bool Machinery::detect(ImageVector & images, JSON & data)
{
// -------------
// Detect motion
return detectMotion(images, data);
}
/*
* processFrame
*
* creates a threshold image of frame, splits the thresholded image into left and
* right halves and then tracks for the configured color in each half, setting the
* left and right paddle positions accordingly
*
* preconditions: frame must be a valid Mat object representing a single frame from
* from a VideoCapture object
* postconditions: sets left and right paddles according to color detected in the
* left and right halves of the frame, respectively
*/
void ColorPaddleDetector::processFrame(Mat &frame)
{
flip(frame, frame, 1);
Mat thres;
createThresholdImg(frame, thres);
// create left and right threshold images for seperate color detection in
// left and right sides of the frame
int x = thres.cols / 2;
int y = thres.rows;
Mat thresholdLeft(thres, Rect(0, 0, 320, 480));
Mat thresholdRight(thres, Rect(320, 0, 320, 480));
// detect motion in the left and right frames
detectMotion(thresholdLeft, frame, IS_RED);
detectMotion(thresholdRight, frame, IS_BLUE);
}
/*
* processFrame
*
* uses sequential images to detect motion in the left and right halves of the frame.
*
* preconditions: frame must be a valid Mat object representing a single frame from
* from a VideoCapture object
* postconditions: sets left and right paddles according to motion detected in the
* left and right halves of the frame, respectively
*/
void MotionPaddleDetector::processFrame(Mat& frame) {
Mat frame2, gray, gray2, thres, diff;
// use sequential images (frame and frame2) for motion detection
// read in frame and convert to grayscale
m_vid->read(frame);
flip(frame, frame, 1);
cvtColor(frame, gray, COLOR_BGR2GRAY);
// read in frame2 and convert to grayscale
m_vid->read(frame2);
flip(frame2, frame2, 1);
cvtColor(frame2, gray2, COLOR_BGR2GRAY);
// create difference image of frame1 and frame2 after being converted to
// grayscale images
absdiff(gray, gray2, diff);
// threshold difference
threshold(diff, thres, THRESHOLD_SENSITIVITY, 255, THRESH_BINARY);
// blur the image. output will be an intensity image
blur(thres, thres, cv::Size(BLUR_SIZE, BLUR_SIZE));
// threshold intensity image to get binary image (after blurring)
threshold(thres, thres, THRESHOLD_SENSITIVITY, 255, THRESH_BINARY);
// split threshold (now binary image) into left and right halves
int x = thres.cols / 2;
int y = thres.rows;
Mat thresholdLeft(thres, Rect(0, 0, x, y));
Mat thresholdRight(thres, Rect(x, 0, x, y));
// detect motion in each half of the binary image
detectMotion(thresholdLeft, frame, IS_RED);
detectMotion(thresholdRight, frame, IS_BLUE);
}
void LKTracker::ShowMotion(cv::Mat& image)
{
MotionVector::iterator iter;
// TRUE- 1 is Right | FALSE- 0 is Left
int motionR, motionL, motionLast, motionLongestL, motionLongestR;
double motionSumL, motionSumR;
for(iter = this->regions.begin(); iter != this->regions.end(); ++iter)
{
Motion *motion = (*iter);
motionR = 0;
motionL = 0;
motionLast = 0;
motionLongestR = 0;
motionLongestL = 0;
motionSumL = 0;
motionSumR = 0;
cv::Rect origin = motion->getRect();
cv::rectangle(image, origin, CV_RGB(255, 0, 255), 2);
for(int i = 0; i < motion->getVx().rows; i++)
{
for(int j = 0; j < motion->getVx().cols; j++)
{
double x_component = motion->getVx().at<double>(i,j);
double y_component = motion->getVy().at<double>(i,j);
//std::cout << "vx " << x_component << " vy " << y_component << std::endl;
cv::Point p1 = cv::Point(j+origin.x, i+origin.y);
cv::Point p2 = cv::Point(j+x_component+origin.x, i + y_component+origin.y);
// distance ?????
// if(cv::norm(px1-px2) < magnitude_treshold || cv::norm(py1-py2) < magnitude_treshold)
if(cv::norm(p1-p2) >= magnitude_treshold)
{
// Draw the vector
cv::circle ( image , p1 , 4 , cv::Scalar(0,255,0) , 2 , 8 );
cv::line(image, p1, p2, CV_RGB(255, 0, 0), 2);
cv::circle ( image , p2 , 1 , cv::Scalar(0,255,0) , 2 , 8 );
}
if(cv::norm(p1-p2) < 5)
{
continue;
}
if(detectMotion(p1, p2) == 1) {
motionSumR += std::abs(x_component);
motionR++;
} else if(detectMotion(p1, p2) == 0) {
motionSumL += std::abs(x_component);
motionL++;
}
/*
// detect gestures
if(detectMotion(p1, p2) == 1 && motionLast == 1)
{
++motionR;
motionLast = 1;
}
else if (detectMotion(p1, p2) == 1 && motionLast == 0)
{
motionR = 1;
motionLast = 1;
// check if last sequence of LEFTS- 0 was longest one
if (motionLongestL < motionL)
{
motionLongestL = motionL;
}
}
else if(detectMotion(p1, p2) == 0 && motionLast == 0)
{
++motionL;
motionLast = 0;
}
else if(detectMotion(p1, p2) == 0 && motionLast == 1)
{
motionL = 1;
motionLast = 0;
// check if last sequence of RIGHTS- 1 was longest one
if (motionLongestR < motionR)
{
motionLongestR = motionR;
}
}
*/
}
}
// print detected motion
/*if (motionLongestR>motionLongestL)
std::cout << "RIGHT" << std::endl;
else if (motionLongestR<motionLongestL)
std::cout << "LEFT" << std::endl;
*/
double av_motion_l = motionSumL / motionL;
double av_motion_r = motionSumR / motionR;
cv::Point p(origin.x+50, origin.y+50);
if (av_motion_r>av_motion_l) {
cv::putText(image, "R", p, cv::FONT_HERSHEY_SIMPLEX, 1.0, cv::Scalar(0,255,0), 3);
}
else if (av_motion_r<av_motion_l) {
cv::putText(image, "L", p, cv::FONT_HERSHEY_SIMPLEX, 1.0, cv::Scalar(0,255,0), 3);
}
}
}
