//
// C++: Mat cv::dnn::blobFromImages(vector_Mat images, double scalefactor = 1.0, Size size = Size(), Scalar mean = Scalar(), bool swapRB = true, bool crop = true)
//
//javadoc: blobFromImages(images, scalefactor, size, mean, swapRB, crop)
public static Mat blobFromImages(List <Mat> images, double scalefactor, Size size, Scalar mean, bool swapRB, bool crop)
{
#if UNITY_PRO_LICENSE || ((UNITY_ANDROID || UNITY_IOS || UNITY_WEBGL) && !UNITY_EDITOR) || UNITY_5 || UNITY_5_3_OR_NEWER
Mat images_mat = Converters.vector_Mat_to_Mat(images);
Mat retVal = new Mat(dnn_Dnn_blobFromImages_10(images_mat.nativeObj, scalefactor, size.width, size.height, mean.val[0], mean.val[1], mean.val[2], mean.val[3], swapRB, crop));
return(retVal);
#else
return(null);
#endif
}
//javadoc: blobFromImages(images, scalefactor, size)
public static Mat blobFromImages(List <Mat> images, double scalefactor, Size size)
{
#if UNITY_PRO_LICENSE || ((UNITY_ANDROID || UNITY_IOS || UNITY_WEBGL) && !UNITY_EDITOR) || UNITY_5 || UNITY_5_3_OR_NEWER
Mat images_mat = Converters.vector_Mat_to_Mat(images);
Mat retVal = new Mat(dnn_Dnn_blobFromImages_13(images_mat.nativeObj, scalefactor, size.width, size.height));
return(retVal);
#else
return(null);
#endif
}
//javadoc: imreadmulti(filename, mats)
public static bool imreadmulti(string filename, List <Mat> mats)
{
#if UNITY_PRO_LICENSE || ((UNITY_ANDROID || UNITY_IOS) && !UNITY_EDITOR) || UNITY_5
Mat mats_mat = Converters.vector_Mat_to_Mat(mats);
bool retVal = imgcodecs_Imgcodecs_imreadmulti_11(filename, mats_mat.nativeObj);
return(retVal);
#else
return(false);
#endif
}
//javadoc: blobFromImages(images)
public static Mat blobFromImages(List <Mat> images)
{
#if UNITY_PRO_LICENSE || ((UNITY_ANDROID || UNITY_IOS || UNITY_WEBGL) && !UNITY_EDITOR) || UNITY_5 || UNITY_5_3_OR_NEWER
Mat images_mat = Converters.vector_Mat_to_Mat(images);
Mat retVal = new Mat(dnn_Dnn_blobFromImages_15(images_mat.nativeObj));
return(retVal);
#else
return(null);
#endif
}
//
// C++: void add(vector_Mat descriptors)
//
//javadoc: javaDescriptorMatcher::add(descriptors)
public void add(List <Mat> descriptors)
{
ThrowIfDisposed();
#if UNITY_PRO_LICENSE || ((UNITY_ANDROID || UNITY_IOS) && !UNITY_EDITOR) || UNITY_5
Mat descriptors_mat = Converters.vector_Mat_to_Mat(descriptors);
features2d_DescriptorMatcher_add_10(nativeObj, descriptors_mat.nativeObj);
return;
#else
return;
#endif
}
//
// C++: void process(vector_Mat src, Mat& dst)
//
//javadoc: MergeMertens::process(src, dst)
public void process(List <Mat> src, Mat dst)
{
ThrowIfDisposed();
#if UNITY_PRO_LICENSE || ((UNITY_ANDROID || UNITY_IOS) && !UNITY_EDITOR) || UNITY_5
Mat src_mat = Converters.vector_Mat_to_Mat(src);
photo_MergeMertens_process_11(nativeObj, src_mat.nativeObj, dst.nativeObj);
return;
#else
return;
#endif
}
//
// C++: void Layer::blobs
//
//javadoc: Layer::set_blobs(blobs)
public void set_blobs(List <Mat> blobs)
{
ThrowIfDisposed();
#if UNITY_PRO_LICENSE || ((UNITY_ANDROID || UNITY_IOS || UNITY_WEBGL) && !UNITY_EDITOR) || UNITY_5 || UNITY_5_3_OR_NEWER
Mat blobs_mat = Converters.vector_Mat_to_Mat(blobs);
