int main(int argc, char **argv)
{
if (argc != 2)
{
printf("use: simpleapp [file path]\n");
return 1;
}
CByteImage image;
// load image
if (!image.LoadFromFile(argv[1]))
{
printf("error: could not open input file\n");
return 1;
}
CByteImage gray_image(image.width, image.height, CByteImage::eGrayScale);
ImageProcessor::ConvertImage(&image, &gray_image);
ImageProcessor::CalculateGradientImageSobel(&gray_image, &gray_image);
gray_image.SaveToFile("output.bmp");
printf("output written to file 'output.bmp'\n");
return 0;
}
void OrbFeaturesFinder::find(const Mat &image, ImageFeatures &features)
{
Mat gray_image;
CV_Assert((image.type() == CV_8UC3) || (image.type() == CV_8UC4) || (image.type() == CV_8UC1));
if (image.type() == CV_8UC3) {
cvtColor(image, gray_image, CV_BGR2GRAY);
} else if (image.type() == CV_8UC4) {
cvtColor(image, gray_image, CV_BGRA2GRAY);
} else if (image.type() == CV_8UC1) {
gray_image=image;
} else {
CV_Error(CV_StsUnsupportedFormat, "");
}
if (grid_size.area() == 1)
(*orb)(gray_image, Mat(), features.keypoints, features.descriptors);
else
{
features.keypoints.clear();
features.descriptors.release();
std::vector<KeyPoint> points;
Mat descriptors;
for (int r = 0; r < grid_size.height; ++r)
for (int c = 0; c < grid_size.width; ++c)
{
int xl = c * gray_image.cols / grid_size.width;
int yl = r * gray_image.rows / grid_size.height;
int xr = (c+1) * gray_image.cols / grid_size.width;
int yr = (r+1) * gray_image.rows / grid_size.height;
// LOGLN("OrbFeaturesFinder::find: gray_image.empty=" << (gray_image.empty()?"true":"false") << ", "
// << " gray_image.size()=(" << gray_image.size().width << "x" << gray_image.size().height << "), "
// << " yl=" << yl << ", yr=" << yr << ", "
// << " xl=" << xl << ", xr=" << xr << ", gray_image.data=" << ((size_t)gray_image.data) << ", "
// << "gray_image.dims=" << gray_image.dims << "\n");
Mat gray_image_part=gray_image(Range(yl, yr), Range(xl, xr));
// LOGLN("OrbFeaturesFinder::find: gray_image_part.empty=" << (gray_image_part.empty()?"true":"false") << ", "
// << " gray_image_part.size()=(" << gray_image_part.size().width << "x" << gray_image_part.size().height << "), "
// << " gray_image_part.dims=" << gray_image_part.dims << ", "
// << " gray_image_part.data=" << ((size_t)gray_image_part.data) << "\n");
(*orb)(gray_image_part, Mat(), points, descriptors);
features.keypoints.reserve(features.keypoints.size() + points.size());
for (std::vector<KeyPoint>::iterator kp = points.begin(); kp != points.end(); ++kp)
{
kp->pt.x += xl;
kp->pt.y += yl;
features.keypoints.push_back(*kp);
}
features.descriptors.push_back(descriptors);
}
}
}
int FeaturesDetectionApplication::main_loop(program_options::variables_map &options)
{
printf("FeaturesDetectionApplication::main_loop says hello world !\n");
//init_gui(options);
//run_gui();
// initialization ---
gst_video_input_p.reset(new GstVideoInput(options));
features_detector_p.reset(new SimpleFAST(options));
// video output ---
rgb8_cimg_t current_image(gst_video_input_p->get_image_dimensions());
gst_video_input_p->get_new_image(current_image.view); // copy the data
CImgDisplay video_display(current_image.dimx(), current_image.dimy(), get_application_title().c_str());
video_display.show();
video_display.display(current_image);
// intermediary image --
gray8_image_t gray_image(current_image.view.dimensions());
// main loop ---
do
{
// get new image --
gst_video_input_p->get_new_image(current_image.view); // copy the data
// color to gray_image
copy_and_convert_pixels(current_image.view, boost::gil::view(gray_image));
// compute features
const vector<FASTFeature> &features =
features_detector_p->detect_features((const_view(gray_image)));
// plot features on output image
draw_features(features, current_image);
video_display.display(current_image);
// add a delay ---
wait_some_seconds(0.1); // [seconds]
}
while (video_display.is_closed == false);
return 0;
}
/*****************************************************
* Process->Function for canny edge detction(class LMS) *
******************************************************/
_lcanny_edge(Mat c_image)
{
cvtColor(c_image, gray_image, CV_RGB2GRAY); // converting extracted object to
// gray image
gray_image = gray_image(Rect(crop_values[0], crop_values[1], crop_values[2],
crop_values[3]));
// writting canny image and computing step size
Step = crop_values[4];
Canny(gray_image, canny_image, 180, 200);
imwrite("C:/AVC/canny_image.jpg",canny_image);
// reading rows and columns from gray image
i_rows = gray_image.rows;
i_cols = gray_image.cols;
return (canny_image);
}
int main()
{
vpImage<unsigned char> gray_image(240, 320);
vpImage<vpRGBa> color_image(240, 320);
gray_image = 128;
vpRGBa color(255, 0, 0);
color_image = color;
unsigned int igray_max = gray_image.getHeight() - 1;
unsigned int jgray_max = gray_image.getWidth() - 1;
std::cout << "Gray image, last pixel intensity: "
<< (int)gray_image[igray_max][jgray_max] << std::endl;
