void WayFinderApp::setup()
{
println("WayFinderApp started.");
// Load destinations from config file.
destinations = Destination::getDestinations();
if(destinations.size() == 0) {
println("No destinations found, check the config file.");
exit(EXIT_FAILURE);
}
println("Destinations loaded.");
// Initialized state.
spotlightRadius = (float)getWindowWidth() / 16.0f;
arrowLength = (float)min(getWindowWidth(), getWindowHeight()) / 2.0f;
spotlightCenter2D = Vec2f((float)getWindowWidth() / 2.0f, (float)getWindowHeight() / 2.0f);
spotlightCenter3D = Vec3f((float)getWindowWidth() / 2.0f, (float)getWindowHeight() / 2.0f, 0.0f);
detected = false;
//capture = Capture::create(WayFinderApp::WIDTH, WayFinderApp::HEIGHT);
capture = Capture::create(getWindowWidth(), getWindowHeight());
capture->start();
//bg.set("bShadowDetection", false);
bg.set("nmixtures", 3);
bg.setBool("detectShadows", true);
debugView = false;
}
JNIEXPORT jintArray JNICALL Java_pris_videotest_JNIClient_detectWithReturn(
JNIEnv * env, jclass, jintArray pixels, jint width, jint height) {
jint * cPixels;
cPixels = env->GetIntArrayElements(pixels, 0);
cv::Mat imgData(height, width, CV_8UC4, (unsigned char*) cPixels);
IplImage *frame = cvCreateImage(cvSize(width, height), IPL_DEPTH_8U, 4);
*frame = imgData.operator _IplImage();
//imgData.release();
cvSmooth(frame, frame, CV_GAUSSIAN, 3, 0, 0); ///<高斯滤波
cv::Mat m_OriFrameMat = frame;
//cvReleaseImage(&frame);
resize(m_OriFrameMat, m_ResizeFrameMat,
cv::Size(m_nVideoResizeW, m_nVideoResizeH), 0, 0, CV_INTER_LINEAR); ///<压缩 640*480 m_pResizeFrame=30*40
//m_OriFrameMat.release();
//cvtColor(m_ResizeFrameMat, m_GrayFrameMat, CV_BGRA2GRAY, 1); ///<灰度化
//m_ResizeFrameMat.release();
m_pBGSubMOG2.operator()(m_ResizeFrameMat, foregroundMat, 0.001);
m_ResizeFrameMat = foregroundMat;
int result[m_nVideoResizeH*m_nVideoResizeW];
for(int i = 0; i<m_nVideoResizeH*m_nVideoResizeW; i++){
result[i] = m_ResizeFrameMat.data[i];
}
env->ReleaseIntArrayElements(pixels, cPixels, 0);
jintArray intArray = env->NewIntArray(m_nVideoResizeH*m_nVideoResizeW);
env->SetIntArrayRegion(intArray, 0, m_nVideoResizeH*m_nVideoResizeW, result);
return intArray;
}
JNIEXPORT jboolean JNICALL Java_pris_videotest_JNIClient_detect(JNIEnv * env,
jclass, jbyteArray pixels, jint width, jint height) {
jbyte * cPixels;
cPixels = env->GetByteArrayElements(pixels, 0);
cv::Mat imgData(height, width, CV_8UC1, (unsigned char*) cPixels);
IplImage *frame = cvCreateImage(cvSize(width, height), IPL_DEPTH_8U, 1);
*frame = imgData.operator _IplImage();
//imgData.release();
cvSmooth(frame, frame, CV_GAUSSIAN, 3, 0, 0); ///<高斯滤波
cv::Mat m_OriFrameMat = frame;
//cvReleaseImage(&frame);
resize(m_OriFrameMat, m_ResizeFrameMat,
cv::Size(m_nVideoResizeW, m_nVideoResizeH), 0, 0, CV_INTER_LINEAR); ///<压缩 640*480 m_pResizeFrame=30*40
//m_OriFrameMat.release();
//cvtColor(m_ResizeFrameMat, m_GrayFrameMat, CV_BGRA2GRAY, 1); ///<灰度化
//m_ResizeFrameMat.release();
m_pBGSubMOG2.operator()(m_ResizeFrameMat, foregroundMat, 0.001);
m_ResizeFrameMat = foregroundMat;
int i, j, k;
k = 0;
for (i = 0; i < m_nVideoResizeH; i++) {
