return 0. -def framesToTime(nFrames, frameRate, initialTime = (0.,0.,0.)): - 'returns hour, minutes and seconds' +def gaussian(center, x, halfwidth): + from numpy import exp + return exp(-((center-x)/halfwidth)**2/2) + +def epanechnikov(center, x, halfwidth): + diff = abs(center-x) + if diff<halfwidth: + return 1.-(diff/halfwidth)**2 + else: + return 0. + +def triangular(center, x, halfwidth): + diff = abs(center-x) + if diff<halfwidth: + return 1.-abs(diff/halfwidth) + else: + return 0. + +def medianSmoothing(x, X, Y, halfwidth): + '''Returns the media of Y's corresponding to X's in the interval [x-halfwidth, x+halfwidth]''' + from numpy import median + return median([y for observedx, y in zip(X,Y) if abs(x-observedx)<halfwidth]) + +def argmaxDict(d): + return max(d, key=d.get) + +def framesToTime(nFrames, frameRate, initialTime = time()): + '''returns a datetime.time for the time in hour, minutes and seconds + initialTime is a datetime.time''' from math import floor - from datetime import time - seconds = int(floor(float(nFrames)/float(frameRate))+initialTime[0]*3600+initialTime[1]*60+initialTime[2]) + seconds = int(floor(float(nFrames)/float(frameRate))+initialTime.hour*3600+initialTime.minute*60+initialTime.second) h = int(floor(seconds/3600.)) seconds = seconds - h*3600 m = int(floor(seconds/60)) seconds = seconds - m*60 return time(h, m, seconds) +def timeToFrames(t, frameRate): + return frameRate*(t.hour*3600+t.minute*60+t.second) + def sortXY(X,Y): 'returns the sorted (x, Y(x)) sorted on X' D = {} @@ -217,14 +240,32 @@ return ceil(v*tens)/tens def inBetween(bound1, bound2, x): - return bound1 <= x <= bound2 or bound2 <= x<= bound1 + return bound1 <= x <= bound2 or bound2 <= x <= bound1 + +def pointDistanceL2(x1,y1,x2,y2): + ''' Compute point-to-point distance (L2 norm, ie Euclidean distance)''' + return sqrt((x2-x1)**2+(y2-y1)**2) def crossProduct(l1, l2): return l1[0]*l2[1]-l1[1]*l2[0] -def filterMovingWindow(input, halfWidth): +def cat_mvgavg(cat_list, halfWidth): + ''' Return a list of categories/values smoothed according to a window. + halfWidth is the search radius on either side''' + from copy import deepcopy + catgories = deepcopy(cat_list) + smoothed = catgories + for point in range(len(catgories)): + lower_bound_check = max(0,point-halfWidth) + upper_bound_check = min(len(catgories)-1,point+halfWidth+1) + window_values = catgories[lower_bound_check:upper_bound_check] + smoothed[point] = max(set(window_values), key=window_values.count) + return smoothed + +def filterMovingWindow(inputSignal, halfWidth): '''Returns an array obtained after the smoothing of the input by a moving average The first and last points are copied from the original.''' + from numpy import ones,convolve,array width = float(halfWidth*2+1) win = ones(width,'d') result = convolve(win/width,array(inputSignal),'same') @@ -251,20 +292,162 @@ return coef ######################### +# iterable section +######################### + +def mostCommon(L): + '''Returns the most frequent element in a iterable + + taken from http://stackoverflow.com/questions/1518522/python-most-common-element-in-a-list''' + from itertools import groupby + from operator import itemgetter + # get an iterable of (item, iterable) pairs + SL = sorted((x, i) for i, x in enumerate(L)) + # print 'SL:', SL + groups = groupby(SL, key=itemgetter(0)) + # auxiliary function to get "quality" for an item + def _auxfun(g): + item, iterable = g + count = 0 + min_index = len(L) + for _, where in iterable: + count += 1 + min_index = min(min_index, where) + # print 'item %r, count %r, minind %r' % (item, count, min_index) + return count, -min_index + # pick the highest-count/earliest item + return max(groups, key=_auxfun)[0] + +######################### +# sequence section +######################### + +class LCSS: + '''Class that keeps the LCSS parameters + and puts together the various computations''' + def __init__(self, similarityFunc, delta = float('inf'), aligned = False, lengthFunc = min): + self.similarityFunc = similarityFunc + self.aligned = aligned + self.delta = delta + self.lengthFunc = lengthFunc + self.subSequenceIndices = [(0,0)] + + def similarities(self, l1, l2, jshift=0): + from numpy import zeros, int as npint + n1 = len(l1) + n2 = len(l2) + self.similarityTable = zeros((n1+1,n2+1), dtype = npint) + for i in xrange(1,n1+1): + for j in xrange(max(1,i-jshift-self.delta),min(n2,i-jshift+self.delta)+1): + if self.similarityFunc(l1[i-1], l2[j-1]): + self.similarityTable[i,j] = self.similarityTable[i-1,j-1]+1 + else: + self.similarityTable[i,j] = max(self.similarityTable[i-1,j], self.similarityTable[i,j-1]) + + def subSequence(self, i, j): + '''Returns the subsequence of two sequences + http://en.wikipedia.org/wiki/Longest_common_subsequence_problem''' + if i == 0 or j == 0: + return [] + elif self.similarityTable[i][j] == self.similarityTable[i][j-1]: + return self.subSequence(i, j-1) + elif self.similarityTable[i][j] == self.similarityTable[i-1][j]: + return self.subSequence(i-1, j) + else: + return self.subSequence(i-1, j-1) + [(i-1,j-1)] + + def _compute(self, _l1, _l2, computeSubSequence = False): + '''returns the longest common subsequence similarity + based on the threshold on distance between two elements of lists l1, l2 + similarityFunc returns True or False whether the two points are considered similar + + if aligned, returns the best matching if using a finite delta by shiftinig the series alignments + + eg distance(p1, p2) < epsilon + ''' + if len(_l2) < len(_l1): # l1 is the shortest + l1 = _l2 + l2 = _l1 + revertIndices = True + else: + l1 = _l1 + l2 = _l2 + revertIndices = False + n1 = len(l1) + n2 = len(l2) + + if self.aligned: + lcssValues = {} + similarityTables = {} + for i in xrange(-n2-self.delta+1, n1+self.delta): # interval such that [i-shift-delta, i-shift+delta] is never empty, which happens when i-shift+delta < 1 or when i-shift-delta > n2 + self.similarities(l1, l2, i) + lcssValues[i] = self.similarityTable.max() + similarityTables[i] = self.similarityTable + #print self.similarityTable + alignmentShift = argmaxDict(lcssValues) # ideally get the medium alignment shift, the one that minimizes distance + self.similarityTable = similarityTables[alignmentShift] + else: + alignmentShift = 0 + self.similarities(l1, l2) + + # threshold values for the useful part of the similarity table are n2-n1-delta and n1-n2-delta + self.similarityTable = self.similarityTable[:min(n1, n2+alignmentShift+self.delta)+1, :min(n2, n1-alignmentShift+self.delta)+1] + + if computeSubSequence: + self.subSequenceIndices = self.subSequence(self.similarityTable.shape[0]-1, self.similarityTable.shape[1]-1) + if revertIndices: + self.subSequenceIndices = [(j,i) for i,j in self.subSequenceIndices] + return self.similarityTable[-1,-1] + + def compute(self, l1, l2, computeSubSequence = False): + '''get methods are to be shadowed in child classes ''' + return self._compute(l1, l2, computeSubSequence) + + def computeAlignment(self): + from numpy import mean + return mean([j-i for i,j in self.subSequenceIndices]) + + def _computeNormalized(self, l1, l2, computeSubSequence = False): + ''' compute the normalized LCSS + ie, the LCSS divided by the min or mean of the indicator lengths (using lengthFunc) + lengthFunc = lambda x,y:float(x,y)/2''' + return float(self._compute(l1, l2, computeSubSequence))/self.lengthFunc(len(l1), len(l2)) + + def computeNormalized(self, l1, l2, computeSubSequence = False): + return self._computeNormalized(l1, l2, computeSubSequence) + + def _computeDistance(self, l1, l2, computeSubSequence = False): + ''' compute the LCSS distance''' + return 1-self._computeNormalized(l1, l2, computeSubSequence) + + def computeDistance(self, l1, l2, computeSubSequence = False): + return self._computeDistance(l1, l2, computeSubSequence) + +######################### # plotting section ######################### -def stepPlot(X, firstX, lastX, initialCount = 0): - '''for each value in x, increment by one the initial count +def plotPolygon(poly, options = ''): + 'Plots shapely polygon poly' + from numpy.core.multiarray import array + from matplotlib.pyplot import plot + from shapely.geometry import Polygon + + tmp = array(poly.exterior) + plot(tmp[:,0], tmp[:,1], options) + +def stepPlot(X, firstX, lastX, initialCount = 0, increment = 1): + '''for each value in X, increment by increment the initial count returns the lists