unsigned int icolor_max = color_image.getHeight() - 1;
unsigned int jcolor_max = color_image.getWidth() - 1;
std::cout << "Color image, last pixel RGB components: "
<< (int)color_image[icolor_max][jcolor_max].R << " "
<< (int)color_image[icolor_max][jcolor_max].G << " "
<< (int)color_image[icolor_max][jcolor_max].B
<< std::endl;
}
void Reader::Detect(Mat &image, Mat &ROI, Point &lpupil, Point &rpupil){
vector<Rect> faces; //vector to hold the rectangles in which there are faces
vector<Mat> RGBchannels; //vector to hold the black and white image
split(image, RGBchannels); //get the gray image
Mat gray_image = RGBchannels[2];
//find the faces in the image
face_cascade.detectMultiScale(gray_image, faces, 1.1, 2, 0 | CV_HAAR_SCALE_IMAGE | CV_HAAR_FIND_BIGGEST_OBJECT, Size(150, 150));
for(unsigned i = 0; i < faces.size(); ++i)
rectangle(image, faces[i], 1234);
//if no faces were found do nothing
if(faces.empty())
return;
Rect face = faces[0];
Mat faceROI = gray_image(face);
ROI = faceROI;
if(kSmoothFaceImage){
double sigma = kSmoothFaceFactor * face.width;
GaussianBlur(faceROI, faceROI, Size(255, 255), sigma);
}
int er_width = face.width * (kEyePercentWidth / 100.0);
int er_height = face.height * (kEyePercentHeight / 100.0);
int er_top = face.height * (kEyePercentTop / 100.0);
Rect lefter (face.width * (kEyePercentSide / 100.0), er_top, er_width, er_height);
Rect righter(face.width - er_width - face.width * (kEyePercentSide / 100.0), er_top, er_width, er_height);
Point left_pupil = FindPupil(faceROI, lefter);
Point right_pupil = FindPupil(faceROI, righter);
left_pupil.x += lefter.x;
left_pupil.y += lefter.y;
right_pupil.x += righter.x;
right_pupil.y += righter.y;
lpupil = left_pupil;
rpupil = right_pupil;
}
void MainWindow::addPerson()
{
QRect selectionRect = addFrameLabel->getSelectionRect();
QImage training_image = addFrameLabel->pixmap()->copy(selectionRect).toImage();
cv::Mat training_image_mat = Utilities::QImageToMat(training_image);
cv::Mat gray_image;
cv::cvtColor(training_image_mat, gray_image, CV_BGR2GRAY);
std::vector<cv::Rect> faces;
cv::CascadeClassifier cascade;
bool cascadeLoaded = Utilities::loadFaceCascade("../facerecognition/android/assets/haarcascade_frontalface_alt.xml", cascade);
if(cascadeLoaded)
{
cascade.detectMultiScale(gray_image, faces, 1.1, 5, 0|CV_HAAR_SCALE_IMAGE, cv::Size(30, 30));
if(faces.size() == 1)
{
cv::Mat face = gray_image(faces[0]);
cv::Mat face_resized;
cv::resize(face, face_resized, cv::Size(100, 100));
QSqlQuery query;
if(ui->nameLine->text().isEmpty())
{
QMessageBox::warning(this, "Name field empty", "The name field is empty. Please enter a name");
return;
}
int newId;
query.exec("SELECT MAX(personID) FROM people");
if(numberImages == 0)
{
if(query.next())
{
newId = query.value(0).toInt() + 1;
}
query.exec(QString("INSERT INTO people VALUES('%1', '%2')").arg(newId).arg(ui->nameLine->text()));
ui->nameLine->setReadOnly(true);
}
else
{
if(query.next())
{
newId = query.value(0).toInt();
}
}
newImages.push_back(face_resized);
newLabels.push_back(newId);
QByteArray data = Utilities::MatToByteArray(face_resized);
int imageId;
query.exec("SELECT MAX(imageID) FROM training_images");
if(query.next())
{
imageId = query.value(0).toInt() + 1;
}
query.prepare(QString("INSERT INTO training_images VALUES('%1', '%2', ?)").arg(imageId).arg(newId));
query.addBindValue(data);
query.exec();
numberImages++;
}
else if(faces.empty())
{
QMessageBox::information(this, "No face detected", "Make sure your face is visible");
}
else
{
QMessageBox::information(this, "Multiple faces", "More than one face detected");
}
}
}
void grab_cb(const ImgBase *img) {
BENCHMARK_THIS_FUNCTION;
int roi_percent = gui["roi_size"].as<int>();
canny.setThresholds(gui["canny_low_th"].as<int>(),gui["canny_high_th"].as<int>());
Img<T> color_original = *img->asImg<T>();
Size size = color_original.getSize();
size.width = std::floor(size.width*((float)roi_percent/100.0));
size.height = std::floor(size.height*((float)roi_percent)/100.0);
size.width = size.width%2+size.width;
size.height = size.height%2+size.height;
Point offset(color_original.getWidth()/2.0-size.width/2.0,color_original.getHeight()/2.0-size.height/2.0);
color_original.setROI(offset,size);
Img<T> color_median(color_original.getParams());
MedianOp median(utils::Size(gui["median_radius"].as<int>(),gui["median_radius"].as<int>()));
bool use_gray = gui["to_gray"].as<bool>();
Img<T> gray_image(color_original.getSize(),core::formatGray);
//color_original.setFormat(core::formatRGB);
Img8u edge_;
if (use_gray) {
core::cc(&color_original,&gray_image);
canny.apply(&gray_image,bpp(edge_));
} else {
canny.apply(&color_original,bpp(edge_));
}
{
BENCHMARK_THIS_SECTION(median_call);
if (use_gray)
median.apply(&gray_image,bpp(color_median));
else
median.apply(&color_original,bpp(color_median));
}
Img8u edge_median;//(color_median.getSize(),core::formatGray);
canny.apply(&color_median,bpp(edge_median));
bi_filter->setRadius(gui["bi_radius"].as<int>());
