int FindFacesRect(const cv::Mat &source, std::vector<cv::Rect> &faces)
{
const std::string cascadeName = "d:/Install/Programming/opencv/data/haarcascades/haarcascade_frontalface_alt.xml";
const double scale = 2;
cv::CascadeClassifier cascade;
if( !cascade.load( cascadeName ) )
{
throw std::runtime_error("ERROR: Could not load classifier cascade");
}
cv::Mat gray;
cv::cvtColor( source, gray, CV_BGR2GRAY );
cv::Mat smallImg(cvRound (source.rows/scale), cvRound(source.cols/scale), CV_8UC1 );
cv::resize( gray, smallImg, smallImg.size(), 0, 0, cv::INTER_LINEAR );
cv::equalizeHist( smallImg, smallImg );
cascade.detectMultiScale( smallImg, faces,
1.1, 2, 0
//|CV_HAAR_FIND_BIGGEST_OBJECT
//|CV_HAAR_DO_ROUGH_SEARCH
|CV_HAAR_SCALE_IMAGE
,
cv::Size(30, 30) );
for( std::vector<cv::Rect>::iterator r = faces.begin(); r != faces.end(); r++)
{
r->x = cvRound(r->x * scale);
r->y = cvRound(r->y * scale);
r->height = cvRound(r->height * scale);
r->width = cvRound(r->width * scale);
}
}
int main (int argc, char **argv)
{
cv::Mat input,mask_img,not_masked;
//loading haar classifier
std::string cascadeName = "/usr/local/share/OpenCV/haarcascades/haarcascade_frontalface_alt.xml";
cv::CascadeClassifier cascade;
if(!cascade.load(cascadeName)){
printf("ERROR: cascadefile見つからん！\n");
return -1;
}
//loading resource file(for face)
input=cv::imread("/Users/naoto/git/opencv_gl/opencv/minematsu1.png",1);
if(input.empty()){
printf("ERROR: image not found!\n");
return 0;
}
double scale = 4.0;
cv::Mat gray, smallImg(cv::saturate_cast<int>(input.rows/scale),cv::saturate_cast<int>(input.cols/scale),CV_8UC1);
cv::cvtColor(input, gray, CV_BGR2GRAY);
cv::resize(gray, smallImg, smallImg.size(),0,0,cv::INTER_LINEAR);
cv::equalizeHist(smallImg, smallImg);//ヒストグラムビンの合計値が 255 になるようヒストグラムを正規化
std::vector<cv::Rect> faces;
cascade.detectMultiScale(smallImg, faces,1.1,2,CV_HAAR_SCALE_IMAGE,cv::Size(20,20));
int i;
printf("deteced faces:%d\n",(int)faces.size());
for (i=0; i<faces.size(); i++) {
cv::Point center,p1,p2;
int radius;
//saturate_castについては http://opencv.jp/opencv-2svn/cpp/operations_on_arrays.html
center.x = cv::saturate_cast<int>((faces[i].x + faces[i].width*0.5)*scale);//scaleはここで戻していることに注意！
center.y = cv::saturate_cast<int>((faces[i].y + faces[i].height*0.5)*scale);
radius = cv::saturate_cast<int>((faces[i].width + faces[i].height)*0.25*scale);
p1.x=center.x-radius;p1.y=center.y-radius;
p2.x=center.x+radius;p2.y=center.y+radius;
cv::Rect roi_rect(center.x-radius,center.y-radius,radius*2,radius*2);//左上のx座標,y座標,width,depthというふうに格納していく
mask_img.create(input.size(), CV_8UC1);
mask_img=cv::Scalar(0,0,0);
not_masked=mask_img(roi_rect);
not_masked=cv::Scalar(255,255,255);
}
cv::namedWindow("result",1);
cv::namedWindow("masked",1);
cv::imshow("result", input);
cv::imshow("masked", mask_img);
cv::waitKey(0);
return 0;
}
std::vector<double> faceToVecMat(cv::Mat img) {
std::vector<double> ret;
cv::Mat smallImg(96, 96, CV_8UC1);
cv::Size smallImgSize = smallImg.size();
cv::resize(img, smallImg, smallImgSize, 0, 0, cv::INTER_LINEAR);
// Generate 128 element face vector using DNN
cv::Mat blob = cv::dnn::blobFromImage(smallImg, 1.0 / 255, smallImgSize,
cv::Scalar(), true, false);
faceRecogNet.setInput(blob);
cv::Mat vec = faceRecogNet.forward();
// Return vector
for (int i = 0; i < vec.rows; ++i)
ret.insert(ret.end(), vec.ptr<float>(i), vec.ptr<float>(i) + vec.cols);
return ret;
}
void FaceDetect::detect(cv::Mat& img, fd_cb_t fd_cb) {
int i = 0;
double t = 0;
std::vector<cv::Rect> faces;
const static cv::Scalar colors[] = { CV_RGB(0,0,255),
CV_RGB(0,128,255),
CV_RGB(0,255,255),
CV_RGB(0,255,0),
CV_RGB(255,128,0),
CV_RGB(255,255,0),
CV_RGB(255,0,0),
CV_RGB(255,0,255)} ;
cv::Mat gray, smallImg( cvRound (img.rows/scale_), cvRound(img.cols/scale_), CV_8UC1 );
