702 lines
24 KiB
C#
702 lines
24 KiB
C#
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using OpenCVForUnity.CoreModule;
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using OpenCVForUnity.UtilsModule;
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using System;
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using System.Collections.Generic;
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using System.Runtime.InteropServices;
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namespace OpenCVForUnity.FaceModule
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{
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// C++: class LBPHFaceRecognizer
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public class LBPHFaceRecognizer : FaceRecognizer
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{
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protected override void Dispose(bool disposing)
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{
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try
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{
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if (disposing)
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{
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}
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if (IsEnabledDispose)
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{
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if (nativeObj != IntPtr.Zero)
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face_LBPHFaceRecognizer_delete(nativeObj);
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nativeObj = IntPtr.Zero;
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}
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}
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finally
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{
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base.Dispose(disposing);
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}
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}
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protected internal LBPHFaceRecognizer(IntPtr addr) : base(addr) { }
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// internal usage only
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public static new LBPHFaceRecognizer __fromPtr__(IntPtr addr) { return new LBPHFaceRecognizer(addr); }
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//
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// C++: int cv::face::LBPHFaceRecognizer::getGridX()
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//
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/**
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* SEE: setGridX
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* return automatically generated
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*/
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public int getGridX()
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{
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ThrowIfDisposed();
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return face_LBPHFaceRecognizer_getGridX_10(nativeObj);
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}
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//
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// C++: void cv::face::LBPHFaceRecognizer::setGridX(int val)
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//
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/**
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* getGridX SEE: getGridX
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* param val automatically generated
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*/
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public void setGridX(int val)
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{
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ThrowIfDisposed();
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face_LBPHFaceRecognizer_setGridX_10(nativeObj, val);
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}
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//
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// C++: int cv::face::LBPHFaceRecognizer::getGridY()
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//
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/**
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* SEE: setGridY
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* return automatically generated
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*/
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public int getGridY()
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{
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ThrowIfDisposed();
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return face_LBPHFaceRecognizer_getGridY_10(nativeObj);
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}
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//
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// C++: void cv::face::LBPHFaceRecognizer::setGridY(int val)
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//
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/**
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* getGridY SEE: getGridY
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* param val automatically generated
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*/
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public void setGridY(int val)
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{
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ThrowIfDisposed();
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face_LBPHFaceRecognizer_setGridY_10(nativeObj, val);
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}
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//
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// C++: int cv::face::LBPHFaceRecognizer::getRadius()
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//
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/**
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* SEE: setRadius
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* return automatically generated
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*/
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public int getRadius()
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{
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ThrowIfDisposed();
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return face_LBPHFaceRecognizer_getRadius_10(nativeObj);
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}
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//
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// C++: void cv::face::LBPHFaceRecognizer::setRadius(int val)
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//
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/**
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* getRadius SEE: getRadius
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* param val automatically generated
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*/
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public void setRadius(int val)
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{
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ThrowIfDisposed();
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face_LBPHFaceRecognizer_setRadius_10(nativeObj, val);
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}
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//
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// C++: int cv::face::LBPHFaceRecognizer::getNeighbors()
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//
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/**
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* SEE: setNeighbors
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* return automatically generated
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*/
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public int getNeighbors()
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{
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ThrowIfDisposed();
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return face_LBPHFaceRecognizer_getNeighbors_10(nativeObj);
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}
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//
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// C++: void cv::face::LBPHFaceRecognizer::setNeighbors(int val)
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//
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/**
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* getNeighbors SEE: getNeighbors
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* param val automatically generated
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*/
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public void setNeighbors(int val)
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{
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ThrowIfDisposed();
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face_LBPHFaceRecognizer_setNeighbors_10(nativeObj, val);
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}
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//
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// C++: double cv::face::LBPHFaceRecognizer::getThreshold()
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//
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/**
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* SEE: setThreshold
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* return automatically generated
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*/
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public double getThreshold()
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{
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ThrowIfDisposed();
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return face_LBPHFaceRecognizer_getThreshold_10(nativeObj);
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}
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//
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// C++: void cv::face::LBPHFaceRecognizer::setThreshold(double val)
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//
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/**
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* getThreshold SEE: getThreshold
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* param val automatically generated
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*/
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public void setThreshold(double val)
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{
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ThrowIfDisposed();
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face_LBPHFaceRecognizer_setThreshold_10(nativeObj, val);
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}
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//
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// C++: vector_Mat cv::face::LBPHFaceRecognizer::getHistograms()
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//
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public List<Mat> getHistograms()
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{
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ThrowIfDisposed();
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List<Mat> retVal = new List<Mat>();
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Mat retValMat = new Mat(DisposableObject.ThrowIfNullIntPtr(face_LBPHFaceRecognizer_getHistograms_10(nativeObj)));
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Converters.Mat_to_vector_Mat(retValMat, retVal);
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return retVal;
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}
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//
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// C++: Mat cv::face::LBPHFaceRecognizer::getLabels()
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//
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public Mat getLabels()
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{
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ThrowIfDisposed();
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return new Mat(DisposableObject.ThrowIfNullIntPtr(face_LBPHFaceRecognizer_getLabels_10(nativeObj)));
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}
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//
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// C++: static Ptr_LBPHFaceRecognizer cv::face::LBPHFaceRecognizer::create(int radius = 1, int neighbors = 8, int grid_x = 8, int grid_y = 8, double threshold = DBL_MAX)
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//
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/**
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* param radius The radius used for building the Circular Local Binary Pattern. The greater the
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* radius, the smoother the image but more spatial information you can get.