bi_filter->setSigmaR(gui["sigma_r"].as<float>());
bi_filter->setSigmaS(gui["sigma_s"].as<float>());
bi_filter->setUseLAB(gui["use_lab"].as<bool>());
Img<T> color_bilateral(color_median.getParams());
{
BENCHMARK_THIS_SECTION(bilateral_filter_call);
if (use_gray)
bi_filter->apply(&color_median,bpp(color_bilateral));
else
bi_filter->apply(&color_median,bpp(color_bilateral));
}
Img8u edge_bi_filtered;//(edge_bi_filtered.getSize(),core::formatGray);
canny.apply(&color_bilateral,bpp(edge_bi_filtered));
// set images
if (use_gray)
gui["view1"] = &gray_image;
else
gui["view1"] = &color_original;
gui["view2"] = &color_median;
gui["view3"] = &color_bilateral;
gui["viewedge1"] = &edge_;
gui["viewedge2"] = &edge_median;
gui["viewedge3"] = &edge_bi_filtered;
// update view:
gui["view1"].render();
gui["view2"].render();
gui["view3"].render();
gui["viewedge1"].render();
gui["viewedge2"].render();
gui["viewedge3"].render();
}
int testfaceLib_pThread ( const char* str_video, int trackerType, int multiviewType, int recognizerType, const char* str_facesetxml, int threads,
bool blink, bool smile, bool gender, bool age, bool recog, bool quiet, bool saveface, const char* sfolder, bool bEnableAutoCluster )
{
FILE* fp_imaginfo = fopen( "imaginfo.txt", "w" );
bool bAutoFocus = false;
IplImage *imgAutoFocus = NULL;
int sampleRate =1;
if(str_facesetxml == NULL)
str_facesetxml = "faceset_model.xml";
int prob_estimate[7];
char sState[256];
EnumViewAngle viewAngle = (EnumViewAngle)multiviewType;
//dynamic clustering for smooth ID registration
//bEnableAutoCluster = true;
CxlibFaceAnalyzer faceAnalyzer(viewAngle, (EnumTrackerType)trackerType, blink, smile, gender, age, recog, sampleRate, str_facesetxml, recognizerType, bEnableAutoCluster);
/////////////////////////////////////////////////////////////////////////////////////
// init GUI window
const char* str_title = "Face Tester";
if( ! quiet )
cvNamedWindow( str_title, CV_WINDOW_AUTOSIZE );
char sCaptionInfo[256] = "";
CvFont *pFont = new CvFont;
cvInitFont(pFont, CV_FONT_HERSHEY_PLAIN, 0.85, 0.85, 0, 1);
// load GUI smile icon images
IplImage *pImgSmileBGR;
IplImage *pImgSmileMask;
if(age == 0)
{ // smile icon
pImgSmileBGR = cvLoadImage( "smile.bmp" );
pImgSmileMask = cvLoadImage( "smilemask.bmp", 0 );
}
else
{ // gender/age/smile icons
pImgSmileBGR = cvLoadImage( "faceicon.bmp" );
pImgSmileMask = cvLoadImage( "faceiconMask.bmp", 0 );
}
IplImage *pImgSmileBGRA = cvCreateImage( cvSize(pImgSmileBGR->width, pImgSmileBGR->height), IPL_DEPTH_8U, 4 );
cvCvtColor(pImgSmileBGR, pImgSmileBGRA, CV_BGR2BGRA );
// open video source
size_t len = strlen( str_video );
bool is_piclist = (0 == stricmp( str_video + len - 4, ".txt" ));
CxImageSeqReader* vidcap = NULL;
if( is_piclist )
vidcap = new CxPicListReader( str_video );
else
vidcap = new CxVideoReader( str_video );
if( cvGetErrStatus() < 0 )
{
cvSetErrStatus( CV_StsOk );
return -1;
}
// when using camera, set to 640x480, 30fps
if( isdigit(str_video[0]) != 0 && str_video[1] == '\0' )
{
vidcap->width( 640 );
vidcap->height( 480 );
vidcap->fps( 30 );
}
// print beginning info
printf( "tracker cascade: '%s'\n", trackerType== TRA_HAAR ? "haar" : (recognizerType== TRA_SURF ? "surf" : "pf tracker SURF"));
printf( "face recognizer: '%s'\n", recognizerType == RECOGNIZER_BOOST_GB240 ? "boost gabor240" : "cascade gloh" );
printf( "video: '%s', %dx%d, %2.1f fps\n", str_video,
vidcap->width(), vidcap->height(), vidcap->fps() );
// set mouse event process
CxMouseParam mouse_faceparam;
mouse_faceparam.updated = false;
mouse_faceparam.play = true;
mouse_faceparam.ret_online_collecting = 0;
static const int MAX_FACES = 16;
if(! quiet)
{
mouse_faceparam.play = true;
mouse_faceparam.updated = false;
mouse_faceparam.face_num = faceAnalyzer.getMaxFaceNum();
mouse_faceparam.rects = faceAnalyzer.getFaceRects();
mouse_faceparam.image = NULL;
mouse_faceparam.cut_big_face= faceAnalyzer.getBigCutFace();
mouse_faceparam.typeRecognizer = 0;
mouse_faceparam.faceRecognizer = &faceAnalyzer;
mouse_faceparam.ret_online_collecting = 0;
cvSetMouseCallback( str_title, my_mouse_callback, (void*)&mouse_faceparam );
faceAnalyzer.setMouseParam(&mouse_faceparam);
}
// init count ticks
int64 ticks, start_ticks, total_ticks;
int64 tracker_total_ticks;
double tracker_fps, total_fps;
start_ticks = total_ticks = 0;
tracker_total_ticks = 0;
// loop for each frame of a video/camera
int frames = 0;
IplImage *pImg = NULL;
while( ! vidcap->eof() )
{
// capture a video frame
if( mouse_faceparam.play == true)
pImg = vidcap->query();
else
continue;
if ( pImg == NULL )
break;
// make a copy, flip if upside-down
CvImage image( cvGetSize(pImg), pImg->depth, pImg->nChannels );
if( pImg->origin == IPL_ORIGIN_BL ) //flip live camera's frame
cvFlip( pImg, image );
else
cvCopy( pImg, image );
// convert to gray_image for face analysis
CvImage gray_image( image.size(), image.depth(), 1 );
if( image.channels() == 3 )
cvCvtColor( image, gray_image, CV_BGR2GRAY );
else