for (j = 0; j < m_nVideoResizeW; j++) {
if (m_ResizeFrameMat.data[i * m_nVideoResizeW + j] != 0) {
k++; ///<计算二值前景图像非0像素点个数
}
}
}
//m_GrayFrameMat.release();
//delete frame;
double k_ratio = (double) k / (double) (m_nVideoResizeW * m_nVideoResizeH);
if (k_ratio <= 0.01) {
env->ReleaseByteArrayElements(pixels, cPixels, 0);
return false;
}
if (k_ratio / m_rFGSegThreshold > 1.5 || k_ratio / m_rFGSegThreshold < 0.79)
m_rFGSegThreshold = k_ratio;
///检测到运动视频段
if (k_ratio >= m_rFGSegThreshold) {
env->ReleaseByteArrayElements(pixels, cPixels, 0);
return true;
}
env->ReleaseByteArrayElements(pixels, cPixels, 0);
return false;
}
int getFirePixelNumber(Mat aFrame) {
const int ROI_WIDTH = 40;
const int ROI_HEIGHT = 30;
unsigned int currentWidth = 0, currentHeight = 0;
unsigned int width, height;
std::vector<std::vector<cv::Point> > contours;
//Mat roi;
Rect roi;
width = aFrame.cols;
height = aFrame.rows;
Mat YCrCbFrame;
Mat YChannel, CrChannel, CbChannel;
Mat Y_Cb, Cr_Cb;
Mat colorMask;
//check for input frame
if(aFrame.empty())
{
return -1;
}
//---------------detect moving pixel------------//
// using BackgroundSubstractMOG2 //
//----------------------------------------------//
bg.operator ()(aFrame, front);
bg.getBackgroundImage(back);
//cv::erode(front,front, cv::Mat());
//cv::dilate(front, front, cv::Mat());
cv::medianBlur(front, front, 5);
cv::findContours(front,contours,CV_RETR_EXTERNAL,CV_CHAIN_APPROX_NONE);
std::vector<std::vector<cv::Point> > contours_poly( contours.size() );
vector<Rect> boundRect( contours.size() );
vector<Point2f>center( contours.size() );
vector<float>radius( contours.size() );
for(unsigned int i = 0; i < contours.size(); i++ )
{
cv::approxPolyDP( contours[i], contours_poly[i], 3.0, true );
boundRect[i] = boundingRect( Mat(contours_poly[i]) );
cv::minEnclosingCircle( contours_poly[i], center[i], radius[i] );
}
for(unsigned int i = 0; i< contours.size(); i++ )
{
Scalar color = Scalar( 255,255,255 );
//params: input output contourIdx color thickness
drawContours( front, contours_poly, i, color, CV_FILLED, 8, vector<Vec4i>(), 0, Point() );
}
//----detect fire color----//
//-------------------------------------------------------------------//
// pixel = fire color when //
// valueY > valueCb && //
// valueCr > valueCb && //
// (valueY > meanY && valueCr > meanCr && valueCb < meanCb) //
//-------------------------------------------------------------------//
//get YCrCb channel
cvtColor(aFrame, YCrCbFrame, CV_BGR2YCrCb);
vector<Mat> channels(3);
split(YCrCbFrame, channels);
YChannel = channels[0];
CrChannel = channels[1];
CbChannel = channels[2];
//calculate mean of 3 channels: => for further use
// unsigned char Y_mean, Cr_mean, Cb_mean;
// Y_mean = (unsigned char)mean(YChannel)[0];
// Cr_mean = (unsigned char)mean(CrChannel)[0];
// Cb_mean = (unsigned char)mean(CbChannel)[0];
colorMask = Mat(aFrame.rows, aFrame.cols, CV_8UC1);
Y_Cb = Mat(aFrame.rows, aFrame.cols, CV_8UC1);//YChannel minus CbChannel
Cr_Cb = Mat(aFrame.rows, aFrame.cols, CV_8UC1);//CrChannel minus CbChannel
subtract(YChannel, CbChannel, Y_Cb); threshold(Y_Cb, Y_Cb, 10, 255, THRESH_BINARY);
