本文代碼來自於網絡
功能：
0、How to detect a face using OpenCV’s Face Detector: 人臉檢測
1、How to preprocess facial images for Face Recognition: 人臉預處理（直方圖均衡）
2、How Eigenfaces can be used for Face Recognition: 平均臉與特徵臉怎樣用於人臉識別
3、Implementing Offline Training: 離線訓練人臉
4、Implementing Offline Recognition: 離線識別人臉
5、Implementing Online Training from a Camera: 在線訓練人臉
6、Implementing Realtime Recognition from a Camera: 在線人臉識別
//////////////////////////////////////////////////////////////////////////////////////
// OnlineFaceRec.cpp, by Shervin Emami (www.shervinemami.info) on 30th Dec 2011.
// Online Face Recognition from a camera using Eigenfaces.
//////////////////////////////////////////////////////////////////////////////////////
//
// Some parts are based on the code example by Robin Hewitt (2007) at:
// "http://www.cognotics.com/opencv/servo_2007_series/part_5/index.html"
//
// Command-line Usage (for offline mode, without a webcam):
//
// First, you need some face images. I used the ORL face database.
// You can download it for free at
// www.cl.cam.ac.uk/research/dtg/attarchive/facedatabase.html
//
// List the training and test face images you want to use in the
// input files train.txt and test.txt. (Example input files are provided
// in the download.) To use these input files exactly as provided, unzip
// the ORL face database, and place train.txt, test.txt, and eigenface.exe
// at the root of the unzipped database.
//
// To run the learning phase of eigenface, enter in the command prompt:
// OnlineFaceRec train <train_file>
// To run the recognition phase, enter:
// OnlineFaceRec test <test_file>
// To run online recognition from a camera, enter:
// OnlineFaceRec
//
//////////////////////////////////////////////////////////////////////////////////////
#include <stdio.h>
#if defined WIN32 || defined _WIN32
#include <conio.h> // For _kbhit() on Windows
#include <direct.h> // For mkdir(path) on Windows
#define snprintf sprintf_s // Visual Studio on Windows comes with sprintf_s() instead of snprintf()
#else
#include <stdio.h> // For getchar() on Linux
#include <termios.h> // For kbhit() on Linux
#include <unistd.h>
#include <sys/types.h>
#include <sys/stat.h> // For mkdir(path, options) on Linux
#endif
#include <vector>
#include <string>
//#include <string.h>
#include "cv.h"
#include "cvaux.h"
#include "highgui.h"
#ifndef BOOL
#define BOOL bool
#endif
using namespace std;
// Haar Cascade file, used for Face Detection.
const char *faceCascadeFilename = "haarcascade_frontalface_alt.xml";
int SAVE_EIGENFACE_IMAGES = 1; // Set to 0 if you dont want images of the Eigenvectors saved to files (for debugging).
//#define USE_MAHALANOBIS_DISTANCE // You might get better recognition accuracy if you enable this.
// Global variables
IplImage ** faceImgArr = 0; // array of face images
CvMat * personNumTruthMat = 0; // array of person numbers
//#define MAX_NAME_LENGTH 256 // Give each name a fixed size for easier code.
//char **personNames = 0; // array of person names (indexed by the person number). Added by Shervin.
vector<string> personNames; // array of person names (indexed by the person number). Added by Shervin.
int faceWidth = 120; // Default dimensions for faces in the face recognition database. Added by Shervin.
int faceHeight = 90; // " " " " " " " "
int nPersons = 0; // the number of people in the training set. Added by Shervin.
int nTrainFaces = 0; // the number of training images
int nEigens = 0; // the number of eigenvalues
IplImage * pAvgTrainImg = 0; // the average image
IplImage ** eigenVectArr = 0; // eigenvectors
CvMat * eigenValMat = 0; // eigenvalues
CvMat * projectedTrainFaceMat = 0; // projected training faces
CvCapture* camera = 0; // The camera device.
// Function prototypes
void printUsage();
void learn(const char *szFileTrain);
void doPCA();
void storeTrainingData();
int loadTrainingData(CvMat ** pTrainPersonNumMat);
int findNearestNeighbor(float * projectedTestFace);
int findNearestNeighbor(float * projectedTestFace, float *pConfidence);
int loadFaceImgArray(const char * filename);
void recognizeFileList(const char *szFileTest);
void recognizeFromCam(void);
IplImage* getCameraFrame(void);
IplImage* convertImageToGreyscale(const IplImage *imageSrc);
IplImage* cropImage(const IplImage *img, const CvRect region);
IplImage* resizeImage(const IplImage *origImg, int newWidth, int newHeight);
IplImage* convertFloatImageToUcharImage(const IplImage *srcImg);
void saveFloatImage(const char *filename, const IplImage *srcImg);
CvRect detectFaceInImage(const IplImage *inputImg, const CvHaarClassifierCascade* cascade );
CvMat* retrainOnline(void);
// Show how to use this program from the command-line.
void printUsage()
{
printf("OnlineFaceRec, created by Shervin Emami (www.shervinemami.co.cc), 2nd Jun 2010.\n"
"Usage: OnlineFaceRec [<command>] \n"
" Valid commands are: \n"
" train <train_file> \n"
" test <test_file> \n"
" (if no args are supplied, then online camera mode is enabled).\n"
);
}
// Startup routine.