cvCopy( image, gray_image );
///////////////////////////////////////////////////////////////////
// do face tracking and face recognition
start_ticks = ticks = cvGetTickCount();
if( is_piclist )
faceAnalyzer.detect(gray_image, prob_estimate, sState);
else
faceAnalyzer.track(gray_image, prob_estimate, sState, image); // track face in each frame but recognize by pthread
//faceAnalyzer.detect(gray_image, prob_estimate, sState);// track and recognizer face in each frame
int face_num = faceAnalyzer.getFaceNum();
ticks = cvGetTickCount() - ticks;
tracker_fps = 1000.0 / ( 1e-3 * ticks / cvGetTickFrequency() );
tracker_total_ticks += ticks;
//set param for mouse event processing
if(!quiet)
{
mouse_faceparam.face_num = face_num;
mouse_faceparam.image = image;
}
if( fp_imaginfo != NULL )
fprintf( fp_imaginfo, "%s %d", vidcap->filename(), face_num );
// blink/smile/gender/age/face recognize section
for( int i=0; i<face_num; i++ )
{
// get face rect and id from face tracker
CvRectItem rectItem = faceAnalyzer.getFaceRect(i);
CvRect rect = rectItem.rc;
int face_trackid = rectItem.fid;
float probSmile = faceAnalyzer.getFaceSmileProb(i);
int bBlink = faceAnalyzer.getFaceBlink(i);
int bSmile = faceAnalyzer.getFaceSmile(i);
int bGender = faceAnalyzer.getFaceGender(i);
int nAgeID = faceAnalyzer.getFaceAge(i);
int nFaceID = faceAnalyzer.getFaceID(i);
float fFaceProb= faceAnalyzer.getFaceProb(i);
char *sFaceCaption = NULL;
char sFaceNameBuff[256];
char *sFaceName = faceAnalyzer.getFaceName(i);
if(sFaceName[0] != '\0')
{
sprintf(sFaceNameBuff, "%s %.2f", sFaceName, fFaceProb);
sFaceCaption = sFaceName;
sFaceCaption = sFaceNameBuff;
}
if( ! quiet )
{
CvPoint2D32f *landmark6 = NULL;
sprintf(sCaptionInfo, "FPS:%04d, %s", (int)tracker_fps, sState);
int trackid = -1; //face_trackid , don't display trackid if -1
cxlibDrawFaceBlob( image, pFont, trackid, rect, landmark6, probSmile,
bBlink, bSmile, bGender, nAgeID, sFaceCaption, NULL,
pImgSmileBGR, pImgSmileBGRA, pImgSmileMask);
}
if( fp_imaginfo != NULL )
fprintf( fp_imaginfo, " %d %d %d %d", rect.x, rect.y, rect.width, rect.height );
}
if( fp_imaginfo != NULL )
fprintf( fp_imaginfo, "\n" );
///////////////////////////////////////////////////////////////////
total_ticks += (cvGetTickCount() - start_ticks);
// frame face_num
frames++;
//auto focus faces
if(quiet == false && bAutoFocus)
{
if(imgAutoFocus)
cvCopy(image, imgAutoFocus);
else
imgAutoFocus = cvCloneImage(image);
CvRectItem *rects = faceAnalyzer.getFaceRects();
cxlibAutoFocusFaceImage(imgAutoFocus, image, rects, face_num);
}
// next frame if quiet
if( quiet )
continue;
else
{
// draw status info for custom interaction
if(mouse_faceparam.ret_online_collecting == 1)
{
sprintf(sCaptionInfo, "Collecting faces for track_id = %d", mouse_faceparam.ret_facetrack_id);
//draw face collecting region
cvLine(image, cvPoint(image.width()/4, 0), cvPoint(image.width()/4, image.height()-1), CV_RGB(255,255,0), 2);
cvLine(image, cvPoint(image.width()*3/4, 0), cvPoint(image.width()*3/4, image.height()-1), CV_RGB(255,255,0), 2);
}
else
sprintf(sCaptionInfo, "FPS:%04d, %s", (int)tracker_fps, sState);
cxlibDrawCaption( image, pFont, sCaptionInfo);
}
//show Image
if (image.width() <= 800)
cvShowImage( str_title, image );
else
{ // display scaled smaller aimge
CvImage scale_image (cvSize(800, image.height()*800/image.width()), image.depth(), 3 );
cvResize (image, scale_image);
cvShowImage( str_title, scale_image );
}
// user interaction
int key = cvWaitKey(1);
//int key = cvWaitKey(0);
if( key == ' ' ) // press space bar to pause the video play
cvWaitKey( 0 );
else if( key == 27 ) // press 'esc' to exit
break;
else if( key == 'a' )
{ // add new face name
if(face_num > 0)
{
CvRect rect = faceAnalyzer.getFaceRect(0).rc;
int x = rect.x+rect.width/2;
int y = rect.y+rect.height/2;
addFaceSet( x, y, &mouse_faceparam);
}
}
else if( key == 'c' )
{ //enable flag to collect face exemplars for the selected face name
mouse_faceparam.ret_online_collecting = 1; //enable online face exemplar collecting
}
else if( key == 'z' )
bAutoFocus = !bAutoFocus;
else if(key >= 0)
{
if(mouse_faceparam.ret_online_collecting == 1)
{ // stop collecting face exemplars
mouse_faceparam.ret_online_collecting = 0; //disable online face exemplar collecting
mouse_faceparam.ret_facetrack_id = -1;
}
if( key == 's')
{
// save faceset xml model
faceAnalyzer.saveFaceModelXML("faceset_model.xml");
sprintf(sCaptionInfo, "%s", "saved the face model");
cxlibDrawCaption( pImg, pFont, sCaptionInfo);
cvShowImage( str_title, pImg );
cvWaitKey( 400 );
}
}
}
// print info about fps
float temp = 1e-6f / cvGetTickFrequency();
tracker_fps = 1.0f / ( tracker_total_ticks * temp / frames );
total_fps = 1.0f / (total_ticks * temp / frames);
printf( "Total frames:%d Speed:%.1f fps\n", frames, total_fps);
printf( "FPS: %.1f ", tracker_fps);
//save updated faceset model
if(mouse_faceparam.updated == true)
{
sprintf(sCaptionInfo, "%s", "press key 's' to save updated face model or other keys to cancel");
