face example error error: undefined reference to `cv::FaceDetectorYN::create(std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > const&, std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > const&,

[gxxzs]

System information (version)

• OpenCV => 4.5.4
• Operating System / Platform => ubuntu 16.04+ros
• Compiler => QT 59

#include <opencv2/dnn.hpp>
#include <opencv2/imgproc.hpp>
#include <opencv2/highgui.hpp>
#include <opencv2/objdetect.hpp>
#include <opencv2/objdetect/face.hpp>
#include<opencv2/core/cvstd.hpp>

#include <iostream>
using namespace cv;
using namespace std;
static
void visualize(Mat& input, int frame, Mat& faces, double fps, int thickness = 2)
{
    std::string fpsString = cv::format("FPS : %.2f", (float)fps);
    if (frame >= 0)
        cout << "Frame " << frame << ", ";
    cout << "FPS: " << fpsString << endl;
    for (int i = 0; i < faces.rows; i++)
    {
        // Print results
        cout << "Face " << i
             << ", top-left coordinates: (" << faces.at<float>(i, 0) << ", " << faces.at<float>(i, 1) << "), "
             << "box width: " << faces.at<float>(i, 2)  << ", box height: " << faces.at<float>(i, 3) << ", "
             << "score: " << cv::format("%.2f", faces.at<float>(i, 14))
             << endl;
        // Draw bounding box
        rectangle(input, Rect2i(int(faces.at<float>(i, 0)), int(faces.at<float>(i, 1)), int(faces.at<float>(i, 2)), int(faces.at<float>(i, 3))), Scalar(0, 255, 0), thickness);
        // Draw landmarks
        circle(input, Point2i(int(faces.at<float>(i, 4)), int(faces.at<float>(i, 5))), 2, Scalar(255, 0, 0), thickness);
        circle(input, Point2i(int(faces.at<float>(i, 6)), int(faces.at<float>(i, 7))), 2, Scalar(0, 0, 255), thickness);
        circle(input, Point2i(int(faces.at<float>(i, 8)), int(faces.at<float>(i, 9))), 2, Scalar(0, 255, 0), thickness);
        circle(input, Point2i(int(faces.at<float>(i, 10)), int(faces.at<float>(i, 11))), 2, Scalar(255, 0, 255), thickness);
        circle(input, Point2i(int(faces.at<float>(i, 12)), int(faces.at<float>(i, 13))), 2, Scalar(0, 255, 255), thickness);
    }
    putText(input, fpsString, Point(0, 15), FONT_HERSHEY_SIMPLEX, 0.5, Scalar(0, 255, 0), 2);
}
int main(int argc, char** argv)
{
    CommandLineParser parser(argc, argv,
        "{help  h           |            | Print this message}"
        "{image1 i1         |            | Path to the input image1. Omit for detecting through VideoCapture}"
        "{image2 i2         |            | Path to the input image2. When image1 and image2 parameters given then the program try to find a face on both images and runs face recognition algorithm}"
        "{video v           | 0          | Path to the input video}"
        "{scale sc          | 1.0        | Scale factor used to resize input video frames}"
        "{fd_model fd       | face_detection_yunet_2021dec.onnx| Path to the model. Download yunet.onnx in https://github.com/opencv/opencv_zoo/tree/master/models/face_detection_yunet}"
        "{fr_model fr       | face_recognition_sface_2021dec.onnx | Path to the face recognition model. Download the model at https://github.com/opencv/opencv_zoo/tree/master/models/face_recognition_sface}"
        "{score_threshold   | 0.9        | Filter out faces of score < score_threshold}"
        "{nms_threshold     | 0.3        | Suppress bounding boxes of iou >= nms_threshold}"
        "{top_k             | 5000       | Keep top_k bounding boxes before NMS}"
        "{save s            | false      | Set true to save results. This flag is invalid when using camera}"
    );
    if (parser.has("help"))
    {
        parser.printMessage();
        return 0;
    }
    cv::String fd_modelPath = parser.get<cv::String>("fd_model");
    cv::String fr_modelPath = parser.get<cv::String>("fr_model");
    float scoreThreshold = parser.get<float>("score_threshold");
    float nmsThreshold = parser.get<float>("nms_threshold");
    int topK = parser.get<int>("top_k");
    bool save = parser.get<bool>("save");
    float scale = parser.get<float>("scale");
    double cosine_similar_thresh = 0.363;
    double l2norm_similar_thresh = 1.128;
    // Initialize FaceDetectorYN
    Ptr<FaceDetectorYN> detector = FaceDetectorYN::create(fd_modelPath, " ", Size(320, 320), scoreThreshold, nmsThreshold, topK);
//    Ptr<FaceDetectorYN> detector;
    TickMeter tm;
    // If input is an image
    if (parser.has("image1"))
    {
        String input1 = parser.get<String>("image1");
        Mat image1 = imread(samples::findFile(input1));
        if (image1.empty())
        {
            std::cerr << "Cannot read image: " << input1 << std::endl;
            return 2;
        }
        int imageWidth = int(image1.cols * scale);
        int imageHeight = int(image1.rows * scale);
        resize(image1, image1, Size(imageWidth, imageHeight));
        tm.start();
        // Set input size before inference
        detector->setInputSize(image1.size());
        Mat faces1;
        detector->detect(image1, faces1);
        if (faces1.rows < 1)
        {
            std::cerr << "Cannot find a face in " << input1 << std::endl;
            return 1;
        }
        tm.stop();
        // Draw results on the input image
        visualize(image1, -1, faces1, tm.getFPS());
        // Save results if save is true
        if (save)
        {
            cout << "Saving result.jpg...\n";
            imwrite("result.jpg", image1);
        }
        // Visualize results