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* param neighbors The number of sample points to build a Circular Local Binary Pattern from. An
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* appropriate value is to use {code 8} sample points. Keep in mind: the more sample points you include,
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* the higher the computational cost.
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* param grid_x The number of cells in the horizontal direction, 8 is a common value used in
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* publications. The more cells, the finer the grid, the higher the dimensionality of the resulting
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* feature vector.
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* param grid_y The number of cells in the vertical direction, 8 is a common value used in
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* publications. The more cells, the finer the grid, the higher the dimensionality of the resulting
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* feature vector.
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* param threshold The threshold applied in the prediction. If the distance to the nearest neighbor
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* is larger than the threshold, this method returns -1.
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*
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* ### Notes:
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*
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* <ul>
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* <li>
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* The Circular Local Binary Patterns (used in training and prediction) expect the data given as
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* grayscale images, use cvtColor to convert between the color spaces.
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* </li>
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* <li>
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* This model supports updating.
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* </li>
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* </ul>
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*
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* ### Model internal data:
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*
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* <ul>
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* <li>
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* radius see LBPHFaceRecognizer::create.
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* </li>
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* <li>
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* neighbors see LBPHFaceRecognizer::create.
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* </li>
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* <li>
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* grid_x see LLBPHFaceRecognizer::create.
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* </li>
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* <li>
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* grid_y see LBPHFaceRecognizer::create.
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* </li>
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* <li>
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* threshold see LBPHFaceRecognizer::create.
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* </li>
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* <li>
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* histograms Local Binary Patterns Histograms calculated from the given training data (empty if
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* none was given).
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* </li>
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* <li>
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* labels Labels corresponding to the calculated Local Binary Patterns Histograms.
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* </li>
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* </ul>
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* return automatically generated
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*/
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public static LBPHFaceRecognizer create(int radius, int neighbors, int grid_x, int grid_y, double threshold)
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{
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return LBPHFaceRecognizer.__fromPtr__(DisposableObject.ThrowIfNullIntPtr(face_LBPHFaceRecognizer_create_10(radius, neighbors, grid_x, grid_y, threshold)));
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}
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/**
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* param radius The radius used for building the Circular Local Binary Pattern. The greater the
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* radius, the smoother the image but more spatial information you can get.
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* param neighbors The number of sample points to build a Circular Local Binary Pattern from. An
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* appropriate value is to use {code 8} sample points. Keep in mind: the more sample points you include,
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* the higher the computational cost.
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* param grid_x The number of cells in the horizontal direction, 8 is a common value used in
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* publications. The more cells, the finer the grid, the higher the dimensionality of the resulting
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* feature vector.
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* param grid_y The number of cells in the vertical direction, 8 is a common value used in
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* publications. The more cells, the finer the grid, the higher the dimensionality of the resulting
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* feature vector.
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* is larger than the threshold, this method returns -1.
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*
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* ### Notes:
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*
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* <ul>
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* <li>
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* The Circular Local Binary Patterns (used in training and prediction) expect the data given as
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* grayscale images, use cvtColor to convert between the color spaces.
