Building Computer Vision Projects with OpenCV 4 and C++

Measuring error

Visually, the results seem very good. However, since we have the ground truth data, we may elect to analytically compare it to the detection and get an error estimate. We can use a standard mean Euclidean distance metric () to tell how close each predicted landmark is to the ground truth on average:

float MeanEuclideanDistance(const vector<Point2f>& A, const vector<Point2f>& B) {    float med = 0.0f;    for (int i = 0; i < A.size(); ++i) {        med += cv::norm(A[i] - B[i]);    }    return med / (float)A.size();}

A visualization of the results with the prediction (red) and ground truth (green) overlaid, shown in the following screenshot:

We ...