//javadoc: CascadeClassifier::detectMultiScale2(image, objects, numDetections)
public void detectMultiScale2(Mat image, MatOfRect objects, MatOfInt numDetections)
{
ThrowIfDisposed();
if (image != null)
{
image.ThrowIfDisposed();
}
if (objects != null)
{
objects.ThrowIfDisposed();
}
if (numDetections != null)
{
numDetections.ThrowIfDisposed();
}
#if ((UNITY_ANDROID || UNITY_IOS || UNITY_WEBGL) && !UNITY_EDITOR) || UNITY_5 || UNITY_5_3_OR_NEWER
Mat objects_mat = objects;
Mat numDetections_mat = numDetections;
objdetect_CascadeClassifier_detectMultiScale2_15(nativeObj, image.nativeObj, objects_mat.nativeObj, numDetections_mat.nativeObj);
return;
#else
return;
#endif
}
/**
* Given the {code input} frame, create input blob, run net and return result detections.
* param classIds Class indexes in result detection.
* param confidences A set of corresponding confidences.
* param boxes A set of bounding boxes.
* param frame automatically generated
*/
public void detect(Mat frame, MatOfInt classIds, MatOfFloat confidences, MatOfRect boxes)
{
ThrowIfDisposed();
if (frame != null)
{
frame.ThrowIfDisposed();
}
if (classIds != null)
{
classIds.ThrowIfDisposed();
}
if (confidences != null)
{
confidences.ThrowIfDisposed();
}
if (boxes != null)
{
boxes.ThrowIfDisposed();
}
Mat classIds_mat = classIds;
Mat confidences_mat = confidences;
Mat boxes_mat = boxes;
dnn_DetectionModel_detect_12(nativeObj, frame.nativeObj, classIds_mat.nativeObj, confidences_mat.nativeObj, boxes_mat.nativeObj);
}
//javadoc: CascadeClassifier::detectMultiScale2(image, objects, numDetections, scaleFactor, minNeighbors, flags, minSize)
public void detectMultiScale2(Mat image, MatOfRect objects, MatOfInt numDetections, double scaleFactor, int minNeighbors, int flags, Size minSize)
{
ThrowIfDisposed();
if (image != null)
{
image.ThrowIfDisposed();
}
if (objects != null)
{
objects.ThrowIfDisposed();
}
if (numDetections != null)
{
numDetections.ThrowIfDisposed();
}
#if ((UNITY_ANDROID || UNITY_IOS || UNITY_WEBGL) && !UNITY_EDITOR) || UNITY_5 || UNITY_5_3_OR_NEWER
Mat objects_mat = objects;
Mat numDetections_mat = numDetections;
objdetect_CascadeClassifier_detectMultiScale2_11(nativeObj, image.nativeObj, objects_mat.nativeObj, numDetections_mat.nativeObj, scaleFactor, minNeighbors, flags, minSize.width, minSize.height);
return;
#else
return;
#endif
}
//
// C++: void cv::xfeatures2d::matchLOGOS(vector_KeyPoint keypoints1, vector_KeyPoint keypoints2, vector_int nn1, vector_int nn2, vector_DMatch matches1to2)
//
/**
* LOGOS (Local geometric support for high-outlier spatial verification) feature matching strategy described in CITE: Lowry2018LOGOSLG .
* param keypoints1 Input keypoints of image1.
* param keypoints2 Input keypoints of image2.
* param nn1 Index to the closest BoW centroid for each descriptors of image1.
* param nn2 Index to the closest BoW centroid for each descriptors of image2.
* param matches1to2 Matches returned by the LOGOS matching strategy.
* <b>Note:</b>
* This matching strategy is suitable for features matching against large scale database.
* First step consists in constructing the bag-of-words (BoW) from a representative image database.
* Image descriptors are then represented by their closest codevector (nearest BoW centroid).
