void RenderContourBounds(CVPipeline * pipe)
{
rng = cv::RNG(5553);
for (int i = 0; i < pipe->contours.Size(); ++i)
{
CVContour * contour = &pipe->contours[i];
cv::Scalar color;
switch(contour->contourClass)
{
case ContourClass::FINGERTIP:
color = cv::Scalar(255, 0, 0);
break;
case ContourClass::FINGER:
color = cv::Scalar(0, 255, 0);
break;
default: color = cv::Scalar(rng.uniform(0,225), rng.uniform(0,225), rng.uniform(0,255));
}
switch(contour->boundingType)
{
case BoundingType::ELLIPSE:
// ellipse
ellipse(pipe->output, contour->boundingEllipse, color, 2, 8 );
break;
case BoundingType::RECT:
{
// rotated rectangle
cv::Point2f rect_points[4];
contour->boundingRect.points( rect_points );
for( int j = 0; j < 4; j++ )
line(pipe->output, rect_points[j], rect_points[(j+1)%4], color, 1, 8 );
break;
}
}
}
}
Plane()
{
n[0] = rng.uniform(-0.5, 0.5);
n[1] = rng.uniform(-0.5, 0.5);
n[2] = -0.3; //rng.uniform(-1.f, 0.5f);
n = n / cv::norm(n);
set_d(rng.uniform(-2.0, 0.6));
}
void RenderPolygons(CVPipeline * pipe)
{
/// Use same random seed every time to avoid rainbow hell..
rng = cv::RNG(12345);
for (int i = 0; i < pipe->approximatedPolygons.size(); ++i)
{
cv::Scalar color = cv::Scalar(rng.uniform(0,255), rng.uniform(0,255), rng.uniform(0,255));
cv::drawContours(pipe->output, pipe->approximatedPolygons, i, color, 2, 8, pipe->contourHierarchy, 0, cv::Point());
}
}
const cv::Vec3b Brush::get_color(const cv::Point center, const cv::Mat& reference_image, const Style& style) const {
cv::Vec3b color = reference_image.at<cv::Vec3b>(center);
color[0] = clamp(color[0] + color[0] * (rng.uniform(-0.5, 0.5) * style.blue_jitter()));
color[1] = clamp(color[1] + color[1] * (rng.uniform(-0.5, 0.5) * style.green_jitter()));
color[2] = clamp(color[2] + color[2] * (rng.uniform(-0.5, 0.5) * style.red_jitter()));
cv::cvtColor(color, color, CV_RGB2HSV);
color[0] = clamp(color[0] + color[0] * (rng.uniform(-0.5, 0.5) * style.hue_jitter()));
color[1] = clamp(color[1] + color[1] * (rng.uniform(-0.5, 0.5) * style.saturation_jitter()));
color[2] = clamp(color[2] + color[2] * (rng.uniform(-0.5, 0.5) * style.value_jitter()));
cv::cvtColor(color, color, CV_HSV2RGB);
return color;
}
void cannyDetector(cv::Mat src, cv::Mat &imgMap)
{
/*Obsolete version for detection of colored contours... date : */
int ratio = 3;
int kernel_size = 3;
cv::Mat srcGray, srcHsv, cannyOutput;
std::vector<std::vector<cv::Point> > contours;
// from color to gray
cv::cvtColor(src, srcGray, cv::COLOR_BGR2GRAY);
// from color to hsv
cv::cvtColor(src, srcHsv, cv::COLOR_BGR2HSV);
/// Reduce noise with a kernel 3x3
cv::blur( srcGray, srcGray, cv::Size(3,3) );
/// Canny detector
cv::Canny( srcGray, cannyOutput, lowThreshold, lowThreshold*ratio, kernel_size );
/// Find contours
cv::findContours(cannyOutput, contours, CV_RETR_EXTERNAL, CV_CHAIN_APPROX_SIMPLE);
// Select orange contours
imgMap = cv::Mat::zeros( srcGray.size(), srcGray.type() );
cv::Rect bRect = cv::Rect(0,0,0,0);
for( int i = 0; i< contours.size(); i++ )
{
if (cv::contourArea(contours[i]) > 0.00001*src.rows*src.cols)
{
bRect = cv::boundingRect(contours[i]);
cv::inRange(srcHsv(bRect), cv::Scalar(h_min,50,50), cv::Scalar(h_max,255,255), imgMap(bRect));
}
}
cv::erode(imgMap, imgMap, getStructuringElement(cv::MORPH_ELLIPSE, cv::Size(1, 1)));
cv::dilate(imgMap, imgMap, getStructuringElement(cv::MORPH_ELLIPSE, cv::Size(3, 3)));
//Draw contours
cv::Mat drawing = cv::Mat::zeros( cannyOutput.size(), CV_8UC3 );
for( int i = 0; i< contours.size(); i++ )
{
cv::Scalar color = cv::Scalar( rng.uniform(0, 255), rng.uniform(0,255), rng.uniform(0,255) );
drawContours( drawing, contours, i, cv::Scalar(0,0,255), 1, 8);
}
/*// Show in a window
cv::namedWindow( "Contours", CV_WINDOW_AUTOSIZE );
cv::imshow( "Contours", drawing );
cv::namedWindow( "imgMap", CV_WINDOW_AUTOSIZE );
cv::imshow( "imgMap", imgMap );*/
}
void RenderConvexityDefects(CVPipeline * pipe)
{
cv::Scalar color = cv::Scalar(rng.uniform(155,255), rng.uniform(125,255), rng.uniform(0,200));
List<cv::Vec4i> defects = pipe->convexityDefects;
for (int i = 0; i < defects.Size(); ++i)
{
cv::Vec4i defect = defects[i];
int farthestPointIndex = defect[2];
cv::Point farthestPoint = pipe->cvContours[0][farthestPointIndex];
// Render point furthest away?
cv::circle(pipe->output, farthestPoint, 3, color, 5);
}
}
void CornerDetection::myShiTomasi_function(cv::Mat img, cv::Mat img_gray, cv::Mat myShiTomasi_dst)
{
myShiTomasi_copy = img.clone();
if( myShiTomasi_qualityLevel < 1 )
myShiTomasi_qualityLevel = 1;
for( int j = 0; j < img_gray.rows; j++ )
for( int i = 0; i < img_gray.cols; i++ )
if( myShiTomasi_dst.at<float>(j,i) > myShiTomasi_minVal + ( myShiTomasi_maxVal - myShiTomasi_minVal )*myShiTomasi_qualityLevel/max_qualityLevel )
cv::circle( myShiTomasi_copy, cv::Point(i,j), 4, cv::Scalar( rng.uniform(0,255), rng.uniform(0,255), rng.uniform(0,255) ), -1, 8, 0 );
cv::imshow( shiTomasi_win, myShiTomasi_copy );
}
void DataProviderDTang<Dtype>::shuffle() {
static cv::RNG rng(time(0));
std::vector<boost::filesystem::path> shuffled_depth_paths(depth_paths_.size());
std::vector<std::vector<cv::Vec3f> > shuffled_annos(annos_.size());
for(size_t idx = 0; idx < depth_paths_.size(); ++idx) {
int rand_idx = rng.uniform(0, depth_paths_.size());
shuffled_depth_paths[idx] = depth_paths_[rand_idx];
shuffled_annos[idx] = annos_[rand_idx];
}
depth_paths_ = shuffled_depth_paths;
annos_ = shuffled_annos;
}
void RenderContours(CVPipeline * pipe)
{
/// Use same random seed every time to avoid rainbow hell..
rng = cv::RNG(12345);
for (int i = 0; i < pipe->contours.Size(); ++i)
{
CVContour * contour = &pipe->contours[i];
if (contour->segments.Size())
RenderContourSegments(contour, pipe);
else
{
cv::Scalar color = cv::Scalar(rng.uniform(0,255), rng.uniform(0,255), rng.uniform(0,255));
cv::drawContours(pipe->output, pipe->cvContours, i, color, 2, 8, pipe->contourHierarchy, 0, cv::Point());
}
}
}
double randomDoubleLog(double minVal, double maxVal)
{
double logMin = log((double)minVal + 1);
double logMax = log((double)maxVal + 1);
double pow = rng.uniform(logMin, logMax);
double v = exp(pow) - 1;
CV_Assert(v >= minVal && (v < maxVal || (v == minVal && v == maxVal)));
return v;
}
void RenderConvexHulls(CVPipeline * pipe)
{
if (pipe->cvContours.size() == 0)
return;
cv::Scalar color = cv::Scalar(rng.uniform(125,255), rng.uniform(125,255), rng.uniform(125,255));
// cv::drawContours(pipe->output, pipe->convexHull, 0, color, 1, 8, std::vector<cv::Vec4i>(), 0, cv::Point() );
std::vector<int> & convexHull = pipe->convexHull;
std::vector<cv::Point> & contour = pipe->cvContours[0];
for (int i = 0; i < convexHull.size(); ++i)
{
int index = convexHull[i];
cv::Point point = contour[index];
cv::circle(pipe->output, point, 15, color);
int nextIndex = convexHull[(i+1) % convexHull.size()];
cv::Point point2 = contour[nextIndex];
// Line!
