public HandDetector()
{
videoCapture = CvCapture.FromCamera(0);
cascade = CvHaarClassifierCascade.FromFile("Hand.Cascade.1.xml");
if (videoCapture == null)
{
throw new Exception("Camera not found.");
}
}
/// <summary>
/// 与えられた画像からオブジェクトを含む様な矩形領域を検出し，それらの領域を矩形の列として返す．
/// </summary>
/// <param name="image">この画像の中からオブジェクトを検出する</param>
/// <param name="cascade">Haar 分類器カスケード の内部表現</param>
/// <param name="storage">オブジェクト候補の矩形が得られた場合に，その矩形列を保存するメモリストレージ</param>
/// <param name="scaleFactor">スキャン毎に探索ウィンドウがスケーリングされる際のスケールファクタ． 例えばこの値が 1.1 ならば，ウィンドウが 10% 大きくなる</param>
/// <param name="minNeighbors">（これから 1 を引いた値が）オブジェクトを構成する近傍矩形の最小数となる． min_neighbors-1 よりも少ない矩形しか含まないようなグループは全て棄却される． もし min_neighbors が 0 である場合，この関数はグループを一つも生成せず，候補となる矩形を全て返す．これはユーザがカスタマイズしたグループ化処理を適用したい場合に有用である． </param>
/// <param name="flags">処理モード</param>
/// <param name="minSize">最小ウィンドウサイズ．デフォルトでは分類器の学習に用いられたサンプルのサイズが設定される（顔検出の場合は，~20×20）.</param>
/// <returns>CvAvgCompを要素とするCvSeq</returns>
#else
/// <summary>
/// Finds rectangular regions in the given image that are likely to contain objects the cascade has been trained for and returns those regions as a sequence of rectangles.
/// </summary>
/// <param name="image">Image to detect objects in. </param>
/// <param name="cascade">Haar classifier cascade in internal representation. </param>
/// <param name="storage">Memory storage to store the resultant sequence of the object candidate rectangles. </param>
/// <param name="scaleFactor">The factor by which the search window is scaled between the subsequent scans, for example, 1.1 means increasing window by 10%. </param>
/// <param name="minNeighbors">Minimum number (minus 1) of neighbor rectangles that makes up an object. All the groups of a smaller number of rectangles than min_neighbors-1 are rejected. If min_neighbors is 0, the function does not any grouping at all and returns all the detected candidate rectangles, which may be useful if the user wants to apply a customized grouping procedure. </param>
/// <param name="flags">Mode of operation. Currently the only flag that may be specified is CV_HAAR_DO_CANNY_PRUNING. If it is set, the function uses Canny edge detector to reject some image regions that contain too few or too much edges and thus can not contain the searched object. The particular threshold values are tuned for face detection and in this case the pruning speeds up the processing. </param>
/// <param name="minSize">Minimum window size. By default, it is set to the size of samples the classifier has been trained on (~20×20 for face detection). </param>
/// <returns></returns>
#endif
public static CvSeq <CvAvgComp> HaarDetectObjects(CvArr image, CvHaarClassifierCascade cascade, CvMemStorage storage, double scaleFactor, int minNeighbors, HaarDetectionType flags, CvSize minSize)
{
if (image == null)
{
throw new ArgumentNullException("image");
}
if (cascade == null)
{
throw new ArgumentNullException("cascade");
}
if (storage == null)
{
throw new ArgumentNullException("storage");
}
IntPtr result = NativeMethods.cvHaarDetectObjects(image.CvPtr, cascade.CvPtr, storage.CvPtr, scaleFactor, minNeighbors, flags, minSize);
if (result == IntPtr.Zero)
{
return(null);
}
else
{
return(new CvSeq <CvAvgComp>(result));
}
}
// Use this for initialization
void Start()
{
cascade = CvHaarClassifierCascade.FromFile(@"./Assets/haarcascade_frontalface_alt.xml");
capture = Cv.CreateCameraCapture(0);
Cv.SetCaptureProperty(capture, CaptureProperty.FrameWidth, CAPTURE_WIDTH);
Cv.SetCaptureProperty(capture, CaptureProperty.FrameHeight, CAPTURE_HEIGHT);
IplImage frame = Cv.QueryFrame(capture);
Cv.NamedWindow("FaceDetect");
CvSVM svm = new CvSVM ();
CvTermCriteria criteria = new CvTermCriteria (CriteriaType.Epsilon, 1000, double.Epsilon);
CvSVMParams param = new CvSVMParams (CvSVM.C_SVC, CvSVM.RBF, 10.0, 8.0, 1.0, 10.0, 0.5, 0.1, null, criteria);
}
/// <summary>
/// 1つ目のカメラでカメラ初期化
/// </summary>
/// <param name="output"></param>
public Camera(logOutput output = null)
{
LogOutput = output;
// カメラの用意
cap = Cv.CreateCameraCapture(0);
Log(cap.CaptureType + ", " + cap.FrameWidth + "x" + cap.FrameHeight + ", " + cap.Mode);
Cv.SetCaptureProperty(cap, CaptureProperty.FrameWidth, WIDTH);
Cv.SetCaptureProperty(cap, CaptureProperty.FrameHeight, HEIGHT);
// 検出器の用意
cvHCC = Cv.Load<CvHaarClassifierCascade>("haarcascade_profileface.xml");
stor = Cv.CreateMemStorage(0);
}
/// <summary>
/// 与えられた画像からオブジェクトを含む様な矩形領域を検出し，それらの領域を矩形の列として返す．
/// </summary>
/// <param name="image">この画像の中からオブジェクトを検出する</param>
/// <param name="cascade">Haar 分類器カスケード の内部表現</param>
/// <param name="storage">オブジェクト候補の矩形が得られた場合に，その矩形列を保存するメモリストレージ</param>
/// <param name="scaleFactor">スキャン毎に探索ウィンドウがスケーリングされる際のスケールファクタ． 例えばこの値が 1.1 ならば，ウィンドウが 10% 大きくなる</param>
/// <param name="minNeighbors">（これから 1 を引いた値が）オブジェクトを構成する近傍矩形の最小数となる． min_neighbors-1 よりも少ない矩形しか含まないようなグループは全て棄却される． もし min_neighbors が 0 である場合，この関数はグループを一つも生成せず，候補となる矩形を全て返す．これはユーザがカスタマイズしたグループ化処理を適用したい場合に有用である． </param>
/// <returns>CvAvgCompを要素とするCvSeq</returns>
#else
/// <summary>
/// Finds rectangular regions in the given image that are likely to contain objects the cascade has been trained for and returns those regions as a sequence of rectangles.
