int main(void) {
printf("Hello World!\n");
cv::Mat depthMat(cv::Size(640,480), CV_16UC1);
cv::Mat depthf(cv::Size(640,480), CV_8UC1);
cv::Mat rgbMat(cv::Size(640,480), CV_8UC3, cv::Scalar(0));
std::list<cv::Mat> images;
Freenect::Freenect freenect;
CvKinect& device = freenect.createDevice<CvKinect>(0);
device.startVideo();
cv::namedWindow("rgb", CV_WINDOW_AUTOSIZE);
cv::namedWindow("depth", CV_WINDOW_AUTOSIZE);
while (true) {
device.getVideo(rgbMat);
// if (images.size() > 0) {
// cv::imshow("rgb", images.front());
// } else {
cv::imshow("rgb", rgbMat);
// }
char key = cv::waitKey(5);
if (key == 'x') {
break;
} else if (key == 's') {
images.push_back(rgbMat.clone());
}
}
device.stopVideo();
std::cout << "Images: " << images.size() << std::endl;
for (std::list<cv::Mat>::iterator iter = images.begin(); iter != images.end(); ++iter) {
cv::imshow("rgb", *iter);
cv::Mat greyMat(cv::Size(640, 480), CV_8UC1, cv::Scalar(0));
cv::cvtColor(*iter, greyMat, CV_RGB2GRAY);
std::vector<cv::Point2f> corners;
bool found = cv::findChessboardCorners(greyMat, cv::Size(7, 7), corners);
std::cout << "Corners found: " << corners.size() << std::endl;
}
cv::destroyWindow("depth");
cv::destroyWindow("rgb");
std::cout << "Exit" << std::endl;
return 0;
}
// Do not call directly even in child
void KinectInputDevice::VideoCallback(void* _rgb, uint32_t timestamp) {
//std::cout << "RGB callback" << std::endl;
uint8_t* rgb = static_cast<uint8_t*>(_rgb);
cv::Mat rgbMat(cv::Size(640,480),CV_8UC3, cv::Scalar(0));
rgbMat.data = rgb;
pthread_mutex_lock(&m_mutex);
m_rgb = rgbMat;
m_new_rgb = true;
pthread_mutex_unlock(&m_mutex);
};
int main(int argc, char **argv) {
bool die(false);
string filename("snapshot");
string suffix(".png");
int i_snap(0),iter(0);
Mat depthMat(Size(640,480),CV_16UC1);
Mat depthf (Size(640,480),CV_8UC1);
Mat rgbMat(Size(640,480),CV_8UC3,Scalar(0));
Mat ownMat(Size(640,480),CV_8UC3,Scalar(0));
Freenect::Freenect<MyFreenectDevice> freenect;
MyFreenectDevice& device = freenect.createDevice(0);
device.setTiltDegrees(10.0);
bool registered = false;
Mat blobMaskOutput = Mat::zeros(Size(640,480),CV_8UC1),outC;
Point midBlob;
int startX = 200, sizeX = 180, num_x_reps = 18, num_y_reps = 48;
double height_over_num_y_reps = 480/num_y_reps,
width_over_num_x_reps = sizeX/num_x_reps;
vector<double> _d(num_x_reps * num_y_reps); //the descriptor
Mat descriptorMat(_d);
// CvNormalBayesClassifier classifier; //doesnt work
CvKNearest classifier;
// CvSVM classifier; //doesnt work
// CvBoost classifier; //only good for 2 classes
// CvDTree classifier;
vector<vector<double> > training_data;
vector<int> label_data;
PCA pca;
Mat labelMat, dataMat;
vector<float> label_counts(4);
bool trained = false, loaded = false;
device.startVideo();
device.startDepth();
while (!die) {
device.getVideo(rgbMat);
device.getDepth(depthMat);
// cv::imshow("rgb", rgbMat);
depthMat.convertTo(depthf, CV_8UC1, 255.0/2048.0);
cv::imshow("depth",depthf);
//interpolation & inpainting
{
Mat _tmp,_tmp1; // = (depthMat - 400.0); //minimum observed value is ~440. so shift a bit
Mat(depthMat - 400.0).convertTo(_tmp1,CV_64FC1);
_tmp.setTo(Scalar(2048), depthMat > 750.0); //cut off at 600 to create a "box" where the user interacts
// _tmp.convertTo(depthf, CV_8UC1, 255.0/1648.0); //values are 0-2048 (11bit), account for -400 = 1648