*/
public static void matchLOGOS(MatOfKeyPoint keypoints1, MatOfKeyPoint keypoints2, MatOfInt nn1, MatOfInt nn2, MatOfDMatch matches1to2)
{
if (keypoints1 != null)
{
keypoints1.ThrowIfDisposed();
}
if (keypoints2 != null)
{
keypoints2.ThrowIfDisposed();
}
if (nn1 != null)
{
nn1.ThrowIfDisposed();
}
if (nn2 != null)
{
nn2.ThrowIfDisposed();
}
if (matches1to2 != null)
{
matches1to2.ThrowIfDisposed();
}
Mat keypoints1_mat = keypoints1;
Mat keypoints2_mat = keypoints2;
Mat nn1_mat = nn1;
Mat nn2_mat = nn2;
Mat matches1to2_mat = matches1to2;
xfeatures2d_Xfeatures2d_matchLOGOS_10(keypoints1_mat.nativeObj, keypoints2_mat.nativeObj, nn1_mat.nativeObj, nn2_mat.nativeObj, matches1to2_mat.nativeObj);
}
//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
}
//javadoc: NMSBoxesRotated(bboxes, scores, score_threshold, nms_threshold, indices)
public static void NMSBoxesRotated(MatOfRotatedRect bboxes, MatOfFloat scores, float score_threshold, float nms_threshold, MatOfInt indices)
{
if (bboxes != null)
{
bboxes.ThrowIfDisposed();
}
if (scores != null)
{
scores.ThrowIfDisposed();
}
if (indices != null)
{
indices.ThrowIfDisposed();
}
#if ((UNITY_ANDROID || UNITY_IOS || UNITY_WEBGL) && !UNITY_EDITOR) || UNITY_5 || UNITY_5_3_OR_NEWER
Mat bboxes_mat = bboxes;
Mat scores_mat = scores;
Mat indices_mat = indices;
dnn_Dnn_NMSBoxesRotated_12(bboxes_mat.nativeObj, scores_mat.nativeObj, score_threshold, nms_threshold, indices_mat.nativeObj);
return;
#else
return;
#endif
}
//
// C++: void cv::dnn::Net::getMemoryConsumption(int layerId, MatShape netInputShape, size_t& weights, size_t& blobs)
//
//javadoc: Net::getMemoryConsumption(layerId, netInputShape, weights, blobs)
public void getMemoryConsumption(int layerId, MatOfInt netInputShape, long[] weights, long[] blobs)
{
ThrowIfDisposed();
if (netInputShape != null)
{
netInputShape.ThrowIfDisposed();
}
#if ((UNITY_ANDROID || UNITY_IOS || UNITY_WEBGL) && !UNITY_EDITOR) || UNITY_5 || UNITY_5_3_OR_NEWER
Mat netInputShape_mat = netInputShape;
double[] weights_out = new double[1];
double[] blobs_out = new double[1];
dnn_Net_getMemoryConsumption_11(nativeObj, layerId, netInputShape_mat.nativeObj, weights_out, blobs_out);
if (weights != null)
{
weights [0] = (long)weights_out [0];
}
if (blobs != null)
{
blobs [0] = (long)blobs_out [0];
}
return;
#else
return;
#endif
}
//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
}
//
// C++: static Ptr_BRISK cv::BRISK::create(int thresh, int octaves, vector_float radiusList, vector_int numberList, float dMax = 5.85f, float dMin = 8.2f, vector_int indexChange = std::vector<int>())
//
//javadoc: BRISK::create(thresh, octaves, radiusList, numberList, dMax, dMin, indexChange)
public static BRISK create(int thresh, int octaves, MatOfFloat radiusList, MatOfInt numberList, float dMax, float dMin, MatOfInt indexChange)
{
if (radiusList != null)
{
radiusList.ThrowIfDisposed();
}
if (numberList != null)
{
numberList.ThrowIfDisposed();
}
if (indexChange != null)
{
indexChange.ThrowIfDisposed();
}
#if ((UNITY_ANDROID || UNITY_IOS || UNITY_WEBGL) && !UNITY_EDITOR) || UNITY_5 || UNITY_5_3_OR_NEWER
Mat radiusList_mat = radiusList;
Mat numberList_mat = numberList;
Mat indexChange_mat = indexChange;
BRISK retVal = BRISK.__fromPtr__(features2d_BRISK_create_10(thresh, octaves, radiusList_mat.nativeObj, numberList_mat.nativeObj, dMax, dMin, indexChange_mat.nativeObj));
return(retVal);
#else
return(null);
#endif
}
//
// C++: static Ptr_FREAK cv::xfeatures2d::FREAK::create(bool orientationNormalized = true, bool scaleNormalized = true, float patternScale = 22.0f, int nOctaves = 4, vector_int selectedPairs = std::vector<int>())
//
/**
* param orientationNormalized Enable orientation normalization.
* param scaleNormalized Enable scale normalization.
* param patternScale Scaling of the description pattern.
* param nOctaves Number of octaves covered by the detected keypoints.
