diff --git a/.github/workflows/build-wheels.yml b/.github/workflows/build-wheels.yml index d64e2c8..78faac7 100644 --- a/.github/workflows/build-wheels.yml +++ b/.github/workflows/build-wheels.yml @@ -1,4 +1,4 @@ -name: Build +name: Build 🐍 📦 + Publish to PyPI on: push: @@ -92,9 +92,11 @@ jobs: -DUPSP_BUILD_TESTING=OFF -DUPSP_BUILD_APPLICATIONS=OFF -DUPSP_BUILD_PYBIND11=ON' CIBW_BEFORE_ALL_LINUX: "yum install -y perl-IPC-Cmd zip ninja-build" CIBW_BEFORE_BUILD_WINDOWS: "pip install cmake" - CIBW_BUILD: "cp39-win_amd64 cp39-macosx_x86_64 cp39-manylinux_x86_64" + CIBW_BUILD: "cp*-win_amd64 cp*-macosx_x86_64 cp*-manylinux_x86_64" CIBW_SKIP: "*-win32 *-manylinux_i686" CIBW_BUILD_VERBOSITY: 1 + CIBW_TEST_REQUIRES: pytest + CIBW_TEST_COMMAND: "pytest {project}/test/python" with: package-dir: . output-dir: wheelhouse @@ -118,3 +120,24 @@ jobs: - uses: actions/upload-artifact@v3 with: path: dist/*.tar.gz + + pypi_publish: + needs: [build_wheels, build_sdist] + runs-on: ubuntu-latest + environment: pypi + permissions: + id-token: write + # Upload for any push of tag starting with 'v' + if: github.event_name == 'push' && startsWith(github.ref, 'refs/tags/v') + steps: + - uses: actions/download-artifact@v3 + with: + # unpacks default artifact into dist/ + # if `name: artifact` is omitted, the action will create extra parent dir + name: artifact + path: dist + + - uses: pypa/gh-action-pypi-publish@release/v1 + # For testing + # with: + # repository_url: https://test.pypi.org/legacy/ diff --git a/.github/workflows/unit-tests.yml b/.github/workflows/unit-tests.yml index 0b87224..eca3f46 100644 --- a/.github/workflows/unit-tests.yml +++ b/.github/workflows/unit-tests.yml @@ -1,5 +1,4 @@ -# Run the uPSP project build system and unit tests. -# Artifacts are installed into the runner's system Python 3 environment. +# Run the uPSP C++ build system and unit tests. # # Notes: # @@ -26,10 +25,9 @@ # - On NAS HECC systems, we build the C/C++ code with dynamic linkage due # to the available system dependencies. This is *not* a default # target supported by vcpkg, but it has a lot of community interest and -# "unofficial" testing support. Ref: https://github.com/microsoft/vcpkg/issues/15006 -# We patch up some of the RPATH quirks using 'patchelf'. +# "unofficial" testing support. # -name: unit-tests +name: C/C++ unit tests on: push: workflow_dispatch: @@ -51,6 +49,8 @@ jobs: steps: - uses: actions/checkout@v3 + with: + fetch-depth: 0 - uses: lukka/get-cmake@latest - name: Install dependencies (Linux) run: | @@ -82,33 +82,22 @@ jobs: runVcpkgInstall: true # Release 2023.04.15 vcpkgGitCommitId: '501db0f17ef6df184fcdbfbe0f87cde2313b6ab1' - - name: Patch vcpkg library headers with patchelf - run: | - cd vcpkg - ${{ github.workspace }}/.github/workflows/vcpkg-fix-rpaths - cd .. - - uses: actions/setup-python@v4 - with: - python-version: ${{ matrix.python-version }} - cache: 'pip' - - name: Upgrade pip to v23+ - run: | - pip install --upgrade pip - pip --version - python --version - - name: Run build+install with pip + - name: Run CMake build run: | - SKBUILD_CONFIGURE_OPTIONS=\ - " -DCMAKE_TOOLCHAIN_FILE=${VCPKG_ROOT}/scripts/buildsystems/vcpkg.cmake"\ - " -DVCPKG_TARGET_TRIPLET=x64-linux-dynamic" pip install -v . + mkdir build + cd build + cmake \ + -DCMAKE_TOOLCHAIN_FILE="${VCPKG_ROOT}/scripts/buildsystems/vcpkg.cmake" \ + -DVCPKG_TARGET_TRIPLET="x64-linux-dynamic" \ + -DUPSP_BUILD_TESTING=ON \ + -DUPSP_BUILD_APPLICATIONS=ON \ + -DUPSP_BUILD_PYBIND11=ON \ + .. + make -j # - name: Setup upterm session # uses: lhotari/action-upterm@v1 - name: Run C/C++ unit tests - # TODO this should be made cross-platform. Currently works only for ubuntu-latest - # TODO fix RUNPATH of built executables to avoid use of LD_LIBRARY_PATH run: | - export LD_LIBRARY_PATH="$LD_LIBRARY_PATH:${{ env.VCPKG_INSTALLED_DIR }}/${{ env.VCPKG_DEFAULT_TRIPLET }}/lib" - echo "LD_LIBRARY_PATH: $LD_LIBRARY_PATH" cd cpp/test - ../../_skbuild/linux-x86_64-3.9/cmake-build/run_tests + ../../build/cpp/test/run_tests diff --git a/.github/workflows/vcpkg-fix-rpaths b/.github/workflows/vcpkg-fix-rpaths deleted file mode 100755 index 072a7b5..0000000 --- a/.github/workflows/vcpkg-fix-rpaths +++ /dev/null @@ -1,54 +0,0 @@ -#!/bin/bash - -# syntax: patchelf -# [--set-interpreter FILENAME] -# [--page-size SIZE] -# [--print-interpreter] -# [--print-soname] Prints 'DT_SONAME' entry of .dynamic section. Raises an error if DT_SONAME doesn't exist -# [--set-soname SONAME] Sets 'DT_SONAME' entry to SONAME. -# [--set-rpath RPATH] -# [--remove-rpath] -# [--shrink-rpath] -# [--allowed-rpath-prefixes PREFIXES] With '--shrink-rpath', reject rpath entries not starting with the allowed prefix -# [--print-rpath] -# [--force-rpath] -# [--add-needed LIBRARY] -# [--remove-needed LIBRARY] -# [--replace-needed LIBRARY NEW_LIBRARY] -# [--print-needed] -# [--no-default-lib] -# [--clear-symbol-version SYMBOL] -# [--output FILE] -# [--debug] -# [--version] -# FILENAME... - - -if [ ! -f "vcpkg" ]; then - echo "ERROR: Must run from vcpkg dir" - exit 1 -fi - -# Find all libs, and patch w/ correct RUNPATH -# '$ORIGIN' is special entry in RUNPATH referring to "this solib's dir", -# shorthand for the LIB_DIR. Alternatively, we could add the actual vcpkg lib dir -# but this makes the vcpkg solib's bulk-relocatable, which is kind of nice. -RUNPATH='$ORIGIN' -TRIPLET=x64-linux-dynamic - -LIB_DIR=$PWD/installed/$TRIPLET/lib -FILES=( $( find $LIB_DIR -type f -name 'lib*.so*' ) ) -# will break on file names with whitespace -for f in "${FILES[@]}"; do - patchelf --set-rpath "$RUNPATH" "$f" - echo "patchelf --set-rpath \"$RUNPATH\" \"$f\"" -done - -LIB_DIR=$PWD/installed/$TRIPLET/debug/lib -FILES=( $( find $LIB_DIR -type f -name 'lib*.so*' ) ) -# will break on file names with whitespace -for f in "${FILES[@]}"; do - patchelf --set-rpath "$RUNPATH" "$f" - echo "patchelf --set-rpath \"$RUNPATH\" \"$f\"" -done - diff --git a/.github/workflows/vcpkg-triplets/x64-linux-dynamic.cmake b/.github/workflows/vcpkg-triplets/x64-linux-dynamic.cmake deleted file mode 100644 index 068e895..0000000 --- a/.github/workflows/vcpkg-triplets/x64-linux-dynamic.cmake +++ /dev/null @@ -1,32 +0,0 @@ -# Custom triplet for building shared libraries (*.so) for -# x64 linux architectures. Default for vcpkg is to build -# static libraries (*.a) only. - -set(VCPKG_TARGET_ARCHITECTURE x64) - -set(VCPKG_CRT_LINKAGE dynamic) -set(VCPKG_LIBRARY_LINKAGE dynamic) - -set(VCPKG_CMAKE_SYSTEM_NAME Linux) - -set(VCPKG_CXX_FLAGS "-Wl,-rpath,'$ORIGIN'") -set(VCPKG_C_FLAGS "-Wl,-rpath,'$ORIGIN'") -set(VCPKG_LINKER_FLAGS "-Wl,-rpath,'$ORIGIN'") - -# use, i.e. don't skip the full RPATH for the build tree -# when building, don't use the install RPATH already -# (but later on when installing) -# set(CMAKE_BUILD_WITH_INSTALL_RPATH FALSE) - -#set(CMAKE_SKIP_RPATH FALSE) -#set(CMAKE_INSTALL_RPATH "${CMAKE_INSTALL_PREFIX}/lib") - -# add the automatically determined parts of the RPATH -# which point to directories outside the build tree to the install RPATH - -# # the RPATH to be used when installing, but only if it's not a system directory -# list(FIND CMAKE_PLATFORM_IMPLICIT_LINK_DIRECTORIES "${CMAKE_INSTALL_PREFIX}/lib" isSystemDir) -# if("${isSystemDir}" STREQUAL "-1") -# set(CMAKE_INSTALL_RPATH "${CMAKE_INSTALL_PREFIX}/lib") -# endif("${isSystemDir}" STREQUAL "-1") - diff --git a/.github/workflows/vcpkg.json b/.github/workflows/vcpkg.json index 0f06721..5768b75 100644 --- a/.github/workflows/vcpkg.json +++ b/.github/workflows/vcpkg.json @@ -15,8 +15,5 @@ "ilmbase", "gtest", "pybind11" - ], - "vcpkg-configuration": { - "overlay-triplets": ["./vcpkg-triplets"] - } + ] } diff --git a/cpp/CMakeLists.txt b/cpp/CMakeLists.txt index 23d1c74..93627f9 100644 --- a/cpp/CMakeLists.txt +++ b/cpp/CMakeLists.txt @@ -1,8 +1,8 @@ include_directories(include) -option(UPSP_BUILD_APPLICATIONS "Build applications" ON) -option(UPSP_BUILD_PYBIND11 "Build Python bindings" ON) -option(UPSP_BUILD_TESTING "Build unit tests" ON) +option(UPSP_BUILD_APPLICATIONS "Build applications" OFF) +option(UPSP_BUILD_PYBIND11 "Build Python bindings" OFF) +option(UPSP_BUILD_TESTING "Build unit tests" OFF) if (UPSP_BUILD_APPLICATIONS) add_subdirectory(exec) diff --git a/cpp/exec/CMakeLists.txt b/cpp/exec/CMakeLists.txt index 246e0c0..1c1c555 100644 --- a/cpp/exec/CMakeLists.txt +++ b/cpp/exec/CMakeLists.txt @@ -26,7 +26,6 @@ target_link_libraries(upsp-extract-frames opencv_videoio) target_include_directories(upsp-extract-frames SYSTEM PRIVATE ${Boost_INCLUDE_DIR}) add_executable(add_field add_field.cpp) -target_link_libraries(add_field upsp_video) target_link_libraries(add_field upsp_utils) target_link_libraries(add_field hdf5::hdf5_cpp-shared hdf5::hdf5_hl_cpp-shared) diff --git a/cpp/exec/psp_process.cpp b/cpp/exec/psp_process.cpp index 8283307..6dc74bc 100644 --- a/cpp/exec/psp_process.cpp +++ b/cpp/exec/psp_process.cpp @@ -1661,7 +1661,21 @@ int phase1(Phase1Settings& sett, P1Elems& elems) { // register the image to the first frame if (ifile.registration == upsp::RegistrationType::Pixel) { cv::Mat warp_matrix; - img = upsp::register_pixel(first_frames_32f[c], img, warp_matrix); + const int max_iters = 50; + const double epsilon = 0.001; + int interpolation_flags = 0; + switch(ifile.pixel_interpolation) { + case upsp::PixelInterpolationType::Linear: + interpolation_flags = cv::INTER_LINEAR; + break; + case upsp::PixelInterpolationType::Nearest: + interpolation_flags = cv::INTER_NEAREST; + break; + } + img = upsp::register_pixel( + first_frames_32f[c], img, warp_matrix, + max_iters, epsilon, interpolation_flags + ); } // patch frame @@ -1762,7 +1776,21 @@ int phase1(Phase1Settings& sett, P1Elems& elems) { // register the image to the first frame if ((f > 0) && (ifile.registration == upsp::RegistrationType::Pixel)) { cv::Mat warp_matrix; - img = upsp::register_pixel(elems.first_frames[c], img, warp_matrix); + const int max_iters = 50; + const double epsilon = 0.001; + int interpolation_flags = 0; + switch(ifile.pixel_interpolation) { + case upsp::PixelInterpolationType::Linear: + interpolation_flags = cv::INTER_LINEAR; + break; + case upsp::PixelInterpolationType::Nearest: + interpolation_flags = cv::INTER_NEAREST; + break; + } + img = upsp::register_pixel( + elems.first_frames[c], img, warp_matrix, + max_iters, epsilon, interpolation_flags + ); } // patch frame diff --git a/cpp/include/registration.h b/cpp/include/registration.h index 634ca7c..6d1d895 100644 --- a/cpp/include/registration.h +++ b/cpp/include/registration.h @@ -53,13 +53,16 @@ struct RegisterImage : std::unary_function { * @param[in] max_iters maximum number of iterations for ECC algorithm * @param[in] epsilon threshold of the increment in correlation coefficient between * two iterations, criteria for ECC algorithm to stop + * @param[in] interpolation_flags pixel interpolation method for image warping + * two iterations, criteria for ECC algorithm to stop * @return @a inp_img warped to align with @a ref_img * * @pre @a ref_img and @a inp_img are grayscale images * @pre @a ref_img is CV_32F */ cv::Mat register_pixel(const cv::Mat& ref_img, const cv::Mat& inp_img, - cv::Mat& warp_matrix, int max_iters=50, double epsilon=0.001); + cv::Mat& warp_matrix, int max_iters=50, double epsilon=0.001, + int interpolation_flags=cv::INTER_LINEAR); /** Remove match pairs where the distance between the points is significantly large * diff --git a/cpp/include/upsp_inputs.h b/cpp/include/upsp_inputs.h index f759204..cd2370c 100644 --- a/cpp/include/upsp_inputs.h +++ b/cpp/include/upsp_inputs.h @@ -26,6 +26,7 @@ enum class TargetPatchType { None, Polynomial }; /** Registration method */ enum class RegistrationType { None, Point, Pixel }; +enum class PixelInterpolationType { Linear, Nearest }; /** Type of spatial filter */ enum class FilterType { None, Gaussian, Box }; @@ -78,6 +79,7 @@ class FileInputs { GridType grid_type; TargetPatchType target_patcher; RegistrationType registration; + PixelInterpolationType pixel_interpolation; FilterType filter; OverlapType overlap; unsigned int filter_size; @@ -174,6 +176,9 @@ std::ostream& operator<<(std::ostream& os, const upsp::TargetPatchType& tpt); /** Add registration type string to stream */ std::ostream& operator<<(std::ostream& os, const upsp::RegistrationType& rt); +/** Add pixel interpolation type string to stream */ +std::ostream& operator<<(std::ostream& os, const upsp::PixelInterpolationType& pt); + /** Add filter type string to stream */ std::ostream& operator<<(std::ostream& os, const upsp::FilterType& ft); diff --git a/cpp/lib/P3DModel.ipp b/cpp/lib/P3DModel.ipp index 853135d..3558385 100644 --- a/cpp/lib/P3DModel.ipp +++ b/cpp/lib/P3DModel.ipp @@ -5,6 +5,7 @@ */ #include "logging.h" #include "plot3d.h" +#include "utils/pspError.h" namespace upsp { diff --git a/cpp/lib/registration.cpp b/cpp/lib/registration.cpp index 83445af..6a67dda 100644 --- a/cpp/lib/registration.cpp +++ b/cpp/lib/registration.cpp @@ -30,7 +30,8 @@ cv::Mat RegisterImage::operator()(cv::Mat inp) { /*****************************************************************************/ cv::Mat register_pixel(const cv::Mat& ref_img, const cv::Mat& inp_img, - cv::Mat& warp_matrix, int max_iters/*=50*/, double epsilon/*=0.001*/) { + cv::Mat& warp_matrix, int max_iters/*=50*/, double epsilon/*=0.001*/, + int interpolation_flags/*=cv::INTER_LINEAR*/) { // grayscale and ref CV_32F assert(ref_img.channels() == 1); @@ -65,11 +66,15 @@ cv::Mat register_pixel(const cv::Mat& ref_img, const cv::Mat& inp_img, // get the warped image cv::Mat warp_img; if (warp_mode != cv::MOTION_HOMOGRAPHY) { - cv::warpAffine(inp_img, warp_img, warp_matrix, ref_img.size(), cv::INTER_LINEAR + - cv::WARP_INVERSE_MAP); + cv::warpAffine( + inp_img, warp_img, warp_matrix, ref_img.size(), + interpolation_flags | cv::WARP_INVERSE_MAP + ); } else { - cv::warpPerspective(inp_img, warp_img, warp_matrix, ref_img.size(), cv::INTER_LINEAR + - cv::WARP_INVERSE_MAP); + cv::warpPerspective( + inp_img, warp_img, warp_matrix, ref_img.size(), + interpolation_flags | cv::WARP_INVERSE_MAP + ); } return warp_img; diff --git a/cpp/lib/upsp_inputs.cpp b/cpp/lib/upsp_inputs.cpp index fc3f1fc..bd5c506 100644 --- a/cpp/lib/upsp_inputs.cpp +++ b/cpp/lib/upsp_inputs.cpp @@ -27,6 +27,7 @@ FileInputs::FileInputs() : run(0), sequence(0), tunnel(""), cameras(0), sds(""), grid(""), active_comps(""), out_name(""), normals(""), out_dir(""), grid_type(GridType::None), target_patcher(TargetPatchType::None), + pixel_interpolation(PixelInterpolationType::Linear), registration(RegistrationType::None), filter(FilterType::None), overlap(OverlapType::AverageViews), filter_size(0), oblique_angle(70), number_frames(0), grid_units("-") {} @@ -323,6 +324,7 @@ void FileInputs::write_file(std::string filename) const { ofs << "@options" << std::endl; ofs << "\ttarget_patcher = " << target_patcher << std::endl; ofs << "\tregistration = " << registration << std::endl; + ofs << "\tpixel_interpolation = " << pixel_interpolation << std::endl; ofs << "\tfilter = " << filter << std::endl; ofs << "\toverlap = " << overlap << std::endl; ofs << "\tfilter_size = " << filter_size << std::endl; @@ -568,6 +570,20 @@ bool FileInputs::load_options(std::ifstream& fs) { fs.close(); return false; } + } else if (tokens[0] == "pixel_interpolation") { + if (tokens[1] == "linear") { + pixel_interpolation = PixelInterpolationType::Linear; + } else if (tokens[1] == "nearest") { + pixel_interpolation = PixelInterpolationType::Nearest; + } else { + LOG_ERROR( + "Error: Could not parse @options:pixel_interpolation." + " Options are 'linear' or 'nearest." + ); + fs.close(); + return false; + } + } else if (tokens[0] == "filter") { if (tokens[1] == "gaussian") { filter = FilterType::Gaussian; @@ -772,6 +788,7 @@ std::ostream& operator<<(std::ostream& os, const FileInputs& fi) { os << "Options:" << std::endl; os << " Target Patcher = " << fi.target_patcher << std::endl; os << " Registration = " << fi.registration << std::endl; + os << " Pixel Interp = " << fi.pixel_interpolation << std::endl; os << " Filter = " << fi.filter << " (" << fi.filter_size << "x"; os << fi.filter_size << ")" << std::endl; os << " Overlap = " << fi.overlap << std::endl; @@ -816,6 +833,22 @@ std::ostream& operator<<(std::ostream& os, const RegistrationType& rt) { return os; } +/*****************************************************************************/ +std::ostream& operator<<(std::ostream& os, const PixelInterpolationType& pt) { + switch (pt) { + case (PixelInterpolationType::Linear) : + os << "linear"; + break; + case (PixelInterpolationType::Nearest) : + os << "nearest"; + break; + default : + os << "undefined interpolation type"; + break; + } + return os; +} + /*****************************************************************************/ std::ostream& operator<<(std::ostream& os, const FilterType& ft) { switch (ft) { diff --git a/cpp/pybind11/CMakeLists.txt b/cpp/pybind11/CMakeLists.txt index 1a5e7b4..d929f04 100644 --- a/cpp/pybind11/CMakeLists.txt +++ b/cpp/pybind11/CMakeLists.txt @@ -26,4 +26,5 @@ install( raycast upsp_kdtree LIBRARY DESTINATION python/upsp + RUNTIME DESTINATION python/upsp ) diff --git a/cpp/raycast/CMakeLists.txt b/cpp/raycast/CMakeLists.txt index 958fc7b..547a805 100644 --- a/cpp/raycast/CMakeLists.txt +++ b/cpp/raycast/CMakeLists.txt @@ -18,7 +18,4 @@ add_library( ) target_link_libraries(upsp_kdtree PUBLIC IlmBase::Imath) -if (UPSP_BUILD_PYBIND11) -else() - install(TARGETS upsp_kdtree) -endif() +install(TARGETS upsp_kdtree) diff --git a/cpp/utils/CMakeLists.txt b/cpp/utils/CMakeLists.txt index 3909f62..bee3148 100644 --- a/cpp/utils/CMakeLists.txt +++ b/cpp/utils/CMakeLists.txt @@ -12,6 +12,7 @@ add_library( general_utils.cpp ) target_link_libraries(upsp_utils PUBLIC opencv_core opencv_imgproc) +target_link_libraries(upsp_utils PUBLIC upsp_video) target_link_libraries(upsp_utils PUBLIC ${LIBRT}) install(TARGETS upsp_utils LIBRARY DESTINATION lib) diff --git a/docs/sphinx/file-formats.rst b/docs/sphinx/file-formats.rst index c0a83aa..d9d86d8 100644 --- a/docs/sphinx/file-formats.rst +++ b/docs/sphinx/file-formats.rst @@ -437,7 +437,14 @@ the variables included in the input deck are included in :numref:`input-deck` - no - decide what type of image registration to perform, supports either ``pixel`` or ``none``; default is ``none`` - + + * - + - pixel_interpolation + - pixel-based image registration interpolation method + - no + - for image registration type ``pixel``, decide what type of interpolation method is + used, supports either ``linear`` or ``nearest``; default is ``linear`` + * - - overlap - multi-view handling diff --git a/docs/sphinx/installation.rst b/docs/sphinx/installation.rst index 060711c..ab3c083 100644 --- a/docs/sphinx/installation.rst +++ b/docs/sphinx/installation.rst @@ -2,34 +2,53 @@ Installation ============ -The project does not currently provide pre-built binary releases. Users must -build the project from source on their local system. The project is built as -a Python package with native applications and extension modules written in C/C++. +There are two components of the uPSP software: +- A C/C++ code base including several utility applications and the + main processing application ``psp-process`` +- A Python code base including batch processing tooling, camera calibration + routines, and data analysis tools. This also includes some limited integration + of C/C++ functionality via ``pybind11`` for 2D-3D raycasting routines. -The following instructions will help the user build and install the package into -their local Python environment as a ``pip`` package. The package build system -leverages ``scikit-build`` and CMake to compile native C/C++ elements and deploy -them alongside pure Python modules in the final package. +To install the C/C++ code base, the user must build from source (instructions +are below). + +To install the Python code base, the user can install from source OR (simpler +solution) they can pull from the binary package distribution on PyPI by running +``pip install upsp``. The Python library is compatible solely with Python 3.9 +(this may be updated soon in an upcoming release). + +Our current strategy at NASA is to build from source for our supercomputing cluster, +and then install from PyPI for local development and data analysis. Prerequisites ============= -- The project is currently tested for use on Linux x86-64 target systems. -- See :ref:`dependencies:Third-Party Dependencies` for a list of third-party libraries that must be installed - on the local system in order to build the project. They can be managed using the - system package manager (``apt`` on Ubuntu, ``yum`` on CentOS, etc.) or with a C/C++ - package management framework---the package maintainers at NASA develop and test using - `vcpkg `_. If using ``vcpkg``, the dependencies will likely not be in a default system folder and - so the following environment variables should be set to point the build system at the - CMake toolchain file provided by your ``vcpkg`` install: - - .. code:: bash - - #!/bin/bash - export SKBUILD_CONFIGURE_OPTIONS=" -DCMAKE_TOOLCHAIN_FILE=${VCPKG_ROOT}/scripts/buildsystems/vcpkg.cmake" - -- Python must be installed on the local system (3.7+), ideally inside a virtual environment. +- The C/C++ project is currently tested for use on Linux x86-64 target systems. +- Python 3.9 must be installed on the local system, ideally inside a virtual environment. - We require ``pip>=22.3.1``. To upgrade, you can run ``pip install --upgrade pip``. +- Several CMake options can be used to control what is built. + - ``UPSP_BUILD_APPLICATIONS``: build core applications + - ``UPSP_BUILD_PYBIND11``: build Python C/C++ extensions (requires ``pybind11``) + - ``UPSP_BUILD_TESTING``: build C/C++ unit tests (requires ``gtest``) +- See :ref:`dependencies:Third-Party Dependencies` for a list of third-party libraries that must be installed + on the local system in order to build the project. + - If building only the Python bindings, then only ``imath`` is required. + - At NASA, we develop and test using `vcpkg `_. If using + ``vcpkg``, the dependencies will likely not be in a default system folder and + so the following environment variables should be set to point the build system at the + CMake toolchain file provided by your ``vcpkg`` install: + + .. code:: bash + + #!/bin/bash + CMAKE_TOOLCHAIN_FILE="${VCPKG_ROOT}/scripts/buildsystems/vcpkg.cmake" + CMAKE_OPTS=() + CMAKE_OPTS+=("-DCMAKE_TOOLCHAIN_FILE=$CMAKE_TOOLCHAIN_FILE") + CMAKE_OPTS+=("-DUPSP_BUILD_APPLICATIONS=ON") + CMAKE_OPTS+=("-DUPSP_BUILD_PYBIND11=ON") + CMAKE_OPTS+=("-DUPSP_BUILD_TESTING=ON") + export SKBUILD_CONFIGURE_OPTIONS="${CMAKE_OPTS[@]}" + Obtaining source code ===================== diff --git a/pyproject.toml b/pyproject.toml index 2415ee9..9624f9e 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,15 +1,18 @@ [project] name = "upsp" description = "NASA uPSP data processing applications and utilities" +readme = { file = "README.md", content-type = "text/markdown" } authors = [{name = "NASA uPSP developers"}] license = {file = "LICENSE.txt"} dynamic = ["version"] +requires-python = "~=3.9.0" dependencies = [ "matplotlib", "numpy", "opencv-python-headless==4.5.2.54", "pandas", "scipy", + "shapely" ] [build-system] diff --git a/python/upsp/cam_cal_utils/camera_tunnel_calibrate.py b/python/upsp/cam_cal_utils/camera_tunnel_calibrate.py index a1ca500..72527b8 100644 --- a/python/upsp/cam_cal_utils/camera_tunnel_calibrate.py +++ b/python/upsp/cam_cal_utils/camera_tunnel_calibrate.py @@ -2,12 +2,14 @@ import os import json +from upsp.target_operations import target_detection from upsp.cam_cal_utils import ( external_calibrate, photogrammetry, parsers, visualization, ) +from upsp.target_operations import target_detection debug_show_3D_targets = False debug_show_img_targets = False @@ -129,13 +131,13 @@ def camera_to_tunnel_calibrate( if debug_show_3D_targets: import matplotlib.pyplot as plt - import target_bumping + from upsp.target_operations import target_bumping fig = plt.figure("3D Targets") ax = fig.add_subplot(projection="3d") tvecs, norms = vis_checker.get_tvecs_and_norms() - ax.scatter(tvecs[:, 0], tvecs[:, 1], tvecs[:, 2]) + ax.scatter(tvecs[:, 0], tvecs[:, 1], tvecs[:, 2], c='b') internals = target_bumping.tgts_get_internals(tgts, vis_checker) internal_names = [internal[0] for internal in internals] @@ -143,7 +145,7 @@ def camera_to_tunnel_calibrate( for tgt in tgts: if tgt["name"] not in internal_names: external_tgts.append(tgt) - visualization.plot_pts_and_norms(external_tgts, ax) + visualization.plot_pts_and_norms(external_tgts, ax, scale=10, c='r') ax.view_init(elev=45, azim=-90) visualization.axisEqual3D(ax) @@ -177,7 +179,7 @@ def camera_to_tunnel_calibrate( img_targets.append(img_target) # Sub-pixel localize the manual img_targets - __, img_targets = external_calibrate.subpixel_localize( + __, img_targets = target_detection.subpixel_localize( img, img_targets, img_targets, test_config ) @@ -202,23 +204,27 @@ def camera_to_tunnel_calibrate( # If debug_show_matches is turned on, generate the debug image if debug_show_matches: visible_init_tgts = photogrammetry.get_visible_targets( - rmat_opt, tvec_opt, tgts, vis_checker + rmat_opt, tvec_opt, cameraMatrix, distCoeffs, tgts, vis_checker ) - external_calibrate.match_targets( - rmat_opt, - tvec_opt, - cameraMatrix, - distCoeffs, - visible_init_tgts, - img_targets, - max_dist=test_config["max_dist"], - debug=[img, str(num), None], + dets = target_detection.template_detection( + img, rmat_opt, tvec_opt, cameraMatrix, distCoeffs, tgts, test_config + ) + tgts_detected, img_targets, num_matches = dets + + proj_pts = photogrammetry.project_targets( + rmat_opt, tvec_opt, cameraMatrix, distCoeffs, tgts + ) + + proj_pts = np.array([proj_pt['proj'] for proj_pt in proj_pts]) + img_targets = np.array([img_target['center'] for img_target in img_targets]) + visualization.show_projection_matching( + img, proj_pts, img_targets, num_matches, name='1_' ) # Transform from camera to tgts to tunnel -> Get camera to tunnel transformation rmat_camera_tunnel = np.matmul(rmat_opt, rmat_tgts_tunnel) tvec_camera_tunnel = tvec_opt + np.matmul(rmat_opt, tvec_tgts_tunnel) - + # Package the camera calibration data uPSP_cameraMatrix = parsers.convert_cv2_cm_to_uPSP_cm(cameraMatrix, img.shape) datum = { @@ -331,7 +337,6 @@ def tf_camera_tgts_thru_tunnel(camera_tunnel_cal, wtd, test_config): tvec__camera_tgts : np.ndarray, shape (3, 1) Translation vector """ - # Turn the wind tunnel data into the transformation from tunnel to targets wtd_transform = tunnel_transform( wtd["ALPHA"], @@ -347,17 +352,13 @@ def tf_camera_tgts_thru_tunnel(camera_tunnel_cal, wtd, test_config): tvec_tgts_tunnel = -np.matmul(rmat_tgts_tunnel, tvec_tunnel_tgts) # Decompose the camera calibration into its parts - ( - rmat__camera_tunnel, - tvec__camera_tunnel, - cameraMatrix, - distCoeffs, - ) = camera_tunnel_cal - - # Combine the transformations to get the transformation from `camera to tgts frame + __, __, cameraMatrix, distCoeffs = camera_tunnel_cal + rmat__camera_tunnel, tvec__camera_tunnel, __, __ = camera_tunnel_cal + + # Combine the transformations to get the transformation from camera to tgts frame rmat__camera_tgts = np.matmul(rmat__camera_tunnel, np.linalg.inv(rmat_tgts_tunnel)) - tvec__camera_tgts = tvec__camera_tunnel + np.matmul( - rmat__camera_tunnel, tvec_tunnel_tgts - ) + + tvec_transform = np.matmul(rmat__camera_tunnel, tvec_tunnel_tgts) + tvec__camera_tgts = tvec__camera_tunnel + tvec_transform return rmat__camera_tgts, tvec__camera_tgts diff --git a/python/upsp/cam_cal_utils/external_calibrate.py b/python/upsp/cam_cal_utils/external_calibrate.py index 247da56..5c2e11b 100644 --- a/python/upsp/cam_cal_utils/external_calibrate.py +++ b/python/upsp/cam_cal_utils/external_calibrate.py @@ -5,17 +5,16 @@ import copy import warnings -from upsp.target_localization import gaussian_fitting_methods +from upsp.target_operations import target_detection + from upsp.cam_cal_utils import ( - visualization, - visibility, camera_tunnel_calibrate, - photogrammetry, img_utils, + photogrammetry, + visibility, + visualization, ) -gauss_fit = gaussian_fitting_methods.gauss_fitter_func("super") - log = logging.getLogger(__name__) # Debugs @@ -24,10 +23,6 @@ debug_refined_matches = True debug_visible_projections = True debug_refined_optimization = True -debug_show_localizations = False - -# --------------------------------------------------------------------------------------- -# Other Functions def compare_poses(pose0, pose1, more_info=False): @@ -82,312 +77,6 @@ def compare_poses(pose0, pose1, more_info=False): return (rvec_rel, tvec_rel) -# --------------------------------------------------------------------------------------- -# Calibration Functions - - -# TODO: make this return a num_matches, all targets, and all unique img_targets just -# like to other targets functions -def subpixel_localize(img, tgts, img_targets, test_config, max_localize_delta=None): - """Find the sub-pixel localized position of the image targets - - Find the location of the image target centers with sub-pixel accuracy. This method - filters common bad localization solutions. I.e The localized position is too far - initial guess to make sense, invalid optimizer solution (None flag), outside the - cropped region or outside the image - - Parameters - ---------- - img : np.ndarray, shape (h, w) - Numpy 2D array of the image - tgts : list - Matched 3D targets. Each target should be a dict. The only strict requirement - set by this function is ``tgts[i]`` is associated with ``img_targets[i]`` - img_targets : list - Matched image targets. Each dict has, at a minimum, keys 'center', and - 'target_type'. 'center' has a value of the image target center location - (tuple/np.ndarray of length 2 of floats) and 'target_type' has the key of the - type of target (string). ``img_targets[i]`` is associated with ``tgts[i]`` - test_config : dict - Processing parameters with, at a minimum, a key for each target type in - `targets` and `img_targets`. The key is `target_type` + '_pad'. This is the - padding around the img target center location to use to sub-pixel localize - max_localize_delta : float, optional - The maximum allowable distance that subpixel_localize can change the - `img_target` position. If None, the max allowable distance will be set to the - padding distance minus 2. - - Returns - ------- - targets : list - Target positions (`tgts`) that have not been filtered out. - img_targets : list - Refined (sub-pixel localized) target positions in the image. - - Notes - ----- - ``targets[i]`` is associated with ``img_targets[i]``. Return lists may not be the - same length as input `tgts` and/or `img_targets` inputs (some - `targets`/`img_targets` from the input may be rejected and thus are not included in - the output) - """ - if max_localize_delta is not None: - filter_dist = max_localize_delta - - out_of_bounds = set() - img_targets_refined = [] - for i, img_target in enumerate(img_targets): - # Integer of center pixel - center_pixel = np.rint( - (img_target["center"][0], img_target["center"][1]) - ).astype(np.int32) - - target_pad = test_config[img_target["target_type"] + "_pad"] - - # cropped region around target (x1, y1), (x2, y2) - bbox = [ - [center_pixel[0] - target_pad, center_pixel[1] - target_pad], - [center_pixel[0] + target_pad + 1, center_pixel[1] + target_pad + 1], - ] - - # If bbox goes out of bounds of the image, ignore it - if ( - (bbox[0][0] < 0) - or (bbox[0][1] < 0) - or (bbox[1][0] >= img.shape[1]) - or (bbox[1][1] >= img.shape[0]) - ): - out_of_bounds.add(i) - img_targets_refined.append( - {"target_type": img_target["target_type"], "center": (None, None)} - ) - continue - - # Cropped image around target - img_cropped = img[bbox[0][1] : bbox[1][1], bbox[0][0] : bbox[1][0]] - - # Perform the sub-pixel localization - gauss_center = gauss_fit( - img_cropped, - target_type=img_target["target_type"], - center=img_target["center"], - img_offset=bbox[0], - )[0] - - # If the optimizer failed, continue - if gauss_center[0] is None: - out_of_bounds.add(i) - img_targets_refined.append( - { - "target_type": img_target["target_type"], - "center": np.array((None, None)), - } - ) - continue - - # Create a new img_target with the updated center location - img_target_refined = copy.deepcopy(img_target) - img_target_refined["center"] = gauss_center - - dist = np.linalg.norm( - [ - gauss_center[0] - img_target_refined["center"][0], - gauss_center[1] - img_target_refined["center"][1], - ] - ) - - # A distance of target_pad - 1 would imply the center is on a pixel in the edge - # of the crop. And since the taret is larger than 1 pixel, by definition this - # is a bad localization. Not even to mention it is likely bad since it is so - # far off from the expected position. We do target_pad - 2 to add a margin of - # safety - if max_localize_delta is None: - filter_dist = target_pad - 2 - - if dist > filter_dist: - out_of_bounds.add(i) - img_targets_refined.append( - { - "target_type": img_target["target_type"], - "center": np.array((None, None)), - } - ) - continue - - if debug_show_localizations: - plt.imshow(img_cropped, cmap="gray") - plt.scatter( - [gauss_center[0] - bbox[0][0]], - [gauss_center[1] - bbox[0][1]], - c="g", - s=2, - ) - plt.scatter( - [img_target["center"][0] - bbox[0][0]], - [img_target["center"][1] - bbox[0][1]], - c="r", - s=2, - ) - plt.savefig( - str(i).rjust(3, "0") - + "_" - + str(np.round(dist, 3)) - + "_" - + str(center_pixel[0]) - + "_" - + str(center_pixel[1]) - + ".png" - ) - plt.close() - - img_targets_refined.append(img_target_refined) - - # Remove the targets that had a bad localization - tgts_loc = [] - img_targets_loc = [] - for i, (tgt, img_target) in enumerate(zip(tgts, img_targets_refined)): - if i not in out_of_bounds: - tgts_loc.append(tgt) - img_targets_loc.append(img_target) - - return tgts_loc, img_targets_loc - - -# TODO: make this return a num_matches, all targets, and all unique img_targets just -# like to other targets functions -def filter_partially_occluded( - rmat, tvec, focal_length, tgts, img_targets, vis_checker, test_config -): - """Checks corners of cropped area used for sub-pixel localization for occlusion - - If the corners of the crop used for sub-pixel localization jumps surfaces, then the - target is likely partially occluded. This most commonly occurs when a booster - partially occludes a target on the core of a launch vehicle - - To get the 3D positions of corners of the cropped area, start at the target tvec. - Approximate the physical distance (in inches) of the cropped area (which is - done in pixels) using the focal length and distance from camera to model. Take - steps along the camera image plane to get to the approximate corner locations in 3D. - - With the corner locations, ensure that they are not inside the model (since the - model is curved the steps along the image plane may put the corners slightly inside - the model. Then check for occlusions. If the corner is occluded, the target is - deemed partially occluded. If none of the corners are occluded, the target is deemed - not occluded (but is still potentially partially occluded). Only the corners are - checked to reduce computation - - Parameters - ---------- - rmat : np.ndarray, shape (3, 3), float - Rotation matrix from camera to object - tvec : np.ndarray, shape (3, 1), float - Translation vector from camera to object - focal_length : float - Focal length of the camera. Most easily accessible from a ``cameraMatrix[0][0]`` - tgts : list - 3D targets. Each target is a dict and has, at a minimum, the keys 'tvec', - 'target_type', 'norm'. 'tvec' has a :class:`numpy.ndarray` (3, 1) representing the - position of the target relative to the model origin for its associated value. - 'norm' has a :class:`numpy.ndarray` (3, 1) representing the normal vector of the - target relative to the model origin for its associated value. 'target_type' has - a string representing the type of target (most commonly 'dot') for its - associated value. ``tgts[i]`` is associated with ``img_targets[i]`` - img_targets : list - Image targets. Each image target should be a dict, but this function does not - set any strict requirements other than that ``img_targets[i]`` is associated - with ``tgts[i]`` - vis_checker : ~upsp.cam_cal_utils.visibility.VisibilityChecker - Visibility checker object with the relevant BVH and oblique viewing angle - test_config : dict - Processign parameters with, at a minimum, a key for each target type of the - targets in `tgts` and `img_targets`. They key is `target_type` + '_pad'. This is - the padding around the img target center location to use to sub-pixel localize - - Returns - ------- - tgts_filtered : list - Target positions (`tgts`) that have not been filtered out - img_targets_filtered : list - Target positions in the image. - """ - # Get the direction for the u and v of pixel space - rmat_inv, tvec_inv = photogrammetry.invTransform(rmat, tvec) - - # Check each target for partial occlusions - tgts_filtered = [] - img_targets_filtered = [] - for tgt, img_target in zip(tgts, img_targets): - # Get the scale factor - obj_dist = np.linalg.norm(tvec.T - tgt["tvec"]) - - # For the scale factor, we use the similar triangles of the object distance by - # actual distance (in) vs focal length by pixel distance (px) - # The scale factor is the object distance divided by the focal length, times the - # pixel distance. We add one to the pad distance for a margin of safety - step_sz = ( - obj_dist * (test_config[tgt["target_type"] + "_pad"] + 1) / focal_length - ) - - # Get the step vector for u and v for the corners - # Get the u and v steps - uv = step_sz * rmat_inv[:, 0:2] - u = uv[:, 0] - v = uv[:, 1] - - # Package the corners. We have 1 corner for steps in the given directions - corners = [] - for x, y in [(-1, -1), (-1, 1), (1, -1), (1, 1)]: - corners.append({"tvec": tgt["tvec"] + x * u + y * v, "norm": tgt["norm"]}) - - # Check for occlusions. Each corner has a boolean and an occlusion position - occlusions = photogrammetry.get_occlusions_targets( - rmat, tvec, corners, vis_checker - ) - - # Check that all corners are visible - are_all_corners_visible = True - for corner, occlusion in zip(corners, occlusions): - # If there was no occlusion, continue - if not occlusion[0]: - continue - - # If there was an occlusion, check the distance from the occlusion to corner - # If that distance is less than sqrt(2) * step_sz (since we step in both - # x and y) it is possible that the corner is inside the model so it is - # unfairly occluded. - # If the distance is greater this corner is just occluded, no questions - dist = np.linalg.norm(occlusion[1] - corner["tvec"]) - if dist > step_sz * np.sqrt(2): - are_all_corners_visible = False - break - - # To fairly check this corner, bump it to the occluded location - bumped_corner = copy.copy(corner) - bumped_corner["tvec"] = occlusion[1] - - # Check this bumped corner for occlusion - bumped_occlusion = photogrammetry.get_occlusions_targets( - rmat, tvec, [bumped_corner], vis_checker - )[0] - - # If there is still an occlusion, this corner is likely occluded - # There is a chance that the distance between the bumped occlusion and - # the original corner is still less than step_sz * np.sqrt(2). However - # the occurrence seems small and not worthy of further checks. - # This is a potential TODO to set up as a while loop - if bumped_occlusion[0]: - are_all_corners_visible = False - break - - # If all corners passed the visibility check, this corner is fine and we can - # move onto the next corner - if are_all_corners_visible: - tgts_filtered.append(tgt) - img_targets_filtered.append(img_target) - - return tgts_filtered, img_targets_filtered - - def filter_min_dist(tgts, img_targets, num_matches, min_dist=8): """Filters the targets and image target that are too close together in the image @@ -1107,7 +796,6 @@ def match_targets( ``tgts_matched[i]`` is matched with ``matching_img_targets[i]`` for i from 0 to `num_matches`. """ - # Match the visible targets to the closest image point matching_img_targets = match_obj_and_img_pts( rmat, tvec, cameraMatrix, distCoeffs, tgts, img_targets, max_dist @@ -1116,6 +804,7 @@ def match_targets( tgts_matched, matching_img_targets, num_matches = filter_nones( tgts, matching_img_targets, len(tgts) ) + tgts_matched, matching_img_targets = post_filter_append( tgts, tgts_matched, img_targets, matching_img_targets ) @@ -1266,10 +955,6 @@ def post_filter_append(tgts, tgts_filtered, img_targets, img_targets_filtered): return tgts_filtered_temp, img_targets_filtered_temp -# --------------------------------------------------------------------------------------- -# External Calibration Wrappers - - def external_calibrate( img, rmat, @@ -1348,51 +1033,83 @@ def external_calibrate( ----- ``tgt_inliers[i]`` is associated with ``img_target_inliers[i]`` """ - # If the inputs are not matched, get the visible targets and match them. - if not isMatched: - # Determine which targets are visible - visibles_tgts = photogrammetry.get_visible_targets( - rmat, tvec, tgts, vis_checker + # If blob_parameters is in test_config, use the deprecated blob detection method + if ("dot_blob_parameters" in test_config.keys()) or ( + "kulite_blob_parameters" in test_config.keys() + ): + deprecated_msg = ("Blob detection has been deprecated and replaced with " + + "upsp.target_operations.template_detection" ) + warnings.warn(deprecated_msg, DeprecationWarning) + + # If the inputs are not matched, get the visible targets and match them. + if not isMatched: + # Determine which targets are visible + visibles_tgts = photogrammetry.get_visible_targets( + rmat, tvec, cameraMatrix, distCoeffs, tgts, vis_checker + ) - # Match the projected locations to the image locations - tgts_matched, img_targets_matched, num_matches_init = match_targets( + # Match the projected locations to the image locations + tgts_matched, img_targets_matched, num_matches_init = match_targets( + rmat, + tvec, + cameraMatrix, + distCoeffs, + visibles_tgts, + img_targets, + test_config["max_dist"], + ) + + # If the input is matched, short circuit the tgt_matched and + # img_targets_matched with the inputs + else: + tgts_matched = tgts + img_targets_matched = img_targets + num_matches_init = len(tgts) + + # Filter the matched targets + tgts_filtered, img_targets_filtered = filter_matches( rmat, tvec, cameraMatrix, distCoeffs, - visibles_tgts, - img_targets, - test_config["max_dist"], + tgts_matched, + img_targets_matched, + num_matches_init, + test_config, ) - - # If the input is matched, short circuit the tgt_matched and - # img_targets_matched with the inputs + + # Sub-pixel localize the image targets + tgts_subpixel, img_targets_subpixel = target_detection.subpixel_localize( + img, + tgts_filtered, + img_targets_filtered, + test_config, + max_localize_delta=max_localize_delta + ) + + # If dot_blob_parameters is not in test_config, use the new cross correlation method else: - tgts_matched = tgts - img_targets_matched = img_targets - num_matches_init = len(tgts) - - # Filter the matched targets - tgts_filtered, img_targets_filtered = filter_matches( - rmat, - tvec, - cameraMatrix, - distCoeffs, - tgts_matched, - img_targets_matched, - num_matches_init, - test_config, - ) + # Filter the matched targets + tgts_filtered, img_targets_filtered, num_matched_filtered = target_detection.filter_matches( + rmat, + tvec, + cameraMatrix, + distCoeffs, + tgts, + img_targets, + len(img_targets), + test_config, + ) - # Sub-pixel localize the image targets - tgts_subpixel, img_targets_subpixel = subpixel_localize( - img, - tgts_filtered, - img_targets_filtered, - test_config, - max_localize_delta=max_localize_delta, - ) + # Sub-pixel localize the image targets + tgts_subpixel, img_targets_subpixel = target_detection.subpixel_localize( + img, + tgts_filtered[:num_matched_filtered], + img_targets_filtered[:num_matched_filtered], + test_config, + max_localize_delta=max_localize_delta, + ) # If there are less than 4 matches, raise an error if len(tgts_subpixel) < 4: @@ -1465,7 +1182,6 @@ def check_external_calibrate_two_stage_inputs( Initial guess of translation vector from camera to object incal : tuple Camera internal calibration. - - ``cameraMatrix`` (:class:`numpy.ndarray`, shape (3, 3)): The (OpenCV formatted) camera matrix for the camera - ``distCoeffs`` (:class:`numpy.ndarray`, shape (1, 5): The (OpenCV formatted) @@ -1479,15 +1195,13 @@ def check_external_calibrate_two_stage_inputs( string representing the type of target (most commonly 'dot') for its associated value. ``tgts[i]`` is associated with ``img_targets[i]`` test_config : dict - Processing parameters with, at a minimum, a key for 'min_dist', 'max_dist', and - 2 keys for the primary target type in targets and img_targets. 