subtract(CrChannel, CbChannel, Cr_Cb);threshold(Cr_Cb, Cr_Cb, 10, 255, THRESH_BINARY);
//colorMask = front & Y_Cb & Y_Cr
bitwise_and(front, Y_Cb, colorMask);
bitwise_and(colorMask, Cr_Cb, colorMask);
for(currentWidth = 0; currentWidth < width; currentWidth+=ROI_WIDTH)
{
for(currentHeight = 0; currentHeight < height; currentHeight+=ROI_HEIGHT)
{
roi = Rect(currentWidth, currentHeight, ROI_WIDTH, ROI_HEIGHT);
cv::Mat testArea = colorMask(roi);
int fireCount = countNonZero(testArea);
if(fireCount > 10)
{
cv::Mat roi_draw = aFrame(roi);
cv::Mat color(roi_draw.size(), CV_8UC3, cv::Scalar (0,125,125));
double alpha = 0.5;
cv::addWeighted(color, alpha, roi_draw, 1.0-alpha, 0.0, roi_draw);
}
}
}
int fireCount = countNonZero(colorMask);
cvtColor(front, front, CV_GRAY2BGR);
cvtColor(Y_Cb, Y_Cb, CV_GRAY2BGR);
cvtColor(Cr_Cb, Cr_Cb, CV_GRAY2BGR);
cvtColor(colorMask, colorMask, CV_GRAY2BGR);
char wName[25];
sprintf(&(wName[0]),"Frames");
cvShowManyImages(wName, aFrame.cols, aFrame.rows, 5, (unsigned char*)aFrame.data, (unsigned char*)front.data, (unsigned char*)Y_Cb.data, (unsigned char*)Cr_Cb.data, (unsigned char*)colorMask.data);
// imshow(wName, frame);
// if(fireCount>fireThreshold)
// {
// //count the frame that contains firePixel surpass threshold
// std::cout << "Fired" << std::endl;
// }
// else
// {
// std::cout << "Not fired" << std::endl;
// }
return fireCount;
}
extern "C" void getbg(int rows, int cols, unsigned char *bgD) {
cv::Mat bg = cv::Mat(rows, cols, CV_8UC3, bgD);
mog.getBackgroundImage(bg);
}
void WayFinderApp::update()
{
if(getElapsedFrames() % FRAME_COUNT_THRESHOLD == 0) {
detected = false;
// TODO: Consider converting capture to grayscale or blurring then thresholding to improve performance.
if(capture && capture->checkNewFrame()) {
frame = toOcv(capture->getSurface());
//cv::Mat frameGray, frameBlurred, frameThresh, foreGray, backGray;
//cvtColor(frame, frameGray, CV_BGR2GRAY);
int blurAmount = 10;
//cv::blur(frame, frameBlurred, cv::Size(blurAmount, blurAmount));
//threshold(frameBlurred, frameThresh, 100, 255, CV_THRESH_BINARY);
// Get all contours.
//bg.operator()(frameThresh,fore);
bg.operator()(frame, fore);
bg.getBackgroundImage(back);
cv::erode(fore, fore, cv::Mat());
cv::dilate(fore, fore, cv::Mat());
cv::findContours(fore, contours, CV_RETR_EXTERNAL, CV_CHAIN_APPROX_NONE);
// Get largest contour: http://stackoverflow.com/questions/15012073/opencv-draw-draw-contours-of-2-largest-objects
unsigned largestIndex = 0;
unsigned largestContour = 0;
for(unsigned i = 0; i < contours.size(); i++) {
if(contours[i].size() > largestContour) {
largestContour = contours[i].size();
largestIndex = i;
}
}
vector<std::vector<cv::Point>> hack;
cv::Rect rect;
cv::Point center;
if(contours.size() > 0) {
hack.push_back(contours[largestIndex]);
// Find bounding rectangle for largest countour.
rect = boundingRect(contours[largestIndex]);
// Make sure the blog is large enough to be a track-worthy.
println("Rext area = " + boost::lexical_cast<std::string>(rect.area()));
if(rect.area() >= 5000) { // TODO: Tweak this value.