//quadratic interpolation
// cv::pow(_tmp,2.0,_tmp1);
// _tmp1 = _tmp1 * 4.0;
// try {
// cv:log(_tmp,_tmp1);
// }
// catch (cv::Exception e) {
// cerr << e.what() << endl;
// exit(0);
// }
Point minLoc; double minval,maxval;
minMaxLoc(_tmp1, &minval, &maxval, NULL, NULL);
_tmp1.convertTo(depthf, CV_8UC1, 255.0/maxval);
Mat small_depthf; resize(depthf,small_depthf,Size(),0.2,0.2);
cv::inpaint(small_depthf,(small_depthf == 255),_tmp1,5.0,INPAINT_TELEA);
resize(_tmp1, _tmp, depthf.size());
_tmp.copyTo(depthf, (depthf == 255));
}
{
// Mat smallDepth = depthf; //cv::resize(depthf,smallDepth,Size(),0.5,0.5);
// Mat edges; //Laplacian(smallDepth, edges, -1, 7, 1.0);
// Sobel(smallDepth, edges, -1, 1, 1, 7);
//medianBlur(edges, edges, 11);
// for (int x=0; x < edges.cols; x+=20) {
// for (int y=0; y < edges.rows; y+=20) {
// //int nz = countNonZero(edges(Range(y,MIN(y+20,edges.rows-1)),Range(x,MIN(x+20,edges.cols-1))));
// Mat _i = edges(Range(y,MIN(y+20,edges.rows-1)),Range(x,MIN(x+20,edges.cols-1)));
// medianBlur(_i, _i, 7);
// //rectangle(edges, Point(x,y), Point(x+20,y+20), Scalar(nz), CV_FILLED);
// }
// }
// imshow("edges", edges);
}
cvtColor(depthf, outC, CV_GRAY2BGR);
Mat blobMaskInput = depthf < 120; //anything not white is "real" depth, TODO: inpainting invalid data
vector<Point> ctr,ctr2;
//closest point to the camera
Point minLoc; double minval,maxval;
minMaxLoc(depthf, &minval, &maxval, &minLoc, NULL, blobMaskInput);
circle(outC, minLoc, 5, Scalar(0,255,0), 3);
blobMaskInput = depthf < (minval + 18);
Scalar blb = refineSegments(Mat(),blobMaskInput,blobMaskOutput,ctr,ctr2,midBlob); //find contours in the foreground, choose biggest
// if (blobMaskOutput.data != NULL) {
// imshow("first", blobMaskOutput);
// }
/////// blb :
//blb[0] = x, blb[1] = y, blb[2] = 1st blob size, blb[3] = 2nd blob size.
if(blb[0]>=0 && blb[2] > 500) { //1st blob detected, and is big enough
//cvtColor(depthf, outC, CV_GRAY2BGR);
Scalar mn,stdv;
meanStdDev(depthf,mn,stdv,blobMaskInput);
//cout << "min: " << minval << ", max: " << maxval << ", mean: " << mn[0] << endl;
//now refining blob by looking at the mean depth value it has...
blobMaskInput = depthf < (mn[0] + stdv[0]);
//(very simple) bias with hand color
{
Mat hsv; cvtColor(rgbMat, hsv, CV_RGB2HSV);
Mat _col_p(hsv.size(),CV_32FC1);
int jump = 5;
for (int x=0; x < hsv.cols; x+=jump) {
for (int y=0; y < hsv.rows; y+=jump) {
Mat _i = hsv(Range(y,MIN(y+jump,hsv.rows-1)),Range(x,MIN(x+jump,hsv.cols-1)));
Scalar hsv_mean = mean(_i);
Vec2i u; u[0] = hsv_mean[0]; u[1] = hsv_mean[1];
Vec2i v; v[0] = 120; v[1] = 110;
rectangle(_col_p, Point(x,y), Point(x+jump,y+jump), Scalar(1.0-MIN(norm(u-v)/125.0,1.0)), CV_FILLED);
}
}
// hsv = hsv - Scalar(0,0,255);
Mat _t = (Mat_<double>(2,3) << 1, 0, 15, 0, 1, -20);
Mat col_p(_col_p.size(),CV_32FC1);
warpAffine(_col_p, col_p, _t, col_p.size());
GaussianBlur(col_p, col_p, Size(11.0,11.0), 2.5);
imshow("hand color",col_p);
// imshow("rgb",rgbMat);
Mat blobMaskInput_32FC1; blobMaskInput.convertTo(blobMaskInput_32FC1, CV_32FC1, 1.0/255.0);
blobMaskInput_32FC1 = blobMaskInput_32FC1.mul(col_p, 1.0);
blobMaskInput_32FC1.convertTo(blobMaskInput, CV_8UC1, 255.0);
blobMaskInput = blobMaskInput > 128;
imshow("blob bias", blobMaskInput);
}
blb = refineSegments(Mat(),blobMaskInput,blobMaskOutput,ctr,ctr2,midBlob);
