Tracking motion using optical flow

In this recipe we will learn how to track motion in the images produced from a webcam using OpenCV using the popular Lucas Kanade optical flow algorithm.

Getting ready

We will need to use OpenCV in this recipe, so please refer to the Integrating with OpenCV recipe from Chapter 3, Using Image Processing Techniques and add OpenCV and it's CinderBlock to your project. Include the following files to your source file:

#include "cinder/Capture.h"
#include "cinder/gl/Texture.h"
#include "CinderOpenCV.h"

Add the following using statements:

using namespace ci;
using namespace ci::app;
using namespace std;

How to do it…

We will read frames from the camera and track motion.

  1. Declare the ci::gl::Texture and ci::Capture objects to display and capture from a camera. Also, declare a cv::Mat object as the previous frame, two std::vector<cv::Point2f> objects to store the current and previous features, and a std::vector<uint8_t> object to store the status of each feature:
        gl::Texture mTexture;
    Capture mCamera;
    cv::Mat mPreviousFrame;
    vector< cv::Point2f > mPreviousFeatures, mFeatures;
    vector< uint8_t > mFeatureStatuses;
  2. In the setup method we will initialize mCamera:
    try{
        mCamera = Capture( 640, 480 );
        mCamera.start();
    } catch( ... ){
        console() << "unable to initialize device" << endl;
    }
  3. In the update method we need to check if mCamera has been correctly initialized and whether it has a new frame available:
        if( mCamera ){
        if( mCamera.checkNewFrame() ){
  4. After those if statements we will get a reference to ci::Surface of mCamera and then copy it to our mTexture for drawing:
                Surface surface = mCamera.getSurface();
            mTexture = gl::Texture( surface );
  5. Now let's create a cv::Mat with the current camera frame. We will also check if mPreviousFrame contains any initialized data, calculate the good features to track, and calculate their motion from the previous camera frame to the current frame:
                cv::Mat frame( toOcv( Channel( surface ) ) );
            if( mPreviousFrame.data != NULL ){
                cv::goodFeaturesToTrack( frame, mFeatures, 300, 0.005f, 3.0f );
                vector<float> errors;
                mPreviousFeatures = mFeatures;
                cv::calcOpticalFlowPyrLK( mPreviousFrame, frame, mPreviousFeatures, mFeatures, mFeatureStatuses, errors );
            }
  6. Now we just need to copy the frame to mPreviousFrame and close the initial if statements:
                mPreviousFrame = frame;
        }
    }
  7. In the draw method we will begin by clearing the background with black and drawing mTexture:
      gl::clear( Color( 0, 0, 0 ) ); 
    if( mTexture ){
        gl::color( Color::white() );
        gl::draw( mTexture, getWindowBounds() );
    }
  8. Next, we will draw red lines on the features we have tracked, using mFeatureStatus to draw the features that have been matched:
        glColor4f( 1.0f, 0.0f, 0.0f, 1.0f );
    for( int i=0; i<mFeatures.size(); i++ ){
        if( (bool)mFeatureStatuses[i] == false ) continue;
        gl::drawSolidCircle( fromOcv( mFeatures[i] ), 5.0f );
    }
  9. Finally, we will draw a line between the previous features and the current ones, also using mFeatureStatus to draw one of the features that has been matched:
        for( int i=0; i<mFeatures.size(); i++ ){
        if( (bool)mFeatureStatuses[i] == false ) continue;
        Vec2f pt1 = fromOcv( mFeatures[i] );
        Vec2f pt2 = fromOcv( mPreviousFeatures[i] );
        gl::drawLine( pt1, pt2 );
    }

    In the following image, the red dots represent good features to track:

    How to do it…

How it works…

The optical flow algorithm will make an estimation of how much the tracked point has moved from one frame to the other.

There's more…

In this recipe we are using the cv::goodFeaturesToTrack object to calculate which features are optimal for tracking, but it is also possible to manually choose which points we wish to track. All we have to do is populate mFeatures manually with whatever points we wish to track and pass it to the cv::calcOpticalFlowPyrLK. object

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