cv::cvtColor( img, gray, CV_BGR2GRAY );
cv::resize( gray, smallImg, smallImg.size(), 0, 0, cv::INTER_LINEAR );
equalizeHist( smallImg, smallImg );
t = (double)cvGetTickCount();
cascade_.detectMultiScale( smallImg, faces,
1.1, 2, 0
//|CV_HAAR_FIND_BIGGEST_OBJECT
//|CV_HAAR_DO_ROUGH_SEARCH
|CV_HAAR_SCALE_IMAGE
,
cv::Size(30, 30) );
fd_cb_t cb = fd_cb ? fd_cb : fd_cb_;
for( std::vector<cv::Rect>::const_iterator r = faces.begin(); r != faces.end(); r++, i++ )
{
cv::Mat smallImgROI;
cv::Point center;
cv::Scalar color = colors[i%8];
int radius;
center.x = cvRound((r->x + r->width*0.5)*scale_);
center.y = cvRound((r->y + r->height*0.5)*scale_);
radius = cvRound((r->width + r->height)*0.25*scale_);
if(cb) {
cb(center.x, center.y, radius);
}
}
}
int main (int argc, char **argv)
{
std::string cascadeName = "/usr/local/share/OpenCV/haarcascades/haarcascade_frontalface_alt.xml";
cv::CascadeClassifier cascade;
if(!cascade.load(cascadeName)){
printf("ERROR: cascadefile見つからん！\n");
return -1;
}
cv::Mat frame;
cv::VideoCapture cap;
cap.open(0);
cap >> frame;
cv::namedWindow("result",1);
cv::createTrackbar("size", "result", &size_of_mosaic, 30,0);
double scale = 4.0;
cv::Mat gray, smallImg(cv::saturate_cast<int>(frame.rows/scale),cv::saturate_cast<int>(frame.cols/scale),CV_8UC1);
for(;;){
cap >> frame;
cv::cvtColor(frame, gray, CV_BGR2GRAY);
cv::resize(gray, smallImg, smallImg.size(),0,0,cv::INTER_LINEAR);
cv::equalizeHist(smallImg, smallImg);
std::vector<cv::Rect> faces;
cascade.detectMultiScale(smallImg, faces,1.1,3,CV_HAAR_SCALE_IMAGE,cv::Size(20,20));
int i;
for (i=0; i<faces.size(); i++) {
#if FLAG //use built-in function
cv::Point center;
int radius;
center.x = cv::saturate_cast<int>((faces[i].x + faces[i].width*0.5)*scale);
center.y = cv::saturate_cast<int>((faces[i].y + faces[i].height*0.5)*scale);
radius = cv::saturate_cast<int>((faces[i].width + faces[i].height)*0.25*scale);
if(size_of_mosaic < 1)size_of_mosaic=1;
cv::Rect roi_rect(center.x-radius,center.y-radius,radius*2,radius*2);
cv::Mat mosaic = frame(roi_rect);
cv::Mat tmp = frame(roi_rect);
cv::resize(mosaic, tmp, cv::Size(radius/size_of_mosaic,radius/size_of_mosaic),0,0);
cv::resize(tmp, mosaic, cv::Size(radius*2,radius*2),0,0,CV_INTER_NN);
#else
//目元の辺りに線を引く
cv::Point center;
int radius;
double eye_ratio=0.2;//顔の中心から半径何割のところに線を引くか
center.x = cv::saturate_cast<int>((faces[i].x + faces[i].width*0.5)*scale);
center.y = cv::saturate_cast<int>((faces[i].y + faces[i].height*0.5)*scale);
radius = cv::saturate_cast<int>((faces[i].width + faces[i].height)*0.25*scale);
cv::line(frame,cv::Point(center.x-radius,center.y-radius*eye_ratio),cv::Point(center.x-radius+radius*2,center.y-radius*eye_ratio) ,cv::Scalar(0),80,8,0);
#endif
}
cv::imshow("result", frame);
int key = cv::waitKey(10);
if(key == 'q' || key == 'Q')
break;
}
return 0;
}
void MyFaceDetect::detectFace( IplImage * imgsrc, CascadeClassifier& cascade, CascadeClassifier& nestedCascade, double scale, bool tryflip )
{
Mat img(imgsrc,0);
int i = 0;
double t = 0;
vector<Rect> faces, faces2;
const static Scalar colors[] = { CV_RGB(0,0,255),
CV_RGB(0,128,255),
CV_RGB(0,255,255),
CV_RGB(0,255,0),
CV_RGB(255,128,0),
CV_RGB(255,255,0),
CV_RGB(255,0,0),
CV_RGB(255,0,255)} ;
Mat gray, smallImg( cvRound (img.rows/scale), cvRound(img.cols/scale), CV_8UC1 );
cvtColor( img, gray, CV_BGR2GRAY );
resize( gray, smallImg, smallImg.size(), 0, 0, INTER_LINEAR );
equalizeHist( smallImg, smallImg );
t = (double)cvGetTickCount();
cascade.detectMultiScale( smallImg, faces,
1.1, 2, 0
//|CV_HAAR_FIND_BIGGEST_OBJECT
//|CV_HAAR_DO_ROUGH_SEARCH
|CV_HAAR_SCALE_IMAGE
,
Size(30, 30) );
if( tryflip )
{
flip(smallImg, smallImg, 1);
cascade.detectMultiScale( smallImg, faces2,