cv::line(pipe->output, point, point2, color, 3);
}
}
Rectangle::Rectangle(const cv::Rect &_rect, const int &_id) :
rect(_rect)
{
tl = _rect.tl();
br = _rect.br();
left = new VerticalLine();
left->set(tl, cv::Point(tl.x, br.y));
top = new HorizontalLine();
top->set(tl, cv::Point(br.x, tl.y));
bottom = new HorizontalLine();
bottom->set(cv::Point(tl.x, br.y), br);
right = new VerticalLine();
right->set(cv::Point(br.x, tl.y), br);
info.set(_id, _rect);
selected = false;
selected_color = RED_COLOR;
offset = 4;
lineOffset = LINE_WEIGHT;
color = cv::Scalar( rng.uniform(0, 255), rng.uniform(0,255), rng.uniform(0,255) );
}
void drawEpipolar( cv::Mat _imga, cv::Mat _imgb,
std::vector<Eigen::VectorXi> _p2Da,
std::vector<Eigen::VectorXi> _p2Db,
std::vector<Eigen::VectorXd> _pda,
std::vector<Eigen::VectorXd> _pdb )
{
char num[] = "12";
for( unsigned int i = 0; i < _p2Da.size(); i++ )
{
Scalar color = Scalar( rng.uniform(0, 255), rng.uniform(0, 255), rng.uniform(0, 255) );
sprintf( num, "%d ", i);
circle( _imga, Point( _p2Da[i](0), _p2Da[i](1)), 5, color, -1, 8, 0 );
putText( _imga, num, Point( _p2Da[i](0), _p2Da[i](1) ), FONT_HERSHEY_SIMPLEX, 1, color, 2, 8, false);
sprintf( num, "%d ", i);
circle( _imgb, Point( _p2Db[i](0), _p2Db[i](1)), 5, color, -1, 8, 0 );
putText( _imgb, num, Point( _p2Db[i](0), _p2Db[i](1) ), FONT_HERSHEY_SIMPLEX, 1, color, 2, 8, false);
Point2f pt1a;
Point2f pt2a;
pt1a.x = _pda[i](0);
pt1a.y = _pda[i](1);
pt2a.x = _pda[i](2);
pt2a.y = _pda[i](3);
line( _imga, pt1a, pt2a, color, 2, CV_AA );
Point2f pt1b;
Point2f pt2b;
pt1b.x = _pdb[i](0);
pt1b.y = _pdb[i](1);
pt2b.x = _pdb[i](2);
pt2b.y = _pdb[i](3);
line( _imgb, pt1b, pt2b, color, 2, CV_AA );
}
}
void CropIm(std::string destFoldPath, cv::Mat Im, double s)
{
int w = Im.rows;
int h = Im.cols;
int wStart;
int hStart;
wStart = rng.uniform(0.0, w*(1-s));
hStart = rng.uniform(0.0, h*(1-s));
cv::Rect myROI(hStart, wStart, h*s, w*s);
cv::Mat croppedImage = Im(myROI);
cv::imwrite(destFoldPath + "_crop" + int2string(int(s * 100)) + ".jpg", croppedImage);
}
void
gen_points_3d(std::vector<Plane>& planes_out, cv::Mat_<unsigned char> &plane_mask, cv::Mat& points3d, cv::Mat& normals,
int n_planes)
{
std::vector<Plane> planes;
for (int i = 0; i < n_planes; i++)
{
Plane px;
for (int j = 0; j < 1; j++)
{
px.set_d(rng.uniform(-3.f, -0.5f));
planes.push_back(px);
}
}
cv::Mat_ < cv::Vec3f > outp(H, W);
cv::Mat_ < cv::Vec3f > outn(H, W);
plane_mask.create(H, W);
// n ( r - r_0) = 0
// n * r_0 = d
//
// r_0 = (0,0,0)
// r[0]
for (int v = 0; v < H; v++)
{
for (int u = 0; u < W; u++)
{
unsigned int plane_index = (u / float(W)) * planes.size();
Plane plane = planes[plane_index];
outp(v, u) = plane.intersection(u, v, Kinv);
outn(v, u) = plane.n;
plane_mask(v, u) = plane_index;
}
}
planes_out = planes;
points3d = outp;
normals = outn;
}
cv::Mat PerspectiveTransform::warpPerspectiveRand( cv::RNG& rng )
{
cv::Mat H;
H.create(3, 3, CV_64FC1);
H.at<double>(0,0) = rng.uniform( 0.8f, 1.2f);
H.at<double>(0,1) = rng.uniform(-0.1f, 0.1f);
//H.at<double>(0,2) = rng.uniform(-0.1f, 0.1f)*src.cols;
H.at<double>(0,2) = rng.uniform(-0.1f, 0.1f);
H.at<double>(1,0) = rng.uniform(-0.1f, 0.1f);
H.at<double>(1,1) = rng.uniform( 0.8f, 1.2f);
//H.at<double>(1,2) = rng.uniform(-0.1f, 0.1f)*src.rows;
H.at<double>(1,2) = rng.uniform(-0.1f, 0.1f);
H.at<double>(2,0) = rng.uniform( -1e-4f, 1e-4f);
H.at<double>(2,1) = rng.uniform( -1e-4f, 1e-4f);
H.at<double>(2,2) = rng.uniform( 0.8f, 1.2f);
return H;
}
void thresh_callback(int, void* )
{
cv::Mat canny_output;
std::vector<std::vector<cv::Point> > contours;
std::vector<cv::Vec4i> hierarchy;
/// Detect edges using canny
cv::threshold( blur, canny_output, thresh, 255 ,1 );
cv::namedWindow("canny",CV_WINDOW_NORMAL);
cv::imshow("canny",canny_output);
/// Find contours
cv::findContours( canny_output, contours, hierarchy, cv::RETR_TREE, cv::CHAIN_APPROX_SIMPLE, cv::Point(0, 0) );
for( int f =0; f <listOfMarks.size();f++)
{
delete listOfMarks[f];
}
listOfMarks.clear();
/// Draw contours
cv::Mat drawing = cv::Mat::zeros( canny_output.size(), CV_8UC3 );
for( size_t i = 0; i< contours.size(); i++ )
{
cv::Scalar color = cv::Scalar( rng.uniform(0, 255), rng.uniform(0,255), rng.uniform(0,255) );
cv::drawContours( drawing, contours, (int)i, cv::Scalar(255,255,255), thick, 8, hierarchy, 0, cv::Point() );
// mark *temp = new mark(contours[i]);
// listOfMarks.push_back(temp);
}
/// Show in a window
cv::namedWindow( "Contours", CV_WINDOW_NORMAL );
cv::imshow( "Contours", drawing );
//-------------------------------------------again
std::vector<std::vector<cv::Point> > contours_again;
std::vector<cv::Vec4i> hierarchy_again;
draw_again = cv::Mat::zeros( canny_output.size(), CV_8UC3 );
colorOutImage = cv::Mat::zeros(canny_output.size(), CV_8UC3);
cv::Mat temp;
cv::cvtColor( drawing,temp, cv::COLOR_BGR2GRAY );
cv::findContours( temp , contours_again, hierarchy_again, cv::RETR_TREE, cv::CHAIN_APPROX_SIMPLE, cv::Point(0, 0) );
for( size_t i = 0; i< contours_again.size(); i++ )
{
// cv::Scalar color = cv::Scalar( rng.uniform(0, 255), rng.uniform(0,255), rng.uniform(0,255) );
cv::Scalar color = cv::Scalar(255,255,255);
cv::drawContours( draw_again, contours_again, (int)i, color , 2, 8, hierarchy, 0, cv::Point() );
mark * temp = new mark(contours_again[i]);
listOfMarks.push_back(temp);
}
/// Show in a window
// cv::namedWindow( "Contours_again", CV_WINDOW_NORMAL );
// cv::imshow( "Contours_again", draw_again );
for(int h = 0; h < listOfMarks.size();h++)
{
int y = (listOfMarks[h]->high_y() + listOfMarks[h]->low_y()) / 2;
int x1 = listOfMarks[h]->high_x() + length;
int x2 = listOfMarks[h]->low_x() - length;
cv::Point one = cv::Point(x1,y);
cv::Point two = cv::Point(x2,y);
cv::Scalar white =cv::Scalar(255,255,255);
cv::line(draw_again, one, two, white, thick,8,0);
}
cv::namedWindow( "Contours_again", CV_WINDOW_NORMAL );
cv::imshow( "Contours_again", draw_again );
std::vector< std::vector<cv::Point> > contour_3;
std::vector<cv::Vec4i> hierarchy_3;
cv::Mat temp_3;
cv::cvtColor( draw_again,temp_3, cv::COLOR_BGR2GRAY );
std::cerr<<"cvtColo" <<std::endl;
cv::findContours( temp_3, contour_3, hierarchy_3, cv::RETR_TREE, cv::CHAIN_APPROX_SIMPLE, cv::Point(0, 0) );
std::cerr<<"found contours" << std::endl;
for( size_t i = 0; i< contour_3.size(); i++ )
{
