链接 OpenCV 库时出错

我目前正在修改 OpenCV 的facerec_demo，在编译代码时收到以下错误。

Creating library C:UsersLop_models20nodes.project20nodes-scenario1.dev64.i0.nt.lib and object C:UsersLop_models20nodes.project20nodes-scenario1.dev64.i0.nt.exp
wlan_mac_hcf.dev64.i0.pr.obj : error LNK2019: unresolved external symbol "class cv::Ptr<class cv::FaceRecognizer> __cdecl cv::createFisherFaceRecognizer(int,double)" (?createFisherFaceRecognizer@cv@@YA?AV?$Ptr@VFaceRecognizer@cv@@@1@HN@Z) referenced in function "void __cdecl faceRecognition(struct _IplImage *,char *,char *,double *,double *,double (* const)[2])" (?faceRecognition@@YAXPEAU_IplImage@@PEAD1PEAN2QEAY01N@Z)
wlan_mac_hcf.dev64.i0.pr.obj : error LNK2019: unresolved external symbol "int __cdecl cv::_interlockedExchangeAdd(int *,int)" (?_interlockedExchangeAdd@cv@@YAHPEAHH@Z) referenced in function "public: __cdecl cv::Mat::Mat(class cv::Mat const &)" (??0Mat@cv@@QEAA@AEBV01@@Z)
C:UsersLop_models20nodes.project20nodes-scenario1.dev64.i0.nt.dll : fatal error LNK1120: 2 unresolved externals
Microsoft (R) Manifest Tool version 5.2.3790.2076

我知道当您忘记在链接器选项中包含库时，通常会出现此问题，因此我仔细检查了我的设置，它们如下：

/LIBPATH:C:OpenCV2.4opencvbuildx64vc9lib
opencv_calib3d240.lib 
opencv_contrib240.lib 
opencv_core240.lib 
opencv_features2d240.lib 
opencv_flann240.lib 
opencv_gpu240.lib 
opencv_haartraining_engine.lib 
opencv_highgui240.lib 
opencv_imgproc240.lib 
opencv_legacy240.lib 
opencv_ml240.lib 
opencv_nonfree240.lib 
opencv_objdetect240.lib 
opencv_photo240.lib 
opencv_stitching240.lib 
opencv_ts240.lib 
opencv_video240.lib 
opencv_videostab240.lib

我不相信我错过了任何库文件，所以我不确定是什么导致了这个问题;这是我的代码供参考。很抱歉弄得一团糟，它仍在进行中。

#include "opencv2corecore.hpp"
#include "opencv2contribcontrib.hpp"
#include "opencv2highguihighgui.hpp"
#include "opencv2imgprocimgproc.hpp"
#include "opencv2objdetectobjdetect.hpp"
void faceRecognition( IplImage* img, char * in,char * d,double *accu, double *error,double truthArray[][2] )
{
  using namespace cv;
 // Get the path to your CSV:
 //string fn_haar = cascade);       // we don't need to load the xml because it is a global assigned elsewhere.
 std::string fn_csv = "faces.csv";
 // These vectors hold the images and corresponding labels:
 std::vector<cv::Mat> images;
 std::vector<int> labels;
 // Read in the data (fails if no valid input filename is given, but you'll get an error message):
 try 
 {
     read_csv(fn_csv, images, labels);
 }
 catch (cv::Exception& e) 
 {
    return;
 }
 // Get the height from the first image. We'll need this
 // later in code to reshape the images to their original
 // size AND we need to reshape incoming faces to this size:
 int im_width = images[0].cols;
 int im_height = images[0].rows;
 // Create a FaceRecognizer and train it on the given images:
 Ptr<FaceRecognizer> model = createFisherFaceRecognizer();
 model->train(images, labels);
 // That's it for learning the Face Recognition model. You now
 // need to create the classifier for the task of Face Detection.
 // We are going to use the haar cascade you have specified in the
 // command line arguments:
 cv::CascadeClassifier haar_cascade;
 //load the xml global assigned elsewhere
 haar_cascade.load(cascade_name);
 // convert input image into Mat format
 cv::Mat frame(img);
 // Clone the current frame:
 cv::Mat original = frame.clone();
 // Convert the current frame to grayscale:
 cv::Mat gray;
 cvtColor(original, gray, CV_BGR2GRAY);
 // Find the faces in the frame:
 std::vector< cv::Rect_<int> > faces;
 haar_cascade.detectMultiScale(gray, faces);
 // At this point you have the position of the faces in
 // faces. Now we'll get the faces, make a prediction and
 // annotate it in the video. Cool or what?
 for(int i = 0; i < faces.size(); i++) 
 {
     // Process face by face:
     cv::Rect face_i = faces[i];
     // Crop the face from the image. So simple with OpenCV C++:
     cv::Mat face = gray(face_i);
     // Resizing the face is necessary for Eigenfaces and Fisherfaces. You can easily
     // verify this, by reading through the face recognition tutorial coming with OpenCV.
     // Resizing IS NOT NEEDED for Local Binary Patterns Histograms, so preparing the
     // input data really depends on the algorithm used.
     //
     // I strongly encourage you to play around with the algorithms. See which work best
     // in your scenario, LBPH should always be a contender for robust face recognition.
     //
     // Since I am showing the Fisherfaces algorithm here, I also show how to resize the
     // face you have just found:
     cv::Mat face_resized;
     cv::resize(face, face_resized, cv::Size(im_width, im_height), 1.0, 1.0, cv::INTER_CUBIC);
     // Now perform the prediction, see how easy that is:
     int prediction = model->predict(face_resized);
     // And finally write all we've found out to the original image!
     // First of all draw a green rectangle around the detected face:
     rectangle(original, face_i, CV_RGB(0, 255,0), 1);
     // Create the text we will annotate the box with:
     std::string box_text = cv::format("Prediction = %d", prediction);
     // Calculate the position for annotated text (make sure we don't
     // put illegal values in there):
     int pos_x = std::max(face_i.tl().x - 10, 0);
     int pos_y = std::max(face_i.tl().y - 10, 0);
     // And now put it into the image:
}