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* </li>
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* <li>
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* This model supports updating.
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* </li>
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* </ul>
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*
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* ### Model internal data:
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*
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* <ul>
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* <li>
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* radius see LBPHFaceRecognizer::create.
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* </li>
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* <li>
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* neighbors see LBPHFaceRecognizer::create.
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* </li>
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* <li>
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* grid_x see LLBPHFaceRecognizer::create.
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* </li>
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* <li>
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* grid_y see LBPHFaceRecognizer::create.
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* </li>
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* <li>
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* threshold see LBPHFaceRecognizer::create.
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* </li>
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* <li>
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* histograms Local Binary Patterns Histograms calculated from the given training data (empty if
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* none was given).
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* </li>
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* <li>
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* labels Labels corresponding to the calculated Local Binary Patterns Histograms.
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* </li>
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* </ul>
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* return automatically generated
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*/
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public static LBPHFaceRecognizer create(int radius, int neighbors, int grid_x, int grid_y)
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{
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return LBPHFaceRecognizer.__fromPtr__(DisposableObject.ThrowIfNullIntPtr(face_LBPHFaceRecognizer_create_11(radius, neighbors, grid_x, grid_y)));
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}
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/**
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* param radius The radius used for building the Circular Local Binary Pattern. The greater the
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* radius, the smoother the image but more spatial information you can get.
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* param neighbors The number of sample points to build a Circular Local Binary Pattern from. An
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* appropriate value is to use {code 8} sample points. Keep in mind: the more sample points you include,
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* the higher the computational cost.
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* param grid_x The number of cells in the horizontal direction, 8 is a common value used in
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* publications. The more cells, the finer the grid, the higher the dimensionality of the resulting
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* feature vector.
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* publications. The more cells, the finer the grid, the higher the dimensionality of the resulting
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* feature vector.
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* is larger than the threshold, this method returns -1.
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*
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* ### Notes:
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*
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* <ul>
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* <li>
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* The Circular Local Binary Patterns (used in training and prediction) expect the data given as
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* grayscale images, use cvtColor to convert between the color spaces.
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* </li>
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* <li>
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* This model supports updating.
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* </li>
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* </ul>
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*
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* ### Model internal data:
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*
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* <ul>
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* <li>
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* radius see LBPHFaceRecognizer::create.
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* </li>
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* <li>
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* neighbors see LBPHFaceRecognizer::create.
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* </li>
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* <li>
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* grid_x see LLBPHFaceRecognizer::create.
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* </li>
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* <li>
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* grid_y see LBPHFaceRecognizer::create.
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* </li>
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* <li>
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* threshold see LBPHFaceRecognizer::create.
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* </li>
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* <li>
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* histograms Local Binary Patterns Histograms calculated from the given training data (empty if
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* none was given).
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* </li>
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* <li>
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* labels Labels corresponding to the calculated Local Binary Patterns Histograms.
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* </li>
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* </ul>
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* return automatically generated
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*/
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public static LBPHFaceRecognizer create(int radius, int neighbors, int grid_x)
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{
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return LBPHFaceRecognizer.__fromPtr__(DisposableObject.ThrowIfNullIntPtr(face_LBPHFaceRecognizer_create_12(radius, neighbors, grid_x)));
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}
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/**
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* param radius The radius used for building the Circular Local Binary Pattern. The greater the
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* radius, the smoother the image but more spatial information you can get.
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* param neighbors The number of sample points to build a Circular Local Binary Pattern from. An
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* appropriate value is to use {code 8} sample points. Keep in mind: the more sample points you include,
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* the higher the computational cost.
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* publications. The more cells, the finer the grid, the higher the dimensionality of the resulting
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* feature vector.
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* publications. The more cells, the finer the grid, the higher the dimensionality of the resulting
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* feature vector.
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* is larger than the threshold, this method returns -1.
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*
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* ### Notes:
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*
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* <ul>
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* <li>
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* The Circular Local Binary Patterns (used in training and prediction) expect the data given as
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* grayscale images, use cvtColor to convert between the color spaces.
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* </li>
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* <li>
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* This model supports updating.
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* </li>
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* </ul>
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*
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* ### Model internal data:
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*
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* <ul>
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* <li>
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* radius see LBPHFaceRecognizer::create.
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* </li>
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* <li>
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* neighbors see LBPHFaceRecognizer::create.