that can be plotted - to obtain a step plot increasing by one for each value in x, from first to last value''' + to obtain a step plot increasing by one for each value in x, from first to last value + firstX and lastX should be respectively smaller and larger than all elements in X''' sortedX = [] counts = [initialCount] for x in sorted(X): sortedX += [x,x] counts.append(counts[-1]) - counts.append(counts[-1]+1) + counts.append(counts[-1]+increment) counts.append(counts[-1]) return [firstX]+sortedX+[lastX], counts @@ -302,50 +485,6 @@ # file I/O section ######################### -def openCheck(filename, option = 'r', quit = False): - '''Open file filename in read mode by default - and checks it is open''' - try: - return open(filename, option) - except IOError: - print 'File %s could not be opened.' % filename - if quit: - from sys import exit - exit() - return None - -def readline(f, commentCharacter = commentChar): - '''Modified readline function to skip comments.''' - s = f.readline() - while (len(s) > 0) and s.startswith(commentCharacter): - s = f.readline() - return s.strip() - -def getLines(f, delimiterCharacter = delimiterChar): - '''Gets a complete entry (all the lines) in between delimiterChar.''' - dataStrings = [] - s = readline(f) - while (len(s) > 0) and (not s.startswith(delimiterCharacter)): - dataStrings += [s.strip()] - s = readline(f) - return dataStrings - -class FakeSecHead(object): - '''Add fake section header [asection] - - from http://stackoverflow.com/questions/2819696/parsing-properties-file-in-python/2819788#2819788 - use read_file in Python 3.2+ - ''' - def __init__(self, fp): - self.fp = fp - self.sechead = '[main]\n' - - def readline(self): - if self.sechead: - try: return self.sechead - finally: self.sechead = None - else: return self.fp.readline() - def removeExtension(filename, delimiter = '.'): '''Returns the filename minus the extension (all characters after last .)''' i = filename.rfind(delimiter) @@ -386,14 +525,6 @@ else: print(filename+' does not exist') -def plotPolygon(poly, options = ''): - from numpy.core.multiarray import array - from matplotlib.pyplot import plot - from shapely.geometry import Polygon - - tmp = array(poly.exterior) - plot(tmp[:,0], tmp[:,1], options) - def line2Floats(l, separator=' '): '''Returns the list of floats corresponding to the string''' return [float(x) for x in l.split(separator)] @@ -403,14 +534,62 @@ return [int(x) for x in l.split(separator)] ######################### -# sqlite +# CLI utils ######################### -def dropTables(connection, tableNames): - 'deletes the table with names in tableNames' - cursor = connection.cursor() - for tableName in tableNames: - cursor.execute('DROP TABLE '+tableName) +def parseCLIOptions(helpMessage, options, cliArgs, optionalOptions=[]): + ''' Simple function to handle similar argument parsing + Returns the dictionary of options and their values + + * cliArgs are most likely directly sys.argv + (only the elements after the first one are considered) + + * options should be a list of strings for getopt options, + eg ['frame=','correspondences=','video='] + A value must be provided for each option, or the program quits''' + import sys, getopt + from numpy.core.fromnumeric import all + optionValues, args = getopt.getopt(cliArgs[1:], 'h', ['help']+options+optionalOptions) + optionValues = dict(optionValues) + + if '--help' in optionValues.keys() or '-h' in optionValues.keys(): + print(helpMessage+ + '\n - Compulsory options: '+' '.join([opt.replace('=','') for opt in options])+ + '\n - Non-compulsory options: '+' '.join([opt.replace('=','') for opt in optionalOptions])) + sys.exit() + + missingArgument = [('--'+opt.replace('=','') in optionValues.keys()) for opt in options] + if not all(missingArgument): + print('Missing argument') + print(optionValues) + sys.exit() + + return optionValues + + +######################### +# Profiling +######################### + +def analyzeProfile(profileFilename, stripDirs = True): + '''Analyze the file produced by cProfile + + obtained by for example: + - call in script (for main() function in script) + import cProfile, os + cProfile.run('main()', os.path.join(os.getcwd(),'main.profile')) + + - or on the command line: + python -m cProfile [-o profile.bin] [-s sort] scriptfile [arg]''' + import pstats, os + p = pstats.Stats(os.path.join(os.pardir, profileFilename)) + if stripDirs: + p.strip_dirs() + p.sort_stats('time') + p.print_stats(.2) + #p.sort_stats('time') + # p.print_callees(.1, 'int_prediction.py:') + return p ######################### # running tests
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/run-tests.sh Fri Dec 05 12:13:53 2014 -0500 @@ -0,0 +1,17 @@ +#!/bin/sh +echo "------------" +echo "Python tests" +cd python +./run-tests.sh +cd .. +echo "------------" +echo "C++ tests" +if [ -f ./bin/tests ] +then + ./bin/tests +else + echo "The test executable has not been compiled" +fi +echo "------------" +echo "Script tests" +./scripts/run-tests.sh
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/samples/TTC/computeTTC.py Fri Dec 05 12:13:53 2014 -0500 @@ -0,0 +1,61 @@ +def computeTTC(databaseFilename,homography,framePerSecond,videoX,videoY,collisionDistanceThreshold,bikAreaOri,bikAreaDes,carAreaOri,carAreaDes): + + import numpy as np + import sys + sys.path.append('/home/sohail/trafficintelligence/python/') + import moving, cvutils, storage + import timeToCollision + + print 'Loading database...' + objects = storage.loadTrajectoriesFromSqlite(databaseFilename, 'object') + + bikCount=0 + carCount=0 + bik=[] + car=[] + bikSpeed=[] + carSpeed=[] + + for obj in objects: + inCarAreaOri = False + inBikAreaOri = False + for time in obj.getTimeInterval(): + x=int(obj.getPositionAtInstant(time).project(homography).x) + y=int(obj.getPositionAtInstant(time).project(homography).y) + x=min(videoX-1,x) + y=min(videoY-1,y) + if bikAreaOri[y,x] == 1 and obj.userType == moving.userType2Num['bicycle']: + inBikAreaOri = True + if bikAreaDes[y,x] == 1 and inBikAreaOri == True: + bikCount += 1 + bik.append(obj) + bikSpeed.append(framePerSecond*3.6*np.median(obj.getSpeeds())) + break + if carAreaOri[y,x] == 1 and obj.userType == moving.userType2Num['car']: + inCarAreaOri = True + if carAreaDes[y,x] == 1 and inCarAreaOri == True: + carCount += 1 + car.append(obj) + carSpeed.append(framePerSecond*3.6*np.median(obj.getSpeeds())) + break + + print 'Computing TTC...' + TTC=[] + potCollision=0 + for obj1 in bik: + for obj2 in car: + ti1=obj1.getTimeInterval() + ti2=obj2.getTimeInterval() + if ti1.first < ti2.last and ti2.first < ti1.last: + potCollision += 1 + ttc=[] + for frameNum in range(max(ti1.first,ti2.first),min(ti1.last,ti2.last)): + ttcp=timeToCollision.timeToCollision(obj1,obj2,collisionDistanceThreshold,frameNum,framePerSecond) + if ttcp < 100: + ttc.append(ttcp) + if ttc != []: + ttc.sort() + TTC.append(ttc[int(.15*len(ttc))]) + + return bikCount,carCount,bikSpeed,carSpeed,TTC,potCollision +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/samples/TTC/ttcCompare.py Fri Dec 05 12:13:53 2014 -0500 @@ -0,0 +1,51 @@ +import matplotlib.image as mpimg +from numpy.linalg.linalg import inv +from numpy import loadtxt +import computeTTC +import numpy as np +import glob + +#databaseName = ['11.sqlite'] +databaseName = glob.glob("*.sqlite") + +bikOri = mpimg.imread('bikeOri.png') +bikAreaOri = bikOri[:,:,0] +bikDes = mpimg.imread('bikeDes.png') +bikAreaDes = bikDes[:,:,0] +carOri = mpimg.imread('carOri.png') +carAreaOri = carOri[:,:,0] +carDes = mpimg.imread('carDes.png') +carAreaDes = carDes[:,:,0] + +videoY = bikOri.shape[0] +videoX = bikOri.shape[1] + +homography=inv(loadtxt('homography.txt')) +framePerSecond=15 +collisionDistanceThreshold=10 + +bikCount=0 +carCount=0 +bikSpeed=[] +carSpeed=[] +ttcDist=[] +potentioalCollision=0 + +for d in databaseName: + print d + bc,cc,bs,cs,ttcdist,potCollision = computeTTC.computeTTC(d,homography,framePerSecond,videoX,videoY,collisionDistanceThreshold,bikAreaOri,bikAreaDes,carAreaOri,carAreaDes) + bikCount += bc + carCount += cc + bikSpeed.extend(bs) + carSpeed.extend(cs) + ttcDist.extend(ttcdist) + potentioalCollision += potCollision + +print 'bikCount :', bikCount +print 'carCount :', carCount +print 'Number of Potential Collisions :', potentioalCollision +np.savetxt('ttc.txt',ttcDist,fmt='%1.2f') +np.savetxt('freq.txt',[bikCount,carCount,potentioalCollision],fmt='%i') +np.savetxt('bikSpeed.txt',bikSpeed,fmt='%1.2f') +np.savetxt('carSpeed.txt',carSpeed,fmt='%1.2f') +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/scripts/classify-objects.py Fri Dec 05 12:13:53 2014 -0500 @@ -0,0 +1,23 @@ +#! /usr/bin/env python + +import numpy as np +import sys, argparse +from cv2 import SVM_RBF, SVM_C_SVC + +import cvutils, moving, ml + +parser = argparse.ArgumentParser(description='The program processes indicators for all pairs of road users in the scene') +parser.add_argument('--cfg', dest = 'configFilename', help = 'name of the configuration file', required = True) +parser.add_argument('-d', dest = 'directoryName', help = 'name of the parent directory containing the videos and extracted trajectories to process', required = True) +# parser.add_argument('-o', dest = 'homographyFilename', help = 'name of the image to world homography file') +# need a classification config file for speed distribution parameters, svm models, frequency parameters, area parameters etc +#parser.add_argument('--cfg', dest = 'svmType', help = 'SVM type', default = SVM_C_SVC, type = long) + + +#parser.add_argument('-s', dest = 'rescaleSize', help = 'rescale size of image samples', default = 64, type = int) +#parser.add_argument('-o', dest = 'nOrientations', help = 'number of orientations in HoG', default = 9, type = int) +#parser.add_argument('-p', dest = 'nPixelsPerCell', help = 'number of pixels per cell', default = 8, type = int) +#parser.add_argument('-c', dest = 'nCellsPerBlock', help = 'number of cells per block', default = 2, type = int) + +args = parser.parse_args() +params = storage.ProcessParameters(args.configFilename)
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/scripts/compute-homography.py Fri Dec 05 12:13:53 2014 -0500 @@ -0,0 +1,129 @@ +#! /usr/bin/env python + +import sys, argparse + +import matplotlib.pyplot as plt +import numpy as np +import cv2 + +import cvutils +import utils + +parser = argparse.ArgumentParser(description='The program computes the homography matrix from at least 4 non-colinear point correspondences inputed in the same order in a video frame and a aerial photo/ground map, or from the list of corresponding points in the two planes.', epilog = '''The point correspondence file contains at least 4 non-colinear point coordinates +with the following format: + - the first two lines are the x and y coordinates in the projected space (usually world space) + - the last two lines are the x and y coordinates in the origin space (usually image space) + +If providing video and world images, with a number of points to input +and a ration to convert pixels to world distance unit (eg meters per pixel), +the images will be shown in turn and the user should click +in the same order the corresponding points in world and image spaces.''', formatter_class=argparse.RawDescriptionHelpFormatter,) + +parser.add_argument('-p', dest = 'pointCorrespondencesFilename', help = 'name of the text file containing the point correspondences') +parser.add_argument('-i', dest = 'videoFrameFilename', help = 'filename of the video frame') +parser.add_argument('-w', dest = 'worldFilename', help = 'filename of the aerial photo/ground map') +parser.add_argument('-n', dest = 'nPoints', help = 'number of corresponding points to input', default = 4, type = int) +parser.add_argument('-u', dest = 'unitsPerPixel', help = 'number of units per pixel', default = 1., type = float) +parser.add_argument('--display', dest = 'displayPoints', help = 'display original and projected points on both images', action = 'store_true') +parser.add_argument('--intrinsic', dest = 'intrinsicCameraMatrixFilename', help = 'name of the intrinsic camera file') +parser.add_argument('--distortion-coefficients', dest = 'distortionCoefficients', help = 'distortion coefficients', nargs = '*', type = float) +parser.add_argument('--undistorted-multiplication', dest = 'undistortedImageMultiplication', help = 'undistorted image multiplication', type = float) +parser.add_argument('--undistort', dest = 'undistort', help = 'undistort the video (because features have been extracted that way', action = 'store_true') +parser.add_argument('--save', dest = 'saveImages', help = 'save the undistorted video frame (display option must be chosen)', action = 'store_true') + +args = parser.parse_args() + +# TODO process camera intrinsic and extrinsic parameters to obtain image to world homography, taking example from Work/src/python/generate-homography.py script +# cameraMat = load(videoFilenamePrefix+'-camera.txt'); +# T1 = cameraMat[3:6,:].copy(); +# A = cameraMat[0:3,0:3].copy(); + +# # pay attention, rotation may be the transpose +# # R = T1[:,0:3].T; +# R = T1[:,0:3]; +# rT = dot(R, T1[:,3]/1000); +# T = zeros((3,4),'f'); +# T[:,0:3] = R[:]; +# T[:,3] = rT; + +# AT = dot(A,T); + +# nPoints = 4; +# worldPoints = cvCreateMat(nPoints, 3, CV_64FC1); +# imagePoints = cvCreateMat(nPoints, 3, CV_64FC1); + +# # extract homography from the camera calibration +# worldPoints = cvCreateMat(4, 3, CV_64FC1); +# imagePoints = cvCreateMat(4, 3, CV_64FC1); + +# worldPoints[0,:] = [[1, 1, 0]]; +# worldPoints[1,:] = [[1, 2, 0]]; +# worldPoints[2,:] = [[2, 1, 0]]; +# worldPoints[3,:] = [[2, 2, 0]]; + +# wPoints = [[1,1,2,2], +# [1,2,1,2], +# [0,0,0,0]]; +# iPoints = utils.worldToImage(AT, wPoints); + +# for i in range(nPoints): +# imagePoints[i,:] = [iPoints[:,i].tolist()]; + +# H = cvCreateMat(3, 3, CV_64FC1); + +# cvFindHomography(imagePoints, worldPoints, H); + + +homography = np.array([]) +if args.pointCorrespondencesFilename != None: + worldPts, videoPts = cvutils.loadPointCorrespondences(args.pointCorrespondencesFilename) + homography, mask = cv2.findHomography(videoPts, worldPts) # method=0, ransacReprojThreshold=3 +elif args.videoFrameFilename != None and args.worldFilename != None: + worldImg = plt.imread(args.worldFilename) + videoImg = plt.imread(args.videoFrameFilename) + if args.undistort: + [map1, map2] = cvutils.computeUndistortMaps(videoImg.shape[1], videoImg.shape[0], args.undistortedImageMultiplication, np.loadtxt(args.intrinsicCameraMatrixFilename), args.distortionCoefficients) + videoImg = cv2.remap(videoImg, map1, map2, interpolation=cv2.INTER_LINEAR) + print('Click on {0} points in the video frame'.format(args.nPoints)) + plt.figure() + plt.imshow(videoImg) + videoPts = np.array(plt.ginput(args.nPoints, timeout=3000)) + print('Click on {0} points in the world image'.format(args.nPoints)) + plt.figure() + plt.imshow(worldImg) + worldPts = args.unitsPerPixel*np.array(plt.ginput(args.nPoints, timeout=3000)) + plt.close('all') + homography, mask = cv2.findHomography(videoPts, worldPts) + # save the points in file + f = open('point-correspondences.txt', 'a') + np.savetxt(f, worldPts.T) + np.savetxt(f, videoPts.T) + f.close() + +if homography.size>0: + np.savetxt('homography.txt',homography) + +if args.displayPoints and args.videoFrameFilename != None and args.worldFilename != None and homography.size>0: + worldImg = cv2.imread(args.worldFilename) + videoImg = cv2.imread(args.videoFrameFilename) + if args.undistort: + [map1, map2] = cvutils.computeUndistortMaps(videoImg.shape[1], videoImg.shape[0], args.undistortedImageMultiplication, np.loadtxt(args.intrinsicCameraMatrixFilename), args.distortionCoefficients) + videoImg = cv2.remap(videoImg, map1, map2, interpolation=cv2.INTER_LINEAR) + if args.saveImages: + cv2.imwrite(utils.removeExtension(args.videoFrameFilename)+'-undistorted.png', videoImg) + invHomography = np.linalg.inv(homography) + projectedWorldPts = cvutils.projectArray(invHomography, worldPts.T).T + projectedVideoPts = cvutils.projectArray(homography, videoPts.T).T + for i in range(worldPts.shape[0]): + # world image + cv2.circle(worldImg,tuple(np.int32(np.round(worldPts[i]/args.unitsPerPixel))),2,cvutils.cvBlue) + cv2.circle(worldImg,tuple(np.int32(np.round(projectedVideoPts[i]/args.unitsPerPixel))),2,cvutils.cvRed) + cv2.putText(worldImg, str(i+1), tuple(np.int32(np.round(worldPts[i]/args.unitsPerPixel))+5), cv2.FONT_HERSHEY_PLAIN, 2., cvutils.cvBlue, 2) + # video image + cv2.circle(videoImg,tuple(np.int32(np.round(videoPts[i]))),2,cvutils.cvBlue) + cv2.circle(videoImg,tuple(np.int32(np.round(projectedWorldPts[i]))),2,cvutils.cvRed) + cv2.putText(videoImg, str(i+1), tuple(np.int32(np.round(videoPts[i])+5)), cv2.FONT_HERSHEY_PLAIN, 2., cvutils.cvBlue, 2) + cv2.imshow('video frame',videoImg) + cv2.imshow('world image',worldImg) + cv2.waitKey() + cv2.destroyAllWindows()