'min_dist' is the - minimum distance between two image targets. 'max_dist' is the maximum allowable - distance between an image target and the projection of a 3D target. The primary - target type is 'dot' if there are 4+ dots in tgts. Otherwise the primary target - type is 'kulite'. The keys for the primary target type are target_type + '_pad' - and `target_type` + '_blob_parameters'. The first is the padding around the img - target center location to use to sub-pixel localize. The second is the blob - detection parameters for that type of target + Processing parameters with, at a minimum, a key for 'min_dist', 'max_dist', + 'crosscorr_coeff', and a key for the primary target type in targets. 'min_dist' + is the minimum distance between two image targets or the projected location of + two 3D targets. 'max_dist' is the maximum allowable distance between an image + target and the projection of a 3D target. The key for the primary target type is + tgt["target_type"] + '_pad' which is the pixel distance of the radius of the 3D + target. This is used for target detection and localization vis_checker : ~upsp.cam_cal_utils.visibility.VisibilityChecker Visibility checker object with the relevant BVH and oblique viewing angle debug : string, optional @@ -1501,7 +1215,7 @@ def check_external_calibrate_two_stage_inputs( # TODO: Any time "isinstance(OBJ, list)" is called, np.array(OBJ) should be inside # of a try-except in case it has degenerate objects - # ----------------------------------------------------------------------------------- + # ---------------------------------------------------------------------------------- # Check img input if isinstance(img, np.ndarray): if img.dtype != np.uint8: @@ -1517,7 +1231,8 @@ def check_external_calibrate_two_stage_inputs( else: # img is not 2 dimensional or 3 dimension. Must be an error print( - "img should be 2 dimensional, or 3 dimensional and single channel. Not ", + "img should be 2 dimensional, or 3 dimensional and single " + + "channel. Not ", len(img.shape), ) return False @@ -1526,9 +1241,8 @@ def check_external_calibrate_two_stage_inputs( print("img type should be np.ndarray. Not ", type(img)) return False - # ----------------------------------------------------------------------------------- + # ---------------------------------------------------------------------------------- # Check rmat_init_guess input - # Check rmat_init_guess is a numpy array of shape (3, 3) if isinstance(rmat_init_guess, np.ndarray): if rmat_init_guess.dtype != np.float32 and rmat_init_guess.dtype != np.float64: @@ -1548,9 +1262,8 @@ def check_external_calibrate_two_stage_inputs( print("rmat_init_guess should be np.ndarray. Not", type(rmat_init_guess)) return False - # ----------------------------------------------------------------------------------- + # ---------------------------------------------------------------------------------- # Check tvec_init_guess input - # Check tvec_init_guess is a numpy array of shape (3, 1) if isinstance(tvec_init_guess, np.ndarray): if tvec_init_guess.dtype != np.float32 and tvec_init_guess.dtype != np.float64: @@ -1560,16 +1273,17 @@ def check_external_calibrate_two_stage_inputs( ) return False if tvec_init_guess.shape != (3, 1): - print("tvec_init_guess.shape should be (3, 1). Not ", tvec_init_guess.shape) + print( + "tvec_init_guess.shape should be (3, 1). Not ", tvec_init_guess.shape + ) return False else: print("tvec_init_guess should be np.ndarray. Not", type(tvec_init_guess)) return False - # ----------------------------------------------------------------------------------- + # ---------------------------------------------------------------------------------- # Check incal input - # if incal[0] is a list, convert it to a numpy array to check the data if isinstance(incal[0], list): incal[0] = np.array(incal[0]) @@ -1613,25 +1327,27 @@ def check_external_calibrate_two_stage_inputs( print("incal[1] should be np.ndarray or list. Not", type(incal[1])) return False - # ----------------------------------------------------------------------------------- + # ---------------------------------------------------------------------------------- # Check tgts input - # Check that each target is as expected for tgt in tgts: if "tvec" not in tgt or "norm" not in tgt or "target_type" not in tgt: print( - "Each item in tgts must have a tvec, norm, and target_type. One or more of the items in tgts did not meet this criteria" + "Each item in tgts must have a tvec, norm, and target_type. " + + "One or more of the items in tgts did not meet this criteria" ) return False if isinstance(tgt["tvec"], np.ndarray) and isinstance(tgt["norm"], np.ndarray): if (tgt["tvec"].shape != (3, 1)) or (tgt["norm"].shape != (3, 1)): print( - "tgt tvec and norm must have shape (3, 1). One or more of the items in tgts did not meet this criteria" + "tgt tvec and norm must have shape (3, 1). " + + "One or more of the items in tgts did not meet this criteria" ) return False else: print( - "tgt tvec and norm must be np.ndarray. One or more of the items in tgts did not meet this criteria" + "tgt tvec and norm must be np.ndarray. " + + "One or more of the items in tgts did not meet this criteria" ) return False @@ -1639,35 +1355,37 @@ def check_external_calibrate_two_stage_inputs( print("tgts must have at least 4 items. Only", len(tgts), "were found") return False - # ----------------------------------------------------------------------------------- + # ---------------------------------------------------------------------------------- # Check test_config input - - if ("dot_blob_parameters" not in test_config.keys()) and ( - "kulite_blob_parameters" not in test_config.keys() + # If blob_parameters is in test_config, use the deprecated blob detection method + if ("dot_blob_parameters" in test_config.keys()) or ( + "kulite_blob_parameters" in test_config.keys() ): - print( - "Neither 'dot_blob_parameters' nor 'kulite_blob_parameters' in test_config.keys()" - ) - return False - - if "dot_blob_parameters" in test_config.keys(): - if not isinstance(test_config["dot_blob_parameters"], list): - print( - "test_config['dot_blob_parameters'] should be a list, not", - type(test_config["dot_blob_parameters"]), - ) - return False - if "kulite_blob_parameters" in test_config.keys(): - if not isinstance(test_config["kulite_blob_parameters"], list): - print( - "test_config['kulite_blob_parameters'] should be a list, not", - type(test_config["kulite_blob_parameters"]), - ) + if "dot_blob_parameters" in test_config.keys(): + if not isinstance(test_config["dot_blob_parameters"], list): + print( + "test_config['dot_blob_parameters'] should be a list, not", + type(test_config["dot_blob_parameters"]), + ) + return False + + if "kulite_blob_parameters" in test_config.keys(): + if not isinstance(test_config["kulite_blob_parameters"], list): + print( + "test_config['kulite_blob_parameters'] should be a list, not", + type(test_config["kulite_blob_parameters"]), + ) + return False + else: + if "crosscorr_coeff" not in test_config.keys(): + # print("'crosscorr_coeff' not in test_config.keys()") + print("test_config must contain 'dot_blob_parameters', " + + "'kulite_blob_parameters', or 'crosscorr_coeff'") return False - # TODO: Need to check that test_config['blob_parameters'] contains all valid - # items. But that is a lot of work, and it fails early enough in - # external_calibration_two_stage that it doesn't matter much + if not isinstance(test_config["crosscorr_coeff"], (float)): + print("test_config['crosscorr_coeff'] must be a floating point number") + return False if "max_dist" not in test_config.keys(): print("'max_dist' not in test_config.keys()") @@ -1685,7 +1403,44 @@ def check_external_calibrate_two_stage_inputs( print("test_config['min_dist'] must be numeric") return False - # ----------------------------------------------------------------------------------- + # Determine the primary class of targets + dot_tgts = [] + for tgt in tgts: + if tgt["target_type"] == "dot": + dot_tgts.append(tgt) + + # If there are 4+ dots, dots are the primary + if len(dot_tgts) >= 4: + if "dot_pad" not in test_config.keys(): + print("dot_pad' not in test_config.keys()") + return False + if not isinstance(test_config["dot_pad"], (int, float)): + print("dot_pad' must be a floating point number") + return False + + # Otherwise check if Kulites can be the primary + else: + # Get the number of kulite targets + kulite_tgts = [] + for tgt in tgts: + if tgt["target_type"] == "kulite": + kulite_tgts.append(tgt) + + # If there are too few, the tgts input is bad + if len(kulite_tgts) < 4: + print("Need 4 dot targets, or 4 kulite targets in tgts") + return False + + # If there are 4+ kulites, make sure kulite_pad is in the test_config input + else: + if "kulite_pad" not in test_config.keys(): + print("kulite_pad' not in test_config.keys()") + return False + if not isinstance(test_config["kulite_pad"], (int, float)): + print("kulite_pad' must be a floating point number") + return False + + # ---------------------------------------------------------------------------------- # Check vis_checker input if not type(vis_checker).__name__ == "VisibilityChecker": @@ -1702,7 +1457,7 @@ def check_external_calibrate_two_stage_inputs( ) return False - # ----------------------------------------------------------------------------------- + # ---------------------------------------------------------------------------------- # Check debug input # If debug is not None object, a string, or integer then it is improper @@ -1738,7 +1493,6 @@ def external_calibrate_two_stage_from_wtd( offset of the tunnel center of rotation for the z axis in inches camera_tunnel_cal : tuple Camera-tunnel calibration - - ``rmat_camera_tunnel`` (:class:`numpy.ndarray`, shape (3, 3)): Rotation matrix from camera to tunnel at wind off condition - ``tvec_camera_tunnel`` (:class:`numpy.ndarray`, shape (3, 1)): Translation @@ -1756,15 +1510,13 @@ def external_calibrate_two_stage_from_wtd( string representing the type of target (most commonly 'dot') for its associated value. ``tgts[i]`` is associated with ``img_targets[i]`` test_config : dict - Processing parameters with, at a minimum, a key for 'min_dist', 'max_dist', and - 2 keys for the primary target type in `targets` and `img_targets`. 'min_dist' is - the minimum distance between two image targets. 'max_dist' is the maximum - allowable distance between an image target and the projection of a 3D target. - The primary target type is 'dot' if there are 4+ dots in tgts. Otherwise the - primary target type is 'kulite'. The keys for the primary target type are - `target_type` + '_pad' and `target_type` + '_blob_parameters'. The first is the - padding around the img target center location to use to sub-pixel localize. The - second is the blob detection parameters for that type of target + Processing parameters with, at a minimum, a key for 'min_dist', 'max_dist', + 'crosscorr_coeff', and a key for the primary target type in targets. 'min_dist' + is the minimum distance between two image targets or the projected location of + two 3D targets. 'max_dist' is the maximum allowable distance between an image + target and the projection of a 3D target. The key for the primary target type is + tgt["target_type"] + '_pad' which is the pixel distance of the radius of the 3D + target. This is used for target detection and localization vis_checker : ~upsp.cam_cal_utils.visibility.VisibilityChecker Visibility checker object with the relevant BVH and oblique viewing angle debug : string, optional @@ -1777,7 +1529,6 @@ def external_calibrate_two_stage_from_wtd( tvec: np.ndarray, shape (3, 1), float camera-to-model external calibration translation vector """ - # Check that the inputs are valid rmat_camera_tunnel, tvec_camera_tunnel, cameraMatrix, distCoeffs = camera_tunnel_cal check_bool = check_external_calibrate_two_stage_inputs( @@ -1793,7 +1544,7 @@ def external_calibrate_two_stage_from_wtd( tunnel_vals_check_bool = True if isinstance(tunnel_vals, dict): - keys = ['ALPHA', 'BETA', 'PHI', 'STRUTZ'] + keys = ["ALPHA", "BETA", "PHI", "STRUTZ"] if len(set(keys).intersection(tunnel_vals.keys())) != 4: tunnel_vals_check_bool = False print( @@ -1823,12 +1574,10 @@ def external_calibrate_two_stage_from_wtd( "One or more bad inputs were given to external_calibrate_two_stage_from_wtd" ) - ( - rmat_init_guess, - tvec_init_guess, - ) = camera_tunnel_calibrate.tf_camera_tgts_thru_tunnel( + excal_init = camera_tunnel_calibrate.tf_camera_tgts_thru_tunnel( camera_tunnel_cal, tunnel_vals, test_config ) + rmat_init_guess, tvec_init_guess = excal_init rmat, tvec = external_calibrate_two_stage( img, @@ -1873,7 +1622,6 @@ def external_calibrate_two_stage( Initial guess of translation vector from camera to object incal : tuple Camera internal calibration. - - ``cameraMatrix`` (:class:`numpy.ndarray`, shape (3, 3)): The (OpenCV formatted) camera matrix for the camera - ``distCoeffs`` (:class:`numpy.ndarray`, shape (1, 5): The (OpenCV formatted) @@ -1887,15 +1635,13 @@ def external_calibrate_two_stage( string representing the type of target (most commonly 'dot') for its associated value. ``tgts[i]`` is associated with ``img_targets[i]`` test_config : dict - Processing parameters with, at a minimum, a key for 'min_dist', 'max_dist', and - 2 keys for the primary target type in targets and img_targets. 'min_dist' is the - minimum distance between two image targets. 'max_dist' is the maximum allowable - distance between an image target and the projection of a 3D target. The primary - target type is 'dot' if there are 4+ dots in tgts. Otherwise the primary target - type is 'kulite'. The keys for the primary target type are target_type + '_pad' - and target_type + '_blob_parameters'. The first is the padding around the img - target center location to use to sub-pixel localize. The second is the blob - detection parameters for that type of target + Processing parameters with, at a minimum, a key for 'min_dist', 'max_dist', + 'crosscorr_coeff', and a key for the primary target type in targets. 'min_dist' + is the minimum distance between two image targets or the projected location of + two 3D targets. 'max_dist' is the maximum allowable distance between an image + target and the projection of a 3D target. The key for the primary target type is + tgt["target_type"] + '_pad' which is the pixel distance of the radius of the 3D + target. This is used for target detection and localization vis_checker : ~upsp.cam_cal_utils.visibility.VisibilityChecker Visibility checker object with the relevant BVH and oblique viewing angle debug : string or None. Optional, default=None @@ -1920,196 +1666,317 @@ def external_calibrate_two_stage( debug, ) - # Scale the image - img = img_utils.scale_image_max_inlier(img) - # Check the inputs if not check_bool: raise ValueError( "One or more bad inputs were given to external_calibrate_two_stage" ) - # Unpack the intrinsics - cameraMatrix, distCoeffs = ( - incal[0], - incal[1], - ) - # Check if there are enough dots to use for targets - dots_found = True primary_tgts = [] for tgt in tgts: if tgt["target_type"] == "dot": primary_tgts.append(tgt) - # If there were enough dots, use the dots blob parameters - if len(primary_tgts) > 4: - blob_parameters = test_config["dot_blob_parameters"] - - # If there were not enough dots, collect the kulites and the kulite blob parameters - else: - # Set the dot_found flag to False - dots_found = False - blob_parameters = test_config["kulite_blob_parameters"] - + # If too few dots were found, use kulites instead + if len(primary_tgts) < 4: primary_tgts = [] for tgt in tgts: if tgt["target_type"] == "kulite": primary_tgts.append(tgt) - # Define the blob detector based on the parameters in the test_config file - params = cv2.SimpleBlobDetector_Params() - for blob_param in blob_parameters: - setattr(params, blob_param[0], blob_param[1]) - - # Run the blob detector - detector = cv2.SimpleBlobDetector_create(params) - keypoints = detector.detect(img) - - # Repackage the blob detector keypoints to function like taret image locations - init_img_targets = [] - for keypoint in keypoints: - init_img_target = { - "target_type": {True: "dot", False: "kulite"}[ - dots_found - ], # target_type depends on if enough dots were found - "center": np.array(keypoint.pt), - } - init_img_targets.append(init_img_target) + # Unpack the intrinsics + cameraMatrix, distCoeffs = incal[0], incal[1] - # Debug for raw matching information - if debug_raw_matches: - tgts_visible = photogrammetry.get_visible_targets( - rmat_init_guess, tvec_init_guess, primary_tgts, vis_checker - ) + # Scale the image + img = img_utils.scale_image_max_inlier(img) - tgts_match_raw, img_targets_match_raw, num_matches_raw = match_targets( + # If blob_parameters is in test_config, use the deprecated blob detection method + if ("dot_blob_parameters" in test_config.keys()) or ( + "kulite_blob_parameters" in test_config.keys() + ): + # Define the blob detector based on the parameters in the test_config file + blob_parameters = test_config["dot_blob_parameters"] + params = cv2.SimpleBlobDetector_Params() + for blob_param in blob_parameters: + setattr(params, blob_param[0], blob_param[1]) + + # Run the blob detector + detector = cv2.SimpleBlobDetector_create(params) + keypoints = detector.detect(img) + + + # Repackage the blob detector keypoints to function like taret image locations + init_img_targets = [] + dots_found = (primary_tgts[0]["target_type"] == 'dot') + for keypoint in keypoints: + init_img_target = { + "target_type": {True: "dot", False: "kulite"}[ + dots_found + ], # target_type depends on if enough dots were found + "center": np.array(keypoint.pt), + } + init_img_targets.append(init_img_target) + + # Check that enough blobs were found + if len(keypoints) < 4: + raise ValueError( + "Less than 4 blobs were found in external_calibrate_two_stage." + ) + + # Do the coarse external calibration + coarse_outputs = external_calibrate( + img, rmat_init_guess, tvec_init_guess, cameraMatrix, distCoeffs, - tgts_visible, + primary_tgts, init_img_targets, - test_config["max_dist"], + vis_checker, + test_config, + isMatched=False, + max_localize_delta=None, + reprojectionError=test_config["max_dist"], ) - tgts_filtered_raw, img_targets_filtered_raw = filter_matches( + else: + # Perform template detection to find and match the targets to image locations + tgts_match_raw, img_targets_match_raw, num_matches_raw = target_detection.template_detection( + img, rmat_init_guess, tvec_init_guess, cameraMatrix, distCoeffs, primary_tgts, test_config) + + # Check that enough targets were detected + if num_matches_raw < 4: + raise ValueError( + "Less than 4 targets were detected in external_calibrate_two_stage." + ) + + # Do the coarse external calibration + coarse_outputs = external_calibrate( + img, rmat_init_guess, tvec_init_guess, cameraMatrix, distCoeffs, - tgts_match_raw, - img_targets_match_raw, - num_matches_raw, - test_config, - ) - - tgts_subpixel_raw, img_targets_subpixel_raw = subpixel_localize( - img, - tgts_filtered_raw, - img_targets_filtered_raw, + tgts_match_raw[:num_matches_raw], + img_targets_match_raw[:num_matches_raw], + vis_checker, test_config, max_localize_delta=None, + reprojectionError=test_config["max_dist"], ) - num_matches = len(img_targets_subpixel_raw) - tgts_subpixel_raw_temp = [copy.deepcopy(tgt) for tgt in tgts_subpixel_raw] - for tgt in tgts_subpixel_raw_temp: - tgt["tvec"] = tgt["tvec"].flatten().tolist() - tgt["norm"] = tgt["norm"].flatten().tolist() + # Debug for raw matching information + if debug_raw_matches: + if ("dot_blob_parameters" in test_config.keys()) or ( + "kulite_blob_parameters" in test_config.keys() + ): + tgts_visible = photogrammetry.get_visible_targets( + rmat_init_guess, + tvec_init_guess, + cameraMatrix, + distCoeffs, + primary_tgts, + vis_checker + ) - rms, max_dist = photogrammetry.reprojection_error( - rmat_init_guess, - tvec_init_guess, - cameraMatrix, - distCoeffs, - tgts_subpixel_raw, - img_targets_subpixel_raw, - ) + tgts_match_raw, img_targets_match_raw, num_matches_raw = match_targets( + rmat_init_guess, + tvec_init_guess, + cameraMatrix, + distCoeffs, + tgts_visible, + init_img_targets, + test_config["max_dist"], + ) - log.info( - "Raw Num Points: %d RMS: %f Max Error: %f", - len(tgts_subpixel_raw), - rms, - max_dist, - ) + tgts_filtered_raw, img_targets_filtered_raw = filter_matches( + rmat_init_guess, + tvec_init_guess, + cameraMatrix, + distCoeffs, + tgts_match_raw, + img_targets_match_raw, + num_matches_raw, + test_config, + ) - # Get a list of the targets that are visible, but weren't matched - visible_but_not_matched = [] - for tgt in tgts_visible: - tgt_temp = copy.deepcopy(tgt) - tgt_temp["tvec"] = tgt_temp["tvec"].flatten().tolist() - tgt_temp["norm"] = tgt_temp["norm"].flatten().tolist() - if tgt_temp not in tgts_subpixel_raw_temp: - visible_but_not_matched.append(tgt) + tgts_subpixel_raw, img_targets_subpixel_raw = target_detection.subpixel_localize( + img, + tgts_filtered_raw, + img_targets_filtered_raw, + test_config, + max_localize_delta=None, + ) + num_matches = len(img_targets_subpixel_raw) - # Get projected location the visible target centers in the image - tgt_projs = photogrammetry.project_targets( - rmat_init_guess, - tvec_init_guess, - cameraMatrix, - distCoeffs, - tgts_subpixel_raw + visible_but_not_matched, - ) - proj_pts = np.array([tgt_proj["proj"] for tgt_proj in tgt_projs]) + tgts_subpixel_raw_temp = [copy.deepcopy(tgt) for tgt in tgts_subpixel_raw] + for tgt in tgts_subpixel_raw_temp: + tgt["tvec"] = tgt["tvec"].flatten().tolist() + tgt["norm"] = tgt["norm"].flatten().tolist() - found_but_not_matched = [] - for tgt_match_raw, img_target_match_raw in zip( - tgts_match_raw, img_targets_match_raw - ): - tgt_temp = copy.deepcopy(tgt_match_raw) - tgt_temp["tvec"] = tgt_temp["tvec"].flatten().tolist() - tgt_temp["norm"] = tgt_temp["norm"].flatten().tolist() - if tgt_temp not in tgts_subpixel_raw_temp: - found_but_not_matched.append(img_target_match_raw) + rms, max_dist = photogrammetry.reprojection_error( + rmat_init_guess, + tvec_init_guess, + cameraMatrix, + distCoeffs, + tgts_subpixel_raw, + img_targets_subpixel_raw, + ) - all_img_targets = img_targets_subpixel_raw + found_but_not_matched - img_centers = [inlier_pt["center"] for inlier_pt in all_img_targets] - img_centers = np.array(img_centers) + log.info( + "Raw Num Points: %d RMS: %f Max Error: %f", + len(tgts_subpixel_raw), + rms, + max_dist, + ) - # Get the debug name. If debug was given, use it otherwise don't - debug_name = str(debug) + "_raw" if debug is not None else "raw" + # Get a list of the targets that are visible, but weren't matched + visible_but_not_matched = [] + for tgt in tgts_visible: + tgt_temp = copy.deepcopy(tgt) + tgt_temp["tvec"] = tgt_temp["tvec"].flatten().tolist() + tgt_temp["norm"] = tgt_temp["norm"].flatten().tolist() + if tgt_temp not in tgts_subpixel_raw_temp: + visible_but_not_matched.append(tgt) - # Output a debug image of the projected locations and image target center locations - visualization.show_projection_matching( - img, - proj_pts, - img_centers, - num_matches=num_matches, - name=debug_name, - scale=2.0, - ) + # Get projected location the visible target centers in the image + tgt_projs = photogrammetry.project_targets( + rmat_init_guess, + tvec_init_guess, + cameraMatrix, + distCoeffs, + tgts_subpixel_raw + visible_but_not_matched, + ) + proj_pts = np.array([tgt_proj["proj"] for tgt_proj in tgt_projs]) - # Check that enough blobs were found - if len(keypoints) < 4: - raise ValueError( - "Less than 4 blobs were found in external_calibrate_two_stage." - ) + found_but_not_matched = [] + for tgt_match_raw, img_target_match_raw in zip( + tgts_match_raw, img_targets_match_raw + ): + tgt_temp = copy.deepcopy(tgt_match_raw) + tgt_temp["tvec"] = tgt_temp["tvec"].flatten().tolist() + tgt_temp["norm"] = tgt_temp["norm"].flatten().tolist() + if tgt_temp not in tgts_subpixel_raw_temp: + found_but_not_matched.append(img_target_match_raw) - # Do the coarse external calibration - coarse_outputs = external_calibrate( - img, - rmat_init_guess, - tvec_init_guess, - cameraMatrix, - distCoeffs, - primary_tgts, - init_img_targets, - vis_checker, - test_config, - isMatched=False, - max_localize_delta=None, - reprojectionError=test_config["max_dist"], - ) + all_img_targets = img_targets_subpixel_raw + found_but_not_matched + img_centers = [inlier_pt["center"] for inlier_pt in all_img_targets] + img_centers = np.array(img_centers) - # Unpack the output variables - ( - rmat_coarse, - tvec_coarse, - tgts_inliers_coarse, - img_target_inliers_coarse, - ) = coarse_outputs + # Get the debug name. If debug was given, use it otherwise don't + debug_name = str(debug) + "_raw" if debug is not None else "raw" + # Output a debug image of the projected locations and image target center locations + visualization.show_projection_matching( + img, + proj_pts, + img_centers, + num_matches=num_matches, + name=debug_name, + scale=2.0, + ) + else: + tgts_visible = photogrammetry.get_visible_targets( + rmat_init_guess, + tvec_init_guess, + cameraMatrix, + distCoeffs, + primary_tgts, + vis_checker + ) + + tgts_filtered_raw, img_targets_filtered_raw = filter_matches( + rmat_init_guess, + tvec_init_guess, + cameraMatrix, + distCoeffs, + tgts_match_raw, + img_targets_match_raw, + num_matches_raw, + test_config, + ) + + tgts_subpixel_raw, img_targets_subpixel_raw = target_detection.subpixel_localize( + img, + tgts_filtered_raw, + img_targets_filtered_raw, + test_config, + max_localize_delta=None, + ) + num_matches = len(img_targets_subpixel_raw) + + tgts_subpixel_raw_temp = [copy.deepcopy(tgt) for tgt in tgts_subpixel_raw] + for tgt in tgts_subpixel_raw_temp: + tgt["tvec"] = tgt["tvec"].flatten().tolist() + tgt["norm"] = tgt["norm"].flatten().tolist() + + rms, max_dist = photogrammetry.reprojection_error( + rmat_init_guess, + tvec_init_guess, + cameraMatrix, + distCoeffs, + tgts_subpixel_raw, + img_targets_subpixel_raw, + ) + + log.info( + "Raw Num Points: %d RMS: %f Max Error: %f", + len(tgts_subpixel_raw), + rms, + max_dist, + ) + + # Get a list of the targets that are visible, but weren't matched + visible_but_not_matched = [] + for tgt in tgts_visible: + tgt_temp = copy.deepcopy(tgt) + tgt_temp["tvec"] = tgt_temp["tvec"].flatten().tolist() + tgt_temp["norm"] = tgt_temp["norm"].flatten().tolist() + if tgt_temp not in tgts_subpixel_raw_temp: + visible_but_not_matched.append(tgt) + + # Get projected location the visible target centers in the image + tgt_projs = photogrammetry.project_targets( + rmat_init_guess, + tvec_init_guess, + cameraMatrix, + distCoeffs, + tgts_subpixel_raw + visible_but_not_matched, + ) + proj_pts = np.array([tgt_proj["proj"] for tgt_proj in tgt_projs]) + + found_but_not_matched = [] + for tgt_match_raw, img_target_match_raw in zip( + tgts_match_raw, img_targets_match_raw + ): + tgt_temp = copy.deepcopy(tgt_match_raw) + tgt_temp["tvec"] = tgt_temp["tvec"].flatten().tolist() + tgt_temp["norm"] = tgt_temp["norm"].flatten().tolist() + if tgt_temp not in tgts_subpixel_raw_temp: + found_but_not_matched.append(img_target_match_raw) + + all_img_targets = img_targets_subpixel_raw + found_but_not_matched + img_centers = [inlier_pt["center"] for inlier_pt in all_img_targets] + img_centers = np.array(img_centers) + + # Get the debug name. If debug was given, use it otherwise don't + debug_name = str(debug) + "_raw" if debug is not None else "raw" + + # Output a debug image of the projected locations and image target center locations + visualization.show_projection_matching( + img, + proj_pts, + img_centers, + num_matches=num_matches, + name=debug_name, + scale=2.0, + ) + + # Unpack the output variables + __, __, tgts_inliers_coarse, img_target_inliers_coarse = coarse_outputs + rmat_coarse, tvec_coarse, __, __ = coarse_outputs + if debug_coarse_optimization: rms, max_dist = photogrammetry.reprojection_error( rmat_coarse, @@ -2160,7 +2027,7 @@ def external_calibrate_two_stage( def external_calibrate_one_step( - img, # Frame specific + img12bit, # Frame specific rmat_coarse, tvec_coarse, # Datapoint specific incal, # Camera specific @@ -2220,7 +2087,7 @@ def external_calibrate_one_step( camera-to-model external calibration translation vector """ # Scale the image - img = img_utils.scale_image_max_inlier(img) + img8bit = img_utils.scale_image_max_inlier(img12bit) # Unpack the intrinsics cameraMatrix, distCoeffs = ( @@ -2230,7 +2097,7 @@ def external_calibrate_one_step( # Get the visible targets visible_tgts = photogrammetry.get_visible_targets( - rmat_coarse, tvec_coarse, tgts, vis_checker + rmat_coarse, tvec_coarse, cameraMatrix, distCoeffs, tgts, vis_checker ) # Get the projections of the visible targets @@ -2242,8 +2109,8 @@ def external_calibrate_one_step( img_targets = [] for tgt_proj in tgt_projs: img_target = { - "target_type": tgt_proj["target_type"], - "center": tgt_proj["proj"], + "target_type" : tgt_proj["target_type"], + "center" : tgt_proj["proj"], } img_targets.append(img_target) @@ -2262,8 +2129,8 @@ def external_calibrate_one_step( ) # Subpixel localize the image targets - tgts_subpixel, img_targets_subpixel = subpixel_localize( - img, + tgts_subpixel, img_targets_subpixel = target_detection.subpixel_localize( + img8bit, tgts_filtered, img_targets_filtered, test_config, @@ -2313,7 +2180,7 @@ def external_calibrate_one_step( # Output a debug image of the projected locations and image target center locations visualization.show_projection_matching( - img, + img8bit, proj_pts, img_centers, num_matches=num_matches, @@ -2323,7 +2190,7 @@ def external_calibrate_one_step( # Run the refined external calibration refined_outputs = external_calibrate( - img, + img8bit, rmat_coarse, tvec_coarse, cameraMatrix, @@ -2338,63 +2205,72 @@ def external_calibrate_one_step( ) # Unpack the refined results - ( - rmat_refined, - tvec_refined, - tgts_inliers_refined, - img_target_inliers_refined, - ) = refined_outputs - - # Debug for refined optimization + __, __, tgts_inliers_refined, img_target_inliers_refined = refined_outputs + rmat_refined, tvec_refined, __, __ = refined_outputs + + # Debug for refined matches if debug_refined_optimization: - rms, max_dist = photogrammetry.reprojection_error( + # Filter the matched targets + filter_outputs = target_detection.filter_matches( rmat_refined, tvec_refined, cameraMatrix, distCoeffs, tgts_inliers_refined, img_target_inliers_refined, + len(img_target_inliers_refined), + test_config, ) + tgts_filtered, img_targets_filtered, num_matched_filtered = filter_outputs - log.info( - "Refined Num Points: %d RMS: %f Max Error: %f", - len(tgts_inliers_refined), - rms, - max_dist, + # Sub-pixel localize the image targets + tgts_subpixel, img_targets_subpixel = target_detection.subpixel_localize( + img8bit, + tgts_filtered[:num_matched_filtered], + img_targets_filtered[:num_matched_filtered], + test_config, + max_localize_delta=None, ) - - # Get projected location the visible targets in the image + + # Get the projections of the visible targets tgt_projs = photogrammetry.project_targets( - rmat_refined, tvec_refined, cameraMatrix, distCoeffs, tgts_inliers_refined + rmat_refined, tvec_refined, cameraMatrix, distCoeffs, tgts_subpixel + ) + + rms, max_dist = photogrammetry.reprojection_error( + rmat_coarse, + tvec_coarse, + cameraMatrix, + distCoeffs, + tgts_subpixel, + img_targets_subpixel, ) + proj_pts = np.array([tgt_proj['proj'] for tgt_proj in tgt_projs]) - # Plot the kulites in blue and dots in red - plt.imshow(img, cmap="gray") - for tgt_proj in tgt_projs: - plt.scatter( - [tgt_proj["proj"][0]], - [tgt_proj["proj"][1]], - c={"kulite": "b", "dot": "r"}[tgt_proj["target_type"]], - marker="o", - s=0.05, - ) + img_centers = np.array( + [img_target["center"] for img_target in img_targets_subpixel] + ) # Get the debug name. If debug was given, use it otherwise don't - debug_name = ( - str(debug) + "_refined_optimization.png" - if debug != None - else "refined_optimization.png" - ) + debug_name = str(debug) + "_refined" if debug is not None else "refined" - plt.savefig(debug_name, dpi=400) - plt.close() + # Output a debug image of the projected locations and image target center + # locations + visualization.show_projection_matching( + img8bit, + proj_pts, + img_centers, + num_matches=len(tgts_subpixel), + name=debug_name, + scale=2.0, + ) # Secondary debug to project all visible targets, not just the targets used in the # optimization if debug_visible_projections: # Get visible targets visible_tgts = photogrammetry.get_visible_targets( - rmat_refined, tvec_refined, tgts, vis_checker + rmat_refined, tvec_refined, cameraMatrix, distCoeffs, tgts, vis_checker ) # Get projected location the visible targets in the image @@ -2403,7 +2279,7 @@ def external_calibrate_one_step( ) # Plot the kulites in blue and dots in red - plt.imshow(img, cmap="gray") + plt.imshow(img8bit, cmap="gray") for i, tgt_proj in enumerate(tgt_projs): plt.scatter( [tgt_proj["proj"][0]], @@ -2484,12 +2360,186 @@ def external_calibrate_RANSAC( Translation vector. May be ``None`` if the maximum number of iterations is met without finding a valid solution. """ - # Seed the random number generator at the start of the routine - np.random.seed(0) + def post_filter_append(tgts, tgts_filtered, img_targets, img_targets_filtered): + tgts_filtered_temp = copy.deepcopy(tgts_filtered) + img_targets_filtered_temp = copy.deepcopy(img_targets_filtered) + + tgts_filter_ls = copy.deepcopy(tgts_filtered) + for i in range(len(tgts_filter_ls)): + tgts_filter_ls[i]["tvec"] = tgts_filter_ls[i]["tvec"].flatten().tolist() + tgts_filter_ls[i]["norm"] = tgts_filter_ls[i]["norm"].flatten().tolist() + + # Add tgts that were not matched + for tgt in tgts: + tgt_list = copy.deepcopy(tgt) + tgt_list["tvec"] = tgt_list["tvec"].flatten().tolist() + tgt_list["norm"] = tgt_list["norm"].flatten().tolist() + if tgt_list not in tgts_filter_ls: + tgts_filtered_temp.append(tgt) + + img_targets_filtered_list = copy.deepcopy(img_targets_filtered) + + for i in range(len(img_targets_filtered_list)): + img_targets_filtered_list[i]["center"] = ( + img_targets_filtered_list[i]["center"].flatten().tolist() + ) + + # Add img_target that were not matched + for img_target in img_targets: + if img_target is not None: + img_target_list = copy.deepcopy(img_target) + img_target_list["center"] = img_target_list["center"].flatten().tolist() + if img_target_list not in img_targets_filtered_list: + img_targets_filtered_temp.append(img_target) + + return tgts_filtered_temp, img_targets_filtered_temp + + def match_obj_and_img_pts( + rmat, tvec, cameraMatrix, distCoeffs, tgts, img_targets, max_dist=np.inf + ): + # Project the points into the image + tgt_projs = photogrammetry.project_targets( + rmat, tvec, cameraMatrix, distCoeffs, tgts + ) + + # For each target, find the matching image point of the same type + # Matches are assumed to be the closest point + matching_img_targets = [] + for tgt_proj in tgt_projs: + match = None + match_dist = max_dist + for img_target in img_targets: + # Check that the target types match + if img_target["target_type"] != tgt_proj["target_type"]: + continue + + # Calculate the distance + dist = np.linalg.norm( + [ + img_target["center"][0] - tgt_proj["proj"][0], + img_target["center"][1] - tgt_proj["proj"][1], + ] + ) + + # If the distance is over the distance threshold and less than current + # lowest distance, make this the new best match + if dist < match_dist: + match_dist = dist + match = copy.deepcopy(img_target) + + matching_img_targets.append(match) + + # Since the target ordering is not changes, we can return the visibles and + # projected as was given + return matching_img_targets + + def filter_nones(tgts, img_targets, num_matches): + # Initialized filtered lists and a count + tgts_filtered = [] + img_targets_filtered = [] + num_matches_filtered = 0 + + # First add the matches where the img_targets are not None + # Increment the count of matched targets + for i in range(num_matches): + tgt, img_target = tgts[i], img_targets[i] + if img_target is not None: + tgts_filtered.append(tgt) + img_targets_filtered.append(img_target) + num_matches_filtered += 1 + + tgts_filtered, img_targets_filtered = post_filter_append( + tgts, tgts_filtered, img_targets, img_targets_filtered + ) + + return tgts_filtered, img_targets_filtered, num_matches_filtered + def match_targets( + rmat, tvec, cameraMatrix, distCoeffs, tgts, img_targets, max_dist=np.inf, + ): + # Match the visible targets to the closest image point + matching_img_targets = match_obj_and_img_pts( + rmat, tvec, cameraMatrix, distCoeffs, tgts, img_targets, max_dist + ) + + tgts_matched, matching_img_targets, num_matches = filter_nones( + tgts, matching_img_targets, len(tgts) + ) + + tgts_matched, matching_img_targets = post_filter_append( + tgts, tgts_matched, img_targets, matching_img_targets + ) + + return tgts_matched, matching_img_targets, num_matches + + def filter_one2one(tgts, img_targets, num_matches): + # Check