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* </li>
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* <li>
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* grid_x see LLBPHFaceRecognizer::create.
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* </li>
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* <li>
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* grid_y see LBPHFaceRecognizer::create.
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* </li>
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* <li>
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* threshold see LBPHFaceRecognizer::create.
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* </li>
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* <li>
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* histograms Local Binary Patterns Histograms calculated from the given training data (empty if
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* none was given).
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* </li>
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* <li>
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* labels Labels corresponding to the calculated Local Binary Patterns Histograms.
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* </li>
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* </ul>
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* return automatically generated
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*/
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public static LBPHFaceRecognizer create(int radius, int neighbors)
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{
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return LBPHFaceRecognizer.__fromPtr__(DisposableObject.ThrowIfNullIntPtr(face_LBPHFaceRecognizer_create_13(radius, neighbors)));
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}
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/**
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* param radius The radius used for building the Circular Local Binary Pattern. The greater the
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* radius, the smoother the image but more spatial information you can get.
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* appropriate value is to use {code 8} sample points. Keep in mind: the more sample points you include,
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* the higher the computational cost.
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* publications. The more cells, the finer the grid, the higher the dimensionality of the resulting
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* feature vector.
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* publications. The more cells, the finer the grid, the higher the dimensionality of the resulting
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* feature vector.
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* is larger than the threshold, this method returns -1.
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*
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* ### Notes:
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*
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* <ul>
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* <li>
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* The Circular Local Binary Patterns (used in training and prediction) expect the data given as
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* grayscale images, use cvtColor to convert between the color spaces.
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* </li>
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* <li>
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* This model supports updating.
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* </li>
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* </ul>
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*
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* ### Model internal data:
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*
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* <ul>
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* <li>
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* radius see LBPHFaceRecognizer::create.
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* </li>
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* <li>
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* neighbors see LBPHFaceRecognizer::create.
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* </li>
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* <li>
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* grid_x see LLBPHFaceRecognizer::create.
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* </li>
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* <li>
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* grid_y see LBPHFaceRecognizer::create.
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* </li>
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* <li>
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* threshold see LBPHFaceRecognizer::create.
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* </li>
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* <li>
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* histograms Local Binary Patterns Histograms calculated from the given training data (empty if
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* none was given).
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* </li>
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* <li>
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* labels Labels corresponding to the calculated Local Binary Patterns Histograms.
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* </li>
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* </ul>
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* return automatically generated
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*/
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public static LBPHFaceRecognizer create(int radius)
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{
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return LBPHFaceRecognizer.__fromPtr__(DisposableObject.ThrowIfNullIntPtr(face_LBPHFaceRecognizer_create_14(radius)));
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}
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/**
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* radius, the smoother the image but more spatial information you can get.
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* appropriate value is to use {code 8} sample points. Keep in mind: the more sample points you include,
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* the higher the computational cost.
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* publications. The more cells, the finer the grid, the higher the dimensionality of the resulting
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* feature vector.
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* publications. The more cells, the finer the grid, the higher the dimensionality of the resulting
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* feature vector.
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* is larger than the threshold, this method returns -1.
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*
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* ### Notes:
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*
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* <ul>
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* <li>
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* The Circular Local Binary Patterns (used in training and prediction) expect the data given as
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* grayscale images, use cvtColor to convert between the color spaces.
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* </li>
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* <li>
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* This model supports updating.
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* </li>
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* </ul>
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*
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* ### Model internal data:
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*
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* <ul>
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* <li>
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* radius see LBPHFaceRecognizer::create.
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* </li>
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* <li>
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* neighbors see LBPHFaceRecognizer::create.
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* </li>
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* <li>
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* grid_x see LLBPHFaceRecognizer::create.
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* </li>
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* <li>
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* grid_y see LBPHFaceRecognizer::create.
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* </li>
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* <li>
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* threshold see LBPHFaceRecognizer::create.
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* </li>
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* <li>
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* histograms Local Binary Patterns Histograms calculated from the given training data (empty if
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* none was given).
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* </li>
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* <li>
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* labels Labels corresponding to the calculated Local Binary Patterns Histograms.