        imshow("image1", image1);
        pollKey();  // handle UI events to show content
        if (parser.has("image2"))
        {
            String input2 = parser.get<String>("image2");
            Mat image2 = imread(samples::findFile(input2));
            if (image2.empty())
            {
                std::cerr << "Cannot read image2: " << input2 << std::endl;
                return 2;
            }
            tm.reset();
            tm.start();
            detector->setInputSize(image2.size());
            Mat faces2;
            detector->detect(image2, faces2);
            if (faces2.rows < 1)
            {
                std::cerr << "Cannot find a face in " << input2 << std::endl;
                return 1;
            }
            tm.stop();
            visualize(image2, -1, faces2, tm.getFPS());
            if (save)
            {
                cout << "Saving result2.jpg...\n";
                imwrite("result2.jpg", image2);
            }
            imshow("image2", image2);
            pollKey();
            // Initialize FaceRecognizerSF
//            Ptr<FaceRecognizerSF> faceRecognizer = FaceRecognizerSF::create(fr_modelPath, "");
            Ptr<FaceRecognizerSF> faceRecognizer;
            // Aligning and cropping facial image through the first face of faces detected.
            Mat aligned_face1, aligned_face2;
            faceRecognizer->alignCrop(image1, faces1.row(0), aligned_face1);
            faceRecognizer->alignCrop(image2, faces2.row(0), aligned_face2);
            // Run feature extraction with given aligned_face
            Mat feature1, feature2;
            faceRecognizer->feature(aligned_face1, feature1);
            feature1 = feature1.clone();
            faceRecognizer->feature(aligned_face2, feature2);
            feature2 = feature2.clone();
            double cos_score = faceRecognizer->match(feature1, feature2, FaceRecognizerSF::DisType::FR_COSINE);
            double L2_score = faceRecognizer->match(feature1, feature2, FaceRecognizerSF::DisType::FR_NORM_L2);
            if (cos_score >= cosine_similar_thresh)
            {
                std::cout << "They have the same identity;";
            }
            else
            {
                std::cout << "They have different identities;";
            }
            std::cout << " Cosine Similarity: " << cos_score << ", threshold: " << cosine_similar_thresh << ". (higher value means higher similarity, max 1.0)\n";
            if (L2_score <= l2norm_similar_thresh)
            {
                std::cout << "They have the same identity;";
            }
            else
            {
                std::cout << "They have different identities.";
            }
            std::cout << " NormL2 Distance: " << L2_score << ", threshold: " << l2norm_similar_thresh << ". (lower value means higher similarity, min 0.0)\n";
        }
        cout << "Press any key to exit..." << endl;
        waitKey(0);
    }
//    else
//    {
//        int frameWidth, frameHeight;
//        VideoCapture capture;
//        std::string video = parser.get<string>("video");
//        if (video.size() == 1 && isdigit(video[0]))
//            capture.open(parser.get<int>("video"));
//        else
//            capture.open(samples::findFileOrKeep(video));  // keep GStreamer pipelines
//        if (capture.isOpened())
//        {
//            frameWidth = int(capture.get(CAP_PROP_FRAME_WIDTH) * scale);
//            frameHeight = int(capture.get(CAP_PROP_FRAME_HEIGHT) * scale);
//            cout << "Video " << video
//                << ": width=" << frameWidth
//                << ", height=" << frameHeight
//                << endl;
//        }
//        else
//        {
//            cout << "Could not initialize video capturing: " << video << "\n";
//            return 1;
//        }
//        detector->setInputSize(Size(frameWidth, frameHeight));
//        cout << "Press 'SPACE' to save frame, any other key to exit..." << endl;
//        int nFrame = 0;
//        for (;;)
//        {
//            // Get frame
//            Mat frame;
//            if (!capture.read(frame))
//            {
//                cerr << "Can't grab frame! Stop\n";
//                break;
//            }
//            resize(frame, frame, Size(frameWidth, frameHeight));
//            // Inference
//            Mat faces;
//            tm.start();
//            detector->detect(frame, faces);
//            tm.stop();
//            Mat result = frame.clone();
//            // Draw results on the input image
//            visualize(result, nFrame, faces, tm.getFPS());
//            // Visualize results
//            imshow("Live", result);
//            int key = waitKey(1);
//            bool saveFrame = save;
//            if (key == ' ')
//            {
//                saveFrame = true;
//                key = 0;  // handled
//            }
//            if (saveFrame)
//            {
//                std::string frame_name = cv::format("frame_%05d.png", nFrame);
//                std::string result_name = cv::format("result_%05d.jpg", nFrame);
//                cout << "Saving '" << frame_name << "' and '" << result_name << "' ...\n";
//                imwrite(frame_name, frame);
//                imwrite(result_name, result);
//            }
//            ++nFrame;
//            if (key > 0)
//                break;
//        }
//        cout << "Processed " << nFrame << " frames" << endl;
//    }
    cout << "Done." << endl;
    return 0;
}