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/scripts/create-bounding-boxes.py Fri Dec 05 12:13:53 2014 -0500 @@ -0,0 +1,21 @@ +#! /usr/bin/env python + +import argparse + +import storage + +from numpy.linalg.linalg import inv +from numpy import loadtxt + +parser = argparse.ArgumentParser(description='The program creates bounding boxes in image space around all features (for display and for comparison to ground truth in the form of bouding boxes.') +parser.add_argument('-d', dest = 'databaseFilename', help = 'name of the Sqlite database file', required = True) +parser.add_argument('-o', dest = 'homography', help = 'name of the image to world homography') + +args = parser.parse_args() + +homography = None +if args.homography != None: + homography = inv(loadtxt(args.homography)) + +storage.createBoundingBoxTable(args.databaseFilename, homography) +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/scripts/delete-tables.py Fri Dec 05 12:13:53 2014 -0500 @@ -0,0 +1,14 @@ +#! /usr/bin/env python + +import sys, argparse + +import utils +import storage + +parser = argparse.ArgumentParser(description='The program deletes (drops) the tables in the database before saving new results (for objects, tables object_features and objects are dropped; for interactions, the tables interactions and indicators are dropped') +#parser.add_argument('configFilename', help = 'name of the configuration file') +parser.add_argument('-d', dest = 'databaseFilename', help = 'name of the Sqlite database', required = True) +parser.add_argument('-t', dest = 'dataType', help = 'type of the data to remove', required = True, choices = ['object','interaction', 'bb']) +args = parser.parse_args() + +storage.deleteFromSqlite(args.databaseFilename, args.dataType)
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/scripts/display-trajectories.py Fri Dec 05 12:13:53 2014 -0500 @@ -0,0 +1,67 @@ +#! /usr/bin/env python + +import sys, argparse + +import storage, cvutils, utils + +from numpy.linalg.linalg import inv +from numpy import loadtxt + +parser = argparse.ArgumentParser(description='The program displays feature or object trajectories overlaid over the video frames.', epilog = 'Either the configuration filename or the other parameters (at least video and database filenames) need to be provided.') +parser.add_argument('--cfg', dest = 'configFilename', help = 'name of the configuration file') +parser.add_argument('-d', dest = 'databaseFilename', help = 'name of the Sqlite database file') +parser.add_argument('-i', dest = 'videoFilename', help = 'name of the video file') +parser.add_argument('-t', dest = 'trajectoryType', help = 'type of trajectories to display', choices = ['feature', 'object'], default = 'feature') +parser.add_argument('-o', dest = 'homographyFilename', help = 'name of the image to world homography file') +parser.add_argument('--intrinsic', dest = 'intrinsicCameraMatrixFilename', help = 'name of the intrinsic camera file') +parser.add_argument('--distortion-coefficients', dest = 'distortionCoefficients', help = 'distortion coefficients', nargs = '*', type = float) +parser.add_argument('--undistorted-multiplication', dest = 'undistortedImageMultiplication', help = 'undistorted image multiplication', type = float) +parser.add_argument('-u', dest = 'undistort', help = 'undistort the video (because features have been extracted that way)', action = 'store_true') +parser.add_argument('-f', dest = 'firstFrameNum', help = 'number of first frame number to display', type = int) +parser.add_argument('-r', dest = 'rescale', help = 'rescaling factor for the displayed image', default = 1., type = float) +parser.add_argument('-s', dest = 'nFramesStep', help = 'number of frames between each display', default = 1, type = int) +parser.add_argument('--save-images', dest = 'saveAllImages', help = 'save all images', action = 'store_true') +parser.add_argument('--last-frame', dest = 'lastFrameNum', help = 'number of last frame number to save (for image saving, no display is made)', type = int) + +args = parser.parse_args() + +if args.configFilename: # consider there is a configuration file + params = storage.ProcessParameters(args.configFilename) + videoFilename = params.videoFilename + databaseFilename = params.databaseFilename + if params.homography != None: + homography = inv(params.homography) + else: + homography = None + intrinsicCameraMatrix = params.intrinsicCameraMatrix + distortionCoefficients = params.distortionCoefficients + undistortedImageMultiplication = params.undistortedImageMultiplication + undistort = params.undistort + firstFrameNum = params.firstFrameNum +else: + homography = None + undistort = False + intrinsicCameraMatrix = None + distortionCoefficients = [] + undistortedImageMultiplication = None + firstFrameNum = 0 + +if not args.configFilename and args.videoFilename != None: + videoFilename = args.videoFilename +if not args.configFilename and args.databaseFilename != None: + databaseFilename = args.databaseFilename +if not args.configFilename and args.homographyFilename != None: + homography = inv(loadtxt(args.homographyFilename)) +if not args.configFilename and args.intrinsicCameraMatrixFilename != None: + intrinsicCameraMatrix = loadtxt(args.intrinsicCameraMatrixFilename) +if not args.configFilename and args.distortionCoefficients != None: + distortionCoefficients = args.distortionCoefficients +if not args.configFilename and args.undistortedImageMultiplication != None: + undistortedImageMultiplication = args.undistortedImageMultiplication +if args.firstFrameNum != None: + firstFrameNum = args.firstFrameNum + + +objects = storage.loadTrajectoriesFromSqlite(databaseFilename, args.trajectoryType) +boundingBoxes = storage.loadBoundingBoxTableForDisplay(databaseFilename) +cvutils.displayTrajectories(videoFilename, objects, boundingBoxes, homography, firstFrameNum, args.lastFrameNum, rescale = args.rescale, nFramesStep = args.nFramesStep, saveAllImages = args.saveAllImages, undistort = (undistort or args.undistort), intrinsicCameraMatrix = intrinsicCameraMatrix, distortionCoefficients = distortionCoefficients, undistortedImageMultiplication = undistortedImageMultiplication)
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/scripts/examples.sql Fri Dec 05 12:13:53 2014 -0500 @@ -0,0 +1,4 @@ + + +# get object id, first and last frame numbers, length and number of features +select OF.object_id, min(P.frame_number) as frame1, max(P.frame_number) as frame2, max(P.frame_number)-min(P.frame_number) as length, count(OF.trajectory_id) as nfeatures from positions P, objects_features OF where P.trajectory_id = OF.trajectory_id group by OF.object_id order by nfeatures asc; \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/scripts/play-video.py Fri Dec 05 12:13:53 2014 -0500 @@ -0,0 +1,23 @@ +#! /usr/bin/env python + +import sys, argparse +import cvutils + + +parser = argparse.ArgumentParser(description='The program displays the video.') +parser.add_argument('-i', dest = 'videoFilename', help = 'name of the video file', required = True) +parser.add_argument('-f', dest = 'firstFrameNum', help = 'number of first frame number to display', default = 0, type = int) +parser.add_argument('--fps', dest = 'frameRate', help = 'approximate frame rate to replay', type = float) +parser.add_argument('-r', dest = 'rescale', help = 'rescaling factor for the displayed image', default = 1., type = float) + +args = parser.parse_args() + +firstFrameNum = 0 +if args.firstFrameNum != None: + firstFrameNum = args.firstFrameNum + +frameRate = -1 +if args.frameRate != None: + frameRate = args.frameRate + +cvutils.playVideo(args.videoFilename, firstFrameNum, frameRate, rescale = args.rescale)