cv::Scalar color = cv::Scalar( rng.uniform(0, 255), rng.uniform(0,255), rng.uniform(0,255) );
// cv::Scalar color = cv::Scalar(255,255,255);
cv::drawContours( colorOutImage, contour_3, (int)i, color , 2, 8, hierarchy_3, 0, cv::Point() );
}
std::cerr<<"drawContours"<<std::endl;
cv::namedWindow( "Contours_3", CV_WINDOW_NORMAL );
cv::imshow( "Contours_3", colorOutImage );
std::cout<<std::endl<<"number of contours -> " <<contours.size();
//again--------------------------------------------------------------------------
// makeZones_callback(0,0);
}
void getDistortionValues(cv::RNG &rng, const Size2i &inputSize, AugParams *agp) {
// This function just gets the random distortion values without modifying the
// image itself. Useful if we need to reapply the same transformations over
// again (e.g. for all frames of a video or for a corresponding target mask)
// colornoise values
// N.B. if _contrastMax == 100, then _colorNoiseStd will be 0.0
for (int i=0; i<3; i++) {
agp->colornoise[i] = rng.gaussian(_colorNoiseStd);
}
// contrast, brightness, saturation
// N.B. all value ranges tied to _contrastMin and _contrastMax
for (int i=0; i<3; i++) {
agp->cbs[i] = rng.uniform(_contrastMin, _contrastMax) / 100.0f;
}
/**************************
* HORIZONTAL FLIP *
***************************/
agp->flip = _flip && rng(2) != 0 ? true : false;
/**************************
* ROTATION ANGLE *
***************************/
agp->angle = rng.uniform(_rotateMin, _rotateMax);
/**************************
* CROP BOX *
***************************/
float shortSide = std::min(inputSize.height, inputSize.width);
// Special case where we just grab the whole image;
if (_scaleMin == 0) {
agp->cropBox = Rect(Point2i(), inputSize);
return;
}
if (_center) {
agp->cropBox.width = shortSide * _width / (float) _scaleMin;
agp->cropBox.height = shortSide * _height / (float) _scaleMin;
agp->cropBox.x = (inputSize.width - agp->cropBox.width) / 2;
agp->cropBox.y = (inputSize.height - agp->cropBox.height) / 2;
} else {
cv::Size2f oSize = inputSize;
// This is a hack for backward compatibility.
// Valid aspect ratio range ( > 100) will override side scaling behavior
if (_aspectRatio == 0) {
float scaleFactor = rng.uniform(_scaleMin, _scaleMax);
agp->cropBox.width = shortSide * _width / scaleFactor;
agp->cropBox.height = shortSide * _height / scaleFactor;
} else {
float mAR = (float) _aspectRatio / 100.0f;
float nAR = rng.uniform(1.0f / mAR, mAR);
float oAR = oSize.width / oSize.height;
// between minscale pct% to 100% subject to aspect ratio limitation
float maxScale = nAR > oAR ? oAR / nAR : nAR / oAR;
float minScale = std::min((float) _scaleMin / 100.0f, maxScale);
float tgtArea = rng.uniform(minScale, maxScale) * oSize.area();
agp->cropBox.height = sqrt(tgtArea / nAR);
agp->cropBox.width = agp->cropBox.height * nAR;
}
agp->cropBox.x = rng.uniform(0, inputSize.width - agp->cropBox.width);
agp->cropBox.y = rng.uniform(0, inputSize.height - agp->cropBox.height);
}
return;
}
int main( int argc, char** argv ) {
/// Load an image
cv::Mat src, greyIm, histeqIm;
src = cv::imread( argv[1] );
if( !src.data ) {
printf("Input file? No? ouuuupsss thooooorryyyyy\n");
return -1;
}
cv::Size s = src.size();
int rows = s.height;
int cols = s.width;
// Setup a rectangle to define your region of interest
cv::Rect myROI(0, rows/2, cols, rows/2);
// Crop the full image to that image contained by the rectangle myROI
// Note that this doesn't copy the data
cv::Mat croppedImage = src(myROI);
cv::imwrite("output/1_low_half.jpg", croppedImage);
cv::cvtColor(croppedImage, greyIm, cv::COLOR_BGR2GRAY);
cv::Size crop_size = croppedImage.size();
int crop_rows = crop_size.height;
int crop_cols = crop_size.width;
cv::imwrite("output/2_grey_scale.jpg", greyIm);
cv::equalizeHist( greyIm, histeqIm );
cv::imwrite("output/3_hist_eq.jpg", histeqIm);
std::vector<std::vector<cv::Point> > contours;
std::vector<cv::Vec4i> hierarchy;
// Reduce noise with kernel 3x3
cv::Mat blurIm;
blur(histeqIm, blurIm, cv::Size(3,3));
cv::imwrite("output/4_blur.jpg", blurIm);
// Canny detector
cv::Mat edgesIm;
Canny(blurIm, edgesIm, thresh, thresh*ratio, kernel_size);
cv::imwrite("output/5_edge.jpg", edgesIm);
// Find contours
cv::findContours(edgesIm, contours, hierarchy, cv::RETR_TREE, cv::CHAIN_APPROX_SIMPLE, cv::Point(0,0));
// Approximate contours to polygons + get bounding rects and circles
std::vector<std::vector<cv::Point> > contours_poly(contours.size());
std::vector<cv::Rect> boundRect(contours.size());
std::vector<cv::Point2f>center(contours.size());
std::vector<float>radius(contours.size());
for (int i = 0; i < contours.size(); i++) {
cv::approxPolyDP(cv::Mat(contours[i]), contours_poly[i], 3, true);
boundRect[i] = cv::boundingRect(cv::Mat(contours_poly[i]));
cv::minEnclosingCircle((cv::Mat)contours_poly[i], center[i], radius[i]);
}
// Draw contours
int j=0;
cv::Mat drawing = cv::Mat::zeros(edgesIm.size(), CV_8UC3);
cv::Mat piece[5], hsvIm[5];
for (int i = 0; i < contours.size(); i++) {
if (!((boundRect[i].height >= boundRect[i].width/5) && (boundRect[i].height <= boundRect[i].width/2)
&& boundRect[i].height<=crop_rows/4 && boundRect[i].width<=crop_cols/2
&& boundRect[i].height>=crop_rows/10 && boundRect[i].width>=crop_cols/6))
continue;
cv::Rect roi = boundRect[i];
piece[j] = croppedImage(roi);
imwrite("output/contour"+std::to_string(j)+".jpg", piece[j]);
j++;
cv::Scalar color = cv::Scalar(rng.uniform(0, 255), rng.uniform(0,255), rng.uniform(0,255));
cv::drawContours(drawing, contours, i, color, 2, 8, hierarchy, 0, cv::Point());
cv::rectangle(drawing, boundRect[i].tl(), boundRect[i].br(), color, 2, 8, 0);
//circle(drawing, center[i], (int)radius[i], color, 2, 8, 0);
}
imwrite("output/6_contours.jpg", drawing);
int h_bins = 50; int s_bins = 60;
int histSize[] = { h_bins, s_bins };
float h_ranges[] = { 0, 180 };
float s_ranges[] = { 0, 256 };
const float* ranges[] = { h_ranges, s_ranges };
int channels[] = { 0, 1 };
cv::Mat hist[5];
for (int i=0; i<j; i++){
cvtColor(piece[i], hsvIm[i], cv::COLOR_BGR2HSV);
imwrite("output/hsvIm"+std::to_string(i)+".jpg", hsvIm[i]);
calcHist( &hsvIm[i], 1, channels, cv::Mat(), hist[i], 2, histSize, ranges, true, false );
//normalize( hsvIm[i], hsvIm[i], 0, 1, cv::NORM_MINMAX, -1, cv::Mat() );
}
return 0;
}
void CVDataFilter::Paint(CVPipeline * pipe)
{
if (returnType == CVReturnType::QUADS)
{
// Draw quads.