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* </li>
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* </ul>
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* return automatically generated
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*/
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public static LBPHFaceRecognizer create()
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{
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return LBPHFaceRecognizer.__fromPtr__(DisposableObject.ThrowIfNullIntPtr(face_LBPHFaceRecognizer_create_15()));
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}
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#if (UNITY_IOS || UNITY_WEBGL) && !UNITY_EDITOR
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const string LIBNAME = "__Internal";
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#else
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const string LIBNAME = "opencvforunity";
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#endif
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// C++: int cv::face::LBPHFaceRecognizer::getGridX()
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[DllImport(LIBNAME)]
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private static extern int face_LBPHFaceRecognizer_getGridX_10(IntPtr nativeObj);
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// C++: void cv::face::LBPHFaceRecognizer::setGridX(int val)
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[DllImport(LIBNAME)]
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private static extern void face_LBPHFaceRecognizer_setGridX_10(IntPtr nativeObj, int val);
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// C++: int cv::face::LBPHFaceRecognizer::getGridY()
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[DllImport(LIBNAME)]
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private static extern int face_LBPHFaceRecognizer_getGridY_10(IntPtr nativeObj);
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// C++: void cv::face::LBPHFaceRecognizer::setGridY(int val)
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[DllImport(LIBNAME)]
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private static extern void face_LBPHFaceRecognizer_setGridY_10(IntPtr nativeObj, int val);
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// C++: int cv::face::LBPHFaceRecognizer::getRadius()
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[DllImport(LIBNAME)]
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private static extern int face_LBPHFaceRecognizer_getRadius_10(IntPtr nativeObj);
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// C++: void cv::face::LBPHFaceRecognizer::setRadius(int val)
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[DllImport(LIBNAME)]
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private static extern void face_LBPHFaceRecognizer_setRadius_10(IntPtr nativeObj, int val);
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// C++: int cv::face::LBPHFaceRecognizer::getNeighbors()
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[DllImport(LIBNAME)]
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private static extern int face_LBPHFaceRecognizer_getNeighbors_10(IntPtr nativeObj);
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// C++: void cv::face::LBPHFaceRecognizer::setNeighbors(int val)
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[DllImport(LIBNAME)]
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private static extern void face_LBPHFaceRecognizer_setNeighbors_10(IntPtr nativeObj, int val);
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// C++: double cv::face::LBPHFaceRecognizer::getThreshold()
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[DllImport(LIBNAME)]
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private static extern double face_LBPHFaceRecognizer_getThreshold_10(IntPtr nativeObj);
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// C++: void cv::face::LBPHFaceRecognizer::setThreshold(double val)
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[DllImport(LIBNAME)]
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private static extern void face_LBPHFaceRecognizer_setThreshold_10(IntPtr nativeObj, double val);
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// C++: vector_Mat cv::face::LBPHFaceRecognizer::getHistograms()
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[DllImport(LIBNAME)]
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private static extern IntPtr face_LBPHFaceRecognizer_getHistograms_10(IntPtr nativeObj);
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// C++: Mat cv::face::LBPHFaceRecognizer::getLabels()
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[DllImport(LIBNAME)]
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private static extern IntPtr face_LBPHFaceRecognizer_getLabels_10(IntPtr nativeObj);
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// C++: static Ptr_LBPHFaceRecognizer cv::face::LBPHFaceRecognizer::create(int radius = 1, int neighbors = 8, int grid_x = 8, int grid_y = 8, double threshold = DBL_MAX)
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[DllImport(LIBNAME)]
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private static extern IntPtr face_LBPHFaceRecognizer_create_10(int radius, int neighbors, int grid_x, int grid_y, double threshold);
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[DllImport(LIBNAME)]
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private static extern IntPtr face_LBPHFaceRecognizer_create_11(int radius, int neighbors, int grid_x, int grid_y);
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[DllImport(LIBNAME)]
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private static extern IntPtr face_LBPHFaceRecognizer_create_12(int radius, int neighbors, int grid_x);
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[DllImport(LIBNAME)]
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private static extern IntPtr face_LBPHFaceRecognizer_create_13(int radius, int neighbors);
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[DllImport(LIBNAME)]
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private static extern IntPtr face_LBPHFaceRecognizer_create_14(int radius);
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[DllImport(LIBNAME)]
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private static extern IntPtr face_LBPHFaceRecognizer_create_15();
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// native support for java finalize()
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[DllImport(LIBNAME)]
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private static extern void face_LBPHFaceRecognizer_delete(IntPtr nativeObj);
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}
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}
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