1.1, 2, 0
//|CV_HAAR_FIND_BIGGEST_OBJECT
//|CV_HAAR_DO_ROUGH_SEARCH
|CV_HAAR_SCALE_IMAGE
,
Size(30, 30) );
for( vector<Rect>::const_iterator r = faces2.begin(); r != faces2.end(); r++ )
{
faces.push_back(Rect(smallImg.cols - r->x - r->width, r->y, r->width, r->height));
}
}
t = (double)cvGetTickCount() - t;
printf( "detection time = %g ms\n", t/((double)cvGetTickFrequency()*1000.) );
for( vector<Rect>::const_iterator r = faces.begin(); r != faces.end(); r++, i++ )
{
Mat smallImgROI;
vector<Rect> nestedObjects;
Point center;
Scalar color = colors[i%8];
int radius;
double aspect_ratio = (double)r->width/r->height;
if( 0.75 < aspect_ratio && aspect_ratio < 1.3 )
{
center.x = cvRound((r->x + r->width*0.5)*scale);
center.y = cvRound((r->y + r->height*0.5)*scale);
radius = cvRound((r->width + r->height)*0.25*scale);
circle( img, center, radius, color, 3, 8, 0 );
}
else
rectangle( img, cvPoint(cvRound(r->x*scale), cvRound(r->y*scale)),
cvPoint(cvRound((r->x + r->width-1)*scale), cvRound((r->y + r->height-1)*scale)),
color, 3, 8, 0);
if( nestedCascade.empty() )
continue;
smallImgROI = smallImg(*r);
nestedCascade.detectMultiScale( smallImgROI, nestedObjects,
1.1, 2, 0
//|CV_HAAR_FIND_BIGGEST_OBJECT
//|CV_HAAR_DO_ROUGH_SEARCH
//|CV_HAAR_DO_CANNY_PRUNING
|CV_HAAR_SCALE_IMAGE
,
Size(30, 30) );
for( vector<Rect>::const_iterator nr = nestedObjects.begin(); nr != nestedObjects.end(); nr++ )
{
center.x = cvRound((r->x + nr->x + nr->width*0.5)*scale);
center.y = cvRound((r->y + nr->y + nr->height*0.5)*scale);
radius = cvRound((nr->width + nr->height)*0.25*scale);
circle( img, center, radius, color, 3, 8, 0 );
}
}
cv::imshow( "result", img );
}
// usage: poisson.exe [source image (color)] [destination image (color] [mask image (gray)] [outout image (color] [offset y] [offset x]
int main(int argc, char** argv){
int i;
int offset[2];
IplImage *im_src = NULL, *im_dst = NULL, *im_mask = NULL;
if(argc != 7){
fprintf(stderr,"usage: poisson.exe [source image (color)] [destination image (color)] [mask image (gray)] [outout image (color)] [offset y] [offset x]\n");
exit(0);
}
//////loading source image(cv::Mat input) & creating mask(cv::Mat mask_img)
cv::Mat input,mask_img,not_masked;
//loading haar classifier
std::string cascadeName = "/usr/local/share/OpenCV/haarcascades/haarcascade_frontalface_alt.xml";
cv::CascadeClassifier cascade;
if(!cascade.load(cascadeName)){
printf("ERROR: cascadefile見つからん！\n");
return -1;
}
//loading resource file(for face)
input=cv::imread(argv[1],1);
if(input.empty()){
printf("ERROR: image not found!\n");
return 0;
}
double scale = 4.0;
cv::Mat gray, smallImg(cv::saturate_cast<int>(input.rows/scale),cv::saturate_cast<int>(input.cols/scale),CV_8UC1);
cv::cvtColor(input, gray, CV_BGR2GRAY);
cv::resize(gray, smallImg, smallImg.size(),0,0,cv::INTER_LINEAR);
cv::equalizeHist(smallImg, smallImg);//ヒストグラムビンの合計値が 255 になるようヒストグラムを正規化
std::vector<cv::Rect> faces;
cascade.detectMultiScale(smallImg, faces,1.1,2,CV_HAAR_SCALE_IMAGE,cv::Size(20,20));
int i;
printf("deteced faces:%d\n",(int)faces.size());
for (i=0; i<faces.size(); i++) {
cv::Point center;
int radius;
//saturate_castについては http://opencv.jp/opencv-2svn/cpp/operations_on_arrays.html
center.x = cv::saturate_cast<int>((faces[i].x + faces[i].width*0.5)*scale);//scaleはここで戻していることに注意！
center.y = cv::saturate_cast<int>((faces[i].y + faces[i].height*0.5)*scale);
radius = cv::saturate_cast<int>((faces[i].width + faces[i].height)*0.25*scale);
cv::Point p1,p2;
p1.x=center.x-radius;p1.y=center.y-radius;
p2.x=center.x+radius;p2.y=center.y+radius;
cv::Rect roi_rect(center.x-radius,center.y-radius,radius*2,radius*2);//左上のx座標,y座標,width,depthというふうに格納していく
mask_img.create(input.size(), CV_8UC1);
mask_img=cv::Scalar(0,0,0);