if (!pipe->quads.Size())
return;
// Copy original input
pipe->initialInput.copyTo(pipe->output);
// Convert to color if needed.
int channelsBefore = pipe->output.channels();
if (channelsBefore == 1)
{
// pipe->output.convertTo(pipe->output, CV_8UC3);
cv::cvtColor(pipe->output, pipe->output, CV_GRAY2RGB);
}
int channelsAfter = pipe->output.channels();
// o.o Paste!
for (int i = 0; i < pipe->quads.Size(); ++i)
{
Quad quad = pipe->quads[i];
#define RGB(r,g,b) cv::Scalar(b,g,r)
rng = cv::RNG(1344);
cv::Scalar color = RGB(rng.uniform(0, 255),rng.uniform(0, 255),rng.uniform(0, 255));
// cv::Mat rectImage = cv::Mat::zeros(pipe->output.size(), CV_8UC3);
cv::rectangle(pipe->output, cv::Point(quad.point1.x, quad.point1.y), cv::Point(quad.point3.x, quad.point3.y), color, CV_FILLED);
float alpha = 0.2f;
float beta = 1 - alpha;
// cv::addWeighted(rectImage, alpha, pipe->output, beta, 0.0, pipe->output);
// rectImage.copyTo(pipe->output);
}
}
else if (returnType == CVReturnType::APPROXIMATED_POLYGONS)
{
// Copy original input..
pipe->initialInput.copyTo(pipe->output);
RenderPolygons(pipe);
}
else if (returnType == CVReturnType::CV_IMAGE)
{
// Do nothing as the image should already be in the ouput matrix of the pipeline.
}
else if (returnType == CVReturnType::LINES ||
returnType == CVReturnType::CV_LINES)
{
// Convert the color to colors again for visualization...
pipe->initialInput.copyTo(pipe->output);
for( size_t i = 0; i < pipe->lines.Size(); i++ )
{
Line & line = pipe->lines[i];
// Multiply the line coordinates with the last used inverted scale.
line.start *= pipe->currentScaleInv;
line.stop *= pipe->currentScaleInv;
cv::line( pipe->output, cv::Point(line.start.x, line.start.y),
cv::Point(line.stop.x, line.stop.y), cv::Scalar(0,0,255), 3, 8 );
}
}
else if (returnType == CVReturnType::CV_CONTOURS)
{
pipe->initialInput.copyTo(pipe->output);
RenderContours(pipe);
}
switch(returnType)
{
case CVReturnType::CV_CONTOUR_SEGMENTS:
{
// Render shit!
pipe->initialInput.copyTo(pipe->output);
RenderContourSegments(pipe);
break;
}
}
if (returnType == CVReturnType::CV_CONTOUR_ELLIPSES)
{
pipe->initialInput.copyTo(pipe->output);
RenderContours(pipe);
RenderContourBounds(pipe);
}
else if (returnType == CVReturnType::CV_CONVEX_HULLS)
{
pipe->initialInput.copyTo(pipe->output);
RenderContours(pipe);
RenderConvexHulls(pipe);
}
else if (returnType == CVReturnType::CV_CONVEXITY_DEFECTS)
{
pipe->initialInput.copyTo(pipe->output);
RenderContours(pipe);
RenderConvexHulls(pipe);
RenderConvexityDefects(pipe);
}
else if (returnType == CVReturnType::HANDS)
{
// Convert image to RGB for easier display
int channelsBefore = pipe->initialInput.channels();
// cv::cvtColor(*pipe->initialInput, pipe->output, CV_GRAY2RGB);
pipe->initialInput.copyTo(pipe->output);
int channelsAfter = pipe->output.channels();
// Check if we got contour-segments, if so prioritize them.
RenderHands(pipe);
}
else if (returnType == CVReturnType::FINGER_STATES)
{
// Render the last known one.
pipe->initialInput.copyTo(pipe->output);
FingerState & state = pipe->fingerStates.Last();
cv::Scalar color(255,0,0,255);
for (int i = 0; i < state.positions.Size(); ++i)
{
Vector3f pos = state.positions[i];
cv::circle(pipe->output, cv::Point(pos.x, pos.y), 5, color, 3);
}
}
else if (returnType == CVReturnType::POINT_CLOUDS)
{
pipe->initialInput.copyTo(pipe->output);
for (int i = 0; i < pipe->pointClouds.Size(); ++i)
{
int r,g,b,a;
r = g = b = a = 255;
if (i % 2 == 0)
g = 0;
if (i % 4 == 0)
b = 0;
cv::Scalar color(r,g,b,a);
CVPointCloud & pc = pipe->pointClouds[i];
for (int j = 0; j < pc.points.Size(); ++j)
{
Vector2f & pos = pc.points[j];
cv::circle(pipe->output, cv::Point(pos.x, pos.y), 2, color, 3);
}
/// Render PCA data if applicable.
for (int j = 0; j < pc.eigenVectors.Size(); ++j)
{
cv::Scalar color = j == 0? CV_RGB(255, 255, 0) : CV_RGB(0, 255, 255);
cv::Point2f eigenVector(pc.eigenVectors[j].x, pc.eigenVectors[j].y);
float eigenValue = pc.eigenValues[j];
cv::Point2f scaledEigenVector = eigenVector * eigenValue * 0.02;
cv::Point2f pcaCenter(pc.pcaCenter.x, pc.pcaCenter.y);
circle(pipe->output, pcaCenter, 3, CV_RGB(255, 0, 255), 2);
line(pipe->output, pcaCenter, pcaCenter + scaledEigenVector, color);
}
}
}
else if (returnType == CVReturnType::CV_OPTICAL_FLOW)
{
// http://stackoverflow.com/questions/7693561/opencv-displaying-a-2-channel-image-optical-flow
// Split the coordinates.
/*
cv::Mat xy[2];
cv::split(pipe->opticalFlow, xy);
cv::Mat magnitude, angle;
// Fetch matrix from pipeline.
cv::cartToPolar(xy[0], xy[1], magnitude, angle, true);
double magMax;
cv::minMaxLoc(magnitude, 0, &magMax);
magnitude.convertTo(magnitude, -1, 1.0 / magMax);
// Build HSV image?
cv::Mat hsvChannels[3], hsv;
hsvChannels[0] = angle;
hsvChannels[1] = cv::Mat::ones(angle.size(), CV_32F);
hsvChannels[2] = magnitude;
cv::merge(hsvChannels, 3, hsv);
// Convert to rgb and send to output.
cv::cvtColor(hsv, pipe->output, cv::COLOR_HSV2RGB);
*/
}
else if (returnType == CVReturnType::NOTHING)
{
// Nothing to render...
}
else if (returnType == -1)
{
// Nothing to render if error.
std::cout<<"\nreturnType variable in filter "<<name<<" not set!";
}
else if (returnType == CVReturnType::RENDER)
// Nothing to render if render.