that each img_target is used at most once + tgts_filtered_temp = [] + img_targets_temp = [] + num_matches_temp = 0 + for i in range(num_matches): + # Unpack the target and match + tgt, match = tgts[i], img_targets[i] + + # Create a copy of the all matching points minus the current + img_targets_subset = copy.deepcopy(img_targets[:num_matches]) + del img_targets_subset[i] + + # Convert the numpy arrays to lists for comparisons + match_temp = copy.deepcopy(match) + match_temp["center"] = match_temp["center"].flatten().tolist() + for j in range(len(img_targets_subset)): + img_targets_subset[j]["center"] = ( + img_targets_subset[j]["center"].flatten().tolist() + ) + + # If match is not in list, there is a 1:1 matching and it can be included + if match_temp not in img_targets_subset: + tgts_filtered_temp.append(tgt) + img_targets_temp.append(match) + num_matches_temp += 1 + + img_targets_subset = None # deleteme + + # Check that each img_target is used at most once + tgts_filtered_one2one = [] + img_targets_one2one = [] + num_matches_one2one = 0 + for i in range(num_matches_temp): + # Unpack the target and match + tgt, match = tgts_filtered_temp[i], img_targets_temp[i] + + # Create a copy of the all matching points minus the current + tgts_subset = copy.deepcopy(tgts_filtered_temp[:num_matches_temp]) + del tgts_subset[i] + + # Convert the numpy arrays to lists for comparisons + tgt_temp = copy.deepcopy(tgt) + tgt_temp["tvec"] = tgt_temp["tvec"].flatten().tolist() + tgt_temp["norm"] = tgt_temp["norm"].flatten().tolist() + for j in range(len(tgts_subset)): + tgts_subset[j]["tvec"] = tgts_subset[j]["tvec"].flatten().tolist() + tgts_subset[j]["norm"] = tgts_subset[j]["norm"].flatten().tolist() + + # If match is not in list, there is a 1:1 matching and it can be included + if tgt_temp not in tgts_subset: + tgts_filtered_one2one.append(tgt) + img_targets_one2one.append(match) + num_matches_one2one += 1 + + tgts_filtered_one2one, img_targets_one2one = post_filter_append( + tgts, tgts_filtered_one2one, img_targets, img_targets_one2one + ) + + return tgts_filtered_one2one, img_targets_one2one, num_matches_one2one + + # Seed the random number generator at the start of the routine + rng = np.random.default_rng(0) + + # Unpack the internal calibration values cameraMatrix, distCoeffs = incal - # 'Fake' visibility checker object with no grid which is used to speed up computation + # 'Fake' visibility checker object with no grid to speed up computation vis_checker_nogrid = visibility.VisibilityChecker( None, oblique_angle=vis_checker.oblique_angle, @@ -2510,7 +2560,7 @@ def external_calibrate_RANSAC( # least 1 additional is required for the smallest consensus) match_thresh = int(max(match_thresh, 4)) - # If max_iter is a float of 1.0, user wanted probability of 1, so repeat infinite times + # If max_iter is 1.0 (float), user wanted probability of 1, so repeat infinite times if isinstance(max_iter, float) and (max_iter == 1.0): max_iter = np.inf @@ -2567,11 +2617,11 @@ def external_calibrate_RANSAC( n = 0 while n < max_iter: # Select 3 random targets - rand_tgts_idxs = np.random.choice(tgt_tvecs_idxs, 3, replace=False) + rand_tgts_idxs = rng.choice(tgt_tvecs_idxs, 3, replace=False) rand_tgts = tgt_tvecs[rand_tgts_idxs] # Select 3 random image targets - rand_img_targets_idxs = np.random.choice( + rand_img_targets_idxs = rng.choice( img_target_projs_idxs, 3, replace=False ) rand_img_targets = img_target_projs[rand_img_targets_idxs] @@ -2593,7 +2643,13 @@ def external_calibrate_RANSAC( # Get the visible targets tgts_vis = photogrammetry.get_visible_targets( - rmat, tvec, tgts, vis_checker_nogrid + rmat, + tvec, + cameraMatrix, + distCoeffs, + tgts, + vis_checker_nogrid, + incal_inputs = {'critical_pt' : None} ) # If there are too few visible targets, continue @@ -2626,7 +2682,13 @@ def external_calibrate_RANSAC( if num_matches >= match_thresh: # Get the visible targets tgts_vis = photogrammetry.get_visible_targets( - rmat, tvec, tgts, vis_checker + rmat, + tvec, + cameraMatrix, + distCoeffs, + tgts, + vis_checker, + incal_inputs = {'critical_pt' : 'first'} ) # Match the visible targets diff --git a/python/upsp/cam_cal_utils/internal_calibration.py b/python/upsp/cam_cal_utils/internal_calibration.py index 36c1a3a..4dd3b81 100644 --- a/python/upsp/cam_cal_utils/internal_calibration.py +++ b/python/upsp/cam_cal_utils/internal_calibration.py @@ -13,17 +13,17 @@ # NOTE: This is implemented for Calib.io version v1.6.5a -def get_safe_pts( +def get_pts_inside_incal( rmat, tvec, cameraMatrix, distCoeffs, obj_pts, - cal_area_is_safe, - cal_vol_is_safe, + cal_area_is_safe=None, + cal_vol_is_safe=None, critical_pt=None, ): - """Determines which `obj_pts` are inside the well behaved region of the internal calibration + """Returns `obj_pts` that are inside the well behaved region of the internal cal Performs 3 checks on each of the `obj_pts`. The first check and second check is that each obj_pt and its projection are within the safe volume and safe area as defined @@ -59,24 +59,27 @@ def get_safe_pts( The (openCV formatted) distortion coefficients for the camera obj_pts : np.ndarray, shape (n, 3), float List of 3d points. Points are relative to the object frame - cal_area_is_safe : callable - ``alpha_shape_is_safe`` function that takes `img_pts` as an array, (shape - ``(n, 2)``) of floats and returns an array-like object of booleans. - ``return[i]`` corresonds to ``img_pts[i]``. If ``return[i]`` is True, then - ``img_pts[i]`` is within the safe internal calibration image area. If it is - False, it is not within that safe area - cal_vol_is_safe : callable - ``alpha_shape_is_safe`` function that takes `obj_pts` and returns a array-like - object of booleans. ``return[i]`` corresonds to ``img_pts[i]``. If ``return[i]`` - is True, then ``img_pts[i]`` is within the safe internal calibration 3D volume. - If it is False, it is not within that safe volume + cal_area_is_safe : callable or None. Optional, default=None + If ``None``, it is assumed all points are safe. If cal_area_is_safe is a + callable, it must be an ``alpha_shape_is_safe`` function. It takes `img_pts` as + an array, (shape ``(n, 2)``) of floats and returns an array-like object of + booleans. ``return[i]`` of that function corresonds to ``img_pts[i]``. If + ``return[i]`` is True, then ``img_pts[i]`` is within the safe internal + calibration image area. If it is False, it is not within that safe area + cal_area_is_safe : callable or None. Optional, default=None + If ``None``, it is assumed all points are safe. If cal_area_is_safe is a + callable, it must be an ``alpha_shape_is_safe`` function. It takes `obj_pts` as + an array, (shape ``(n, 3)``) of floats and returns an array-like object of + booleans. ``return[i]`` of that function corresonds to ``obj_pts[i]``. If + ``return[i]`` is True, then ``obj_pts[i]`` is within the safe internal + calibration image area. If it is False, it is not within that safe area critical_pt : str, optional Criteria for step 3. Must be one of: - If 'first' then `obj_pts` cannot have a homogeneous coordinate with a magnitude past the first maxima of the distortion curve. This is the most useful option for the majority of lenses. - - If 'infinity', the magnitude must be less than the final maxima of the + - If 'final', the magnitude must be less than the final maxima of the distortion curve, and the distortion curve must be decreasing after the final maxima. This is the most useful option for mustache or otherwise complex distortion, if the mustache distortion effect is accurate @@ -88,16 +91,22 @@ def get_safe_pts( Returns ------- obj_pts_safe : np.ndarray, shape (n,), bool - Array of booleans where ``return[i]`` cooresponds to ``obj_pts[i]``. If - ``return[i]`` is True, ``obj_pts[i]`` is well behaved. If ``return[i]`` is - False, ``obj_pts[i]`` may not be well behaved - + Array of indices ``obj_pts[i]``. If i is in return, then ``obj_pts[i]`` is well + behaved. Otherwise, ``obj_pts[i]`` is not well behaved + See Also -------- incal_calibration_bounds : creates ``alpha_shape_is_safe`` functions """ assert critical_pt in ["first", "final", None] + # Populate the cal area objects if they were not given + if cal_area_is_safe is None: + cal_area_is_safe = incal_calibration_bounds_debug()[0] + + if cal_vol_is_safe is None: + cal_vol_is_safe = incal_calibration_bounds_debug()[1] + # Define a list of booleans to denote if a variable is safe obj_pts_safe = np.full(len(obj_pts), True) @@ -115,12 +124,12 @@ def get_safe_pts( # First shortcut skips if step 3 is omitted if critical_pt is None: - return obj_pts_safe + return np.argwhere(obj_pts_safe==True).flatten() # Second shortcut skips if all of the distortion coefficients are non-negative, # since there is no maxima if (distCoeffs >= 0).all(): - return obj_pts_safe + return np.argwhere(obj_pts_safe==True).flatten() # Third shortcut skips if critical_pt == 'final' and the projection curve has a # positive derivative as the magnitude of the homogeneous coordinates goes to inf @@ -142,7 +151,7 @@ def get_safe_pts( else min(p2, p1) ) if (critical_pt == "final") and highest_nonzero_term > 0.0: - return obj_pts_safe + return np.argwhere(obj_pts_safe==True).flatten() # Step 3b: Perform the full step 3 @@ -150,8 +159,8 @@ def get_safe_pts( obj_pts_rel_cam = photogrammetry.transform_3d_point(rmat, tvec, obj_pts) # Define the homogenous coordiantes - x_homo_vals = (obj_pts_rel_cam[:, 0] / obj_pts_rel_cam[:, 2]).astype(np.complex) - y_homo_vals = (obj_pts_rel_cam[:, 1] / obj_pts_rel_cam[:, 2]).astype(np.complex) + x_homo_vals = (obj_pts_rel_cam[:, 0] / obj_pts_rel_cam[:, 2]).astype(complex) + y_homo_vals = (obj_pts_rel_cam[:, 1] / obj_pts_rel_cam[:, 2]).astype(complex) # Define the distortion terms, and vectorize calculating of powers of x_homo_vals # and y_homo_vals @@ -267,7 +276,7 @@ def get_safe_pts( obj_pts_safe *= np.where(y_homo_vals < y_homo_max, True, False) # Return the list of is_safe booleans - return obj_pts_safe + return np.argwhere(obj_pts_safe==True).flatten() def incal_from_calibio(calibio_path): @@ -336,6 +345,7 @@ def write_incal_from_calibio(calibio_path, camera_name, sensor_size, save_dir=No ---------- calibio_path : str Path to the calib.io saved calibration json + (Calib.io: File > Save Calibration Project) camera_name : str Name of the camera sensor_size : np.ndarray, shape (2,) of floats @@ -697,7 +707,7 @@ def alpha_shape_is_safe(pts, alpha): # Get the number of dimensions num_dims = tess.simplices.shape[1] - 1 - # Get the radius of the circumsphere (circumcircle in 2D) for each of the tessellation + # Get radius of the circumsphere (circumcircle in 2D) for each of the tessellation # Equations for circumcircle and circumsphere radius from wolframalpha: # https://mathworld.wolfram.com/Circumcircle.html # https://mathworld.wolfram.com/Circumsphere.html diff --git a/python/upsp/cam_cal_utils/parsers.py b/python/upsp/cam_cal_utils/parsers.py index 6c5c07e..b7a8423 100644 --- a/python/upsp/cam_cal_utils/parsers.py +++ b/python/upsp/cam_cal_utils/parsers.py @@ -351,7 +351,7 @@ def read_internal_params(internal_cal_path, dims, read_sensor=False): def read_camera_tunnel_cal(cal_path, dims, read_sensor=False): - """ Returns the internal (intrinsic) and external (extrinsic) camera calibration parameters + """ Returns internal (intrinsic) and external (extrinsic) camera cal parameters Parameters ---------- diff --git a/python/upsp/cam_cal_utils/photogrammetry.py b/python/upsp/cam_cal_utils/photogrammetry.py index 49a5333..ac66ef2 100644 --- a/python/upsp/cam_cal_utils/photogrammetry.py +++ b/python/upsp/cam_cal_utils/photogrammetry.py @@ -27,7 +27,6 @@ def rot(angle, axis): Rotation matrix of an identity matrix rotated about the given axis by the given amount """ - assert_error_msg = "Error in photogrammetry.rot. Axis must be 'x', 'y', or 'z'." assert axis in ['x', 'y', 'z'], assert_error_msg @@ -66,7 +65,6 @@ def invTransform(R, t): tvec_i : np.ndarray, shape (3, 1), float Inverse translation vector """ - R_transpose = R.transpose() return (R_transpose, -np.matmul(R_transpose, t)) @@ -84,7 +82,6 @@ def isRotationMatrix(R): bool True if R is a valid rotation matrix, and False if it is not """ - Rt = np.transpose(R) shouldBeIdentity = np.dot(Rt, R) n = np.linalg.norm(np.identity(3, dtype=R.dtype) - shouldBeIdentity) @@ -157,11 +154,23 @@ def transform_3d_point(rmat, tvec, obj_pts): np.ndarray (n, 3) of floats Array of transformed points. return[i] is the transformed obj_pts[i] """ - obj_pts_tf = np.squeeze(np.array([np.matmul(rmat, np.expand_dims(pt, 1)) + tvec for pt in obj_pts]), axis=2) + if not len(obj_pts): + return np.array([]) + + obj_pts_tf = [np.matmul(rmat, np.expand_dims(pt, 1)) + tvec for pt in obj_pts] + obj_pts_tf = np.squeeze(np.array(obj_pts_tf), axis=2) return obj_pts_tf -def project_3d_point(rmat, tvec, cameraMatrix, distCoeffs, obj_pts, ret_jac=False, ret_full_jac=False): +def project_3d_point( + rmat, + tvec, + cameraMatrix, + distCoeffs, + obj_pts, + ret_jac=False, + ret_full_jac=False +): """Projects targets into an image. Parameters @@ -254,7 +263,6 @@ def transform_targets(rmat, tvec, tgts): List of targets. Each target has the attributes of the input targets, except the transformed targets are transformed to be relative to the camera """ - tgts_tf = [] for tgt in tgts: # Transform the target tvec and norm to be relative to the camera @@ -377,13 +385,23 @@ def get_occlusions_targets(rmat, tvec, tgts, vis_checker): origin = node + vis_checker.epsilon * normal # If it is occluded, mark it occluded and move on to the next node - occlusions.append(vis_checker.does_intersect(origin, direction, return_pos=True)) + occlusions.append(vis_checker.does_intersect( + origin, direction, return_pos=True) + ) return occlusions -def get_visible_targets(rmat, tvec, tgts, vis_checker): - """Wrapper around :meth:`~upsp.cam_cal_utils.visibility.VisibilityChecker.is_visible` +def get_visible_targets( + rmat, + tvec, + cameraMatrix, + distCoeffs, + tgts, + vis_checker, + incal_inputs=None +): + """Wrapper of :meth:`~upsp.cam_cal_utils.visibility.VisibilityChecker.is_visible` Parameters ---------- @@ -405,9 +423,12 @@ def get_visible_targets(rmat, tvec, tgts, vis_checker): List of visible targets. Returned targets are references to input targets. Returned list is the subset of the input targets that are visible to the camera """ - - # Get the position and orientation of the camera in the tgts frame - rmat_model2camera, tvec_model2camera = invTransform(rmat, tvec) + if (len(tgts) == 0): + return [] + + # Populate incal_inputs if it was not given + if incal_inputs is None: + incal_inputs = {'critical_pt':'first'} # Package the tvecs and normals of the targets tvecs = [] @@ -419,7 +440,9 @@ def get_visible_targets(rmat, tvec, tgts, vis_checker): norms = np.squeeze(np.array(norms), 2) # Get the visible targets and return - visible_indices = vis_checker.is_visible(tvec_model2camera, tvecs, norms) + visible_indices = vis_checker.is_visible_and_inside_incal( + rmat, tvec, cameraMatrix, distCoeffs, tvecs, norms, incal_inputs + ) tgts_visibles = [tgts[x] for x in visible_indices] return tgts_visibles @@ -452,7 +475,6 @@ def reprojection_error(rmat, tvec, cameraMatrix, distCoeffs, tgts, img_targets): rms, max_dist : float RMS distance and maximum distance """ - if len(tgts) == 0: return np.inf, np.inf diff --git a/python/upsp/cam_cal_utils/visibility.py b/python/upsp/cam_cal_utils/visibility.py index f496d8b..eea564f 100644 --- a/python/upsp/cam_cal_utils/visibility.py +++ b/python/upsp/cam_cal_utils/visibility.py @@ -6,6 +6,11 @@ import upsp.processing.p3d_conversions as p2g import upsp.raycast +from upsp.cam_cal_utils import ( + internal_calibration, + photogrammetry, +) + log = logging.getLogger(__name__) @@ -57,10 +62,19 @@ class VisibilityChecker: primitive (effectively having 'no grid'). This makes BVH operations much faster. """ - def __init__(self, grid_path, oblique_angle=70, epsilon=1e-4, debug=False, debug_nogrid=False): + def __init__( + self, + grid_path, + oblique_angle=70, + epsilon=1e-4, + debug=False, + debug_nogrid=False + ): # If debug_nogrid is used, create a fake BVH with one very small primitive if debug_nogrid: - primitives = np.array([0.00001, 0.0, 0.0, 0.0, 0.00001, 0.0, 0.0, 0.0, 0.00001]) + primitives = np.array( + [0.00001, 0.0, 0.0, 0.0, 0.00001, 0.0, 0.0, 0.0, 0.00001] + ) # When debug is True (and debug_nogrid is False), we are allowed to cache & load # the grid. This makes debugging faster, but cached items can become stale @@ -76,7 +90,7 @@ def __init__(self, grid_path, oblique_angle=70, epsilon=1e-4, debug=False, debug primitives = np.load(CACHE_FILENAME) log.debug('Loaded BVH primitives cache "%s"', CACHE_FILENAME) - # If the primitives are not available, load the grid from scratch and cache it + # If primitives are not available, load the grid from scratch and cache it else: t = self.load_mesh(grid_path) @@ -147,7 +161,7 @@ def load_mesh(self, grid_path): grd = p3d.read_p3d_grid(grid_path) t = p2g.p3d_to_gltf_triangles(grd) t["vertices"] = np.array(t["vertices"]) - t["indices"] = np.array(t["indices"]) + t["indices"] = np.array(t["indices"], dtype=int) return t def package_primitives(self, t): @@ -240,7 +254,8 @@ def angle_between(self, v1, v2): """ v1_u = self.unit_vector(v1) v2_u = self.unit_vector(v2) - return np.rad2deg(np.arccos(np.clip(np.sum(np.multiply(v1_u, v2_u), axis=1), -1.0, 1.0))) + angle_r = np.arccos(np.clip(np.sum(np.multiply(v1_u, v2_u), axis=1), -1.0, 1.0)) + return np.rad2deg(angle_r) def is_back_facing(self, t, n): """This is the 'slow' version of the back face culling check @@ -277,7 +292,7 @@ def is_back_facing(self, t, n): return False def is_back_facing_fast(self, t, n): - r"""Returns True if the node is back facing, otherwise returns False + """Returns True if the node is back facing, otherwise returns False This re-write is significantly faster than the naive approach, but not as fast as the vectorized approach. This function is mostly for legacy/regression @@ -477,7 +492,77 @@ def is_visible(self, tvec_model_to_camera, nodes, normals, return_angles=False): if return_angles: return np.array(visible), np.array(angles[visible]) - return np.array(visible) + return np.array(visible, dtype=int) + + def is_visible_and_inside_incal( + self, + rmat, + tvec, + cameraMatrix, + distCoeffs, + nodes, + normals, + incal_inputs=None + ): + """ Returns indices that pass the visibility and internal calibration checks + + Parameters: + ----------- + rmat : np.ndarray, shape (3, 3), float + Rotation matrix from camera to object + tvec : np.ndarray, shape (3, 1), float + Translation vector from camera to object + cameraMatrix : np.ndarray, shape (3, 3), float + The (openCV formatted) camera matrix for the camera + distCoeffs : np.ndarray, shape (1, 5), float + The (openCV formatted) distortion coefficients for the camera + nodes : np.ndarray, shape (N, 3), float + X, Y, Z position of nodes to be checked. ``nodes[i]`` is associated with + ``normals[i]`` + normals : np.ndarray, shape (N, 3), float + Normal vectors. ``normals[i]`` is associated with ``nodes[i]`` + incal_inputs : dict. Optional, default={'critical_pt':'first'} + Additional parameter inputs for internal_calibration.get_pts_inside_incal + + Returns + ---------- + inside_incal : np.ndarray + Numpy array of the indices of the nodes that are inside the internal + calibration boundaries + """ + + """ + # slow way + is_inside_incal_idxs = self.check_inside_incal( + rmat, tvec, cameraMatrix, distCoeffs, nodes, incal_inputs + ) + + rmat_model_to_cam, tvec_model_to_cam = photogrammetry.invTransform(rmat, tvec) + is_visible_idxs = self.is_visible( + tvec_model_to_cam, nodes, normals, return_angles=False + ) + in_incal_and_vis_idxs = np.intersect1d(is_inside_incal_idxs, is_visible_idxs) + return np.sort(in_incal_and_vis_idxs) + """ + # Populate incal_inputs if it was not given + if incal_inputs is None: + incal_inputs = {'critical_pt':'first'} + + # Get the visible nodes + rmat_model_to_cam, tvec_model_to_cam = photogrammetry.invTransform(rmat, tvec) + is_visible_idxs = self.is_visible( + tvec_model_to_cam, nodes, normals, return_angles=False + ) + + # Of the visible nodes, get the nodes that are also in the incal bounds + is_inside_incal_idxs = internal_calibration.get_pts_inside_incal( + rmat, tvec, cameraMatrix, distCoeffs, nodes[is_visible_idxs], **incal_inputs + ) + + # is_inside_incal_idxs is the indices of is_visible_idxs that are visible + # Get the indices of nodes that are visible and in the incal bounds + in_incal_and_vis_idxs = is_visible_idxs[is_inside_incal_idxs] + return np.sort(in_incal_and_vis_idxs) def tri_normal(self, face): """Returns the normal of a face with vertices ordered counter-clockwise @@ -493,7 +578,6 @@ def tri_normal(self, face): normal : np.ndarray, shape (3,) Normal vector of input face """ - # https://www.scratchapixel.com/lessons/3d-basic-rendering/ray-tracing-rendering-a-triangle/geometry-of-a-triangle A = face[1] - face[0] # Edge 1 B = face[2] - face[1] # Edge 2 C = np.cross(A, B) @@ -525,7 +609,7 @@ def get_faces_and_face_normals(self): # Get all the faces and the face normals faces = [] face_normals = [] - for face_v_idx in faces_v_idx: #[np.random.choice(range(len(faces_v_idx)), 25000)]: + for face_v_idx in faces_v_idx: face = np.array([verts[face_v_idx[0]], verts[face_v_idx[1]], verts[face_v_idx[2]]]) diff --git a/python/upsp/intensity_mapping/node_pixel_mapping.py b/python/upsp/intensity_mapping/node_pixel_mapping.py index 4d8bfcf..c54d17a 100644 --- a/python/upsp/intensity_mapping/node_pixel_mapping.py +++ b/python/upsp/intensity_mapping/node_pixel_mapping.py @@ -1,12 +1,24 @@ import numpy as np import cv2 -from upsp.cam_cal_utils import photogrammetry +from upsp.cam_cal_utils import ( + photogrammetry, + internal_calibration, +) np.set_printoptions(linewidth=np.inf, precision=4, threshold=np.inf) -def node_to_pixel_mapping_keyframe(rmat, tvec, cameraMatrix, distCoeffs, vis_checker, nodes, normals): +def node_to_pixel_mapping_keyframe( + rmat, + tvec, + cameraMatrix, + distCoeffs, + vis_checker, + nodes, + normals, + get_pts_inside_incal_inputs=None +): """Returns data on the projected visible nodes for a keyframe Returns a numpy array of the pixel positions of the projected locations of the @@ -15,6 +27,12 @@ def node_to_pixel_mapping_keyframe(rmat, tvec, cameraMatrix, distCoeffs, vis_che position and gradient value of NAN. Additionally returns a sorted numpy array of the indices of the nodes that are visible. + Nodes are also checked for 'safety' with regards to the internal calibration. Points + outside the 'safe' region of the internal calibration are rejected. This is most + often the case for poorly-defined internal calibrations. But can happen to high + quality internal calibrations if the nodes given are very far outside the frame of + the image. See internal_calibration.get_pts_inside_incal for more information. + Parameters ---------- rmat : np.ndarray, shape (3, 3), float @@ -33,7 +51,10 @@ def node_to_pixel_mapping_keyframe(rmat, tvec, cameraMatrix, distCoeffs, vis_che normals : np.ndarray, shape (N, 3), float A numpy array of the i, j, and k values of the node normals. ``normals[n]`` is associated with ``nodes[n]`` - + get_safe_pts_inputs : dict. Optional, default={} + *get_safe_pts_inputs is given to internal_calibration.get_safe_pts when finding + the nodes inside the safe region of the internal calibration. + Returns ------- projections : np.ndarray, shape (N, 2), float @@ -50,30 +71,48 @@ def node_to_pixel_mapping_keyframe(rmat, tvec, cameraMatrix, distCoeffs, vis_che -------- node_to_pixel_mapping_non_keyframe : use outputs to quickly map non-keyframes """ - + if get_pts_inside_incal_inputs is None: + get_pts_inside_incal_inputs = {'critical_pt':'first'} + # Get the visible nodes rmat_model2camera, tvec_model2camera = photogrammetry.invTransform(rmat, tvec) - vis_idxs = vis_checker.is_visible(tvec_model2camera, nodes, normals) - vis_idxs = np.sort(vis_idxs) - vis_nodes = nodes[vis_idxs] - # Project all visible nodes - projs, jacs = photogrammetry.project_3d_point(rmat, tvec, cameraMatrix, distCoeffs, vis_nodes, ret_jac=True) + in_incal_and_vis_idxs = vis_checker.is_visible_and_inside_incal( + rmat, + tvec, + cameraMatrix, + distCoeffs, + nodes, + normals, + get_pts_inside_incal_inputs + ) + in_incal_and_vis_idxs_nodes = nodes[in_incal_and_vis_idxs] - jacs_swapped = np.swapaxes(jacs, 0, 1) + # Project all visible nodes + projs, jacs = photogrammetry.project_3d_point( + rmat, tvec, cameraMatrix, distCoeffs, in_incal_and_vis_idxs_nodes, ret_jac=True + ) # Initialize an output of all NANs projs_fin = np.full((len(nodes), 2), np.NAN) jacs_fin = np.full((len(nodes), 2, 6), np.NAN) # For visible nodes, replace the NANs with data - projs_fin[vis_idxs] = projs - jacs_fin[vis_idxs] = jacs + projs_fin[in_incal_and_vis_idxs] = projs + jacs_fin[in_incal_and_vis_idxs] = jacs - return projs_fin, jacs_fin, vis_idxs + return projs_fin, jacs_fin, in_incal_and_vis_idxs -def node_to_pixel_mapping_non_keyframe(rmat_key, tvec_key, rmat_curr, tvec_curr, projs, jacs, vis_idxs): +def node_to_pixel_mapping_non_keyframe( + rmat_key, + tvec_key, + rmat_curr, + tvec_curr, + projs, + jacs, + vis_idxs +): """Returns data on the projected visible nodes for a non-keyframe Returns a numpy array of the pixel positions of the projected locations of the @@ -130,7 +169,14 @@ def node_to_pixel_mapping_non_keyframe(rmat_key, tvec_key, rmat_curr, tvec_curr, return projs_fin -def node_to_pixel_mapping_non_keyframe_full(rmat, tvec, cameraMatrix, distCoeffs, nodes, vis_idxs): +def node_to_pixel_mapping_non_keyframe_full( + rmat, + tvec, + cameraMatrix, + distCoeffs, + nodes, + vis_idxs +): """Returns data on the projected visible nodes for a non-keyframe Returns a numpy array of the pixel positions of the projected locations of the @@ -168,7 +214,9 @@ def node_to_pixel_mapping_non_keyframe_full(rmat, tvec, cameraMatrix, distCoeffs vis_nodes = nodes[vis_idxs] # Project all visible nodes - projs = photogrammetry.project_3d_point(rmat, tvec, cameraMatrix, distCoeffs, vis_nodes, ret_jac=False) + projs = photogrammetry.project_3d_point( + rmat, tvec, cameraMatrix, distCoeffs, vis_nodes, ret_jac=False + ) # Initialize an output of all NANs projs_fin = np.full((len(nodes), 2), np.NAN) diff --git a/python/upsp/intensity_mapping/patching.py b/python/upsp/intensity_mapping/patching.py index 3c30805..061f34f 100644 --- a/python/upsp/intensity_mapping/patching.py +++ b/python/upsp/intensity_mapping/patching.py @@ -11,13 +11,15 @@ degree = 3 # TODO: Another potential method to look into is a 2D taylor expansion -# F(t_0 + delta_t) = sum n from 0 to inf: 1/n! (n-th derivative of F w.r.t. t) * delta_t^n +# F(t_0 + delta_t) = sum n from 0 to inf: +# 1/n! (n-th derivative of F w.r.t. t) * delta_t^n # Where F(t) = f(x_o + t * delta_x, y_o + t * delta_y) # (f is the function we want, F(t) is easy to take a taylor series of) # n-th derivative of F(t): # sum m from 0 to n: # (n choose m) * (m-th partial derivative w.r.t. x and (n-m)-th partial -# derivative w.r.t. y of f) evaluated at (x_o, y_o) * delta_x^m * delta_y^(n-m) +# derivative w.r.t. y of f) evaluated at +# (x_o, y_o) * delta_x^m * delta_y^(n-m) # TODO: Another thing to try (with the current method or Taylor expansion) is weighting # Each pixel gets exponentially less weight on the current patch based on distance @@ -75,7 +77,9 @@ def get_target_node_idxs(nodes, tgts, buffer_thickness_in): # flagged as potential invalid nodes heuristic_invalid_nodes = set() for tgt in tgts: - manhattan_dist = np.linalg.norm(np.absolute(nodes - tgt['tvec'].T), ord=1, axis=1) + manhattan_dist = np.linalg.norm( + np.absolute(nodes - tgt['tvec'].T), ord=1, axis=1 + ) heuristic_invalid_nodes.update(np.squeeze(np.argwhere(manhattan_dist < (np.sqrt(3) * (tgt['size'] / 2 + buffer_thickness_in))), axis=1).tolist()) heuristic_invalid_nodes = list(heuristic_invalid_nodes) @@ -88,7 +92,16 @@ def get_target_node_idxs(nodes, tgts, buffer_thickness_in): return np.array(sorted(invalid_nodes), dtype=np.int32) -def patchFiducials(fiduals_visible, inp_img, rmat, tvec, cameraMatrix, distCoeffs, boundary_thickness, buffer_thickness_in): +def patchFiducials( + fiduals_visible, + inp_img, + rmat, + tvec, + cameraMatrix, + distCoeffs, + boundary_thickness, + buffer_thickness_in +): """Patches clusters in the `inp_img` Parameters @@ -131,20 +144,51 @@ def patchFiducials(fiduals_visible, inp_img, rmat, tvec, cameraMatrix, distCoeff out_img = copy.deepcopy(inp_img).astype(np.float32) # Cluster the fiducials - clusters = clusterFiducials(fiduals_visible, rmat, tvec, cameraMatrix, distCoeffs, boundary_thickness, buffer_thickness_in) + clusters = clusterFiducials( + fiduals_visible, + rmat, + tvec, + cameraMatrix, + distCoeffs, + boundary_thickness, + buffer_thickness_in + ) # Patch every cluster in the output image - for cluster in clusters: + for i, cluster in enumerate(clusters): # Get the internal and boundary points of the cluster if (len(cluster) == 1): - internals, bounds = get_fiducial_internal_and_boundary(cluster[0], rmat, tvec, cameraMatrix, distCoeffs, boundary_thickness, buffer_thickness_in) + internals, bounds = get_fiducial_internal_and_boundary( + cluster[0], + rmat, + tvec, + cameraMatrix, + distCoeffs, + boundary_thickness, + buffer_thickness_in) else: - internals, bounds = get_cluster_internal_and_boundary(cluster, rmat, tvec, cameraMatrix, distCoeffs, boundary_thickness, buffer_thickness_in) + internals, bounds = get_cluster_internal_and_boundary( + cluster, + rmat, + tvec, + cameraMatrix, + distCoeffs, + boundary_thickness, + buffer_thickness_in + ) # Skip if there are too few points to perform patching if (len(bounds) < ((degree+2)*(degree+1)/2)): continue + # Skip if the fiducial is outside the image + if min(bounds[:, 0]) < 0 or min(bounds[:, 1]) < 0: + continue + + # Skip if the fiducial is outside the image (continued) + if max(bounds[:, 0]) > inp_img.shape[1] or max(bounds[:, 1]) > inp_img.shape[0]: + continue + # Convert the positions to local positions min_bounds = (np.min(bounds[:,0]), np.min(bounds[:,1])) local_internals = internals - min_bounds @@ -166,7 +210,15 @@ def patchFiducials(fiduals_visible, inp_img, rmat, tvec, cameraMatrix, distCoeff return out_img -def clusterFiducials(fiduals_visible, rmat, tvec, cameraMatrix, distCoeffs, boundary_thickness, buffer_thickness_in): +def clusterFiducials( + fiduals_visible, + rmat, + tvec, + cameraMatrix, + distCoeffs, + boundary_thickness, + buffer_thickness_in +): """Clusters input fiducials based on image location and image size A cluster is made of all fiducials with a path of overlap between them, i.e. A is @@ -204,18 +256,23 @@ def clusterFiducials(fiduals_visible, rmat, tvec, cameraMatrix, distCoeffs, boun `fiducials_visible`. """ # Represent fiducials as circles with center and radius - unclustered_fiducial_sets = [] #deleteme? + unclustered_fiducial_sets = [] # unclustered_image_fiducials = [] #deleteme? for fiducial in fiduals_visible: - internals, bounds = get_fiducial_internal_and_boundary(fiducial, rmat, tvec, cameraMatrix, distCoeffs, boundary_thickness, buffer_thickness_in) + internals, bounds = get_fiducial_internal_and_boundary( + fiducial, + rmat, + tvec, + cameraMatrix, + distCoeffs, + boundary_thickness, + buffer_thickness_in + ) internal_set = set(tuple(internal) for internal in internals) - internal_and_boundary_set = set(set(tuple(boundary) for boundary in bounds)).union(internal_set) + boundary_set = set(tuple(boundary) for boundary in bounds) + internal_and_boundary_set = boundary_set.union(internal_set) unclustered_fiducial_sets.append([internal_set, internal_and_boundary_set]) - # tgt_proj, targ_size_px, __, __ = get_fiducial_pixel_properties(fiducial, rmat, tvec, cameraMatrix, distCoeffs, boundary_thickness, buffer_thickness_in) - # # tgt_proj['proj'] is center, targ_size_px is diameter - # unclustered_image_fiducials.append((tgt_proj['proj'], targ_size_px / 2)) - # unclustered_image_fiducials_orig = copy.deepcopy(unclustered_image_fiducials) unclustered_fiducial_sets_orig = copy.deepcopy(unclustered_fiducial_sets) @@ -282,7 +339,15 @@ def clusterFiducials(fiduals_visible, rmat, tvec, cameraMatrix, distCoeffs, boun # TODO: automatically find fiducials that are close together to group into clusters -def get_cluster_internal_and_boundary(cluster, rmat, tvec, cameraMatrix, distCoeffs, boundary_thickness, buffer_thickness_in): +def get_cluster_internal_and_boundary( + cluster, + rmat, + tvec, + cameraMatrix, + distCoeffs, + boundary_thickness, + buffer_thickness_in +): """Returns a list of internal and boundary pixels for the input cluster An internal pixel is either directly a part of the fiducials or in between @@ -326,13 +391,21 @@ def get_cluster_internal_and_boundary(cluster, rmat, tvec, cameraMatrix, distCoe # First stage: Get the minimum, axis aligned rectangle that contains all fiducials # of this cluster t_min = np.array([np.iinfo(np.int32).max, np.iinfo(np.int32).max], dtype=np.int32) - t_max = np.array([0, 0], dtype=np.int32) + t_max = np.array([np.iinfo(np.int32).min, np.iinfo(np.int32).min], dtype=np.int32) # Iterate over every fiducial, and take the leftmost, topmost, rightmost, and # bottommost fiducial point. Save them in t_min and t_max respectively for tgt in cluster: # Get the minimum, axis-aligned bounding box for this fiducial - tgt_proj, targ_size_px, t_min_temp, t_max_temp = get_fiducial_pixel_properties(tgt, rmat, tvec, cameraMatrix, distCoeffs, boundary_thickness, buffer_thickness_in) + tgt_proj, targ_size_px, t_min_temp, t_max_temp = get_fiducial_pixel_properties( + tgt, + rmat, + tvec, + cameraMatrix, + distCoeffs, + boundary_thickness, + buffer_thickness_in + ) # Update t_min and t_max t_min = np.minimum(t_min, t_min_temp) @@ -340,15 +413,23 @@ def get_cluster_internal_and_boundary(cluster, rmat, tvec, cameraMatrix, distCoe # Second Stage: make a mini images the size of that minimum axis aligned rectangle # and mark internal and boundary pixels. An internal pixel is either directly a - # part of the fiducials or in between fiducials. A boundary pixel is any pixel within - # buffer pixels of an internal pixel and is not an internal itself + # part of the fiducials or in between fiducials. A boundary pixel is any pixel + # within buffer pixels of an internal pixel and is not an internal itself # Mini image to contain the internal points internal_map = np.zeros((t_max[1] - t_min[1], t_max[0] - t_min[0])) # Mark all points that are directly a part of the fiducials as internal for tgt in cluster: - internal_points, __ = get_fiducial_internal_and_boundary(tgt, rmat, tvec, cameraMatrix, distCoeffs, boundary_thickness, buffer_thickness_in) + internal_points, __ = get_fiducial_internal_and_boundary( + tgt, + rmat, + tvec, + cameraMatrix, + distCoeffs, + boundary_thickness, + buffer_thickness_in + ) # Mark every internal point as internal for x, y in internal_points: @@ -365,18 +446,18 @@ def get_cluster_internal_and_boundary(cluster, rmat, tvec, cameraMatrix, distCoe # For each column, find the topmost and bottommost internal pixel. Fill in # everything between them - for x in range(t_max[0] - t_min[0]): - min_y = t_max[1] - 1 + for x in range(internal_map.shape[1]): + min_y = internal_map.shape[0] - 1 max_y = 0 # Find the topmost internal pixel of this row - for y in range(t_max[1] - t_min[1]): + for y in range(internal_map.shape[0]): if internal_map[y][x]: min_y = y break # Find the bottommost internal pixel of this row - for y in reversed(range(t_max[1] - t_min[1])): + for y in reversed(range(internal_map.shape[0])): if internal_map[y][x]: max_y = y break @@ -387,18 +468,18 @@ def get_cluster_internal_and_boundary(cluster, rmat, tvec, cameraMatrix, distCoe # For each row, find the leftmost and rightmost internal pixel. Fill in everything # between them - for y in range(t_max[1] - t_min[1]): - min_x = t_max[0] - 1 + for y in range(internal_map.shape[0]): + min_x = internal_map.shape[1] - 1 max_x = 0 # Find the topmost internal pixel of this row - for x in range(t_max[0] - t_min[0]): + for x in range(internal_map.shape[1]): if internal_map[y][x]: min_x = x break # Find the bottommost internal pixel of this row - for x in reversed(range(t_max[0] - t_min[0])): + for x in reversed(range(internal_map.shape[1])): if internal_map[y][x]: max_x = x break @@ -407,8 +488,11 @@ def get_cluster_internal_and_boundary(cluster, rmat, tvec, cameraMatrix, distCoe for x in range(min_x, max_x): internal_map[y][x] = 1 - # Get all pixels that are not internal and are within boundary_thickness of an internal - boundary_map = get_fiducial_boundary_map_from_internal_map(internal_map, boundary_thickness) + # Get all pixels that are not internal and are within boundary_thickness of an + # internal + boundary_map = get_fiducial_boundary_map_from_internal_map( + internal_map, boundary_thickness + ) # Third Stage: Turn those maps into a list of points @@ -423,7 +507,15 @@ def get_cluster_internal_and_boundary(cluster, rmat, tvec, cameraMatrix, distCoe return internals, bounds -def get_fiducial_internal_and_boundary(tgt, rmat, tvec, cameraMatrix, distCoeffs, boundary_thickness, buffer_thickness_in): +def get_fiducial_internal_and_boundary( + tgt, + rmat, + tvec, + cameraMatrix, + distCoeffs, + boundary_thickness, + buffer_thickness_in +): """Return internal and boundary pixel positions for the input fiducial An internal pixel a part of the fiducials (minimum axis aligned bounding rectangle). @@ -467,7 +559,15 @@ def get_fiducial_internal_and_boundary(tgt, rmat, tvec, cameraMatrix, distCoeffs assert type(boundary_thickness) is int # delteme # Get the tgt_proj and the minimum, axis-aligned bounding box for this fiducial - tgt_proj, targ_size_px, t_min, t_max = get_fiducial_pixel_properties(tgt, rmat, tvec, cameraMatrix, distCoeffs, boundary_thickness, buffer_thickness_in) + tgt_proj, targ_size_px, t_min, t_max = get_fiducial_pixel_properties( + tgt, + rmat, + tvec, + cameraMatrix, + distCoeffs, + boundary_thickness, + buffer_thickness_in + ) # Make a mini image the size of the region covered by the fiducial # In this image, 0 is safe and 1 is internals @@ -496,13 +596,18 @@ def get_fiducial_internal_and_boundary(tgt, rmat, tvec, cameraMatrix, distCoeffs # For each of the closest points, if it is within the radius of the center it is # internal and needs to be marked as such in the internal_map - closest_distances = np.linalg.norm([Xns - tgt_proj_local[0], Yns - tgt_proj_local[1]], axis=0) + closest_distances = np.linalg.norm( + [Xns - tgt_proj_local[0], Yns - tgt_proj_local[1]], axis=0 + ) internals_idxs = np.argwhere(closest_distances < (targ_size_px / 2))[:, 0] internals = np.stack((ys[internals_idxs], xs[internals_idxs]), axis=1) internal_map[tuple(internals.T)] = 1 - # Get all pixels that are not internal and are within boundary_thickness of an internal - boundary_map = get_fiducial_boundary_map_from_internal_map(internal_map, boundary_thickness) + # Get all pixels that are not internal and are within boundary_thickness of an + # internal + boundary_map = get_fiducial_boundary_map_from_internal_map( + internal_map, boundary_thickness + ) # Get all internal and boundary points internals = np.argwhere(internal_map == 1)[:, [1,0]] @@ -558,7 +663,15 @@ def get_fiducial_boundary_map_from_internal_map(internal_map, boundary_thickness return boundary_map -def get_fiducial_pixel_properties(tgt, rmat, tvec, cameraMatrix, distCoeffs, boundary_thickness, buffer_thickness_in): +def get_fiducial_pixel_properties( + tgt, + rmat, + tvec, + cameraMatrix, + distCoeffs, + boundary_thickness, + buffer_thickness_in +): """Returns the pixel properties of the input fiducial Pixel properties refers to projected location, pixel size (adjusted for focal @@ -617,7 +730,10 @@ def get_fiducial_pixel_properties(tgt, rmat, tvec, cameraMatrix, distCoeffs, bou targ_size_px = cameraMatrix[1][1] * targ_size_in / cam2tgt_dist # Get the tgt projection - tgt_proj = photogrammetry.project_targets(rmat, tvec, cameraMatrix, distCoeffs, [tgt])[0] + tgt_projs = photogrammetry.project_targets( + rmat, tvec, cameraMatrix, distCoeffs, [tgt] + ) + tgt_proj = tgt_projs[0] # Get the an axis aligned minimum rectangle for this fiducial (plus buffer) t_min = [tgt_proj['proj'][0] - 0.5 * targ_size_px - boundary_thickness - 1, @@ -661,8 +777,10 @@ def polyfit2D(bounds, Is): # Equivalent to 1 + 2 + ... + degree + (degree+1) = (degree+2)*(degree+1)/2 num_coeffs = int((degree + 2) * (degree + 1) / 2) - # Number of boundary terms must be greater than or equal to the number of coefficients - assert(len(bounds) >= num_coeffs), "Not enough boundary terms, please increase boundary_thickness or decrease degree" + # Number of boundary terms must be greater than or equal to the number of + # coefficients + assert(len(bounds) >= num_coeffs), ("Not enough boundary terms, please increase " + + "boundary_thickness or decrease degree") # Initialize the least squares input matrix # Ax ~= b where ~= is least squares solution diff --git a/python/upsp/processing/kulite_utilities.py b/python/upsp/processing/kulite_utilities.py index 975cb9e..5ef2d10 100644 --- a/python/upsp/processing/kulite_utilities.py +++ b/python/upsp/processing/kulite_utilities.py @@ -54,7 +54,7 @@ def __init__(self, data_dir, run, seq, kulites='all', # Get a list of all info files. There are a couple variations, # auto-detect using regexes. info_files = self.