dnn_Layer_set_1blobs_10(nativeObj, blobs_mat.nativeObj);
return;
#else
return;
#endif
}
//
// C++: bool getProjPixel(vector_Mat patternImages, int x, int y, Point projPix)
//
//javadoc: GrayCodePattern::getProjPixel(patternImages, x, y, projPix)
public bool getProjPixel(List <Mat> patternImages, int x, int y, Point projPix)
{
ThrowIfDisposed();
#if UNITY_PRO_LICENSE || ((UNITY_ANDROID || UNITY_IOS || UNITY_WEBGL) && !UNITY_EDITOR) || UNITY_5 || UNITY_5_3_OR_NEWER
Mat patternImages_mat = Converters.vector_Mat_to_Mat(patternImages);
bool retVal = structured_1light_GrayCodePattern_getProjPixel_10(nativeObj, patternImages_mat.nativeObj, x, y, projPix.x, projPix.y);
return(retVal);
#else
return(false);
#endif
}
//
// C++: void cv::face::MACE::train(vector_Mat images)
//
//javadoc: MACE::train(images)
public void train(List <Mat> images)
{
ThrowIfDisposed();
#if UNITY_PRO_LICENSE || ((UNITY_ANDROID || UNITY_IOS || UNITY_WEBGL) && !UNITY_EDITOR) || UNITY_5 || UNITY_5_3_OR_NEWER
Mat images_mat = Converters.vector_Mat_to_Mat(images);
face_MACE_train_10(nativeObj, images_mat.nativeObj);
return;
#else
return;
#endif
}
//
// C++: void computeSignatures(vector_Mat images, vector_Mat signatures)
//
//javadoc: PCTSignatures::computeSignatures(images, signatures)
public void computeSignatures(List <Mat> images, List <Mat> signatures)
{
ThrowIfDisposed();
#if UNITY_PRO_LICENSE || ((UNITY_ANDROID || UNITY_IOS || UNITY_WEBGL) && !UNITY_EDITOR) || UNITY_5 || UNITY_5_3_OR_NEWER
Mat images_mat = Converters.vector_Mat_to_Mat(images);
Mat signatures_mat = Converters.vector_Mat_to_Mat(signatures);
xfeatures2d_PCTSignatures_computeSignatures_10(nativeObj, images_mat.nativeObj, signatures_mat.nativeObj);
return;
#else
return;
#endif
}
//
// C++: void process(vector_Mat src, vector_Mat dst, Mat times, Mat response)
//
//javadoc: AlignExposures::process(src, dst, times, response)
public virtual void process(List <Mat> src, List <Mat> dst, Mat times, Mat response)
{
ThrowIfDisposed();
#if UNITY_PRO_LICENSE || ((UNITY_ANDROID || UNITY_IOS) && !UNITY_EDITOR) || UNITY_5
Mat src_mat = Converters.vector_Mat_to_Mat(src);
Mat dst_mat = Converters.vector_Mat_to_Mat(dst);
photo_AlignExposures_process_10(nativeObj, src_mat.nativeObj, dst_mat.nativeObj, times.nativeObj, response.nativeObj);
return;
#else
return;
#endif
}
//
// C++: vector_Mat cv::dnn::Layer::finalize(vector_Mat inputs)
//
//javadoc: Layer::finalize(inputs)
public List <Mat> finalize(List <Mat> inputs)
{
ThrowIfDisposed();
#if UNITY_PRO_LICENSE || ((UNITY_ANDROID || UNITY_IOS || UNITY_WEBGL) && !UNITY_EDITOR) || UNITY_5 || UNITY_5_3_OR_NEWER
Mat inputs_mat = Converters.vector_Mat_to_Mat(inputs);
List <Mat> retVal = new List <Mat>();
Mat retValMat = new Mat(dnn_Layer_finalize_10(nativeObj, inputs_mat.nativeObj));
Converters.Mat_to_vector_Mat(retValMat, retVal);
return(retVal);
#else
return(null);
#endif
}
//javadoc: javaFeatureDetector::detect(images, keypoints)
public void detect(List <Mat> images, List <MatOfKeyPoint> keypoints)
{
ThrowIfDisposed();
#if UNITY_PRO_LICENSE || ((UNITY_ANDROID || UNITY_IOS) && !UNITY_EDITOR) || UNITY_5
