//javadoc: CascadeClassifier::detectMultiScale3(image, objects, rejectLevels, levelWeights)
public void detectMultiScale3(Mat image, MatOfRect objects, MatOfInt rejectLevels, MatOfDouble levelWeights)
{
ThrowIfDisposed();
if (image != null)
{
image.ThrowIfDisposed();
}
if (objects != null)
{
objects.ThrowIfDisposed();
}
if (rejectLevels != null)
{
rejectLevels.ThrowIfDisposed();
}
if (levelWeights != null)
{
levelWeights.ThrowIfDisposed();
}
#if ((UNITY_ANDROID || UNITY_IOS || UNITY_WEBGL) && !UNITY_EDITOR) || UNITY_5 || UNITY_5_3_OR_NEWER
Mat objects_mat = objects;
Mat rejectLevels_mat = rejectLevels;
Mat levelWeights_mat = levelWeights;
objdetect_CascadeClassifier_detectMultiScale3_16(nativeObj, image.nativeObj, objects_mat.nativeObj, rejectLevels_mat.nativeObj, levelWeights_mat.nativeObj);
return;
#else
return;
#endif
}
//javadoc: CascadeClassifier::detectMultiScale3(image, objects, rejectLevels, levelWeights, scaleFactor, minNeighbors, flags, minSize)
public void detectMultiScale3(Mat image, MatOfRect objects, MatOfInt rejectLevels, MatOfDouble levelWeights, double scaleFactor, int minNeighbors, int flags, Size minSize)
{
ThrowIfDisposed();
if (image != null)
{
image.ThrowIfDisposed();
}
if (objects != null)
{
objects.ThrowIfDisposed();
}
if (rejectLevels != null)
{
rejectLevels.ThrowIfDisposed();
}
if (levelWeights != null)
{
levelWeights.ThrowIfDisposed();
}
#if ((UNITY_ANDROID || UNITY_IOS || UNITY_WEBGL) && !UNITY_EDITOR) || UNITY_5 || UNITY_5_3_OR_NEWER
Mat objects_mat = objects;
Mat rejectLevels_mat = rejectLevels;
Mat levelWeights_mat = levelWeights;
objdetect_CascadeClassifier_detectMultiScale3_12(nativeObj, image.nativeObj, objects_mat.nativeObj, rejectLevels_mat.nativeObj, levelWeights_mat.nativeObj, scaleFactor, minNeighbors, flags, minSize.width, minSize.height);
return;
#else
return;
#endif
}
//
// C++: int64 cv::dnn::Net::getPerfProfile(vector_double& timings)
//
/**
* Returns overall time for inference and timings (in ticks) for layers.
* Indexes in returned vector correspond to layers ids. Some layers can be fused with others,
* in this case zero ticks count will be return for that skipped layers.
* param timings vector for tick timings for all layers.
* return overall ticks for model inference.
*/
public long getPerfProfile(MatOfDouble timings)
{
ThrowIfDisposed();
if (timings != null)
{
timings.ThrowIfDisposed();
}
Mat timings_mat = timings;
return(dnn_Net_getPerfProfile_10(nativeObj, timings_mat.nativeObj));
}
//
// C++: int64 cv::dnn::Net::getPerfProfile(vector_double& timings)
//
//javadoc: Net::getPerfProfile(timings)
public long getPerfProfile(MatOfDouble timings)
{
ThrowIfDisposed();
if (timings != null)
{
timings.ThrowIfDisposed();
}
#if ((UNITY_ANDROID || UNITY_IOS || UNITY_WEBGL) && !UNITY_EDITOR) || UNITY_5 || UNITY_5_3_OR_NEWER
Mat timings_mat = timings;
long retVal = dnn_Net_getPerfProfile_10(nativeObj, timings_mat.nativeObj);
return(retVal);
#else
return(-1);
#endif
}