cxlibDrawCaption( pImg, pFont, sCaptionInfo);
cvShowImage( str_title, pImg );
int key = cvWaitKey();
if( key == 's')
faceAnalyzer.saveFaceModelXML("faceset_model.xml");
}
//save merged face model for dynamic clustering of smoothID
vFaceSet vMergedFaceSet;
int minWeight =10;
faceAnalyzer.getMergedFaceSet(vMergedFaceSet, minWeight);
faceAnalyzer.saveFaceModelXML("faceset_modelMerged.xml", &vMergedFaceSet);
//release global GUI data
if( !quiet )
cvDestroyWindow( str_title );
cvReleaseImage(&pImgSmileBGR);
cvReleaseImage(&pImgSmileBGRA);
cvReleaseImage(&pImgSmileMask);
delete pFont;
delete vidcap;
if( fp_imaginfo != NULL )
fclose( fp_imaginfo );
return 0;
}
int testfaceLib_sThread ( const char* str_video, int trackerType, int multiviewType, int recognizerType, const char* str_facesetxml, int threads,
bool blink, bool smile, bool gender, bool age, bool recog, bool quiet, bool saveface, const char* sfolder, bool bEnableAutoCluster)
{
int faceimgID = 0;
char driver[8];
char dir[1024];
char fname[1024];
char ext[8];
char sImgPath[1024];
if(sfolder)
{
char sysCommand[128];
sprintf (sysCommand, "mkdir %s", sfolder);
system (sysCommand);
sprintf(sImgPath, "%s//%s", sfolder, "imaginfo.txt");
sprintf(fname, "%s//%s", sfolder, "faceinfo.txt");
}
else
{
sprintf(sImgPath, "%s", "imaginfo.txt");
sprintf(fname, "%s", "faceinfo.txt");
}
FILE* fp_imaginfo = fopen( sImgPath, "wt" );
FILE* fp_faceinfo = fopen( fname, "wt" );
bool bAutoFocus = false;
IplImage *imgAutoFocus = NULL;
/////////////////////////////////////////////////////////////////////////////////////
// init GUI window
const char* str_title = "Face Tester";
if( ! quiet )
cvNamedWindow( str_title, CV_WINDOW_AUTOSIZE );
char sCaptionInfo[256]="";
CvFont *pFont = new CvFont;
cvInitFont(pFont, CV_FONT_HERSHEY_PLAIN, 0.85, 0.85, 0, 1);
// load GUI smile icon images
IplImage *pImgSmileBGR;
IplImage *pImgSmileMask;
if(age == 0)
{
pImgSmileBGR = cvLoadImage( "smile.bmp" );
pImgSmileMask = cvLoadImage( "smilemask.bmp", 0 );
}
else
{
pImgSmileBGR = cvLoadImage( "faceicon.bmp" );
pImgSmileMask = cvLoadImage( "faceiconMask.bmp", 0 );
}
IplImage *pImgSmileBGRA = cvCreateImage( cvSize(pImgSmileBGR->width, pImgSmileBGR->height), IPL_DEPTH_8U, 4 );
cvCvtColor(pImgSmileBGR, pImgSmileBGRA, CV_BGR2BGRA );
// open video source
size_t len = strlen( str_video );
bool is_piclist = (0 == stricmp( str_video + len - 4, ".txt" ));
CxImageSeqReader* vidcap = NULL;
if( is_piclist )
vidcap = new CxPicListReader( str_video );
else
vidcap = new CxVideoReader( str_video );
if( cvGetErrStatus() < 0 )
{
cvSetErrStatus( CV_StsOk );
return -1;
}
// when using camera, set to 640x480, 30fps
if( isdigit(str_video[0]) != 0 && str_video[1] == '\0' )
{
vidcap->width( 640 );
vidcap->height( 480 );
vidcap->fps( 30 );
}
// print beginning info
printf( "tracker cascade: '%s'\n", trackerType == TRA_HAAR ? "haar" : (trackerType== TRA_SURF ? "surf" : "pf tracker SURF"));
printf( "face recognizer: '%s'\n", recognizerType == RECOGNIZER_BOOST_GB240 ? "boost gabor240" : "cascade gloh" );
printf( "video: '%s', %dx%d, %2.1f fps\n", str_video,
vidcap->width(), vidcap->height(), vidcap->fps() );
// config face tracker
const int face_max = 16;
CvRectItem rects[face_max];
tagDetectConfig configParam;
EnumViewAngle viewAngle = (EnumViewAngle)multiviewType;
CxlibFaceDetector detector;
detector.init(viewAngle, (EnumFeaType)trackerType);
detector.config( configParam );
CxlibFaceTracker tracker;
tracker.init(viewAngle, (EnumTrackerType)trackerType);
tracker.config( configParam, TR_NLEVEL_3 );
if( cvGetErrStatus() < 0 )
{
cvSetErrStatus( CV_StsOk );
return -1;
}
// config landmark detector
CvPoint2D32f landmark6[6+1]; // consider both 6-pt and 7-pt
float parameters[16];
bool bLandmark = false;
CxlibLandmarkDetector landmarkDetector(LDM_6PT);
int size_smallface = 64;
int size_bigface = 128;
CxlibAlignFace cutFace(size_smallface, size_bigface);
// config blink/smile/gender detector
int bBlink = 0, bSmile = 0, bGender = 0, bAge = 0; //+1, -1, otherwise 0: no process
float probBlink = 0, probSmile = 0, probGender = 0, probAge[4];
int nAgeID = 0;
CxlibBlinkDetector blinkDetector(size_smallface);
CxlibSmileDetector smileDetector(size_smallface);
CxlibGenderDetector genderDetector(size_smallface);
CxlibAgeDetector ageDetector(size_bigface);
// config face recognizer
float probFaceID = 0;
if(str_facesetxml == NULL)
str_facesetxml = "faceset_model.xml";
CxlibFaceRecognizer faceRecognizer( size_bigface, recognizerType );
if(recog) faceRecognizer.loadFaceModelXML(str_facesetxml);
// set mouse event process
CxMouseParam mouse_faceparam;
mouse_faceparam.updated = false;
mouse_faceparam.play = true;
mouse_faceparam.ret_online_collecting = 0;
if(! quiet)
{
mouse_faceparam.face_num = face_max;
mouse_faceparam.rects = rects;
mouse_faceparam.image = NULL;
mouse_faceparam.cut_big_face= cutFace.getBigCutFace();
mouse_faceparam.typeRecognizer = 1;
mouse_faceparam.faceRecognizer = &faceRecognizer;