not_masked=mask_img(roi_rect);
not_masked=cv::Scalar(255,255,255);
}
// for debug(checking masks)
// cv::namedWindow("result",1);
// cv::namedWindow("masked",1);
// cv::imshow("result", input);
// cv::imshow("masked", mask_img);
// cv::waitKey(0);
//loading destination image() & find position and size
//resizing source image & calc offset
if( (im_src = cvLoadImage( argv[1], CV_LOAD_IMAGE_COLOR)) == 0 ){
fprintf(stderr,"No such file %s", argv[1]);
exit(0);
}
if( (im_dst = cvLoadImage( argv[2], CV_LOAD_IMAGE_COLOR)) == 0 ){
fprintf(stderr,"No such file %s", argv[2]);
exit(0);
}
if( (im_mask = cvLoadImage( argv[3], CV_LOAD_IMAGE_GRAYSCALE)) == 0 ){
fprintf(stderr,"No such file %s", argv[3]);
exit(0);
}
offset[0]=atoi(argv[5]);
offset[1]=atoi(argv[6]);
for(i=0;i<3;i++){// i:channnels
quasi_poisson_solver(im_src, im_dst, im_mask, i, offset);
//poisson_solver(im_src, im_dst, im_mask, i, offset);
}
cvSaveImage(argv[4],im_dst);
cvReleaseImage(&im_src);
cvReleaseImage(&im_dst);
cvReleaseImage(&im_mask);
return 0;
}
double CDetectionAlgs::BoostingDetection( vector<Rect>& objs,
const CascadeClassifier& cascade,
const Mat& img,
const Rect* confinedArea,
const double scale,
Size sSize,
Size bSize)
{
double res = (double)cvGetTickCount();
objs.clear();
Mat confinedImg;
if(confinedArea)
confinedImg = img(*confinedArea);
else
confinedImg = img;
Mat gray, smallImg( cvRound (confinedImg.rows/scale),
cvRound(confinedImg.cols/scale),
CV_8UC1 );
if(confinedImg.channels() == 3)
cvtColor( confinedImg, gray, CV_BGR2GRAY );
else
gray = confinedImg;
resize( gray, smallImg, smallImg.size(), 0, 0, INTER_LINEAR );
equalizeHist( smallImg, smallImg );
/////////////////detection/////////////////////////////////////////
//t = (double)cvGetTickCount();
const_cast<CascadeClassifier&>(cascade).detectMultiScale(
smallImg,
objs,
1.1,
2,
0,
//|CascadeClassifier::DO_CANNY_PRUNING
//|CascadeClassifier::FIND_BIGGEST_OBJECT
//|CascadeClassifier::DO_ROUGH_SEARCH
//|CascadeClassifier::SCALE_IMAGE,
sSize,
bSize);
///////////////////////sort///////////////////////////////////////
if (objs.size() > 0)
{
qsort( (void *)&(objs[0]), objs.size(), sizeof(Size), cvSizeCompare );
// re-position
if (confinedArea)
{
for (int i = 0; i < objs.size(); ++i)
{
objs[i].x = objs[i].x*scale+confinedArea->x;
objs[i].y = objs[i].y*scale+confinedArea->y;
}
}
else
{
for (int i = 0; i < objs.size(); ++i)
{
objs[i].x = objs[i].x*scale;
objs[i].y = objs[i].y*scale;
}
}
//scale back
for ( int i = 0; i < objs.size(); ++i)
{
objs[i].width *= scale;
objs[i].height *= scale;
}
}
res = ((double)cvGetTickCount() - res)
/ ((double)cvGetTickFrequency()*1000.);
return res;
}
int main(int argc, char *argv[])
{
// 1. load classifier
std::string cascadeName = "/usr/local/share/OpenCV/haarcascades/haarcascade_frontalface_alt.xml"; //Haar-like
cv::CascadeClassifier cascade;
if(!cascade.load(cascadeName)){
printf("ERROR: cascadeFile not found\n");
return -1;
}
// 2. initialize VideoCapture
cv::Mat frame;
cv::VideoCapture cap;
cap.open(0);
cap >> frame;
// 3. prepare window and trackbar
cv::namedWindow("result", 1);
cv::createTrackbar("size", "result", &size_of_mosaic, 30, 0);
double scale = 4.0;
cv::Mat gray, smallImg(cv::saturate_cast<int>(frame.rows/scale),
cv::saturate_cast<int>(frame.cols/scale), CV_8UC1);
for(;;){
// 4. capture frame
cap >> frame;
//convert to gray scale
cv::cvtColor( frame, gray, CV_BGR2GRAY );
// 5. scale-down the image
cv::resize(gray, smallImg, smallImg.size(), 0, 0, cv::INTER_LINEAR);
cv::equalizeHist(smallImg, smallImg);
// 6. detect face using Haar-classifier
std::vector<cv::Rect> faces;
///multi-scale face searching
// image, size, scale, num, flag, smallest rect
cascade.detectMultiScale(smallImg, faces,
1.1,
2,//この引数を大きくすると検出が早くなる