;
else
; // std::cout<<"\nCVDataFilter::Paint called. Forgot to subclass the paint-method?";
}
JNIEXPORT void JNICALL
Java_ph_edu_dlsu_circles_CameraActivity_process(JNIEnv *env, jobject instance,
jobject pTarget, jbyteArray pSource, jint thresh) {
uint32_t t;
cv::Mat srcBGR;
cv::Mat edges;
cv::Scalar color;
std::vector<cv::Vec3f> circles;
AndroidBitmapInfo bitmapInfo;
uint32_t* bitmapContent;
if(AndroidBitmap_getInfo(env, pTarget, &bitmapInfo) < 0) abort();
if(bitmapInfo.format != ANDROID_BITMAP_FORMAT_RGBA_8888) abort();
if(AndroidBitmap_lockPixels(env, pTarget, (void**)&bitmapContent) < 0) abort();
/// Access source array data... OK
jbyte* source = (jbyte*)env->GetPrimitiveArrayCritical(pSource, 0);
if (source == NULL) abort();
/// cv::Mat for YUV420sp source and output BGRA
cv::Mat srcGray(bitmapInfo.height, bitmapInfo.width, CV_8UC1, (unsigned char *)source);
cv::Mat src(bitmapInfo.height + bitmapInfo.height/2, bitmapInfo.width, CV_8UC1, (unsigned char *)source);
cv::Mat mbgra(bitmapInfo.height, bitmapInfo.width, CV_8UC4, (unsigned char *)bitmapContent);
/***********************************************************************************************/
/// Native Image Processing HERE...
t = cv::getTickCount();
if(srcBGR.empty())
srcBGR = cv::Mat(bitmapInfo.height, bitmapInfo.width, CV_8UC3);
cv::cvtColor(src, srcBGR, CV_YUV420sp2RGB);
// Reduce noise
cv::GaussianBlur( srcGray, srcGray, cv::Size(9, 9), 2, 2 );
// Detect the circles
cv::HoughCircles(srcGray, circles, CV_HOUGH_GRADIENT, 1, srcGray.rows/4, 200, thresh > 0 ? thresh : 1, 5);
// Draw the circles
for(size_t i = 0; i < circles.size(); i++) {
cv::Point center(cvRound(circles[i][0]), cvRound(circles[i][1]));
int radius = cvRound(circles[i][2]);
color = cv::Scalar( rng.uniform(0, 255), rng.uniform(0,255), rng.uniform(0,255) );
// draw the circle center
cv::circle(srcBGR, center, 3, color, -1, 8, 0);
// draw the circle outline
cv::circle(srcBGR, center, radius, color, 3, 8, 0);
}
LOGI("Processing took %0.2f ms.", 1000*((float)cv::getTickCount() - t)/(float)cv::getTickFrequency());
cvtColor(srcBGR, mbgra, CV_BGR2BGRA);
/************************************************************************************************/
/// Release Java byte buffer and unlock backing bitmap
//env-> ReleasePrimitiveArrayCritical(pSource,source,0);
/*
* If 0, then JNI should copy the modified array back into the initial Java
* array and tell JNI to release its temporary memory buffer.
*
* */
env-> ReleasePrimitiveArrayCritical(pSource, source, JNI_COMMIT);
/*
* If JNI_COMMIT, then JNI should copy the modified array back into the
* initial array but without releasing the memory. That way, the client code
* can transmit the result back to Java while still pursuing its work on the
* memory buffer
*
* */
/*
* Get<Primitive>ArrayCritical() and Release<Primitive>ArrayCritical()
* are similar to Get<Primitive>ArrayElements() and Release<Primitive>ArrayElements()
* but are only available to provide a direct access to the target array
* (instead of a copy). In exchange, the caller must not perform blocking
* or JNI calls and should not hold the array for a long time
*
*/
if (AndroidBitmap_unlockPixels(env, pTarget) < 0) abort();
}
int main_center_of_mass(int argc, char* argv[]){
char* file = "contours.png";
int thresh = 100;
//src.convertTo(src_gray,CV_8U);
while(true){
cv::Mat src = cv::imread(file);
cv::Mat src_gray;// = cv::imread(file,0);
cv::cvtColor(src,src_gray,CV_RGB2GRAY);
cv::dilate( src_gray, src_gray, cv::Mat() );
cv::erode( src_gray, src_gray, cv::Mat() );
//cv::threshold(src,src_gray,0.5,255,CV_THRESH_BINARY);
cv::Mat1b src_g;
cv::cvtColor(src,src_g,CV_RGB2GRAY);
cv::Mat canny_output;
cv::vector<cv::vector<cv::Point> > contours;
cv::vector<cv::Vec4i> hierarchy;
/// Detect edges using canny
cv::Canny( src_gray, canny_output, thresh, thresh*2, 3 );
cv::imshow("canny",canny_output);
/// Find contours
cv::findContours( canny_output, contours, hierarchy, CV_RETR_LIST, CV_CHAIN_APPROX_SIMPLE, cv::Point(0, 0) );
cv::Mat drawing = cv::Mat::zeros( canny_output.size(), CV_8UC3 );
int idx = 0;
int max = -1;
for(unsigned int i = 0; i< contours.size(); i++ ){
cv::Scalar color3 = cv::Scalar( rng.uniform(0, 255), rng.uniform(0,255), rng.uniform(0,255) );
drawContours( drawing, contours, i, color3, 2, 8, hierarchy, 0, cv::Point() );
int coiso = contours.at(i).size();
if(coiso > max){
idx = i;
max = coiso;
}
}
cv::Moments mm = moments( contours[idx], false );
cv::Point2f center = cv::Point2f((float)(mm.m10/mm.m00) , (float)(mm.m01/mm.m00));
cv::Rect rect = cv::boundingRect(cv::Mat(contours[idx]));
for(unsigned int i = rect.y ; i < rect.y + rect.height; i++){
for(unsigned int j = rect.x ; j < rect.x + rect.width ; j++){
cv::Scalar color2 = cv::Scalar( rng.uniform(0, 255), rng.uniform(0,255), rng.uniform(0,255) );
if(src_gray.ptr<unsigned char>(i)[j]){
drawing.ptr<unsigned char>(i)[3*j] = (unsigned char)color2.val[0]; // first channel
drawing.ptr<unsigned char>(i)[3*j+1]= (unsigned char)color2.val[1]; // second channel
drawing.ptr<unsigned char>(i)[3*j+2]= (unsigned char)color2.val[2]; // third channel
//cv::circle( drawing, pt, 1, color2, -1, 2, 0 );
}
}
}
cv::Scalar color = cv::Scalar( 255, 255, 255 );
drawContours( drawing, contours, idx, color, 2, 8, hierarchy, 0, cv::Point() );
cv::circle( drawing, center, 4, color, -1, 8, 0 );
cv::rectangle(src,rect,color);
/* /// Get the moments
cv::vector<cv::Moments> mu(contours.size() );
for( int i = 0; i < contours.size(); i++ )
{ mu[i] = moments( contours[i], false ); }
/// Get the mass centers:
cv::vector<cv::Point2f> mc( contours.size() );
for( int i = 0; i < contours.size(); i++ )
{ mc[i] = cv::Point2f( mu[i].m10/mu[i].m00 , mu[i].m01/mu[i].m00 ); }
cv::Mat drawing = cv::Mat::zeros( canny_output.size(), CV_8UC3 );
cv::Scalar color = cv::Scalar( rng.uniform(0, 255), rng.uniform(0,255), rng.uniform(0,255) );
drawContours( drawing, contours, idx, color, 2, 8, hierarchy, 0, cv::Point() );
cv::circle( src, mc[idx], 4, color, -1, 8, 0 );*/
cv::imshow("src2",src);
cv::imshow("cont",drawing);
cv::waitKey(22);
}
return 0;
}
PERF_TEST_P(Size_Dp_MinDist, OCL_HoughCircles,
testing::Combine(
testing::Values(perf::sz720p, perf::szSXGA, perf::sz1080p),
testing::Values(1.0f, 2.0f, 4.0f),
testing::Values(1.0f, 10.0f)))
{
const cv::Size size = std::tr1::get<0>(GetParam());
const float dp = std::tr1::get<1>(GetParam());
const float minDist = std::tr1::get<2>(GetParam());
const int minRadius = 10;
const int maxRadius = 30;
const int cannyThreshold = 100;
const int votesThreshold = 15;
cv::RNG rng(123456789);
cv::Mat src(size, CV_8UC1, cv::Scalar::all(0));
const int numCircles = rng.uniform(50, 100);
for (int i = 0; i < numCircles; ++i)
{
cv::Point center(rng.uniform(0, src.cols), rng.uniform(0, src.rows));
const int radius = rng.uniform(minRadius, maxRadius + 1);
cv::circle(src, center, radius, cv::Scalar::all(255), -1);
}
cv::ocl::oclMat ocl_src(src);
cv::ocl::oclMat ocl_circles;
Mat randomMat(Size size, int type, double minVal, double maxVal, bool useRoi = false)
{