Mat images_mat = Converters.vector_Mat_to_Mat(images);
Mat keypoints_mat = new Mat();
features2d_FeatureDetector_detect_13(nativeObj, images_mat.nativeObj, keypoints_mat.nativeObj);
Converters.Mat_to_vector_vector_KeyPoint(keypoints_mat, keypoints);
return;
#else
return;
#endif
}
//
// C++: void Algorithm::setMatVector(string name, vector_Mat value)
//
public void setMatVector(string name, List <Mat> value)
{
ThrowIfDisposed();
#if UNITY_PRO_LICENSE || ((UNITY_ANDROID || UNITY_IOS) && !UNITY_EDITOR) || UNITY_5
Mat value_mat = Converters.vector_Mat_to_Mat(value);
core_Algorithm_setMatVector_10(nativeObj, name, value_mat.nativeObj);
return;
#else
return;
#endif
}
//
// C++: void process(vector_Mat src, vector_Mat dst)
//
//javadoc: AlignMTB::process(src, dst)
public void process(List <Mat> src, List <Mat> dst)
{
ThrowIfDisposed();
#if UNITY_PRO_LICENSE || ((UNITY_ANDROID || UNITY_IOS || UNITY_WEBGL) && !UNITY_EDITOR) || UNITY_5 || UNITY_5_3_OR_NEWER
Mat src_mat = Converters.vector_Mat_to_Mat(src);
Mat dst_mat = Converters.vector_Mat_to_Mat(dst);
photo_AlignMTB_process_11(nativeObj, src_mat.nativeObj, dst_mat.nativeObj);
return;
#else
return;
#endif
}
//
// C++: void cv::dnn::Layer::finalize(vector_Mat inputs, vector_Mat& outputs)
//
//javadoc: Layer::finalize(inputs, outputs)
public void finalize(List <Mat> inputs, List <Mat> outputs)
{
ThrowIfDisposed();
#if UNITY_PRO_LICENSE || ((UNITY_ANDROID || UNITY_IOS || UNITY_WEBGL) && !UNITY_EDITOR) || UNITY_5 || UNITY_5_3_OR_NEWER
Mat inputs_mat = Converters.vector_Mat_to_Mat(inputs);
Mat outputs_mat = new Mat();
dnn_Layer_finalize_11(nativeObj, inputs_mat.nativeObj, outputs_mat.nativeObj);
Converters.Mat_to_vector_Mat(outputs_mat, outputs);
outputs_mat.release();
return;
#else
return;
#endif
}
//javadoc: denoise_TVL1(observations, result)
public static void denoise_TVL1(List <Mat> observations, Mat result)
{
if (result != null)
{
result.ThrowIfDisposed();
}
#if UNITY_PRO_LICENSE || ((UNITY_ANDROID || UNITY_IOS || UNITY_WEBGL) && !UNITY_EDITOR) || UNITY_5 || UNITY_5_3_OR_NEWER
Mat observations_mat = Converters.vector_Mat_to_Mat(observations);
photo_Photo_denoise_1TVL1_11(observations_mat.nativeObj, result.nativeObj);
return;
#else
return;
#endif
}
//javadoc: fastNlMeansDenoisingMulti(srcImgs, dst, imgToDenoiseIndex, temporalWindowSize)
public static void fastNlMeansDenoisingMulti(List <Mat> srcImgs, Mat dst, int imgToDenoiseIndex, int temporalWindowSize)
{
if (dst != null)
{
dst.ThrowIfDisposed();
}
#if UNITY_PRO_LICENSE || ((UNITY_ANDROID || UNITY_IOS || UNITY_WEBGL) && !UNITY_EDITOR) || UNITY_5 || UNITY_5_3_OR_NEWER
Mat srcImgs_mat = Converters.vector_Mat_to_Mat(srcImgs);
photo_Photo_fastNlMeansDenoisingMulti_11(srcImgs_mat.nativeObj, dst.nativeObj, imgToDenoiseIndex, temporalWindowSize);
return;
#else
return;
#endif
}
//
// C++: void cv::Feature2D::detect(vector_Mat images, vector_vector_KeyPoint& keypoints, vector_Mat masks = vector_Mat())
//
//javadoc: Feature2D::detect(images, keypoints, masks)
public void detect(List <Mat> images, List <MatOfKeyPoint> keypoints, List <Mat> masks)
{
ThrowIfDisposed();
