我正在尝试在OpenCV提供的视频示例中使用人脸识别。我所做的唯一修改是：我没有使用命令行参数来提供CSV和Cascade分类器路径，而是直接在代码中给出了它们。这是代码：
#include "stdafx.h"
#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"
#include <iostream>
#include <fstream>
#include <sstream>
using namespace cv;
using namespace std;
static void read_csv(const string& filename, vector<Mat>& images, vector<int>& labels, char separator = ';') {
std::ifstream file(filename.c_str(), ifstream::in);
if (!file) {
string error_message = "No valid input file was given, please check the given filename.";
CV_Error(CV_StsBadArg, error_message);
}
string line, path, classlabel;
while (getline(file, line)) {
stringstream liness(line);
getline(liness, path, separator);
getline(liness, classlabel);
if(!path.empty() && !classlabel.empty()) {
images.push_back(imread(path, 0));
labels.push_back(atoi(classlabel.c_str()));
}
}
}
int main(int argc, const char *argv[]) {
// Check for valid command line arguments, print usage
// if no arguments were given.
if (argc != 4) {
cout << "usage: " << argv[0] << " </path/to/haar_cascade> </path/to/csv.ext> </path/to/device id>" << endl;
cout << "\t </path/to/haar_cascade> -- Path to the Haar Cascade for face detection." << endl;
cout << "\t </path/to/csv.ext> -- Path to the CSV file with the face database." << endl;
cout << "\t <device id> -- The webcam device id to grab frames from." << endl;
//exit(1);
}
// Get the path to your CSV:
string fn_haar = "C:\\OpenCV-2.4.2\\opencv\\data\\haarcascades\\haarcascade_frontalface_default.xml";
string fn_csv = "C:\\Users\\gaspl\\Desktop\\train.txt";
int deviceId = 1;
// These vectors hold the images and corresponding labels:
vector<Mat> images;
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) {
cerr << "Error opening file \"" << fn_csv << "\". Reason: " << e.msg << endl;
// nothing more we can do
exit(1);
}
// 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:
//
CascadeClassifier haar_cascade;
haar_cascade.load(fn_haar);
// Get a handle to the Video device:
VideoCapture cap(deviceId);
// Check if we can use this device at all:
if(!cap.isOpened()) {
cerr << "Capture Device ID " << deviceId << "cannot be opened." << endl;
return -1;
}
// Holds the current frame from the Video device:
Mat frame;
for(;;) {
cap >> frame;
// Clone the current frame:
Mat original = frame.clone();
// Convert the current frame to grayscale:
Mat gray;
cvtColor(original, gray, CV_BGR2GRAY);
// Find the faces in the frame:
vector< 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:
Rect face_i = faces[i];
// Crop the face from the image. So simple with OpenCV C++:
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:
Mat face_resized;
cv::resize(face, face_resized, Size(im_width, im_height), 1.0, 1.0, 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:
string box_text = 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:
putText(original, box_text, Point(pos_x, pos_y), FONT_HERSHEY_PLAIN, 1.0, CV_RGB(0,255,0), 2.0);
}
// Show the result:
imshow("face_recognizer", original);
// And display it:
char key = (char) waitKey(20);
// Exit this loop on escape:
if(key == 27)
break;
}
return 0;
}
这就是我的train.txt CSV文件：
C:\\Training\\extract0.jpg;0
C:\\Training\\extract1.jpg;0
C:\\Training\\extract2.jpg;0
但是，当我尝试运行该示例（它构建正常）时，我收到了这些错误，它要求我Break：
First-chance exception at 0x000007FEFE04CAED in facrec.exe: Microsoft C++ exception: cv::Exception at memory location 0x000000000025C530.
Unhandled exception at at 0x000007FEFE04CAED in facrec.exe: Microsoft C++ exception: cv::Exception at memory location 0x000000000025C530.
错误发生在我初始化Fisher识别器的位置：
Ptr<FaceRecognizer> model = createFisherFaceRecognizer();
model->train(images, labels);
我在Visual Studio 2012和OpenCV 2.4.2中使用Windows 7 64位（构建配置：x64-Release）
我已经尝试过面部检测和面部提取，它们在我的计算机上工作正常（If anyone wants to check out the code），因此我的Visual Studio项目设置（链接器或C / C ++）显然没有问题。
There is a similar question here但它仍然无法解决任何问题。
我正在做的事情有什么问题吗？
答案 0 :(得分：11)
我们已经通过邮件结束了。发生这种情况，因为您的CSV文件中只提供了一个标签：
C:\Training\extract0.jpg;0 C:\Training\extract1.jpg;0 C:\Training\extract2.jpg;0
Fisherfaces方法至少需要两个类来学习模型。这个案例应该被捕获，OpenCV 2.4.2在我的系统上抛出以下异常：
OpenCV Error: Bad argument (At least two classes are needed to perform a LDA. Reason: Only one class was given!) in lda, file /home/philipp/github/libfacerec/src/subspace.cpp, line 150 terminate called after throwing an instance of 'cv::Exception' what(): /home/philipp/github/libfacerec/src/subspace.cpp:150: error: (-5) At least two classes are needed to perform a LDA. Reason: Only one class was given! in function lda
这使得训练数据中的错误非常清楚。我不知道，为什么不会在Windows 7安装上抛出此异常，但我会设置一个测试系统，以便尽快重现并进行相应的修复。