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/scripts/polytracktopdtv.py Fri Dec 05 12:13:53 2014 -0500 @@ -0,0 +1,231 @@ +#! /usr/bin/env python + +from pdtv import TsaiCamera, ZipVideo, SyncedVideos, TrackSet, Track, State +import sys, os, datetime, argparse +import shutil +import sqlite3 +import zipfile +import utils +import cvutils +import cv2 + + +def zipFolder(inputFolder, outputFile): + '''Method to compress the content of the inputFolder in the outputFile''' + zip = zipfile.ZipFile(outputFile, 'w') + for root, dirs, files in os.walk(inputFolder): + for file in files: + zip.write(root+file, file) + zip.close() + + + + +def getTypeDict(cursor): + '''Return a dictionnary with integer key and associated type string + i.e.: "0" -> "unknown" + "1" -> "car" + "2" -> "pedestrians" + "3" -> "motorcycle" + "4" -> "bicycle" + "5" -> "bus" + "6" -> "truck" + ... and other type if the objects_type table is defined in SQLite''' + typeDict = dict() + cursor.execute("SELECT name FROM sqlite_master WHERE type='table' AND name='objects_type'") + data = cursor.fetchone() + + if(data == None): + typeDict["0"] = "unknown" + typeDict["1"] = "car" + typeDict["2"] = "pedestrians" + typeDict["3"] = "motorcycle" + typeDict["4"] = "bicycle" + typeDict["5"] = "bus" + typeDict["6"] = "truck" + + else: + cursor.execute("SELECT road_user_type, type_string FROM objects_type") + for row in cursor: + typeDict[row[0]]= row[1] + + return typeDict + +def extractFrames(videoFile, framePath, fps, time, firstFrameNum = 0, lastFrameNum = None): + '''Method to extract all the frames of the video''' + print('Extracting frame') + deltaTimestamp = 1000.0/float(fps); + time+=datetime.timedelta(microseconds=firstFrameNum*deltaTimestamp*1000) + + inc = 1000 #How many frame we fetch in the video at a time + + if lastFrameNum != None: + delta = lastFrameNum-firstFrameNum + if delta < inc: + inc = delta + + currentIdx = firstFrameNum + frameList = cvutils.getImagesFromVideo(videoFile, firstFrameNum = currentIdx, nFrames = inc) + + while len(frameList) == inc and inc > 0: + + for f in frameList: + cv2.imwrite(os.path.join(framePath,time.strftime("%Y%m%d-%H%M%S.%f")[:-3]+'.jpg'), f) + time += datetime.timedelta(microseconds=deltaTimestamp*1000) + currentIdx = currentIdx + inc + + if lastFrameNum != None: + delta = lastFrameNum-currentIdx + if delta < inc: + inc = delta + if inc: + frameList = cvutils.getImagesFromVideo(videoFile, firstFrameNum = currentIdx, nFrames = inc) + print('Extracting frame ' + str(currentIdx)) + return len(frameList) > 0 + + + +def convertDatabase(workDirname, sectionName, sceneFilename = None, databaseFilename = None, videoFilename = None, videoFolderExist = False, firstFrameNum = 0, lastFrameNum = None, cameraCalibrationFilename = None, outputFileName = 'roaduser.json'): + ''' + Method to convert database from polytrack to PDTV: + workDirname is the current working directory + sceneFilename is the path to the .cfg file + sectionName is the name of the section we want to process in this file + videoFolderExist specifiy if we want to reextract the video frame or if they already exist at workdir/videoframes/ + firstFrameNum is the first frame we want to extract + lastFrameNum is the last frame we want to extract (or None if we want to extract everything) + ''' + error = False + if sceneFilename != None: + scene = utils.SceneParameters.loadConfigFile(os.path.join(workDirname, sceneFilename)) + time = scene[sectionName].date + inputDb = os.path.join(workDirname, scene[sectionName].databaseFilename) + videoFile = os.path.join(workDirname, scene[sectionName].videoFilename) + + if databaseFilename != None: + inputDb = os.path.join(workDirname, databaseFilename) + if videoFilename != None: + videoFile = os.path.join(workDirname, videoFilename) + # elif videoFolderExist == False: + # print('No video path specified') + # error = True + + videoFolderPath = os.path.join(workDirname, "videoframes/") + fileName = sectionName + + if videoFile != None: + fps = cvutils.getFPS(videoFile) + print('Video should run at ' + str(fps) + ' fps') + deltaTimestamp = 1000.0/float(fps); + if videoFolderExist == False: + if os.path.exists(videoFolderPath): + shutil.rmtree(videoFolderPath) + utils.mkdir(videoFolderPath) + if extractFrames(videoFile, videoFolderPath, fps, time, firstFrameNum, lastFrameNum) == 0: + print("Error. Frame were not extracted") + error = True + + + if not error: + masterTimestamp = 0.0 + masterTimestampList = list() + video_timestringsList = list() + frameNumberToMasterTimestamp = dict() + for r,d,f in os.walk(videoFolderPath): + i = firstFrameNum + for files in f: + name, ext = os.path.splitext(files) + video_timestringsList.append(name) + masterTimestampList.append(masterTimestamp) + frameNumberToMasterTimestamp[i] = masterTimestamp + i = i +1 + masterTimestamp = masterTimestamp+deltaTimestamp + + inputZipVideoName = fileName+".zip" + print('Zipping files...') + if not os.path.exists(inputZipVideoName) or not videoFolderExist: + zipFolder(videoFolderPath, inputZipVideoName) + print('Zipping files...Done.') + #We generate the structure for ZipVideo + if cameraCalibrationFilename != None: + calibrationFile = cameraCalibrationFilename + else: + calibrationFile = 'calib.json' + zipVideo = ZipVideo(video_zip_file=inputZipVideoName, + time_offset=0.0, time_scale=1.0, master_timestamps=masterTimestampList, calibration_file=calibrationFile) + zipVideo.save(os.path.join(workDirname, 'video.json')) + + print('ZipVideo saved') + obj = SyncedVideos(master_timestamps = [masterTimestamp], + video_timestrings = [video_timestringsList], + video_files = ['video.json'], + fps=fps) + obj.save(os.path.join(workDirname, 'syncedvideo.json')) + + print('SyncedVideos saved') + + print('Opening db') + connection = sqlite3.connect(inputDb) + cursor = connection.cursor() + + + #Tracket database + trackset = TrackSet(synced_file = ['syncedvideo.json']) + + + #1) We build the type index dictionnary + typeDict = getTypeDict(cursor) + + idToTrackDict = dict() + #2) We read the object database + cursor.execute("SELECT object_id, road_user_type FROM objects") + for row in cursor: + objectId = row[0] + objectType = row[1] + t = Track(type=typeDict.get(objectType, "unknown")) + idToTrackDict[objectId] = t; + trackset.append(t) + print('Reading boundingbox table') + #3) We read the bounding box table + cursor.execute("SELECT name FROM sqlite_master WHERE type='table' AND name='bounding_boxes'") + data = cursor.fetchone() + if data == None: + print('No bounding box table. Maybe it was not generated ?') + else: + cursor.execute("SELECT object_id, frame_number, x_top_left, y_top_left, x_bottom_right, y_bottom_right FROM bounding_boxes") + for row in cursor: + objectId = row[0] + frameNumber = row[1] + x_top_left = row[2] + y_top_left = row[3] + x_bottom_right = row[4] + y_bottom_right = row[5] + + idToTrackDict[objectId].append(State(frame=int(frameNumber), world_position = [0.,0.], + master_timestamp=frameNumberToMasterTimestamp[int(frameNumber)], + bounding_boxes=[[(x_top_left, x_bottom_right), (y_top_left, y_bottom_right)]])) + print('Saving db in json') + trackset.save(os.path.join(workDirname, outputFileName)) + connection.close() + print('Done.') + + + + +if __name__ == "__main__": + parser = argparse.ArgumentParser(description='The program convert polytrack.sqlite database to pdtv bounding boxes', epilog = 'Either the configuration filename or the other parameters (at least video and database filenames) need to be provided.') + parser.add_argument('-w', dest = 'workDirname', help = 'use a different work directory', default = "./",type = str) + parser.add_argument('--scene', dest = 'sceneFilename', help = 'name of the configuration file', type = str, default = None) + parser.add_argument('--section', dest = 'sectionName', help = 'name of the section', type = str, default = None) + parser.add_argument('-d', dest = 'databaseFilename', help = 'name of the Sqlite database file', type = str, default = None) + parser.add_argument('-i', dest = 'videoFilename', help = 'name of the video file', type = str, default = None) + parser.add_argument('-c', dest = 'cameraCalibrationFilename', help = 'name of the camera json file', type = str, default = None) + parser.add_argument('-o', dest = 'outputFilename', help = 'name of the output json file', type = str, default = 'roaduser.json') + parser.add_argument('-s', dest = 'firstFrameNum', help = 'forced start frame', type = int, default = 0) + parser.add_argument('-e', dest = 'lastFrameNum', help = 'forced end frame', type = int, default = None) + #parser.add_argument('-t', dest = 'useDatabaseTimestamp', help = 'use the timestamp of the database', default= False, type = bool) + parser.add_argument('-u', dest = 'useCurrentVideoFile', help = 'use the previously generated video file', action = 'store_true') + args = parser.parse_args() + #convertDatabase(args) + + convertDatabase(args.workDirname, args.sectionName, args.sceneFilename, videoFilename = args.videoFilename, databaseFilename = args.databaseFilename, videoFolderExist = args.useCurrentVideoFile, firstFrameNum = args.firstFrameNum, lastFrameNum = args.lastFrameNum, cameraCalibrationFilename = args.cameraCalibrationFilename, outputFileName=args.outputFilename )