RNG dataRng(rng.next());
return cvtest::randomMat(dataRng, size, type, minVal, maxVal, useRoi);
}
void Controller::generate_model(){
//Create Average images;
//this->_model_depth_average = cv::Mat::zeros(cv::Size(640,480),CV_16UC1);
//this->_model_color_average = cv::Mat::zeros(cv::Size(640,480),CV_8UC3);
//cv::Mat average_counter = cv::Mat::zeros(cv::Size(640,480),CV_8UC1);
int old_step = this->_property_manager->_3d_step;
cv::Mat1i freq_n(cv::Size(640,480),0);
cv::Mat1f freq_v(cv::Size(640,480),0);
cv::Mat freq_c = cv::Mat::zeros(cv::Size(640,480),CV_32FC3);
int idx, idx_clr;
float* ptr_freq_v = (float*)freq_v.data;
int* ptr_freq_n = (int*)freq_n.data;
float* ptr_freq_c = (float*)freq_c.data;
//Create Depth and Color Sum Image
for(int k = 0 ; k < this->_model_frames_depth.size() ; ++k){
UINT16* ptr_16u = (UINT16*)this->_model_frames_depth[k].data;
uint8_t* ptr_clr = (uint8_t*)this->_model_frames_color[k].data;
for(int y = 0; y < XN_VGA_Y_RES ; ++y) {
for(int x = 0; x < XN_VGA_X_RES ; ++x) {
idx = y * XN_VGA_X_RES + x;
if(ptr_16u[idx]){
ptr_freq_n[idx]++;
ptr_freq_v[idx]+=ptr_16u[idx];
}
idx_clr = y * XN_VGA_X_RES * 3 + x* 3;
ptr_freq_c[idx_clr + 0] += ptr_clr[idx_clr + 0];
ptr_freq_c[idx_clr + 1] += ptr_clr[idx_clr + 1];
ptr_freq_c[idx_clr + 2] += ptr_clr[idx_clr + 2];
}
}
}
//Create Depth and Color Average Image
for(int y = 0; y < XN_VGA_Y_RES ; ++y) {
for(int x = 0; x < XN_VGA_X_RES ; ++x) {
idx = y * XN_VGA_X_RES + x;
if(ptr_freq_n[idx]){
ptr_freq_v[idx] /= ptr_freq_n[idx];
}
}
}
freq_c /= this->_model_frames_color.size();
freq_c.convertTo(this->_model_color_average,CV_8UC3);
freq_v.convertTo(this->_model_depth_average,CV_16UC1);
//cv::Mat t8u;
//this->_model_depth_average.convertTo(t8u,CV_8UC1,0.05);
//cv::imshow("win1",this->_model_color_average);
////cv::imshow("win2",this->_mat_color_bgr);
//cv::imshow("win3",t8u);
//cv::imshow("win4",this->_mat_depth8UC1);
//cv::waitKey();
//Create Masks for avg images
cv::Mat main_mask;
cv::inRange(this->_model_depth_average,
this->_property_manager->_depth_min,
this->_property_manager->_depth_max,
main_mask);
cv::bitwise_and(main_mask,this->_mask_mirrors_and_foor,main_mask);
//Contours to remove noise
cv::Mat t8u;
main_mask.convertTo(t8u,CV_8UC1,0.05);
//cv::Mat image_temp;
//this->_model_depth_average.copyTo(image_temp);
cv::vector<cv::vector<cv::Point> > contours;
cv::vector<cv::Vec4i> hierarchy;
cv::findContours( t8u, contours, hierarchy, CV_RETR_EXTERNAL, CV_CHAIN_APPROX_SIMPLE, cv::Point(0, 0) );
for(unsigned int i = 0; i< contours.size(); i++ ){
if(contours[i].size() < 50){
cv::Scalar clr = cv::Scalar(/*255);// */rng.uniform(0, 255), rng.uniform(0,255), rng.uniform(0,255) );
cv::Rect rect = cv::boundingRect(contours[i]);
drawContours( this->_model_color_average, contours, i, clr, -1, 8, hierarchy, 0, cv::Point() );
//cv::rectangle(top_view_color,rect, clr);
}
}
Mirror* mirror;
uchar* ptr_mirror_mask = this->_mask_mirrors.data;
//Generate 3D
this->_model_n_points;
uchar* ptr_main_mask = main_mask.data;
//uchar* ptr_mirror_mask = this->_mask_mirrors.data;
UINT16* ptr_16u = (UINT16*)this->_model_depth_average.data;
for(int y = 0; y < XN_VGA_Y_RES ; ++y /*y += this->_property_manager->_3d_step*/) {
for(int x = 0; x < XN_VGA_X_RES ; ++x/*x += this->_property_manager->_3d_step*/) {
idx = y * XN_VGA_X_RES + x;
if(ptr_main_mask[idx]){
XnPoint3D point1;
point1.X = x;
point1.Y = y;
point1.Z = ptr_16u[idx];
if(ptr_mirror_mask[idx]){
for(int j = 0 ; j < this->_mirrors.size() ; ++j){
mirror = this->_mirrors[j];
if(mirror && mirror->_area_mask.data[idx]){
if(point1.Z > mirror->_depth_min && point1.Z < mirror->_depth_max){
this->_model_back_3d_to_2d[this->_model_n_points][XX] = x;
this->_model_back_3d_to_2d[this->_model_n_points][YY] = y;
this->_model_projective[this->_model_n_points++] = point1;
}
}
}
}
else{
this->_model_back_3d_to_2d[this->_model_n_points][XX] = x;
this->_model_back_3d_to_2d[this->_model_n_points][YY] = y;
this->_model_projective[this->_model_n_points++] = point1;
}
}
}
}
bool result = this->_kinect->convert_to_realworld( this->_model_n_points,
this->_model_projective,
this->_model_realworld);
//uint8_t* ptr_clr = (uint8_t*)this->_model_color_average.data;
//Generate PCL-3D Floor
//uchar* ptr_mask_floor = mask_floor.data;
//for(int y = 0; y < XN_VGA_Y_RES ; y += this->_property_manager->_3d_step) {
// for(int x = 0; x < XN_VGA_X_RES ; x += this->_property_manager->_3d_step) {
// //if(ptr_mask_floor[y * XN_VGA_X_RES + x]){
// //}
// }
//}
//Generate PCL-3D from Mirrors
_pcl_cloud.clear();
uchar* ptr = this->_mask_main.data;
uint8_t* ptr_clr = (uint8_t*)this->_model_color_average.data;
_model_cloud.clear();
int xx, yy;
double dist;
std::vector<double> nx,ny,nz;
for(int i = 0 ; i < this->_mirrors.size() ; ++i){
double nx_,ny_,nz_;
mirror = this->_mirrors[i];
if(mirror){
mirror->_n_points = 0;
mirror->_plane.get_normal(&nx_,&ny_,&nz_);
nx_ *= -1; ny_ *= -1; nz_ *= -1;
nx.push_back(nx_);
ny.push_back(ny_);
nz.push_back(nz_);
}
}
for(int i = 0 ; i < this->_model_n_points ; ++i){
xx = this->_model_back_3d_to_2d[i][XX];
yy = this->_model_back_3d_to_2d[i][YY];
idx = yy * XN_VGA_X_RES + xx;
if(ptr_mirror_mask[idx]){
for(int j = 0 ; j < this->_mirrors.size() ; ++j){
mirror = this->_mirrors[j];
if(mirror && mirror->_area_mask.data[idx]){
//if(this->_model_realworld[i].Z > mirror->_depth_min && this->_model_realworld[i].Z < mirror->_depth_max){
dist = mirror->_plane.distance_to_plane( this->_model_realworld[i].X,
this->_model_realworld[i].Y,
this->_model_realworld[i].Z);
this->_model_realworld[i].X += 2 * dist * nx[j];
this->_model_realworld[i].Y += 2 * dist * ny[j];
this->_model_realworld[i].Z += 2 * dist * nz[j];
//}
}
}
}
dist = this->_floor->_plane.distance_to_plane( this->_model_realworld[i].X,
this->_model_realworld[i].Y,
this->_model_realworld[i].Z);
if(dist > this->_floor->_thresh){
pcl::PointXYZRGB pt(ptr_clr[yy * XN_VGA_X_RES * 3 + xx* 3 + 2],
ptr_clr[yy * XN_VGA_X_RES * 3 + xx* 3 + 1],
ptr_clr[yy * XN_VGA_X_RES * 3 + xx* 3 + 0]);
pt.x = this->_model_realworld[i].X;
pt.y = this->_model_realworld[i].Y;
pt.z = this->_model_realworld[i].Z;
this->_model_cloud.push_back(pt);
}
}
this->_property_manager->_3d_step = old_step;
this->_property_manager->_flag_processed[PropertyManager::P_CAPTURE_SHOW] = true;
}
double randomDouble(double minVal, double maxVal)
{
return rng.uniform(minVal, maxVal);
}
static cv::Scalar randomScalar() {
static cv::RNG rng(12345);
return cv::Scalar(rng.uniform(0, 255), rng.uniform(0, 255), rng.uniform(0, 255));
}
void extractPatch(cv::Mat &img,
int *hull,
int bbW,
int bbH,
unsigned char filledColor,
cv::RNG &rng,
double noise,
double angle,
double shift,
double scale,
cv::Mat & noiseM,
cv::Mat &result) {
int cpX = (hull[0] + hull[2]) / 2;
int cpY = (hull[1] + hull[3]) / 2;
cv::Mat h = cv::Mat::eye(3, 3, CV_64FC1);
cv::Mat temp = cv::Mat::eye(3, 3, CV_64FC1);
double *tempPtr = temp.ptr<double>();
double sc;
double ang, ca, sa;
double shR, shC;
double xMin, yMin, xMax, yMax;
unsigned char *resPtr;
double *noisePtr;
int size;
//****Translating...
