// // 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 }