cvSetMouseCallback( str_title, my_mouse_callback, (void*)&mouse_faceparam );
}
// init count ticks
int64 ticks, start_ticks, total_ticks;
int64 tracker_total_ticks, landmark_total_ticks, align_total_ticks,
blink_total_ticks, smile_total_ticks, gender_total_ticks, age_total_ticks, recg_total_ticks;
double frame_fps, tracker_fps, landmark_fps, align_fps, blink_fps, smile_fps, gender_fps, age_fps, recg_fps, total_fps;
start_ticks = total_ticks = 0;
tracker_total_ticks = landmark_total_ticks = align_total_ticks = 0;
blink_total_ticks = smile_total_ticks = gender_total_ticks = age_total_ticks = recg_total_ticks = 0;
tracker_fps = landmark_fps = align_fps = blink_fps = smile_fps = gender_fps = age_fps = recg_fps = total_fps = 0.0;
// loop for each frame of a video/camera
int frames = 0;
IplImage *pImg = NULL;
int print_faceid=-1;
float print_score = 0;
std::string print_facename;
bool bRunLandmark = blink || smile|| gender|| age|| recog || saveface;
IplImage *thumbnailImg = cvCreateImage(cvSize(THUMBNAIL_WIDTH, THUMBNAIL_HEIGHT), IPL_DEPTH_8U, 3);
//dynamic clustering for smooth ID registration
//bEnableAutoCluster = true;
if( is_piclist ) bEnableAutoCluster = false;
while( ! vidcap->eof() )
{
// capture a video frame
if( mouse_faceparam.play == true)
pImg = vidcap->query();
else
continue;
if ( pImg == NULL )
continue;
// make a copy, flip if upside-down
CvImage image( cvGetSize(pImg), pImg->depth, pImg->nChannels );
if( pImg->origin == IPL_ORIGIN_BL ) //flip live camera's frame
cvFlip( pImg, image );
else
cvCopy( pImg, image );
// convert to gray_image for face analysis
CvImage gray_image( image.size(), image.depth(), 1 );
if( image.channels() == 3 )
cvCvtColor( image, gray_image, CV_BGR2GRAY );
else
cvCopy( image, gray_image );
// do face tracking
start_ticks = ticks = cvGetTickCount();
int face_num = 0;
if( is_piclist )
face_num = detector.detect( gray_image, rects, face_max );
else
face_num = tracker.track( gray_image, rects, face_max, image ); // track in a video for faster speed
//face_num = tracker.detect( gray_image, rects, face_max ); // detect in an image
//set param for mouse event processing
if(!quiet)
{
mouse_faceparam.face_num = face_num;
mouse_faceparam.image = image;
}
ticks = cvGetTickCount() - ticks;
tracker_fps = 1000.0 / ( 1e-3 * ticks / cvGetTickFrequency() );
tracker_total_ticks += ticks;
if( fp_imaginfo != NULL )
fprintf( fp_imaginfo, "%s %d", vidcap->filename(), face_num );
// blink/smile/gender/age/face recognize section
for( int i=0; i<face_num; i++ )
//for( int i=0; i< MIN(1,face_num); i++ )
{
// get face rect and id from face tracker
CvRect rect = rects[i].rc;
if( fp_imaginfo != NULL )
fprintf( fp_imaginfo, " %d %d %d %d %f", rect.x, rect.y, rect.width, rect.height, rects[i].prob );
int face_trackid = rects[i].fid;
float like = rects[i].prob;
int angle= rects[i].angle;
// filter out outer faces
if( rect.x+rect.width > gray_image.width() || rect.x < 0 ) continue;
if( rect.y+rect.height > gray_image.height() || rect.y < 0 ) continue;
//tracker.getThumbnail(image, rect, thumbnailImg);
// detect landmark points
ticks = cvGetTickCount();
if(bRunLandmark)
{
if( is_piclist )
bLandmark = landmarkDetector.detect( gray_image, &rect, landmark6, parameters, angle ); //detect in an image
else
bLandmark = landmarkDetector.track( gray_image, &rect, landmark6, parameters, angle ); // track in a video for faster speed
ticks = cvGetTickCount() - ticks;
landmark_fps = 1000.0 / ( 1e-3 * ticks / cvGetTickFrequency() );
landmark_total_ticks += ticks;
}
else
bLandmark = false;
if(quiet == false && bLandmark == false)
{
//DrawFaceRect
cxlibDrawFaceRect(image, rect);
continue;
}
// warped align face and hist eq to delighting
ticks = cvGetTickCount();
cutFace.init(gray_image, rect, landmark6);
ticks = cvGetTickCount() - ticks;
if(ticks > 1)
align_fps = 1000.0 / ( 1e-3 * ticks / cvGetTickFrequency() );
else
{ align_fps = 0;
ticks = 0;
}
align_total_ticks += ticks;
if(saveface) //save face icon for training later
{
//save cutfaces
if(sfolder)
{
#ifdef WIN32
_splitpath(vidcap->filename(),driver,dir,fname,ext);
sprintf(sImgPath, "%s//%s%s", sfolder, fname,ext);
#else
sprintf(sImgPath, "%s//%06d.jpg", sfolder, faceimgID++);
#endif
}
else
sprintf(sImgPath, "%s#.jpg", vidcap->filename());
cvSaveImage(sImgPath, cutFace.getBigCutFace());
}
// detect blink
bBlink = 0;
probBlink = 0;
if(blink && bLandmark)
{
ticks = cvGetTickCount();
float blink_threshold = blinkDetector.getDefThreshold();//0.5;
int ret = blinkDetector.predict( &cutFace, &probBlink);
if(probBlink > blink_threshold )
bBlink = 1; //eye close
else
bBlink = -1; //eye open
ticks = cvGetTickCount() - ticks;
blink_fps = 1000.0/(1e-3*ticks/cvGetTickFrequency());
blink_total_ticks += ticks;
print_score = probBlink;
}
else blink_fps = 0;
// detect smile
bSmile = 0;
probSmile = 0;
if ( smile && bLandmark )
{
ticks = cvGetTickCount();
float smile_threshold = smileDetector.getDefThreshold(); //0.48;