__autodetect_info_files(data_dir, run, seq) - log.info('Autodetected info files: %s', str(info_files)) + log.debug('Autodetected info files: %s', str(info_files)) # Check each info file for the kulites of interest for info_file in sorted(info_files): @@ -186,7 +186,7 @@ def __read_info(self, info_file): # the numbering corresponds to what channel they are plugged into. # if 'User Name' not in chanconfig_df.columns: - log.warning(''.join([ + log.debug(''.join([ '"User Name" column not found in %s. ', 'Will infer Kulite-to-channel mapping: ', 'K01 -> Channel 1, K02 -> Channel 2, etc.', @@ -224,7 +224,7 @@ def __read_info(self, info_file): "Can't determine file format. Did Kevin change it again?" ) - log.info('Detected info file version: %d', Version) + log.debug('Detected info file version: %d', Version) accepted_versions = [1, 4] if Version not in accepted_versions: raise Exception( @@ -278,7 +278,6 @@ def __read_data(self, info, channels, f_type): # f_type, filename, inferred_filename # ) filename = inferred_filename - print(filename) def try_load_cal(): prefix = os.path.splitext(filename)[0] @@ -314,7 +313,7 @@ def try_load_cal(): cal = try_load_cal() if cal: - print('calibration matrix:', cal['calibrationmatrix'].shape) + log.debug('calibration matrix: %s', str(cal['calibrationmatrix'].shape)) # Perform import via numpy array # type is nChannel columns of int32 (i4) @@ -343,7 +342,7 @@ def try_load_cal(): (10**(chan['EU Gain (dB)'][channel] / 20)) + \ chan['EU C0'][channel] - print( + log.debug( 'C[%02d]' % (channel + 1,), vraw.shape, 'Counts:', f'{np.min(vraw):+10d}', f'{np.max(vraw):+10d}', 'V:', f'{np.min(vnative):.3f}', f'{np.max(vnative):.3f}', @@ -612,7 +611,7 @@ def create_pressure_array(kuls, data_type=np.float32): leng = len(kuls.data[list(kuls.data.keys())[0]]) data_array = np.empty(size, dtype=data_type) - log.info("Creating kulite pressure file of %d kulites, history of %d," + log.debug("Creating kulite pressure file of %d kulites, history of %d," + " size %d, and type %s..", len(kuls.data), leng, size, data_type) diff --git a/python/upsp/processing/tree.py b/python/upsp/processing/tree.py index 1d7effa..663ac11 100644 --- a/python/upsp/processing/tree.py +++ b/python/upsp/processing/tree.py @@ -294,7 +294,6 @@ def _launcher_env_sh(cfg: dict): - Load any required system libraries that aren't available by default... for example, system-provided MPI libraries. - - Prefix the PATH with the directory of our current python interpreter. Ensures any scripts keying off "/usr/bin/env python" resolve the correct interpreter at runtime. @@ -302,7 +301,7 @@ def _launcher_env_sh(cfg: dict): env_sh_lines = [ "source /usr/local/lib/global.profile", "module purge", - "module load mpi-hpe/mpt.2.25", + "module load mpi-hpe/mpt", "export PATH=%s:$PATH" % (os.path.dirname(shutil.which("python"))) ] return "\n".join(env_sh_lines) @@ -548,10 +547,11 @@ def _configuration_name(cfg: dict): # - The alias for the uPSP processing software parameter values alias_input_files = cfg["__meta__"]["datapoints"]["config_name"] alias_processing_params = cfg["__meta__"]["processing"]["name"] - if alias_input_files == alias_processing_params: - return alias_input_files - else: - return "+".join([alias_input_files, alias_processing_params]) + toks = [alias_input_files, alias_processing_params] + filtered_toks = [t for t in toks if t != "default"] + if not filtered_toks: + filtered_toks = ["default"] + return "+".join(filtered_toks) def _version_configuration_name(cfg: dict): diff --git a/python/upsp/target_localization/__init__.py b/python/upsp/target_operations/__init__.py similarity index 100% rename from python/upsp/target_localization/__init__.py rename to python/upsp/target_operations/__init__.py diff --git a/python/upsp/target_localization/blob_detector_methods.py b/python/upsp/target_operations/blob_localization_methods.py similarity index 100% rename from python/upsp/target_localization/blob_detector_methods.py rename to python/upsp/target_operations/blob_localization_methods.py diff --git a/python/upsp/target_localization/gaussian_fitting_methods.py b/python/upsp/target_operations/gaussian_localization_methods.py similarity index 89% rename from python/upsp/target_localization/gaussian_fitting_methods.py rename to python/upsp/target_operations/gaussian_localization_methods.py index f480ee4..0b755c4 100644 --- a/python/upsp/target_localization/gaussian_fitting_methods.py +++ b/python/upsp/target_operations/gaussian_localization_methods.py @@ -4,8 +4,7 @@ import warnings from scipy.optimize import OptimizeWarning -from upsp.target_localization.blob_detector_methods import blob_func - +from upsp.target_operations import blob_localization_methods np.set_printoptions(linewidth=180, precision=8, threshold=np.inf) @@ -18,8 +17,6 @@ def super_twoD_Gaussian(pt, amplitude, xo, yo, sx, sy, theta, offset, p): """Returns the value of a super 2D Gaussian function at the given point - See https://en.wikipedia.org/wiki/Gaussian_function#Higher-order_Gaussian_or_super-Gaussian_function - Parameters ---------- pt : np.ndarray, shape (N, 2), float @@ -86,7 +83,17 @@ def twoD_Gaussian(pt, amplitude, xo, yo, sx, sy, theta, offset): return super_twoD_Gaussian(pt, amplitude, xo, yo, sx, sy, theta, offset, 1) -def super_twoD_Gaussian_nobounds(pt, ln_amplitude, xo, yo, ln_sx, ln_sy, theta, offset, ln_p): +def super_twoD_Gaussian_nobounds( + pt, + ln_amplitude, + xo, + yo, + ln_sx, + ln_sy, + theta, + offset, + ln_p +): """Wrapper to call :func:`super_twoD_Gaussian` with no bounds on inputs For :func:`super_twoD_Gaussian`, the function does not make physical sense if @@ -127,7 +134,17 @@ def super_twoD_Gaussian_nobounds(pt, ln_amplitude, xo, yo, ln_sx, ln_sy, theta, super_twoD_Gaussian : Returns the value of a super 2D Gaussian function at the given point """ - return super_twoD_Gaussian(pt, np.exp(ln_amplitude), xo, yo, np.exp(ln_sx), np.exp(ln_sy), theta, offset, np.exp(ln_p) + 1) + return super_twoD_Gaussian( + pt, + np.exp(ln_amplitude), + xo, + yo, + np.exp(ln_sx), + np.exp(ln_sy), + theta, + offset, + np.exp(ln_p) + 1 + ) #--------------------------------------------------------------------------------------- @@ -197,8 +214,12 @@ def gauss_fitter_func(fit, get_rms=False, curve_fit_kwds=None, debug=False): print("fitter must be 'super' or 'normal'") quit() - blob_detector = blob_func('detector_all', 'last') - warnings.filterwarnings("ignore", category=OptimizeWarning, message=".*Covariance of the parameters could not be estimated*.") + blob_detector = blob_localization_methods.blob_func('detector_all', 'last') + warnings.filterwarnings( + "ignore", + category=OptimizeWarning, + message=".*Covariance of the parameters could not be estimated*." + ) if curve_fit_kwds is None: curve_fit_kwds = {'maxfev': 25000, 'ftol': 1e-4, 'xtol': 1e-4} @@ -237,8 +258,8 @@ def fit_gaussian(img, center=None, target_type=None, img_offset=None): parameters as the first item, and the covariance matrix as the second item. If gauss_fitter_func's get_rms is True, return tuple contains rms error as the second item. If get_rms is False, return tuple has no second item. - If there is an error in the optimization, all None's will be returned for the - center, optimizer parameters, covatiance matrix, and rms as needed. + If there is an error in the optimization, all None's will be returned for + the center, optimizer parameters, covatiance matrix, and rms as needed. """ # If a keypoint was not given as input, find one with a blob detector if center is None: @@ -301,11 +322,15 @@ def fit_gaussian(img, center=None, target_type=None, img_offset=None): np.average(img)] # Set the bounds for the inputs - bounds = [(0.0, np.inf), # Amplitude bounds - (0.0, img.shape[1]), (0.0, img.shape[0]), # center location bounds - (np.finfo(float).eps, np.inf), (np.finfo(float).eps, np.inf), # std dev bounds - (-np.inf, np.inf), # angle bounds - (min(img.flatten()), max(img.flatten()))] + bounds = [ + (0.0, np.inf), # Amplitude bounds + (0.0, img.shape[1]), # center x location bounds + (0.0, img.shape[0]), # center y location bounds + (np.finfo(float).eps, np.inf), # x std dev bound + (np.finfo(float).eps, np.inf), # y std dev bound + (-np.inf, np.inf), # angle bounds + (min(img.flatten()), max(img.flatten())) # image offset bounds + ] # If this is using a super gauss add the additional p term for the initial # guess and bounds diff --git a/python/upsp/cam_cal_utils/target_bumping.py b/python/upsp/target_operations/target_bumping.py similarity index 94% rename from python/upsp/cam_cal_utils/target_bumping.py rename to python/upsp/target_operations/target_bumping.py index 64b11f6..0b359b5 100644 --- a/python/upsp/cam_cal_utils/target_bumping.py +++ b/python/upsp/target_operations/target_bumping.py @@ -194,7 +194,9 @@ def tgt_bump_internals(tgts, vis_checker, bump_eps=1e-5, tgts_tol=1e-1, grid_tol if bumping_occlusion[0]: if debug_bump_distance: - dist = np.linalg.norm(np.array(bumping_occlusion[1]) - np.array(tgt['tvec'])) + dist = np.linalg.norm( + np.array(bumping_occlusion[1]) - np.array(tgt['tvec']) + ) print('\tInternal Target:', tgt['name'], 'Dist:', dist) # If the occlusion is artificial (differentiably inside the mode), bump the @@ -210,8 +212,10 @@ def tgt_bump_internals(tgts, vis_checker, bump_eps=1e-5, tgts_tol=1e-1, grid_tol bumped_tgt['norm'] = np.array(tgt['norm'], dtype=np.float64) bumped_tgt['norm'] /= np.linalg.norm(tgt['norm']) - # Bump the target to the occlusion point plus some small tolerance along the normal - bumped_tgt['tvec'] = np.array(bumping_occlusion[1]) + bump_eps * bumped_tgt['norm'] + # Bump the target to the occlusion point plus some small tolerance along + # the normal + bump = bump_eps * bumped_tgt['norm'] + bumped_tgt['tvec'] = np.array(bumping_occlusion[1]) + bump # Add the bumped target to the new list of targets tgts_bumped.append(bumped_tgt) @@ -262,8 +266,11 @@ def tgt_bump_externals(tgts, vis_checker, bump_eps=1e-5): for tgt in tgts: # Create a copy of the target, but with an inverted normal - tgt_inv_norm = {'tvec': np.array(tgt['tvec']) + 100 * bump_eps * np.array(tgt['norm']), - 'norm': -np.array(tgt['norm'])} + bump = 100 * bump_eps * np.array(tgt['norm']) + tgt_inv_norm = { + 'tvec': np.array(tgt['tvec']) + bump, + 'norm': -np.array(tgt['norm']) + } # Get the point on the model surface just behind the target bumping_occlusion = get_bumping_occlusion(tgt_inv_norm, vis_checker) @@ -275,8 +282,9 @@ def tgt_bump_externals(tgts, vis_checker, bump_eps=1e-5): bumped_tgt['norm'] = np.array(tgt['norm'], dtype=np.float64) bumped_tgt['norm'] /= np.linalg.norm(tgt['norm']) - # Bump the target to the occlusion point plus some small tolerance along the normal - bumped_tgt['tvec'] = np.array(bumping_occlusion[1]) + bump_eps * bumped_tgt['norm'] + # Bump target to the occlusion point plus some small tolerance along the normal + bump = bump_eps * bumped_tgt['norm'] + bumped_tgt['tvec'] = np.array(bumping_occlusion[1]) + bump # Add the bumped target to the new list of targets tgts_bumped.append(bumped_tgt) @@ -335,8 +343,9 @@ def tgts_bumper(tgts, vis_checker, bump_eps=1e-5, tgts_tol=1e-1, grid_tol=1e-3): print('Internal Bump Iteration', internal_bump_count) while was_bumped: if internal_bump_count > max_bump_iterations: - print('Number of internal bumps exceeds max allowed. Skiping to external bump.' + - 'Targets are still be internal. Please modify bump_eps, tgts_tol, or grid_tol') + print('Number of internal bumps exceeds max allowed. Skiping to external ' + + 'bump. Targets are still be internal. Please modify bump_eps, ' + + 'tgts_tol, or grid_tol') break bumped_internal_targets, was_bumped = tgt_bump_internals( @@ -357,7 +366,9 @@ def tgts_bumper(tgts, vis_checker, bump_eps=1e-5, tgts_tol=1e-1, grid_tol=1e-3): break # Bump the external targets - bumped_external_targets = tgt_bump_externals(bumped_internal_targets, vis_checker, bump_eps) + bumped_external_targets = tgt_bump_externals( + bumped_internal_targets, vis_checker, bump_eps + ) bumped_internal_targets = bumped_external_targets _, was_bumped = tgt_bump_internals( @@ -372,7 +383,14 @@ def tgts_bumper(tgts, vis_checker, bump_eps=1e-5, tgts_tol=1e-1, grid_tol=1e-3): return bumped_external_targets -def tgts_file_bumper(tgts_file_path, vis_checker, bump_eps=1e-5, tgts_tol=1e-1, grid_tol=1e-3): +def tgts_file_bumper( + tgts_file_path, + vis_checker, + bump_eps=1e-5, + tgts_tol=1e-1, + grid_tol=1e-3, + new_tgts_file_path = None +): """Creates a tgts file with all internal targets bumped along their normal to be slightly external and very external targets to be slightly external @@ -403,8 +421,9 @@ def tgts_file_bumper(tgts_file_path, vis_checker, bump_eps=1e-5, tgts_tol=1e-1, # Get the filepath of the new tgts file filename, file_ext = os.path.splitext(os.path.basename(tgts_file_path)) - new_tgts_file_path = os.path.join(os.path.dirname(tgts_file_path), - filename + '_bumped' + file_ext) + if new_tgts_file_path is None: + new_tgts_file_path = os.path.join(os.path.dirname(tgts_file_path), + filename + '_bumped' + file_ext) # Open the new tgts file with open(new_tgts_file_path, 'w') as f_write: diff --git a/python/upsp/target_operations/target_detection.py b/python/upsp/target_operations/target_detection.py new file mode 100644 index 0000000..2dfad8c --- /dev/null +++ b/python/upsp/target_operations/target_detection.py @@ -0,0 +1,916 @@ +import cv2 +import numpy as np +from shapely.geometry import Polygon +import copy + +from upsp.cam_cal_utils import ( + photogrammetry, +) + +from upsp.target_operations import gaussian_localization_methods +gauss_fit = gaussian_localization_methods.gauss_fitter_func("super") + +debug_show_localizations = False + +num_poly_pts = 100 + + +def subpixel_localize(img, tgts, img_targets, test_config, max_localize_delta=None): + """Find the sub-pixel localized position of the image targets + + Find the location of the image target centers with sub-pixel accuracy. This method + filters common bad localization solutions. I.e The localized position is too far + initial guess to make sense, invalid optimizer solution (None flag), outside the + cropped region or outside the image + + Parameters + ---------- + img : np.ndarray, shape (h, w) + Numpy 2D array of the image + tgts : list + Matched 3D targets. Each target should be a dict. The only strict requirement + set by this function is ``tgts[i]`` is associated with ``img_targets[i]`` + img_targets : list + Matched image targets. Each dict has, at a minimum, keys 'center', and + 'target_type'. 'center' has a value of the image target center location + (tuple/np.ndarray of length 2 of floats) and 'target_type' has the key of the + type of target (string). ``img_targets[i]`` is associated with ``tgts[i]`` + test_config : dict + Processing parameters with, at a minimum, a key for each target type in + `targets` and `img_targets`. The key is `target_type` + '_pad'. This is the + padding around the img target center location to use to sub-pixel localize + max_localize_delta : float, optional + The maximum allowable distance that subpixel_localize can change the + `img_target` position. If None, the max allowable distance will be set to the + padding distance minus 2. + + Returns + ------- + targets : list + Target positions (`tgts`) that have not been filtered out. + img_targets : list + Refined (sub-pixel localized) target positions in the image. + + Notes + ----- + ``targets[i]`` is associated with ``img_targets[i]``. Return lists may not be the + same length as input `tgts` and/or `img_targets` inputs (some + `targets`/`img_targets` from the input may be rejected and thus are not included in + the output) + """ + if max_localize_delta is not None: + filter_dist = max_localize_delta + + out_of_bounds = set() + img_targets_refined = [] + for i, img_target in enumerate(img_targets): + # Integer of center pixel + center_pixel = np.rint( + (img_target["center"][0], img_target["center"][1]) + ).astype(np.int32) + + target_pad = test_config[img_target["target_type"] + "_pad"] + + # cropped region around target (x1, y1), (x2, y2) + bbox = [ + [center_pixel[0] - target_pad, center_pixel[1] - target_pad], + [center_pixel[0] + target_pad + 1, center_pixel[1] + target_pad + 1], + ] + + # If bbox goes out of bounds of the image, ignore it + if ( + (bbox[0][0] < 0) + or (bbox[0][1] < 0) + or (bbox[1][0] >= img.shape[1]) + or (bbox[1][1] >= img.shape[0]) + ): + out_of_bounds.add(i) + img_targets_refined.append( + {"target_type": img_target["target_type"], "center": (None, None)} + ) + continue + + # Cropped image around target + img_cropped = img[bbox[0][1] : bbox[1][1], bbox[0][0] : bbox[1][0]] + + # Perform the sub-pixel localization + gauss_center = gauss_fit( + img_cropped, + target_type=img_target["target_type"], + center=img_target["center"], + img_offset=bbox[0], + )[0] + + # If the optimizer failed, continue + if gauss_center[0] is None: + out_of_bounds.add(i) + img_targets_refined.append( + { + "target_type": img_target["target_type"], + "center": np.array((None, None)), + } + ) + continue + + # Create a new img_target with the updated center location + img_target_refined = copy.deepcopy(img_target) + img_target_refined["center"] = gauss_center + + dist = np.linalg.norm( + [ + gauss_center[0] - img_target_refined["center"][0], + gauss_center[1] - img_target_refined["center"][1], + ] + ) + + # A distance of target_pad - 1 would imply the center is on a pixel in the edge + # of the crop. And since the taret is larger than 1 pixel, by definition this + # is a bad localization. Not even to mention it is likely bad since it is so + # far off from the expected position. We do target_pad - 2 to add a margin of + # safety + if max_localize_delta is None: + filter_dist = target_pad - 2 + + if dist > filter_dist: + out_of_bounds.add(i) + img_targets_refined.append( + { + "target_type": img_target["target_type"], + "center": np.array((None, None)), + } + ) + continue + + if debug_show_localizations: + plt.imshow(img_cropped, cmap="gray") + plt.scatter( + [gauss_center[0] - bbox[0][0]], + [gauss_center[1] - bbox[0][1]], + c="g", + s=2, + ) + plt.scatter( + [img_target["center"][0] - bbox[0][0]], + [img_target["center"][1] - bbox[0][1]], + c="r", + s=2, + ) + plt.savefig( + str(i).rjust(3, "0") + + "_" + + str(np.round(dist, 3)) + + "_" + + str(center_pixel[0]) + + "_" + + str(center_pixel[1]) + + ".png" + ) + plt.close() + + img_targets_refined.append(img_target_refined) + + # Remove the targets that had a bad localization + tgts_loc = [] + img_targets_loc = [] + for i, (tgt, img_target) in enumerate(zip(tgts, img_targets_refined)): + if i not in out_of_bounds: + tgts_loc.append(tgt) + img_targets_loc.append(img_target) + + return tgts_loc, img_targets_loc + + +def pixel_2_polygon(u, v): + """ Returns a square Polygon object of length 1 centered at (u, v) + + Parameters + ---------- + u : float + Pixel x position + v : float + Pixel y position + + Returns + ---------- + Polygon object + Polygon of square pixel returned as a shapely polygon object + """ + + return Polygon([[u - 0.5, v - 0.5], [u + 0.5, v - 0.5], + [u + 0.5, v + 0.5], [u - 0.5, v + 0.5]]) + + +def area_overlap(target, pixel_coord): + """Calculates area overlap between target and pixel + + Parameters + ---------- + target : Polygon + Shapely Polygon object. Suggested to create target using ellipse_2_polygon + pixel_coords : tuple + Length 2 iterable (tuple, np.ndarray, etc). First index is x coordinate (u) and + second index is y coordinate (v) + + Returns + ---------- + float + Returns the area overlap between the target and pixel. Effectively the amount + that the pixel is covered by the ellipse (assumes area of pixel is 1) + """ + px = pixel_2_polygon(*pixel_coord) + return px.intersection(target).area + + +def template_detection( + img, rmat, tvec, cameraMatrix, distCoeffs, visible_tgts, test_config +): + """ This function uses template matching to detect targets + + The function needs the internal calibration (cameraMatrix and distCoeffs), the 3D + positions of the targets assumed to be visible (visible_tgts), and an initial guess + of the external calibration (rmat and tvec). Hyper parameters given in test_config + (max_dist, target_pad, and crosscorr_coeff) tune the search parameters. + + test_config[tgt["target_type"] + "_pad"] is the radius of the target for tgt with + tgt["target_type"]. The external calibration initial guess needs to be close enough + to the true external calibration that the projected target position within a pixel + distance of test_config["max_dist"] for the detection to occur. + + For each target: + 1) using the internal cand external calibrations, a template of the target is + generated. Using the 3D target diameter given in visible_tgts[i]['size'], the points + on the edge of the target (in 3D space) are projected into the image to make a + polygon approximation of the target in image space. That polygon is used to make an + image of the target, which acts as the template. Two templates are made. One using + the projected points, and one with the projected points shifted diagonally by 0.5 + pixels in the x and y direction. Since the template is very small, shifting by 0.5 + of a pixel can significantly change the template. + 2) The image is cropped to the region around the projected position of the target. + The crop is a square with a side length of 2 * (test_config["max_dist"] + + test_config[tgt["target_type"] + "_pad"]). This region is used so that the crop + contains all allowable positions of the target, and contains the entire target. If + only 2 * test_config["max_dist"] used, the image target's center could be on the + edge of the crop, but half the image target would be outside the crop so the + template wouldn't match. The crop is square so that OpenCV's methods can be used + (since OpenCV is highly optimized). + 3) Template matching is used to find all sites within the crop where the cross + correlation coefficient is above test_config["crosscorr_coeff"]. This step uses the + union of the sets of target sites found with both templates (original and shifted + template) + 3.5) If there are no target sites found, skip to the next target + 4) Target sites farther than a distance of test_config["max_dist"] from the the + center of the crop are removed. This makes is so the image targets are within a + distance of test_config["max_dist"], and also targets farther than + test_config["max_dist"] do not invalidate the detection in the next step + 4.5) If there is exactly 1 target site remaining, that is used for the detection + of this target + 5) If there are more than 1 target sites remaining, we will use the detection with + the highest cross correlation coefficient (amoung both templates). The caveat is we + can only use that detection if all target sites are for the same target. I.e. if the + detections are on opposite sides of the crop, they are likely for different targets. + This is computed by finding the bounding box for the target sites, then checking if + the bounding box fits within a circle with radius + test_config[tgt["target_type"] + "_pad"]. If it does, then we can use the detection. + + After all detections are made, the targets of visible_tgts that have an associated + detectiona are copied into tgts_matched. The targets of visible_tgts that do not + have an associated detectiona are copied into tgts_unmatched. The output + tgts_detected is then tgts_matched + tgts_unmatched. And num_matches is + len(tgts_matched). That way the first num_matches elements of tgts_detected are + associated 1-1 with the first num_matches elements of img_targets + + Parameters + ---------- + img : np.ndarray, shape (height, width) + Image to use for calibration + rmat : np.ndarray, shape (3, 3), float + Rotation matrix from camera to object + tvec : np.ndarray, shape (3, 1), float + Translation vector from camera to object + cameraMatrix : np.ndarray, shape (3, 3), float + The (openCV formatted) camera matrix for the camera + distCoeffs : np.ndarray, shape (1, 5), float + The (openCV formatted) distortion coefficients for the camera + visible_tgts : list of dict + Each target is a dict that has, at a minimum, the keys 'tvec', and + 'target_type'. If isMatched is False, 'norm' is additionally needed as a key. + 'tvec' has a np.ndarray (3, 1) representing the position of the target relative + to the model origin for its associated value. 'target_type' has a string + representing the type of target (most commonly 'dot') for its associated value. + ``tgts[i]`` is associated with ``img_targets[i]`` for i from 0 to `num_matches`. + 'norm' has a :class:`numpy.ndarray` (3, 1) representing the normal vector of the + target relative to the model coordinate system for its associated value + test_config : dict + dict with, at a minimum, a key for 'max_dist', 'crosscorr_coeff' and + '[target_type] + "_pad"' for each target type of the targets in tgts. 'max_dist' + is the search distance used for the template detection. 'max_dist' is the max + pixel distance between the projected position of the target using the given + external calibration, and the discovered image location of the target. + 'crosscorr_coeff' is the crosscorrelation coefficient threshold for thetemplate + matching. '[target_type] + "_pad"' is the distance used to determine if multiple + template matches correspond to one target, or to multiple targets + + Returns + ------- + tgts_detected : list of dict + Reordered version of visible_targets so that ``img_targets[i]`` is associated + with ``tgts_detected[i]`` for i from 0 to num_matches + img_targets : list of dict + Matched image targets. Each dict has, the keys 'center', and + 'target_type'. 'center' is the image target center location (tuple/np.ndarray of + length 2 of floats) and 'target_type' has the key of the type of target + (string). ``img_targets[i]`` is associated with ``tgts_detected[i]`` for i from + 0 to num_matches + num_matches : int + Number of detections/matches found + """ + + # Assume the target is small relative to the curvature of the model + # Assume the target is a perfect circle + # Find the plane relatively to the camera that the target lies on + # Project points from the target into the camera + # Use those points to make a Polygon + + img_targets = [] + for tgt in visible_tgts: + # Basis vectors of plane based on a pt at the origin and normal (a, b, c) + # Step 1, find another point on the plane + # Equation of plane and pt (x0, y0, z0) + # a(x-x0) + b(y-y0) + c (z-z0) = 0 + # ax + by + cz = ax0 + by0 + cz0 + # x and y are free variables, so use (1, 1) + # cz = ax0 + by0 + cz0 - a - b + # (x0, y0, z0) = (0, 0, 0) + # z = -(a + b) / c + # Normalize Vector - Basis Vector 1 + # Basis Vector 2 - Cross Product between Normal and Basis Vector 1 + + # z location of point on plane with x = 1 and y = 1 + z0 = -(tgt['norm'][0][0] + tgt['norm'][1][0]) / tgt['norm'][2][0] + + # Normalize x, y, and z + basis_v0 = [1, 1, z0] / np.linalg.norm([1, 1, z0]) + + # Get second basis vector + basis_v1 = np.cross(tgt['norm'][:, 0], basis_v0) + basis_v1 /= np.linalg.norm(basis_v1) + + # Scale based on the target size (size is diameter, we want radius) + basis_v0 = basis_v0 * tgt['size'] / 2 + basis_v1 = basis_v1 * tgt['size'] / 2 + + # Convert to (3, 1) vectors + basis_v0 = basis_v0.reshape(3, 1) + basis_v1 = basis_v1.reshape(3, 1) + + # Get points along the edge of the target + thetas = np.linspace(0, 2*np.pi, num_poly_pts).reshape(1, -1) + target_edge_points = np.cos(thetas) * basis_v0 + np.sin(thetas) * basis_v1 + target_edge_points += tgt['tvec'] + + # Project those points into the image + img_points = photogrammetry.project_3d_point( + rmat, tvec, cameraMatrix, distCoeffs, target_edge_points + ) + + # Get a shapely polygon representation of the polyon + tgt_poly0 = Polygon(img_points) + + # Get the bounding box of the projected points + x_min, x_max = min(img_points[:, 0]+0.5), max(img_points[:, 0]-0.5) + y_min, y_max = min(img_points[:, 1]+0.5), max(img_points[:, 1]-0.5) + + projected_center = [(x_min + x_max)/2, (y_min + y_max)/2] + + x_min, y_min = np.floor(x_min).astype(int), np.floor(y_min).astype(int) + x_max, y_max = np.ceil(x_max).astype(int), np.ceil(y_max).astype(int) + + # Get the crop of the image near the target + crop_radius = test_config["max_dist"] + test_config[tgt["target_type"] + "_pad"] + bbox = np.array([ + [ + np.floor(projected_center[0] - crop_radius), + np.floor(projected_center[1] - crop_radius) + ], + [ + np.ceil(projected_center[0] + crop_radius), + np.ceil(projected_center[1] + crop_radius) + ], + ]).astype(int) + + # If bbox goes out of bounds of the image, ignore it + if ( + (bbox[0][0] < 0) + or (bbox[0][1] < 0) + or (bbox[1][0] >= img.shape[1]) + or (bbox[1][1] >= img.shape[0]) + ): + img_targets.append(None) + continue + + img_cropped = copy.deepcopy( + img[bbox[0][1] : bbox[1][1], bbox[0][0] : bbox[1][0]] + ) + img_cropped -= img_cropped.min() + img_cropped = 255 * (img_cropped / img_cropped.max()) + img_cropped = np.rint(img_cropped).astype(np.uint8) + + # For each pixel in the bounding box, find the overlap + # This creates a template of the target + template0 = np.zeros((y_max - y_min + 1, x_max - x_min + 1)) + for x in range(x_min, x_max + 1): + for y in range(y_min, y_max + 1): + template0[y - y_min][x - x_min] = area_overlap(tgt_poly0, (x, y)) + template0 = ~np.rint(255 * template0).astype(np.uint8) + + # Get a shapely representation of the polygon shifted by 1/2 a pixel + img_points_shifted = img_points + 0.5 + tgt_poly0pt5 = Polygon(img_points_shifted) + + # Get the bounding box of the projected points + x_min = min(img_points_shifted[:, 0]+0.5) + x_max = max(img_points_shifted[:, 0]-0.5) + + y_min = min(img_points_shifted[:, 1]+0.5) + y_max = max(img_points_shifted[:, 1]-0.5) + + x_min = np.floor(x_min).astype(int) + y_min = np.floor(y_min).astype(int) + + x_max = np.ceil(x_max).astype(int) + y_max = np.ceil(y_max).astype(int) + + # Shift the projected points by half a pixel and create a second template + template0pt5 = np.zeros((y_max - y_min + 1, x_max - x_min + 1)) + for x in range(x_min, x_max + 1): + for y in range(y_min, y_max + 1): + template0pt5[y - y_min][x - x_min] = area_overlap(tgt_poly0pt5, (x, y)) + template0pt5 = ~np.rint(255 * template0pt5).astype(np.uint8) + + # Perform template matching to find the target + # All the methods but cv2.TM_CORR gave the same results on small test set + # cv2.TM_CCOEFF_NORMED has nice theoretical properties because it corrects for + # brightness (looks for correlation) and is normalized to correct variations + # in template size + # cv2.CORR and its normed variant latch into the brightest part of the image + # cv2.TM_SQDIFF and its normed variant are wildly inconsistent with the + # threshold, potentially due to noise (camera and surface texture) + res0 = cv2.matchTemplate(img_cropped, template0, cv2.TM_CCOEFF_NORMED) + target_sites0 = np.argwhere(res0 > test_config["crosscorr_coeff"]) + + res0pt5 = cv2.matchTemplate(img_cropped, template0pt5, cv2.TM_CCOEFF_NORMED) + target_sites0pt5 = np.argwhere(res0pt5 > test_config["crosscorr_coeff"]) + + # Combine the found target sites + target_sites = np.concatenate([target_sites0, target_sites0pt5], 0) + + # If none above the threshold were found, ignore this target, we missed it + if len(target_sites) == 0: + center = None + + # If 1 or more site were found, get the 'correct' site + else: + # Filter the target sites for those less than test_config["max_dist"] from + # the center of the image + crop_center = (np.array(img_cropped.shape) - (1, 1)) / 2 + dists = np.linalg.norm(target_sites - crop_center, axis=1) + valid_sites = np.argwhere(dists < test_config["max_dist"])[:, 0] + target_sites = np.array([target_sites[i, :] for i in valid_sites]) + + # If none are within the required distance, ignore this target, we missed it + if len(target_sites) == 0: + center = None + + # If exactly one target site was found, that's the spot we think it is in + elif len(target_sites) == 1: + center = target_sites[0] + np.array(template0.shape[::-1]) / 2 + + # If more than 1 target site was found, check if they are all for the same + # target. If they are, use the one with the largest correlation + else: + x_max = max(target_sites[:, 1]) + x_min = min(target_sites[:, 1]) + y_max = max(target_sites[:, 0]) + y_min = min(target_sites[:, 0]) + + target_pad = test_config[tgt["target_type"] + "_pad"] + if np.linalg.norm([[x_max - x_min], [y_max - y_min]]) < target_pad: + # Get the res with the largest correlation + res = res0 if res0.argmax() > res0pt5.argmax() else res0pt5 + + # Get the position in that res of the largest correlation + top_left = np.unravel_index(res.argmax(), res.shape)[::-1] + + # Get center of the template (both templates have the same shape) + center = top_left + np.array(template0.shape[::-1]) / 2 + + else: + center = None + + # If a valid target site was not found, there was no detection + if center is None: + img_targets.append(None) + + # If a valid target site was found, add it to img_targets + else: + # 'Center' is given in image crop coordinates. Need to offset that by the + # top left corner of the bounding box to put it into image coordinates + center += (bbox[0][0], bbox[0][1]) + img_targets.append({'target_type' : tgt['target_type'], 'center' : center}) + + # Reorder the targets and image targets + # The first n items of tgts corresponds to the first n items of img_targets 1:1 + # where n is num_matches. After that, they are unmatched tgts + # (The length of img_targets will always be equal to or less the length of tgts + # due to the way this routine is set up) + tgts_matched, img_targets_matched = [], [] + tgts_unmatched = [] + for tgt, img_target in zip(visible_tgts, img_targets): + if img_target is None: + tgts_unmatched.append(tgt) + else: + tgts_matched.append(tgt) + img_targets_matched.append(img_target) + + num_matches = len(tgts_matched) + tgts_detected = tgts_matched + tgts_unmatched + + return tgts_detected, img_targets_matched, num_matches + + +def filter_dist_filter( + rmat, + tvec, + cameraMatrix, + distCoeffs, + tgts, + img_targets, + num_matches, + intra_dist, + inter_dist +): + """Filters the targets and image target that are too close + + Conditions Enforced: + 1) The projected location of each target has at most 1 img_target within a radius of + inter_dist from it + 2) Each img_target has at most 1 projected target location within a radius of + inter_dist from it + 3) The projected location of each target is seperated from the projected location of + each other target by a distance of at least intra_dist + 4) Each img_target is seperated from each other img_target by a distance of at least + intra_dist + + Parameters + ---------- + rmat : np.ndarray, shape (3, 3), float + Rotation matrix from camera to object + tvec : np.ndarray, shape (3, 1), float + Translation vector from camera to object + cameraMatrix : np.ndarray, shape (3, 3), float + The (openCV formatted) camera matrix for the camera + distCoeffs : np.ndarray, shape (1, 5), float + The (openCV formatted) distortion coefficients for the camera + tgts : list + 3D targets. Each target is a dict and has, at a minimum, the keys 'tvec', and + 'target_type'. 'tvec' has a :class:`numpy.ndarray` (3, 1) representing the + position of the target relative to the model origin for its associated value. + 'target_type' has a string representing the type of target (most commonly 'dot') + for its associated value. ``tgts[i]`` is associated with ``img_targets[i]`` for + i from 0 to `num_matches` + img_targets : list + Matched image targets. Each dict has, at a minimum, the key 'center' which has a + value of is the image target center location (tuple/np.ndarray of floats). + ``img_targets[i]`` is associated with ``tgts[i]`` for i from 0 to `num_matches` + num_matches : int + An integer of the number of matches. Must be less than or equal to + ``min(len(tgts), len(img_targets))`` + max_dist : float + The maximum distance between two image targets. Any image targets farther than + this distance are filtered out. + + Returns + ------- + tgts_bi_filter : list + Target positions (`tgts`) that have been bifiltered. + img_targets_bi_filter : list + Target positions in the image. + num_matches_bi_filter : int + + Notes + ----- + ``tgts_matched_bi_filter[i]`` is associated with ``img_targets_bi_filter[i]`` for i + from 0 to `num_matches_bi_filter`. + """ + + def inter_filter(tgts, img_targets, num_matches): + """ Helper function that enforces conditions 1 and 2 + """ + # Project the points into the image + tgt_projs = photogrammetry.project_targets( + rmat, tvec, cameraMatrix, distCoeffs, tgts + ) + + # Get the indices of the matched targets whose projected image location has + # exactly 1 img target within a distance of inter_dist + targets_inter_pass = set() + for i in range(num_matches): + inter_target_key = True + for j in range(len(img_targets)): + if i == j: + continue + + dist = np.linalg.norm(tgt_projs[i]["proj"] - img_targets[j]["center"]) + + # If the distance is less than inter_dist, this target fails the filter + if dist < inter_dist: + inter_target_key = False + break + + # If this target passed the filter, add it to the pass lists + if inter_target_key: + targets_inter_pass.add(i) + + # Get the indices of the matched img_targets that have exactly 1 target with a + # projected image location within a distance of inter_dist + targets_intra_pass = set() + for i in range(num_matches): + intra_target_key = True + for j in range(len(tgt_projs)): + if i == j: + continue + + dist = np.linalg.norm(img_targets[i]["center"] - tgt_projs[j]["proj"]) + + # If the distance is less than intra_dist, this target fails the filter + if dist < intra_dist: + intra_target_key = False + break + + # If this target passed the filter, add it to the pass lists + if intra_target_key: + targets_intra_pass.add(i) + + # Get the intersection of the two sets + inter_pass = targets_inter_pass.intersection(targets_intra_pass) + + # Add the matched tgts and matched img_targets that pass the filter to new lists + tgts_inter_filter, img_targets_inter_filter = [], [] + for i in range(num_matches): + if i in inter_pass: + tgts_inter_filter.append(tgts[i]) + img_targets_inter_filter.append(img_targets[i]) + + # Add the tgts and img_targets that were not matched and/or did pass the filter. + # Since they have indices greater than the return value of num_matches, they + # are known to be not matched + for i in range(max(len(tgts), len(img_targets))): + if i not in inter_pass: + if i < len(tgts): + tgts_inter_filter.append(tgts[i]) + if i < len(img_targets): + img_targets_inter_filter.append(img_targets[i]) + + return tgts_inter_filter, img_targets_inter_filter, len(inter_pass) + + def intra_filter(tgts, img_targets, num_matches): + """ Helper function that enforces conditions 3 and 4 + """ + # Project the points into the image + tgt_projs = photogrammetry.project_targets( + rmat, tvec, cameraMatrix, distCoeffs, tgts + ) + + # Find the projected target locations that are too close together + tgts_intra_pass = set() + for i in range(len(tgts)): + intra_target_key = True + for j in range(len(tgts)): + if i == j: + continue + + # Calculate the distance between point i and j + dist = np.linalg.norm( + [ + tgt_projs[i]["proj"][0] - tgt_projs[j]["proj"][0], + tgt_projs[i]["proj"][1] - tgt_projs[j]["proj"][1], + ] + ) + + # If the distance is less than intra_dist, this target fails the filter + if dist < intra_dist: + intra_target_key = False + break + + if intra_target_key: + tgts_intra_pass.add(i) + + # Find the projected target locations that are too close together + img_targets_intra_pass = set() + for i in range(len(img_targets)): + intra_target_key = True + for j in range(len(img_targets)): + if i == j: + continue + + # Calculate the distance between point i and j + dist = np.linalg.norm( + [ + img_targets[i]["center"][0] - img_targets[j]["center"][0], + img_targets[i]["center"][1] - img_targets[j]["center"][1], + ] + ) + + # If the distance is less than intra_dist, this target fails the filter + if dist < intra_dist: + intra_target_key = False + break + + if intra_target_key: + img_targets_intra_pass.add(i) + + # Get the intersection of the two sets + intra_pass = tgts_intra_pass.intersection(img_targets_intra_pass) + + # Add the matched tgts and matched img_targets that pass the filter to new lists + tgts_inter_filter, img_targets_inter_filter = [], [] + for i in range(num_matches): + if i in intra_pass: + tgts_inter_filter.append(tgts[i]) + img_targets_inter_filter.append(img_targets[i]) + + # Add the tgts and img_targets that were not matched and/or did pass the filter. + # Since they have indices greater than the return value of num_matches, they + # are known to be not matched + for i in range(max(len(tgts), len(img_targets))): + if i not in intra_pass: + if i < len(tgts): + tgts_inter_filter.append(tgts[i]) + if i < len(img_targets): + img_targets_inter_filter.append(img_targets[i]) + + return tgts_inter_filter, img_targets_inter_filter, len(intra_pass) + + # Perform the inter filter (enforce conditions 1 and 2) + inter_filer_retval = inter_filter(tgts, img_targets, num_matches) + tgts_inter, img_targets_inter, num_matched_inter = inter_filer_retval + + # Perform the intra filter (enforce conditions 3 and 4) + filtered_retval = intra_filter(tgts_inter, img_targets_inter, num_matched_inter) + tgts_filtered, img_targets_filtered, num_matched_filtered = filtered_retval + + return tgts_filtered, img_targets_filtered, num_matched_filtered + + +# TODO: Implement this function properly +# TODO: make this return a num_matches, all targets, and all unique img_targets just +# like to other targets functions +def filter_partially_occluded( + rmat, tvec, focal_length, tgts, img_targets, vis_checker, test_config +): + """Checks corners of cropped area used for sub-pixel localization for occlusion + + If the corners of the crop used for sub-pixel localization jumps surfaces, then the + target is likely partially occluded. This most commonly occurs when a booster + partially occludes a target on the core of a launch vehicle + + To get the 3D positions of corners of the cropped area, start at the target tvec. + Approximate the physical distance (in inches) of the cropped area (which is + done in pixels) using the focal length and distance from camera to model. Take + steps along the camera image plane to get to the approximate corner locations in 3D. + + With the corner locations, ensure that they are not inside the model (since the + model is curved the steps along the image plane may put the corners slightly inside + the model. Then check for occlusions. If the corner is occluded, the target is + deemed partially occluded. If none of the corners are occluded, the target is deemed + not occluded (but is still potentially partially occluded). Only the corners are + checked to reduce computation + + Parameters + ---------- + rmat : np.ndarray, shape (3, 3), float + Rotation matrix from camera to object + tvec : np.ndarray, shape (3, 1), float + Translation vector from camera to object + focal_length : float + Focal length of the camera. Most easily accessible from a ``cameraMatrix[0][0]`` + tgts : list + 3D targets. Each target is a dict and has, at a minimum, the keys 'tvec', + 'target_type', 'norm'. 