* param selectedPairs (Optional) user defined selected pairs indexes,
* return automatically generated
*/
public static FREAK create(bool orientationNormalized, bool scaleNormalized, float patternScale, int nOctaves, MatOfInt selectedPairs)
{
if (selectedPairs != null)
{
selectedPairs.ThrowIfDisposed();
}
Mat selectedPairs_mat = selectedPairs;
return(FREAK.__fromPtr__(xfeatures2d_FREAK_create_10(orientationNormalized, scaleNormalized, patternScale, nOctaves, selectedPairs_mat.nativeObj)));
}
//
// C++: cv::VideoWriter::VideoWriter(String filename, int apiPreference, int fourcc, double fps, Size frameSize, vector_int _params)
//
public VideoWriter(string filename, int apiPreference, int fourcc, double fps, Size frameSize, MatOfInt _params)
{
if (_params != null)
{
_params.ThrowIfDisposed();
}
Mat _params_mat = _params;
nativeObj = videoio_VideoWriter_VideoWriter_12(filename, apiPreference, fourcc, fps, frameSize.width, frameSize.height, _params_mat.nativeObj);
}
//
// C++: void cv::xfeatures2d::PCTSignatures::setInitSeedIndexes(vector_int initSeedIndexes)
//
/**
* Initial seed indexes for the k-means algorithm.
* param initSeedIndexes automatically generated
*/
public void setInitSeedIndexes(MatOfInt initSeedIndexes)
{
ThrowIfDisposed();
if (initSeedIndexes != null)
{
initSeedIndexes.ThrowIfDisposed();
}
Mat initSeedIndexes_mat = initSeedIndexes;
xfeatures2d_PCTSignatures_setInitSeedIndexes_10(nativeObj, initSeedIndexes_mat.nativeObj);
}
//
// C++: bool cv::VideoWriter::open(String filename, int fourcc, double fps, Size frameSize, vector_int _params)
//
public bool open(string filename, int fourcc, double fps, Size frameSize, MatOfInt _params)
{
ThrowIfDisposed();
if (_params != null)
{
_params.ThrowIfDisposed();
}
Mat _params_mat = _params;
return(videoio_VideoWriter_open_15(nativeObj, filename, fourcc, fps, frameSize.width, frameSize.height, _params_mat.nativeObj));
}
//
// C++: void cv::dnn::Net::setInputShape(String inputName, MatShape shape)
//
/**
* Specify shape of network input.
* param inputName automatically generated
* param shape automatically generated
*/
public void setInputShape(string inputName, MatOfInt shape)
{
ThrowIfDisposed();
if (shape != null)
{
shape.ThrowIfDisposed();
}
Mat shape_mat = shape;
dnn_Net_setInputShape_10(nativeObj, inputName, shape_mat.nativeObj);
}
//
// C++: void cv::Subdiv2D::getLeadingEdgeList(vector_int& leadingEdgeList)
//
/**
* Returns a list of the leading edge ID connected to each triangle.
*
* param leadingEdgeList Output vector.
*
* The function gives one edge ID for each triangle.
*/
public void getLeadingEdgeList(MatOfInt leadingEdgeList)
{
ThrowIfDisposed();
if (leadingEdgeList != null)
{
leadingEdgeList.ThrowIfDisposed();
}
Mat leadingEdgeList_mat = leadingEdgeList;
imgproc_Subdiv2D_getLeadingEdgeList_10(nativeObj, leadingEdgeList_mat.nativeObj);
}
//
// C++: int64 cv::dnn::Net::getFLOPS(int layerId, MatShape netInputShape)
//
public long getFLOPS(int layerId, MatOfInt netInputShape)
{
ThrowIfDisposed();
if (netInputShape != null)
{
netInputShape.ThrowIfDisposed();
}
Mat netInputShape_mat = netInputShape;
return(dnn_Net_getFLOPS_11(nativeObj, layerId, netInputShape_mat.nativeObj));
}
//
// C++: bool cv::imwrite(String filename, Mat img, vector_int _params = std::vector<int>())
//
/**
* Saves an image to a specified file.
*
* The function imwrite saves the image to the specified file. The image format is chosen based on the
* filename extension (see cv::imread for the list of extensions). In general, only 8-bit
* single-channel or 3-channel (with 'BGR' channel order) images
* can be saved using this function, with these exceptions:
*
* <ul>
* <li>
* 16-bit unsigned (CV_16U) images can be saved in the case of PNG, JPEG 2000, and TIFF formats
* </li>
* <li>
* 32-bit float (CV_32F) images can be saved in PFM, TIFF, OpenEXR, and Radiance HDR formats;
* 3-channel (CV_32FC3) TIFF images will be saved using the LogLuv high dynamic range encoding
* (4 bytes per pixel)
* </li>
* <li>
* PNG images with an alpha channel can be saved using this function. To do this, create
* 8-bit (or 16-bit) 4-channel image BGRA, where the alpha channel goes last. Fully transparent pixels
* should have alpha set to 0, fully opaque pixels should have alpha set to 255/65535 (see the code sample below).