CV_HAAR_SCALE_IMAGE,
cv::Size(30,30));
// 7. mosaic(pixelate) face-region
//std::vector<cv::Rect>::const_iterator r = faces.begin();
//for(; r != faces.end(); ++r) {
int i;
for(i=0;i<faces.size();++i){
cv::Point center;
int radius;
center.x = cv::saturate_cast<int>((faces[i].x + faces[i].width*0.5)*scale);
center.y = cv::saturate_cast<int>((faces[i].y + faces[i].height*0.5)*scale);
radius = cv::saturate_cast<int>((faces[i].width + faces[i].height)*0.25*scale);
//mosaic
if(size_of_mosaic < 1) size_of_mosaic = 1;
cv::Rect roi_rect(center.x-radius,center.y-radius,radius*2,radius*2);
cv::Mat mosaic = frame(roi_rect);
cv::Mat tmp;
cv::resize(mosaic,tmp,cv::Size(radius / size_of_mosaic, radius / size_of_mosaic),0,0);
cv::resize(tmp,mosaic, cv::Size(radius*2, radius*2),0,0,CV_INTER_NN);
}
// 8. show mosaiced image to window
cv::imshow("result", frame );
int key = cv::waitKey(10);
if(key == 'q' || key == 'Q')
break;
}
return 0;
}
// usage: poisson.exe [source image (color)] [destination image (color] [mask image (gray)] [outout image (color] [offset y] [offset x]
int main(int argc, char** argv){
int offset[2];
IplImage *im_src =NULL, *im_dst = NULL, *im_mask = NULL;
cv::Mat input,dst_img,mask_img,not_masked,mask_img_gray;
if(argc != 4){
fprintf(stderr,"usage: poisson.exe [source image (color)] [destination image (color)] [outout image (color)] [offset y] [offset x]\n");
exit(0);
}
//////loading source image(cv::Mat input) & creating mask(cv::Mat mask_img)
//loading haar classifier
std::string cascadeName = "/usr/local/share/OpenCV/haarcascades/haarcascade_frontalface_alt.xml";
cv::CascadeClassifier cascade;
if(!cascade.load(cascadeName)){
printf("ERROR: cascadefile見つからん！\n");
return -1;
}
//////loading destination image() & find position and size
//loading destination file(for face)
dst_img=cv::imread(argv[2],1);
if(dst_img.empty()){
printf("ERROR:distination image not found!\n");
return 0;
}
//preprocessing
double scale = 4.0;
cv::Mat gray1, smallImg1(cv::saturate_cast<int>(dst_img.rows/scale),cv::saturate_cast<int>(dst_img.cols/scale),CV_8UC1);
cv::cvtColor(dst_img, gray1, CV_BGR2GRAY);
cv::resize(gray1, smallImg1, smallImg1.size(),0,0,cv::INTER_LINEAR);
cv::equalizeHist(smallImg1, smallImg1);//ヒストグラムビンの合計値が 255 になるようヒストグラムを正規化
std::vector<cv::Rect> faces,faces1,faces2;
cv::Point center2,center1;
int radius,radius1,radius2;
cv::Point p1,p2;
//find face size
cascade.detectMultiScale(smallImg1, faces1,1.1,2,CV_HAAR_SCALE_IMAGE,cv::Size(20,20));
printf("deteced faces:%d\n",(int)faces1.size());
for (int i=0; i<faces1.size(); i++) {
//saturate_castについては http://opencv.jp/opencv-2svn/cpp/operations_on_arrays.html
center1.x = cv::saturate_cast<int>((faces1[i].x + faces1[i].width*0.5)*scale);//scaleはここで戻していることに注意！
center1.y = cv::saturate_cast<int>((faces1[i].y + faces1[i].height*0.5)*scale);
radius1 = cv::saturate_cast<int>((faces1[i].width + faces1[i].height)*0.25*scale);
}
//loading resource file(for face)
input=cv::imread(argv[1],1);
if(input.empty()){
printf("ERROR:resource image not found!\n");
return 0;
}
//preprocessing
cv::Mat gray, smallImg(cv::saturate_cast<int>(input.rows/scale),cv::saturate_cast<int>(input.cols/scale),CV_8UC1);
cv::cvtColor(input, gray, CV_BGR2GRAY);
cv::resize(gray, smallImg, smallImg.size(),0,0,cv::INTER_LINEAR);
cv::equalizeHist(smallImg, smallImg);//ヒストグラムビンの合計値が 255 になるようヒストグラムを正規化
//find face size and generating mask
cascade.detectMultiScale(smallImg, faces2,1.1,2,CV_HAAR_SCALE_IMAGE,cv::Size(20,20));
printf("deteced faces:%d\n",(int)faces2.size());
for (int i=0; i<1; i++) {
//saturate_castについては http://opencv.jp/opencv-2svn/cpp/operations_on_arrays.html
center2.x = cv::saturate_cast<int>((faces2[i].x + faces2[i].width*0.5)*scale);//scaleはここで戻していることに注意！