int main( int argc, char** argv )
{
printUsage();
if( argc >= 2 && strcmp(argv[1], "train") == 0 ) {
char *szFileTrain;
if (argc == 3)
szFileTrain = argv[2]; // use the given arg
else {
printf("ERROR: No training file given.\n");
return 1;
}
learn(szFileTrain);
}
else if( argc >= 2 && strcmp(argv[1], "test") == 0) {
char *szFileTest;
if (argc == 3)
szFileTest = argv[2]; // use the given arg
else {
printf("ERROR: No testing file given.\n");
return 1;
}
recognizeFileList(szFileTest);
}
else {
recognizeFromCam();
}
return 0;
}
#if defined WIN32 || defined _WIN32
// Wrappers of kbhit() and getch() for Windows:
#define changeKeyboardMode
#define kbhit _kbhit
#else
// Create an equivalent to kbhit() and getch() for Linux,
// based on "http://cboard.cprogramming.com/c-programming/63166-kbhit-linux.html":
#define VK_ESCAPE 0x1B // Escape character
// If 'dir' is 1, get the Linux terminal to return the 1st keypress instead of waiting for an ENTER key.
// If 'dir' is 0, will reset the terminal back to the original settings.
void changeKeyboardMode(int dir)
{
static struct termios oldt, newt;
if ( dir == 1 ) {
tcgetattr( STDIN_FILENO, &oldt);
newt = oldt;
newt.c_lflag &= ~( ICANON | ECHO );
tcsetattr( STDIN_FILENO, TCSANOW, &newt);
}
else
tcsetattr( STDIN_FILENO, TCSANOW, &oldt);
}
// Get the next keypress.
int kbhit(void)
{
struct timeval tv;
fd_set rdfs;
tv.tv_sec = 0;
tv.tv_usec = 0;
FD_ZERO(&rdfs);
FD_SET (STDIN_FILENO, &rdfs);
select(STDIN_FILENO+1, &rdfs, NULL, NULL, &tv);
return FD_ISSET(STDIN_FILENO, &rdfs);
}
// Use getchar() on Linux instead of getch().
#define getch() getchar()
#endif
// Save all the eigenvectors as images, so that they can be checked.
void storeEigenfaceImages()
{
// Store the average image to a file
printf("Saving the image of the average face as 'out_averageImage.bmp'.\n");
cvSaveImage("out_averageImage.bmp", pAvgTrainImg);
// Create a large image made of many eigenface images.
// Must also convert each eigenface image to a normal 8-bit UCHAR image instead of a 32-bit float image.
printf("Saving the %d eigenvector images as 'out_eigenfaces.bmp'\n", nEigens);
if (nEigens > 0) {
// Put all the eigenfaces next to each other.
int COLUMNS = 8; // Put upto 8 images on a row.
int nCols = min(nEigens, COLUMNS);
int nRows = 1 + (nEigens / COLUMNS); // Put the rest on new rows.
int w = eigenVectArr[0]->width;
int h = eigenVectArr[0]->height;
CvSize size;
size = cvSize(nCols * w, nRows * h);
IplImage *bigImg = cvCreateImage(size, IPL_DEPTH_8U, 1); // 8-bit Greyscale UCHAR image
for (int i=0; i<nEigens; i++) {
// Get the eigenface image.
IplImage *byteImg = convertFloatImageToUcharImage(eigenVectArr[i]);
// Paste it into the correct position.
int x = w * (i % COLUMNS);
int y = h * (i / COLUMNS);
CvRect ROI = cvRect(x, y, w, h);
cvSetImageROI(bigImg, ROI);
cvCopyImage(byteImg, bigImg);
cvResetImageROI(bigImg);
cvReleaseImage(&byteImg);
}
cvSaveImage("out_eigenfaces.bmp", bigImg);
cvReleaseImage(&bigImg);
}
}
// Train from the data in the given text file, and store the trained data into the file 'facedata.xml'.
void learn(const char *szFileTrain)
{
int i, offset;
// load training data
printf("Loading the training images in '%s'\n", szFileTrain);
nTrainFaces = loadFaceImgArray(szFileTrain);
printf("Got %d training images.\n", nTrainFaces);
if( nTrainFaces < 2 )
{
fprintf(stderr,
"Need 2 or more training faces\n"
"Input file contains only %d\n", nTrainFaces);
return;
}
// do PCA on the training faces
doPCA();
// project the training images onto the PCA subspace
projectedTrainFaceMat = cvCreateMat( nTrainFaces, nEigens, CV_32FC1 );
offset = projectedTrainFaceMat->step / sizeof(float);
for(i=0; i<nTrainFaces; i++)
{
//int offset = i * nEigens;
cvEigenDecomposite(
faceImgArr[i],
nEigens,
eigenVectArr,
0, 0,
pAvgTrainImg,
//projectedTrainFaceMat->data.fl + i*nEigens);
projectedTrainFaceMat->data.fl + i*offset);
}
// store the recognition data as an xml file
storeTrainingData();
// Save all the eigenvectors as images, so that they can be checked.
