int FaceDetect(const T *img, int width, int height, const char *classifier,
int *out, int maxdetect)
{
cv::Mat cvimg(width,height,CV_8UC1);
double maxval = *std::max_element(img,img+width*height);
double scale = (maxval==0)?1:255/maxval;
/* 8bitデータへの変換 */
for(int i=0; i<height; ++i) {
for(int j=0; j<width; ++j) {
*cvimg.ptr(i,j) = static_cast<unsigned char>(img[i*width+j] * scale);
}
}
std::vector<cv::Rect> faces;
// 顔検出器のロード
{
cv::CascadeClassifier cascade;
if(cascade.load( classifier )) {
// 顔検出の実行
cascade.detectMultiScale( cvimg, faces, 1.1, 2,
CV_HAAR_SCALE_IMAGE, cv::Size(30, 30) );
}
}
int n = std::min<int>(maxdetect,faces.size());
/* 顔位置データの引渡し (配列への変換) */
for(int i=0; i<n; ++i) {
out[4*i+0] = faces[i].x;
out[4*i+1] = faces[i].y;
out[4*i+2] = faces[i].width;
out[4*i+3] = faces[i].height;
}
return n;
}
openMVG::image::Image<openMVG::image::RGBColor> OpenCVHelper::createThumbnail(
const openMVG::image::Image<openMVG::image::RGBColor> &orig, int thWidth, int thHeight)
{
const int width = orig.Width();
const int height = orig.Height();
const float image_aspect = static_cast<float>(width) / static_cast<float>(height);
const float thumb_aspect = static_cast<float>(thWidth) / static_cast<float>(thHeight);
int rescale_width, rescale_height;
if(image_aspect > thumb_aspect)
{
rescale_width = std::ceil(thHeight * image_aspect);
rescale_height = thHeight;
}
else
{
rescale_width = thWidth;
rescale_height = std::ceil(thWidth / image_aspect);
}
// Convert image to OpenCV data
cv::Mat cvimg(height, width, CV_8UC3);
cv::Vec3b *cvPtr = cvimg.ptr<cv::Vec3b>(0);
for(int y = 0; y < height; y++)
{
for(int x = 0; x < width; x++)
{
(*cvPtr)[2] = orig(y, x).r(); // OpenCV usually stores BGR, not RGB
(*cvPtr)[1] = orig(y, x).g();
(*cvPtr)[0] = orig(y, x).b();
cvPtr++;
}
}
// Resize
cv::Mat cvoutimg;
cv::resize(cvimg, cvoutimg, cv::Size(thWidth, thHeight), 0, 0, cv::INTER_AREA);
// Convert back to openMVG::image
cvPtr = cvoutimg.ptr<cv::Vec3b>(0);
openMVG::image::Image<openMVG::image::RGBColor> outImg;
outImg.resize(thWidth, thHeight);
for(int y = 0; y < thHeight; y++)
{
for(int x = 0; x < thWidth; x++)
{
outImg(y, x).r() = (*cvPtr)[2]; // OpenCV usually stores BGR, not RGB
outImg(y, x).g() = (*cvPtr)[1];
outImg(y, x).b() = (*cvPtr)[0];
cvPtr++;
}
}
return outImg;
}
int GoodFeaturesToTrack(const T *img, int width, int height, int maxpos,
double *pos, double quality, double mindist, bool bHarris, bool bSubPix){
cv::Mat cvimg(width,height,CV_8UC1);
double maxval = *std::max_element(img,img+width*height);
double scale = (maxval==0)?1:255/maxval;
/* 8bitデータへの変換 */
for(int i=0;i<height;++i){
for(int j=0;j<width;++j){
*cvimg.ptr(i,j) = static_cast<unsigned char>(img[i*width+j] * scale);
}}
std::vector<cv::Point2f> cvpnts;
cv::goodFeaturesToTrack(cvimg,cvpnts,maxpos,quality,mindist,cv::Mat(),3,bHarris);
if(bSubPix){
cv::TermCriteria criteria(CV_TERMCRIT_EPS | CV_TERMCRIT_ITER, 20, 0.03);
cv::cornerSubPix(cvimg,cvpnts,cv::Size(10,10), cv::Size(-1,-1), criteria);
}
for(int i=0;i<cvpnts.size();++i){
pos[2*i ] = cvpnts[i].x;
pos[2*i+1] = cvpnts[i].y;
}
return cvpnts.size();
