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Merge pull request #4 from llDev-Rootll/development
Merging Phase 1 development to branch : master
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‎.gitignore

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# VS Code files
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*.VC.db*
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.vscode/
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# OS X files
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.DS_Store
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# Build artifacts
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build/
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*.weights
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# Python
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*.pyc

‎.travis.yml

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# We need a decent ubuntu dist
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sudo: required
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dist: xenial
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# Modern cpp settings from
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# http://genbattle.bitbucket.org/blog/2016/01/17/c++-travis-ci/
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language: cpp
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matrix:
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include:
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- os: linux
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compiler: gcc
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addons:
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apt:
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sources:
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- ubuntu-toolchain-r-test
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- george-edison55-precise-backports
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packages:
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- g++-5
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- cmake
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- cmake-data
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env: COMPILER=g++-5
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before_install:
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- pip install --user cpp-coveralls
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- sudo chmod a+x scripts/getModels.sh
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- ./scripts/getModels.sh
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install:
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- sudo chmod a+x scripts/install_dependencies.sh
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- ./scripts/install_dependencies.sh
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script:
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- export CXX=$COMPILER;
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- mkdir build
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- cd build
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- cmake -DCOVERAGE=ON -DCMAKE_BUILD_TYPE=Debug ../
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- make
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- make code_coverage
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- test/cpp-test
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after_success:
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- coveralls --root .. -E ".*external.*" -E ".*CMakeFiles.*" -E ".*test/.*.cpp.*"
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notifications:
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email: false

‎.ycm_extra_conf.py

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# Generated by YCM Generator at 2016-10-18 10:13:21.653665
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# This file is NOT licensed under the GPLv3, which is the license for the rest
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# of YouCompleteMe.
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#
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# Here's the license text for this file:
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#
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# This is free and unencumbered software released into the public domain.
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#
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# Anyone is free to copy, modify, publish, use, compile, sell, or
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# distribute this software, either in source code form or as a compiled
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# binary, for any purpose, commercial or non-commercial, and by any
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# means.
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#
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# In jurisdictions that recognize copyright laws, the author or authors
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# of this software dedicate any and all copyright interest in the
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# software to the public domain. We make this dedication for the benefit
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# of the public at large and to the detriment of our heirs and
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# successors. We intend this dedication to be an overt act of
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# relinquishment in perpetuity of all present and future rights to this
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# software under copyright law.
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#
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# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
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# EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
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# MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.
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# IN NO EVENT SHALL THE AUTHORS BE LIABLE FOR ANY CLAIM, DAMAGES OR
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# OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE,
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# ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR
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# OTHER DEALINGS IN THE SOFTWARE.
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#
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# For more information, please refer to <http://unlicense.org/>
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import os
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import ycm_core
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flags = [
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'-x',
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'c++',
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'-DGTEST_HAS_PTHREAD=1',
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'-I/Users/david/code/scratch/cpp/include',
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'-I/Users/david/code/scratch/cpp/test/../vendor/googletest/googletest/include',
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'-I/Users/david/code/scratch/cpp/vendor/boost',
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'-I/Users/david/code/scratch/cpp/vendor/googletest/googletest',
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'-I/Users/david/code/scratch/cpp/vendor/googletest/googletest/include',
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'-Wall',
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'-Wextra',
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'-Wpedantic',
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'-std=c++14',
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]
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# Set this to the absolute path to the folder (NOT the file!) containing the
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# compile_commands.json file to use that instead of 'flags'. See here for
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# more details: http://clang.llvm.org/docs/JSONCompilationDatabase.html
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#
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# You can get CMake to generate this file for you by adding:
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# set( CMAKE_EXPORT_COMPILE_COMMANDS 1 )
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# to your CMakeLists.txt file.
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#
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# Most projects will NOT need to set this to anything; you can just change the
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# 'flags' list of compilation flags. Notice that YCM itself uses that approach.
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compilation_database_folder = ''
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if os.path.exists( compilation_database_folder ):
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database = ycm_core.CompilationDatabase( compilation_database_folder )
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else:
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database = None
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SOURCE_EXTENSIONS = [ '.C', '.cpp', '.cxx', '.cc', '.c', '.m', '.mm' ]
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def DirectoryOfThisScript():
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return os.path.dirname( os.path.abspath( __file__ ) )
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def MakeRelativePathsInFlagsAbsolute( flags, working_directory ):
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if not working_directory:
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return list( flags )
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new_flags = []
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make_next_absolute = False
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path_flags = [ '-isystem', '-I', '-iquote', '--sysroot=' ]
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for flag in flags:
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new_flag = flag
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if make_next_absolute:
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make_next_absolute = False
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if not flag.startswith( '/' ):
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new_flag = os.path.join( working_directory, flag )
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for path_flag in path_flags:
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if flag == path_flag:
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make_next_absolute = True
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break
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if flag.startswith( path_flag ):
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path = flag[ len( path_flag ): ]
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new_flag = path_flag + os.path.join( working_directory, path )
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break
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if new_flag:
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new_flags.append( new_flag )
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return new_flags
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def IsHeaderFile( filename ):
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extension = os.path.splitext( filename )[ 1 ]
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return extension in [ '.H', '.h', '.hxx', '.hpp', '.hh' ]
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def GetCompilationInfoForFile( filename ):
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# The compilation_commands.json file generated by CMake does not have entries
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# for header files. So we do our best by asking the db for flags for a
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# corresponding source file, if any. If one exists, the flags for that file
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# should be good enough.
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if IsHeaderFile( filename ):
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basename = os.path.splitext( filename )[ 0 ]
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for extension in SOURCE_EXTENSIONS:
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replacement_file = basename + extension
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if os.path.exists( replacement_file ):
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compilation_info = database.GetCompilationInfoForFile(
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replacement_file )
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if compilation_info.compiler_flags_:
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return compilation_info
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return None
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return database.GetCompilationInfoForFile( filename )
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def FlagsForFile( filename, **kwargs ):
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if database:
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# Bear in mind that compilation_info.compiler_flags_ does NOT return a
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# python list, but a "list-like" StringVec object
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compilation_info = GetCompilationInfoForFile( filename )
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if not compilation_info:
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return None
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final_flags = MakeRelativePathsInFlagsAbsolute(
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compilation_info.compiler_flags_,
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compilation_info.compiler_working_dir_ )
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else:
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relative_to = DirectoryOfThisScript()
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final_flags = MakeRelativePathsInFlagsAbsolute( flags, relative_to )
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return {
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'flags': final_flags,
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'do_cache': True
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}
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‎Activity_Diagram.pdf