if (SAVE_EIGENFACE_IMAGES) {
storeEigenfaceImages();
}
}
// Open the training data from the file 'facedata.xml'.
int loadTrainingData(CvMat ** pTrainPersonNumMat)
{
CvFileStorage * fileStorage;
int i;
// create a file-storage interface
fileStorage = cvOpenFileStorage( "facedata.xml", 0, CV_STORAGE_READ );
if( !fileStorage ) {
printf("Can't open training database file 'facedata.xml'.\n");
return 0;
}
// Load the person names. Added by Shervin.
personNames.clear(); // Make sure it starts as empty.
nPersons = cvReadIntByName( fileStorage, 0, "nPersons", 0 );
if (nPersons == 0) {
printf("No people found in the training database 'facedata.xml'.\n");
return 0;
}
// Load each person's name.
for (i=0; i<nPersons; i++) {
string sPersonName;
char varname[200];
snprintf( varname, sizeof(varname)-1, "personName_%d", (i+1) );
sPersonName = cvReadStringByName(fileStorage, 0, varname );
personNames.push_back( sPersonName );
}
// Load the data
nEigens = cvReadIntByName(fileStorage, 0, "nEigens", 0);
nTrainFaces = cvReadIntByName(fileStorage, 0, "nTrainFaces", 0);
*pTrainPersonNumMat = (CvMat *)cvReadByName(fileStorage, 0, "trainPersonNumMat", 0);
eigenValMat = (CvMat *)cvReadByName(fileStorage, 0, "eigenValMat", 0);
projectedTrainFaceMat = (CvMat *)cvReadByName(fileStorage, 0, "projectedTrainFaceMat", 0);
pAvgTrainImg = (IplImage *)cvReadByName(fileStorage, 0, "avgTrainImg", 0);
eigenVectArr = (IplImage **)cvAlloc(nTrainFaces*sizeof(IplImage *));
for(i=0; i<nEigens; i++)
{
char varname[200];
snprintf( varname, sizeof(varname)-1, "eigenVect_%d", i );
eigenVectArr[i] = (IplImage *)cvReadByName(fileStorage, 0, varname, 0);
}
// release the file-storage interface
cvReleaseFileStorage( &fileStorage );
printf("Training data loaded (%d training images of %d people):\n", nTrainFaces, nPersons);
printf("People: ");
if (nPersons > 0)
printf("<%s>", personNames[0].c_str());
for (i=1; i<nPersons; i++) {
printf(", <%s>", personNames[i].c_str());
}
printf(".\n");
return 1;
}
// Save the training data to the file 'facedata.xml'.
void storeTrainingData()
{
CvFileStorage * fileStorage;
int i;
// create a file-storage interface
fileStorage = cvOpenFileStorage( "facedata.xml", 0, CV_STORAGE_WRITE );
// Store the person names. Added by Shervin.
cvWriteInt( fileStorage, "nPersons", nPersons );
for (i=0; i<nPersons; i++) {
char varname[200];
snprintf( varname, sizeof(varname)-1, "personName_%d", (i+1) );
cvWriteString(fileStorage, varname, personNames[i].c_str(), 0);
}
// store all the data
cvWriteInt( fileStorage, "nEigens", nEigens );
cvWriteInt( fileStorage, "nTrainFaces", nTrainFaces );
cvWrite(fileStorage, "trainPersonNumMat", personNumTruthMat, cvAttrList(0,0));
cvWrite(fileStorage, "eigenValMat", eigenValMat, cvAttrList(0,0));
cvWrite(fileStorage, "projectedTrainFaceMat", projectedTrainFaceMat, cvAttrList(0,0));
cvWrite(fileStorage, "avgTrainImg", pAvgTrainImg, cvAttrList(0,0));
for(i=0; i<nEigens; i++)
{
char varname[200];
snprintf( varname, sizeof(varname)-1, "eigenVect_%d", i );
cvWrite(fileStorage, varname, eigenVectArr[i], cvAttrList(0,0));
}
// release the file-storage interface
cvReleaseFileStorage( &fileStorage );
}
// Find the most likely person based on a detection. Returns the index, and stores the confidence value into pConfidence.
int findNearestNeighbor(float * projectedTestFace, float *pConfidence)
{
//double leastDistSq = 1e12;
double leastDistSq = DBL_MAX;
int i, iTrain, iNearest = 0;
for(iTrain=0; iTrain<nTrainFaces; iTrain++)
{
double distSq=0;
for(i=0; i<nEigens; i++)
{
float d_i = projectedTestFace[i] - projectedTrainFaceMat->data.fl[iTrain*nEigens + i];
#ifdef USE_MAHALANOBIS_DISTANCE
distSq += d_i*d_i / eigenValMat->data.fl[i]; // Mahalanobis distance (might give better results than Eucalidean distance)
#else
distSq += d_i*d_i; // Euclidean distance.
#endif
}
if(distSq < leastDistSq)
{
leastDistSq = distSq;
iNearest = iTrain;
}
}
// Return the confidence level based on the Euclidean distance,
// so that similar images should give a confidence between 0.5 to 1.0,
// and very different images should give a confidence between 0.0 to 0.5.