}
bool DominantColorSegmentation::getHSIColorHistogram(QImage *img)
{
//Create Mat images
int w = img->width(), h = img->height();
int bl = img->bytesPerLine();
uchar *fin_p = img->bits();
cv::Mat cvimg(h,w,CV_8UC4);
uchar *cvpointer = cvimg.data;
memcpy(cvpointer, fin_p, h*bl);
cv::Mat BGRmat(h,w,CV_8UC3);
uchar *BGRpointer = BGRmat.data;
int bplbgr = BGRmat.step;
cv::Mat HSVmat(h,w,CV_8UC3);
cv::cvtColor(cvimg, BGRmat, COLOR_BGRA2BGR);
cv::cvtColor(BGRmat, HSVmat, COLOR_BGR2HSV);
uchar *HSVpointer = HSVmat.data;
int blpHSV = HSVmat.step;
//Histograms
int H_Hist[255];
int S_Hist[255];
int I_Hist[255];
int Hpeak = 0;
int Speak = 0;
int Ipeak = 0;
int ihpeak;
int ispeak;
int iipeak;
for(int i=0; i<256; i++)
{
H_Hist[i] = 0;
S_Hist[i] = 0;
I_Hist[i] = 0;
}
//Haciendo los Histogramas
for (int i = 0; i < w; ++i) {
for (int j = 0; j < h; ++j)
{
int B = cvpointer[i*4 + j*bl + 0];
int G = cvpointer[i*4 + j*bl + 1];
int R = cvpointer[i*4 + j*bl + 2];
// int A = cvpointer[i*4 + j*bl + 3];
int H = HSVpointer[i*3 + j*blpHSV + 0];
int S = HSVpointer[i*3 + j*blpHSV + 1];
// int V = HSVpointer[i*3 + j*blpHSV + 2];
int I = (R+G+B)/3;
// std::cout << H << "\t" << S << "\t" << I << "\t";
// std::cout << R << "\t" << G << "\t" << B << "\n";
// cvpointer[i*4 + j*bl + 0] = H;
// cvpointer[i*4 + j*bl + 2] = H;
// cvpointer[i*4 + j*bl + 1] = H;
H_Hist[H]++;
S_Hist[S]++;
I_Hist[I]++;
//Obtain max peaks of each histograms
if(H_Hist[H] > Hpeak)
{
ihpeak = H;
Hpeak = H_Hist[H];
}
if(S_Hist[S] > Speak && S<255)
{
ispeak = S;
Speak = S_Hist[S];
}
if(I_Hist[H] > Ipeak)
{
iipeak = I;
Ipeak = I_Hist[I];
}
}
}
/**Finding Histogram mean**/
Hmean = find_histogram_mean(H_Hist, Hpeak, ihpeak);
Smean = find_histogram_mean(S_Hist, Speak, ispeak);
Imean = find_histogram_mean(I_Hist, Ipeak, iipeak);
cv::Mat fg2(h,w,CV_8UC1);
uchar *fg2bits = fg2.data;
int fg2bpl = fg2.step;
uchar *fgbits = m_data->fgImage->bits();
int fgbpl = m_data->fgImage->bytesPerLine();
for (int i = 0; i < w; ++i) {
for (int j = 0; j < h; ++j)
{
//MASK
if(j > (148-0.049*i) && j < (844-0.085*i))
{
int B = cvpointer[i*4 + j*bl + 0];
int G = cvpointer[i*4 + j*bl + 1];
int R = cvpointer[i*4 + j*bl + 2];
int H = HSVpointer[i*3 + j*blpHSV + 0];
int S = HSVpointer[i*3 + j*blpHSV + 1];
int I = (R+G+B)/3;
double cylindricaldist = get_cylindrical_dist(H,S,I);
if(cylindricaldist > Tcolor)
fg2bits[i + j*fg2bpl + 0] = 255;
else
fg2bits[i + j*fg2bpl + 0] = 0;
}
else
fg2bits[i + j*fg2bpl + 0] = 0;
}
}
Mat element = cv::getStructuringElement( cv::MORPH_ELLIPSE,
cv::Size( 3, 3 ),
cv::Point( 1, 1 ) );
//EROSION
cv::erode(fg2, fg2, element, cv::Point(-1,-1), 1);
cv::dilate(fg2, fg2, element, cv::Point(-1,-1), 1);
//DILATACION
cv::dilate(fg2, fg2, element, cv::Point(-1,-1), 1);
cv::erode(fg2, fg2, element, cv::Point(-1,-1), 1);
memcpy(fgbits, fg2bits, h*fgbpl);
/**EXTRA**/
// cv::Mat blurimg(h,w,CV_8UC4);
// blur( cvimg, blurimg, Size( 15, 15));
// uchar *blurpointer = blurimg.data;
// for (int i = 0; i < w; ++i) {
// for (int j = 0; j < h; ++j)
// {
// int B = blurpointer[i*4 + j*bl + 0];
// int G = blurpointer[i*4 + j*bl + 1];