CMakeLists.txt

cmake_minimum_required(VERSION 2.8.3)

set(CMAKE_CXX_STANDARD 11)
set(CMAKE_CXX_STANDARD_REQUIRED ON)
project(dog_pose)

## Compile as C++11, supported in ROS Kinetic and newer
# add_compile_options(-std=c++11)

## Find catkin macros and libraries
## if COMPONENTS list like find_package(catkin REQUIRED COMPONENTS xyz)
## is used, also find other catkin packages
find_package(catkin REQUIRED COMPONENTS
  rospy
  roscpp
  sensor_msgs
  #std_msgs
  pcl_ros
  pcl_conversions
  cv_bridge
  sensor_msgs
  visualization_msgs
  tf
  cmake_modules
  geometry_msgs
  pcl_msgs
  kdl_parser

)



find_package(Boost REQUIRED)
find_package(Eigen REQUIRED)
find_package(octomap REQUIRED)

link_libraries(${OCTOMAP_LIBRARIES})


catkin_package(
    INCLUDE_DIRS
    CATKIN_DEPENDS roscpp rospy
    orocos_kdl
    kdl_parser
#    intera_core_msgs
    DEPENDS system_lib
    )
set(PCL_DIR "/home/zs/pcl-1.8/share/pcl-1.8")
find_package(PCL 1.8 REQUIRED)
#find_package(PCL REQUIRED)
 include_directories(
  ${PCL_INCLUDE_DIRS}
)
link_directories(
  ${PCL_LIBRARY_DIRS}
)

find_package(OpenCV PATHS /usr/local/opencv454/lib/cmake/opencv4)
include_directories(include ${catkin_INCLUDE_DIRS} /usr/local/opencv454/include/opencv4)
link_directories(/usr/local/opencv454/lib)
#find_package(OpenCV 4 REQUIRED)

#set(OpenCV_DIR /usr/local/opencv455/lib/cmake/OpenCV4)
#find_package(OpenCV REQUIRED)
#find_package(OpenCV 3 REQUIRED)
#include_directories(${OpenCV_INCLUDE_DIRS})
## System dependencies are found with CMake's conventions
# find_package(Boost REQUIRED COMPONENTS system)


