I am trying to find the centroid of a contour but am having trouble implementing the example code in C++ (OpenCV 2.3.1). Can anyone help me out?
To get the area of the contours, we can implement the function cv2. contourArea() . Why don't we try several contours here? If you input the first, second and third contours, you'll get the decreasing values as shown below.
The cv2. boundingRect() function of OpenCV is used to draw an approximate rectangle around the binary image. This function is used mainly to highlight the region of interest after obtaining contours from an image. As per the documentation there are two types of bounding rectangles: Straight Bounding Rectangle.
The function cv2.approxPolyDP() approximates a contour shape to another shape with less number of vertices. It accepts the following arguments − cnt − The array of the contour points. epsilon − Maximum distance from contour to approximated contour. A wise selection of epsilon is needed to get the correct output.
If you have the mask of the contour area, you can find the centroid location as follows:
cv::Point computeCentroid(const cv::Mat &mask) {
cv::Moments m = moments(mask, true);
cv::Point center(m.m10/m.m00, m.m01/m.m00);
return center;
}
This approach is useful when one has the mask but not the contour. In that case the above method is computationally more efficient vs. using cv::findContours(...)
and then finding mass center.
Here's the source
Given the contour points, and the formula from Wikipedia, the centroid can be efficiently computed like this:
template <typename T>
cv::Point_<T> computeCentroid(const std::vector<cv::Point_<T> >& in) {
if (in.size() > 2) {
T doubleArea = 0;
cv::Point_<T> p(0,0);
cv::Point_<T> p0 = in->back();
for (const cv::Point_<T>& p1 : in) {//C++11
T a = p0.x * p1.y - p0.y * p1.x; //cross product, (signed) double area of triangle of vertices (origin,p0,p1)
p += (p0 + p1) * a;
doubleArea += a;
p0 = p1;
}
if (doubleArea != 0)
return p * (1 / (3 * doubleArea) ); //Operator / does not exist for cv::Point
}
///If we get here,
///All points lies on one line, you can compute a fallback value,
///e.g. the average of the input vertices
[...]
}
Note:
p
and of the return value to Point2f
or Point2d
,
and to add a cast to float
or double
to the denominator in the return statement.To find the centroid of a contour, you can use the method of moments. And functions are implemented OpenCV.
Check out these moments function (central and spatial moments).
Below code is taken from OpenCV 2.3 docs tutorial. Full code here.
/// Find contours
findContours( canny_output, contours, hierarchy, CV_RETR_TREE, CV_CHAIN_APPROX_SIMPLE, Point(0, 0) );
/// Get the moments
vector<Moments> mu(contours.size() );
for( int i = 0; i < contours.size(); i++ )
{ mu[i] = moments( contours[i], false ); }
/// Get the mass centers:
vector<Point2f> mc( contours.size() );
for( int i = 0; i < contours.size(); i++ )
{ mc[i] = Point2f( mu[i].m10/mu[i].m00 , mu[i].m01/mu[i].m00 ); }
Also check out this SOF, although it is in Python, it would be useful. It finds all parameters of a contour.
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