* </li>
* <li>
* Multiple images (vector of Mat) can be saved in TIFF format (see the code sample below).
* </li>
* </ul>
*
* If the format, depth or channel order is different, use
* Mat::convertTo and cv::cvtColor to convert it before saving. Or, use the universal FileStorage I/O
* functions to save the image to XML or YAML format.
*
* The sample below shows how to create a BGRA image, how to set custom compression parameters and save it to a PNG file.
* It also demonstrates how to save multiple images in a TIFF file:
* INCLUDE: snippets/imgcodecs_imwrite.cpp
* param filename Name of the file.
* param img (Mat or vector of Mat) Image or Images to be saved.
* param _params automatically generated
* return automatically generated
*/
public static bool imwrite(string filename, Mat img, MatOfInt _params)
{
if (img != null)
{
img.ThrowIfDisposed();
}
if (_params != null)
{
_params.ThrowIfDisposed();
}
Mat _params_mat = _params;
return(imgcodecs_Imgcodecs_imwrite_10(filename, img.nativeObj, _params_mat.nativeObj));
}
//
// C++: static Ptr_FREAK cv::xfeatures2d::FREAK::create(bool orientationNormalized = true, bool scaleNormalized = true, float patternScale = 22.0f, int nOctaves = 4, vector_int selectedPairs = std::vector<int>())
//
//javadoc: FREAK::create(orientationNormalized, scaleNormalized, patternScale, nOctaves, selectedPairs)
public static FREAK create(bool orientationNormalized, bool scaleNormalized, float patternScale, int nOctaves, MatOfInt selectedPairs)
{
if (selectedPairs != null)
{
selectedPairs.ThrowIfDisposed();
}
#if ((UNITY_ANDROID || UNITY_IOS || UNITY_WEBGL) && !UNITY_EDITOR) || UNITY_5 || UNITY_5_3_OR_NEWER
Mat selectedPairs_mat = selectedPairs;
FREAK retVal = FREAK.__fromPtr__(xfeatures2d_FREAK_create_10(orientationNormalized, scaleNormalized, patternScale, nOctaves, selectedPairs_mat.nativeObj));
return(retVal);
#else
return(null);
#endif
}
/**
* The BRISK constructor for a custom pattern, detection threshold and octaves
*
* param thresh AGAST detection threshold score.
* param octaves detection octaves. Use 0 to do single scale.
* param radiusList defines the radii (in pixels) where the samples around a keypoint are taken (for
* keypoint scale 1).
* param numberList defines the number of sampling points on the sampling circle. Must be the same
* size as radiusList..
* param dMax threshold for the short pairings used for descriptor formation (in pixels for keypoint
* scale 1).
* keypoint scale 1).
* return automatically generated
*/
public static BRISK create(int thresh, int octaves, MatOfFloat radiusList, MatOfInt numberList, float dMax)
{
if (radiusList != null)
{
radiusList.ThrowIfDisposed();
}
if (numberList != null)
{
numberList.ThrowIfDisposed();
}
Mat radiusList_mat = radiusList;
Mat numberList_mat = numberList;
return(BRISK.__fromPtr__(features2d_BRISK_create_12(thresh, octaves, radiusList_mat.nativeObj, numberList_mat.nativeObj, dMax)));
}
/**
* The BRISK constructor for a custom pattern
*
* param radiusList defines the radii (in pixels) where the samples around a keypoint are taken (for
* keypoint scale 1).
* param numberList defines the number of sampling points on the sampling circle. Must be the same
* size as radiusList..
* scale 1).
* keypoint scale 1).