## Uncomment this if the package has a setup.py. This macro ensures
## modules and global scripts declared therein get installed
## See http://ros.org/doc/api/catkin/html/user_guide/setup_dot_py.html
# catkin_python_setup()

################################################
## Declare ROS messages, services and actions ##
################################################

## To declare and build messages, services or actions from within this
## package, follow these steps:
## * Let MSG_DEP_SET be the set of packages whose message types you use in
##   your messages/services/actions (e.g. std_msgs, actionlib_msgs, ...).
## * In the file package.xml:
##   * add a build_depend tag for "message_generation"
##   * add a build_depend and a run_depend tag for each package in MSG_DEP_SET
##   * If MSG_DEP_SET isn't empty the following dependency has been pulled in
##     but can be declared for certainty nonetheless:
##     * add a run_depend tag for "message_runtime"
## * In this file (CMakeLists.txt):
##   * add "message_generation" and every package in MSG_DEP_SET to
##     find_package(catkin REQUIRED COMPONENTS ...)
##   * add "message_runtime" and every package in MSG_DEP_SET to
##     catkin_package(CATKIN_DEPENDS ...)
##   * uncomment the add_*_files sections below as needed
##     and list every .msg/.srv/.action file to be processed
##   * uncomment the generate_messages entry below
##   * add every package in MSG_DEP_SET to generate_messages(DEPENDENCIES ...)

## Generate messages in the 'msg' folder
# add_message_files(
#   FILES
#   Message1.msg
#   Message2.msg
# )

## Generate services in the 'srv' folder
# add_service_files(
#   FILES
#   Service1.srv
#   Service2.srv
# )

## Generate actions in the 'action' folder
# add_action_files(
#   FILES
#   Action1.action
#   Action2.action
# )

## Generate added messages and services with any dependencies listed here
# generate_messages(
#   DEPENDENCIES
#   std_msgs
# )

################################################
## Declare ROS dynamic reconfigure parameters ##
################################################

## To declare and build dynamic reconfigure parameters within this
## package, follow these steps:
## * In the file package.xml:
##   * add a build_depend and a run_depend tag for "dynamic_reconfigure"
## * In this file (CMakeLists.txt):
##   * add "dynamic_reconfigure" to
##     find_package(catkin REQUIRED COMPONENTS ...)
##   * uncomment the "generate_dynamic_reconfigure_options" section below
##     and list every .cfg file to be processed

## Generate dynamic reconfigure parameters in the 'cfg' folder
# generate_dynamic_reconfigure_options(
#   cfg/DynReconf1.cfg
#   cfg/DynReconf2.cfg
# )

###################################
## catkin specific configuration ##
###################################
## The catkin_package macro generates cmake config files for your package
## Declare things to be passed to dependent projects
## INCLUDE_DIRS: uncomment this if you package contains header files
## LIBRARIES: libraries you create in this project that dependent projects also need
## CATKIN_DEPENDS: catkin_packages dependent projects also need
## DEPENDS: system dependencies of this project that dependent projects also need
catkin_package(
    CATKIN_DEPENDS pcl_conversions pcl_msgs pcl_ros sensor_msgs
  #  DEPENDS system_libCATKIN_DEPENDS
    DEPENDS
      Boost
      Eigen
      PCL
      orocos_kdl
      kdl_parser
      #kdl_parser
      DEPENDS system_lib
#  LIBRARIES project_1
#  CATKIN_DEPENDS rospy std_msgs
#  DEPENDS system_lib
)

###########
## Build ##
###########

## Specify additional locations of header files
## Your package locations should be listed before other locations
# include_directories(include)
include_directories(
  ${catkin_INCLUDE_DIRS}
#  /home/zs/pcl_ws/src/pcl_object_tracking/project_1/include
)

## Declare a C++ library
# add_library(project_1
#   src/${PROJECT_NAME}/project_1.cpp
# )

## Add cmake target dependencies of the library
## as an example, code may need to be generated before libraries
## either from message generation or dynamic reconfigure
# add_dependencies(project_1 ${${PROJECT_NAME}_EXPORTED_TARGETS} ${catkin_EXPORTED_TARGETS})