*pConfidence = 1.0f - sqrt( leastDistSq / (float)(nTrainFaces * nEigens) ) / 255.0f;
// Return the found index.
return iNearest;
}
// Do the Principal Component Analysis, finding the average image
// and the eigenfaces that represent any image in the given dataset.
void doPCA()
{
int i;
CvTermCriteria calcLimit;
CvSize faceImgSize;
// set the number of eigenvalues to use
nEigens = nTrainFaces-1;
// allocate the eigenvector images
faceImgSize.width = faceImgArr[0]->width;
faceImgSize.height = faceImgArr[0]->height;
eigenVectArr = (IplImage**)cvAlloc(sizeof(IplImage*) * nEigens);
for(i=0; i<nEigens; i++)
eigenVectArr[i] = cvCreateImage(faceImgSize, IPL_DEPTH_32F, 1);
// allocate the eigenvalue array
eigenValMat = cvCreateMat( 1, nEigens, CV_32FC1 );
// allocate the averaged image
pAvgTrainImg = cvCreateImage(faceImgSize, IPL_DEPTH_32F, 1);
// set the PCA termination criterion
calcLimit = cvTermCriteria( CV_TERMCRIT_ITER, nEigens, 1);
// compute average image, eigenvalues, and eigenvectors
cvCalcEigenObjects(
nTrainFaces,
(void*)faceImgArr,
(void*)eigenVectArr,
CV_EIGOBJ_NO_CALLBACK,
0,
0,
&calcLimit,
pAvgTrainImg,
eigenValMat->data.fl);
cvNormalize(eigenValMat, eigenValMat, 1, 0, CV_L1, 0);
}
// Read the names & image filenames of people from a text file, and load all those images listed.
int loadFaceImgArray(const char * filename)
{
FILE * imgListFile = 0;
char imgFilename[512];
int iFace, nFaces=0;
int i;
// open the input file
if( !(imgListFile = fopen(filename, "r")) )
{
fprintf(stderr, "Can\'t open file %s\n", filename);
return 0;
}
// count the number of faces
while( fgets(imgFilename, sizeof(imgFilename)-1, imgListFile) ) ++nFaces;
rewind(imgListFile);
// allocate the face-image array and person number matrix
faceImgArr = (IplImage **)cvAlloc( nFaces*sizeof(IplImage *) );
personNumTruthMat = cvCreateMat( 1, nFaces, CV_32SC1 );
personNames.clear(); // Make sure it starts as empty.
nPersons = 0;
// store the face images in an array
for(iFace=0; iFace<nFaces; iFace++)
{
char personName[256];
string sPersonName;
int personNumber;
// read person number (beginning with 1), their name and the image filename.
fscanf(imgListFile, "%d %s %s", &personNumber, personName, imgFilename);
sPersonName = personName;
//printf("Got %d: %d, <%s>, <%s>.\n", iFace, personNumber, personName, imgFilename);
// Check if a new person is being loaded.
if (personNumber > nPersons) {
// Allocate memory for the extra person (or possibly multiple), using this new person's name.
for (i=nPersons; i < personNumber; i++) {
personNames.push_back( sPersonName );
}
nPersons = personNumber;
//printf("Got new person <%s> -> nPersons = %d [%d]\n", sPersonName.c_str(), nPersons, personNames.size());
}
// Keep the data
personNumTruthMat->data.i[iFace] = personNumber;
// load the face image
faceImgArr[iFace] = cvLoadImage(imgFilename, CV_LOAD_IMAGE_GRAYSCALE);
if( !faceImgArr[iFace] )
{
fprintf(stderr, "Can\'t load image from %s\n", imgFilename);
return 0;
}
}
fclose(imgListFile);
printf("Data loaded from '%s': (%d images of %d people).\n", filename, nFaces, nPersons);
printf("People: ");
if (nPersons > 0)
printf("<%s>", personNames[0].c_str());
for (i=1; i<nPersons; i++) {
printf(", <%s>", personNames[i].c_str());
}
printf(".\n");
return nFaces;
}
// Recognize the face in each of the test images given, and compare the results with the truth.
void recognizeFileList(const char *szFileTest)
{
int i, nTestFaces = 0; // the number of test images
CvMat * trainPersonNumMat = 0; // the person numbers during training
float * projectedTestFace = 0;
const char *answer;
int nCorrect = 0;
int nWrong = 0;
double timeFaceRecognizeStart;
double tallyFaceRecognizeTime;
float confidence;
// load test images and ground truth for person number
nTestFaces = loadFaceImgArray(szFileTest);
printf("%d test faces loaded\n", nTestFaces);
// load the saved training data
if( !loadTrainingData( &trainPersonNumMat ) ) return;
// project the test images onto the PCA subspace
projectedTestFace = (float *)cvAlloc( nEigens*sizeof(float) );
timeFaceRecognizeStart = (double)cvGetTickCount(); // Record the timing.