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/scripts/replay-event-annotation.py Fri Dec 05 12:13:53 2014 -0500 @@ -0,0 +1,26 @@ +#! /usr/bin/env python + +import sys, argparse, datetime + +import storage, cvutils, utils + +import matplotlib.pylab as pylab +import matplotlib.pyplot as plt +import numpy as np + + +annotations = pylab.csv2rec(sys.argv[1]) + +frameRate = 30 +dirname = "/home/nicolas/Research/Data/montreal/infractions-pietons/" +videoDirnames = {'amherst': '2011-06-22-sherbrooke-amherst/', + 'iberville': '2011-06-28-sherbrooke-iberville/'} + +# for amherst, subtract 40 seconds: add a delta + +for annotation in annotations: + video = annotation['video_name'].lower() + print('{} {}'.format(annotation['conflict_start_time'], annotation['conflict_end_time'])) + print(annotation['road_user_1']+' '+annotation['road_user_2']+' '+annotation['conflict_quality']) + print(annotation['comments']) + cvutils.playVideo(dirname+videoDirnames[video]+video+'-{}.avi'.format(annotation['video_start_time']), utils.timeToFrames(annotation['conflict_start_time']+datetime.timedelta(seconds=-40), frameRate), frameRate, True, False, annotation['road_user_1']+' '+annotation['road_user_2']+' '+annotation['conflict_quality'])
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/scripts/rescale-homography.py Fri Dec 05 12:13:53 2014 -0500 @@ -0,0 +1,32 @@ +#! /usr/bin/env python + +import sys + +import matplotlib.pyplot as plt +import numpy as np +import cv2 + +import cvutils +import utils + +if len(sys.argv) < 4: + print('Usage: {} homography_filename original_size new_size (size can be width or height)'.format(sys.argv[0])) + sys.exit() + +homography = np.loadtxt(sys.argv[1]) + +imgPoints = np.array([[10,10], + [10,20], + [20,20], + [20,10]]) + +wldPoints = cvutils.projectArray(homography, imgPoints.T).T + +newSize = float(sys.argv[3]) +originalSize = float(sys.argv[2]) +imgPoints = imgPoints*newSize/originalSize + +newHomography, mask = cv2.findHomography(imgPoints, wldPoints) + +np.savetxt(sys.argv[1]+'.new', newHomography) +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/scripts/run-tests.sh Fri Dec 05 12:13:53 2014 -0500 @@ -0,0 +1,2 @@ +#!/bin/sh +echo 'no tests' \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/scripts/safety-analysis.py Fri Dec 05 12:13:53 2014 -0500 @@ -0,0 +1,75 @@ +#! /usr/bin/env python + +import storage, prediction, events, moving + +import sys, argparse, random + +import matplotlib.pyplot as plt +import numpy as np + +# todo: very slow if too many predicted trajectories +# add computation of probality of unsucessful evasive action + +parser = argparse.ArgumentParser(description='The program processes indicators for all pairs of road users in the scene') +parser.add_argument('--cfg', dest = 'configFilename', help = 'name of the configuration file', required = True) +parser.add_argument('--prediction-method', dest = 'predictionMethod', help = 'prediction method (constant velocity (vector computation), constant velocity, normal adaptation, point set prediction)', choices = ['cvd', 'cv', 'na', 'ps']) +parser.add_argument('--display-cp', dest = 'displayCollisionPoints', help = 'display collision points', action = 'store_true') +parser.add_argument('-n', dest = 'nProcesses', help = 'number of processes to run in parallel', type = int, default = 1) +args = parser.parse_args() + +params = storage.ProcessParameters(args.configFilename) + +# parameters for prediction methods +if args.predictionMethod: + predictionMethod = args.predictionMethod +else: + predictionMethod = params.predictionMethod + +def accelerationDistribution(): + return random.triangular(-params.maxNormalAcceleration, params.maxNormalAcceleration, 0.) +def steeringDistribution(): + return random.triangular(-params.maxNormalSteering, params.maxNormalSteering, 0.) + +if predictionMethod == 'cvd': # TODO add cve: constant velocity exact (Sohail's) + predictionParameters = prediction.CVDirectPredictionParameters() +elif predictionMethod == 'cv': + predictionParameters = prediction.ConstantPredictionParameters(params.maxPredictedSpeed) +elif predictionMethod == 'na': + predictionParameters = prediction.NormalAdaptationPredictionParameters(params.maxPredictedSpeed, + params.nPredictedTrajectories, + accelerationDistribution, + steeringDistribution, + params.useFeaturesForPrediction) +elif predictionMethod == 'ps': + predictionParameters = prediction.PointSetPredictionParameters(params.maxPredictedSpeed) +# no else required, since parameters is required as argument + +# evasiveActionPredictionParameters = prediction.EvasiveActionPredictionParameters(params.maxPredictedSpeed, +# params.nPredictedTrajectories, +# params.minExtremeAcceleration, +# params.maxExtremeAcceleration, +# params.maxExtremeSteering, +# params.useFeaturesForPrediction) + +objects = storage.loadTrajectoriesFromSqlite(params.databaseFilename,'object') +if params.useFeaturesForPrediction: + features = storage.loadTrajectoriesFromSqlite(params.databaseFilename,'feature') # needed if normal adaptation + for obj in objects: + obj.setFeatures(features) + +interactions = events.createInteractions(objects) +for inter in interactions: + inter.computeIndicators() + inter.computeCrossingsCollisions(predictionParameters, params.collisionDistance, params.predictionTimeHorizon, params.crossingZones, nProcesses = args.nProcesses) + +storage.saveIndicators(params.databaseFilename, interactions) + +if args.displayCollisionPoints: + plt.figure() + allCollisionPoints = [] + for inter in interactions: + for collisionPoints in inter.collisionPoints.values(): + allCollisionPoints += collisionPoints + moving.Point.plotAll(allCollisionPoints) + plt.axis('equal') +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/scripts/setup-tracking.sh Fri Dec 05 12:13:53 2014 -0500 @@ -0,0 +1,39 @@ +version="$(wget -q -O - http://sourceforge.net/projects/opencvlibrary/files/opencv-unix | egrep -m1 -o '\"[0-9](\.[0-9])+' | cut -c2-)" +echo "Removing any pre-installed ffmpeg and x264" +sudo apt-get -qq remove ffmpeg x264 libx264-dev +echo "Installing Dependencies" +sudo apt-get -qq install libopencv-dev build-essential checkinstall cmake pkg-config yasm libtiff4-dev libjpeg-dev libjasper-dev libavcodec-dev libavformat-dev libswscale-dev libdc1394-22-dev libxine-dev libgstreamer0.10-dev libgstreamer-plugins-base0.10-dev libv4l-dev python-dev python-numpy libtbb-dev libqt4-dev libgtk2.0-dev libfaac-dev libmp3lame-dev libopencore-amrnb-dev libopencore-amrwb-dev libtheora-dev libvorbis-dev libxvidcore-dev x264 v4l-utils ffmpeg +sudo apt-get -qq install libavfilter-dev libboost-dev libboost-program-options-dev libboost-graph-dev python-setuptools python-dev libcppunit-dev sqlite3 libsqlite3-dev cmake-qt-gui libboost-all-dev +sudo easy_install -U mercurial +echo "Installing OpenCV" $version +cd +mkdir OpenCV +cd OpenCV +echo "Downloading OpenCV" $version +wget -O OpenCV-$version.tar.gz http://sourceforge.net/projects/opencvlibrary/files/opencv-unix/$version/opencv-"$version".tar.gz/download +echo "Installing OpenCV" $version +tar -xvf OpenCV-$version.tar.gz +cd opencv-$version +mkdir release +cd release +cmake -D CMAKE_BUILD_TYPE=RELEASE -D CMAKE_INSTALL_PREFIX=/usr/local .. +make +sudo make install +echo "OpenCV" $version "ready to be used" + +echo "Installing Traffic Intelligence..." +cd +mkdir Research +cd Research +mkdir Code +cd Code +hg clone https://Nicolas@bitbucket.org/trajectories/trajectorymanagementandanalysis +hg clone https://Nicolas@bitbucket.org/Nicolas/trafficintelligence +cd trajectorymanagementandanalysis/trunk/src/TrajectoryManagementAndAnalysis/ +cmake . +make TrajectoryManagementAndAnalysis +cd +cd trafficintelligence/c/ +make feature-based-tracking +cd +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/scripts/test-compute-object-position-from-features.py Fri Dec 05 12:13:53 2014 -0500 @@ -0,0 +1,141 @@ +#!