* return automatically generated
*/
public static BRISK create(MatOfFloat radiusList, MatOfInt numberList)
{
if (radiusList != null)
{
radiusList.ThrowIfDisposed();
}
if (numberList != null)
{
numberList.ThrowIfDisposed();
}
Mat radiusList_mat = radiusList;
Mat numberList_mat = numberList;
return(BRISK.__fromPtr__(features2d_BRISK_create_111(radiusList_mat.nativeObj, numberList_mat.nativeObj)));
}
public static void groupRectangles(MatOfRect rectList, MatOfInt weights, int groupThreshold)
{
if (rectList != null)
{
rectList.ThrowIfDisposed();
}
if (weights != null)
{
weights.ThrowIfDisposed();
}
Mat rectList_mat = rectList;
Mat weights_mat = weights;
objdetect_Objdetect_groupRectangles_11(rectList_mat.nativeObj, weights_mat.nativeObj, groupThreshold);
}
//
// C++: static Ptr_PCTSignatures cv::xfeatures2d::PCTSignatures::create(vector_Point2f initSamplingPoints, vector_int initClusterSeedIndexes)
//
/**
* Creates PCTSignatures algorithm using pre-generated sampling points
* and clusterization seeds indexes.
* param initSamplingPoints Sampling points used in image sampling.
* param initClusterSeedIndexes Indexes of initial clusterization seeds.
* Its size must be lower or equal to initSamplingPoints.size().
* return Created algorithm.
*/
public static PCTSignatures create(MatOfPoint2f initSamplingPoints, MatOfInt initClusterSeedIndexes)
{
if (initSamplingPoints != null)
{
initSamplingPoints.ThrowIfDisposed();
}
if (initClusterSeedIndexes != null)
{
initClusterSeedIndexes.ThrowIfDisposed();
}
Mat initSamplingPoints_mat = initSamplingPoints;
Mat initClusterSeedIndexes_mat = initClusterSeedIndexes;
return(PCTSignatures.__fromPtr__(xfeatures2d_PCTSignatures_create_15(initSamplingPoints_mat.nativeObj, initClusterSeedIndexes_mat.nativeObj)));
}
//
// C++: void cv::xfeatures2d::PCTSignatures::setInitSeedIndexes(vector_int initSeedIndexes)
//
//javadoc: PCTSignatures::setInitSeedIndexes(initSeedIndexes)
public void setInitSeedIndexes(MatOfInt initSeedIndexes)
{
ThrowIfDisposed();
if (initSeedIndexes != null)
{
initSeedIndexes.ThrowIfDisposed();
}
#if ((UNITY_ANDROID || UNITY_IOS || UNITY_WEBGL) && !UNITY_EDITOR) || UNITY_5 || UNITY_5_3_OR_NEWER
Mat initSeedIndexes_mat = initSeedIndexes;
xfeatures2d_PCTSignatures_setInitSeedIndexes_10(nativeObj, initSeedIndexes_mat.nativeObj);
return;
#else
return;
#endif
}
//
// C++: void cv::Subdiv2D::getLeadingEdgeList(vector_int& leadingEdgeList)
//
//javadoc: Subdiv2D::getLeadingEdgeList(leadingEdgeList)
public void getLeadingEdgeList(MatOfInt leadingEdgeList)
{
ThrowIfDisposed();
if (leadingEdgeList != null)
{
leadingEdgeList.ThrowIfDisposed();
}
#if ((UNITY_ANDROID || UNITY_IOS || UNITY_WEBGL) && !UNITY_EDITOR) || UNITY_5 || UNITY_5_3_OR_NEWER
Mat leadingEdgeList_mat = leadingEdgeList;
imgproc_Subdiv2D_getLeadingEdgeList_10(nativeObj, leadingEdgeList_mat.nativeObj);
return;
#else
return;
#endif
}
//
// C++: int64 cv::dnn::Net::getFLOPS(int layerId, MatShape netInputShape)
//
//javadoc: Net::getFLOPS(layerId, netInputShape)
public long getFLOPS(int layerId, MatOfInt netInputShape)
{
ThrowIfDisposed();
if (netInputShape != null)
{
netInputShape.ThrowIfDisposed();
}
#if ((UNITY_ANDROID || UNITY_IOS || UNITY_WEBGL) && !UNITY_EDITOR) || UNITY_5 || UNITY_5_3_OR_NEWER
Mat netInputShape_mat = netInputShape;
long retVal = dnn_Net_getFLOPS_11(nativeObj, layerId, netInputShape_mat.nativeObj);
return(retVal);
#else
return(-1);
#endif
}
//
// C++: bool cv::imwrite(String filename, Mat img, vector_int _params = std::vector<int>())
//
//javadoc: imwrite(filename, img, _params)
public static bool imwrite(string filename, Mat img, MatOfInt _params)
{
if (img != null)
{
img.ThrowIfDisposed();
}
if (_params != null)
{
_params.ThrowIfDisposed();
}
#if ((UNITY_ANDROID || UNITY_IOS || UNITY_WEBGL) && !UNITY_EDITOR) || UNITY_5 || UNITY_5_3_OR_NEWER
Mat _params_mat = _params;
bool retVal = imgcodecs_Imgcodecs_imwrite_10(filename, img.nativeObj, _params_mat.nativeObj);
return(retVal);
#else
return(false);
#endif
}
//
// C++: void cv::Subdiv2D::getVoronoiFacetList(vector_int idx, vector_vector_Point2f& facetList, vector_Point2f& facetCenters)
//
/**
* Returns a list of all Voronoi facets.