for(i=0; i<nTestFaces; i++)
{
int iNearest, nearest, truth;
// project the test image onto the PCA subspace
cvEigenDecomposite(
faceImgArr[i],
nEigens,
eigenVectArr,
0, 0,
pAvgTrainImg,
projectedTestFace);
iNearest = findNearestNeighbor(projectedTestFace, &confidence);
truth = personNumTruthMat->data.i[i];
nearest = trainPersonNumMat->data.i[iNearest];
if (nearest == truth) {
answer = "Correct";
nCorrect++;
}
else {
answer = "WRONG!";
nWrong++;
}
printf("nearest = %d, Truth = %d (%s). Confidence = %f\n", nearest, truth, answer, confidence);
}
tallyFaceRecognizeTime = (double)cvGetTickCount() - timeFaceRecognizeStart;
if (nCorrect+nWrong > 0) {
printf("TOTAL ACCURACY: %d%% out of %d tests.\n", nCorrect * 100/(nCorrect+nWrong), (nCorrect+nWrong));
printf("TOTAL TIME: %.1fms average.\n", tallyFaceRecognizeTime/((double)cvGetTickFrequency() * 1000.0 * (nCorrect+nWrong) ) );
}
}
// Grab the next camera frame. Waits until the next frame is ready,
// and provides direct access to it, so do NOT modify the returned image or free it!
// Will automatically initialize the camera on the first frame.
IplImage* getCameraFrame(void)
{
IplImage *frame;
// If the camera hasn't been initialized, then open it.
if (!camera) {
printf("Acessing the camera ...\n");
camera = cvCaptureFromCAM( 0 );
if (!camera) {
printf("ERROR in getCameraFrame(): Couldn't access the camera.\n");
exit(1);
}
// Try to set the camera resolution
cvSetCaptureProperty( camera, CV_CAP_PROP_FRAME_WIDTH, 320 );
cvSetCaptureProperty( camera, CV_CAP_PROP_FRAME_HEIGHT, 240 );
// Wait a little, so that the camera can auto-adjust itself
#if defined WIN32 || defined _WIN32
Sleep(1000); // (in milliseconds)
#endif
frame = cvQueryFrame( camera ); // get the first frame, to make sure the camera is initialized.
if (frame) {
printf("Got a camera using a resolution of %dx%d.\n", (int)cvGetCaptureProperty( camera, CV_CAP_PROP_FRAME_WIDTH), (int)cvGetCaptureProperty( camera, CV_CAP_PROP_FRAME_HEIGHT) );
}
}
frame = cvQueryFrame( camera );
if (!frame) {
fprintf(stderr, "ERROR in recognizeFromCam(): Could not access the camera or video file.\n");
exit(1);
//return NULL;
}
return frame;
}
// Return a new image that is always greyscale, whether the input image was RGB or Greyscale.
// Remember to free the returned image using cvReleaseImage() when finished.
IplImage* convertImageToGreyscale(const IplImage *imageSrc)
{
IplImage *imageGrey;
// Either convert the image to greyscale, or make a copy of the existing greyscale image.
// This is to make sure that the user can always call cvReleaseImage() on the output, whether it was greyscale or not.
if (imageSrc->nChannels == 3) {
imageGrey = cvCreateImage( cvGetSize(imageSrc), IPL_DEPTH_8U, 1 );
cvCvtColor( imageSrc, imageGrey, CV_BGR2GRAY );
}
else {
imageGrey = cvCloneImage(imageSrc);
}
return imageGrey;
}
// Creates a new image copy that is of a desired size.
// Remember to free the new image later.
IplImage* resizeImage(const IplImage *origImg, int newWidth, int newHeight)
{
IplImage *outImg = 0;
int origWidth;
int origHeight;
if (origImg) {
origWidth = origImg->width;
origHeight = origImg->height;
}
if (newWidth <= 0 || newHeight <= 0 || origImg == 0 || origWidth <= 0 || origHeight <= 0) {
printf("ERROR in resizeImage: Bad desired image size of %dx%d\n.", newWidth, newHeight);
exit(1);
}
// Scale the image to the new dimensions, even if the aspect ratio will be changed.
outImg = cvCreateImage(cvSize(newWidth, newHeight), origImg->depth, origImg->nChannels);
if (newWidth > origImg->width && newHeight > origImg->height) {
// Make the image larger
cvResetImageROI((IplImage*)origImg);
cvResize(origImg, outImg, CV_INTER_LINEAR); // CV_INTER_CUBIC or CV_INTER_LINEAR is good for enlarging
}
else {
// Make the image smaller
cvResetImageROI((IplImage*)origImg);
cvResize(origImg, outImg, CV_INTER_AREA); // CV_INTER_AREA is good for shrinking / decimation, but bad at enlarging.
}
return outImg;
}
// Returns a new image that is a cropped version of the original image.