#if UNITY_PRO_LICENSE || ((UNITY_ANDROID || UNITY_IOS || UNITY_WEBGL) && !UNITY_EDITOR) || UNITY_5 || UNITY_5_3_OR_NEWER
Mat images_mat = Converters.vector_Mat_to_Mat(images);
Mat keypoints_mat = new Mat();
Mat masks_mat = Converters.vector_Mat_to_Mat(masks);
features2d_Feature2D_detect_12(nativeObj, images_mat.nativeObj, keypoints_mat.nativeObj, masks_mat.nativeObj);
Converters.Mat_to_vector_vector_KeyPoint(keypoints_mat, keypoints);
keypoints_mat.release();
return;
#else
return;
#endif
}
//javadoc: SinusoidalPattern::computePhaseMap(patternImages, wrappedPhaseMap)
public void computePhaseMap(List <Mat> patternImages, Mat wrappedPhaseMap)
{
ThrowIfDisposed();
if (wrappedPhaseMap != null)
{
wrappedPhaseMap.ThrowIfDisposed();
}
#if UNITY_PRO_LICENSE || ((UNITY_ANDROID || UNITY_IOS || UNITY_WEBGL) && !UNITY_EDITOR) || UNITY_5 || UNITY_5_3_OR_NEWER
Mat patternImages_mat = Converters.vector_Mat_to_Mat(patternImages);
structured_1light_SinusoidalPattern_computePhaseMap_12(nativeObj, patternImages_mat.nativeObj, wrappedPhaseMap.nativeObj);
return;
#else
return;
#endif
}
//
// C++: void update(vector_Mat src, Mat labels)
//
//javadoc: FaceRecognizer::update(src, labels)
public void update(List <Mat> src, Mat labels)
{
ThrowIfDisposed();
if (labels != null)
{
labels.ThrowIfDisposed();
}
#if UNITY_PRO_LICENSE || ((UNITY_ANDROID || UNITY_IOS || UNITY_WEBGL) && !UNITY_EDITOR) || UNITY_5 || UNITY_5_3_OR_NEWER
Mat src_mat = Converters.vector_Mat_to_Mat(src);
face_FaceRecognizer_update_10(nativeObj, src_mat.nativeObj, labels.nativeObj);
return;
#else
return;
#endif
}
//
// C++: void fastNlMeansDenoisingColoredMulti(vector_Mat srcImgs, Mat& dst, int imgToDenoiseIndex, int temporalWindowSize, float h = 3, float hColor = 3, int templateWindowSize = 7, int searchWindowSize = 21)
//
//javadoc: fastNlMeansDenoisingColoredMulti(srcImgs, dst, imgToDenoiseIndex, temporalWindowSize, h, hColor, templateWindowSize, searchWindowSize)
public static void fastNlMeansDenoisingColoredMulti(List <Mat> srcImgs, Mat dst, int imgToDenoiseIndex, int temporalWindowSize, float h, float hColor, int templateWindowSize, int searchWindowSize)
{
if (dst != null)
{
dst.ThrowIfDisposed();
}
#if UNITY_PRO_LICENSE || ((UNITY_ANDROID || UNITY_IOS) && !UNITY_EDITOR) || UNITY_5
Mat srcImgs_mat = Converters.vector_Mat_to_Mat(srcImgs);
photo_Photo_fastNlMeansDenoisingColoredMulti_10(srcImgs_mat.nativeObj, dst.nativeObj, imgToDenoiseIndex, temporalWindowSize, h, hColor, templateWindowSize, searchWindowSize);
return;
#else
return;
#endif
}
//javadoc: SinusoidalPattern::unwrapPhaseMap(wrappedPhaseMap, unwrappedPhaseMap, camSize)
public void unwrapPhaseMap(List <Mat> wrappedPhaseMap, Mat unwrappedPhaseMap, Size camSize)
{
ThrowIfDisposed();
if (unwrappedPhaseMap != null)
{
unwrappedPhaseMap.ThrowIfDisposed();
}
#if UNITY_PRO_LICENSE || ((UNITY_ANDROID || UNITY_IOS || UNITY_WEBGL) && !UNITY_EDITOR) || UNITY_5 || UNITY_5_3_OR_NEWER
Mat wrappedPhaseMap_mat = Converters.vector_Mat_to_Mat(wrappedPhaseMap);
structured_1light_SinusoidalPattern_unwrapPhaseMap_11(nativeObj, wrappedPhaseMap_mat.nativeObj, unwrappedPhaseMap.nativeObj, camSize.width, camSize.height);
