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Я в настоящее время изменяю facerec_demo из OpenCV, и я получаю следующие ошибки при компиляции моего кода.Ошибка связывания библиотек OpenCV
Creating library C:\Users\L\op_models\20nodes.project\20nodes-scenario1.dev64.i0.nt.lib and object C:\Users\L\op_models\20nodes.project\20nodes-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)" ([email protected]@@[email protected]@[email protected]@@[email protected]@Z) referenced in function "void __cdecl faceRecognition(struct _IplImage *,char *,char *,double *,double *,double (* const)[2])" ([email protected]@[email protected]@[email protected])
wlan_mac_hcf.dev64.i0.pr.obj : error LNK2019: unresolved external symbol "int __cdecl cv::_interlockedExchangeAdd(int *,int)" ([email protected]@@[email protected]) referenced in function "public: __cdecl cv::Mat::Mat(class cv::Mat const &)" ([email protected]@@[email protected]@@Z)
C:\Users\L\op_models\20nodes.project\20nodes-scenario1.dev64.i0.nt.dll : fatal error LNK1120: 2 unresolved externals
Microsoft (R) Manifest Tool version 5.2.3790.2076
Я знаю, что эта проблема обычно возникает, когда вы забыли включить библиотеку в опции компоновщика, поэтому я дважды проверил мои настройки и они заключаются в следующем:
/LIBPATH:C:\OpenCV2.4\opencv\build\x64\vc9\lib
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
Я не верю что я пропустил файлы библиотеки, поэтому я не уверен, что может вызвать проблему; вот мой код для справки. Извините за беспорядок, это все еще продолжается.
#include "opencv2\core\core.hpp"
#include "opencv2\contrib\contrib.hpp"
#include "opencv2\highgui\highgui.hpp"
#include "opencv2\imgproc\imgproc.hpp"
#include "opencv2\objdetect\objdetect.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:
}