IplImage* cropImage(const IplImage *img, const CvRect region)
{
IplImage *imageTmp;
IplImage *imageRGB;
CvSize size;
size.height = img->height;
size.width = img->width;
if (img->depth != IPL_DEPTH_8U) {
printf("ERROR in cropImage: Unknown image depth of %d given in cropImage() instead of 8 bits per pixel.\n", img->depth);
exit(1);
}
// First create a new (color or greyscale) IPL Image and copy contents of img into it.
imageTmp = cvCreateImage(size, IPL_DEPTH_8U, img->nChannels);
cvCopy(img, imageTmp, NULL);
// Create a new image of the detected region
// Set region of interest to that surrounding the face
cvSetImageROI(imageTmp, region);
// Copy region of interest (i.e. face) into a new iplImage (imageRGB) and return it
size.width = region.width;
size.height = region.height;
imageRGB = cvCreateImage(size, IPL_DEPTH_8U, img->nChannels);
cvCopy(imageTmp, imageRGB, NULL); // Copy just the region.
cvReleaseImage( &imageTmp );
return imageRGB;
}
// Get an 8-bit equivalent of the 32-bit Float image.
// Returns a new image, so remember to call 'cvReleaseImage()' on the result.
IplImage* convertFloatImageToUcharImage(const IplImage *srcImg)
{
IplImage *dstImg = 0;
if ((srcImg) && (srcImg->width > 0 && srcImg->height > 0)) {
// Spread the 32bit floating point pixels to fit within 8bit pixel range.
double minVal, maxVal;
cvMinMaxLoc(srcImg, &minVal, &maxVal);
//cout << "FloatImage:(minV=" << minVal << ", maxV=" << maxVal << ")." << endl;
// Deal with NaN and extreme values, since the DFT seems to give some NaN results.
if (cvIsNaN(minVal) || minVal < -1e30)
minVal = -1e30;
if (cvIsNaN(maxVal) || maxVal > 1e30)
maxVal = 1e30;
if (maxVal-minVal == 0.0f)
maxVal = minVal + 0.001; // remove potential divide by zero errors.
// Convert the format
dstImg = cvCreateImage(cvSize(srcImg->width, srcImg->height), 8, 1);
cvConvertScale(srcImg, dstImg, 255.0 / (maxVal - minVal), - minVal * 255.0 / (maxVal-minVal));
}
return dstImg;
}
// Store a greyscale floating-point CvMat image into a BMP/JPG/GIF/PNG image,
// since cvSaveImage() can only handle 8bit images (not 32bit float images).
void saveFloatImage(const char *filename, const IplImage *srcImg)
{
//cout << "Saving Float Image '" << filename << "' (" << srcImg->width << "," << srcImg->height << "). " << endl;
IplImage *byteImg = convertFloatImageToUcharImage(srcImg);
cvSaveImage(filename, byteImg);
cvReleaseImage(&byteImg);
}
// Perform face detection on the input image, using the given Haar cascade classifier.
// Returns a rectangle for the detected region in the given image.
CvRect detectFaceInImage(const IplImage *inputImg, const CvHaarClassifierCascade* cascade )
{
const CvSize minFeatureSize = cvSize(20, 20);
const int flags = CV_HAAR_FIND_BIGGEST_OBJECT | CV_HAAR_DO_ROUGH_SEARCH; // Only search for 1 face.
const float search_scale_factor = 1.1f;
IplImage *detectImg;
IplImage *greyImg = 0;
CvMemStorage* storage;
CvRect rc;
double t;
CvSeq* rects;
int i;
storage = cvCreateMemStorage(0);
cvClearMemStorage( storage );
// If the image is color, use a greyscale copy of the image.
detectImg = (IplImage*)inputImg; // Assume the input image is to be used.
if (inputImg->nChannels > 1)
{
greyImg = cvCreateImage(cvSize(inputImg->width, inputImg->height), IPL_DEPTH_8U, 1 );
cvCvtColor( inputImg, greyImg, CV_BGR2GRAY );
detectImg = greyImg; // Use the greyscale version as the input.
}
// Detect all the faces.
t = (double)cvGetTickCount();
rects = cvHaarDetectObjects( detectImg, (CvHaarClassifierCascade*)cascade, storage,
search_scale_factor, 3, flags, minFeatureSize );
t = (double)cvGetTickCount() - t;
printf("[Face Detection took %d ms and found %d objects]\n", cvRound( t/((double)cvGetTickFrequency()*1000.0) ), rects->total );
// Get the first detected face (the biggest).
if (rects->total > 0) {
rc = *(CvRect*)cvGetSeqElem( rects, 0 );
}
else
rc = cvRect(-1,-1,-1,-1); // Couldn't find the face.
//cvReleaseHaarClassifierCascade( &cascade );
//cvReleaseImage( &detectImg );
if (greyImg)
cvReleaseImage( &greyImg );
cvReleaseMemStorage( &storage );
return rc; // Return the biggest face found, or (-1,-1,-1,-1).
}
// Re-train the new face rec database without shutting down.
// Depending on the number of images in the training set and number of people, it might take 30 seconds or so.