'tvec' has a :class:`numpy.ndarray` (3, 1) representing + the position of the target relative to the model origin for its associated + value. 'norm' has a :class:`numpy.ndarray` (3, 1) representing the normal vector + of the target relative to the model origin for its associated value. + 'target_type' has a string representing the type of target (most commonly 'dot') + for its associated value. ``tgts[i]`` is associated with ``img_targets[i]`` + img_targets : list + Image targets. Each image target should be a dict, but this function does not + set any strict requirements other than that ``img_targets[i]`` is associated + with ``tgts[i]`` + vis_checker : ~upsp.cam_cal_utils.visibility.VisibilityChecker + Visibility checker object with the relevant BVH and oblique viewing angle + test_config : dict + Processign parameters with, at a minimum, a key for each target type of the + targets in `tgts` and `img_targets`. They key is `target_type` + '_pad'. This is + the padding around the img target center location to use to sub-pixel localize + + Returns + ------- + tgts_filtered : list + Target positions (`tgts`) that have not been filtered out + img_targets_filtered : list + Target positions in the image. + """ + # Get the direction for the u and v of pixel space + rmat_inv, tvec_inv = photogrammetry.invTransform(rmat, tvec) + + # Check each target for partial occlusions + tgts_filtered = [] + img_targets_filtered = [] + for tgt, img_target in zip(tgts, img_targets): + # Get the scale factor + obj_dist = np.linalg.norm(tvec.T - tgt["tvec"]) + + # For the scale factor, we use the similar triangles of the object distance by + # actual distance (in) vs focal length by pixel distance (px) + # The scale factor is the object distance divided by the focal length, times the + # pixel distance. We add one to the pad distance for a margin of safety + step_sz = ( + obj_dist * (test_config[tgt["target_type"] + "_pad"] + 1) / focal_length + ) + + # Get the step vector for u and v for the corners + # Get the u and v steps + uv = step_sz * rmat_inv[:, 0:2] + u = uv[:, 0] + v = uv[:, 1] + + # Package the corners. We have 1 corner for steps in the given directions + corners = [] + for x, y in [(-1, -1), (-1, 1), (1, -1), (1, 1)]: + corners.append({"tvec": tgt["tvec"] + x * u + y * v, "norm": tgt["norm"]}) + + # Check for occlusions. Each corner has a boolean and an occlusion position + occlusions = photogrammetry.get_occlusions_targets( + rmat, tvec, corners, vis_checker + ) + + # Check that all corners are visible + are_all_corners_visible = True + for corner, occlusion in zip(corners, occlusions): + # If there was no occlusion, continue + if not occlusion[0]: + continue + + # If there was an occlusion, check the distance from the occlusion to corner + # If that distance is less than sqrt(2) * step_sz (since we step in both + # x and y) it is possible that the corner is inside the model so it is + # unfairly occluded. + # If the distance is greater this corner is just occluded, no questions + dist = np.linalg.norm(occlusion[1] - corner["tvec"]) + if dist > step_sz * np.sqrt(2): + are_all_corners_visible = False + break + + # To fairly check this corner, bump it to the occluded location + bumped_corner = copy.copy(corner) + bumped_corner["tvec"] = occlusion[1] + + # Check this bumped corner for occlusion + bumped_occlusion = photogrammetry.get_occlusions_targets( + rmat, tvec, [bumped_corner], vis_checker + )[0] + + # If there is still an occlusion, this corner is likely occluded + # There is a chance that the distance between the bumped occlusion and + # the original corner is still less than step_sz * np.sqrt(2). However + # the occurrence seems small and not worthy of further checks. + # This is a potential TODO to set up as a while loop + if bumped_occlusion[0]: + are_all_corners_visible = False + break + + # If all corners passed the visibility check, this corner is fine and we can + # move onto the next corner + if are_all_corners_visible: + tgts_filtered.append(tgt) + img_targets_filtered.append(img_target) + + return tgts_filtered, img_targets_filtered + + +def filter_matches( + rmat, tvec, cameraMatrix, distCoeffs, tgts, img_targets, num_matches, test_config +): + tgts_filtered, img_targets_filtered, num_matched_filtered = filter_dist_filter( + rmat, + tvec, + cameraMatrix, + distCoeffs, + tgts, + img_targets, + num_matches, + test_config["max_dist"], + test_config["min_dist"] + ) + + return tgts_filtered, img_targets_filtered, num_matched_filtered diff --git a/python/upsp/video/__init__.py b/python/upsp/video/__init__.py new file mode 100644 index 0000000..25b4e75 --- /dev/null +++ b/python/upsp/video/__init__.py @@ -0,0 +1,60 @@ +"""Video file reading and parsing + +Typical usage of this subpackage involves the use of the high-level :func:`video_reader` +context manager, which accepts a path to a video file and yields an appropriate reader +object. + +For a Phantom Cine file (:class:`~upsp.video.cine.CineReader`):: + + from upsp.video import video_reader + + with video_reader("video.cine") as reader: + nframes = reader.frame_count + imgs = reader.read_frames(10) + +Or for a Photron MRAW file (:class:`~upsp.video.mraw.MrawReader`):: + + with video_reader("video.mraw") as reader: + bpp = reader.bit_depth + img = reader.read_frame(0) + +This subpackage also functions as a tool for inspecting a video, displaying some of the +video properties and (optionally) displaying the first frame:: + + $ python -m upsp.video video.cine +""" + +import contextlib +from pathlib import Path + +from .base import VideoReader +from .cine import CineReader +from .mraw import MrawReader + + +@contextlib.contextmanager +def video_reader(path): + """Retrieve an appropriate video file reader for a given video file + + Parameters + ---------- + path : path-like + Path to the video. + + Yields + ------ + reader : ~upsp.video.base.VideoReader + An open video reader instance. + """ + path = path if isinstance(path, Path) else Path(path) + ext = path.suffix + + if ext == ".cine": + cls = CineReader + elif ext in {".mraw", ".cih"}: + cls = MrawReader + else: + raise ValueError(f"Video file with extension {ext} not supported") + + with cls(path) as reader: + yield reader diff --git a/python/upsp/video/__main__.py b/python/upsp/video/__main__.py new file mode 100644 index 0000000..c657448 --- /dev/null +++ b/python/upsp/video/__main__.py @@ -0,0 +1,43 @@ +import argparse +import matplotlib.pyplot as plt + +from . import video_reader + + +def main(argv=None): + parser = argparse.ArgumentParser() + parser.add_argument("video", help="Path to a video file") + parser.add_argument("--no-plot", action="store_true", help="Only print metadata") + args = parser.parse_args(argv) + + with video_reader(args.video) as vid: + print("Video Properties") + print(30 * "-") + for attr, unit in [ + ("frame_count", ""), + ("frame_rate", "Hz"), + ("width", "px"), + ("height", "px"), + ("bit_depth", "bits"), + ("raw_bit_depth", "bits"), + ]: + val = getattr(vid, attr) + name = attr.replace("_", " ") + print(f"{name:20s} {val} {unit}") + + if not args.no_plot: + img = vid.read_frame(0) + + plt.matshow( + img, + vmin=0, + vmax=2**vid.bit_depth - 1, + interpolation="none", + cmap="magma", + ) + plt.colorbar() + plt.show() + + +if __name__ == "__main__": + main() diff --git a/python/upsp/video/base.py b/python/upsp/video/base.py new file mode 100644 index 0000000..407791e --- /dev/null +++ b/python/upsp/video/base.py @@ -0,0 +1,201 @@ +"""Generic video reader abstraction layer""" + +import abc + +import numpy + + +class VideoReader(abc.ABC): + """Video reader interface. + + This provides filetype-specific readers with default implementations of file + handling, so they can uniformly be used with the context manager pattern, e.g.:: + + with ReaderImpl("some_file.ext") as vid: + print(vid.frame_count) + vid.read_frame(0) + + Parameters + ---------- + path : path-like + Path to the video file to read. + """ + + dtype = numpy.uint16 + + def __init__(self, path): + self.path = path + self.fd = None + + def open(self): + """Open the video to begin reading + + This is normally handled via context manager. + """ + self.fd = open(self.path, "rb") + self.initialize() + + def close(self): + """Close the video, preventing further reading + + This is normally handled via context manager. + """ + if self.fd is not None: + self.fd.close() + + def __enter__(self): + self.open() + return self + + def __exit__(self, exc_type, exc_val, exc_tb): + self.close() + + def _validate_index(self, idx): + """Check if the video has a frame with a given index + + Raises + ------ + ValueError : `idx` is out of range + """ + if idx >= self.frame_count: + raise ValueError( + f"Invalid frame index {idx}. File has {self.frame_count} frames" + ) + + @property + @abc.abstractmethod + def frame_count(self): + """Total number of frames in the file""" + return + + @property + @abc.abstractmethod + def frame_rate(self): + """Video frame rate, in Hz""" + return + + @property + @abc.abstractmethod + def width(self): + """Video frame width, in pixels""" + return + + @property + @abc.abstractmethod + def height(self): + """Video frame height, in pixels""" + return + + @property + @abc.abstractmethod + def bit_depth(self): + """Bit depth of frame data, following any post-processing + + This property should be used for determining the range of values possible in + frame data returned by :meth:`read_frame` (i.e. :math:`[0, 2^d - 1]`), since it + represents the bit depth following any post-processing operations such as a + lookup table expansion. + + See Also + -------- + VideoReader.raw_bit_depth + Bit depth as stored in the file, prior to any processing. + """ + return + + @property + def raw_bit_depth(self): + """Bit depth of frame data as stored in the file""" + return self.bit_depth + + @abc.abstractmethod + def initialize(self): + """One-time initialization performed on file open + + Concrete implementations should perform any operations necessary to obtain video + properties such as bit depth, frame size, etc. + """ + return + + @abc.abstractmethod + def read_frame(self, idx): + """Read a single frame by index. + + Parameters + ---------- + idx : int + Index of the frame to read. + + Returns + ------- + img : ndarray, shape (height, width) + """ + return + + def read_frames(self, nframes, start=0, out=None): + """Read multiple consectutive frames from the video + + Parameters + ---------- + nframes : int + Number of frames to read. + start : int, optional + First frame to read. Default is the first frame in the file. + out : ndarray, optional + Array to place results in. Must have the correct shape (nframes, height, + width). The dtype may be different from that of the reader, in which case + casting is done on the fly (per frame). + + Returns + ------- + out : ndarray, shape (nframes, height, width) + + Notes + ----- + Subclasses may want to override this method for efficiency (e.g. one contiguous + read). The base implementation simply calls :meth:`read_frames` in a loop. + """ + # subclasses may want to override for efficiency + self._validate_index(start + nframes - 1) + shape = (nframes, self.height, self.width) + if out is None: + out = numpy.empty((nframes, self.height, self.width), dtype=self.dtype) + elif out.shape != shape: + raise ValueError(f"Expected shape {shape}, got {out.shape}") + + for i, f in enumerate(range(start, start + nframes)): + out[i] = self.read_frame(f) + + return out + + def iter_frames(self, nframes, start=0, frames_per_chunk=100, dtype=None): + """Iterate over chunks of frames in a video + + Parameters + ---------- + nframes : int + Total number of frames to read. + start : int, optional + First frame to read. Default is the first frame in the file. + frames_per_chunk : int, optional + Number of frames to read per iteration. The final iteration may produce + fewer than `frames_per_chunk` if `nframes` is not an integer multiple. + dtype : dtype, optional + Data type to return. See :meth:`read_frames` for handling of conversion. + + Yields + ------ + imgs : ndarray, shape (nframes_in_chunk, height, width) + Image frames. This is a view of an array that is recycled on each read, so + it may need to be copied each time, depending on usage. + """ + self._validate_index(start + nframes - 1) + shape = (min(frames_per_chunk, self.frame_count), self.height, self.width) + dtype = self.dtype if dtype is None else dtype + out_buf = numpy.empty(shape, dtype=dtype) + for iframe in range(start, start + nframes, frames_per_chunk): + iframe_end = min(iframe + frames_per_chunk, start + nframes) + frames_to_read = min(frames_per_chunk, iframe_end - iframe) + yield self.read_frames( + frames_to_read, start=iframe, out=out_buf[:frames_to_read] + ) diff --git a/python/upsp/video/cine.py b/python/upsp/video/cine.py new file mode 100644 index 0000000..fe09ca3 --- /dev/null +++ b/python/upsp/video/cine.py @@ -0,0 +1,504 @@ +"""Python module for reading cine files.""" + +import struct +from ctypes import ( + Structure, + c_char, + c_double, + c_float, + c_int16, + c_int32, + c_uint8, + c_uint16, + c_uint32, + sizeof, +) + +import numpy + +from .base import VideoReader +from .util import unpack_10bpp, unpack_12bpp + +MAXLENDESCRIPTION_OLD = 121 +MAXLENDESCRIPTION = 4096 +OLDMAXFILENAME = 65 + + +class PrintableStructure(Structure): + def __repr__(self): + return "\n".join( + "{}: {}".format(f[0], getattr(self, f[0])) for f in self._fields_ + ) + + +class PrintableSubStructure(Structure): + def __repr__(self): + return "{}\n".format(self.__class__.__name__) + "\n".join( + "\t{}: {}".format(f[0], getattr(self, f[0])) for f in self._fields_ + ) + + +class TIME64(PrintableSubStructure): + _fields_ = [ + ("fractions", c_uint32), + ("seconds", c_uint32), + ] + + +class TC(PrintableSubStructure): + _fields_ = [ + ("framesU", c_uint8, 4), + ("framesT", c_uint8, 2), + ("dropFrameFlag", c_uint8, 1), + ("colorFrameFlag", c_uint8, 1), + ("secondsU", c_uint8, 4), + ("secondsT", c_uint8, 3), + ("flag1", c_uint8, 1), + ("minutesU", c_uint8, 4), + ("minutesT", c_uint8, 3), + ("flag2", c_uint8, 1), + ("hoursU", c_uint8, 4), + ("hoursT", c_uint8, 2), + ("flag3", c_uint8, 1), + ("flag4", c_uint8, 1), + ("userBitData", c_uint32), + ] + + +class WBGAIN(PrintableSubStructure): + _fields_ = [ + ("R", c_float), + ("B", c_float), + ] + + +class RECT(PrintableSubStructure): + _fields_ = [ + ("left", c_int32), + ("top", c_int32), + ("right", c_int32), + ("bottom", c_int32), + ] + + +class IMFILTER(PrintableSubStructure): + _fields_ = [ + ("dim", c_int32), + ("shifts", c_int32), + ("bias", c_int32), + ("Coef", c_int32 * 25), + ] + + +class CINEFILEHEADER(PrintableStructure): + _fields_ = [ + ("Type", c_uint16), + ("Headersize", c_uint16), + ("Compression", c_uint16), + ("Version", c_uint16), + ("FirstMovieImage", c_int32), + ("TotalImageCount", c_uint32), + ("FirstImageNo", c_int32), + ("ImageCount", c_uint32), + ("OffImageHeader", c_uint32), + ("OffSetup", c_uint32), + ("OffImageOffsets", c_uint32), + ("TriggerTime", TIME64), + ] + + +class BITMAPINFOHEADER(PrintableStructure): + _fields_ = [ + ("biSize", c_uint32), # header size, 40 + ("biWidth", c_int32), # bitmap width, pixels + ("biHeight", c_int32), # bitmap height, pixels + ("biPlanes", c_uint16), # always 1 + ("biBitCount", c_uint16), # not real depth + ("biCompression", c_uint32), # + ("biSizeImage", c_uint32), # image size, bytes + ("biXPelsPerMeter", c_int32), # x pixels per meter + ("biYPelsPerMeter", c_int32), # y pixels per meter + ("biClrUsed", c_uint32), # + ("biClrImportant", c_uint32), # color indices for display + ] + + +class SETUP(PrintableStructure): + _pack_ = 1 + _fields_ = [ + ("FrameRate16", c_uint16), + ("Shutter16", c_uint16), + ("PostTrigger16", c_uint16), + ("FrameDelay16", c_uint16), + ("AspectRatio", c_uint16), + ("Res7", c_uint16), + ("Res8", c_uint16), + ("Res9", c_uint8), + ("Res10", c_uint8), + ("Res11", c_uint8), + ("TrigFrame", c_uint8), + ("Res12", c_uint8), + ("DescriptionOld", c_char * MAXLENDESCRIPTION_OLD), + ("Mark", c_uint16), + ("Length", c_uint16), + ("Res13", c_uint16), + ("SigOption", c_uint16), + ("BinChannels", c_int16), + ("SamplesPerImage", c_uint8), + ("BinName", c_char * (8 * 11)), + ("AnaOption", c_uint16), + ("AnaChannels", c_int16), + ("Res6", c_uint8), + ("AnaBoard", c_uint8), + ("ChOption", c_int16 * 8), + ("AnaGain", c_float * 8), + ("AnaUnit", c_char * (8 * 6)), + ("AnaName", c_char * (8 * 11)), + ("lFirstImage", c_int32), + ("dwImageCount", c_uint32), + ("nQFactor", c_int16), + ("wCineFileType", c_uint16), + ("szCinePath", c_char * (4 * OLDMAXFILENAME)), + ("Res14", c_uint16), + ("Res15", c_uint8), + ("Res16", c_uint8), + ("Res17", c_uint16), + ("Res18", c_double), + ("Res19", c_double), + ("Res20", c_uint16), + ("Res1", c_int32), + ("Res2", c_int32), + ("Res3", c_int32), + ("ImWidth", c_uint16), + ("ImHeight", c_uint16), + ("EDRShutter16", c_uint16), + ("Serial", c_uint32), + ("Saturation", c_int32), + ("Res5", c_uint8), + ("AutoExposure", c_uint32), + ("bFlipH", c_uint32), + ("bFlipV", c_uint32), + ("Grid", c_uint32), + ("FrameRate", c_uint32), + ("Shutter", c_uint32), + ("EDRShutter", c_uint32), + ("PostTrigger", c_uint32), + ("FrameDelay", c_uint32), + ("bEnableColor", c_uint32), + ("CameraVersion", c_uint32), + ("FirmwareVersion", c_uint32), + ("SoftwareVersion", c_uint32), + ("RecordingTimeZone", c_int32), + ("CFA", c_uint32), + ("Bright", c_int32), + ("Contrast", c_int32), + ("Gamma", c_int32), + ("Res21", c_uint32), + ("AutoExpLevel", c_uint32), + ("AutoExpSpeed", c_uint32), + ("AutoExpRect", RECT), + ("WBGain", WBGAIN * 4), + ("Rotate", c_int32), + ("WBView", WBGAIN), + ("RealBPP", c_uint32), + ("Conv8Min", c_uint32), + ("Conv8Max", c_uint32), + ("FilterCode", c_int32), + ("FilterParam", c_int32), + ("UF", IMFILTER), + ("BlackCalSVer", c_uint32), + ("WhiteCalSVer", c_uint32), + ("GrayCalsVer", c_uint32), + ("bStampTime", c_uint32), + ("SoundDest", c_uint32), + ("FRPSteps", c_uint32), + ("FRPImgNr", c_int32 * 16), + ("FRPRate", c_uint32 * 16), + ("FRPExp", c_uint32 * 16), + ("MCCnt", c_int32), + ("MCPercent", c_float * 64), + ("CICAlib", c_uint32), + ("CalibWidth", c_uint32), + ("CalibHeight", c_uint32), + ("CalibRate", c_uint32), + ("CalibExp", c_uint32), + ("CalibEDR", c_uint32), + ("CalibTemp", c_uint32), + ("HeadSerial", c_uint32 * 4), + ("RangeCode", c_uint32), + ("RangeSize", c_uint32), + ("Decimation", c_uint32), + ("MasterSerial", c_uint32), + ("Sensor", c_uint32), + ("ShutterNs", c_uint32), + ("EDRShutterNs", c_uint32), + ("FrameDelayNs", c_uint32), + ("ImPosXAcq", c_uint32), + ("ImPosYAcq", c_uint32), + ("ImWidthAcq", c_uint32), + ("ImHeightAcq", c_uint32), + ("Description", c_char * MAXLENDESCRIPTION), + ("RisingEdge", c_uint32), + ("FilterTime", c_uint32), + ("LongReady", c_uint32), + ("ShutterOff", c_uint32), + ("Res4", c_uint8 * 16), + ("bMetaWB", c_uint32), + ("Hue", c_int32), + ("BlackLevel", c_int32), + ("WhiteLevel", c_int32), + ("LensDescription", c_char * 256), + ("LensAperture", c_float), + ("LensFocusDistance", c_float), + ("LensFocalLength", c_float), + ("fOffset", c_float), + ("fGain", c_float), + ("fSaturation", c_float), + ("fHue", c_float), + ("fGamma", c_float), + ("fGammaR", c_float), + ("fGammaB", c_float), + ("fFlare", c_float), + ("fPedestalR", c_float), + ("fPedestalG", c_float), + ("fPedestalB", c_float), + ("fChroma", c_float), + ("ToneLabel", c_char * 256), + ("TonePoints", c_int32), + ("fTone", c_float * (32 * 2)), + ("UserMatrixLabel", c_char * 256), + ("EnableMatrices", c_uint32), + ("fUserMatrix", c_float * 9), + ("EnableCrop", c_uint32), + ("CropRect", RECT), + ("EnableResample", c_uint32), + ("ResampleWidth", c_uint32), + ("ResampleHeight", c_uint32), + ("fGain16_8", c_float), + ("FRPShape", c_uint32 * 16), + ("TrigTC", TC), + ("fPbRate", c_float), + ("fTcRate", c_float), + ("CineName", c_char * 256), + ] + + +class CineReader(VideoReader): + """Phantom Cine file reader. + + Attributes + ---------- + cfinfo : CINEFILEHEADER + All data contained in the cine file header. + bminfo : BITMAPINFOHEADER + All data contained in the bitmap info header. + setup : SETUP + All data contained in the setup header. + tagged_info : dict + Dictionary of tagged information blocks. Types depend on the data stored in the + file when the cine was captured. + """ + + # tagged information block type code -> name, data type (per frame) + tinfo_types = { + 1002: ("time_only", TIME64), + 1003: ("exposure", c_uint32), + 1007: ("timecode", TC), + } + + def initialize(self): + """Read cine file headers, annotations, and offsets""" + self._read_headers() + if self.cfinfo.OffSetup + self.setup.Length < self.cfinfo.OffImageOffsets: + self._read_annotations() + self._read_offsets() + + @property + def width(self): + return self.bminfo.biWidth + + @property + def height(self): + return self.bminfo.biHeight + + @property + def bit_depth(self): + # special case 10 bit with LUT expansion + return 12 if self.raw_bit_depth == 10 else self.raw_bit_depth + + @property + def raw_bit_depth(self): + return self.setup.RealBPP + + @property + def frame_rate(self): + return self.setup.FrameRate + + @property + def frame_count(self): + return self.cfinfo.ImageCount + + @property + def _packed(self): + """Whether pixel values in the file are packed or not""" + # doc says packed if this field == 256, but some have 1024 + return self.bminfo.biCompression != 0 + + def _read_headers(self): + """Read header data from the beginning of the file. + + Fields are read into 3 different structs (cfinfo, bminfo, and setup). These are + documented in the Vision Research Cine File Format document. + """ + self.cfinfo = CINEFILEHEADER() + self.bminfo = BITMAPINFOHEADER() + self.setup = SETUP() + + self.fd.readinto(self.cfinfo) + self.fd.readinto(self.bminfo) + self.fd.readinto(self.setup) + + bpp = self.setup.RealBPP + if bpp not in (10, 12): + raise NotImplementedError( + f"Unpacking frames with bit depth {bpp} not supported yet" + ) + + def _read_annotations(self): + """Read all tagged information blocks into a dict.""" + self.tagged_info = {} + self.fd.seek(self.cfinfo.OffSetup + self.setup.Length) + while self.fd.tell() < self.cfinfo.OffImageOffsets: + bsize = struct.unpack("<---- | -- pix 1 - ] + # byte 2: [ ---------> | <--pix 2 - ] + # byte 3: [ ---------- | ----><---- ] + # byte 4: [ - pix 3 -- | ---------> ] + pix = numpy.zeros(len(buf) * 8 // 10, dtype=numpy.uint16) + b = numpy.frombuffer(buf, dtype=numpy.uint8).astype(numpy.uint16) + pix[::4] = (b[::5] << 2) | (b[1::5] >> 6) + pix[1::4] = ((b[1::5] & 0x3F) << 4) | (b[2::5] >> 4) + pix[2::4] = ((b[2::5] & 0x0F) << 6) | (b[3::5] >> 2) + pix[3::4] = ((b[3::5] & 0x03) << 8) | b[4::5] + return pix + + +def unpack_12bpp(buf): + """Unpack 12-bit pixels into a 1D uint16 ndarray.""" + # pixel MSBits first + # byte 0: [ <- pix 0 - | ---------- ] + # byte 1: [ ---------> | <- pix 1 - ] + # byte 2: [ ---------- | ---------> ] + pix = numpy.zeros(len(buf) * 8 // 12, dtype=numpy.uint16) + b = numpy.frombuffer(buf, dtype=numpy.uint8).astype(numpy.uint16) + pix[::2] = (b[::3] << 4) | (b[1::3] >> 4) + pix[1::2] = ((b[1::3] & 0x0F) << 8) | b[2::3] + return pix + + +def pack_12bpp(pix): + """Pack a 1D array of pixel values into a 12-bit packed buffer.""" + # byte 0: [ <- pix 0 - | ---------- ] + # byte 1: [ ---------> | <- pix 1 - ] + # byte 2: [ ---------- | ---------> ] + # condition for interpreting as 12-bit unsigned values + pix[pix < 0] = 0 + pix = pix.astype(numpy.uint16) + pix[pix > 2**12 - 1] = 2**12 - 1 + + buf = numpy.zeros(pix.size * 12 // 8, dtype=numpy.uint8) + buf[0::3] = pix[0::2] >> 4 + buf[1::3] = ((pix[0::2] & 0x0F) << 4) | (pix[1::2] >> 8) + buf[2::3] = pix[1::2] + + return buf diff --git a/scripts/upsp-make-processing-tree b/scripts/upsp-make-processing-tree index 943b878..07f332b 100755 --- a/scripts/upsp-make-processing-tree +++ b/scripts/upsp-make-processing-tree @@ -27,6 +27,7 @@ if __name__ == "__main__": ap.add_argument("--proc", help="upsp processing parameters json file") ap.add_argument("--plot", help="upsp plotting parameters json file") args = ap.parse_args() + outdir = os.path.realpath(args.outdir) upsp.processing.tree.create( - args.outdir, args.data, args.user, args.proc, args.plot + outdir, args.data, args.user, args.proc, args.plot ) diff --git a/test/data/camera-tunnel-calibration/camera01_35_6.json b/test/data/camera-tunnel-calibration/camera01_35_6.json new file mode 100755 index 0000000..195a429 --- /dev/null +++ b/test/data/camera-tunnel-calibration/camera01_35_6.json @@ -0,0 +1,11 @@ +{"uPSP_cameraMatrix": [[1380.2632820187425, 0.0, 21.908701486902032], + [0.0, 1380.2632820187425, 0.778541140320840], + [0.0, 0.0, 1.0]], + "distCoeffs": [[-0.09098491035825468, 0.0, 0.0, 0.0, 0.0]], + "rmat": [[-0.99972648056921220, -0.012978713450675507, 0.019455514536029755], + [-0.01318372430061382, 0.999858520536128000, -0.010446450347859564], + [-0.01931718049462227, -0.010700089180476265, -0.999756147582633200]], + "tvec": [-5.093035986816083, -0.07716666965650254, 11.556054197934923], + "sensor_resolution": [800, 1280, 1], + "sensor_size": [0.8818898, 1.4110236] +} \ No newline at end of file diff --git a/test/data/camera-tunnel-calibration/camera05_35_6.json b/test/data/camera-tunnel-calibration/camera05_35_6.json new file mode 100755 index 0000000..9813fac --- /dev/null +++ b/test/data/camera-tunnel-calibration/camera05_35_6.json @@ -0,0 +1,10 @@ +{"uPSP_cameraMatrix": [[1370.401138555427, 0.0, 3.847886235332112], + [0.0, 1370.401138555427, -26.186844807195087], + [0.0, 0.0, 1.0]], + "distCoeffs": [[-0.08189309959000819, 0.0, 0.0, 0.0, 0.0]], + "rmat": [[-0.99949825438508370, -0.026555355059734578, 0.017264199918389986], + [-0.026483021959245870, 0.999639557708500200, 0.004405021255353153], + [-0.017374954074092074, 0.003945602869707133, -0.999841258995105600]], + "tvec": [3.8237429795798445, 0.5239595649430541, 11.4979244429552], + "sensor_resolution": [800, 1280, 1], + "sensor_size": [0.8818898, 1.4110236]} \ No newline at end of file diff --git a/test/data/data.csv b/test/data/data.csv new file mode 100644 index 0000000..69785f5 --- /dev/null +++ b/test/data/data.csv @@ -0,0 +1,7 @@ +../data,"['camera-tunnel-calibration', 'detection', 'images', 'internal-calibration', 'output']","['data.csv', 'fml_tc3_volume.grid', 'fml_tc3_volume.tgts', 'test_config.json', 'wtd_test.wtd']" +../data/camera-tunnel-calibration,[],"['camera01_35_6.json', 'camera05_35_6.json']" +../data/detection,['Annotations'],[] +../data/detection/Annotations,[],"['CAM1_RUN8_CINE02_Y20000209H11294501.00001.xml', 'CAM5_RUN8_CINE02_Y20220209H11291505.00001.xml']" +../data/images,[],"['CAM1_RUN8_CINE02_Y20000209H11294501.00001.png', 'CAM5_RUN8_CINE02_Y20220209H11291505.00001.png']" +../data/internal-calibration,[],"['calibio_incal_test.json', 'camera01_35_6.json', 'camera05_35_6.json']" +../data/output,[],['output.placeholder'] diff --git a/test/data/detection/Annotations/CAM1_RUN8_CINE02_Y20000209H11294501.00001.xml b/test/data/detection/Annotations/CAM1_RUN8_CINE02_Y20000209H11294501.00001.xml new file mode 100755 index 0000000..bc2477e --- /dev/null +++ b/test/data/detection/Annotations/CAM1_RUN8_CINE02_Y20000209H11294501.00001.xml @@ -0,0 +1,218 @@ + + 35mm_2 + CAM1_RUN8_CINE02_Y20000209H112945.png + /mnt/c/Users/ncalifan/Desktop/TC3_2022/frames1/35mm_2/CAM1_RUN8_CINE02_Y20000209H112945.png + + Unknown + + + 1280 + 800 + 1 + + 0 + + dot + Unspecified + 0 + 0 + + 202 + 208 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a/test/data/detection/Annotations/CAM5_RUN8_CINE02_Y20220209H11291505.00001.xml b/test/data/detection/Annotations/CAM5_RUN8_CINE02_Y20220209H11291505.00001.xml new file mode 100755 index 0000000..a6c8f9e --- /dev/null +++ b/test/data/detection/Annotations/CAM5_RUN8_CINE02_Y20220209H11291505.00001.xml @@ -0,0 +1,194 @@ + + 35mm_2 + CAM5_RUN8_CINE02_Y20220209H112915.png + /mnt/c/Users/ncalifan/Desktop/TC3_2022/frames1/35mm_2/CAM5_RUN8_CINE02_Y20220209H112915.png + + Unknown + + + 1280 + 800 + 1 + + 0 + + dot + Unspecified + 0 + 0 + + 150 + 218 + 159 + 228 + + + + dot + Unspecified + 0 + 0 + + 280 + 389 + 290 + 397 + + + + dot + Unspecified + 0 + 0 + + 143 + 561 + 154 + 571 + + + + dot + Unspecified + 0 + 0 + + 464 + 551 + 470 + 557 + + + + dot + Unspecified + 0 + 0 + + 459 + 246 + 471 + 257 + + + + dot + Unspecified + 0 + 0 + + 629 + 329 + 639 + 337 + + + + dot + Unspecified + 0 + 0 + + 629 + 517 + 639 + 525 + + + + dot + Unspecified + 0 + 0 + + 750 + 401 + 758 + 411 + + + + dot + 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b/test/data/internal-calibration/camera01_35_6.json new file mode 100755 index 0000000..f3452e0 --- /dev/null +++ b/test/data/internal-calibration/camera01_35_6.json @@ -0,0 +1,6 @@ +{"uPSP_cameraMatrix": [[1380.2632820187426, 0.0, 21.9087014869020], + [0.0, 1380.2632820187426, 0.77854114032086], + [0.0, 0.0, 1.0]], +"distCoeffs": [[-0.09098491035825468, 0.0, 0.0, 0.0, 0.0]], +"sensor_resolution" : [800, 1280, 1], +"sensor_size" : [0.8818898, 1.4110236]} \ No newline at end of file diff --git a/test/data/internal-calibration/camera05_35_6.json b/test/data/internal-calibration/camera05_35_6.json new file mode 100755 index 0000000..3bc7b88 --- /dev/null +++ b/test/data/internal-calibration/camera05_35_6.json @@ -0,0 +1,6 @@ +{"uPSP_cameraMatrix": [[1370.401138555427, 0.0, 3.8478862353321], + [0.0, 1370.401138555427, -26.1868448071951], + [0.0, 0.0, 1.0]], +"distCoeffs": [[-0.08189309959000819, 0.0, 0.0, 0.0, 0.0]], +"sensor_resolution" : [800, 1280, 1], +"sensor_size" : [0.8818898, 1.4110236]} \ No newline at end of file diff --git a/test/data/output/output.placeholder b/test/data/output/output.placeholder new file mode 100644 index 0000000..e69de29 diff --git a/test/data/test_config.json b/test/data/test_config.json new file mode 100755 index 0000000..7f9f73d --- /dev/null +++ b/test/data/test_config.json @@ -0,0 +1,12 @@ +{"oblique_angle" : 70, +"tunnel-cor_to_tgts_tvec" : [0.0, 0.0, 0.0], +"tunnel-cor_to_tgts_rmat": [[1.0, 0, 0], [0, 1.0, 0], [0, 0, 1.0]], +"tunnel-cor_to_tunnel-origin_tvec": [0.0, 0.0, 0.0], +"tunnel-cor_to_tunnel-origin_rmat": [[1.0, 0, 0], [0, 1.0, 0], [0, 0, 1.0]], +"crosscorr_coeff" : 0.75, +"dot_pad" : 4, +"kulite_pad" : 3, +"max_dist" : 6, +"min_dist" : 8, +"Updated" : "03/13/2023" +} diff --git a/test/data/wtd_test.wtd b/test/data/wtd_test.wtd new file mode 100755 index 0000000..8b29b31 --- /dev/null +++ b/test/data/wtd_test.wtd @@ -0,0 +1,15 @@ +RUN 3016 2 +# MACH ALPHA BETA PHI PTOT TTF PS Q TRC RNU STRUTZ KS_STING_AKS KS_STING_BKS T102 T103 T104 T105 T106 T107 T108 CONFIG RPM DATE_ACQUIRED TIME_ACQUIRED +1.00 0.05 0.12 0.90 0.00 0.00 0.00 0.00 0.00 0.00 10.00 0.00 0.00 99999.000000 99999.000000 99999.000000 99999.000000 0.00 0.00 0.00 0.00 0.00 20230322.000000 0.00 + +# Tap CP + 1 9.999900 + 2 9.999900 + 3 9.999900 + 4 9.999900 + 5 9.999900 + 6 9.999900 + 7 9.999900 + 8 9.999900 + 9 9.999900 + 10 9.999900 \ No newline at end of file diff --git a/test/python/file_structure.py b/test/python/file_structure.py new file mode 100644 index 0000000..ba64bc4 --- /dev/null +++ b/test/python/file_structure.py @@ -0,0 +1,82 @@ +import os +import csv +import pathlib + + +def setUp(data_dir): + """ + Checks files are set up properly in the given data_dir + + Parameters + ---------- + data_dir : string + Absolute path to data directory + """ + # walk through upsp-proc/test/data and get the file structure + # Get paths relative to this file + relevant_file_setup = [] + file_setup = os.walk(data_dir) + for f in file_setup: + f = list(f) + f[0] = pathlib.Path(os.path.relpath(f[0], os.path.dirname(__file__))).as_posix() + f[1] = sorted(f[1]) + f[2] = sorted(f[2]) + relevant_file_setup.append(f) + + # convert the file structure to strings to match what is written in the csv + relevant_file_setup = [[str(x) for x in f] for f in relevant_file_setup] + + # Read the ground truth file structure csv file + relevant_ground_truth = [] + file_ground_truth = os.path.join(data_dir, os.path.basename(data_dir) + '.csv') + with open(file_ground_truth, 'r') as fp: + read = csv.reader(fp) + for line in read: + relevant_ground_truth.append(line) + + # Check that all files in the ground truth are in upsp-proc/test/data + for rgt in relevant_ground_truth: + try: + assert (rgt in relevant_file_setup) + except AssertionError as e: + message = "File structure error in " + data_dir + e.args = (message,) + raise + + # Check that all files in upsp-proc/test/data are in the ground truth + for rfs in relevant_file_setup: + try: + assert (rfs in relevant_ground_truth) + except AssertionError as e: + message = "File structure error in " + data_dir + e.args = (message,) + raise + + + # If all ground truth are in upsp-proc/test/data, and vice versa, they are the same + return + + +def file_setup_writer(data_dir): + """ + Writes a csv file of all files in data_dir, recursively + + Parameters + ---------- + data_dir : string + Absolute path to data directory + """ + + with open(os.path.join(data_dir, 'data.csv'), 'w') as fp: + wri = csv.writer(fp) + for files in os.walk(files_dir): + files = list(files) + files[0] = os.path.relpath(files[0], os.path.dirname(__file__)) + files[1] = sorted(files[1]) + files[2] = sorted(files[2]) + wri.writerow(files) + + +if __name__ == '__main__': + files_dir = os.path.join(os.path.dirname(os.path.dirname(__file__)), 'data') + file_setup_writer(files_dir) diff --git a/test/python/test_camera_tunnel_calibration.py b/test/python/test_camera_tunnel_calibration.py new file mode 100644 index 0000000..d6c52ca --- /dev/null +++ b/test/python/test_camera_tunnel_calibration.py @@ -0,0 +1,278 @@ +import unittest +import numpy as np +import os +import sys +import cv2 + +import file_structure +from upsp.cam_cal_utils import ( + camera_tunnel_calibrate as ctc, + external_calibrate, + parsers, + photogrammetry, + visibility, +) + +files_dir = os.path.join(os.path.dirname(os.path.dirname(__file__)), 'data') +ctc_dir = os.path.join(files_dir, 'output') + +# If you are debug, setting this to True will unmute the function calls. +# If this is False (by default), then function calls will be muted +test_debug = False + + +class CameraTunnelCalibrationTestCase(unittest.TestCase): + """ + Tests for the camera_tunnel_calibrate module + """ + + @classmethod + def setUpClass(cls): + pass + + def setUp(self): + """ + Checks files set up properly before each running test + """ + # Check file structure + file_structure.setUp(files_dir) + + ctc.debug_show_img_targets = False + ctc.debug_show_matches = False + ctc.debug_show_3D_targets = False + + external_calibrate.debug_raw_matches = False + external_calibrate.debug_coarse_optimization = False + external_calibrate.debug_visible_projections = False + external_calibrate.debug_refined_optimization = False + external_calibrate.debug_show_localizations = False + + def tearDown(self): + """ + Checks files are still set up properly after each running test + """ + + # We can rerun setUp because the file structure should be unchanged + self.setUp() + + def test_camera_to_tunnel_calibrate(self): + """ + Regression test for camera_to_tunnel_calibrate + """ + if not test_debug: + f = open(os.devnull, 'w') + std_stdout_orig = sys.stdout + sys.stdout = f + + # Get pathings to the data + tgts_filepath = os.path.join(files_dir, 'fml_tc3_volume.tgts') + grid_filepath = os.path.join(files_dir, 'fml_tc3_volume.grid') + test_config_filepath = os.path.join(files_dir, 'test_config.json') + + # Read tgts file and wtd file + tgts_all = parsers.read_tgts(tgts_filepath) + + # Read in the test_config file + test_config = parsers.read_test_config(test_config_filepath) + + # Get the visibility checker from the grid + vis_checker = visibility.VisibilityChecker(grid_filepath, + test_config['oblique_angle']) + + # Populate the model's tunnel position with all 0's since it was in a lab + tunnel_vals = {'ALPHA' : 0.0, 'BETA' : 0.0, 'PHI' : 0.0, 'STRUTZ' : 0.0} + + # Run the calibration script + img_path = os.path.join( + files_dir, 'images', 'CAM1_RUN8_CINE02_Y20000209H11294501.00001.png' + ) + img = cv2.imread(img_path, cv2.IMREAD_GRAYSCALE + cv2.IMREAD_ANYDEPTH) + + # Load the internal calibration for this camera + incal_path = os.path.join( + files_dir, 'internal-calibration', 'camera01_35_6.json' + ) + internal_cal = parsers.read_internal_params( + incal_path, img.shape, read_sensor=True + ) + + # Read in manual bboxes + manual_det_filename = 'CAM1_RUN8_CINE02_Y20000209H11294501.00001.xml' + manual_detections_path = os.path.join( + files_dir, 'detection', 'Annotations', manual_det_filename + ) + manual_detection = parsers.read_pascal_voc(manual_detections_path) + + ctc.camera_to_tunnel_calibrate( + ctc_dir, + [img], + [internal_cal], + [manual_detection], + tunnel_vals, + tgts_all, + test_config, + vis_checker + ) + + # Compare the generated camera calibrations to the baseline. The baseline + # solutions were manually verified visually with the debug outputs + regression_path = os.path.join( + files_dir, 'camera-tunnel-calibration', 'camera01_35_6.json' + ) + regression_cal = parsers.read_camera_tunnel_cal(regression_path, (512, 1024)) + rmat_reg, tvec_reg, cameraMatrix_reg, distCoeffs_reg = regression_cal + + output_path = os.path.join(ctc_dir, 'camera01.json') + output_cal = parsers.read_camera_tunnel_cal(output_path, (512, 1024)) + rmat_ctc, tvec_ctc, cameraMatrix_ctc, distCoeffs_ctc = output_cal + + np.testing.assert_array_almost_equal(rmat_reg, rmat_ctc, decimal=1) + np.testing.assert_array_almost_equal(tvec_reg, tvec_ctc, decimal=1) + np.testing.assert_array_almost_equal(cameraMatrix_reg, cameraMatrix_ctc, decimal=1) + np.testing.assert_array_almost_equal(distCoeffs_reg, distCoeffs_ctc, decimal=1) + + if not test_debug: + sys.stdout = std_stdout_orig + f.close() + + # Deleted the generated camera_tunnel_calibration json file + ctc_json = os.path.join(ctc_dir, 'camera01.json') + os.remove(ctc_json) + + def test_tunnel_transform(self): + """ + Regression test for tunnel_transform + + The solutions were manually verified visually with the debug outputs + """ + + # Seed the random generator for consistency between tests + # It should work for any test, but if there is an error associated with a + # given input this will allow for better error tracing + rng = np.random.default_rng(0) + + # This is a cached version of the function. Should the function be changed in + # the future, we want to ensure consistency between the old and new function + def regression_tunnel_transform(ALPHA, BETA, PHI, STRUTZ, sting_offset): + alpha_rad = np.deg2rad(ALPHA) + beta_rad = np.deg2rad(BETA) + phi_rad = np.deg2rad(PHI) + + c_alpha = np.cos(alpha_rad) + s_alpha = np.sin(alpha_rad) + pitch = np.array([[c_alpha, 0, -s_alpha], [0, 1, 0], [s_alpha, 0, c_alpha]]) + + c_beta = np.cos(beta_rad) + s_beta = np.sin(beta_rad) + yaw = np.array([[c_beta, s_beta, 0], [-s_beta, c_beta, 0], [0, 0, 1]]) + + c_phi = np.cos(phi_rad) + s_phi = np.sin(phi_rad) + roll = np.array([[1, 0, 0], [0, c_phi, -s_phi], [0, s_phi, c_phi]]) + + rotation_matrix = np.matmul(pitch, np.matmul(yaw, roll)) + rotation_matrix = np.linalg.inv(rotation_matrix) + + tvec__cor_tgts__tgts_frame = sting_offset + + tvec__knuckle_tgts = np.matmul(rotation_matrix, tvec__cor_tgts__tgts_frame) + tvec__tunnel_tgts__tunnel_frame = tvec__knuckle_tgts + np.array([0, 0, STRUTZ]) + + return rotation_matrix, tvec__tunnel_tgts__tunnel_frame.reshape(3, 1) + + # Generate 50 random inputs + alphas = (360 * rng.random(50)) - 180 + betas = (360 * rng.random(50)) - 180 + phis = (360 * rng.random(50)) - 180 + strutzs = (200 * rng.random(50)) - 100 + s_os = (200 * rng.random((50, 3))) - 100 + + # Test that the output of photogrammtery.tunnel_transformation matches the + # cached version for all the random inputs + for alpha, beta, phi, strutz, s_o in zip(alphas, betas, phis, strutzs, s_os): + p_tt = ctc.tunnel_transform(alpha, beta, phi, strutz, s_o.reshape(3, 1)) + r_tt = regression_tunnel_transform(alpha, beta, phi, strutz, s_o) + + np.testing.assert_array_almost_equal(p_tt[0], r_tt[0], decimal=3) + np.testing.assert_array_almost_equal(p_tt[1], r_tt[1], decimal=3) + + np.testing.assert_array_almost_equal(p_tt[0].shape, (3, 3)) + np.testing.assert_array_almost_equal(p_tt[1].shape, (3, 1)) + + def test_tf_camera_tgts_thru_tunnel(self): + """ + Regression test of tf_camera_tgts_thru_tunnel + + The solutions were manually verified visually with the debug outputs + """ + + # Seed the random generator for consistency between tests + # It should work for any test, but if there is an error associated with a + # given input this will allow for better error tracing + rng = np.random.default_rng(0) + + # This is a cached version of the function. Should the function be changed in + # the future, we want to ensure consistency between the old and new function + def regression_tf_camera_tgts_thru_tunnel(camera_cal, wtd, test_config): + # Turn the wind tunnel data into the transformation from tunnel to targets + wtd_transform = ctc.tunnel_transform(wtd['ALPHA'], wtd['BETA'], wtd['PHI'], + wtd['STRUTZ'], test_config['tunnel-cor_to_tgts_tvec']) + rmat_tunnel_tgts, tvec_tunnel_tgts = wtd_transform + + # Transformation from tgts frame to runnel frame + rmat_tgts_tunnel = np.linalg.inv(rmat_tunnel_tgts) + tvec_tgts_tunnel = -np.matmul(rmat_tgts_tunnel, tvec_tunnel_tgts) + + # Decompose the camera calibration into its parts + rmat__camera_tunnel, tvec__camera_tunnel, cameraMatrix, distCoeffs = camera_cal + + # Combine the transformations to get the transformation from `camera to tgts frame + rmat__camera_tgts = np.matmul(rmat__camera_tunnel, np.linalg.inv(rmat_tgts_tunnel)) + tvec__camera_tgts = tvec__camera_tunnel + np.matmul(rmat__camera_tunnel, + tvec_tunnel_tgts) + + return rmat__camera_tgts, tvec__camera_tgts.reshape(3, 1) + + # Generate 50 random inputs + strutzs = (200 * rng.random(50)) - 100 + s_os = (200 * rng.random((50, 3))) - 100 + + # Test 50 random inputs + for i in range(50): + cameraMatrix = np.eye(3) + cameraMatrix[0, 2] = 512 + cameraMatrix[1, 2] = 256 + distCoeffs = (2 * rng.random(5)) - 1 + + # Generate a random but valid rotation matrix + angles = (360 * rng.random(3)) - 180 + rotx = photogrammetry.rot(angles[0], 'x') + roty = photogrammetry.rot(angles[1], 'y') + rotz = photogrammetry.rot(angles[2], 'z') + rmat = np.matmul(rotz, np.matmul(roty, rotx)) + + # Generate a random translation vector + tvec = (200 * rng.random((3, 1))) - 100 + + # Package the camera calibration + camera_cal = rmat, tvec, cameraMatrix, distCoeffs + + tunnel_angles = (360 * rng.random(3)) - 180 + wtd = {'ALPHA' : tunnel_angles[0], + 'BETA' : tunnel_angles[1], + 'PHI' : tunnel_angles[2], + 'STRUTZ' : strutzs[i]} + test_config = {'tunnel-cor_to_tgts_tvec' : s_os[i].reshape(3, 1)} + + # Test the tf_camera_tgts_thru_tunnel against the cached function + reg = regression_tf_camera_tgts_thru_tunnel(camera_cal, wtd, test_config) + pho = ctc.tf_camera_tgts_thru_tunnel(camera_cal, wtd, test_config) + + np.testing.assert_array_almost_equal(reg[0], pho[0], decimal=3) + np.testing.assert_array_almost_equal(reg[1], pho[1], decimal=3) + + np.testing.assert_array_almost_equal(pho[0].shape, (3, 3)) + np.testing.assert_array_almost_equal(pho[1].shape, (3, 1)) + +if __name__ == "__main__": + unittest.main() diff --git a/test/python/test_external_calibrate.py b/test/python/test_external_calibrate.py new file mode 100644 index 0000000..3732469 --- /dev/null +++ b/test/python/test_external_calibrate.py @@ -0,0 +1,945 @@ +import unittest +import numpy as np +import cv2 +import os +import sys +import copy + +import file_structure +from upsp.target_operations import target_detection +from upsp.cam_cal_utils import ( + external_calibrate, + img_utils, + parsers, + photogrammetry, + visibility, +) + +files_dir = os.path.join(os.path.dirname(os.path.dirname(__file__)), 'data') + +# If you are debug, setting this to True will unmute the function calls. +# If this is False (by default), then function calls will be muted +test_external_calibrate_debug = False + +class ExternalCalibrateTestCase(unittest.TestCase): + """ + Tests for external calibrate + """ + + @classmethod + def setUpClass(cls): + # Read in the image + img_path = os.path.join( + files_dir, 'images', 'CAM1_RUN8_CINE02_Y20000209H11294501.00001.png' + ) + cls.img = cv2.imread(img_path, cv2.IMREAD_GRAYSCALE + cv2.IMREAD_ANYDEPTH) + + # Get the camera calibration for this camera + cam_tun_path = os.path.join( + files_dir, 'camera-tunnel-calibration', 'camera01_35_6.json' + ) + camera_cal_tunnel = parsers.read_camera_tunnel_cal( + cam_tun_path, cls.img.shape + ) + cls.rmat, cls.tvec, cls.cameraMatrix, cls.distCoeffs = camera_cal_tunnel + + # Read the rest configuration file + test_config_file = os.path.join(files_dir, 'test_config.json') + cls.test_config = parsers.read_test_config(test_config_file) + cls.test_config["crosscorr_coeff"] = 0.5 + + # Get the visibility checker from the grid + grd_file = os.path.join(files_dir, 'fml_tc3_volume.grid') + cls.vis_checker = visibility.VisibilityChecker( + grd_file, 70, epsilon=1e-4 + ) + + # Read in the targets file + tgts_file = os.path.join(files_dir, 'fml_tc3_volume.tgts') + cls.tgts = parsers.read_tgts(tgts_file) + + cls.tgts_visible = photogrammetry.get_visible_targets( + cls.rmat, + cls.tvec, + cls.cameraMatrix, + cls.distCoeffs, + cls.tgts, + cls.vis_checker + ) + + def setUp(self): + """ + Checks files are set up properly before each running test + """ + test_external_calibrate_debug = False + + # Set debugs to False + external_calibrate.debug_raw_matches = False + external_calibrate.debug_coarse_optimization = False + external_calibrate.debug_refined_matches = False + external_calibrate.debug_visible_projections = False + external_calibrate.debug_refined_optimization = False + + file_structure.setUp(files_dir) + + def tearDown(self): + """ + Checks files are still set up properly after each running test + """ + + # We can rerun setUp because the file structure should be unchanged + self.setUp() + + def test_compare_poses(self): + """ + Regression test for compare_poses + + Many randomly generated inputs tests known properties of the outputs + """ + + # Seed the random generator for consistency between tests + # It should work for any test, but if there is an error associated with a + # given input this will allow for better error tracing + rng = np.random.default_rng(0) + + # Generate 100 random inputs + rotation = (360 * rng.random(600)) - 180 + + xs = (100 * rng.random(200)) - 50 + ys = (100 * rng.random(200)) - 50 + zs = (100 * rng.random(200)) - 50 + + for i in range(100): + # Get random rotation matries + I = np.eye(3) + R_a1 = np.matmul(photogrammetry.rot(rotation[6 * i + 0], 'x'), I) + R_a2 = np.matmul(photogrammetry.rot(rotation[6 * i + 1], 'y'), R_a1) + R_a3 = np.matmul(photogrammetry.rot(rotation[6 * i + 2], 'z'), R_a2) + + R_b1 = np.matmul(photogrammetry.rot(rotation[6 * i + 3], 'x'), I) + R_b2 = np.matmul(photogrammetry.rot(rotation[6 * i + 4], 'y'), R_b1) + R_b3 = np.matmul(photogrammetry.rot(rotation[6 * i + 5], 'z'), R_b2) + + # Get random translation vectors + x1, y1, z1 = xs[2 * i], ys[2 * i], zs[2* i] + x2, y2, z2 = xs[2 * i + 1], ys[2 * i + 1], zs[2 * i + 1] + + # Calculate correct theta and dist + rmat_rel = np.matmul(R_b3, R_a3.T) + tvec_tf = -np.matmul(rmat_rel, np.array([x2, y2, z2])) + tvec_rel = np.array([x1, y1, z1]) + tvec_tf + dist_gt = np.linalg.norm(tvec_rel) + rvec_rel, _ = cv2.Rodrigues(rmat_rel) + theta_gt_rad = np.linalg.norm(rvec_rel) + theta_gt = np.rad2deg(theta_gt_rad) + + # Calculate theta and dist with excal + theta, dist = external_calibrate.compare_poses( + [R_a3, np.array([x2, y2, z2])], [R_b3, np.array([x1, y1, z1])] + ) + + # Compare to ensure consistency + np.testing.assert_array_almost_equal(theta, theta_gt, decimal=4) + np.testing.assert_array_almost_equal(dist, dist_gt, decimal=4) + + def test_external_calibrate(self): + """ + Regression test of external_calibrate + + Outputs were manually verified to be as expected + """ + # Initialize input values + rmat = np.matmul(photogrammetry.rot(0.25, 'x'), self.rmat) + rmat = np.matmul(photogrammetry.rot(-0.25, 'y'), rmat) + tvec = self.tvec + np.array([[0.02], [-0.01], [0.02]]) + camera_tunnel_cal = [rmat, tvec, self.cameraMatrix, self.distCoeffs] + + # Scale the image + img8bit = img_utils.scale_image_max_inlier(self.img) + + # Perform template detection to find and match the targets to image locations + template_detection_output = target_detection.template_detection( + img8bit, + rmat, + tvec, + self.cameraMatrix, + self.distCoeffs, + self.tgts, + self.test_config + ) + tgts_match, img_targets_match, num_matches = template_detection_output + + # Run the external calibration function + ex_cal_out = external_calibrate.external_calibrate( + self.img, + rmat, + tvec, + self.cameraMatrix, + self.distCoeffs, + tgts_match[:num_matches], + img_targets_match[:num_matches], + self.vis_checker, + self.test_config, + isMatched=False, + max_localize_delta=None, + reprojectionError=6.0 + ) + rmat, tvec, tgt_inliers, img_target_inliers = ex_cal_out + + rmat_gt = [[-0.9996968381, -0.0131838521, 0.0207946606], + [-0.0133185088, 0.999891139, -0.0063503932], + [-0.0207086742, -0.0066254219, -0.9997635994]] + tvec_gt = [[-5.0804278566], [-0.0481402216], [11.556791127]] + + # Test that the function output closely matches the ground truth + np.testing.assert_array_almost_equal(rmat, rmat_gt, decimal=3) + np.testing.assert_array_almost_equal(tvec, tvec_gt, decimal=3) + + # Check that external calibrate raises an error when too few targets are matched + with self.assertRaises(ValueError): + ex_cal_out = external_calibrate.external_calibrate( + self.img, + self.rmat, + self.tvec, + self.cameraMatrix, + self.distCoeffs, + tgts_match[:num_matches][:2], + img_targets_match[:num_matches][:2], + self.vis_checker, + self.test_config, + isMatched=False, + max_localize_delta=None, + reprojectionError=6.0 + ) + + def test_check_external_calibrate_two_stage_inputs(self): + """ + Regression test of check_external_calibrate_two_stage_inputs + + Outputs were manually verified to be as expected + """ + # Direct the std out to nothing temporarily to avoid error messages + if not test_external_calibrate_debug: + f = open(os.devnull, 'w') + std_stdout_orig = sys.stdout + sys.stdout = f + + check_excal = external_calibrate.check_external_calibrate_two_stage_inputs + + #------------------------------------------------------------------------------- + # Load all proper inputs + camera_tunnel_cal = [self.rmat, self.tvec, self.cameraMatrix, self.distCoeffs] + rmat, tvec = self.rmat, self.tvec + cameraMatrix, distCoeffs = self.cameraMatrix, self.distCoeffs + incal = camera_tunnel_cal[2:] + + tgts = self.tgts + vis_checker = self.vis_checker + img = self.img + test_config = self.test_config + + # Pass the proper inputs and check for success + check_bool = check_excal( + img, rmat, tvec, incal, tgts, test_config, vis_checker, None + ) + self.assertEqual(check_bool, True) + + # Verify img checks + + # Pass None object + check_bool = check_excal( + None, rmat, tvec, incal, tgts, test_config, vis_checker, None + ) + self.assertEqual(check_bool, False) + + # Pass an object with an np.int32 object type + check_bool = check_excal( + img.astype(np.int32), + rmat, + tvec, + incal, + tgts, + test_config, + vis_checker, + None + ) + self.assertEqual(check_bool, False) + + # Pass 3 dimensional image. Since it is single channel, it should return True + img_3 = np.expand_dims(img, axis=2) + check_bool = check_excal( + img_3, rmat, tvec, incal, tgts, test_config, vis_checker, None + ) + self.assertEqual(check_bool, True) + + # Pass 3 dimensional image. Since it is 3 channel, it should return False + check_bool = check_excal( + np.concatenate((img_3, img_3, img_3), axis=2), + rmat, + tvec, + incal, + tgts, + test_config, + vis_checker, + None + ) + self.assertEqual(check_bool, False) + + # Pass 4 dimensional image. Should return False + check_bool = check_excal( + np.expand_dims(img_3, axis=3), + rmat, + tvec, + incal, + tgts, + test_config, + vis_checker, + None + ) + self.assertEqual(check_bool, False) + + #------------------------------------------------------------------------------- + # Verify rmat checks + + # Passing a list version for reference as well + check_bool = check_excal( + img, rmat.tolist(), tvec, incal, tgts, test_config, vis_checker, None + ) + self.assertEqual(check_bool, False) + + # Passing an rmat with improper dimensions + rmat_3 = np.expand_dims(rmat, 1) + check_bool = check_excal( + img, rmat_3, tvec, incal, tgts, test_config, vis_checker, None + ) + self.assertEqual(check_bool, False) + + # Passing an rmat with improper dimensions + check_bool = check_excal( + img, + [[0.0, 0.0], [0.0, 0.0]], + tvec, + incal, + tgts, + test_config, + vis_checker, + None + ) + self.assertEqual(check_bool, False) + + # Passing an rmat with the wrong datatype (integer) + check_bool = check_excal( + img, + [[1, 0, 0], [0, 1, 0], [0, 0, 1]], + tvec, + incal, + tgts, + test_config, + vis_checker, + None + ) + self.assertEqual(check_bool, False) + + # Pass an invalid rotation matrix + check_bool = check_excal( + img, + [[1.0, 1.0, 0.0], [0.0, 1.0, 0.0], [0.0, 0.0, 1.0]], + tvec, + incal, + tgts, + test_config, + vis_checker, + None + ) + self.assertEqual(check_bool, False) + + #------------------------------------------------------------------------------- + # Verify tvec checks + + # Passing a list version for reference as well + check_bool = check_excal( + img, + rmat, + tvec.tolist(), + incal, + tgts, + test_config, + vis_checker, + None + ) + self.assertEqual(check_bool, False) + + # Passing (3,1) version for reference as well + tvec = tvec.reshape((3, 1)) + check_bool = check_excal( + img, + rmat, + tvec, + incal, + tgts, + test_config, + vis_checker, + None + ) + self.assertEqual(check_bool, True) + + # Passing an tvec with improper dimensions + check_bool = check_excal( + img, + rmat, + np.expand_dims(tvec, 1), + incal, + tgts, + test_config, + vis_checker, + None + ) + self.assertEqual(check_bool, False) + + # Passing an tvec with improper dimensions + check_bool = check_excal( + img, + rmat, + [[0.0, 0.0], [0.0, 0.0]], + incal, + tgts, + test_config, + vis_checker, + None + ) + self.assertEqual(check_bool, False) + + # Passing an tvec with the wrong datatype (integer) + check_bool = check_excal( + img, + rmat, + [1, 2, 3], + incal, + tgts, + test_config, + vis_checker, + None + ) + self.assertEqual(check_bool, False) + + #------------------------------------------------------------------------------- + # Verify incal checks + + # Passing a list version of incal[0] for reference as well + incal_test = [incal[0].tolist()] + list(incal[1:]) + check_bool = check_excal( + img, + rmat, + tvec, + incal, + tgts, + test_config, + vis_checker, + None + ) + self.assertEqual(check_bool, True) + + # Passing an incal[0] with improper dimensions + incal_test = [np.expand_dims(incal[0], 1)] + list(incal[1:]) + check_bool = check_excal( + img, + rmat, + tvec, + incal_test, + tgts, + test_config, + vis_checker, + None + ) + self.assertEqual(check_bool, False) + + # Passing an incal[0] with improper dimensions + bad_cm = [np.array([[663.4, 0.0, 512.0], [0.0, 664.3, 1024.0]])] + incal_test = [bad_cm, list(incal[1:])] + check_bool = check_excal( + img, + rmat, + tvec, + incal_test, + tgts, + test_config, + vis_checker, + None + ) + self.assertEqual(check_bool, False) + + # Passing an incal[0] with the wrong datatype (integer) + bad_cm = [np.array([[600, 0, 512], [0, 800, 1024], [0, 0, 1]])] + incal_test = [bad_cm, list(incal[1:])] + check_bool = check_excal( + img, + rmat, + tvec, + incal_test, + tgts, + test_config, + vis_checker, + None + ) + self.assertEqual(check_bool, False) + + # Pass an invalid incal[0] versions + bad_cm = np.array([[660.0, 1.0, 512.0], [0.0, 660., 1024.0], [0.0, 0.0, 1.0]]) + incal_test = [bad_cm, list(incal[1:])] + check_bool = check_excal( + img, + rmat, + tvec, + incal_test, + tgts, + test_config, + vis_checker, + None + ) + self.assertEqual(check_bool, False) + + bad_cm = np.array([[660.0, 0.0, 512.0], [1.0, 660., 1024.0], [0.0, 0.0, 1.0]]) + incal_test = [bad_cm, list(incal[1:])] + check_bool = check_excal( + img, + rmat, + tvec, + incal_test, + tgts, + test_config, + vis_checker, + None + ) + self.assertEqual(check_bool, False) + + bad_cm = np.array([[660.0, 0.0, 512.0], [0.0, 660., 1024.0], [1.0, 0.0, 1.0]]) + incal_test = [bad_cm, list(incal[1:])] + check_bool = check_excal( + img, + rmat, + tvec, + incal_test, + tgts, + test_config, + vis_checker, + None + ) + self.assertEqual(check_bool, False) + + bad_cm = np.array([[660.0, 0.0, 512.0], [0.0, 660., 1024.0], [0.0, 1.0, 1.0]]) + incal_test = [bad_cm, list(incal[1:])] + check_bool = check_excal( + img, + rmat, + tvec, + incal_test, + tgts, + test_config, + vis_checker, + None + ) + self.assertEqual(check_bool, False) + + bad_cm = np.array([[660.0, 0.0, 512.0], [0.0, 660., 1024.0], [0.0, 0.0, 2.0]]) + incal_test = [bad_cm, list(incal[1:])] + check_bool = check_excal( + img, + rmat, + tvec, + incal_test, + tgts, + test_config, + vis_checker, + None + ) + self.assertEqual(check_bool, False) + + # Passing a list version of incal[1] for reference as well + incal_test = [incal[0]] + [incal[1].tolist()] + check_bool = check_excal( + img, + rmat, + tvec, + incal_test, + tgts, + test_config, + vis_checker, + None + ) + self.assertEqual(check_bool, True) + + # Passing an incal[1] with improper dimensions + incal_test = [incal[0]] + [np.expand_dims(incal[1], 1)] + check_bool = check_excal( + img, + rmat, + tvec, + incal_test, + tgts, + test_config, + vis_checker, + None + ) + self.assertEqual(check_bool, False) + + # Passing an incal[1] with improper dimensions + incal_test = [incal[0]] + [np.array([663.4, 0.0, 512.0]).reshape((3, 1))] + check_bool = check_excal( + img, rmat, tvec, incal_test, tgts, test_config, vis_checker, None + ) + self.assertEqual(check_bool, False) + + # Passing an incal[1] with the wrong datatype (integer) + incal_test = [incal[0]] + [np.array([[1, 0, 0, 0, 0]])] + check_bool = check_excal( + img, rmat, tvec, incal_test, tgts, test_config, vis_checker, None + ) + self.assertEqual(check_bool, False) + + #------------------------------------------------------------------------------- + # Check tgts input + + # Pass a blank list for tgts + check_bool = check_excal( + img, rmat, tvec, incal, [], test_config, vis_checker, None + ) + self.assertEqual(check_bool, False) + + # Remove tvec from one of the tgts + tgts_test = copy.deepcopy(tgts) + del tgts_test[0]['tvec'] + check_bool = check_excal( + img, rmat, tvec, incal, tgts_test, test_config, vis_checker, None + ) + self.assertEqual(check_bool, False) + + # Pass a tgts with list instead of numpy arrays + tgts_test = copy.deepcopy(tgts) + tgts_test[0]['tvec'] = tgts_test[0]['tvec'].tolist() + check_bool = check_excal( + img, rmat, tvec, incal, tgts_test, test_config, vis_checker, None + ) + self.assertEqual(check_bool, False) + + #------------------------------------------------------------------------------- + # Check test_config input + + # Check a float and integer for test_config_test['max_dist'] + # (both are proper inputs) + test_config_test = copy.deepcopy(test_config) + test_config_test['max_dist'] = 32.0 + check_bool = check_excal( + img, rmat, tvec, incal, tgts, test_config_test, vis_checker, None + ) + self.assertEqual(check_bool, True) + + test_config_test['max_dist'] = 32 + check_bool = check_excal( + img, rmat, tvec, incal, tgts, test_config_test, vis_checker, None + ) + self.assertEqual(check_bool, True) + + # Passing a string for test_config_test['max_dist'] is improper + test_config_test['max_dist'] = '32' + check_bool = check_excal( + img, rmat, tvec, incal, tgts, test_config_test, vis_checker, None + ) + self.assertEqual(check_bool, False) + + # Pasing test_config without a max_dist item is improper + del test_config_test['max_dist'] + check_bool = check_excal( + img, rmat, tvec, incal, tgts, test_config_test, vis_checker, None + ) + self.assertEqual(check_bool, False) + + # Check a float and integer for test_config_test['min_dist'] + # (both are proper inputs) + test_config_test = copy.deepcopy(test_config) + test_config_test['min_dist'] = 32.0 + check_bool = check_excal( + img, rmat, tvec, incal, tgts, test_config_test, vis_checker, None + ) + self.assertEqual(check_bool, True) + + test_config_test['min_dist'] = 32 + check_bool = check_excal( + img, rmat, tvec, incal, tgts, test_config_test, vis_checker, None + ) + self.assertEqual(check_bool, True) + + # Passing a string for test_config_test['min_dist'] is improper + test_config_test['min_dist'] = '32' + check_bool = check_excal( + img, rmat, tvec, incal, tgts, test_config_test, vis_checker, None + ) + self.assertEqual(check_bool, False) + + # Pasing test_config without a min_dist item is improper + del test_config_test['min_dist'] + check_bool = check_excal( + img, rmat, tvec, incal, tgts, test_config_test, vis_checker, None + ) + self.assertEqual(check_bool, False) + + #------------------------------------------------------------------------------- + # Check vis_checker input + + # Pass the wrong vis_checker object + check_bool = check_excal( + img, rmat, tvec, incal, tgts, test_config, 432, None + ) + self.assertEqual(check_bool, False) + + # Pass the wrong vis_checker object + check_bool = check_excal( + img, rmat, tvec, incal, tgts, test_config, '432', None + ) + self.assertEqual(check_bool, False) + + # Pass the wrong vis_checker object + check_bool = check_excal( + img, rmat, tvec, incal, tgts, test_config, None, None + ) + self.assertEqual(check_bool, False) + + # Pass the vis_checker with the wrong vis_checker_test.scene object + vis_checker_test = copy.copy(vis_checker) + vis_checker_test.scene = None + check_bool = check_excal( + img, rmat, tvec, incal, tgts, test_config, vis_checker_test, None + ) + self.assertEqual(check_bool, False) + + # Pass the vis_checker with the wrong vis_checker_test.scene object + vis_checker_test.scene = "BVH" + check_bool = check_excal( + img, rmat, tvec, incal, tgts, test_config, vis_checker_test, None + ) + self.assertEqual(check_bool, False) + + #------------------------------------------------------------------------------- + # Check debug input + + # debug can be None, int, or string. Check those values pass the check + check_bool = check_excal( + img, rmat, tvec, incal, tgts, test_config, vis_checker, None + ) + self.assertEqual(check_bool, True) + + check_bool = check_excal( + img, rmat, tvec, incal, tgts, test_config, vis_checker, 2 + ) + self.assertEqual(check_bool, True) + + check_bool = check_excal( + img, rmat, tvec, incal, tgts, test_config, vis_checker, '3' + ) + self.assertEqual(check_bool, True) + + # Any other object should fail + check_bool = check_excal( + img, rmat, tvec, incal, tgts, test_config, vis_checker, 43.0 + ) + self.assertEqual(check_bool, False) + + check_bool = check_excal( + img, rmat, tvec, incal, tgts, test_config, vis_checker, [43]) + self.assertEqual(check_bool, False) + + check_bool = check_excal( + img, rmat, tvec, incal, tgts, test_config, vis_checker, (4, 3) + ) + self.assertEqual(check_bool, False) + + # Redirect the std out to the original location + if not test_external_calibrate_debug: + sys.stdout = std_stdout_orig + f.close() + + def test_external_calibrate_two_stage_from_wtd(self): + """ + Regression test of external_calibrate_two_stage_from_wtd + + Outputs were manually verified to be as expected + """ + # Initialize input values + tunnel_vals = {"ALPHA" : 0.0, "BETA" : 0.0, "PHI" : 0.0, "STRUTZ" : 0.0} + + rmat = np.matmul(photogrammetry.rot(0.25, 'x'), self.rmat) + rmat = np.matmul(photogrammetry.rot(-0.25, 'y'), rmat) + tvec = self.tvec + np.array([[0.02], [-0.01], [0.02]]) + camera_tunnel_cal = [rmat, tvec, self.cameraMatrix, self.distCoeffs] + + # Direct the std out to nothing temporarily to avoid error messages + if not test_external_calibrate_debug: + f = open(os.devnull, 'w') + std_stdout_orig = sys.stdout + sys.stdout = f + + # Get the fine-tuned external_calibration output + rmat_opt, tvec_opt = external_calibrate.external_calibrate_two_stage_from_wtd( + self.img, + tunnel_vals, + camera_tunnel_cal, + self.tgts, + self.test_config, + self.vis_checker + ) + + # Redirect the std out to the original location + if not test_external_calibrate_debug: + sys.stdout = std_stdout_orig + f.close() + + # Check that the function output closely matches the expected output + np.testing.assert_array_almost_equal(rmat_opt, self.rmat, decimal=3) + np.testing.assert_array_almost_equal(tvec_opt, self.tvec, decimal=3) + + def test_external_calibrate_two_stage(self): + """ + Regression test of external_calibrate_two_stage + + Outputs were manually verified to be as expected + """ + # Initialize input values + rmat = np.matmul(photogrammetry.rot(0.25, 'x'), self.rmat) + rmat = np.matmul(photogrammetry.rot(-0.25, 'y'), rmat) + tvec = self.tvec + np.array([[0.02], [-0.01], [0.02]]) + camera_tunnel_cal = [rmat, tvec, self.cameraMatrix, self.distCoeffs] + + # Direct the std out to nothing temporarily to avoid error messages + if not test_external_calibrate_debug: + f = open(os.devnull, 'w') + std_stdout_orig = sys.stdout + sys.stdout = f + + # Get the fine-tuned external_calibration output + incal = camera_tunnel_cal[2:] + rmat_opt, tvec_opt = external_calibrate.external_calibrate_two_stage( + self.img, rmat, tvec, incal, self.tgts, self.test_config, self.vis_checker + ) + + # Redirect the std out to the original location + if not test_external_calibrate_debug: + sys.stdout = std_stdout_orig + f.close() + + # Check that the function output closely matches the expected output + np.testing.assert_array_almost_equal(rmat_opt, self.rmat, decimal=3) + np.testing.assert_array_almost_equal(tvec_opt, self.tvec, decimal=3) + + def test_external_calibrate_one_step(self): + """ + Regression test of external_calibrate_one_step + + Outputs were manually verified to be as expected + """ + # Initialize input values + rmat = np.matmul(photogrammetry.rot(0.25, 'x'), self.rmat) + rmat = np.matmul(photogrammetry.rot(-0.25, 'y'), rmat) + tvec = self.tvec + np.array([[0.02], [-0.01], [0.02]]) + camera_tunnel_cal = [rmat, tvec, self.cameraMatrix, self.distCoeffs] + + # Scale the image + img8bit = img_utils.scale_image_max_inlier(self.img) + + # Perform template detection to find and match the targets to image locations + template_detection_output = target_detection.template_detection( + img8bit, + rmat, + tvec, + self.cameraMatrix, + self.distCoeffs, + self.tgts, + self.test_config + ) + tgts_match, img_targets_match, num_matches = template_detection_output + + # Do the coarse external calibration + coarse_outputs = external_calibrate.external_calibrate( + img8bit, + rmat, + tvec, + self.cameraMatrix, + self.distCoeffs, + tgts_match[:num_matches], + img_targets_match[:num_matches], + self.vis_checker, + self.test_config, + max_localize_delta=None, + reprojectionError=self.test_config["max_dist"], + ) + + # Unpack the output variables + __, __, tgts_inliers_coarse, img_target_inliers_coarse = coarse_outputs + rmat_coarse, tvec_coarse, __, __ = coarse_outputs + + # Get the fine-tuned external_calibration output + incal = camera_tunnel_cal[2:] + rmat_opt, tvec_opt = external_calibrate.external_calibrate_one_step( + self.img, + rmat_coarse, + tvec_coarse, + incal, + self.tgts, + self.test_config, + self.vis_checker + ) + + # Check that the function output closely matches the expected output + np.testing.assert_array_almost_equal(rmat_opt, self.rmat, decimal=3) + np.testing.assert_array_almost_equal(tvec_opt, self.tvec, decimal=3) + + def test_external_calibrate_RANSAC(self): + """ + Regression test of external_calibrate_RANSAC + + Outputs were manually verified to be as expected + """ + if not test_external_calibrate_debug: + f = open(os.devnull, 'w') + std_stdout_orig = sys.stdout + sys.stdout = f + + # Get image location of tgts using manual bboxes and Gaussian localization + # Read in manual bboxes + manual_det_filename = 'CAM1_RUN8_CINE02_Y20000209H11294501.00001.xml' + manual_detections_path = os.path.join( + files_dir, 'detection', 'Annotations', manual_det_filename + ) + + # Get center of each image target + img_targets = [] + for bbox in parsers.read_pascal_voc(manual_detections_path): + # If it is a target of the appropriate class, and not flagged as difficult + # add it to the targets list + if (bbox['class'] == 'dot') and not bbox['difficult']: + x1, y1, x2, y2 = bbox['x1'], bbox['y1'], bbox['x2'], bbox['y2'] + center = np.array([(x1 + x2) / 2, (y1 + y2) / 2]) + img_target = {'target_type' : 'dot', + 'center' : center} + img_targets.append(img_target) + + rmat_opt, tvec_opt = external_calibrate.external_calibrate_RANSAC( + [self.cameraMatrix, self.distCoeffs], + self.tgts, img_targets, self.vis_checker, max_iter=0.999, + max_dist=8, match_thresh=0.80) + + rmat_gt = [[-0.999685595, -0.01305803, 0.0214055819], + [-0.0132301035, 0.9998811364, -0.0079169066], + [-0.0212996584, -0.0081976155, -0.9997395279]] + tvec_gt = [[-5.0715497626], [-0.0496114238], [11.5443579906]] + + # Check that the function output closely matches the expected output + np.testing.assert_array_almost_equal(rmat_opt, rmat_gt, decimal=2) + np.testing.assert_array_almost_equal(tvec_opt, tvec_gt, decimal=2) + + # Redirect the std out to the original location + if not test_external_calibrate_debug: + sys.stdout = std_stdout_orig + f.close() + + +if __name__ == "__main__": + unittest.main() diff --git a/test/python/test_img_utils.py b/test/python/test_img_utils.py new file mode 100644 index 0000000..423fa98 --- /dev/null +++ b/test/python/test_img_utils.py @@ -0,0 +1,86 @@ +import unittest +import numpy as np +import os +import cv2 + +from upsp.cam_cal_utils import img_utils + +files_dir = os.path.join(os.path.dirname(os.path.dirname(__file__)), 'data') + + +class ImgUtilsTestCase(unittest.TestCase): + """ + Tests for the img_utils module + """ + + def test_convert_12bit_to_8bit(self): + """ + Regression tests convert_12bit_to_8bit + + Outputs were manually verified to be as expected + """ + + # Tests that the output is being correctly scaled + inp = np.array([0, 4095, 4078, 128, 369, 3184], dtype=np.uint16) + out_true = np.array([0, 255, 254, 8, 23, 198], dtype=np.uint8) + out_func = img_utils.convert_12bit_to_8bit(inp) + np.testing.assert_array_equal(out_func, out_true) + + # Test for when 1 output is incorrectly scaled + inp = np.array([0, 4095, 4078, 128, 369, 3184], dtype=np.uint16) + out_true = np.array([1, 255, 254, 8, 23, 198], dtype=np.uint8) + out_func = img_utils.convert_12bit_to_8bit(inp) + np.testing.assert_equal(np.any(np.not_equal(out_true, out_func)), True) + + # Test for when all outputs are incorrectly scaled + inp = np.array([0, 4095, 4078, 128, 369, 3184], dtype=np.uint16) + out_true = np.array([42, 42, 42, 42, 42, 42], dtype=np.uint8) + out_func = img_utils.convert_12bit_to_8bit(inp) + np.testing.assert_equal(np.any(np.not_equal(out_true, out_func)), True) + + # scale_image is tested via test_convert_12bit_to_8bit and + # test_scale_image_max_inlier + + def test_scale_image_max_inlier(self): + """ + Regression tests scale_image_max_inlier + + Outputs were manually verified to be as expected + """ + + # Test for when the 2nd greatest value is within 98% of the 1st greatest value + # No change from convert_12bit_to_8bit + inp = np.array([0, 4095, 4078, 128, 369, 3184], dtype=np.uint16) + out_true = np.array([0, 255, 254, 8, 23, 198], dtype=np.uint8) + out_func = img_utils.scale_image_max_inlier(inp) + np.testing.assert_array_equal(out_func, out_true) + + # Test for when 2nd greatest value is NOT within 98% of 1st greatest value, but + # 3rd greatest value is within 98% of 2nd greatest value + inp = np.array([0, 4095, 2000, 1999, 1002, 918], dtype=np.uint16) + out_true = np.array([0, 255, 255, 255, 128, 117], dtype=np.uint8) + out_func = img_utils.scale_image_max_inlier(inp) + np.testing.assert_array_equal(out_func, out_true) + + def test_interp(self): + """ + Regression test of interp + + Outputs were manually verified to be as expected + """ + img_path = os.path.join( + files_dir, 'images', 'CAM1_RUN8_CINE02_Y20000209H11294501.00001.png' + ) + + img = cv2.imread(img_path, cv2.IMREAD_GRAYSCALE + cv2.IMREAD_ANYDEPTH) + pts = np.array([[1.4, 1.2], [500.3, 254.1], [741.9, 452.8], [1011.0, 400.]]) + + res = img_utils.interp(pts, img, method='nearest') + np.testing.assert_equal(res, [2., 133., 8., 118.]) + + res = img_utils.interp(pts, img, method='linear') + np.testing.assert_almost_equal(res, [1.92, 133.09, 7.72, 118.]) + + +if __name__ == "__main__": + unittest.main() diff --git a/test/python/test_internal_calibration.py b/test/python/test_internal_calibration.py new file mode 100644 index 0000000..e8c9248 --- /dev/null +++ b/test/python/test_internal_calibration.py @@ -0,0 +1,205 @@ +import unittest +import numpy as np +import os + +import file_structure +from upsp.cam_cal_utils import ( + internal_calibration, + parsers, +) + +files_dir = os.path.join(os.path.dirname(os.path.dirname(__file__)), 'data') + + +class InternalCalibrationTestCase(unittest.TestCase): + """ + Tests for the img_utils module + """ + + @classmethod + def setUpClass(cls): + pass + + def setUp(self): + """ + Checks files set up properly before each running test + """ + # Check file structure + file_structure.setUp(files_dir) + + # Set the debug boolean to False + internal_calibration.debug_show_cal_bounds = False + + def tearDown(self): + """ + Checks files are still set up properly after each running test + """ + # We can rerun setUp because the file structure should be unchanged + self.setUp() + + def test_get_pts_inside_incal(self): + """ + Regression test for get_pts_inside_incal + + Outputs were manually verified to be as expected + """ + area_debug, vol_debug = internal_calibration.incal_calibration_bounds_debug() + obj_pts = [] + max_val = 50 + for x in range(max_val+1): + for y in range(max_val+1): + obj_pts.append((x, y, 10.)) + obj_pts = np.array(obj_pts) + obj_pts -= (max_val/2, max_val/2, 0.) + + safe_obj_idxs = internal_calibration.get_pts_inside_incal( + np.eye(3), np.zeros((3, 1)), np.eye(3), np.array([[-0.05, 0., 0., 0., 0.]]), + obj_pts, area_debug, vol_debug, critical_pt = 'first') + safe_obj_pts = set([tuple(obj_pts[i].tolist()) for i in safe_obj_idxs]) + + safe_obj_pts_gt = [ + ( 22.0, 11.0, 10.0), (-5.0, 2.0, 10.0), ( 25.0, -7.0, 10.0), + (-17.0, 0.0, 10.0), (-3.0, -9.0, 10.0), (-17.0, -3.0, 10.0), + ( -8.0, -7.0, 10.0), (-4.0, 8.0, 10.0), (-10.0, 23.0, 10.0), + ( -3.0, -22.0, 10.0), (10.0, 14.0, 10.0), (-12.0, -6.0, 10.0) + ] + + not_safe_obj_pts_gt = [ + (25.0, 16.0, 10.0), ( 23.0, -23.0, 10.0), (-25.0, 20.0, 10.0), + (17.0, 24.0, 10.0), (-22.0, -24.0, 10.0), ( 25.0, 13.0, 10.0), + (-15.0, 25.0, 10.0), (-21.0, 24.0, 10.0), (-22.0, 25.0, 10.0), + (20.0, 25.0, 10.0), (-23.0, 21.0, 10.0), (-25.0, -13.0, 10.0) + ] + + self.assertEqual(len(safe_obj_pts), 2413) + for pt in safe_obj_pts_gt: + self.assertTrue(pt in safe_obj_pts) + for pt in not_safe_obj_pts_gt: + self.assertTrue(pt not in safe_obj_pts) + + # incal_from_calibio is tested via write_incal_from_calibio + + def test_write_incal_from_calibio(self): + """ + Unit test for write_incal_from_calibio + """ + calibio_path = os.path.join( + files_dir, 'internal-calibration', 'calibio_incal_test.json' + ) + + output_dir = os.path.join(files_dir, 'output') + internal_calibration.write_incal_from_calibio( + calibio_path, 'written_camera', [0.8, 1.2], output_dir + ) + incal_output_dir = os.path.join(output_dir, 'written_camera.json') + + incal_output = parsers.read_internal_params( + incal_output_dir, dims=(1000, 1000), read_sensor=True + ) + cameraMatrix_wi, distCoeffs_wi, img_size_wi, sensor_sise_wi = incal_output + + cameraMatrix_gt = [[2.0037e+03, 0.0000e+00, 5.1452e+02], + [0.0000e+00, 2.0037e+03, 4.8666e+02], + [0.0000e+00, 0.0000e+00, 1.0000e+00]] + distCoeffs_gt = [[-0.174, 0.1268, 0., 0., 0. ]] + img_size_gt = [ 800, 1280] + sensor_sise_gt = [0.8, 1.2] + + np.testing.assert_array_almost_equal( + cameraMatrix_gt, cameraMatrix_wi, decimal=2 + ) + np.testing.assert_array_almost_equal(distCoeffs_gt, distCoeffs_wi, decimal=2) + np.testing.assert_array_almost_equal(img_size_gt, img_size_wi, decimal=2) + np.testing.assert_array_almost_equal(sensor_sise_gt, sensor_sise_wi, decimal=2) + + # Deleted the generated internal calibration json file + os.remove(incal_output_dir) + + def test_uncertainties_from_calibio(self): + """ + Unit test for uncertainties_from_calibio + """ + calibio_path = os.path.join( + files_dir, 'internal-calibration', 'calibio_incal_test.json' + ) + uncertainties = internal_calibration.uncertainties_from_calibio(calibio_path) + uncertainties_gt = [1.5056e+00,8.2730e-01, 9.8265e-01, 9.1580e-04, 4.0277e-03, + 0.0000e+00, 0.0000e+00, 0.0000e+00] + np.testing.assert_array_almost_equal( + uncertainties, uncertainties_gt, decimal=4 + ) + + def test_incal_calibration_bounds(self): + """ + Regression test for incal_calibration_bounds + + Outputs were manually verified to be as expected + """ + calibio_path = os.path.join( + files_dir, 'internal-calibration', 'calibio_incal_test.json' + ) + cal_is_safes = internal_calibration.incal_calibration_bounds( + calibio_path, 0.075, 0.025 + ) + cal_area_is_safe, cal_vol_is_safe = cal_is_safes + + incal_from_calib = internal_calibration.incal_from_calibio(calibio_path) + img_size, uPSP_cameraMatrix, distCoeffs = incal_from_calib + cameraMatrix = parsers.convert_uPSP_cm_to_cv2_cm(uPSP_cameraMatrix, img_size) + + pixels = np.array(list(np.ndindex(img_size[1], img_size[0]))) + pixel_bools = cal_area_is_safe(pixels) + unsafe_points = np.array(pixels[np.where(~pixel_bools)[0]]) + safe_points = np.array(pixels[np.where(pixel_bools)[0]]) + + self.assertEqual(len(safe_points), 904491) + self.assertEqual(len(unsafe_points), 119509) + + z = 95.0 + xs = (pixels[:, 0] - cameraMatrix[0][2]) / cameraMatrix[0][0] * z + ys = (pixels[:, 1] - cameraMatrix[1][2]) / cameraMatrix[0][0] * z + zs = np.full(xs.shape, z) + pts = np.array([xs, ys, zs]).T + + pt_bools = cal_vol_is_safe(pts) + unsafe_points = np.array(pixels[np.where(~pt_bools)[0]]) + safe_points = np.array(pixels[np.where(pt_bools)[0]]) + + self.assertEqual(len(safe_points), 786715) + self.assertEqual(len(unsafe_points), 237285) + + def test_incal_calibration_bounds_debug(self): + """ + Unit test for incal_calibration_bounds_debug + """ + is_safe_debugs = internal_calibration.incal_calibration_bounds_debug() + cal_area_is_safe, cal_vol_is_safe = is_safe_debugs + img_size = (512, 1024) + cameraMatrix = np.array([[650, 0, 256], [650, 0, 512], [0, 0, 1]], dtype=float) + + pixels = np.array(list(np.ndindex(img_size[1], img_size[0]))) + pixel_bools = cal_area_is_safe(pixels) + unsafe_points = np.array(pixels[np.where(~pixel_bools)[0]]) + safe_points = np.array(pixels[np.where(pixel_bools)[0]]) + + self.assertEqual(len(safe_points), 524288) + self.assertEqual(len(unsafe_points), 0) + + z = 95.0 + xs = (pixels[:, 0] - cameraMatrix[0][2]) / cameraMatrix[0][0] * z + ys = (pixels[:, 1] - cameraMatrix[1][2]) / cameraMatrix[0][0] * z + zs = np.full(xs.shape, z) + pts = np.array([xs, ys, zs]).T + + pt_bools = cal_vol_is_safe(pts) + unsafe_points = np.array(pixels[np.where(~pt_bools)[0]]) + safe_points = np.array(pixels[np.where(pt_bools)[0]]) + + self.assertEqual(len(safe_points), 524288) + self.assertEqual(len(unsafe_points), 0) + + # alpha_shape_is_safe is tested via incal_calibration_bounds + + +if __name__ == "__main__": + unittest.main() diff --git a/test/python/test_node_pixel_mapping.py b/test/python/test_node_pixel_mapping.py new file mode 100644 index 0000000..517344b --- /dev/null +++ b/test/python/test_node_pixel_mapping.py @@ -0,0 +1,162 @@ +import unittest +import numpy as np +import cv2 +import os + +import file_structure +from upsp.cam_cal_utils import ( + camera_tunnel_calibrate, + parsers, + visibility, +) +from upsp.intensity_mapping import node_pixel_mapping + +files_dir = os.path.join(os.path.dirname(os.path.dirname(__file__)), 'data') + + +class NodePixelMappingTestCase(unittest.TestCase): + """ + Tests for node-pixel mapping + """ + + @classmethod + def setUpClass(cls): + # Read tgts file + tgts_file_path = os.path.join(files_dir, 'fml_tc3_volume.tgts') + cls.tgts_all = parsers.read_tgts(tgts_file_path, output_target_types=None) + cls.tgts_dot = parsers.read_tgts(tgts_file_path, output_target_types='dot') + + # Read the rest configuration file + cls.test_config = {"oblique_angle" : 70, + "tunnel-cor_to_tgts_tvec" : np.zeros((3, 1)), + "tunnel-cor_to_tgts_rmat": np.eye(3), + "tunnel-cor_to_tunnel-origin_tvec": np.zeros((3, 1)), + "tunnel-cor_to_tunnel-origin_rmat": np.eye(3), + } + + # Read the tunnel vals + tunnel_vals = {"ALPHA" : 0.0, "BETA" : 0.0, "PHI" : 0.0, "STRUTZ" : 0.0} + + # Get the visibility checker from the grid + grd = os.path.join(files_dir, 'fml_tc3_volume.grid') + cls.vis_checker = visibility.VisibilityChecker( + grd, cls.test_config['oblique_angle'] + ) + + # Load the frame + frame_path = os.path.join( + files_dir, 'images', 'CAM1_RUN8_CINE02_Y20000209H11294501.00001.png' + ) + cls.frame = cv2.imread( + frame_path, cv2.IMREAD_GRAYSCALE + cv2.IMREAD_ANYDEPTH + ) + + # Get the camera calibration for this camera + cam_tun_path = os.path.join( + files_dir, 'camera-tunnel-calibration', 'camera01_35_6.json' + ) + camera_cal_tunnel = parsers.read_camera_tunnel_cal( + cam_tun_path, cls.frame.shape + ) + __, __, cls.cameraMatrix, cls.distCoeffs = camera_cal_tunnel + cls.rmat, cls.tvec = camera_tunnel_calibrate.tf_camera_tgts_thru_tunnel( + camera_cal_tunnel, tunnel_vals, cls.test_config + ) + + # Get vertex and normal information + cls.verts, cls.normals = cls.vis_checker.get_tvecs_and_norms() + + def setUp(self): + """ + Checks files are set up properly before each running test + """ + + file_structure.setUp(files_dir) + + def tearDown(self): + """ + Checks files are still set up properly after each running test + """ + + # We can rerun setUp because the file structure should be unchanged + self.setUp() + + def test_node_to_pixel_mapping_keyframe(self): + """ Regression test for node_to_pixel_mapping_keyframe + + The solutions were manually verified visually with the debug outputs + """ + projs, jacs, vis_idxs = node_pixel_mapping.node_to_pixel_mapping_keyframe( + self.rmat, + self.tvec, + self.cameraMatrix, + self.distCoeffs, + self.vis_checker, + self.verts, + self.normals + ) + + self.assertEqual(len(self.verts), projs.shape[0]) + self.assertEqual(len(self.verts), jacs.shape[0]) + + not_nans = np.argwhere(np.isnan(projs[:, 0]) == False) + self.assertEqual(len(not_nans), vis_idxs.shape[0]) + + self.assertTrue(148823 * 0.95 < len(not_nans) < 148823 * 1.05) + + def test_node_to_pixel_mapping_non_keyframe(self): + """ Regression test for node_to_pixel_mapping_non_keyframe + + The solutions were manually verified visually with the debug outputs + """ + projs_key, jacs, vis_idxs = node_pixel_mapping.node_to_pixel_mapping_keyframe( + self.rmat, + self.tvec, + self.cameraMatrix, + self.distCoeffs, + self.vis_checker, + self.verts, + self.normals + ) + + proj_grad = node_pixel_mapping.node_to_pixel_mapping_non_keyframe( + self.rmat, + self.tvec, + self.rmat, + self.tvec, + projs_key, + jacs, + vis_idxs + ) + + np.testing.assert_allclose(projs_key, proj_grad, atol=0.5) + + def test_node_to_pixel_mapping_non_keyframe_full(self): + """ Regression test for node_to_pixel_mapping_non_keyframe_full + + The solutions were manually verified visually with the debug outputs + """ + projs_key, jacs, vis_idxs = node_pixel_mapping.node_to_pixel_mapping_keyframe( + self.rmat, + self.tvec, + self.cameraMatrix, + self.distCoeffs, + self.vis_checker, + self.verts, + self.normals + ) + + projs_full = node_pixel_mapping.node_to_pixel_mapping_non_keyframe_full( + self.rmat, + self.tvec, + self.cameraMatrix, + self.distCoeffs, + self.verts, + vis_idxs + ) + + np.testing.assert_equal(projs_full, projs_key) + + +if __name__ == "__main__": + unittest.main() diff --git a/test/python/test_parsers.py b/test/python/test_parsers.py new file mode 100644 index 0000000..34c86fb --- /dev/null +++ b/test/python/test_parsers.py @@ -0,0 +1,289 @@ +import unittest +import numpy as np +import os + +import file_structure +from upsp.cam_cal_utils import parsers + +files_dir = os.path.join(os.path.dirname(os.path.dirname(__file__)), 'data') + + +class ParsersTestCase(unittest.TestCase): + """ + Unit tests for the parsers module + """ + + def setUp(self): + """ + Checks files set up properly before each running test + """ + + file_structure.setUp(files_dir) + + def tearDown(self): + """ + Checks files are still set up properly after each running test + """ + + # We can rerun setUp because the file structure should be unchanged + self.setUp() + + def test_read_tgts(self): + """ + Unit test for read_tgts + """ + # Read in the test tgts file + tgts_path = os.path.join(files_dir, 'fml_tc3_volume.tgts') + + # Spot check on a couple of items with different, valid inputs + tgts = parsers.read_tgts(tgts_path, 'dot') + np.testing.assert_array_almost_equal( + tgts[6]['tvec'], np.array([[-7.4388], [-0.145], [-7.0]]), decimal=4 + ) + np.testing.assert_array_equal(tgts[6]['tvec'].shape, (3, 1)) + + tgts = parsers.read_tgts(tgts_path, ['dot']) + np.testing.assert_array_almost_equal( + tgts[6]['tvec'], np.array([[-7.4388], [-0.145], [-7.0]]), decimal=4 + ) + + def test_read_pascal_voc(self): + """ + Unit test for read_pascal_voc + """ + # Define the filepath and the ground truth results + pv_path = os.path.join( + files_dir, + 'detection', + 'Annotations', + 'CAM1_RUN8_CINE02_Y20000209H11294501.00001.xml' + ) + pv_gt_1 = { + 'class': 'dot', + 'x1': 605, + 'x2': 612, + 'y1': 397, + 'y2': 404, + 'difficult': 0, + 'truncated': 0 + } + pv_gt_2 = { + 'class': 'dot', + 'x1': 804, + 'x2': 810, + 'y1': 685, + 'y2': 691, + 'difficult': 0, + 'truncated': 0 + } + + # Tests capability to read list of annots + pv = parsers.read_pascal_voc([pv_path, pv_path]) + + self.assertEqual(pv[0][4], pv_gt_1) + self.assertEqual(pv[0][11], pv_gt_2) + + # Tests capability to read single annot + pv = parsers.read_pascal_voc(pv_path) + self.assertEqual(pv[4], pv_gt_1) + self.assertEqual(pv[11], pv_gt_2) + + def test_read_wind_tunnel_data(self): + """ + Unit test for read_wind_tunnel_data + """ + # Read the wind tunnel data file then compare to the baseline + wtd = parsers.read_wind_tunnel_data(os.path.join(files_dir, 'wtd_test.wtd')) + self.assertEqual( + wtd, {'ALPHA': 0.05, 'BETA': 0.12, 'PHI': 0.90, 'STRUTZ': 10.0} + ) + + def test_convert_cv2_cm_to_uPSP_cm(self): + """ + Unit test for convert_cv2_cm_to_uPSP_cm + """ + # Define a traditional camera matrix (with realistic parameters) + cm = np.array([[1380.2632820187425, 0.0, 533.908701486902032], + [0.0, 1380.2632820187425, 256.778541140320840], + [0.0, 0.0, 1.0]]) + + # Convert to a uPSP camera matrix + uPSP_cm = parsers.convert_cv2_cm_to_uPSP_cm(cm, (512, 1024)) + + # Define the ground truth for the uPSP camera matrix + uPSP_cm_gt = np.array([[1380.2632820187425, 0.0, 21.908701486902032], + [0.0, 1380.2632820187425, 0.778541140320840], + [0.0, 0.0, 1.0]]) + + # Compare the parsers' converted matrix to the ground truth + np.testing.assert_array_almost_equal(uPSP_cm, uPSP_cm_gt, decimal=4) + np.testing.assert_array_equal(uPSP_cm.shape, (3, 3)) + + def test_convert_uPSP_cm_to_cv2_cm(self): + """ + Unit test for convert_uPSP_cm_to_cv2_cm + """ + # Define a uPSP camera matrix (with realistic parameters) + uPSP_cm = np.array([[1380.2632820187425, 0.0, 21.908701486902032], + [0.0, 1380.2632820187425, 0.778541140320840], + [0.0, 0.0, 1.0]]) + + # Convert to a traditional camera matrix + cm = parsers.convert_uPSP_cm_to_cv2_cm(uPSP_cm, (512, 1024)) + + # Define the ground truth for the camera matrix + cm_gt = np.array([[1380.2632820187425, 0.0, 533.908701486902032], + [0.0, 1380.2632820187425, 256.778541140320840], + [0.0, 0.0, 1.0]]) + + # Compare the parsers' converted uPSP matrix to the ground truth + np.testing.assert_array_almost_equal(cm, cm_gt, decimal=4) + np.testing.assert_array_equal(cm.shape, (3, 3)) + + def test_read_internal_params(self): + """ + Unit test for read_internal_params + """ + # Read the given intrinsics file + intrinsics_path = os.path.join( + files_dir, 'internal-calibration', 'camera01_35_6.json' + ) + + cm, dists, sr, ss = parsers.read_internal_params( + intrinsics_path, (512, 1024), read_sensor=True + ) + + # Compare the ground truth camera matrix to the parsed matrix + cm_gt = np.array([[1380.2632820187425, 0.0, 533.908701486902032], + [0.0, 1380.2632820187425, 256.778541140320840], + [0.0, 0.0, 1.0]]) + np.testing.assert_array_almost_equal(cm, cm_gt, decimal=4) + np.testing.assert_array_equal(cm.shape, (3, 3)) + + # Compare the ground truth distortion coefficients to the parsed coefficients + dists_gt = np.array([[-0.09098491035825468, 0.0, 0.0, 0.0, 0.0]]) + np.testing.assert_array_almost_equal(dists, dists_gt, decimal=4) + np.testing.assert_array_equal(dists.shape, (1, 5)) + + # Compare sensor resolutoin and sensor size + sensor_resolution_gt = np.array([800, 1280, 1]) + np.testing.assert_array_almost_equal(sr, sensor_resolution_gt, decimal=4) + + sensor_size_gt = np.array([0.8818898, 1.4110236]) + np.testing.assert_array_almost_equal(ss, sensor_size_gt, decimal=4) + + def test_read_camera_tunnel_cal(self): + """ + Unit test for read_camera_tunnel_cal + """ + # Read in the camera calibration + camera_tunnel_cal_path = os.path.join( + files_dir, 'camera-tunnel-calibration', 'camera01_35_6.json' + ) + rmat, tvec, cm, dists, sr, ss = parsers.read_camera_tunnel_cal( + camera_tunnel_cal_path, (512, 1024), read_sensor=True + ) + + # Compare the ground truth camera matrix to the parsed matrix + cm_gt = np.array([[1380.2632820187425, 0.0, 533.908701486902032], + [0.0, 1380.2632820187425, 256.778541140320840], + [0.0, 0.0, 1.0]]) + np.testing.assert_array_almost_equal(cm, cm_gt, decimal=1) + np.testing.assert_array_equal(cm.shape, (3, 3)) + + # Compare the ground truth distortion coefficients to the parsed coefficients + dists_gt = np.array([[-0.09098491035825468, 0.0, 0.0, 0.0, 0.0]]) + np.testing.assert_array_almost_equal(dists, dists_gt, decimal=1) + np.testing.assert_array_equal(dists.shape, (1, 5)) + + # Compare the ground truth rotation matrix to the parsed matrix + rmat_gt = np.array([[-0.9997264805692, -0.01297871345068, 0.01945551453603], + [-0.0131837243006, 0.99985852053613, -0.01044645034786], + [-0.0193171804946, -0.01070008918048, -0.99975614758263]]) + np.testing.assert_array_almost_equal(rmat, rmat_gt, decimal=1) + np.testing.assert_array_equal(rmat.shape, (3, 3)) + + # Compare the ground truth translation vector to the parsed vector + tvec_gt = np.expand_dims([-5.09303598682, -0.0771666696565, 11.55605419793], 1) + np.testing.assert_array_almost_equal(tvec, tvec_gt, decimal=1) + np.testing.assert_array_equal(tvec.shape, (3, 1)) + + # Compare sensor resolutoin and sensor size + sensor_resolution_gt = np.array([800, 1280, 1]) + np.testing.assert_array_almost_equal(sr, sensor_resolution_gt, decimal=4) + + sensor_size_gt = np.array([0.8818898, 1.4110236]) + np.testing.assert_array_almost_equal(ss, sensor_size_gt, decimal=4) + + def test_read_json(self): + """ + Unit test for read_json + """ + # Read in the test config as a regular json + tc = parsers.read_json(os.path.join(files_dir, 'test_config.json')) + + # Assert that Updated is an item in the test_config file + # Remove it since the cached version might be more updated than the read version + assert "Updated" in tc + del tc["Updated"] + + # Define the ground truth + tc_gt = {"oblique_angle" : 70, + "tunnel-cor_to_tgts_tvec" : np.zeros(3), + "tunnel-cor_to_tgts_rmat": np.eye(3), + "tunnel-cor_to_tunnel-origin_tvec": np.zeros(3), + "tunnel-cor_to_tunnel-origin_rmat": np.eye(3), + "crosscorr_coeff" : 0.75, + "dot_pad" : 4, + "kulite_pad" : 3, + "max_dist" : 6, + "min_dist" : 8} + + # Compare the parsed to the ground truth + for key in tc_gt.keys(): + np.testing.assert_array_equal(tc[key], tc_gt[key]) + for key in tc.keys(): + np.testing.assert_array_equal(tc[key], tc_gt[key]) + + def test_read_test_config(self): + """ + Unit test for read_test_config + """ + # Read in the test config file + tc = parsers.read_test_config(os.path.join(files_dir, 'test_config.json')) + + # Assert that Updated is an item in the test_config file + # Remove it since the cached version might be more updated than the read version + assert "Updated" in tc + del tc["Updated"] + + # Define the ground truth + tc_gt = {"oblique_angle" : 70, + "tunnel-cor_to_tgts_tvec" : np.zeros((3, 1)), + "tunnel-cor_to_tgts_rmat": np.eye(3), + "tunnel-cor_to_tunnel-origin_tvec": np.zeros((3, 1)), + "tunnel-cor_to_tunnel-origin_rmat": np.eye(3), + "crosscorr_coeff" : 0.75, + "dot_pad" : 4, + "kulite_pad" : 3, + "max_dist" : 6, + "min_dist" : 8} + + # Compare the parsed to the ground truth + for key in tc_gt.keys(): + np.testing.assert_array_equal(tc[key], tc_gt[key]) + for key in tc.keys(): + np.testing.assert_array_equal(tc[key], tc_gt[key]) + + np.testing.assert_array_equal(tc['tunnel-cor_to_tgts_tvec'].shape, (3, 1)) + np.testing.assert_array_equal(tc['tunnel-cor_to_tgts_rmat'].shape, (3, 3)) + np.testing.assert_array_equal( + tc['tunnel-cor_to_tunnel-origin_tvec'].shape, (3, 1) + ) + np.testing.assert_array_equal( + tc['tunnel-cor_to_tunnel-origin_rmat'].shape, (3, 3) + ) + + +if __name__ == "__main__": + unittest.main() diff --git a/test/python/test_patching.py b/test/python/test_patching.py new file mode 100644 index 0000000..06edd27 --- /dev/null +++ b/test/python/test_patching.py @@ -0,0 +1,553 @@ +import unittest +import numpy as np +import cv2 +import os +import copy + +import file_structure +from upsp.cam_cal_utils import ( + camera_tunnel_calibrate, + parsers, + photogrammetry, + visibility, +) +from upsp.intensity_mapping import patching + +files_dir = os.path.join(os.path.dirname(os.path.dirname(__file__)), 'data') + + +class PatchingTestCase(unittest.TestCase): + """ + Tests for patching + """ + + @classmethod + def setUpClass(cls): + # Read tgts file + tgts_file_path = os.path.join(files_dir, 'fml_tc3_volume.tgts') + cls.tgts = parsers.read_tgts(tgts_file_path) + + # Read the rest configuration file + cls.test_config = parsers.read_test_config( + os.path.join(files_dir, 'test_config.json') + ) + + # Read the tunnel vals + tunnel_vals = {"ALPHA" : 0.0, "BETA" : 0.0, "PHI" : 0.0, "STRUTZ" : 0.0} + + # Get the visibility checker from the grid + cls.grd = os.path.join(files_dir, 'fml_tc3_volume.grid') + cls.vis_checker = visibility.VisibilityChecker( + cls.grd, cls.test_config['oblique_angle'] + ) + + # Load the frame + frame_path = os.path.join( + files_dir, 'images', 'CAM1_RUN8_CINE02_Y20000209H11294501.00001.png' + ) + cls.frame = cv2.imread(frame_path, cv2.IMREAD_GRAYSCALE + cv2.IMREAD_ANYDEPTH) + + # Get the camera calibration for this camera + cam_tun_path = os.path.join( + files_dir, 'camera-tunnel-calibration', 'camera01_35_6.json' + ) + camera_cal_tunnel = parsers.read_camera_tunnel_cal( + cam_tun_path, cls.frame.shape + ) + __, __, cls.cameraMatrix, cls.distCoeffs = camera_cal_tunnel + cls.rmat, cls.tvec = camera_tunnel_calibrate.tf_camera_tgts_thru_tunnel( + camera_cal_tunnel, tunnel_vals, cls.test_config + ) + + cls.tgts_visible = photogrammetry.get_visible_targets( + cls.rmat, + cls.tvec, + cls.cameraMatrix, + cls.distCoeffs, + cls.tgts, + cls.vis_checker + ) + + def setUp(self): + """ + Checks files are set up properly before each running test + """ + + file_structure.setUp(files_dir) + + def tearDown(self): + """ + Checks files are still set up properly after each running test + """ + + # We can rerun setUp because the file structure should be unchanged + self.setUp() + + def test_get_target_node_idxs(self): + """ Regression test for get_target_node_idxs + + The solutions were manually verified visually with the debug outputs + """ + verts, normals = self.vis_checker.get_tvecs_and_norms() + tgt_vert_idxs = patching.get_target_node_idxs(verts, self.tgts_visible, 0.1) + + self.assertTrue(526 * 0.95 < len(tgt_vert_idxs) < 526 * 1.05) + + def test_patchFiducials(self): + """ Regression test for patchFiducials + + The solutions were manually verified visually with the debug outputs + """ + internal_intensities_gt = [[106.03251, 106.21941], + [107.01004, 107.28066], + [121.48262, 121.870865], + [123.34717, 123.45227], + [122.19859, 123.282555], + [106.937965, 107.00953], + [130.03972, 125.14807], + [102.69896, 102.12065], + [7.301373, 7.8519864], + [130.2384, 130.08713], + [155.92165, 156.675], + [134.46587, 134.05843], + [125.855385, 125.172966], + [114.238075, 115.599174], + [98.09196, 97.20015], + [89.30197, 90.37857], + [84.49397, 85.29867], + [74.0, 76.0], + [73.0, 71.0]] + + # Define patch parameters + boundary_thickness = 2 + buffer_thickness_in = 0.1 + + out_img = patching.patchFiducials( + self.tgts_visible, + self.frame, + self.rmat, + self.tvec, + self.cameraMatrix, + self.distCoeffs, + boundary_thickness, + buffer_thickness_in + ) + + # Generate image with all targets highlighted + ny, nx = self.frame.shape + xs, ys = np.meshgrid(np.arange(nx), np.arange(ny)) + pts = np.stack((xs, ys), axis=2) + + all_targs = np.zeros(self.frame.shape) + for i, fiducial in enumerate(self.tgts_visible): + tgt_proj, targ_size_px, __, __ = patching.get_fiducial_pixel_properties( + fiducial, + self.rmat, + self.tvec, + self.cameraMatrix, + self.distCoeffs, + boundary_thickness, + buffer_thickness_in + ) + + tgt_proj_formatted = np.array(tgt_proj['proj']).reshape(1, 1, 2) + dists = np.linalg.norm(pts - tgt_proj['proj'], axis=2) + + internals = np.array(np.where(dists <= targ_size_px/2)) + + # If there are no internals (ie the target is outside the image) then + # the input image should not have been changed + if internals.shape[1] == 0: + continue + + np.testing.assert_array_almost_equal( + out_img[internals.T[0][0]][internals.T[0][1]], + internal_intensities_gt[i][0], + decimal=4 + ) + + np.testing.assert_array_almost_equal( + out_img[internals.T[17][0]][internals.T[17][1]], + internal_intensities_gt[i][1], + decimal=4 + ) + + def test_clusterFiducials(self): + """ Regression test for clusterFiducials + + The solutions were manually verified visually with the debug outputs + """ + # Define patch parameters + boundary_thickness = 2 + buffer_thickness_in = 0.03 + + # Create a copy of the visible targets to make a cluster + tgts_visible_cp = copy.deepcopy(self.tgts_visible) + fake_tgt = copy.deepcopy(tgts_visible_cp[-1]) + fake_tgt['tvec'][0] += 0.1 + tgts_visible_cp.append(fake_tgt) + + # Cluster the fiducials + fiducial_clusters = patching.clusterFiducials( + tgts_visible_cp, + self.rmat, + self.tvec, + self.cameraMatrix, + self.distCoeffs, + boundary_thickness, + buffer_thickness_in + ) + + # Check that none of the clusters overlap + internals_set = set() + + bounds_set = set() + for i, cluster in enumerate(fiducial_clusters): + internals, bounds = patching.get_cluster_internal_and_boundary( + cluster, + self.rmat, + self.tvec, + self.cameraMatrix, + self.distCoeffs, + boundary_thickness, + buffer_thickness_in + ) + + # Check if the internals of this cluster overlap with any previous clusters + # Check if the internals overlap with any boundaries + for internal in internals: + internal_tuple = tuple(internal.tolist()) + + # Assert that the internal is not in the bounds + self.assertFalse(internal_tuple in bounds_set) + + # Assert that the internal is not in another internal + self.assertFalse(internal_tuple in internals_set) + + # Add this internal to the internals set + internals_set.add(internal_tuple) + + # Check if the boundaries of this cluster overlap with any previous clusters + # Check if the bounds overlap with any internals + for bound in bounds: + bound_tuple = tuple(bound.tolist()) + + # Assert that the bounds are not in the internal + self.assertFalse(bound_tuple in internals_set) + + # Add this bound to the boundary set + bounds_set.add(bound_tuple) + + # Make sure there are the correct number of clusters + self.assertEqual(len(fiducial_clusters), 24) + + # Make sure the clusters have the correct number of fiducials + self.assertEqual(len(fiducial_clusters[0]), 2) + for fiducial_cluster in fiducial_clusters[1:]: + self.assertEqual(len(fiducial_cluster), 1) + + def test_get_cluster_internal_and_boundary(self): + """ Regression test for get_cluster_internal_and_boundary + + The solutions were manually verified visually with the debug outputs + """ + boundary_thickness = 2 + buffer_thickness_in = 0.03 + tgts_sub = copy.deepcopy([self.tgts_visible[x] for x in [10, 12, 13, 21]]) + + spot_check_internals = [[(( 6, 15), 1), (( 2, 3), 0)], + [(( 8, 5), 1), ((12, 20), 1)], + [((12, 10), 0), (( 2, 5), 0)], + [((10, 10), 1), ((12, 20), 0)]] + spot_check_bounds = [[((10, 5), 0), ((10, 1), 1)], + [((12, 0), 0), ((14, 15), 0)], + [(( 2, 5), 1), (( 6, 15), 0)], + [(( 0, 5), 1), ((12, 25), 1)]] + + # Check that the function works for single fiducial clusters + for i, tgt in enumerate(tgts_sub): + internals_f, bounds_f = patching.get_fiducial_internal_and_boundary( + tgt, + self.rmat, + self.tvec, + self.cameraMatrix, + self.distCoeffs, + boundary_thickness, + buffer_thickness_in + ) + + internals_f_set = set() + for internal_f in internals_f: + internals_f_set.add(tuple(internal_f.tolist())) + + internals_c, bounds_c = patching.get_cluster_internal_and_boundary( + [tgt], + self.rmat, + self.tvec, + self.cameraMatrix, + self.distCoeffs, + boundary_thickness, + buffer_thickness_in + ) + + internals_c_set = set() + for internal_c in internals_c: + internals_c_set.add(tuple(internal_c.tolist())) + + np.testing.assert_equal( + len(internals_f_set), + len(internals_f_set.intersection(internals_c_set)) + ) + + # Check that the function works for multi fiducial clusters + for i, tgt in enumerate(tgts_sub): + tgt_copy = copy.deepcopy(tgt) + tgt_copy['tvec'][0] += 0.20 # moves fiducial in image x (mostly) + tgt_copy['tvec'][2] += 0.06 # moves fiducial in image y (mostly) + + internals, bounds = patching.get_cluster_internal_and_boundary( + [tgt, tgt_copy], + self.rmat, + self.tvec, + self.cameraMatrix, + self.distCoeffs, + boundary_thickness, + buffer_thickness_in + ) + + # print the debugs + min_bounds = (np.min(bounds[:,0]), np.min(bounds[:,1])) + max_bounds = (np.max(bounds[:,0]), np.max(bounds[:,1])) + + internals_map = np.zeros((max_bounds[1] - min_bounds[1] + 1, + max_bounds[0] - min_bounds[0] + 1)) + + for in_pt in internals: + local_in_pt = (in_pt[0] - min_bounds[0], in_pt[1] - min_bounds[1]) + internals_map[local_in_pt[1]][local_in_pt[0]] = 1 + + bounds_map = np.zeros( + (max_bounds[1] - min_bounds[1] + 1, max_bounds[0] - min_bounds[0] + 1) + ) + for bd_pt in bounds: + local_bd_pt = (bd_pt[0] - min_bounds[0], bd_pt[1] - min_bounds[1]) + bounds_map[local_bd_pt[1]][local_bd_pt[0]] = 1 + + np.testing.assert_array_less(internals_map + bounds_map, 2) + for spot_check in spot_check_internals[i]: + np.testing.assert_equal(internals_map[spot_check[0]], spot_check[1]) + + for spot_check in spot_check_bounds[i]: + np.testing.assert_equal(bounds_map[spot_check[0]], spot_check[1]) + + def test_get_fiducial_internal_and_boundary(self): + """ Regression test for get_fiducial_internal_and_boundary + + The solutions were manually verified visually with the debug outputs + """ + tgts_sub = copy.deepcopy([self.tgts_visible[x] for x in [10, 12, 13]]) + + spot_check_internals = [[(( 5, 10), 1), ((20, 2), 0)], + [((10, 10), 1), ((20, 20), 0)], + [((20, 20), 0), (( 2, 20), 0)]] + spot_check_bounds = [[(( 5, 2), 1), (( 5, 10), 0)], + [((20, 5), 1), ((20, 20), 1)], + [((15, 1), 1), ((20, 5), 1)]] + + for i, tgt in enumerate(tgts_sub): + internals, bounds = patching.get_fiducial_internal_and_boundary( + tgt, + self.rmat, self.tvec, self.cameraMatrix, self.distCoeffs, + 2, 0.1 + ) + + # print the debugs + min_bounds = (np.min(bounds[:,0]), np.min(bounds[:,1])) + max_bounds = (np.max(bounds[:,0]), np.max(bounds[:,1])) + + internals_map = np.zeros((max_bounds[1] - min_bounds[1] + 1, + max_bounds[0] - min_bounds[0] + 1)) + + for in_pt in internals: + local_in_pt = (in_pt[0] - min_bounds[0], in_pt[1] - min_bounds[1]) + internals_map[local_in_pt[1]][local_in_pt[0]] = 1 + + bounds_map = np.zeros( + (max_bounds[1] - min_bounds[1] + 1, max_bounds[0] - min_bounds[0] + 1) + ) + for bd_pt in bounds: + local_bd_pt = (bd_pt[0] - min_bounds[0], bd_pt[1] - min_bounds[1]) + bounds_map[local_bd_pt[1]][local_bd_pt[0]] = 1 + + np.testing.assert_array_less(internals_map + bounds_map, 2) + for spot_check in spot_check_internals[i]: + np.testing.assert_equal(internals_map[spot_check[0]], spot_check[1]) + + for spot_check in spot_check_bounds[i]: + np.testing.assert_equal(bounds_map[spot_check[0]], spot_check[1]) + + def test_get_fiducial_boundary_map_from_internal_map(self): + """ Regression test for get_fiducial_boundary_map_from_internal_map + + The solutions were manually verified visually with the debug outputs + """ + boundary_thickness = 2 + def get_fiducial_boundary_map_from_internal_map_regr(internal_map): + # Dilate the internals image to get an image of the internals and boundary + kernel = np.full((3, 3), 1) + int_and_bound_map = copy.deepcopy(internal_map).astype(dtype=np.uint8) + for i in range(boundary_thickness): + int_and_bound_map = cv2.dilate(int_and_bound_map, kernel) + + # Get the boundary by subtracting the internals + boundary_map = int_and_bound_map - internal_map + return boundary_map + + for i, tgt in enumerate(self.tgts_visible): + internals, bounds = patching.get_fiducial_internal_and_boundary( + tgt, + self.rmat, + self.tvec, + self.cameraMatrix, + self.distCoeffs, + boundary_thickness, + 0.1 + ) + + internals_map = np.zeros(self.frame.shape) + for internal in internals: + x, y = internal + if x < 0 or x > self.frame.shape[1]: + continue + if y < 0 or y > self.frame.shape[0]: + continue + internals_map[y][x] = 1 + + bounds_map = np.zeros(self.frame.shape) + for bound in bounds: + x, y = bound + if x < 0 or x > self.frame.shape[1]: + continue + if y < 0 or y > self.frame.shape[0]: + continue + bounds_map[y][x] = 1 + + np.testing.assert_array_equal( + bounds_map, + get_fiducial_boundary_map_from_internal_map_regr(internals_map) + ) + + def test_get_fiducial_pixel_properties(self): + """ Regression test for get_fiducial_pixel_properties + + The solutions were manually verified visually with the debug outputs + """ + targ_size_px_gt = [11.442733103728068, 11.441378433873822, 11.738253646918416, + 11.764717542333987, 11.801661542160153, 11.720571915645861, + 11.94827679861624, 11.745977350478295, 13.478404742018785, + 11.843501749982838, 12.05934648991549, 11.96534823545594, + 11.782260632481044, 11.794715194262851, 11.401202760114563, + 11.418828875986328, 11.272951268319456, 10.909527128683477, + 10.911584769089375, 10.329888893588022, 10.372276760939084, + 9.793731185954318, 9.289072105360367, 9.29690065203498] + tgt_proj_gt = [[1062.1596, 262.6362], [1049.5059, 602.7571], + [ 958.7569, 401.9505], [ 912.806, 277.4822], + [ 896.4405, 540.9792], [ 833.6571, 147.9847], + [ 824.7844, 397.0226], [ 806.4481, 686.5303], + [ 730.9645, 406.4167], [ 608.0831, 151.1053], + [ 608.3256, 398.896 ], [ 609.248, 589.4952], + [ 375.6581, 260.3785], [ 366.0181, 537.7377], + [ 206.7432, 213.7723], [ 203.3067, 594.6966], + [ 113.9483, 397.4618], [ -0.847, 328.4902], + [ 0.6552, 512.2042], [-158.6912, 252.179 ], + [-154.1934, 544.9812], [-318.6646, 390.3488], + [-430.5195, 231.9778], [-436.5539, 557.0394]] + + # Define patch parameters + boundary_thickness = 2 + buffer_thickness_in = 0.05 + + for i, fiducial in enumerate(self.tgts_visible): + tgt_proj, targ_size_px, __, __ = patching.get_fiducial_pixel_properties( + fiducial, self.rmat, self.tvec, self.cameraMatrix, self.distCoeffs, + boundary_thickness, buffer_thickness_in + ) + np.testing.assert_almost_equal(targ_size_px_gt[i], targ_size_px, decimal=2) + np.testing.assert_almost_equal(tgt_proj_gt[i], tgt_proj['proj'], decimal=2) + + def test_polyfit2D(self): + """ Regression test for polyfit2D + + The solutions were manually verified visually with the debug outputs + """ + coeff_gts = [[ 1.6494e+02, -1.8998e+00, 1.4664e-01, -4.0182e-03, -1.3818e+00, + 1.0423e-01, -6.6948e-04, 1.0484e-01, -3.5790e-03, -2.2306e-03], + [ 1.3130e+02, -1.4366e+00, 1.3644e-01, -3.3798e-03, -9.3938e-01, + -1.1125e-02, -3.4015e-04, 1.2540e-01, 1.9413e-03, -4.2443e-03], + [ 1.1783e+02, -8.8762e-01, 6.5684e-02, -8.7675e-04, -1.0222e-01, + 4.5181e-03, -1.7885e-03, 2.3825e-02, 1.5239e-03, -1.2054e-03]] + + tgts_sub = copy.deepcopy([self.tgts_visible[x] for x in [10, 12, 13]]) + + for i, tgt in enumerate(tgts_sub): + # Get the bounds + internals, bounds = patching.get_fiducial_internal_and_boundary( + tgt, self.rmat, self.tvec, self.cameraMatrix, self.distCoeffs, 2, 0.1) + + # Get the intensities of the bounds + Is = [self.frame[bound[1]][bound[0]] for bound in bounds] + + # Convert the bounds to local bounds + min_bounds = (np.min(bounds[:,0]), np.min(bounds[:,1])) + local_bounds = bounds - min_bounds + local_internals = internals - min_bounds + + # fit a 2D polynomial to the bounds + coeffs = patching.polyfit2D(local_bounds, Is) + np.testing.assert_almost_equal(coeff_gts[i], coeffs, decimal=2) + + def test_polyval2D(self): + """ Regression test for polyval2D + + The solutions were manually verified visually with the debug outputs + """ + # Define patch parameters + boundary_thickness = 2 + buffer_thickness_in = 0.05 + + internal_intensities_gts = [[157.0578855264, 156.61471620885, 158.33528562904], + [125.6815396239, 125.39472392504, 126.34666003293], + [115.0199014473, 115.84445115081, 115.74647936438]] + tgts_sub = copy.deepcopy([self.tgts_visible[x] for x in [10, 12, 13]]) + + for i, tgt in enumerate(tgts_sub): + # Get the bounds + internals, bounds = patching.get_fiducial_internal_and_boundary( + tgt, self.rmat, self.tvec, self.cameraMatrix, self.distCoeffs, 2, 0.05) + + # Get the intensities of the bounds + Is = [self.frame[bound[1]][bound[0]] for bound in bounds] + + # Convert the bounds to local bounds + min_bounds = (np.min(bounds[:,0]), np.min(bounds[:,1])) + local_bounds = bounds - min_bounds + local_internals = internals - min_bounds + + # fit a 2D polynomial to the bounds + coeffs = patching.polyfit2D(local_bounds, Is) + + # Get the internal intensities based on the fit + internal_intensities = patching.polyval2D(local_internals, coeffs) + np.testing.assert_almost_equal( + internal_intensities_gts[i][0], internal_intensities[ 0], decimal=4 + ) + np.testing.assert_almost_equal( + internal_intensities_gts[i][1], internal_intensities[ 7], decimal=4 + ) + np.testing.assert_almost_equal( + internal_intensities_gts[i][2], internal_intensities[23], decimal=4 + ) + + +if __name__ == "__main__": + unittest.main() diff --git a/test/python/test_photogrammetry.py b/test/python/test_photogrammetry.py new file mode 100644 index 0000000..cf91649 --- /dev/null +++ b/test/python/test_photogrammetry.py @@ -0,0 +1,506 @@ +import unittest +import numpy as np +import os +import copy + +import file_structure +from upsp.cam_cal_utils import ( + camera_tunnel_calibrate, + parsers, + photogrammetry, + visibility, +) + +files_dir = os.path.join(os.path.dirname(os.path.dirname(__file__)), 'data') + + +class PhotogrammetryTestCase(unittest.TestCase): + """ + Tests for photogrammetry + """ + + @classmethod + def setUpClass(cls): + # Define the rotation matrix and translation vector + cls.rmat = np.array([[-0.999726480569, -0.0129787134506, 0.0194555145360], + [-0.013183724300, 0.9998585205361, -0.0104464503478], + [-0.019317180494, -0.0107000891804, -0.9997561475826]]) + cls.tvec = np.array([[-5.093035986816], [-0.07716666965650], [11.556054197934]]) + + # Define the camera matrix and distortion coefficients + cls.cameraMatrix = np.array([[1380.2632820187425, 0.0, 533.908701486902032], + [0.0, 1380.2632820187425, 256.778541140320840], + [0.0, 0.0, 1.0]]) + cls.distCoeffs = np.array([[-0.09098491035825468, 0.0, 0.0, 0.0, 0.0]]) + + # Read the rest configuration file + test_config_file = os.path.join(files_dir, 'test_config.json') + test_config = parsers.read_json(test_config_file) + + # Get the visibility checker from the grid + grd_file = os.path.join(files_dir, 'fml_tc3_volume.grid') + cls.vis_checker = visibility.VisibilityChecker(grd_file, + test_config['oblique_angle'], epsilon=1e-4 + ) + + # Read in the targets file + tgts_file = os.path.join(files_dir, 'fml_tc3_volume.tgts') + cls.tgts = parsers.read_tgts(tgts_file) + + cls.tgts_visible = photogrammetry.get_visible_targets( + cls.rmat, + cls.tvec, + cls.cameraMatrix, + cls.distCoeffs, + cls.tgts, + cls.vis_checker + ) + + def setUp(self): + """ + Checks files set up properly before each running test + """ + + file_structure.setUp(files_dir) + + def tearDown(self): + """ + Checks files are still set up properly after each running test + """ + + # We can rerun setUp because the file structure should be unchanged + self.setUp() + + def test_rot(self): + """ + Regression test for rot + + Randomly generated inputs tests against a version of the function cached on + 03/11/2021. Outputs of function at time were manually tested and verified + to be correct + """ + + # Seed the random generator for consistency between tests + # It should work for any test, but if there is an error associated with a + # given input this will allow for better error tracing + rng = np.random.default_rng(0) + + # This is a cached version of the function. Should the function be changed in + # the future, we want to ensure consistency between the old and new function + def regression_rot(angle, axis): + angle_rad = np.deg2rad(angle) + + if axis == 'x': + return np.array([[1, 0, 0], + [0, np.cos(angle_rad), -np.sin(angle_rad)], + [0, np.sin(angle_rad), np.cos(angle_rad)]]) + + if axis == 'y': + return np.array([[np.cos(angle_rad), 0, np.sin(angle_rad)], + [0, 1, 0], + [-np.sin(angle_rad), 0, np.cos(angle_rad)]]) + + if axis == 'z': + return np.array([[np.cos(angle_rad), -np.sin(angle_rad), 0], + [np.sin(angle_rad), np.cos(angle_rad), 0], + [0, 0, 1]]) + + # Generate 50 random angles and axes + random_angles = (360 * rng.random(50)) - 180 + axes = rng.choice(['x', 'y', 'z'], 50, replace=True) + + # Test photogrammetry.rot against the cached version + for angle, axis in zip(random_angles, axes): + p_rot = photogrammetry.rot(angle, axis) + r_rot = regression_rot(angle, axis) + np.testing.assert_array_almost_equal(p_rot, r_rot, decimal=3) + np.testing.assert_array_almost_equal(p_rot.shape, (3, 3)) + + def test_invTransform(self): + """ + Unit test for invTransform + + Many randomly generated inputs tests known properties of the outputs + """ + + # Seed the random generator for consistency between tests + # It should work for any test, but if there is an error associated with a + # given input this will allow for better error tracing + rng = np.random.default_rng(0) + + # Test 50 times (50 random inputs) + for i in range(50): + # Generate 3 random rotation matrices + angles = (360 * rng.random(3)) - 180 + rotx = photogrammetry.rot(angles[0], 'x') + roty = photogrammetry.rot(angles[1], 'y') + rotz = photogrammetry.rot(angles[2], 'z') + + # Combine the 3 random rotation matrices with a random translation vector + rmat = np.matmul(rotx, roty, rotz) + tvec = (360 * rng.random((3, 1))) - 180 + + # Get the inverse of the rotation and translation + inv_rmat, inv_tvec = photogrammetry.invTransform(rmat, tvec) + + # To test the inverse rotation matrix, use the fact that RR' = I + # Allow for machine precision errors + near_eye_rmat = np.matmul(rmat, inv_rmat) + np.testing.assert_array_almost_equal(near_eye_rmat, np.eye(3), decimal=3) + np.testing.assert_array_almost_equal(near_eye_rmat.shape, (3, 3)) + + # To test the translation vector, use the fact that Rt' + t = 0 + # Allow for machine precision errors + near_zero_tvec = tvec + np.matmul(rmat, inv_tvec) + np.testing.assert_array_almost_equal( + near_zero_tvec, np.zeros((3, 1)), decimal=3 + ) + np.testing.assert_array_almost_equal(near_zero_tvec.shape, (3, 1)) + + def test_isRotationMatrix(self): + """ + Unit test for isRotationMatrix + + Many randomly generated inputs tests known properties of the outputs + """ + + # Seed the random generator for consistency between tests + # It should work for any test, but if there is an error associated with a + # given input this will allow for better error tracing + rng = np.random.default_rng(0) + + # Test 50 times + for i in range(50): + # Generate a random but valid rotation matrix + angles = (360 * rng.random(3)) - 180 + rotx = photogrammetry.rot(angles[0], 'x') + roty = photogrammetry.rot(angles[1], 'y') + rotz = photogrammetry.rot(angles[2], 'z') + rmat = np.matmul(rotz, roty, rotx) + + # Test if the rotation matrix is valid + self.assertEqual(photogrammetry.isRotationMatrix(rmat), True) + + # Test against an obviously invalid rotation matrix + rmat = np.zeros((3, 3)) + self.assertEqual(photogrammetry.isRotationMatrix(rmat), False) + + # Test against an obviously invalid rotation matrix + rmat = np.full((3, 3), 1) + self.assertEqual(photogrammetry.isRotationMatrix(rmat), False) + + def test_isRotationMatrixRightHanded(self): + """ + Unit test for isRotationMatrixRightHanded + + Begin with a left handed and right handed matrix. Perform random rotations + of those matrixes. Rotated left handed should always be not-right-handed and + rotated right handed should always be right handed + """ + righty = np.eye(3) + lefty = np.array([[1, 0, 0], [0, 1, 0], [0, 0, -1]]) + + rng = np.random.default_rng(0) + randoms = (360 * rng.random((50, 3))) - 180 + for random in randoms: + righty_i = np.matmul(righty, photogrammetry.rot(random[0], 'x')) + righty_i = np.matmul(righty_i, photogrammetry.rot(random[1], 'y')) + righty_i = np.matmul(righty_i, photogrammetry.rot(random[2], 'z')) + + lefty_i = np.matmul(lefty, photogrammetry.rot(random[0], 'x')) + lefty_i = np.matmul(lefty_i, photogrammetry.rot(random[1], 'y')) + lefty_i = np.matmul(lefty_i, photogrammetry.rot(random[2], 'z')) + + self.assertEqual( + photogrammetry.isRotationMatrixRightHanded(righty_i), True + ) + self.assertEqual( + photogrammetry.isRotationMatrixRightHanded(lefty_i), False + ) + + def test_rotationMatrixToTunnelAngles(self): + """ + Unit test for rotationMatrixToTunnelAngles + + Many randomly generated inputs tests known properties of the outputs + """ + + # Seed the random generator for consistency between tests + # It should work for any test, but if there is an error associated with a + # given input this will allow for better error tracing + rng = np.random.default_rng(0) + + # Test with 50 random inputs + for i in range(50): + angles = (179.998 * rng.random(3)) - 89.999 + + # Generate the rotation matrix + rmat, tvec = camera_tunnel_calibrate.tunnel_transform( + angles[0], angles[1], angles[2], 0, [0, 0, 0] + ) + + # Convert rotation matrix to euler angles + angles_calculated = photogrammetry.rotationMatrixToTunnelAngles(rmat) + + # Test that the rotation matrix converted from euler angles is the same as + # the original rotation matrix + np.testing.assert_array_almost_equal( + np.expand_dims(angles, 1), angles_calculated, decimal=3 + ) + + def test_transform_3d_point(self): + """ + Regression test for transform_3d_point + + Tests a subset of the targets for projected pixel location and pixel jacobian + """ + obj_pts = np.squeeze([tgt['tvec'] for tgt in self.tgts_visible], axis=2) + + # Get the transformed targets + transforms = photogrammetry.transform_3d_point(self.rmat, self.tvec, obj_pts) + + # Check the transformed targets