imshow("blob", blobMaskOutput);
if(blb[0] >= 0 && blb[2] > 300) {
//draw contour
Scalar color(0,0,255);
for (int idx=0; idx<ctr.size()-1; idx++)
line(outC, ctr[idx], ctr[idx+1], color, 1);
line(outC, ctr[ctr.size()-1], ctr[0], color, 1);
if(ctr2.size() > 0) { //second blob detected
Scalar color2(255,0,255);
for (int idx=0; idx<ctr2.size()-1; idx++)
line(outC, ctr2[idx], ctr2[idx+1], color2, 2);
line(outC, ctr2[ctr2.size()-1], ctr2[0], color2, 2);
}
//blob center
circle(outC, Point(blb[0],blb[1]), 50, Scalar(255,0,0), 3);
{
Mat hsv; cvtColor(rgbMat, hsv, CV_RGB2HSV);
Scalar hsv_mean,hsv_stddev; meanStdDev(hsv, hsv_mean, hsv_stddev, blobMaskOutput);
stringstream ss; ss << hsv_mean[0] << "," << hsv_mean[1] << "," << hsv_mean[2];
putText(outC, ss.str(), Point(blb[0],blb[1]), CV_FONT_HERSHEY_PLAIN, 1.0, Scalar(0,255,255));
}
Mat blobDepth,blobEdge;
depthf.copyTo(blobDepth,blobMaskOutput);
Laplacian(blobDepth, blobEdge, 8);
// equalizeHist(blobEdge, blobEdge);//just for visualization
Mat logPolar(depthf.size(),CV_8UC1);
cvLogPolar(&((IplImage)blobEdge), &((IplImage)logPolar), Point2f(blb[0],blb[1]), 80.0);
// for (int i=0; i<num_x_reps+1; i++) {
// //verical lines
// line(logPolar, Point(startX+i*width_over_num_x_reps, 0), Point(startX+i*width_over_num_x_reps,479), Scalar(255), 1);
// }
// for(int i=0; i<num_y_reps+1; i++) {
// //horizontal
// line(logPolar, Point(startX, i*height_over_num_y_reps), Point(startX+sizeX,i*height_over_num_y_reps), Scalar(255), 1);
// }
double total = 0.0;
//histogram
for (int i=0; i<num_x_reps; i++) {
for(int j=0; j<num_y_reps; j++) {
Mat part = logPolar(
Range(j*height_over_num_y_reps,(j+1)*height_over_num_y_reps),
Range(startX+i*width_over_num_x_reps,startX+(i+1)*width_over_num_x_reps)
);
// int count = countNonZero(part); //TODO: use calcHist
// _d[i*num_x_reps + j] = count;
Scalar mn = mean(part);
// part.setTo(Scalar(mn[0])); //for debug: show the value in the image
_d[i*num_x_reps + j] = mn[0];
total += mn[0];
}
}
descriptorMat = descriptorMat / total;
/*
Mat images[1] = {logPolar(Range(0,30),Range(0,30))};
int nimages = 1;
int channels[1] = {0};
int dims = 1;
float range_0[]={0,256};
float* ranges[] = { range_0 };
int histSize[1] = { 5 };
calcHist(, <#int nimages#>, <#const int *channels#>, <#const Mat mask#>, <#MatND hist#>, <#int dims#>, <#const int *histSize#>, <#const float **ranges#>, <#bool uniform#>, <#bool accumulate#>)
*/
// Mat _tmp(logPolar.size(),CV_8UC1);
// cvLogPolar(&((IplImage)logPolar), &((IplImage)_tmp),Point2f(blb[0],blb[1]), 80.0, CV_WARP_INVERSE_MAP);
// imshow("descriptor", _tmp);
// imshow("logpolar", logPolar);
}
}
if(trained) {
Mat results(1,1,CV_32FC1);
Mat samples; Mat(Mat(_d).t()).convertTo(samples,CV_32FC1);
Mat samplesAfterPCA = samples; //pca.project(samples);
classifier.find_nearest(&((CvMat)samplesAfterPCA), 1, &((CvMat)results));
// ((float*)results.data)[0] = classifier.predict(&((CvMat)samples))->value;
Mat lc(label_counts); lc *= 0.9;
// label_counts[(int)((float*)results.data)[0]] *= 0.9;
label_counts[(int)((float*)results.data)[0]] += 0.1;
Point maxLoc;
minMaxLoc(lc, NULL, NULL, NULL, &maxLoc);
int res = maxLoc.y;
stringstream ss; ss << "prediction: ";
if (res == LABEL_OPEN) {
ss << "Open hand";
}
if (res == LABEL_FIST) {
ss << "Fist";
}
if (res == LABEL_THUMB) {
ss << "Thumb";