return;
#else
return;
#endif
}
//
// C++: void denoise_TVL1(vector_Mat observations, Mat result, double lambda = 1.0, int niters = 30)
//
//javadoc: denoise_TVL1(observations, result, lambda, niters)
public static void denoise_TVL1(List <Mat> observations, Mat result, double lambda, int niters)
{
if (result != null)
{
result.ThrowIfDisposed();
}
#if UNITY_PRO_LICENSE || ((UNITY_ANDROID || UNITY_IOS) && !UNITY_EDITOR) || UNITY_5
Mat observations_mat = Converters.vector_Mat_to_Mat(observations);
photo_Photo_denoise_1TVL1_10(observations_mat.nativeObj, result.nativeObj, lambda, niters);
return;
#else
return;
#endif
}
//
// C++: void FaceRecognizer::train(vector_Mat src, Mat labels)
//
/**
* <p>Trains a FaceRecognizer with given data and associated labels.</p>
*
* <p>The following source code snippet shows you how to learn a Fisherfaces model
* on a given set of images. The images are read with "imread" and pushed into a
* <code>std.vector<Mat></code>. The labels of each image are stored within a
* <code>std.vector<int></code> (you could also use a "Mat" of type
* "CV_32SC1"). Think of the label as the subject (the person) this image
* belongs to, so same subjects (persons) should have the same label. For the
* available "FaceRecognizer" you don't have to pay any attention to the order
* of the labels, just make sure same persons have the same label: // holds
* images and labels <code></p>
*
* <p>// C++ code:</p>
*
* <p>vector<Mat> images;</p>
*
* <p>vector<int> labels;</p>
*
* <p>// images for first person</p>
*
* <p>images.push_back(imread("person0/0.jpg", CV_LOAD_IMAGE_GRAYSCALE));
* labels.push_back(0);</p>
*
* <p>images.push_back(imread("person0/1.jpg", CV_LOAD_IMAGE_GRAYSCALE));
* labels.push_back(0);</p>
*
* <p>images.push_back(imread("person0/2.jpg", CV_LOAD_IMAGE_GRAYSCALE));
* labels.push_back(0);</p>
*
* <p>// images for second person</p>
*
* <p>images.push_back(imread("person1/0.jpg", CV_LOAD_IMAGE_GRAYSCALE));
* labels.push_back(1);</p>
*
* <p>images.push_back(imread("person1/1.jpg", CV_LOAD_IMAGE_GRAYSCALE));
* labels.push_back(1);</p>
*
* <p>images.push_back(imread("person1/2.jpg", CV_LOAD_IMAGE_GRAYSCALE));
* labels.push_back(1);</p>
*
* <p>Now that you have read some images, we can create a new "FaceRecognizer". In
* this example I'll create a Fisherfaces model and decide to keep all of the
* possible Fisherfaces: </code></p>
*
* <p>// Create a new Fisherfaces model and retain all available Fisherfaces,
* <code></p>
*
* <p>// C++ code:</p>
*
* <p>// this is the most common usage of this specific FaceRecognizer:</p>
*
* <p>//</p>
*
* <p>Ptr<FaceRecognizer> model = createFisherFaceRecognizer();</p>
*
* <p>And finally train it on the given dataset (the face images and labels):
* </code></p>
*
* <p>// This is the common interface to train all of the available
* cv.FaceRecognizer <code></p>
*
* <p>// C++ code:</p>
*
* <p>// implementations:</p>
*
* <p>//</p>
*
* <p>model->train(images, labels);</p>
*
* @param src The training images, that means the faces you want to learn. The
* data has to be given as a <code>vector<Mat></code>.