are as expected + self.assertEqual(transforms.shape, (24, 3)) + np.testing.assert_array_almost_equal( + transforms[0], [5.49454348, -1.89638399, 18.782807], decimal=3 + ) + np.testing.assert_array_almost_equal( + transforms[1], [5.30912805, 2.76660932, 18.730475], decimal=3 + ) + np.testing.assert_array_almost_equal( + transforms[2], [4.04636575, 0.01597572, 18.7346435], decimal=3 + ) + + def test_project_3d_point(self): + """ + Regression test for project_3d_point + + Tests a subset of the targets for projected pixel location and pixel jacobian + """ + obj_pts = np.array([tgt['tvec'] for tgt in self.tgts]) + projs = photogrammetry.project_3d_point( + self.rmat, self.tvec, self.cameraMatrix, self.distCoeffs, obj_pts + ) + + # Check that projections are correct + np.testing.assert_array_equal(projs.shape, (24, 2)) + + np.testing.assert_array_almost_equal(projs[0], [934.159, 118.636], decimal=2) + np.testing.assert_array_almost_equal(projs[2], [830.756, 257.950], decimal=2) + np.testing.assert_array_almost_equal(projs[4], [768.440, 396.979], decimal=2) + np.testing.assert_array_almost_equal(projs[7], [678.448, 542.530], decimal=2) + np.testing.assert_array_almost_equal(projs[11], [481.248, 445.495], decimal=2) + np.testing.assert_array_almost_equal(projs[17], [-128.84, 184.490], decimal=2) + np.testing.assert_array_almost_equal(projs[23], [-564.553, 413.039], decimal=2) + np.testing.assert_array_almost_equal(projs[-1], [-564.553, 413.039], decimal=2) + + projs, jac = photogrammetry.project_3d_point( + self.rmat, + self.tvec, + self.cameraMatrix, + self.distCoeffs, + obj_pts, + ret_jac=True + ) + + jac7 = np.array([[ 1.81736046e+02, 5.78473766e+02, -2.05491850e+01, + 7.34788956e+01, -2.94818066e-01, -7.67152670e+00], + [-3.35503179e+02, 1.01839467e+02, -3.67421363e+02, + -2.94818066e-01, 7.30451713e+01, -1.51664631e+01]]) + + jac19 = np.array([[-96.40308249, 719.56242991, -48.06341937, + 66.9415494, -0.936783, 41.16804995], + [287.24119638, 46.27631763, -330.32092328, + -0.936783, 71.94503581, 7.45497706]]) + + np.testing.assert_array_equal(jac.shape, (24, 2, 6)) + np.testing.assert_array_almost_equal(jac[7], jac7, decimal=2) + np.testing.assert_array_almost_equal(jac[19], jac19, decimal=2) + + def test_transform_targets(self): + """ + Regression test for transform_targets + + The solutions were manually verified visually with the debug outputs + """ + + # Define the rotation matrix and translation vector + rmat = np.array([[-0.99972648056921, -0.012978713450675, 0.019455514536029], + [-0.01318372430061, 0.999858520536128, -0.010446450347859], + [-0.01931718049462, -0.010700089180476, -0.999756147582633]]) + tvec = np.array([[-5.0930359869], [-0.077166669657], [11.5560541979]]) + + # Read in the targets file + tgts_file = os.path.join(files_dir, 'fml_tc3_volume.tgts') + tgts = parsers.read_tgts(tgts_file) + + cameraMatrix = np.array([[1380.2632820187425, 0.0, 533.908701486902032], + [0.0, 1380.2632820187425, 256.778541140320840], + [0.0, 0.0, 1.0]]) + distCoeffs = np.array([[-0.09098491035825468, 0.0, 0.0, 0.0, 0.0]]) + + tgts = photogrammetry.get_visible_targets( + rmat, + tvec, + cameraMatrix, + distCoeffs, + tgts, + self.vis_checker + ) + + # Get the transformed targets + transforms = photogrammetry.transform_targets(rmat, tvec, tgts) + + tvec0 = np.array([[5.49454348], [-1.89638399], [18.78280763]]) + norm0 = np.array([[ 0.01945551], [-0.01044645], [-0.99975615]]) + + tvec1 = np.array([[5.30912805], [2.76660932], [18.73047579]]) + norm1 = np.array([[0.01945551], [-0.01044645], [-0.99975615]]) + + tvec2 = np.array([[4.04636575], [0.0159757297], [18.7346435]]) + norm2 = np.array([[0.01945551], [-0.01044645], [-0.99975615]]) + + # Check the transformed targets are as expected + np.testing.assert_array_almost_equal(transforms[0]['tvec'], tvec0, decimal=3) + np.testing.assert_array_almost_equal(transforms[0]['norm'], norm0, decimal=3) + + np.testing.assert_array_almost_equal(transforms[1]['tvec'], tvec1, decimal=3) + np.testing.assert_array_almost_equal(transforms[1]['norm'], norm1, decimal=3) + + np.testing.assert_array_almost_equal(transforms[2]['tvec'], tvec2, decimal=3) + np.testing.assert_array_almost_equal(transforms[2]['norm'], norm2, decimal=3) + + def test_project_targets(self): + """ + Regression test for project_targets + + The solutions were manually verified visually with the debug outputs + """ + # Project the targets into the image + projs = photogrammetry.project_targets( + self.rmat, self.tvec, self.cameraMatrix, self.distCoeffs, self.tgts_visible) + + # Test the target projections are as expected + self.assertEqual(len(projs), len(self.tgts_visible)) + np.testing.assert_array_almost_equal( + projs[0]['proj'], [934.160, 118.63616256], decimal=2 + ) + np.testing.assert_array_almost_equal( + projs[1]['proj'], [921.506, 458.75711298], decimal=2 + ) + np.testing.assert_array_almost_equal( + projs[2]['proj'], [830.757, 257.9505475 ], decimal=2 + ) + np.testing.assert_array_almost_equal( + projs[3]['proj'], [784.80597529, 133.48220928], decimal=2 + ) + np.testing.assert_array_almost_equal( + projs[4]['proj'], [768.44052177, 396.97917061], decimal=2 + ) + + def test_get_occlusions_targets(self): + """ + Regression test for get_occlusions_targets + + The solutions were manually verified visually with the debug outputs + """ + tgts = copy.deepcopy(self.tgts_visible) + + tgt_fake1 = copy.deepcopy(tgts[0]) + tgt_fake1['tvec'][2] += 0.25 + tgts.append(tgt_fake1) + + tgt_fake2 = copy.deepcopy(tgts[0]) + tgt_fake2['norm'] *= -1 + tgts.append(tgt_fake2) + + occlusion_results = photogrammetry.get_occlusions_targets( + self.rmat, self.tvec, tgts, self.vis_checker) + + # Assert all but the last occlusion_results are False + for occlusion_result in occlusion_results[:-1]: + self.assertFalse(occlusion_result[0]) + + # Assert the last occlusion_result is True and have the following values: + np.testing.assert_array_almost_equal( + occlusion_results[-1][1], + np.array([[-10.70026875], [ -2.03368831], [ -7.]]) + ) + + def test_get_visible_targets(self): + """ + Regression test for get_visible_targets + + The solutions were manually verified visually with the debug outputs + """ + # Add a fake, and not visible target + tgt_fake = copy.deepcopy(self.tgts_visible[-1]) + tgt_fake['norm'][2] *= -1 + tgts_extra = self.tgts_visible + [tgt_fake] + + # Get the visible targets + visible_targets = photogrammetry.get_visible_targets( + self.rmat, + self.tvec, + self.cameraMatrix, + self.distCoeffs, + tgts_extra, + self.vis_checker + ) + + # Check that the number of visibile targets is as expected + self.assertEqual(len(visible_targets), len(tgts_extra) - 1) + + def test_reprojection_error(self): + """ + Regression test for reprojection_error + + The solutions were manually verified visually with the debug outputs + """ + + # Hardcoded Extrinsics (30160201) + rmat = np.eye(3) + tvec = np.array([[0., 0., 0.]]) + + # Hardcoded Intrinsics + cameraMatrix = np.array([[600, 0.0, 512], + [0.0, 660, 256], + [0.0, 0.0, 1.0]]) + distCoeffs = np.array([[0.0, 0.0, 0.0, 0.0, 0.0]]) + + target_tgts = [{'tvec' : [0., 0., 100.], 'target_type' : 'dot'}, + {'tvec' : [10., 0., 100.], 'target_type' : 'dot'}, + {'tvec' : [0., 10., 100.], 'target_type' : 'dot'}, + {'tvec' : [10., 10., 100.], 'target_type' : 'dot'}] + + targ_projs = photogrammetry.project_targets(rmat, tvec, cameraMatrix, + distCoeffs, target_tgts) + + matching_img_points = [] + for targ_proj in targ_projs: + matching_img_points.append( + {'center' : targ_proj['proj'], 'target_type' : 'dot'} + ) + + error = photogrammetry.reprojection_error( + rmat, tvec, cameraMatrix, distCoeffs, target_tgts, matching_img_points) + + self.assertEqual(error, (0.0, 0.0)) + + matching_img_points[0]['center'][0] += 1 + error = photogrammetry.reprojection_error( + rmat, tvec, cameraMatrix, distCoeffs, target_tgts, matching_img_points) + + self.assertEqual(error, (0.5, 1.0)) + + # Note: element 0 still has (+1, +0) since we don't reset it + matching_img_points[1]['center'][1] += 1 + matching_img_points[3]['center'][1] += 1 + error = photogrammetry.reprojection_error( + rmat, tvec, cameraMatrix, distCoeffs, target_tgts, matching_img_points) + + self.assertEqual(error, (np.sqrt(3)/2, 1.0)) + + +if __name__ == "__main__": + unittest.main() diff --git a/test/python/test_target_bumping.py b/test/python/test_target_bumping.py new file mode 100644 index 0000000..ff17b15 --- /dev/null +++ b/test/python/test_target_bumping.py @@ -0,0 +1,191 @@ +import unittest +import numpy as np +import os +import copy + +import file_structure +from upsp.target_operations import target_bumping +from upsp.cam_cal_utils import ( + parsers, + visibility, +) + +files_dir = os.path.join(os.path.dirname(os.path.dirname(__file__)), 'data') + + +class TargetBumpingTestCase(unittest.TestCase): + """ + Tests for target_bumping + """ + + @classmethod + def setUpClass(cls): + # Read the rest configuration file + test_config_file = os.path.join(files_dir, 'test_config.json') + cls.test_config = parsers.read_json(test_config_file) + + # Load the tgts file + cls.tgts_file_path = os.path.join(files_dir, 'fml_tc3_volume.tgts') + cls.tgts = parsers.read_tgts(cls.tgts_file_path) + + # Get the visibility checker from the grid + grd_file = os.path.join(files_dir, 'fml_tc3_volume.grid') + + cls.vis_checker = visibility.VisibilityChecker( + grd_file, 70, epsilon=1e-4 + ) + + def setUp(self): + """ + Checks files set up properly before each running test + """ + + target_bumping.debug_print_bumping = False + target_bumping.debug_bump_distance = False + + file_structure.setUp(files_dir) + + def tearDown(self): + """ + Checks files are still set up properly after each running test + """ + + # We can rerun setUp because the file structure should be unchanged + self.setUp() + + # target_bumping.get_bumping_occlusion is tested via tgt_bump_internals and + # tgt_bump_externals + + # target_bumping.is_real_occlusion is tested via tgt_bump_internals + + def test_tgts_get_internals(self): + """ + Regression test for tgts_get_internals + + The solutions were manually verified visually with the debug outputs + """ + tgts = copy.deepcopy(self.tgts) + internals = [1, 2, 3, 5, 8, 13, 21] + internal_names_gt = [tgts[i]['name'] for i in internals] + for i, tgt in enumerate(tgts): + if i in internals: + tgt['tvec'] -= 0.02 * tgt['norm'] + + # Get the bumped targets + internals = target_bumping.tgts_get_internals( + tgts, self.vis_checker, tgts_tol=1e-4, grid_tol=1e-3 + ) + + internal_names = [] + for internal in internals: + if internal[2]: + internal_names.append(internal[0]) + + self.assertEqual(internal_names, internal_names_gt) + + def test_tgt_bump_internals(self): + """ + Regression test for tgt_bump_internals + + The solutions were manually verified visually with the debug outputs + """ + tgts = copy.deepcopy(self.tgts) + internals = [1, 2, 3, 5, 8, 13, 21] + internal_names_gt = [tgts[i]['name'] for i in internals] + for i, tgt in enumerate(tgts): + if i in internals: + tgt['tvec'] -= 0.02 * tgt['norm'] + + # Get the bumped targets + bumped_internals, was_bumped = target_bumping.tgt_bump_internals( + tgts, self.vis_checker, bump_eps=1e-5, tgts_tol=1e-1, grid_tol=1e-3 + ) + + for i, tgt in enumerate(tgts): + for bi in bumped_internals: + if tgt['name'] == bi['name']: + diff = tgt['tvec'] - bi['tvec'] + if i in internals: + self.assertAlmostEqual(np.linalg.norm(diff), 0.02, 2) + else: + self.assertLess(np.linalg.norm(diff), 1e-3) + + # Check that the bumped targets don't change when bumping them again + # Since they should be outside the model it shouldn't modify them + # We know from manually checking that double bumping shouldn't change them + # so long as the tgt_bump_internals function is working properly + double_bumped_internals, was_bumped = target_bumping.tgt_bump_internals( + bumped_internals, + self.vis_checker, + bump_eps=1e-5, + tgts_tol=1e-4, + grid_tol=1e-3 + ) + + # Targets should not be changed in the double bumped, check them against the + # single bumped ones + for double, single in zip(double_bumped_internals, bumped_internals): + np.testing.assert_array_almost_equal( + double['tvec'], single['tvec'], decimal=3 + ) + np.testing.assert_array_almost_equal( + double['norm'], single['norm'], decimal=3 + ) + + def test_tgt_bump_externals(self): + """ + Regression test for tgt_bump_externals + + The solutions were manually verified visually with the debug outputs + """ + tgts = copy.deepcopy(self.tgts) + internals = [1, 2, 3, 5, 8, 13, 21] + internal_names_gt = [tgts[i]['name'] for i in internals] + for i, tgt in enumerate(tgts): + if i in internals: + tgt['tvec'] += 0.02 * tgt['norm'] + + # Do the external bumping + externals = target_bumping.tgt_bump_externals( + tgts, self.vis_checker, bump_eps=1e-5 + ) + + for i, tgt in enumerate(tgts): + for ex in externals: + if tgt['name'] == ex['name']: + diff = tgt['tvec'] - ex['tvec'] + if i in internals: + self.assertAlmostEqual(np.linalg.norm(diff), 0.02, 2) + else: + self.assertLess(np.linalg.norm(diff), 1e-3) + + # target_bumping.tgts_bumper is tested via tgts_file_bumper + + def test_tgts_file_bumper(self): + """ + Regression test for tgts_file_bumper + + The solutions were manually verified visually with the debug outputs + """ + new_tgts_file_path = os.path.join(files_dir, 'output', 'tgts_bumped.tgts') + + target_bumping.tgts_file_bumper( + self.tgts_file_path, + self.vis_checker, + bump_eps=1e-1, + tgts_tol=1e-1, + grid_tol=1e-3, + new_tgts_file_path=new_tgts_file_path + ) + + bumped_tgts = parsers.read_tgts(new_tgts_file_path) + + for tgt, bumped_tgt in zip(self.tgts, bumped_tgts): + diff = tgt['tvec'] - bumped_tgt['tvec'] + self.assertAlmostEqual(np.linalg.norm(diff), 0.1, 2) + + # Deleted the generated bumped tgts file + os.remove(new_tgts_file_path) + +if __name__ == "__main__": + unittest.main() diff --git a/test/python/test_target_detection.py b/test/python/test_target_detection.py new file mode 100644 index 0000000..a5aac47 --- /dev/null +++ b/test/python/test_target_detection.py @@ -0,0 +1,256 @@ +import unittest +import numpy as np +import os +import copy +import cv2 + +import file_structure +from upsp.target_operations import target_detection +from upsp.cam_cal_utils import ( + img_utils, + parsers, + photogrammetry, + visibility, +) + +files_dir = os.path.join(os.path.dirname(os.path.dirname(__file__)), 'data') + + +class TargetDetectionTestCase(unittest.TestCase): + """ + Tests for target_detection + """ + + @classmethod + def setUpClass(cls): + # Read in the image + img_path = os.path.join( + files_dir, 'images', 'CAM1_RUN8_CINE02_Y20000209H11294501.00001.png' + ) + cls.img = cv2.imread(img_path, cv2.IMREAD_GRAYSCALE + cv2.IMREAD_ANYDEPTH) + + # Get the camera calibration for this camera + cam_tun_path = os.path.join( + files_dir, 'camera-tunnel-calibration', 'camera01_35_6.json' + ) + camera_cal_tunnel = parsers.read_camera_tunnel_cal( + cam_tun_path, cls.img.shape + ) + cls.rmat, cls.tvec, cls.cameraMatrix, cls.distCoeffs = camera_cal_tunnel + + # Read the rest configuration file + test_config_file = os.path.join(files_dir, 'test_config.json') + cls.test_config = parsers.read_test_config(test_config_file) + cls.test_config["crosscorr_coeff"] = 0.5 + + # Get the visibility checker from the grid + grd_file = os.path.join(files_dir, 'fml_tc3_volume.grid') + cls.vis_checker = visibility.VisibilityChecker( + grd_file, 70, epsilon=1e-4 + ) + + # Read in the targets file + tgts_file = os.path.join(files_dir, 'fml_tc3_volume.tgts') + cls.tgts = parsers.read_tgts(tgts_file) + + cls.tgts_visible = photogrammetry.get_visible_targets( + cls.rmat, + cls.tvec, + cls.cameraMatrix, + cls.distCoeffs, + cls.tgts, + cls.vis_checker + ) + + def test_subpixel_localize(self): + """ + Regression test for subpixel_localize + + Outputs were manually verified to be as expected + """ + # Initialize input values + rmat = np.matmul(photogrammetry.rot(0.25, 'x'), self.rmat) + rmat = np.matmul(photogrammetry.rot(-0.25, 'y'), rmat) + tvec = self.tvec + np.array([[0.02], [-0.01], [0.02]]) + camera_tunnel_cal = [rmat, tvec, self.cameraMatrix, self.distCoeffs] + + # Scale the image + img8bit = img_utils.scale_image_max_inlier(self.img) + + # Perform template detection to find and match the targets to image locations + template_detection_output = target_detection.template_detection( + img8bit, + rmat, + tvec, + self.cameraMatrix, + self.distCoeffs, + copy.deepcopy(self.tgts_visible), + self.test_config + ) + tgts_match, img_targets_match, num_matches = template_detection_output + + # Run the subpixel localization + tgts_sub, img_targets_sub = target_detection.subpixel_localize( + img8bit, + tgts_match[:num_matches], + img_targets_match, + self.test_config, + max_localize_delta=None + ) + + img_targets_sub_gt = [[1061.32947596, 262.49474001], + [1049.19571548, 602.58437063], + [959.21813484, 401.63819636], + [824.76577736, 396.81149221], + [806.58913148, 687.05189197], + [731.56243383, 407.50854921], + [607.99036642, 150.81565999], + [608.23270776, 399.98498769], + [366.61396903, 538.20378513], + [206.82895966, 212.73496326], + [114.70997899, 397.03944153]] + + for i in range(len(img_targets_sub)): + np.testing.assert_array_almost_equal( + img_targets_sub[i]['center'], + img_targets_sub_gt[i], + decimal=2, + ) + + # pixel_2_polygon is tested indirectly with the test_template_detection + + # area_overlap is tested indirectly with the test_template_detection + + def test_template_detection(self): + """ + Regression test for template_detection + + Outputs were manually verified to be as expected + """ + # Initialize input values + rmat = np.matmul(photogrammetry.rot(0.25, 'x'), self.rmat) + rmat = np.matmul(photogrammetry.rot(-0.25, 'y'), rmat) + tvec = self.tvec + np.array([[0.02], [-0.01], [0.02]]) + camera_tunnel_cal = [rmat, tvec, self.cameraMatrix, self.distCoeffs] + + # Scale the image + img8bit = img_utils.scale_image_max_inlier(self.img) + + # Perform template detection to find and match the targets to image locations + template_detection_output = target_detection.template_detection( + img8bit, + rmat, + tvec, + self.cameraMatrix, + self.distCoeffs, + copy.deepcopy(self.tgts_visible), + self.test_config + ) + tgts_match, img_targets_match, num_matches = template_detection_output + self.assertEqual(num_matches, 11) + + img_targets_match_gt = [[1061.5, 262.5], + [1049.5, 603.5], + [959.5, 402.5], + [825., 397.5], + [806.5, 688. ], + [732., 408. ], + [609., 152. ], + [609., 400.5], + [367., 539. ], + [207.5, 213.5], + [116., 398. ]] + + for i in range(num_matches): + np.testing.assert_array_almost_equal( + img_targets_match_gt[i], img_targets_match[i]['center'] + ) + + def test_filter_dist_filter(self): + """ + Unit test of filter_dist_filter for basic cases + """ + # Initialize input values + rmat = np.matmul(photogrammetry.rot(0.25, 'x'), self.rmat) + rmat = np.matmul(photogrammetry.rot(-0.25, 'y'), rmat) + tvec = self.tvec + np.array([[0.02], [-0.01], [0.02]]) + camera_tunnel_cal = [rmat, tvec, self.cameraMatrix, self.distCoeffs] + + # Scale the image + img8bit = img_utils.scale_image_max_inlier(self.img) + + # Perform template detection to find and match the targets to image locations + template_detection_output = target_detection.template_detection( + img8bit, + rmat, + tvec, + self.cameraMatrix, + self.distCoeffs, + copy.deepcopy(self.tgts_visible), + self.test_config + ) + tgts_match, img_targets_match, num_matches = template_detection_output + + # Check 1 + tgts_test = copy.deepcopy(tgts_match) + tgt_fake = copy.deepcopy(tgts_test[num_matches-1]) + tgt_fake['tvec'][0] += 0.1 + tgts_test.append(tgt_fake) + tgts_det, img_targets, num_matches_filter = target_detection.filter_dist_filter( + rmat, + tvec, + self.cameraMatrix, + self.distCoeffs, + tgts_test, + img_targets_match, + num_matches, + 8, + 6 + ) + self.assertEqual(num_matches_filter, num_matches - 1) + + # Check 2 + img_targets_test = copy.deepcopy(img_targets_match) + img_target_fake = copy.deepcopy(img_targets_test[num_matches-1]) + img_target_fake['center'][0] += 2 + img_target_fake['center'][1] += 2 + img_targets_test.append(img_target_fake) + tgts_det, img_targets, num_matches_filter = target_detection.filter_dist_filter( + rmat, + tvec, + self.cameraMatrix, + self.distCoeffs, + tgts_match, + img_targets_test, + num_matches, + 8, + 6 + ) + self.assertEqual(num_matches_filter, num_matches - 1) + + # Check 3 + img_targets_test = copy.deepcopy(img_targets_match) + img_target_fake = copy.deepcopy(img_targets_test[-1]) + img_target_fake['center'] = 113.5, 401.5 + img_targets_test.append(img_target_fake) + tgts_det, img_targets, num_matches_filter = target_detection.filter_dist_filter( + rmat, + tvec, + self.cameraMatrix, + self.distCoeffs, + tgts_match, + img_targets_test, + num_matches, + 8, + 6 + ) + self.assertEqual(num_matches_filter, num_matches - 1) + + # filter_partially_occluded is not yet implemented in the filtering pipeline, so it + # doesn't have to be tested yet + + # filter_matches at the moment only implements filter_dist_filter, so if + # filter_dist_filter passes, filter_matches will pass as well + +if __name__ == "__main__": + unittest.main() diff --git a/test/python/test_target_localization_methods.py b/test/python/test_target_localization_methods.py new file mode 100644 index 0000000..a3e3ec8 --- /dev/null +++ b/test/python/test_target_localization_methods.py @@ -0,0 +1,233 @@ +import unittest +import numpy as np +import os +from shapely.geometry import Polygon + +import file_structure +from upsp.target_operations import ( + blob_localization_methods, + gaussian_localization_methods, +) + + +class TargetLocalizationMethodsTestCase(unittest.TestCase): + """ + Tests for the target localization methods + """ + + def test_gaussian_fitting_methods_gaussians(self): + """ + This is halfway between a regression test and a unit test. + I re-implemented the 2D Gaussian function from the reference and check the + fitting methods function against it. The solutions were manually verified + visually with debug outputs + + Tests python/upsp/target_localization/gaussian_fitting_methods.twoD_Gaussian + python/upsp/target_localization/ + gaussian_fitting_methods.super_twoD_Gaussian + """ + + # Iterate over a bunch of parameters + x, y = np.linspace(0, 10, 5), np.linspace(0, 10, 5) + x, y = np.meshgrid(x, y) + + sigma_Xs, sigma_Ys = np.linspace(1, 10, 3), np.linspace(1, 10, 3) + sigma_Xs, sigma_Ys = np.meshgrid(sigma_Xs, sigma_Ys) + sigma_Xs, sigma_Ys = sigma_Xs.ravel(), sigma_Ys.ravel() + + x0s, y0s = np.linspace(-10, 10, 5), np.linspace(-10, 10, 5) + x0s, y0s = np.meshgrid(x0s, y0s) + x0s, y0s = x0s.ravel(), y0s.ravel() + + offsets = np.linspace(0, 100, 3) + amplitudes = np.linspace(1, 10, 3) + offsets, amplitudes = np.meshgrid(offsets, amplitudes) + offsets = offsets.ravel() + amplitudes = amplitudes.ravel() + + for theta in np.linspace(0, np.pi, 3): + for sigma_X, sigma_Y in zip(sigma_Xs, sigma_Ys): + for x0, y0 in zip(x0s, y0s): + for offset, amplitude in zip(offsets, amplitudes): + a = np.cos(theta)**2/(2*sigma_X**2) + a += np.sin(theta)**2/(2*sigma_Y**2) + + b = -np.sin(2*theta)/(4*sigma_X**2) + b += np.sin(2*theta)/(4*sigma_Y**2) + + c = np.sin(theta)**2/(2*sigma_X**2) + c += np.cos(theta)**2/(2*sigma_Y**2) + + gauss_inner = a*(x-x0)**2 + 2*b*(x-x0)*(y-y0) + c*(y-y0)**2 + + z = np.exp(-gauss_inner) + z *= amplitude + z += offset + + # Test the traditional 2D Gaussian function + z_tl = gaussian_localization_methods.twoD_Gaussian( + (x, y), amplitude, x0, y0, sigma_X, sigma_Y, theta, offset) + np.testing.assert_array_almost_equal(z_tl, z, decimal=8) + + # Test the super 2D Gaussian function + for power in np.linspace(2, 8, 3): + z_super = np.exp(-np.power(gauss_inner, power)) + z_super *= amplitude + z_super += offset + + z_tl = gaussian_localization_methods.super_twoD_Gaussian( + (x, y), amplitude, x0, y0, sigma_X, sigma_Y, theta, + offset, power) + np.testing.assert_array_almost_equal( + z_tl, z_super, decimal=8 + ) + + # super_twoD_Gaussian is tested indirectly with the test_localization_funcs + + # twoD_Gaussian is tested indirectly with the test_localization_funcs + + def test_localization_funcs(self): + """ + Unit test for gaussian and blob localization + """ + def ellipse_2_polygon(a, b, alpha, h, k): + """ Function copied from internal synthetic target generation module + """ + # The ellipse is approximated with as a polygon with num_poly_pts sides + # Therefore we need to trace out that many points along the ellipse + # and so we need that many angle values + thetas = np.linspace(0, 360., 100) + + # Get the x and y values of the points along the ellipse for each value of + # theta. This ellipse is centered at (0,0) and is not rotated + x = a * np.cos(np.deg2rad(thetas)) + y = b * np.sin(np.deg2rad(thetas)) + + # Rotate these points by the given alpha + # For each point, get the distance from that point to the origin. This + # distance is r. The angle between the line to that point, and the + # positive x axis is theta. The theta values are known because thetas was + # used to make the ellipse. + # Now we rotate that point by alpha degrees along a circle of radius r. + # By doing so we rotate the polygon by alpha degrees which is equivalent to + # rotating the approximation of the ellipse + for i, theta in enumerate(thetas): + # Determine the distance from the point to the origin + r = np.linalg.norm([x[i], y[i]]) + + # Get the new angle by adding alpha (degrees) + phi = np.rad2deg(np.arctan2(y[i], x[i])) + alpha + + # Update the point to be rotated by alpha degrees + x[i] = r * np.cos(np.deg2rad(phi)) + y[i] = r * np.sin(np.deg2rad(phi)) + + # Finally, shift the center of the ellipse to be at (h, k) + x = x + h + y = y + k + + # Need to restructure the points to be acceptably by + # shapely.geometry.Polygon + pts = [] + + # Exclude the last point, Shapely automatically closes polygon loops + for i in range(len(x) - 1): + pts.append([x[i], y[i]]) + + # Return the polygon approximation of the ellipse + return Polygon(pts) + + def area_overlap(target, pixel_coords): + """ Function copied from internal synthetic target generation module + """ + px = pixel_2_polygon(*pixel_coords) + return px.intersection(target).area + + def pixel_2_polygon(u, v): + """ Function copied from internal synthetic target generation module + """ + return Polygon([[u - 0.5, v - 0.5], [u + 0.5, v - 0.5], + [u + 0.5, v + 0.5], [u - 0.5, v + 0.5]]) + + # Seed the random generator for consistency between tests + # It should work for any test, but if there is an error associated with a + # given input this will allow for better error tracing + rng = np.random.default_rng(0) + + # Create both a normal and super gauss fitter + normal_gauss_fitter = gaussian_localization_methods.gauss_fitter_func('normal') + super_gauss_fitter = gaussian_localization_methods.gauss_fitter_func('super') + + # Create a blob fitter for all 5 blob fitter types + blob_fitters = [] + for x in ['biggest', 'smallest', 'first', 'last', 'random']: + blob_fitters.append(blob_localization_methods.blob_func('detector_all', x)) + + # Helper function that generates an image with a target of the given input + def target_maker(img, a, b, alpha, h, k): + target = ellipse_2_polygon(a, b, alpha, h, k) + xx, yy = target.exterior.coords.xy + x_min, x_max = np.floor(min(xx)), np.ceil(max(xx)) + y_min, y_max = np.floor(min(yy)), np.ceil(max(yy)) + + out = np.copy(img) + for x in range(out.shape[1]): + if x <= x_min or x >= x_max: + continue + for y in range(out.shape[0]): + if y <= y_min or y >= y_max: + continue + + out[y][x] -= area_overlap(target, (x, y))*64 + + return out + + # Make a blank image with uneven side lengths + blank_img = np.full((17, 19), 128, dtype=np.uint8) + + # Generate 100 targets and test that the fitter functions find a sufficient + # number of them within the allowable error + normal_gauss_tp_count = 0 + super_gauss_tp_count = 0 + blob_tp_counts = [0] * len(blob_fitters) + for i in range(100): + # Generate random parameters + a = (2 * rng.random(1)) + 1 + b = (0.7 * a * rng.random(1)) + (0.3 * a) + alpha = np.pi * 2 * rng.random(1) + h = (2 * rng.random(1)) + 5.5 + k = (2 * rng.random(1)) + 7.5 + + # Generate a target image + target_img = target_maker(blank_img, a, b, alpha, h, k) + + # Get the result from the normal gauss fitter. Check if it is within 1 pixel + normal_pred = normal_gauss_fitter(target_img)[0] + normal_error = np.linalg.norm([normal_pred[0]-h, normal_pred[1]-k]) + normal_gauss_tp_count += 1 if normal_error < 1 else 0 + + # Get the result from the super gauss fitter. Check if it is within 1 pixel + super_pred = super_gauss_fitter(target_img)[0] + super_error = np.linalg.norm([super_pred[0]-h, super_pred[1]-k]) + super_gauss_tp_count += 1 if super_error < 1 else 0 + + # Get the result from the blob fitters. Keep track of which ones are within + # 2 pixels + for i in range(len(blob_fitters)): + blob_pred = blob_fitters[i](target_img)[0] + blob_error = np.linalg.norm([blob_pred[0]-h, blob_pred[1]-k]) + blob_tp_counts[i] += 1 if blob_error < 2 else 0 + + return + + # Strict requirements on the output of the gauss fitters + np.testing.assert_array_less(95, normal_gauss_tp_count) + np.testing.assert_array_less(95, super_gauss_tp_count) + + # Loser restrictions on the output of the blob fitter + for i in range(len(blob_fitters)): + np.testing.assert_array_less(50, blob_tp_counts[i]) + + +if __name__ == "__main__": + unittest.main() diff --git a/test/python/test_video.py b/test/python/test_video.py new file mode 100644 index 0000000..6f8005f --- /dev/null +++ b/test/python/test_video.py @@ -0,0 +1,35 @@ +import unittest +from pathlib import Path + +from upsp.video import MrawReader, video_reader + +# test/python -> cpp/test/mraw +here = Path(__file__).parent +mraw_dir = here.parent.parent / "cpp" / "test" / "mraw" + + +class VideoReaderTest(unittest.TestCase): + def test_video_reader_mraw(self): + with video_reader(mraw_dir / "12bitMRAW.mraw") as vid: + self.assertEqual(vid.frame_count, 2) + vid.read_frame(0) + + +class MrawTest(unittest.TestCase): + def test_12bit(self): + with MrawReader(mraw_dir / "12bitMRAW.mraw") as vid: + # properties + self.assertEqual(vid.height, 1024) + self.assertEqual(vid.width, 1024) + self.assertEqual(vid.bit_depth, 12) + self.assertEqual(vid.frame_count, 2) + + # frame reading + f0 = vid.read_frame(1) + vid.read_frames(2) + + self.assertEqual(f0.shape, (1024, 1024)) + + +if __name__ == "__main__": + unittest.main() diff --git a/test/python/test_visibility.py b/test/python/test_visibility.py new file mode 100644 index 0000000..6860a89 --- /dev/null +++ b/test/python/test_visibility.py @@ -0,0 +1,326 @@ +import unittest +import numpy as np +import os +import cv2 +import copy + +import file_structure +from upsp.cam_cal_utils import ( + parsers, + photogrammetry, + visibility, +) +import upsp.raycast + +files_dir = os.path.join(os.path.dirname(os.path.dirname(__file__)), 'data') + + +# TODO: This really needs to test the edge cases of the visibility. Viewing angles near +# the maximum oblique angle, near zero, above it, etc. Just barely visible and not +# visible. Etc +class VisibilityTestCase(unittest.TestCase): + """ + Unit tests for the visibility module + """ + + @classmethod + def setUpClass(cls): + # Checks files set up properly before each running test + file_structure.setUp(files_dir) + + # Initialize VisibilityChecker + # Simple scene with several triangle primitives. + # [t0p0x, t0p0y, t0p0z, t0p1x, t0p1y, t0p1z, t0p2x, t0p2y, t0p2z, ...] + t0 = [0., 0., 1., 0., 1., 0., 1., 0., 0.] + t1 = [0., 0., 1., 0., 1., 0., 0., 1., 1.] + tris = t0 + t1 + + # Initialize visibility checker with fml_tc3_volume.grid + cls.vis_checker_nogrid = visibility.VisibilityChecker( + None, oblique_angle=70, epsilon=1e-4, debug_nogrid=True + ) + cls.vis_checker_nogrid.scene = upsp.raycast.CreateBVH(tris, 3) + + # Initialize VisibilityChecker with fml_tc3_volume.grid + test_grd = os.path.join(files_dir, 'fml_tc3_volume.grid') + cls.vis_checker_grid = visibility.VisibilityChecker( + grid_path=test_grd, oblique_angle=70, epsilon=1e-4 + ) + cls.faces, cls.face_normals = cls.vis_checker_grid.get_faces_and_face_normals() + cls.nodes, cls.norms = cls.vis_checker_grid.get_tvecs_and_norms() + + # Read in the image + img_path = os.path.join( + files_dir, 'images', 'CAM1_RUN8_CINE02_Y20000209H11294501.00001.png' + ) + img = cv2.imread(img_path, cv2.IMREAD_GRAYSCALE + cv2.IMREAD_ANYDEPTH) + + # Get the camera calibration for this camera + cam_tun_path = os.path.join( + files_dir, 'camera-tunnel-calibration', 'camera01_35_6.json' + ) + + camera_tunnel_cal = parsers.read_camera_tunnel_cal(cam_tun_path, img.shape) + cls.rmat, cls.tvec, cls.cameraMatrix, cls.distCoeffs = camera_tunnel_cal + + def test_update_oblique_angle(self): + """ + Unit test for update_oblique_angle + """ + self.assertEqual(self.vis_checker_nogrid.oblique_angle, 70) + self.assertEqual( + self.vis_checker_nogrid.squared_cos_angle, np.cos(np.deg2rad(70))**2 + ) + + self.vis_checker_nogrid.update_oblique_angle(40) + self.assertEqual(self.vis_checker_nogrid.oblique_angle, 40) + self.assertEqual( + self.vis_checker_nogrid.squared_cos_angle, np.cos(np.deg2rad(40))**2 + ) + + self.vis_checker_nogrid.update_oblique_angle(70) + self.assertEqual(self.vis_checker_nogrid.oblique_angle, 70) + self.assertEqual( + self.vis_checker_nogrid.squared_cos_angle, np.cos(np.deg2rad(70))**2 + ) + + # load_mesh is tested in setUpClass when combined with the regression tests + + # package_primitives is tested in setUpClass when combined with the regression tests + + def test_unit_vector(self): + """ + Unit test for unit_vector + """ + vec = np.array([np.sqrt(2), -np.sqrt(2), 0.]).reshape(1, 3) + np.testing.assert_array_equal( + self.vis_checker_nogrid.unit_vector(vec), np.array(vec/2) + ) + + vecs = np.array([[np.sqrt(3), -np.sqrt(3), np.sqrt(3)], + [np.sqrt(2), -np.sqrt(2), 0.], + [0., 0., 1.]] + ) + gt = np.divide(vecs, np.array([3, 2, 1]).reshape(3, 1)) + np.testing.assert_array_almost_equal( + self.vis_checker_nogrid.unit_vector(vecs), gt + ) + + vecs = np.array([[ np.sqrt(3), -np.sqrt(3), np.sqrt(3)], + [ np.sqrt(2), -np.sqrt(2), 0.], + [-np.sqrt(2), -np.sqrt(2), 0.], + [ np.sqrt(2), np.sqrt(2), 0.], + [0., 0., 1.]] + ) + gt = np.divide(vecs, np.array([3, 2, 2, 2, 1]).reshape(5, 1)) + np.testing.assert_array_almost_equal( + self.vis_checker_nogrid.unit_vector(vecs), gt + ) + np.testing.assert_array_almost_equal( + self.vis_checker_nogrid.unit_vector(vecs).shape, (5, 3) + ) + + def test_angle_between(self): + """ + Unit test for angle_between + """ + x = np.array([[ 1., 0., 0.], + [ np.sqrt(2), -np.sqrt(2), 0.], + [-np.sqrt(2), -np.sqrt(2), 0.], + [ 1., 0., 0.]] + ) + y = np.array([[ 0., 1., 0.], + [ 0., 1., 0.], + [ 0., 1., 0.], + [ 0., 1., 0.]] + ) + + np.testing.assert_array_almost_equal( + self.vis_checker_nogrid.angle_between(x, y), + [90., 135., 135., 90.] + ) + + # is_back_facing_fast_vectorized is tested indirectly with the following tests + + # does_intersect is tested indirectly with the following tests + + # is_visible is tested indirectly with the following tests + + def test_is_visible_and_inside_incal(self): + distCoeffs = copy.deepcopy(self.distCoeffs) + distCoeffs[0][0] *= -1 + distCoeffs[0][1] = -0.4 + + R, tvec_model_to_camera = photogrammetry.invTransform(self.rmat, self.tvec) + visible = self.vis_checker_grid.is_visible( + tvec_model_to_camera, + self.nodes, + self.norms, + ) + + visible_incal = self.vis_checker_grid.is_visible_and_inside_incal( + self.rmat, + self.tvec, + self.cameraMatrix, + distCoeffs, + self.nodes, + self.norms, + {'critical_pt':'first'} + ) + + self.assertEqual(len(visible_incal), 117553) + + def test_tri_normal(self): + """ + Unit test for tri_normal + """ + def unit_vector(vector): + return (vector / np.linalg.norm(vector)).reshape(3, 1) + + face = np.array([[0., 0., 0.], [0., 1., 0.], [0, 0, 1]]) + vis_check_norm = self.vis_checker_nogrid.tri_normal(face) + np.testing.assert_array_equal( + unit_vector(vis_check_norm), np.array([1, 0, 0]).reshape(3, 1) + ) + + face = np.array([[0., 0., 0.], [0., 0.5, 0.], [0, 0, 0.5]]) + vis_check_norm = self.vis_checker_nogrid.tri_normal(face) + np.testing.assert_array_equal( + unit_vector(vis_check_norm), np.array([1, 0, 0]).reshape(3, 1) + ) + + face = np.array([[0., 0., 0.], [1., 0., 0.], [0, 1, 0]]) + vis_check_norm = self.vis_checker_nogrid.tri_normal(face) + np.testing.assert_array_equal( + unit_vector(vis_check_norm), np.array([0, 0, 1]).reshape(3, 1) + ) + + face = np.array([[0., 0., 0.], [0.5, 0., 0.], [0, 0.5, 0]]) + vis_check_norm = self.vis_checker_nogrid.tri_normal(face) + np.testing.assert_array_equal( + unit_vector(vis_check_norm), np.array([0, 0, 1]).reshape(3, 1) + ) + + face = np.array([[0., 0., 0.], [1, 0., 0.], [0, 0, 1]]) + vis_check_norm = self.vis_checker_nogrid.tri_normal(face) + np.testing.assert_array_equal( + unit_vector(vis_check_norm), np.array([0, -1, 0]).reshape(3, 1) + ) + + # Seed the random generator for consistency between tests + # It should work for any test, but if there is an error associated with a + # given input this will allow for better error tracing + rng = np.random.default_rng(0) + + # Generate 50 random faces + random_faces = (2 * rng.random((50, 3, 3))) - 2 + + # Test photogrammetry.rot against the cached version + for face in random_faces: + A = face[1] - face[0] + B = face[2] - face[1] + vis_check_norm = self.vis_checker_nogrid.tri_normal(face) + np.testing.assert_array_equal( + unit_vector(vis_check_norm), unit_vector(np.cross(A, B)) + ) + + def test_get_faces_and_face_normals(self): + """ + Unit test for get_faces_and_face_normals + """ + faces, face_normals = self.vis_checker_grid.get_faces_and_face_normals() + self.assertEqual(faces.shape, (609120, 3, 3)) + self.assertEqual(face_normals.shape, (609120, 3)) + + def test_get_tvecs_and_norms(self): + """ + Unit test for get_tvecs_and_norms + """ + tvecs, norms = self.vis_checker_grid.get_tvecs_and_norms() + self.assertEqual(tvecs.shape, (304566, 3)) + self.assertEqual(norms.shape, (304566, 3)) + + def test_camera01(self): + """ + Regression test of is_visible and indirectly tests load_mesh, + package_primitives, is_back_facing (or similar), and does_intersect + """ + R, tvec_model_to_camera = photogrammetry.invTransform(self.rmat, self.tvec) + visible = self.vis_checker_grid.is_visible( + tvec_model_to_camera, + self.nodes, + self.norms, + ) + self.assertEqual(len(visible), 148608) + + def test_back_facing(self): + """ + Regression test of is_visible and indirectly tests load_mesh, + package_primitives, is_back_facing (or similar), and does_intersect + """ + camera_t = np.array([1., 1., 1.]).reshape(3, 1) + nodes = np.array([[2., 2., 2.], [3., 3., 3.]]).reshape(-1, 3) + normals = np.array([[1., 1., 1.], [1., 1., 1.]]).reshape(-1, 3) + + result = self.vis_checker_nogrid.is_visible(camera_t, nodes, normals) + self.assertEqual(result.tolist(), []) + + def test_occluded(self): + """ + Regression test of is_visible and indirectly tests load_mesh, + package_primitives, is_back_facing (or similar), and does_intersect + """ + camera_t = np.array([1., 1., 1.]).reshape(3, 1) + nodes = np.array([[0., 0., 0.], [-1., -1., -1.]]).reshape(-1, 3) + normals = np.array([[0.9, 0.9, 0.9], [1.1, 1.1, 1.1]]).reshape(-1, 3) + + result = self.vis_checker_nogrid.is_visible(camera_t, nodes, normals) + self.assertEqual(result.tolist(), []) + + def test_all_visible(self): + """ + Regression test of is_visible and indirectly tests load_mesh, + package_primitives, is_back_facing (or similar), and does_intersect + """ + camera_t = np.array([-8., -8., 0.]) + nodes = np.array([[-5., -5., 0.], [-5., -1., 0]]).reshape(-1, 3) + normals = np.array([[-0.9, 0., 0.], [0., -1.2, 0.]]).reshape(-1, 3) + + result = self.vis_checker_nogrid.is_visible(camera_t, nodes, normals) + self.assertEqual(result.tolist(), [0, 1]) + + camera_t = np.array([-2., -2., -2.]).reshape(3, 1) + nodes = np.array([[-1., -1., -1.], [0., 0., 0.]]).reshape(-1, 3) + normals = np.array([[-1., -1., -1.], [-1., -1., -1.]]).reshape(-1, 3) + + result = self.vis_checker_nogrid.is_visible(camera_t, nodes, normals) + self.assertEqual(result.tolist(), [0, 1]) + + def test_some_visible(self): + """ + Regression test of is_visible and indirectly tests load_mesh, + package_primitives, is_back_facing (or similar), and does_intersect + """ + camera_t = np.array([1., 1., 1.]).reshape(3, 1) + nodes = [[2., 2., 2.], + [3., 3., 3.], + [0.9, 0.9, 0.9], + [0.5, 0.5, 0.5], + [0., 0., 0.], + [-1., -1., -1.]] + nodes = np.array(nodes).reshape(-1, 3) + + normals = [[1., 1., 1.], + [1., 1., 1.], + [1., 1., 1.], + [1., 1., 1.], + [1., 1., 1.], + [1., 1., 1.]] + normals = np.array(normals).reshape(-1, 3) + + result = self.vis_checker_nogrid.is_visible(camera_t, nodes, normals) + self.assertEqual(result.tolist(), [2, 3]) + + +if __name__ == "__main__": + unittest.main()