/// <summary>
/// Given the input frame, prepare network input, run network inference, post-process network output and return result detections.
/// </summary>
/// <param name="frame">The input image</param>
/// <param name="detections">Array with detections' RotationRect results</param>
/// <param name="confidences">Array with detection confidences</param>
public void DetectTextRectangles(IInputArray frame, VectorOfRotatedRect detections, VectorOfFloat confidences)
{
using (InputArray iaFrame = frame.GetInputArray())
{
DnnInvoke.cveDnnTextDetectionModelDetectTextRectangles(_textDetectionModel, iaFrame, detections, confidences);
}
}
/// <summary>
/// Performs non maximum suppression given boxes and corresponding scores.
/// </summary>
/// <param name="bboxes">a set of bounding boxes to apply NMS.</param>
/// <param name="scores">a set of corresponding confidences.</param>
/// <param name="scoreThreshold">a threshold used to filter boxes by score.</param>
/// <param name="nmsThreshold">a threshold used in non maximum suppression.</param>
/// <param name="indices">the kept indices of bboxes after NMS.</param>
/// <param name="eta">a coefficient in adaptive threshold formula</param>
/// <param name="topK">if `>0`, keep at most @p top_k picked indices.</param>
// ReSharper disable once IdentifierTypo
public static void NMSBoxes(IEnumerable <RotatedRect> bboxes, IEnumerable <float> scores,
float scoreThreshold, float nmsThreshold,
out int[] indices,
float eta = 1.0f, int topK = 0)
{
if (bboxes == null)
{
throw new ArgumentNullException(nameof(bboxes));
}
if (scores == null)
{
throw new ArgumentNullException(nameof(scores));
}
// ReSharper disable once IdentifierTypo
using (var bboxesVec = new VectorOfRotatedRect(bboxes))
using (var scoresVec = new VectorOfFloat(scores))
using (var indicesVec = new VectorOfInt32())
{
NativeMethods.dnn_NMSBoxes_RotatedRect(
bboxesVec.CvPtr, scoresVec.CvPtr, scoreThreshold, nmsThreshold,
indicesVec.CvPtr, eta, topK);
indices = indicesVec.ToArray();
}
}
/// <summary>
/// Performs non maximum suppression given boxes and corresponding scores.
/// </summary>
/// <param name="bboxes">A set of bounding boxes to apply NMS.</param>
/// <param name="scores">A set of corresponding confidences.</param>
/// <param name="scoreThreshold">A threshold used to filter boxes by score.</param>
/// <param name="nmsThreshold">A threshold used in non maximum suppression.</param>
/// <param name="eta">A coefficient in adaptive threshold</param>
/// <param name="topK">If >0, keep at most top_k picked indices.</param>
/// <returns>The indices of the boxes to keep after NMS</returns>
public static int[] NMSBoxes(RotatedRect[] bboxes, float[] scores, float scoreThreshold, float nmsThreshold, float eta = 1.0f, int topK = 0)
{
using (VectorOfRotatedRect vBoxes = new VectorOfRotatedRect(bboxes))
using (VectorOfFloat vScores = new VectorOfFloat(scores))
using (VectorOfInt indices = new VectorOfInt())
{
NMSBoxes(vBoxes, vScores, scoreThreshold, nmsThreshold, indices, eta, topK);
return(indices.ToArray());
}
}
/// <summary>
/// Performs non maximum suppression given boxes and corresponding scores.
/// </summary>
/// <param name="bboxes">A set of bounding boxes to apply NMS.</param>
/// <param name="scores">A set of corresponding confidences.</param>
/// <param name="scoreThreshold">A threshold used to filter boxes by score.</param>
/// <param name="nmsThreshold">A threshold used in non maximum suppression.</param>
/// <param name="indices">The kept indices of bboxes after NMS.</param>
/// <param name="eta">A coefficient in adaptive threshold</param>
/// <param name="topK">If >0, keep at most top_k picked indices.</param>
public static void NMSBoxes(VectorOfRotatedRect bboxes, VectorOfFloat scores, float scoreThreshold, float nmsThreshold, VectorOfInt indices, float eta = 1.0f, int topK = 0)
{
cveDnnNMSBoxes2(bboxes, scores, scoreThreshold, nmsThreshold, indices, eta, topK);
}