int ret = smileDetector.predict(&cutFace, &probSmile);
if(probSmile > smile_threshold)
bSmile = 1; //smile
else
bSmile = -1; //not smile
ticks = cvGetTickCount() - ticks;
smile_fps = 1000.0 /( 1e-3 * ticks / cvGetTickFrequency() );
smile_total_ticks += ticks;
print_score = probSmile;
}
else smile_fps = 0;
//detect gender
bGender = 0;
probGender = 0;
if(gender && bLandmark)
{
ticks = cvGetTickCount();
float gender_threshold = genderDetector.getDefThreshold(); // 0.42;
int ret = genderDetector.predict(&cutFace, &probGender);
if(probGender > gender_threshold)
bGender = 1; //female
else
bGender = -1; //male
//bGender = -1:male, 1:female, 0: null
// smooth prediction result
if( ! is_piclist )
bGender = genderDetector.voteLabel(face_trackid, bGender);
ticks = cvGetTickCount() - ticks;
gender_fps = 1000.0/(1e-3*ticks/cvGetTickFrequency());
gender_total_ticks += ticks;
print_score = probGender;
}
else gender_fps = 0;
//detect age
nAgeID = -1;
if(age && bLandmark && rect.width*rect.height > 40*40)
{
ticks = cvGetTickCount();
//nAgeID = 0:"Baby", 1:"Kid", 2:"Adult", 3:"Senior"
nAgeID = ageDetector.predict(&cutFace, probAge);
// smooth prediction result
if( ! is_piclist )
nAgeID = ageDetector.voteLabel(face_trackid, nAgeID);
ticks = cvGetTickCount() - ticks;
age_fps = 1000.0/(1e-3*ticks/cvGetTickFrequency());
age_total_ticks += ticks;
print_score = probAge[nAgeID];
//if( ! quiet ) cxDrawAignFace2Image(image, pCutFace2);
}
else
{
age_fps = 0;
}
// recognize the face id
// we only do recognition every 5 frames,interval
char *sFaceCaption = NULL;
char sFaceCaptionBuff[256];
int face_id = 0;
probFaceID = 0;
if ( recog && bLandmark )
{
ticks = cvGetTickCount();
float face_threshold = faceRecognizer.getDefThreshold();
/////////////////////////////////////////////////////////////////////////////////////////
int face_id = -1;
if(bEnableAutoCluster & !is_piclist)
{
bool bAutocluster = true;
if(mouse_faceparam.ret_online_collecting) bAutocluster = false;
//face clustering
face_id = faceRecognizer.predict(&cutFace, &probFaceID, bAutocluster, face_trackid, frames);
}
else//face recognition
face_id = faceRecognizer.predict(&cutFace, &probFaceID);
/////////////////////////////////////////////////////////////////////////////////////////
ticks = cvGetTickCount() - ticks;
recg_fps = 1000.0f / ( 1e-3 * ticks / cvGetTickFrequency() );
recg_total_ticks += ticks;
// smooth prediction result
if( ! is_piclist && !bEnableAutoCluster)
{
if(probFaceID > face_threshold*1.0)
face_id = faceRecognizer.voteLabel(face_trackid, face_id);
else
face_id = faceRecognizer.voteLabel(face_trackid, -1);
}
else if(probFaceID <= face_threshold)
{
face_id =-1;
}
//set face name caption
if(face_id >= 0)
{
// recognized face name
const char* sFaceName = faceRecognizer.getFaceName(face_id);
sprintf(sFaceCaptionBuff, "%s %.2f", sFaceName, probFaceID);
//sprintf(sFaceCaptionBuff, "%s", sFaceName); //dispaly score
sFaceCaption = sFaceCaptionBuff;
print_score = probFaceID;
print_faceid = face_id;
}
else
{ // failed to recognize
//sprintf(sFaceCaptionBuff, "N\A %.2f", probFaceID);
//sFaceCaption = sFaceCaptionBuff;
}
// collect and save unknown face exemplars
if(probFaceID < face_threshold*0.9 || face_id != mouse_faceparam.ret_faceset_id )
{
if(mouse_faceparam.ret_online_collecting && (face_num ==1 || face_trackid == mouse_faceparam.ret_facetrack_id))
{
if( rect.x > gray_image.width()/4 && rect.x+rect.width < gray_image.width()*3/4 )
{
mouse_faceparam.updated = true;
int nFaceSetIdx = faceRecognizer.getFaceSetIdx(mouse_faceparam.ret_faceset_id);
bool bflag = faceRecognizer.tryInsertFace(cutFace.getBigCutFace(), nFaceSetIdx);
//printf("insert flag %d\n", bflag);
}
}
}
}
else recg_fps = 0;
if( ! quiet )
{
sprintf(sCaptionInfo, "FPS: %03d Fd:%04d Ld:%04d Fa:%04d Bl:%04d Sm:%04d Ge:%04d Ag:%03d Rc:%03d",
(int)frame_fps, (int)tracker_fps, (int)landmark_fps, (int)align_fps,
(int)blink_fps, (int)smile_fps, (int)gender_fps, (int)age_fps, (int)recg_fps);
//sprintf(sFaceCaptionBuff, "%.2f", print_score);
//sFaceCaption = sFaceCaptionBuff;
int trackid = -1; //face_trackid. don't display trackid if -1
cxlibDrawFaceBlob( image, pFont, trackid, rect, landmark6, probSmile,
bBlink, bSmile, bGender, nAgeID, sFaceCaption, NULL,
pImgSmileBGR, pImgSmileBGRA, pImgSmileMask);
}
// log file
if( fp_faceinfo != NULL )
{
// index, rect, landmark6, bBlink, probBlink, bSmile, probSmile, bGender, probGender, nAgeID, probAge[nAgeID], face_id, probFaceID
//fprintf( fp_faceinfo, "#%s# @%s@ ", vidcap->filename(), sImgPath);
fprintf( fp_faceinfo, "#%s# ", vidcap->filename());
fprintf( fp_faceinfo, "faceidx=( %06d %02d )", vidcap->index(), i+1 );
fprintf( fp_faceinfo, " rect=( %3d %3d %3d %3d )", rect.x, rect.y, rect.width, rect.height );
fprintf( fp_faceinfo, " landmark6=(" );
int l;
for( l = 0; l < 6; l++ )
fprintf( fp_faceinfo, " %3.0f %3.0f", landmark6[l].x, landmark6[l].y );
fprintf( fp_faceinfo, " )");
fprintf( fp_faceinfo, " blink=( %+d %f )", bBlink, probBlink );