/usr/bin/env python + +import sys + +import matplotlib.mlab as pylab +import matplotlib.pyplot as plt +import numpy as np + +import cv +import utils +import cvutils +import ubc_utils +import moving + +# use something like getopt to manage arguments if necessary + +if len(sys.argv) < 3: + print('Usage: {0} <video-filename> <n-objects>'.format(sys.argv[0])) + sys.exit() + +if sys.argv[1].endswith('.avi'): + videoFilenamePrefix = utils.removeExtension(sys.argv[1],'.') +else: + videoFilenamePrefix = sys.argv[1] + +objectNum = int(sys.argv[2]) + +objects = ubc_utils.loadTrajectories(videoFilenamePrefix+'-objects.txt', objectNum+1) +obj = objects[objectNum] +features = ubc_utils.loadTrajectories(videoFilenamePrefix+'-features.txt', max(obj.featureNumbers)+1) +h = np.loadtxt(videoFilenamePrefix+'-homography.txt') + +invh = cvutils.invertHomography(h) + +def computeGroundTrajectory(features, homography, timeInterval = None): + '''Computes a trajectory for the set of features as the closes point to the ground + using the homography in image space''' + if not timeInterval: + raise Exception('not implemented') # compute from the features + + yCoordinates = -np.ones((len(features),int(timeInterval.length()))) + for i,f in enumerate(features): + traj = f.getPositions().asArray() + imgTraj = cvutils.projectArray(homography, traj) + yCoordinates[i,f.getFirstInstant()-timeInterval.first:f.getLastInstant()+1-timeInterval.first] = imgTraj[1,:] + + indices = np.argmax(yCoordinates,0) + newTraj = moving.Trajectory() + for j,idx in enumerate(indices): + newTraj.addPosition(features[idx].getPositionAtInstant(j+timeInterval.first)) + #newVelocities.addPosition(features[obj.featureNumbers[idx]].getVelocityAtInstant(j+obj.getFirstInstant())) + + return newTraj + +def computeMedianTrajectory(features, timeInterval = None): + if not timeInterval: + raise Exception('not implemented') # compute from the features + + newTraj = moving.Trajectory() + for t in timeInterval: + points = [] + for f in features: + if f.existsAtInstant(t): + points.append(f.getPositionAtInstant(t).aslist()) + med = np.median(np.array(points), 0) + newTraj.addPositionXY(med[0], med[1]) + + return newTraj + +# TODO version median: conversion to large matrix will not work, have to do it frame by frame + +def kalmanFilter(positions, velocities, processNoiseCov, measurementNoiseCov): + kalman=cv.CreateKalman(6, 4) + kalman.transition_matrix[0,2]=1 + kalman.transition_matrix[0,4]=1./2 + kalman.transition_matrix[1,3]=1 + kalman.transition_matrix[1,5]=1./2 + kalman.transition_matrix[2,4]=1 + kalman.transition_matrix[3,5]=1 + + cv.SetIdentity(kalman.measurement_matrix, 1.) + cv.SetIdentity(kalman.process_noise_cov, processNoiseCov) + cv.SetIdentity(kalman.measurement_noise_cov, measurementNoiseCov) + cv.SetIdentity(kalman.error_cov_post, 1.) + + p = positions[0] + v = velocities[0] + v2 = velocities[2] + a = (v2-v).multiply(0.5) + kalman.state_post[0,0]=p.x + kalman.state_post[1,0]=p.y + kalman.state_post[2,0]=v.x + kalman.state_post[3,0]=v.y + kalman.state_post[4,0]=a.x + kalman.state_post[5,0]=a.y + + filteredPositions = moving.Trajectory() + filteredVelocities = moving.Trajectory() + measurement = cv.CreateMat(4,1,cv.CV_32FC1) + for i in xrange(positions.length()): + cv.KalmanPredict(kalman) # no control + p = positions[i] + v = velocities[i] + measurement[0,0] = p.x + measurement[1,0] = p.y + measurement[2,0] = v.x + measurement[3,0] = v.y + cv.KalmanCorrect(kalman, measurement) + filteredPositions.addPositionXY(kalman.state_post[0,0], kalman.state_post[1,0]) + filteredVelocities.addPositionXY(kalman.state_post[2,0], kalman.state_post[3,0]) + + return (filteredPositions, filteredVelocities) + +groundTrajectory = computeGroundTrajectory([features[i] for i in obj.featureNumbers], invh, obj.getTimeInterval()) +(filteredPositions, filteredVelocities) = kalmanFilter(groundTrajectory, obj.getVelocities(), 0.1, 0.1) + +#medianTrajectory = computeMedianTrajectory([features[i] for i in obj.featureNumbers], obj.getTimeInterval()) + +delta = [] +for t in obj.getTimeInterval(): + p1 = obj.getPositionAtInstant(t) + p2 = groundTrajectory[t-obj.getFirstInstant()] + delta.append((p1-p2).aslist()) + +delta = np.median(delta, 0) + +translated = moving.Trajectory() +for t in obj.getTimeInterval(): + p1 = obj.getPositionAtInstant(t) + p1.x -= delta[0] + p1.y -= delta[1] + translated.addPosition(p1) + +plt.clf() +obj.draw('rx-') +for fnum in obj.featureNumbers: features[fnum].draw() +groundTrajectory.draw('bx-') +filteredPositions.draw('gx-') +translated.draw('kx-') +#medianTrajectory.draw('kx-') +plt.axis('equal')
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/scripts/train-object-classification.py Fri Dec 05 12:13:53 2014 -0500 @@ -0,0 +1,66 @@ +#! /usr/bin/env python + +import numpy as np +import sys, argparse +from cv2 import SVM_RBF, SVM_C_SVC + +import cvutils, moving, ml + +parser = argparse.ArgumentParser(description='The program processes indicators for all pairs of road users in the scene') +parser.add_argument('-d', dest = 'directoryName', help = 'parent directory name for the directories containing the samples for the different road users', required = True) +parser.add_argument('--kernel', dest = 'kernelType', help = 'kernel type for the support vector machine (SVM)', default = SVM_RBF, type = long) +parser.add_argument('--svm', dest = 'svmType', help = 'SVM type', default = SVM_C_SVC, type = long) +parser.add_argument('-s', dest = 'rescaleSize', help = 'rescale size of image samples', default = 64, type = int) +parser.add_argument('-o', dest = 'nOrientations', help = 'number of orientations in HoG', default = 9, type = int) +parser.add_argument('-p', dest = 'nPixelsPerCell', help = 'number of pixels per cell', default = 8, type = int) +parser.add_argument('-c', dest = 'nCellsPerBlock', help = 'number of cells per block', default = 2, type = int) +args = parser.parse_args() + +rescaleSize = (args.rescaleSize, args.rescaleSize) +nPixelsPerCell = (args.nPixelsPerCell, args.nPixelsPerCell) +nCellsPerBlock = (args.nCellsPerBlock, args.nCellsPerBlock) + +imageDirectories = {'pedestrian': args.directoryName + "/Pedestrians/", + 'bicycle': args.directoryName + "/Cyclists/", + 'car': args.directoryName + "/Vehicles/"} + +#directory_model = args.directoryName +trainingSamplesPBV = {} +trainingLabelsPBV = {} +trainingSamplesBV = {} +trainingLabelsBV = {} +trainingSamplesPB = {} +trainingLabelsPB = {} +trainingSamplesPV = {} +trainingLabelsPV = {} + +for k, v in imageDirectories.iteritems(): + print('Loading {} samples'.format(k)) + trainingSamplesPBV[k], trainingLabelsPBV[k] = cvutils.createHOGTrainingSet(v, moving.userType2Num[k], rescaleSize, args.nOrientations, nPixelsPerCell, nCellsPerBlock) + if k != 'pedestrian': + trainingSamplesBV[k], trainingLabelsBV[k] = cvutils.createHOGTrainingSet(v, moving.userType2Num[k], rescaleSize, args.nOrientations, nPixelsPerCell, nCellsPerBlock) + if k != 'car': + trainingSamplesPB[k], trainingLabelsPB[k] = cvutils.createHOGTrainingSet(v, moving.userType2Num[k], rescaleSize, args.nOrientations, nPixelsPerCell, nCellsPerBlock) + if k != 'bicycle': + trainingSamplesPV[k], trainingLabelsPV[k] = cvutils.createHOGTrainingSet(v, moving.userType2Num[k], rescaleSize, args.nOrientations, nPixelsPerCell, nCellsPerBlock) + +# Training the Support Vector Machine +print "Training Pedestrian-Cyclist-Vehicle Model" +model = ml.SVM(args.svmType, args.kernelType) +model.train(np.concatenate(trainingSamplesPBV.values()), np.concatenate(trainingLabelsPBV.values())) +model.save(args.directoryName + "/modelPBV.xml") + +print "Training Cyclist-Vehicle Model" +model = ml.SVM(args.svmType, args.kernelType) +model.train(np.concatenate(trainingSamplesBV.values()), np.concatenate(trainingLabelsBV.values())) +model.save(args.directoryName + "/modelBV.xml") + +print "Training Pedestrian-Cyclist Model" +model = ml.SVM(args.svmType, args.kernelType) +model.train(np.concatenate(trainingSamplesPB.values()), np.concatenate(trainingLabelsPB.values())) +model.save(args.directoryName + "/modelPB.xml") + +print "Training Pedestrian-Vehicle Model" +model = ml.SVM(args.svmType, args.kernelType) +model.train(np.concatenate(trainingSamplesPV.values()), np.concatenate(trainingLabelsPV.values())) +model.save(args.directoryName + "/modelPV.xml")