center2.y = cv::saturate_cast<int>((faces2[i].y + faces2[i].height*0.5)*scale);
radius2 = cv::saturate_cast<int>((faces2[i].width + faces2[i].height)*0.25*scale);
p1.x=center2.x-radius;p1.y=center2.y-radius;
p2.x=center2.x+radius;p2.y=center2.y+radius;
cv::Rect roi_rect(center2.x-radius2,center2.y-radius2,radius2*2,radius2*2);//左上のx座標,y座標,width,depthというふうに格納していく
mask_img.create(input.size(), CV_8UC3);
mask_img=cv::Scalar(0,0,0);//真っ黒に
not_masked=mask_img(roi_rect);
not_masked=cv::Scalar(255,255,255);//真っ白に
cvtColor(mask_img,mask_img_gray,CV_RGB2GRAY);
input(roi_rect).copyTo(not_masked);
}
double ratio=(double)radius2/(double)radius1;//
int difx=center1.x*ratio-center2.x;
int dify=center1.y*ratio-center2.y;
printf("%f%d%d\n",ratio,difx,dify);
cv::Mat expanded_output(cv::saturate_cast<int>(dst_img.rows*ratio),cv::saturate_cast<int>(dst_img.cols*ratio),CV_8UC1);
cv::resize(dst_img,expanded_output,expanded_output.size(),0,0,cv::INTER_LINEAR);
//resizing source image & calc offset
// //for debug(checking masks)
// cv::namedWindow("input",1);
// cv::namedWindow("result",1);
// cv::namedWindow("masked",1);
// cv::imshow("input", input);
// cv::imshow("result", expanded_output);
// cv::imshow("masked", mask_img);
//ref:http://bicycle.life.coocan.jp/takamints/index.php/doc/opencv/doc/Mat_conversion//
//http://d.hatena.ne.jp/kamekamekame877/20110621
IplImage buf1=input;//特殊なコピーコンストラクタが呼ばれてるかららしい
IplImage buf2=mask_img_gray;//同じく
IplImage buf3=expanded_output;
im_src=&buf1;
im_mask=&buf2;
im_dst=&buf3;
offset[0]=dify;
offset[1]=difx;
for(int i=0;i<3;i++){// i:channnels
quasi_poisson_solver(im_src, im_dst, im_mask, i, offset);
//poisson_solver(im_src, im_dst, im_mask, i, offset);
}
cvSaveImage(argv[3],im_dst);
// cvReleaseImage(&im_src);
// cvReleaseImage(&im_dst);
// cvReleaseImage(&im_mask);
return 0;
}
void MatchGesture::detectAndDraw(Mat &img, CascadeClassifier &cascade, CascadeClassifier &nestedCascade, double scale)
{
//cout<<"A Detected"<<endl;
int i = 0;
double t = 0;
vector<Rect> faces;
const static Scalar colors[] = { CV_RGB(0,0,255),
CV_RGB(0,128,255),
CV_RGB(0,255,255),
CV_RGB(0,255,0),
CV_RGB(255,128,0),
CV_RGB(255,255,0),
CV_RGB(255,0,0),
CV_RGB(255,0,255)
};
Mat gray, smallImg( cvRound (img.rows/scale), cvRound(img.cols/scale), CV_8UC1 );
cvtColor( img, gray, CV_BGR2GRAY );
cv::resize( gray, smallImg, smallImg.size(), 0, 0, INTER_LINEAR );
equalizeHist( smallImg, smallImg );
t = (double)cvGetTickCount();
cascade.detectMultiScale( smallImg, faces,
1.1, 2, 0
//|CV_HAAR_FIND_BIGGEST_OBJECT
//|CV_HAAR_DO_ROUGH_SEARCH
|CV_HAAR_SCALE_IMAGE
,
Size(30, 30) );
t = (double)cvGetTickCount() - t;
// printf( "detection time = %g ms\n", t/((double)cvGetTickFrequency()*1000.) );
for( vector<Rect>::const_iterator r = faces.begin(); r != faces.end(); r++, i++ )
{
Mat smallImgROI;
vector<Rect> nestedObjects;
nestedObjects.clear();
Point center;
center.x = 0;
center.y = 0;
Scalar color = colors[i%8];
int radius;
center.x = cvRound((r->x + r->width*0.5)*scale);
center.y = cvRound((r->y + r->height*0.5)*scale);
radius = cvRound((r->width + r->height)*0.25*scale);
if(center.x != 0 || center.y != 0)
{
matchedCharacter = 'A';
int tempIndex = (int)matchedCharacter-48;
charactersOccurences[tempIndex]=charactersOccurences[tempIndex]+1;
if(maxTillNow<charactersOccurences[tempIndex])
{
maxTillNow = charactersOccurences[tempIndex];
maxIndexTillNow = tempIndex;
std::cout<<"Max Index Till now - "<<maxIndexTillNow<<std::endl;
}
//cout<<"A Detected"<<endl;
// system("espeak < speak.txt");
//emit sendForDisplay('A');
}
circle( img, center, radius, color, 3, 8, 0 );
if( nestedCascade.empty() )
continue;
std::cout<<"First circle command"<<std::endl;
smallImgROI = smallImg(*r);