}
if (res == LABEL_GARBAGE) {
ss << "Garbage";
}
putText(outC, ss.str(), Point(20,50), CV_FONT_HERSHEY_PLAIN, 3.0, Scalar(0,0,255), 2);
}
stringstream ss; ss << "samples: " << training_data.size();
putText(outC, ss.str(), Point(30,outC.rows - 30), CV_FONT_HERSHEY_PLAIN, 2.0, Scalar(0,0,255), 1);
imshow("blobs", outC);
char k = cvWaitKey(5);
if( k == 27 ){
break;
}
if( k == 8 ) {
std::ostringstream file;
file << filename << i_snap << suffix;
cv::imwrite(file.str(),rgbMat);
i_snap++;
}
if (k == 'g') {
//put into training as 'garbage'
training_data.push_back(_d);
label_data.push_back(LABEL_GARBAGE);
cout << "learn grabage" << endl;
}
if(k == 'o') {
//put into training as 'open'
training_data.push_back(_d);
label_data.push_back(LABEL_OPEN);
cout << "learn open" << endl;
}
if(k == 'f') {
//put into training as 'fist'
training_data.push_back(_d);
label_data.push_back(LABEL_FIST);
cout << "learn fist" << endl;
}
if(k == 'h') {
//put into training as 'thumb'
training_data.push_back(_d);
label_data.push_back(LABEL_THUMB);
cout << "learn thumb" << endl;
}
if (k=='t') {
//train model
cout << "train model" << endl;
if(loaded != true) {
dataMat = Mat(training_data.size(),_d.size(),CV_32FC1); //descriptors as matrix rows
for (uint i=0; i<training_data.size(); i++) {
Mat v = dataMat(Range(i,i+1),Range::all());
Mat(Mat(training_data[i]).t()).convertTo(v,CV_32FC1,1.0);
}
Mat(label_data).convertTo(labelMat,CV_32FC1);
}
// pca = pca(dataMat,Mat(),CV_PCA_DATA_AS_ROW,15);
Mat dataAfterPCA = dataMat;
// pca.project(dataMat,dataAfterPCA);
classifier.train(&((CvMat)dataAfterPCA), &((CvMat)labelMat));
trained = true;
}
// if(k=='p' && trained) {
// //predict
// Mat results(1,1,CV_32FC1);
// Mat samples(1,64,CV_32FC1); Mat(Mat(_d).t()).convertTo(samples,CV_32FC1);
// classifier.find_nearest(&((CvMat)samples), 1, &((CvMat)results));
// cout << "prediction: " << ((float*)results.data)[0] << endl;
// }
if(k=='s') {
cout << "save training data" << endl;
// classifier.save("knn-classifier-open-fist-thumb.yaml"); //not implemented
dataMat = Mat(training_data.size(),_d.size(),CV_32FC1); //descriptors as matrix rows
for (uint i=0; i<training_data.size(); i++) {
Mat v = dataMat(Range(i,i+1),Range::all());
Mat(Mat(training_data[i]).t()).convertTo(v,CV_32FC1,1.0);
}
Mat(label_data).convertTo(labelMat,CV_32FC1);
FileStorage fs;
fs.open("data-samples-labels.yaml", CV_STORAGE_WRITE);
if (fs.isOpened()) {
fs << "samples" << dataMat;
fs << "labels" << labelMat;
fs << "startX" << startX;
fs << "sizeX" << sizeX;
fs << "num_x_reps" << num_x_reps;
fs << "num_y_reps" << num_y_reps;
loaded = true;
fs.release();
} else {
cerr << "can't open saved data" << endl;
}
}
if(k=='l') {
cout << "try to load training data" << endl;
FileStorage fs;
fs.open("data-samples-labels.yaml", CV_STORAGE_READ);
if (fs.isOpened()) {
fs["samples"] >> dataMat;
fs["labels"] >> labelMat;
fs["startX"] >> startX;
fs["sizeX"] >> sizeX;
fs["num_x_reps"] >> num_x_reps;
fs["num_y_reps"] >> num_y_reps;
height_over_num_y_reps = 480/num_y_reps;
width_over_num_x_reps = sizeX/num_x_reps;
loaded = true;
fs.release();
} else {
Rgbimage::Rgbimage(libfreenect2::Frame * image, libfreenect2::Frame::Type type) : Frame(image,type)
{
std::cout << "rgb const" << std::endl;
cv::Mat rgbMat(image->height,image->width,CV_8UC4,image->data);
image_ = rgbMat;
}