CvMat* retrainOnline(void)
{
CvMat *trainPersonNumMat;
int i;
// Free & Re-initialize the global variables.
if (faceImgArr) {
for (i=0; i<nTrainFaces; i++) {
if (faceImgArr[i])
cvReleaseImage( &faceImgArr[i] );
}
}
cvFree( &faceImgArr ); // array of face images
cvFree( &personNumTruthMat ); // array of person numbers
personNames.clear(); // array of person names (indexed by the person number). Added by Shervin.
nPersons = 0; // the number of people in the training set. Added by Shervin.
nTrainFaces = 0; // the number of training images
nEigens = 0; // the number of eigenvalues
cvReleaseImage( &pAvgTrainImg ); // the average image
for (i=0; i<nTrainFaces; i++) {
if (eigenVectArr[i])
cvReleaseImage( &eigenVectArr[i] );
}
cvFree( &eigenVectArr ); // eigenvectors
cvFree( &eigenValMat ); // eigenvalues
cvFree( &projectedTrainFaceMat ); // projected training faces
// Retrain from the data in the files
printf("Retraining with the new person ...\n");
learn("train.txt");
printf("Done retraining.\n");
// Load the previously saved training data
if( !loadTrainingData( &trainPersonNumMat ) ) {
printf("ERROR in recognizeFromCam(): Couldn't load the training data!\n");
exit(1);
}
return trainPersonNumMat;
}
// Continuously recognize the person in the camera.
void recognizeFromCam(void)
{
int i;
CvMat * trainPersonNumMat; // the person numbers during training
float * projectedTestFace;
double timeFaceRecognizeStart;
double tallyFaceRecognizeTime;
CvHaarClassifierCascade* faceCascade;
char cstr[256];
BOOL saveNextFaces = FALSE;
char newPersonName[256];
int newPersonFaces;
trainPersonNumMat = 0; // the person numbers during training
projectedTestFace = 0;
saveNextFaces = FALSE;
newPersonFaces = 0;
printf("Recognizing person in the camera ...\n");
// Load the previously saved training data
if( loadTrainingData( &trainPersonNumMat ) ) {
faceWidth = pAvgTrainImg->width;
faceHeight = pAvgTrainImg->height;
}
else {
//printf("ERROR in recognizeFromCam(): Couldn't load the training data!\n");
//exit(1);
}
// Project the test images onto the PCA subspace
projectedTestFace = (float *)cvAlloc( nEigens*sizeof(float) );
// Create a GUI window for the user to see the camera image.
cvNamedWindow("Input", CV_WINDOW_AUTOSIZE);
// Make sure there is a "data" folder, for storing the new person.
#if defined WIN32 || defined _WIN32
mkdir("data");
#else
// For Linux, make the folder to be Read-Write-Executable for this user & group but only Readable for others.
mkdir("data", S_IRWXU | S_IRWXG | S_IROTH);
#endif
// Load the HaarCascade classifier for face detection.
faceCascade = (CvHaarClassifierCascade*)cvLoad(faceCascadeFilename, 0, 0, 0 );
if( !faceCascade ) {
printf("ERROR in recognizeFromCam(): Could not load Haar cascade Face detection classifier in '%s'.\n", faceCascadeFilename);
exit(1);
}
// Tell the Linux terminal to return the 1st keypress instead of waiting for an ENTER key.
changeKeyboardMode(1);
timeFaceRecognizeStart = (double)cvGetTickCount(); // Record the timing.
while (1)
{
int iNearest, nearest, truth;
IplImage *camImg;
IplImage *greyImg;
IplImage *faceImg;
IplImage *sizedImg;
IplImage *equalizedImg;
IplImage *processedFaceImg;
CvRect faceRect;
IplImage *shownImg;
int keyPressed = 0;
FILE *trainFile;
float confidence;
// Handle non-blocking keyboard input in the console.
if (kbhit())
keyPressed = getch();
if (keyPressed == VK_ESCAPE) { // Check if the user hit the 'Escape' key
break; // Stop processing input.
}
switch (keyPressed) {
case 'n': // Add a new person to the training set.
// Train from the following images.
printf("Enter your name: ");
strcpy(newPersonName, "newPerson");
// Read a string from the console. Waits until they hit ENTER.
changeKeyboardMode(0);
fgets(newPersonName, sizeof(newPersonName)-1, stdin);
changeKeyboardMode(1);
// Remove 1 or 2 newline characters if they were appended (eg: Linux).
i = strlen(newPersonName);
if (i > 0 && (newPersonName[i-1] == 10 || newPersonName[i-1] == 13)) {
newPersonName[i-1] = 0;
i--;
}
if (i > 0 && (newPersonName[i-1] == 10 || newPersonName[i-1] == 13)) {
newPersonName[i-1] = 0;
i--;
}
if (i > 0) {
printf("Collecting all images until you hit 't', to start Training the images as '%s' ...\n", newPersonName);
newPersonFaces = 0; // restart training a new person
saveNextFaces = TRUE;
}
else {
printf("Did not get a valid name from you, so will ignore it. Hit 'n' to retry.\n");
}
break;
case 't': // Start training
saveNextFaces = FALSE; // stop saving next faces.
// Store the saved data into the training file.
printf("Storing the training data for new person '%s'.\n", newPersonName);
// Append the new person to the end of the training data.
trainFile = fopen("train.txt", "a");
for (i=0; i<newPersonFaces; i++) {
snprintf(cstr, sizeof(cstr)-1, "data/%d_%s%d.pgm", nPersons+1, newPersonName, i+1);
fprintf(trainFile, "%d %s %s\n", nPersons+1, newPersonName, cstr);
}
fclose(trainFile);
// Now there is one more person in the database, ready for retraining.