nestedCascade.detectMultiScale( smallImgROI, nestedObjects,
1.1, 2, 0
//|CV_HAAR_FIND_BIGGEST_OBJECT
//|CV_HAAR_DO_ROUGH_SEARCH
//|CV_HAAR_DO_CANNY_PRUNING
|CV_HAAR_SCALE_IMAGE
,
Size(30, 30) );
for( vector<Rect>::const_iterator nr = nestedObjects.begin(); nr != nestedObjects.end(); nr++ )
{
center.x = cvRound((r->x + nr->x + nr->width*0.5)*scale);
center.y = cvRound((r->y + nr->y + nr->height*0.5)*scale);
radius = cvRound((nr->width + nr->height)*0.25*scale);
circle( img, center, radius, color, 3, 8, 0 );
std::cout<<"Enter circle drawing - "<<center.x<<"\t"<<center.y<<std::endl;
}
}
}
// Detect faces in a photo
std::vector<FaceRect> detectFaces(cv::String inputName, cv::String cascadeName, double scale, bool infer = false) {
cv::CascadeClassifier cascade;
if (!cascade.load(cascadeName)) {
std::cout << "error;Could not load classifier cascade. Filename: \"" << cascadeName << "\"" << std::endl;
}
if (inputName.empty()) {
std::cout << "error;You must specify the file to process." << std::endl;
}
cv::Mat img = cv::imread(inputName, 1);
if (img.empty()) {
std::cout << "error;Could not load the file to process. Filename: \"" << inputName << "\"" << std::endl;
}
std::vector<cv::Rect> faces;
cv::Size smallImgSize;
static bool disableDnn;
#ifdef HAS_OPENCV_DNN
disableDnn = faceDetectNet.empty();
#else
disableDnn = true;
#endif
if (disableDnn) {
// Classical face detection
cv::Mat gray;
cvtColor(img, gray, CV_BGR2GRAY);
cv::Mat smallImg(cvRound(img.rows / scale), cvRound(img.cols / scale), CV_8UC1);
smallImgSize = smallImg.size();
cv::resize(gray, smallImg, smallImgSize, 0, 0, cv::INTER_LINEAR);
cv::equalizeHist(smallImg, smallImg);
cascade.detectMultiScale(smallImg, faces, 1.1, 2, CV_HAAR_SCALE_IMAGE, cv::Size(30, 30));
} else {
#ifdef HAS_OPENCV_DNN
// DNN based face detection
faces = detectFacesMat(img);
smallImgSize = img.size(); // Not using the small image here
#endif
}
std::vector<FaceRect> scaled;
for (std::vector<cv::Rect>::const_iterator r = faces.begin(); r != faces.end(); r++) {
FaceRect i;
i.x = (float) r->x / smallImgSize.width;
i.y = (float) r->y / smallImgSize.height;
i.width = (float) r->width / smallImgSize.width;
i.height = (float) r->height / smallImgSize.height;
#ifdef HAS_OPENCV_DNN
if (infer && !faceRecogNet.empty()) {
// Get colour image for vector generation
cv::Mat colourImg;
cv::resize(img, colourImg, smallImgSize, 0, 0, cv::INTER_LINEAR);
i.vec = faceToVecMat(colourImg(*r)); // Run vector conversion on the face
} else {
i.vec.assign(128, 0);
}
#else
i.vec.assign(128, 0);
#endif
scaled.push_back(i);
}
return scaled;
}
int main(int argc, char *argv[])
{
// 1. load classifier
std::string cascadeeyeName = /*"Nariz.xml";*/"/usr/local/share/OpenCV/haarcascades/haarcascade_eye.xml";/*"frontalEyes35x16.xml";*//*"/usr/local/share/OpenCV/haarcascades/haarcascade_frontalface_alt.xml"; *///Haar-like
std::string cascademouthName = "/usr/local/share/OpenCV/haarcascades/haarcascade_mcs_mouth.xml";
cv::CascadeClassifier cascadeeye, cascademouth;
if(!cascadeeye.load(cascadeeyeName)){
printf("ERROR: cascadeeyeFile not found\n");
return -1;
}
if(!cascademouth.load(cascademouthName)){
printf("ERROR: cascademouthFile not found\n");
return -1;
}
cv::Mat doraemon = cv::imread(doraemon_file);
cv::Mat doraemon_resized;
// 2. initialize image
char *input_file;
// 1. prepare Mat objects for input-image and output-image
cv::Mat input;
if(argc == 2){
input_file = argv[1];
}
else{
input_file = preset_file;
}
// 2. read an image from the specified file
input = cv::imread(input_file,1);
if(input.empty()){
fprintf(stderr, "cannot open %s\n", input_file);
exit(0);
}
// 3. prepare window and trackbar
cv::namedWindow("result", 1);
//cv::namedWindow("mosaic", 1);
cv::createTrackbar("size", "result", &size_of_mosaic, 30, 0);
