internal static extern Seq cvHaarDetectObjects(
Arr image,
HaarClassifierCascade cascade,
MemStorage storage,
double scale_factor,
int min_neighbors,
HaarDetectObjectFlags flags,
Size min_size,
Size max_size);
/// <summary>
/// Detects objects in the image.
/// </summary>
/// <param name="image">Image to detect objects in.</param>
/// <param name="storage">Memory storage to store the resultant sequence of the object candidate rectangles.</param>
/// <param name="scaleFactor">
/// The factor by which the search window is scaled between the subsequent scans, 1.1 means increasing window by 10%.
/// </param>
/// <param name="minNeighbors">
/// Minimum number (minus 1) of neighbor rectangles that make up an object. All the groups of a smaller number of
/// rectangles than <paramref name="minNeighbors"/>-1 are rejected. If it is 0, the method does not do any grouping
/// at all and returns all the detected candidate rectangles, which may be useful if the user wants to apply a
/// customized grouping procedure.
/// </param>
/// <param name="flags">Mode of operation.</param>
/// <param name="minSize">
/// Minimum window size. By default, it is set to the size of samples the classifier has been trained on
/// (20x20 for face detection).
/// </param>
/// <param name="maxSize">Maximum window size. By default, it is set to the total image size.</param>
/// <returns>The sequence of grouped (or ungrouped) object rectangles.</returns>
public Seq DetectObjects(
Arr image,
MemStorage storage,
double scaleFactor = 1.1,
int minNeighbors = 3,
HaarDetectObjectFlags flags = HaarDetectObjectFlags.None,
Size minSize = default(Size),
Size maxSize = default(Size))
{
var objects = NativeMethods.cvHaarDetectObjects(image, this, storage, scaleFactor, minNeighbors, flags, minSize, maxSize);
if (objects.IsInvalid)
{
return(null);
}
objects.SetOwnerStorage(storage);
return(objects);
}