fprintf( fp_faceinfo, " smile=( %+d %f )", bSmile, probSmile );
fprintf( fp_faceinfo, " gender=( %+d %f )", bGender, probGender );
fprintf( fp_faceinfo, " agegroup=( %+d %f )", nAgeID, (nAgeID >= 0 && nAgeID < 4) ? probAge[nAgeID] : 1.0f );
fprintf( fp_faceinfo, " identity=( %+d %f )", face_id, probFaceID );
fprintf( fp_faceinfo, "\n" );
}
}
if( fp_imaginfo != NULL )
fprintf( fp_imaginfo, "\n" );
ticks = cvGetTickCount() - start_ticks;
total_ticks += (ticks);
frame_fps = 1000.0f / ( 1e-3 * ticks / cvGetTickFrequency() );
// frame face_num
frames++;
//auto focus faces
if(quiet == false && bAutoFocus)
{
if(imgAutoFocus)
cvCopy(image, imgAutoFocus);
else
imgAutoFocus = cvCloneImage(image);
cxlibAutoFocusFaceImage(imgAutoFocus, image, rects, face_num);
}
// next frame if quiet
if( quiet )
continue;
else
{
// draw status info for custom interaction
if(mouse_faceparam.ret_online_collecting == 1)
{
sprintf(sCaptionInfo, "Collecting faces for track_id = %d", mouse_faceparam.ret_facetrack_id);
//draw face collecting region
cvLine(image, cvPoint(image.width()/4, 0), cvPoint(image.width()/4, image.height()-1), CV_RGB(255,255,0), 2);
cvLine(image, cvPoint(image.width()*3/4, 0), cvPoint(image.width()*3/4, image.height()-1), CV_RGB(255,255,0), 2);
}
else
sprintf(sCaptionInfo, "FPS: %03d Fd:%04d Ld:%04d Fa:%04d Bl:%04d Sm:%04d Ge:%04d Ag:%03d Rc:%03d",
(int)frame_fps, (int)tracker_fps, (int)landmark_fps, (int)align_fps,
(int)blink_fps, (int)smile_fps, (int)gender_fps, (int)age_fps, (int)recg_fps);
cxlibDrawCaption( image, pFont, sCaptionInfo);
}
//show Image
if (image.width() <= 800)
{
//show image
cvShowImage( str_title, image );
}
else
{ // show scaled smaller image
CvImage scale_image (cvSize(800, image.height()*800/image.width()), image.depth(), 3 );
cvResize (image, scale_image);
cvShowImage( str_title, scale_image );
}
// user interaction
int key = cvWaitKey( 30 );
//int key = cvWaitKey( );
if( key == ' ' ) // press the spacebar to pause the video play
cvWaitKey( 0 );
else if( key == 27 )
break; // press 'esc' to exit
else if( key == 'a' )
{ // add new face name
if(face_num > 0)
{
CvRect rect = rects[0].rc;
int x = rect.x+rect.width/2;
int y = rect.y+rect.height/2;
addFaceSet( x, y, &mouse_faceparam);
}
}
else if( key == 'c' )
{ // collect face exemplars for current selected facename
mouse_faceparam.ret_online_collecting = 1; //enable online face exemplar collecting
}
else if( key == 'z' )
// turn on/off the autofocus flag
bAutoFocus = !bAutoFocus;
else if(key >= 0)
{
if(mouse_faceparam.ret_online_collecting == 1)
{ // stop collecting faces
mouse_faceparam.ret_online_collecting = 0; //disable online face exemplar collecting
mouse_faceparam.ret_facetrack_id = -1;
}
if( key == 's')
{ // save face models
faceRecognizer.saveFaceModelXML("faceset_model.xml");
sprintf(sCaptionInfo, "%s", "saved the face model");
cxlibDrawCaption( pImg, pFont, sCaptionInfo);
cvShowImage( str_title, pImg );
cvWaitKey( 400 );
}
}
}
// print speed info about fps
float temp = 1e-6f / cvGetTickFrequency();
tracker_fps = 1.0f / ( tracker_total_ticks * temp / frames );
if (landmark_total_ticks != 0.0)
landmark_fps = 1.0f / ( landmark_total_ticks * temp / frames );
if (align_total_ticks != 0.0)
align_fps = 1.0f / ( align_total_ticks * temp / frames );
if (blink_total_ticks != 0.0)
blink_fps = 1.0f / (blink_total_ticks * temp / frames);
if (smile_total_ticks != 0.0)
smile_fps = 1.0f / (smile_total_ticks * temp / frames);
if (gender_total_ticks != 0.0)
gender_fps = 1.0f / (gender_total_ticks * temp / frames);
if (age_total_ticks != 0.0)
age_fps = 1.0f / (age_total_ticks * temp / frames);
if (recg_total_ticks != 0.0)
recg_fps = 1.0f / (recg_total_ticks * temp / frames);
total_fps = 1.0f / (total_ticks * temp / frames);
printf( "Total frames:%d Speed:%.1f fps\n", frames, total_fps);
printf( "FPS: Fd:%.1f Ld:%.1f Fa:%.1f Bl:%.1f Sm:%.1f Ge:%.1f Ag:%.1f Rc:%.1f",
tracker_fps, landmark_fps, align_fps,
blink_fps, smile_fps, gender_fps, age_fps, recg_fps);
//save updated face model
if(mouse_faceparam.updated == true)
{
sprintf(sCaptionInfo, "%s", "press key 's' to save updated face model or other keys to cancel");
cxlibDrawCaption( pImg, pFont, sCaptionInfo);
cvShowImage( str_title, pImg );
int key = cvWaitKey();
if( key == 's')
faceRecognizer.saveFaceModelXML("faceset_model.xml");
}
//save merged face model for dynamic clustering of smoothID
vFaceSet vMergedFaceSet;
int minWeight = 10;
faceRecognizer.getMergedFaceSet(vMergedFaceSet, minWeight);
faceRecognizer.saveFaceModelXML("faceset_modelMerged.xml", &vMergedFaceSet);
//faceRecognizer.saveFaceModelXML("faceset_modelMerged#.xml");
//release buff
//release global GUI data
if( !quiet )
cvDestroyWindow( str_title );
cvReleaseImage(&thumbnailImg);
cvReleaseImage(&pImgSmileBGR);
cvReleaseImage(&pImgSmileBGRA);
cvReleaseImage(&pImgSmileMask);
delete pFont;
delete vidcap;
if( fp_imaginfo != NULL )
fclose( fp_imaginfo );
if( fp_faceinfo != NULL )
fclose( fp_faceinfo );
return 0;
}