* @param labels The labels corresponding to the images have to be given either
* as a <code>vector<int></code> or a
*
* @see <a href="http://docs.opencv.org/modules/contrib/doc/facerec_api.html#facerecognizer-train">org.opencv.contrib.FaceRecognizer.train</a>
*/
public void train(List <Mat> src, Mat labels)
{
if (labels != null)
{
labels.ThrowIfDisposed();
}
ThrowIfDisposed();
#if UNITY_PRO_LICENSE || ((UNITY_ANDROID || UNITY_IOS) && !UNITY_EDITOR) || UNITY_5
Mat src_mat = Converters.vector_Mat_to_Mat(src);
contrib_FaceRecognizer_train_10(nativeObj, src_mat.nativeObj, labels.nativeObj);
return;
#else
return;
#endif
}
//
// C++: void javaDescriptorExtractor::compute(vector_Mat images, vector_vector_KeyPoint& keypoints, vector_Mat& descriptors)
//
/**
* <p>Computes the descriptors for a set of keypoints detected in an image (first
* variant) or image set (second variant).</p>
*
* @param images Image set.
* @param keypoints Input collection of keypoints. Keypoints for which a
* descriptor cannot be computed are removed and the remaining ones may be
* reordered. Sometimes new keypoints can be added, for example:
* <code>SIFT</code> duplicates a keypoint with several dominant orientations
* (for each orientation).
* @param descriptors Computed descriptors. In the second variant of the method
* <code>descriptors[i]</code> are descriptors computed for a <code>keypoints[i]</code>.
* Row <code>j</code> is the <code>keypoints</code> (or <code>keypoints[i]</code>)
* is the descriptor for keypoint <code>j</code>-th keypoint.
*
* @see <a href="http://docs.opencv.org/modules/features2d/doc/common_interfaces_of_descriptor_extractors.html#descriptorextractor-compute">org.opencv.features2d.DescriptorExtractor.compute</a>
*/
public void compute(List <Mat> images, List <MatOfKeyPoint> keypoints, List <Mat> descriptors)
{
ThrowIfDisposed();
#if UNITY_PRO_LICENSE || ((UNITY_ANDROID || UNITY_IOS) && !UNITY_EDITOR) || UNITY_5
Mat images_mat = Converters.vector_Mat_to_Mat(images);
List <Mat> keypoints_tmplm = new List <Mat> ((keypoints != null) ? keypoints.Count : 0);
Mat keypoints_mat = Converters.vector_vector_KeyPoint_to_Mat(keypoints, keypoints_tmplm);
Mat descriptors_mat = new Mat();
features2d_DescriptorExtractor_compute_11(nativeObj, images_mat.nativeObj, keypoints_mat.nativeObj, descriptors_mat.nativeObj);
Converters.Mat_to_vector_vector_KeyPoint(keypoints_mat, keypoints);
Converters.Mat_to_vector_Mat(descriptors_mat, descriptors);
return;
#else
return;
#endif
}
//javadoc: DescriptorMatcher::radiusMatch(queryDescriptors, matches, maxDistance, masks)
public void radiusMatch(Mat queryDescriptors, List <MatOfDMatch> matches, float maxDistance, List <Mat> masks)
{
ThrowIfDisposed();
if (queryDescriptors != null)