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/scripts/undistort-video.py Fri Dec 05 12:13:53 2014 -0500 @@ -0,0 +1,47 @@ +#! /usr/bin/env python + +import sys, argparse + +import numpy as np +import cv2 + +import cvutils +from math import ceil, log10 + +parser = argparse.ArgumentParser(description='''The program converts a video into a series of images corrected for distortion. One can then use mencoder to generate a movie, eg +$ mencoder 'mf://./*.png' -mf fps=[framerate]:type=png -ovc xvid -xvidencopts bitrate=[bitrate] -nosound -o [output.avi]''') + +parser.add_argument('-i', dest = 'videoFilename', help = 'filename of the video sequence') +parser.add_argument('--intrinsic', dest = 'intrinsicCameraMatrixFilename', help = 'name of the intrinsic camera file') +parser.add_argument('--distortion-coefficients', dest = 'distortionCoefficients', help = 'distortion coefficients', nargs = '*', type = float) +parser.add_argument('--undistorted-multiplication', dest = 'undistortedImageMultiplication', help = 'undistorted image multiplication', type = float) +parser.add_argument('-f', dest = 'firstFrameNum', help = 'number of first frame number to display', type = int) +parser.add_argument('-l', dest = 'lastFrameNum', help = 'number of last frame number to save', type = int) + +args = parser.parse_args() + +intrinsicCameraMatrix = np.loadtxt(args.intrinsicCameraMatrixFilename) +#distortionCoefficients = args.distortionCoefficients +#undistortedImageMultiplication = args.undistortedImageMultiplication +#firstFrameNum = params.firstFrameNum + +capture = cv2.VideoCapture(args.videoFilename) +width = int(capture.get(cv2.cv.CV_CAP_PROP_FRAME_WIDTH)) +height = int(capture.get(cv2.cv.CV_CAP_PROP_FRAME_HEIGHT)) +[map1, map2] = cvutils.computeUndistortMaps(width, height, args.undistortedImageMultiplication, intrinsicCameraMatrix, args.distortionCoefficients) +if capture.isOpened(): + ret = True + frameNum = args.firstFrameNum + capture.set(cv2.cv.CV_CAP_PROP_POS_FRAMES, args.firstFrameNum) + if args.lastFrameNum == None: + from sys import maxint + lastFrameNum = maxint + else: + lastFrameNum = args.lastFrameNum + nZerosFilename = int(ceil(log10(lastFrameNum))) + while ret and frameNum < lastFrameNum: + ret, img = capture.read() + if ret: + img = cv2.remap(img, map1, map2, interpolation=cv2.INTER_LINEAR) + cv2.imwrite('undistorted-{{:0{}}}.png'.format(nZerosFilename).format(frameNum), img) + frameNum += 1
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/scripts/uninstall-scripts.sh Fri Dec 05 12:13:53 2014 -0500 @@ -0,0 +1,7 @@ +#!/bin/sh +installDirname='/usr/local/bin' +for filename in * +do + echo 'rm '$installDirname/$filename + rm $installDirname/$filename +done \ No newline at end of file
--- a/tracking.cfg Thu Apr 18 15:29:33 2013 -0400 +++ b/tracking.cfg Fri Dec 05 12:13:53 2014 -0500 @@ -1,16 +1,30 @@ # filename of the video to process -video-filename = ~/Research/Data/minnesota/Rice-and-University-12_50.avi +video-filename = laurier.avi # filename of the database where results are saved -database-filename = ~/Research/Data/minnesota/results.sqlite +database-filename = laurier.sqlite +# filename of the homography matrix +homography-filename = laurier-homography.txt # filename of the homography matrix -homography-filename = ~/Research/Data/minnesota/Rice-and-University-12_50-homography.txt +intrinsic-camera-filename = intrinsic-camera.txt +# -0.11759321 0.0148536 0.00030756 -0.00020578 -0.00091816 +distortion-coefficients = -0.11759321 +distortion-coefficients = 0.0148536 +distortion-coefficients = 0.00030756 +distortion-coefficients = -0.00020578 +distortion-coefficients = -0.00091816 +# undistorted image multiplication +undistorted-size-multiplication = 1.31 +# Interpolation method for remapping image when correcting for distortion: 0 for INTER_NEAREST - a nearest-neighbor interpolation; 1 for INTER_LINEAR - a bilinear interpolation (used by default); 2 for INTER_CUBIC - a bicubic interpolation over 4x4 pixel neighborhood; 3 for INTER_LANCZOS4 +interpolation-method = 1 # filename of the mask image (where features are detected) -mask-filename = ~/Research/Data/minnesota/Rice-and-University-12_50-mask.png +mask-filename = none +# undistort the video for feature tracking +undistort = false # load features from database load-features = false # display trajectories on the video display = false -# original video frame rate +# original video frame rate (number of frames/s) video-fps = 29.97 # number of digits of precision for all measurements derived from video # measurement-precision = 3 @@ -25,12 +39,14 @@ feature-quality = 0.1 # minimum distance between features min-feature-distanceklt = 5 -# size of the search window at each pyramid level -window-size = 7 +# size of the block for feature characteristics +block-size = 7 # use of Harris corner detector use-harris-detector = false # k parameter to detect good features to track (OpenCV) k = 0.4 +# size of the search window at each pyramid level +window-size = 7 # maximal pyramid level in the feature tracking algorithm pyramid-level = 5 # number of displacement to test minimum feature motion @@ -46,9 +62,11 @@ # number of frames to compute velocities #nframes-velocity = 5 # maximum number of iterations to stop feature tracking -max-number-iterations = 20 +max-number-iterations = 30 # minimum error to reach to stop feature tracking -min-tracking-error = 0.3 +min-tracking-error = 0.01 +# minimum eigen value of a 2x2 normal matrix of optical flow equations +min-feature-eig-threshold = 1e-4 # minimum length of a feature (number of frames) to consider a feature for grouping min-feature-time = 20 # Min Max similarity parameters (Beymer et al. method) @@ -62,3 +80,46 @@ min-velocity-cosine = 0.8 # minimum average number of features per frame to create a vehicle hypothesis min-nfeatures-group = 3 +# Road user classification +# filename of the general ped/cyc/veh SVM classifier +pbv-svm-filename = modelPBV.xml +# filename of the cyc/veh SVM classifier +pbv-svm-filename = modelBV.xml +# maximum pedestrian speed (agregate: mean, median, 85th centile, etc.) 3.6 m/s +max-ped-speed = 0.12 +# maximum cyclist speed (agregate: mean, median, 85th centile, etc.) 10.8 m/s (3xped) +max-cyc-speed = 0.36 +# mean pedestrian speed and standard deviation (in a normal distribution) 1.36+-0.24 m/s +mean-ped-speed = 0.45 +std-ped-speed = 0.008 +# mean cyclist speed and standard deviation (in a log-normal distribution) 1.36+-0.24 m/s +mean-cyc-speed = 0.45 +std-cyc-speed = 0.008 +# mean vehicle speed and standard deviation (in a normal distribution) 5.12+-2.11 m/s +mean-veh-speed = 0.17 +std-veh-speed = 0.07 +# Safety analysis +# maximum speed when predicting future motion (km/h) +max-predicted-speed = 50 +# time horizon for collision prediction (s) +prediction-time-horizon = 5 +# collision distance threshold (m) +collision-distance = 1.8 +# option to compute crossing zones and predicted PET +crossing-zones = false +# prediction method: cv, na, ps +prediction-method = na +# number of predicted trajectories (use depends on prediction method) +npredicted-trajectories = 10 +# maximum acceleration for normal adaptation input symmetric distribution (m/s2) +max-normal-acceleration = 2 +# maximum steering for normal adaptation input symmetric distribution (rad/s) +max-normal-steering = 0.2 +# minimum acceleration for input distribution (m/s2) (extreme values used for evasive action distributions) +min-extreme-acceleration = -9.1 +# maximum acceleration for input distribution (m/s2) (extreme values used for evasive action distributions) +max-extreme-acceleration = 4.3 +# maximum steering for input distribution (rad/s) (extreme values used for evasive action distributions) +max-extreme-steering = 0.5 +# use feature positions and velocities for prediction +use-features-prediction = true
--- a/trafficintelligence.vcxproj Thu Apr 18 15:29:33 2013 -0400 +++ b/trafficintelligence.vcxproj Fri Dec 05 12:13:53 2014 -0500 @@ -14,6 +14,8 @@ <ClCompile Include="..\trajectorymanagementandanalysis\trunk\src\TrajectoryManagementAndAnalysis\src\DBSQLiteAccess.cpp" /> <ClCompile Include="c\cvutils.cpp" /> <ClCompile Include="c\feature-based-tracking.cpp" /> + <ClCompile Include="c\InputFrameListModule.cpp" /> + <ClCompile Include="c\InputVideoFileModule.cpp" /> <ClCompile Include="c\Motion.cpp" /> <ClCompile Include="c\Parameters.cpp" /> <ClCompile Include="c\utils.cpp" /> @@ -21,6 +23,9 @@ <ItemGroup> <ClInclude Include="include\catch.hpp" /> <ClInclude Include="include\cvutils.hpp" /> + <ClInclude Include="include\InputFrameListModule.h" /> + <ClInclude Include="include\InputFrameProviderIface.h" /> + <ClInclude Include="include\InputVideoFileModule.h" /> <ClInclude Include="include\learning.hpp" /> <ClInclude Include="include\Motion.hpp" /> <ClInclude Include="include\Parameters.hpp" />