shR = shift * bbH * (rng.uniform(1e-4, 1.)-0.5);
shC = shift * bbW * (rng.uniform(1e-4, 1.)-0.5);
*(tempPtr + 2) = shC;
*(tempPtr + temp.cols + 2) = shR;
h *= temp;
//Reset...
*(tempPtr + 2) = 0.0;
*(tempPtr + temp.cols + 2) = 0.0;
//****Rotating...
ang = 2 * CV_PI / 360.0 * angle * (rng.uniform(1e-4, 1.)-0.5);
ca = cos(ang);
sa = sin(ang);
*tempPtr = ca;
*(tempPtr + 1) = -sa;
*(tempPtr + temp.cols) = sa;
*(tempPtr + temp.cols + 1) = ca;
h *= temp;
//Reset...
*tempPtr = 1.0;
*(tempPtr + 1) = 0.0;
*(tempPtr + temp.cols) = 0.0;
*(tempPtr + temp.cols + 1) = 1.0;
//****Scaling...
sc = 1.0 - scale*(rng.uniform(1e-4, 1.)-0.5);
*tempPtr = (double)sc;
*(tempPtr + temp.cols + 1) = (double)sc;
h *= temp;
//Reset...
*tempPtr = 1.0;
*(tempPtr + temp.cols + 1) = 1.0;
//****Shifting Center of BB to (0, 0)...
*(tempPtr + 2) = -cpX;
*(tempPtr + temp.cols + 2) = -cpY;
h *= temp;
//Now Warp the Patch...
bbW--;
bbH--;
xMin = -bbW / 2.0;
yMin = -bbH / 2.0;
xMax = bbW / 2.0;
yMax = bbH / 2.0;
warpImageROI(img,
xMin,
yMin,
xMax,
yMax,
bbW,
bbH,
h,
filledColor,
result.data);
//Add Random Noise...
rng.fill(noiseM,
cv::RNG::NORMAL,
cv::Mat::zeros(1,1,CV_64FC1),
cv::Mat::ones(1,1,CV_64FC1));
noiseM *= noise;
//Here OpenCV Applies Saturation Arithmetic by Itself...
cv::add(result, noise, result, cv::noArray(), CV_8UC1);
}
int randomInt(int minVal, int maxVal)
{
return rng.uniform(minVal, maxVal);
}
/**
* Detect people using background segmentation and contours
* BSN2013
*/
static vector<cv::Mat> detectPeopleSegment(cv::Mat image) {
vector<cv::Mat> points;
// convert to HSV
cv::Mat imageHSV;
cv::cvtColor(image, imageHSV, CV_BGR2HSV);
vector<cv::Mat> imageHSVSlices;
cv::split(imageHSV, imageHSVSlices);
//cv::threshold(imageHSVSlices[0], imageHSVSlices[0], 160, 200, cv::THRESH_BINARY);
// background subtraction
cv::Mat fgMask;
bgmodel(image, fgMask, learningRate);
// tidy foreground mask
cv::GaussianBlur(fgMask, fgMask, cv::Size(1, 1), 0, 0);
int erosionSize = 5;
cv::Mat element = cv::getStructuringElement( cv::MORPH_ELLIPSE,
cv::Size(2*erosionSize+1, 2*erosionSize+1),
cv::Point( erosionSize, erosionSize ));
cv::dilate(fgMask, fgMask, element);
cv::erode(fgMask, fgMask, element);
cv::erode(fgMask, fgMask, element);
cv::dilate(fgMask, fgMask, element);
cv::Mat background;
bgmodel.getBackgroundImage(background);
//cv::imshow("back", background);
// subtract background from original image
cv::Mat foreground;
//cv::not
cv::threshold(fgMask, fgMask, 128, 255, cv::THRESH_BINARY);
image.copyTo(foreground, fgMask);
cv::imshow("fg", fgMask);
cv::imshow("fore", foreground);
// edge information
int lowThreshold = 100;
int ratio = 3;
int kernelSize = 3;
cv::Mat imageCanny;
cv::Canny(foreground, imageCanny, lowThreshold, lowThreshold*ratio, kernelSize);
// weight map and weighted-gradient image
// apply Gaussian filter (size = 9 and sigma = 1.5) to edge information from foreground image
// create Gaussian filter
// weight map
cv::Mat weightMap;
cv::GaussianBlur(imageCanny, weightMap, cv::Size(9, 9), 1.5, 1.5);
// gradient image
cv::Mat imageGray;
cv::cvtColor(image, imageGray, CV_BGR2GRAY);
cv::Mat imageGradient;
cv::Mat imageGradientX;
cv::Mat imageGradientY;
cv::Mat imageAbsGradientX;
cv::Mat imageAbsGradientY;
cv::Sobel(imageGray, imageGradientX, CV_16S, 1, 0, 3, 1, 0, cv::BORDER_DEFAULT);
cv::Sobel(imageGray, imageGradientY, CV_16S, 0, 1, 3, 1, 0, cv::BORDER_DEFAULT);
cv::convertScaleAbs(imageGradientX, imageAbsGradientX);
cv::convertScaleAbs(imageGradientY, imageAbsGradientY);
cv::addWeighted(imageAbsGradientX, 0.5, imageAbsGradientY, 0.5, 0, imageGradient);
// weighted-gradient image
cv::Mat weightedGradient;
cv::Mat colourWeightMap;
weightedGradient = imageGradient.mul(weightMap);
// object (body) contours
vector< vector<cv::Point> > objectContours;
vector<cv::Vec4i> objectHierarchy;
cv::findContours(fgMask, objectContours, objectHierarchy, CV_RETR_TREE, CV_CHAIN_APPROX_SIMPLE, cv::Point(0, 0));
// bodies and heads
// store index of detected body contours and position of head
vector<int> bodies;
vector<cv::Point2f> headCenter;
vector<float> headRadius;
// detect big bodies
for (int i = 0; i < objectContours.size(); i++) {
// if contour is too big
if (getContourRadius(objectContours[i]) > BODYSIZE*2) {
// increment merged counter
numMerged++;
cout << "Merged object" << endl;
// TODO cut down to size
// TODO consider just slicing it
// process contour by eroding it
cv::Mat largeContour = cv::Mat::zeros(imageCanny.size(), CV_8UC3);
drawContours(largeContour, objectContours, i, colourRed, CV_FILLED, 8, objectHierarchy, 0, cv::Point());
// erode until large contour becomes 2+
vector< vector<cv::Point> > largeContours;
vector<cv::Vec4i> largeHierarchy;
do {
cv::erode(largeContour, largeContour, element);
cv::Canny(largeContour, largeContour, lowThreshold, lowThreshold*ratio, kernelSize);
cv::findContours(largeContour, largeContours, largeHierarchy, CV_RETR_TREE, CV_CHAIN_APPROX_SIMPLE, cv::Point(0, 0));
} while (largeContours.size() == 1); // || (largeContours.size() == 1 && getContourRadius(largeContours[0]) >= BODYSIZE)); // TODO potential infinite bug here
if (largeContours.size() > 1) {
// increment split counter
numSplit++;
cout << "Split object" << endl;
}
else if (largeContours.size() == 1) {
// increment unsplit counter
numUnsplit++;
cout << "No split - size still 1" << endl;
}
for (int j = 0; j < largeContours.size(); j++) {
objectContours.push_back(largeContours[j]);
}
}
}
cv::Mat bodiesHeads = cv::Mat::zeros(image.size(), CV_8UC3);
// detect bodies
for (int i = 0; i < objectContours.size(); i++) {
if (isBody(objectContours[i])) {
// predict head position
cv::Point2f defaultHeadCenter;
// body bounding box
cv::RotatedRect minBodyRect;
minBodyRect = cv::minAreaRect(cv::Mat(objectContours[i]));
// body bounding circle radius
float headOffset = getContourRadius(objectContours[i]); //*0.7;
// image centre
cv::Point2f imageCentre(image.size().width/2, image.size().height/2);