## Declare a C++ executable
# add_executable(project_1_node src/project_1_node.cpp)

## Add cmake target dependencies of the executable
## same as for the library above
# add_dependencies(project_1_node ${${PROJECT_NAME}_EXPORTED_TARGETS} ${catkin_EXPORTED_TARGETS})

## Specify libraries to link a library or executable target against
# target_link_libraries(project_1_node
#   ${catkin_LIBRARIES}
# )

#############
## Install ##
#############

# all install targets should use catkin DESTINATION variables
# See http://ros.org/doc/api/catkin/html/adv_user_guide/variables.html

## Mark executable scripts (Python etc.) for installation
## in contrast to setup.py, you can choose the destination
# install(PROGRAMS
#   scripts/my_python_script
#   DESTINATION ${CATKIN_PACKAGE_BIN_DESTINATION}
# )

## Mark executables and/or libraries for installation
# install(TARGETS project_1 project_1_node
#   ARCHIVE DESTINATION ${CATKIN_PACKAGE_LIB_DESTINATION}
#   LIBRARY DESTINATION ${CATKIN_PACKAGE_LIB_DESTINATION}
#   RUNTIME DESTINATION ${CATKIN_PACKAGE_BIN_DESTINATION}
# )

## Mark cpp header files for installation
# install(DIRECTORY include/${PROJECT_NAME}/
#   DESTINATION ${CATKIN_PACKAGE_INCLUDE_DESTINATION}
#   FILES_MATCHING PATTERN "*.h"
#   PATTERN ".svn" EXCLUDE
# )

## Mark other files for installation (e.g. launch and bag files, etc.)
# install(FILES
#   # myfile1
#   # myfile2
#   DESTINATION ${CATKIN_PACKAGE_SHARE_DESTINATION}
# )

#############
## Testing ##
#############


#add_executable(face_3d_pose src/face_3d_pose.cpp)
#target_link_libraries(face_3d_pose ${catkin_LIBRARIES}   ${OpenCV_LIBRARIES})

add_executable(face_detection src/face_detection.cpp)
target_link_libraries(face_detection ${catkin_LIBRARIES}   ${OpenCV_LIBRARIES}
    /usr/local/opencv454/lib/libopencv_highgui.so
    /usr/local/opencv454/lib/libopencv_core.so
    /usr/local/opencv454/lib/libopencv_imgproc.so
    /usr/local/opencv454/lib/libopencv_imgcodecs.so
    /usr/local/opencv454/lib/libopencv_dnn.so
    /usr/local/opencv454/lib/libopencv_dnn.so)

Error output

face_detection.cpp:67: error: undefined reference to `cv::FaceDetectorYN::create(std::__cxx11::basic_string<char, std::char_traits, std::allocator > const&, std::_cxx11::basic_string<char, std::char_traits, std::allocator > const&, cv::Size const&, float, float, int, int, int)'

[fengyuentau]

target_link_libraries(face_detection ${catkin_LIBRARIES} ${OpenCV_LIBRARIES}
/usr/local/opencv454/lib/libopencv_highgui.so
/usr/local/opencv454/lib/libopencv_core.so
/usr/local/opencv454/lib/libopencv_imgproc.so
/usr/local/opencv454/lib/libopencv_imgcodecs.so
/usr/local/opencv454/lib/libopencv_dnn.so
/usr/local/opencv454/lib/libopencv_dnn.so)

@gxxzs You need to check whether ${OpenCV_LIBRARIES} has libopencv_objdetect.so, and if it doesn't, please add /usr/local/opencv454/lib/libopencv_objdetect.so to the list of linking libraries.

[gxxzs]

target_link_libraries(face_detection catkinLIBRARIES{OpenCV_LIBRARIES}
/usr/local/opencv454/lib/libopencv_highgui.so
/usr/local/opencv454/lib/libopencv_core.so
/usr/local/opencv454/lib/libopencv_imgproc.so
/usr/local/opencv454/lib/libopencv_imgcodecs.so
/usr/local/opencv454/lib/libopencv_dnn.so
/usr/local/opencv454/lib/libopencv_dnn.so)

@gxxzs You need to check whether ${OpenCV_LIBRARIES} has libopencv_objdetect.so, and if it doesn't, please add /usr/local/opencv454/lib/libopencv_objdetect.so to the list of linking libraries.

/usr/local/opencv454/lib/libopencv_objdetect.so solve the problem. Thanks a lot.