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‎CMakeLists.txt

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cmake_minimum_required(VERSION 3.2.1)
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project(scratch)
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# Add project cmake modules to path.
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set(CMAKE_MODULE_PATH ${CMAKE_MODULE_PATH} ${PROJECT_SOURCE_DIR}/cmake ${CMAKE_CURRENT_SOURCE_DIR}/cmake)
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#set(OpenCV_DIR ${PROJECT_SOURCE_DIR}/cmake/)
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#set path for openCV and Eigen3
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find_package(OpenCV REQUIRED)
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find_package(Eigen3 REQUIRED)
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#Add openCV
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include_directories(${OpenCV_INCLUDE_DIRS})
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INCLUDE_DIRECTORIES(${EIGEN3_INCLUDE_DIR})
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# We probably don't want this to run on every build.
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option(COVERAGE "Generate Coverage Data" OFF)
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if (COVERAGE)
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include(CodeCoverage)
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set(LCOV_REMOVE_EXTRA "'vendor/*'")
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setup_target_for_coverage(code_coverage test/cpp-test coverage)
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set(COVERAGE_SRCS app/main.cpp test/include/test_Robot.h test/include/test_HumanDetector.h test/src/test_Robot.cpp test/src/test_HumanDetector.cpp)
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SET(CMAKE_CXX_FLAGS "-g -O0 -fprofile-arcs -ftest-coverage")
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SET(CMAKE_C_FLAGS "-g -O0 -fprofile-arcs -ftest-coverage")
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SET(CMAKE_EXE_LINKER_FLAGS "-fprofile-arcs -ftest-coverage")
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else()
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set(CMAKE_CXX_FLAGS "-Wall -Wextra -Wpedantic -g")
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endif()
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include(CMakeToolsHelpers OPTIONAL)
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set(CMAKE_EXPORT_COMPILE_COMMANDS ON)
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set(CMAKE_CXX_STANDARD 14)
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add_subdirectory(app)
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add_subdirectory(src)
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add_subdirectory(test)
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add_subdirectory(test/src)
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add_subdirectory(vendor/googletest/googletest)