// find gradient
float m = (minBodyRect.center.y - imageCentre.y)/(minBodyRect.center.x - imageCentre.x);
// find angle
double angle;
if (minBodyRect.center.x <= imageCentre.x && minBodyRect.center.y < imageCentre.y) {
// top left quad
angle = atan((imageCentre.x - minBodyRect.center.x)/(imageCentre.y - minBodyRect.center.y));
}
else if (minBodyRect.center.x <= imageCentre.x) {
// bottom left quad
angle = PI - atan((imageCentre.x - minBodyRect.center.x)/(minBodyRect.center.y - imageCentre.y));
}
else if (minBodyRect.center.x > imageCentre.x && minBodyRect.center.y > imageCentre.y) {
// bottom right quad
angle = PI + atan((minBodyRect.center.x - imageCentre.x)/(minBodyRect.center.y - imageCentre.y));
}
else {
// top right quad
angle = 2*PI - atan((minBodyRect.center.x - imageCentre.x)/(imageCentre.y - minBodyRect.center.y));
}
do {
headOffset *= 0.7;
defaultHeadCenter = cv::Point2f(minBodyRect.center.x - headOffset * sin(angle), minBodyRect.center.y - headOffset * cos(angle));
} while (cv::pointPolygonTest(objectContours[i], defaultHeadCenter, true) <= 0 && headOffset >= 1);
// store body and head if body big enough for head
if (headOffset >= 1) {
// store body
bodies.push_back(i);
//angle = angle * 180/PI;
headCenter.push_back(defaultHeadCenter);
headRadius.push_back(0); // default head size
// get detailed contours of body
cv::Mat bodyMask = cv::Mat::zeros(image.size(), CV_8UC1);
drawContours(bodyMask, objectContours, i, colourWhite, CV_FILLED, 8, objectHierarchy, 0, cv::Point());
//cv::floodFill(bodyMask, cv::Point2i(0, 0), cv::Scalar(1));
cv::Mat body;
image.copyTo(body, bodyMask);
//cv::imshow("B", body);
// body edges
cv::Mat bodyCanny;
cv::Canny(body, bodyCanny, lowThreshold, lowThreshold*ratio, kernelSize);
// weight map
cv::Mat bodyWeightMap;
cv::GaussianBlur(bodyCanny, bodyWeightMap, cv::Size(9, 9), 1.5, 1.5);
// gradient image
cv::Mat bodyGray;
cv::cvtColor(body, bodyGray, CV_BGR2GRAY);
cv::Mat bodyGradient;
cv::Mat bodyGradientX;
cv::Mat bodyGradientY;
cv::Mat bodyAbsGradientX;
cv::Mat bodyAbsGradientY;
cv::Sobel(bodyGray, bodyGradientX, CV_16S, 1, 0, 3, 1, 0, cv::BORDER_DEFAULT);
cv::Sobel(bodyGray, bodyGradientY, CV_16S, 0, 1, 3, 1, 0, cv::BORDER_DEFAULT);
cv::convertScaleAbs(bodyGradientX, bodyAbsGradientX);
cv::convertScaleAbs(bodyGradientY, bodyAbsGradientY);
cv::addWeighted(bodyAbsGradientX, 0.5, bodyAbsGradientY, 0.5, 0, bodyGradient);
// weighted-gradient image
cv::Mat bodyWeightedGradient;
bodyWeightedGradient = bodyGradient.mul(bodyWeightMap);
// body contours
vector< vector<cv::Point> > bodyContours;
vector<cv::Vec4i> bodyHierarchy;
cv::findContours(bodyWeightedGradient, bodyContours, bodyHierarchy, CV_RETR_TREE, CV_CHAIN_APPROX_SIMPLE, cv::Point(0, 0));
// detect head
for (int j = 0; j < bodyContours.size(); j++) {
// process contour by eroding it
cv::Mat aContour = cv::Mat::zeros(image.size(), CV_8UC3);
drawContours(aContour, bodyContours, j, colourWhite, CV_FILLED, 8, bodyHierarchy, 0, cv::Point());
drawContours(bodiesHeads, bodyContours, j, colourWhite, 2, 8, bodyHierarchy, 0, cv::Point());
cv::erode(aContour, aContour, element);
//cv::erode(aContour, aContour, element);
//cv::dilate(aContour, aContour, element);
cv::Canny(aContour, aContour, lowThreshold, lowThreshold*ratio, kernelSize);
vector< vector<cv::Point> > subContours;
vector<cv::Vec4i> subHierarchy;
cv::findContours(aContour, subContours, subHierarchy, CV_RETR_TREE, CV_CHAIN_APPROX_SIMPLE, cv::Point(0, 0));
//
for (int k = 0; k < subContours.size(); k++) {
//cv::drawContours(imageContours, subContours, k, cv::Scalar(0, 255, 0), 2, 8, subHierarchy, 0, cv::Point());
if (isHead(subContours[k], objectContours[i])) {
vector<cv::Point> contourPoly;
cv::Point2f center;
float radius;
if (subContours.size() > 1) {
approxPolyDP(cv::Mat(subContours[k]), contourPoly, 3, true);
}
else {
approxPolyDP(cv::Mat(bodyContours[j]), contourPoly, 3, true);
}
minEnclosingCircle((cv::Mat)contourPoly, center, radius);
float distanceOld = euclideanDistance(headCenter[headCenter.size() - 1], defaultHeadCenter);
float distanceNew = euclideanDistance(center, defaultHeadCenter);
if (headRadius[headRadius.size() - 1] == 0 || (distanceOld > 0 && distanceNew < distanceOld)) {
// store first detected head or store if it is a better detection
headCenter[headCenter.size() - 1] = center;
headRadius[headRadius.size() - 1] = radius;
}
}
}
}
if (headRadius[headRadius.size() - 1] == 0) {
headRadius[headRadius.size() - 1] = 10;
}
}
}
}
// draw bodies and heads
//cv::Mat bodiesHeads = cv::Mat::zeros(image.size(), CV_8UC3);
for (int i = 0; i < bodies.size(); i++) {
// draw body
cv::Scalar colour = cv::Scalar(rng.uniform(0, 255), rng.uniform(0, 255), rng.uniform(0, 255));
drawContours(foreground, objectContours, bodies[i], colour, 2, 8, objectHierarchy, 0, cv::Point());
circle(foreground, headCenter[i], (int)headRadius[i], colour, 2, 8, 0);
// body bounding box
cv::RotatedRect bodyRect;
bodyRect = cv::minAreaRect(cv::Mat(objectContours[bodies[i]]));
// output
cout << imageNum;
cout << "," << headCenter[i].x << "," << headCenter[i].y << "," << headRadius[i]; // head info
cout << "," << bodyRect.center.x << "," << bodyRect.center.y;
cout << "," << cv::contourArea(objectContours[bodies[i]]);
cout << endl;
// output points
cv::Mat point(2, 1, CV_32FC1);
point.at<float>(0) = headCenter[i].x;
point.at<float>(1) = headCenter[i].y;
points.push_back(point);
}
// increment frame counter
numFrames++;
cv::imshow("Original", image);
//cv::imshow("Hue", imageHSVSlices[0]);
//cv::imshow("Saturation", imageHSVSlices[1]);
//cv::imshow("Value", imageHSVSlices[2]);
cv::imshow("fgMask", fgMask);
cv::imshow("Foreground", foreground);
cv::imshow("Canny", imageCanny);
cv::imshow("WeightMap", weightMap);
cv::imshow("Gradient Image", imageGradient);
cv::imshow("Weighted-Gradient Image", weightedGradient);
//cv::imshow("Contours", imageContours);
cv::imshow("Body & Head", bodiesHeads);
cvWaitKey(delay); //5
return points;
}