// Get center of rectangle.
center = cv::Point(
rect.x + (rect.width / 2),
rect.y + (rect.height / 2)
);
// Show guide.
spotlightCenter2D.x = (float)center.x;
spotlightCenter2D.y = (float)center.y;
spotlightCenter3D.x = (float)center.x;
spotlightCenter3D.y = (float)center.y;
//spotlightRadius = (rect.width + rect.y) / 2;
detected = true;
}
}
// When debug mode is off, the background should be black.
if(debugView) {
if(contours.size() > 0) {
cv::drawContours(frame, contours, -1, cv::Scalar(0, 0, 255), 2);
cv::drawContours(frame, hack, -1, cv::Scalar(255, 0, 0), 2);
rectangle(frame, rect, cv::Scalar(0, 255, 0), 3);
circle(frame, center, 10, cv::Scalar(0, 255, 0), 3);
}
mTexture = gl::Texture(fromOcv(frame));
}
}
// TODO: Create control panel for all inputs.
}
}
void processVideo(char* videoFilename) {
//create the capture object
IplImage *labelImg;//foreground
CTracker openTracker((float)0.033, (float)0.6, (double)20.0, 10, 3000);
CvTracks tracks;
VideoCapture capture(videoFilename);
if(!capture.isOpened()){
//error in opening the video input
cerr << "Unable to open video file: " << videoFilename << endl;
exit(EXIT_FAILURE);
}
bool bInitialized = false;
//read input data. ESC or 'q' for quitting
while( (char)keyboard != 'q' && (char)keyboard != 27 ){
//read the current frame
if(!capture.read(frame)) {
cerr << "Unable to read next frame." << endl;
cerr << "Exiting..." << endl;
exit(EXIT_FAILURE);
}
if(bInitialized==false)
{
cv::Size frameSize(static_cast<int>(frame.cols), static_cast<int>(frame.rows));
labelImg = cvCreateImage(frameSize, IPL_DEPTH_LABEL, 1);
bInitialized = true;
}
//update the background model
pMOG2.operator ()(frame,fgMaskMOG2);
//open operator.
cv::erode(fgMaskMOG2,fgMaskMOG2,cv::Mat(),cv::Point(-1,-1),1);
cv::dilate(fgMaskMOG2,fgMaskMOG2,cv::Mat(),cv::Point(-1,-1),4);
// step 2::blob analysis
CvBlobs blobs;
unsigned int result = cvLabel(&(IplImage)fgMaskMOG2, labelImg, blobs);
cvFilterByArea(blobs, 125, 10000);
cvRenderBlobs(labelImg, blobs, &(IplImage)frame, &(IplImage)frame, CV_BLOB_RENDER_BOUNDING_BOX);
//cvUpdateTracks(blobs, tracks, 200, 5);
//cvRenderTracks(tracks, &(IplImage)frame, &(IplImage)frame, CV_TRACK_RENDER_ID|CV_TRACK_RENDER_BOUNDING_BOX|CV_TRACK_RENDER_TO_LOG);
//convert the blobs into detection structure;
vector<Detection*> detections;
for (CvBlobs::const_iterator it=blobs.begin();it!=blobs.end();++it)
{
CvBlob *blob=(*it).second;
Detection *_detection = new Detection;
_detection->centroid.x= blob->centroid.x;
_detection->centroid.y= blob->centroid.y;
_detection->brect.x = blob->minx;
_detection->brect.y = blob->miny;
_detection->brect.height = blob->maxy - blob->miny;
_detection->brect.width = blob->maxx - blob->minx;
detections.push_back(_detection);
}
//Step 3 : give the list of all centroids of all detected contours to tracker. Track return the trace of the track, whose values are Kalman-filtered
if(blobs.size() > 0)
{
openTracker.Update(detections);
int i, j;
for(i=0; i < openTracker.tracks.size(); i++)
{
//add a threshold to de-noise, if the contour just appeared, maybe noise. set a threshold