{
queryDescriptors.ThrowIfDisposed();
}
#if UNITY_PRO_LICENSE || ((UNITY_ANDROID || UNITY_IOS || UNITY_WEBGL) && !UNITY_EDITOR) || UNITY_5 || UNITY_5_3_OR_NEWER
Mat matches_mat = new Mat();
Mat masks_mat = Converters.vector_Mat_to_Mat(masks);
features2d_DescriptorMatcher_radiusMatch_14(nativeObj, queryDescriptors.nativeObj, matches_mat.nativeObj, maxDistance, masks_mat.nativeObj);
Converters.Mat_to_vector_vector_DMatch(matches_mat, matches);
matches_mat.release();
return;
#else
return;
#endif
}
//
// C++: void cv::DescriptorMatcher::knnMatch(Mat queryDescriptors, vector_vector_DMatch& matches, int k, vector_Mat masks = vector_Mat(), bool compactResult = false)
//
//javadoc: DescriptorMatcher::knnMatch(queryDescriptors, matches, k, masks, compactResult)
public void knnMatch(Mat queryDescriptors, List <MatOfDMatch> matches, int k, List <Mat> masks, bool compactResult)
{
ThrowIfDisposed();
if (queryDescriptors != null)
{
queryDescriptors.ThrowIfDisposed();
}
#if UNITY_PRO_LICENSE || ((UNITY_ANDROID || UNITY_IOS || UNITY_WEBGL) && !UNITY_EDITOR) || UNITY_5 || UNITY_5_3_OR_NEWER
Mat matches_mat = new Mat();
Mat masks_mat = Converters.vector_Mat_to_Mat(masks);
features2d_DescriptorMatcher_knnMatch_13(nativeObj, queryDescriptors.nativeObj, matches_mat.nativeObj, k, masks_mat.nativeObj, compactResult);
Converters.Mat_to_vector_vector_DMatch(matches_mat, matches);
matches_mat.release();
return;
#else
return;
#endif
}
//
// C++: static Ptr_Board cv::aruco::Board::create(vector_Mat objPoints, Ptr_Dictionary dictionary, Mat ids)
//
//javadoc: Board::create(objPoints, dictionary, ids)
public static Board create(List <Mat> objPoints, Dictionary dictionary, Mat ids)
{
if (dictionary != null)
{
dictionary.ThrowIfDisposed();
}
if (ids != null)
{
ids.ThrowIfDisposed();
}
#if UNITY_PRO_LICENSE || ((UNITY_ANDROID || UNITY_IOS || UNITY_WEBGL) && !UNITY_EDITOR) || UNITY_5 || UNITY_5_3_OR_NEWER
Mat objPoints_mat = Converters.vector_Mat_to_Mat(objPoints);
Board retVal = Board.__fromPtr__(aruco_Board_create_10(objPoints_mat.nativeObj, dictionary.getNativeObjAddr(), ids.nativeObj));
return(retVal);
#else
return(null);
#endif
}
//
// C++: void cv::MergeRobertson::process(vector_Mat src, Mat& dst, Mat times)
//
//javadoc: MergeRobertson::process(src, dst, times)
public void process(List <Mat> src, Mat dst, Mat times)
{
ThrowIfDisposed();
if (dst != null)
{
dst.ThrowIfDisposed();
}
if (times != null)
{
times.ThrowIfDisposed();
}
#if UNITY_PRO_LICENSE || ((UNITY_ANDROID || UNITY_IOS || UNITY_WEBGL) && !UNITY_EDITOR) || UNITY_5 || UNITY_5_3_OR_NEWER
Mat src_mat = Converters.vector_Mat_to_Mat(src);
photo_MergeRobertson_process_11(nativeObj, src_mat.nativeObj, dst.nativeObj, times.nativeObj);
return;
#else
return;
#endif
}