//nPersons++;
//break;
//case 'r':
// Re-initialize the local data.
projectedTestFace = 0;
saveNextFaces = FALSE;
newPersonFaces = 0;
// Retrain from the new database without shutting down.
// Depending on the number of images in the training set and number of people, it might take 30 seconds or so.
cvFree( &trainPersonNumMat ); // Free the previous data before getting new data
trainPersonNumMat = retrainOnline();
// Project the test images onto the PCA subspace
cvFree(&projectedTestFace); // Free the previous data before getting new data
projectedTestFace = (float *)cvAlloc( nEigens*sizeof(float) );
printf("Recognizing person in the camera ...\n");
continue; // Begin with the next frame.
break;
}
// Get the camera frame
camImg = getCameraFrame();
if (!camImg) {
printf("ERROR in recognizeFromCam(): Bad input image!\n");
exit(1);
}
// Make sure the image is greyscale, since the Eigenfaces is only done on greyscale image.
greyImg = convertImageToGreyscale(camImg);
// Perform face detection on the input image, using the given Haar cascade classifier.
faceRect = detectFaceInImage(greyImg, faceCascade );
// Make sure a valid face was detected.
if (faceRect.width > 0) {
faceImg = cropImage(greyImg, faceRect); // Get the detected face image.
// Make sure the image is the same dimensions as the training images.
sizedImg = resizeImage(faceImg, faceWidth, faceHeight);
// Give the image a standard brightness and contrast, in case it was too dark or low contrast.
equalizedImg = cvCreateImage(cvGetSize(sizedImg), 8, 1); // Create an empty greyscale image
cvEqualizeHist(sizedImg, equalizedImg);
processedFaceImg = equalizedImg;
if (!processedFaceImg) {
printf("ERROR in recognizeFromCam(): Don't have input image!\n");
exit(1);
}
// If the face rec database has been loaded, then try to recognize the person currently detected.
if (nEigens > 0) {
// project the test image onto the PCA subspace
cvEigenDecomposite(
processedFaceImg,
nEigens,
eigenVectArr,
0, 0,
pAvgTrainImg,
projectedTestFace);
// Check which person it is most likely to be.
iNearest = findNearestNeighbor(projectedTestFace, &confidence);
nearest = trainPersonNumMat->data.i[iNearest];
printf("Most likely person in camera: '%s' (confidence=%f).\n", personNames[nearest-1].c_str(), confidence);
}//endif nEigens
// Possibly save the processed face to the training set.
if (saveNextFaces) {
// MAYBE GET IT TO ONLY TRAIN SOME IMAGES ?
// Use a different filename each time.
snprintf(cstr, sizeof(cstr)-1, "data/%d_%s%d.pgm", nPersons+1, newPersonName, newPersonFaces+1);
printf("Storing the current face of '%s' into image '%s'.\n", newPersonName, cstr);
cvSaveImage(cstr, processedFaceImg, NULL);
newPersonFaces++;
}
// Free the resources used for this frame.
cvReleaseImage( &greyImg );
cvReleaseImage( &faceImg );
cvReleaseImage( &sizedImg );
cvReleaseImage( &equalizedImg );
}
// Show the data on the screen.
shownImg = cvCloneImage(camImg);
if (faceRect.width > 0) { // Check if a face was detected.
// Show the detected face region.
cvRectangle(shownImg, cvPoint(faceRect.x, faceRect.y), cvPoint(faceRect.x + faceRect.width-1, faceRect.y + faceRect.height-1), CV_RGB(0,255,0), 1, 8, 0);
if (nEigens > 0) { // Check if the face recognition database is loaded and a person was recognized.
// Show the name of the recognized person, overlayed on the image below their face.
CvFont font;
cvInitFont(&font,CV_FONT_HERSHEY_PLAIN, 1.0, 1.0, 0,1,CV_AA);
CvScalar textColor = CV_RGB(0,255,255); // light blue text
char text[256];
snprintf(text, sizeof(text)-1, "Name: '%s'", personNames[nearest-1].c_str());
cvPutText(shownImg, text, cvPoint(faceRect.x, faceRect.y + faceRect.height + 15), &font, textColor);
snprintf(text, sizeof(text)-1, "Confidence: %f", confidence);
cvPutText(shownImg, text, cvPoint(faceRect.x, faceRect.y + faceRect.height + 30), &font, textColor);
}
}
// Display the image.
cvShowImage("Input", shownImg);
// Give some time for OpenCV to draw the GUI and check if the user has pressed something in the GUI window.
keyPressed = cvWaitKey(10);
if (keyPressed == VK_ESCAPE) { // Check if the user hit the 'Escape' key in the GUI window.
break; // Stop processing input.
}
cvReleaseImage( &shownImg );
}
tallyFaceRecognizeTime = (double)cvGetTickCount() - timeFaceRecognizeStart;
// Reset the Linux terminal back to the original settings.
changeKeyboardMode(0);
// Free the camera and memory resources used.
cvReleaseCapture( &camera );
cvReleaseHaarClassifierCascade( &faceCascade );
}