if(openTracker.tracks[i]->trace.size() > 10)
{
for(j = 0; j < (openTracker.tracks[i]->trace.size() - 2); j++)
{
cv::rectangle(frame, openTracker.tracks[i]->brect, Scalar(255,0,0));
//line(fore, openTracker.tracks[i]->trace[j], openTracker.tracks[i]->trace[j+1], Colors[openTracker.tracks[i]->track_id % 9], 1, CV_AA);
line(frame, openTracker.tracks[i]->trace[j], openTracker.tracks[i]->trace[j+1], Scalar(255,0,0), 1, CV_AA);
}
stringstream ss;
ss << openTracker.tracks[i]->track_id;
string str = ss.str();
putText(frame, str, openTracker.tracks[i]->trace[j], FONT_HERSHEY_SIMPLEX, 0.5, Scalar(255,0,0), 1);
}
}
}
//get the frame number and write it on the current frame
stringstream ss;
rectangle(frame, cv::Point(10, 2), cv::Point(100,20),cv::Scalar(255,255,255), -1);
//show the current frame and the fg masks
imshow("Frame", frame);
imshow("FG Mask MOG 2", fgMaskMOG2);
//get the input from the keyboard
keyboard = waitKey( 30 );
}
//delete capture object
capture.release();
cvReleaseImage(&labelImg);
}
int main(int argc, const char** argv) {
int persons = 1;
const bool recalibrate = true;
// read args
if (argc > 1)
persons = atoi(argv[1]);
// setup path to file
char* filepath;
filepath = "/home/charence/Workspace/biomotion-vision/images/set2/%d/10/frame%04d.jpg";
// is it my mac?
#ifdef MYMAC
filepath = "/Users/charence/Workspace/biomotion-vision/images/set2/%d/10/frame%04d.jpg";
#endif
// is it on doc?
#ifdef ONDOC
filepath = "/media/Charence500/Data/20121221/10/%d/frame%04d.jpg";
#endif
int start = 0;
int end = 2485;
switch(persons) {
case 1: end = 2485; break;
case 2: end = 3621; break;
case 3: end = 4489; break;
}
// setup background model
bgmodel.set("history", history);
bgmodel.set("varThreshold", varThreshold);
bgmodel.set("detectShadows", true);
// setup homography
if (recalibrate) {
}
else {
}
// setup tracker
pointTracker.setArguments(3.5, 50);
//cout << "ImageNum,ContourArea,RectCentreX,RectCentreY,RectAngle,RectWidth,RectHeight,CircCentreX,CircCentreY,Radius" << endl;
//cout << "ImageNum,ContourArea,CircCentreX,CircCentreY,Radius" << endl;
// process sequence
for (int i = start; i <= end; i++) {
if (i > 200)
i = 999; //1050; // 999; // 1050;*/
char filename [128];
sprintf(filename, filepath, persons, i);
//cout << "In: " << filename << endl;
imageNum = i;
// load image
cv::Mat image = cv::imread(filename);
if (image.empty()) {
throw runtime_error("Could not load image");
}
// detect objects
learningRate = (i > 200) ? 0.00005 : 0.01;
//delay = (i > 900) ? 500 : 5; // slows down update so I can get screencaps
//vector<ObjectInfo> detectedObjects = detectPeople(image);
vector<cv::Mat> detectedObjects = detectPeople(image);
// homography
//detectedObjects = ho
// track objects
//objectTracker->update(detectedObjects);
if (i > 200) {
trackObjects(detectedObjects);
predictObjects(image);
}
// translate coordinates
}
cout << "Total frames: " << numFrames << endl;
cout << "Merged frames: " << numMerged << endl;
cout << "Split frames: " << numSplit << endl;
cout << "Unsplit frames: " << numUnsplit << endl;
return 0;
}
/**
* 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;
}