‎Quad_Chart.pdf

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‎README.md

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# Human_Detection_Tracking-CPP
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[![Build Status](https://app.travis-ci.com/llDev-Rootll/Human_Detection_Tracking-CPP.svg?branch=master)](https://app.travis-ci.com/llDev-Rootll/Human_Detection_Tracking-CPP)
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[![Coverage Status](https://coveralls.io/repos/github/llDev-Rootll/Human_Detection_Tracking-CPP/badge.svg?branch=master)](https://coveralls.io/github/llDev-Rootll/Human_Detection_Tracking-CPP?branch=master)
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[![Build Status](https://app.travis-ci.com/aditiramadwar/Human_Detection_Tracking-CPP.svg?branch=Phase_1)](https://app.travis-ci.com/aditiramadwar/Human_Detection_Tracking-CPP)
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[![Coverage Status](https://coveralls.io/repos/github/aditiramadwar/Human_Detection_Tracking-CPP/badge.svg?branch=Phase_1)](https://coveralls.io/github/aditiramadwar/Human_Detection_Tracking-CPP?branch=Phase_1)
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[![License: MIT](https://img.shields.io/badge/License-MIT-blue.svg)](https://opensource.org/licenses/MIT)
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A C++ module to detect and track humans which outputs location information directly in a robot's reference frame.
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## Authors
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- Arunava Basu (Navigator)
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- Aditi Ramadwar (Driver)
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## Introduction
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This repository contains a robust human obstacle detector and tracker using a monocular camera, directly usable in a robot’s reference frame.
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Human detection or person detection is the computer vision task of the localization and classification of human being(s) in an image. This is a key part in robotic applications for various reasons such as safety, obstacle avoidance, collaborative robotics, etc.
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We aim to design and deliver a robust robust human obstacle detector and tracker using a monocular camera, directly usable in a robot’s reference frame according to the requirement specifications provided to us by ACME robotics's RnD division for integration into a future product.
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Our system is built using C++ and will employ the robust YOLOv3 neural network model trained on the COCO dataset for human detection and tracking as it is one of the most accurate real-time object detection algorithms. A one time calibration is performed to calculate certain calibration metrics for the transformation of location info into the camera frame. An image from a monocular camera is pre-processed and passed to the model which outputs the location info in the image frame. It is then converted to the camera frame by using the calibration constants and then transformed into the robot's frame.
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## Project Collaterals
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The Agile Iterative Process will be used for the development of this system consisting of two sprints.
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The project proposal can be found [here](https://github.com/llDev-Rootll/Human_Detection_Tracking-CPP/blob/development/assets/Human%20Detector%20%26%20Tracker%20-%20Proposal.pdf).
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The quad chart can be found [here](https://github.com/llDev-Rootll/Human_Detection_Tracking-CPP/blob/development/assets/Quad_Chart.pdf).
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The overall backlog table and the tables for each sprints can be found [here](https://docs.google.com/spreadsheets/d/1tjJKUd9B4bBSYAHnrwuMjWNl_lUBmqeB6lw7iTNKZSg/edit?usp=sharing).
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## Requirements
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| Name | Version |
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| :--- | :---: |
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| C++ | 14++ |
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| CMake | min 3.2.1 |
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Continuous integration is tracked by using Travis CI and code coverage is tracked using coveralls.
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## Dependencies
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| Name | Version | License |
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| :--- | :---: | ---: |
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| OpenCV | 4.5.1 | [Apache 2](https://github.com/opencv/opencv/blob/master/LICENSE) |
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| Eigen | 3.X.X | [MPL2](https://www.mozilla.org/en-US/MPL/2.0/FAQ/) |
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Run this command in the root directory to install the dependencies :
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```
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chmod a+x scripts/install_dependencies.sh
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./scripts/install_dependencies.sh
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```
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## System Architecture
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The following shows the activity diagram for our proposed schema :
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<img alt="activity" src="assets/activity.png" width="75%" />
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*Fig 1 : Activity Diagram*
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The corresponding class diagram can be found [here](https://github.com/llDev-Rootll/Human_Detection_Tracking-CPP/blob/development/UML/revised/Revised_Class_Diagram.pdf).
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## Testing
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Unit Testing will be used to test each submodule and ensure complete code coverage. For this Google Gtest will be leveraged and identical test classes and methods will be created with minimal modification in order to facilitate testing.
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Additionally, a small test subset of the COCO test data will be used to validate model accuracy.
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The initial design is as follows :
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## Building without code coverage
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Execute these commands in the root folder :
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```
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chmod a+x scripts/build_without_coverage.sh
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./scripts/build_without_coverage.sh
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```
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## Building with code coverage
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<img alt="design" src="assets/design.gif" width="75%" />
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Execute these commands in the root folder :
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```
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sudo apt-get install lcov
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chmod a+x scripts/build_with_coverage.sh
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./scripts/build_with_coverage.sh
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```
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## Phase 1
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*Fig 1 : High Level Arhitecture Design*
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- Defined Robot, HumanDetector and test classes according to the UML diagrams.
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- Implemented all Robot and HumanDetector methods except for transformToRobotFrame method.
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- Definition and implementation of test cases are planned for Phase 2.
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Please refer to the backlog table, [here](https://docs.google.com/spreadsheets/d/1tjJKUd9B4bBSYAHnrwuMjWNl_lUBmqeB6lw7iTNKZSg/edit?usp=sharing), for an exhaustive list tasks completed in Phase 1.
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## Currently under development in the "development" branch [here](https://github.com/llDev-Rootll/Human_Detection_Tracking-CPP/tree/development)

‎UML/initial/Activity_Diagram.pdf

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‎UML/initial/Class_Diagram.pdf

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‎UML/revised/Revised_Activity Diagram.pdf

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