double scale = 4.0;
cv::Mat gray, smallImg(cv::saturate_cast<int>(input.rows/scale),
cv::saturate_cast<int>(input.cols/scale), CV_8UC1);
//convert to gray scale
cv::cvtColor( input, gray, CV_BGR2GRAY );
// 5. scale-down the image
cv::resize(gray, smallImg, smallImg.size(), 0, 0, cv::INTER_LINEAR);
cv::equalizeHist(smallImg, smallImg);
// 6. detect face using Haar-classifier
std::vector<cv::Rect> faces;
///multi-scale face searching
// image, size, scale, num, flag, smallest rect
cascadeeye.detectMultiScale(smallImg, faces,
1.1,
4,//この引数を大きくすると検出が早くなる
CV_HAAR_SCALE_IMAGE);/*,
cv::Size(30,30)*/
// 7. mosaic(pixelate) face-region
//std::vector<cv::Rect>::const_iterator r = faces.begin();
int i;
for(i=0;i<faces.size();++i){
cv::Point center;
int radius;
double radiusx, radiusy;
center.x = cv::saturate_cast<int>((faces[i].x + faces[i].width*0.5)*scale);
center.y = cv::saturate_cast<int>((faces[i].y + faces[i].height*0.5)*scale);
radius = cv::saturate_cast<int>((faces[i].width + faces[i].height)*0.25*scale);
//画像の縦横比で正規化
if(doraemon.size().width > doraemon.size().height) {
radiusx = (double)radius;
radiusy = (double)radius * doraemon.size().height / doraemon.size().width;
} else {
radiusy = (double)radius;
radiusx = (double)radius * doraemon.size().width / doraemon.size().height;
}
//rakutencardman
cv::Rect roi_rect(center.x-radiusx,center.y-radiusy, radiusx*2, radiusy*2);
cv::Mat mosaic = input(roi_rect);//顔の部分を切り出している
cv::resize(doraemon, doraemon_resized, mosaic.size());
mosaic = cv::Scalar(0,0,0);// doraemon_resized;
cv::add(doraemon_resized, mosaic, mosaic);
//cv::imshow("mosaic", mosaic);
}
printf("%d", (int)faces.size());
// 8. show mosaiced image to window
cv::imshow("result", input );
while(1){
int c = cv::waitKey();
// 7. process according to input
switch(c){
case 27://ESC
case 'q':
break;
case 10://ENTER
cv::imwrite("rakuten_cardman.jpg", input);
break;
}
break;
}
return 0;
}
Mat detect_Face_and_eyes( Mat& img, double scale, QVector <face> &find_faces)
{
vector<Rect> faces;
const static Scalar colors[] =
{
Scalar(255,0,0),
Scalar(255,128,0),
Scalar(255,255,0),
Scalar(0,255,0),
Scalar(0,128,255),
Scalar(0,255,255),
Scalar(0,0,255),
Scalar(255,0,255)
};
Mat gray, smallImg;
cvtColor( img, gray, COLOR_BGR2GRAY);
double fx = 1 / scale;
resize( gray, smallImg, Size(), fx, fx, INTER_LINEAR );
equalizeHist( smallImg, smallImg );
obj.cascade.detectMultiScale( smallImg, faces,
1.1, 2, 0
|CASCADE_SCALE_IMAGE,
Size(30, 30) );
for ( size_t i = 0; i < faces.size(); i++ )
{
Scalar color = colors[i%8];
int radius;
Rect r = faces[i];
Mat smallImgROI;
vector<Rect> nestedObjects;
Point center;
face temp;
find_faces.push_back(temp);
double aspect_ratio = (double)r.width/r.height;
if( 0.75 < aspect_ratio && aspect_ratio < 1.3 )
{
center.x = cvRound((r.x + r.width*0.5)*scale);
center.y = cvRound((r.y + r.height*0.5)*scale);
}
smallImgROI = smallImg( r );
obj.nestedCascade.detectMultiScale(smallImgROI, nestedObjects,
1.1, 2, 0
|CASCADE_SCALE_IMAGE,
Size(30, 30) );
find_faces.value(i).set_coord_face(center);
QVector <Point> write_eyes_array;
QVector <int> write_radius_eyes_array;
for ( size_t j = 0; j < nestedObjects.size(); j++ )
{
Rect nr = nestedObjects[j];
center.x = cvRound((r.x + nr.x + nr.width*0.5)*scale);
center.y = cvRound((r.y + nr.y + nr.height*0.5)*scale);
radius = cvRound((nr.width + nr.height)*0.25*scale);
if((radius>=20)&&((center.x>10)&&(center.x<img.size().width-10))&&((center.y>10)&&(center.x<img.size().height-10)))
{
write_radius_eyes_array.push_back(radius);
write_eyes_array.push_back(center);
circle(img, center, radius, color, 3, 8, 0 );
}
}
find_faces[i].set_coord_eyes(write_eyes_array);
find_faces[i].set_radius_eyes(write_radius_eyes_array);
}
return img;
}