*
* param idx Vector of vertices IDs to consider. For all vertices you can pass empty vector.
* param facetList Output vector of the Voronoi facets.
* param facetCenters Output vector of the Voronoi facets center points.
*/
public void getVoronoiFacetList(MatOfInt idx, List <MatOfPoint2f> facetList, MatOfPoint2f facetCenters)
{
ThrowIfDisposed();
if (idx != null)
{
idx.ThrowIfDisposed();
}
if (facetCenters != null)
{
facetCenters.ThrowIfDisposed();
}
Mat idx_mat = idx;
Mat facetList_mat = new Mat();
Mat facetCenters_mat = facetCenters;
imgproc_Subdiv2D_getVoronoiFacetList_10(nativeObj, idx_mat.nativeObj, facetList_mat.nativeObj, facetCenters_mat.nativeObj);
Converters.Mat_to_vector_vector_Point2f(facetList_mat, facetList);
facetList_mat.release();
}
//
// C++: bool cv::imencode(String ext, Mat img, vector_uchar& buf, vector_int _params = std::vector<int>())
//
/**
* Encodes an image into a memory buffer.
*
* The function imencode compresses the image and stores it in the memory buffer that is resized to fit the
* result. See cv::imwrite for the list of supported formats and flags description.
*
* param ext File extension that defines the output format.
* param img Image to be written.
* param buf Output buffer resized to fit the compressed image.
* param _params automatically generated
* return automatically generated
*/
public static bool imencode(string ext, Mat img, MatOfByte buf, MatOfInt _params)
{
if (img != null)
{
img.ThrowIfDisposed();
}
if (buf != null)
{
buf.ThrowIfDisposed();
}
if (_params != null)
{
_params.ThrowIfDisposed();
}
Mat buf_mat = buf;
Mat _params_mat = _params;
return(imgcodecs_Imgcodecs_imencode_10(ext, img.nativeObj, buf_mat.nativeObj, _params_mat.nativeObj));
}
//javadoc: BRISK::create(radiusList, numberList)
public static BRISK create(MatOfFloat radiusList, MatOfInt numberList)
{
if (radiusList != null)
{
radiusList.ThrowIfDisposed();
}
if (numberList != null)
{
numberList.ThrowIfDisposed();
}
#if ((UNITY_ANDROID || UNITY_IOS || UNITY_WEBGL) && !UNITY_EDITOR) || UNITY_5 || UNITY_5_3_OR_NEWER
Mat radiusList_mat = radiusList;
Mat numberList_mat = numberList;
BRISK retVal = BRISK.__fromPtr__(features2d_BRISK_create_111(radiusList_mat.nativeObj, numberList_mat.nativeObj));
return(retVal);
#else
return(null);
#endif
}
//
// C++: static Ptr_PCTSignatures cv::xfeatures2d::PCTSignatures::create(vector_Point2f initSamplingPoints, vector_int initClusterSeedIndexes)
//
//javadoc: PCTSignatures::create(initSamplingPoints, initClusterSeedIndexes)
public static PCTSignatures create(MatOfPoint2f initSamplingPoints, MatOfInt initClusterSeedIndexes)
{
if (initSamplingPoints != null)
{
initSamplingPoints.ThrowIfDisposed();
}
if (initClusterSeedIndexes != null)
{
initClusterSeedIndexes.ThrowIfDisposed();
}
#if ((UNITY_ANDROID || UNITY_IOS || UNITY_WEBGL) && !UNITY_EDITOR) || UNITY_5 || UNITY_5_3_OR_NEWER
Mat initSamplingPoints_mat = initSamplingPoints;
Mat initClusterSeedIndexes_mat = initClusterSeedIndexes;
PCTSignatures retVal = PCTSignatures.__fromPtr__(xfeatures2d_PCTSignatures_create_15(initSamplingPoints_mat.nativeObj, initClusterSeedIndexes_mat.nativeObj));
return(retVal);
#else
return(null);
#endif
}