// int R = blurpointer[i*4 + j*bl + 2];
// int I = (R+G+B)/3;
// if(I+j*upperpenalty<IntensityThreshold)
// {
// cvpointer[i*4 + j*bl + 0] = 0;
// cvpointer[i*4 + j*bl + 1] = 0;
// cvpointer[i*4 + j*bl + 2] = 0;
// }
// else
// {
// cvpointer[i*4 + j*bl + 0] = BGRpointer[i*3 + j*bplbgr + 0];
// cvpointer[i*4 + j*bl + 1] = BGRpointer[i*3 + j*bplbgr + 1];
// cvpointer[i*4 + j*bl + 2] = BGRpointer[i*3 + j*bplbgr + 2];
// }
// }
// }
// memcpy(fin_p, cvpointer, h*bl);
return true;
}
void BallClasificationModule::dominantcolor()
{
//Parametros
HistogramPeakThreshold = 0.2;
Tcolor = 120;
upperpenalty = 0.5;
IntensityThreshold = 70;
//CREANDO IMAGEN EN MAT
QImage *img = m_data->currentImage;
int w = img->width(), h = img->height();
int bl = img->bytesPerLine();
uchar *fin_p = img->bits();
cv::Mat cvimg(h,w,CV_8UC4);
uchar *cvpointer = cvimg.data;
memcpy(cvpointer, fin_p, h*bl);
//CAMBIANDO DIMENCIONES DE COLOR
cv::Mat BGRmat(h,w,CV_8UC3);
cv::Mat HSVmat(h,w,CV_8UC3);
cv::cvtColor(cvimg, BGRmat, COLOR_BGRA2BGR);
cv::cvtColor(BGRmat, HSVmat, COLOR_BGR2HSV);
uchar *HSVpointer = HSVmat.data;
int blpHSV = HSVmat.step;
//Histograms
int H_Hist[255];
int S_Hist[255];
int I_Hist[255];
int Hpeak = 0;
int Speak = 0;
int Ipeak = 0;
int ihpeak;
int ispeak;
int iipeak;
for(int i=0; i<256; i++)
{
H_Hist[i] = 0;
S_Hist[i] = 0;
I_Hist[i] = 0;
}
//Haciendo los Histogramas
for (int i = 0; i < w; ++i) {
for (int j = 0; j < h; ++j)
{
int B = cvpointer[i*4 + j*bl + 0];
int G = cvpointer[i*4 + j*bl + 1];
int R = cvpointer[i*4 + j*bl + 2];
// int A = cvpointer[i*4 + j*bl + 3];
int H = HSVpointer[i*3 + j*blpHSV + 0];
int S = HSVpointer[i*3 + j*blpHSV + 1];
// int V = HSVpointer[i*3 + j*blpHSV + 2];
int I = (R+G+B)/3;
// std::cout << H << "\t" << S << "\t" << I << "\t";
// std::cout << R << "\t" << G << "\t" << B << "\n";
// cvpointer[i*4 + j*bl + 0] = H;
// cvpointer[i*4 + j*bl + 2] = H;
// cvpointer[i*4 + j*bl + 1] = H;
H_Hist[H]++;
S_Hist[S]++;
I_Hist[I]++;
//Obtain max peaks of each histograms
if(H_Hist[H] > Hpeak)
{
ihpeak = H;
Hpeak = H_Hist[H];
}
if(S_Hist[S] > Speak && S<255)
{
ispeak = S;
Speak = S_Hist[S];
}
if(I_Hist[H] > Ipeak)
{
iipeak = I;
Ipeak = I_Hist[I];
}
// /**TEST HISTOGRAMS**/
// if(H>=48 && H<=53 )
// {
// cvpointer[i*4 + j*bl + 0] = 255;
// cvpointer[i*4 + j*bl + 1] = 0;
// cvpointer[i*4 + j*bl + 2] = 0;
// }
}
}
/**Finding Histogram mean**/
Hmean = find_histogram_mean(H_Hist, Hpeak, ihpeak);
Smean = find_histogram_mean(S_Hist, Speak, ispeak);
Imean = find_histogram_mean(I_Hist, Ipeak, iipeak);
/**Comprobando segmentacion**/
for (int i = 0; i < w; ++i) {
for (int j = 0; j < h; ++j)
{
int B = cvpointer[i*4 + j*bl + 0];
int G = cvpointer[i*4 + j*bl + 1];
int R = cvpointer[i*4 + j*bl + 2];
int H = HSVpointer[i*3 + j*blpHSV + 0];
int S = HSVpointer[i*3 + j*blpHSV + 1];
int I = (R+G+B)/3;
double cylindricaldist = get_cylindrical_dist(H,S,I);
if(cylindricaldist > Tcolor)
{
cvpointer[i*4 + j*bl + 0] = 255;
cvpointer[i*4 + j*bl + 1] = 0;
cvpointer[i*4 + j*bl + 2] = 0;
}
}
}
namedWindow("asdasd",WINDOW_NORMAL);
imshow("asdasd", cvimg);
return;
}