/// </summary>
/// <param name="image">Image to detect objects in. </param>
/// <param name="cascade">Haar classifier cascade in internal representation. </param>
/// <param name="storage">Memory storage to store the resultant sequence of the object candidate rectangles. </param>
/// <param name="scaleFactor">The factor by which the search window is scaled between the subsequent scans, for example, 1.1 means increasing window by 10%. </param>
/// <param name="minNeighbors">Minimum number (minus 1) of neighbor rectangles that makes up an object. All the groups of a smaller number of rectangles than min_neighbors-1 are rejected. If min_neighbors is 0, the function does not any grouping at all and returns all the detected candidate rectangles, which may be useful if the user wants to apply a customized grouping procedure. </param>
/// <returns></returns>
#endif
public static CvSeq <CvAvgComp> HaarDetectObjects(CvArr image, CvHaarClassifierCascade cascade, CvMemStorage storage, double scaleFactor, int minNeighbors)
{
return(HaarDetectObjects(image, cascade, storage, scaleFactor, minNeighbors, HaarDetectionType.Zero, new CvSize(0, 0)));
}
/// <summary>
/// 与えられた画像からオブジェクトを含む様な矩形領域を検出し，それらの領域を矩形の列として返す．
/// </summary>
/// <param name="image">この画像の中からオブジェクトを検出する</param>
/// <param name="cascade">Haar 分類器カスケード の内部表現</param>
/// <param name="storage">オブジェクト候補の矩形が得られた場合に，その矩形列を保存するメモリストレージ</param>
/// <returns>CvAvgCompを要素とするCvSeq</returns>
#else
/// <summary>
/// Finds rectangular regions in the given image that are likely to contain objects the cascade has been trained for and returns those regions as a sequence of rectangles.
/// </summary>
/// <param name="image">Image to detect objects in. </param>
/// <param name="cascade">Haar classifier cascade in internal representation. </param>
/// <param name="storage">Memory storage to store the resultant sequence of the object candidate rectangles. </param>
/// <returns></returns>
#endif
public static CvSeq <CvAvgComp> HaarDetectObjects(CvArr image, CvHaarClassifierCascade cascade, CvMemStorage storage)
{
return(HaarDetectObjects(image, cascade, storage, 1.1, 3, HaarDetectionType.Zero, new CvSize(0, 0)));
}
/// <summary>
/// 与えられた画像からオブジェクトを含む様な矩形領域を検出し，それらの領域を矩形の列として返す．
/// </summary>
/// <param name="image">この画像の中からオブジェクトを検出する</param>
/// <param name="cascade">Haar 分類器カスケード の内部表現</param>
/// <param name="storage">オブジェクト候補の矩形が得られた場合に，その矩形列を保存するメモリストレージ</param>
/// <param name="scaleFactor">スキャン毎に探索ウィンドウがスケーリングされる際のスケールファクタ． 例えばこの値が 1.1 ならば，ウィンドウが 10% 大きくなる</param>
/// <param name="minNeighbors">（これから 1 を引いた値が）オブジェクトを構成する近傍矩形の最小数となる． min_neighbors-1 よりも少ない矩形しか含まないようなグループは全て棄却される． もし min_neighbors が 0 である場合，この関数はグループを一つも生成せず，候補となる矩形を全て返す．これはユーザがカスタマイズしたグループ化処理を適用したい場合に有用である． </param>
/// <returns>CvAvgCompを要素とするCvSeq</returns>
#else
/// <summary>
/// Finds rectangular regions in the given image that are likely to contain objects the cascade has been trained for and returns those regions as a sequence of rectangles.
/// </summary>
/// <param name="image">Image to detect objects in. </param>
/// <param name="cascade">Haar classifier cascade in internal representation. </param>
/// <param name="storage">Memory storage to store the resultant sequence of the object candidate rectangles. </param>
/// <param name="scaleFactor">The factor by which the search window is scaled between the subsequent scans, for example, 1.1 means increasing window by 10%. </param>
/// <param name="minNeighbors">Minimum number (minus 1) of neighbor rectangles that makes up an object. All the groups of a smaller number of rectangles than min_neighbors-1 are rejected. If min_neighbors is 0, the function does not any grouping at all and returns all the detected candidate rectangles, which may be useful if the user wants to apply a customized grouping procedure. </param>
/// <returns></returns>
#endif
public static CvSeq<CvAvgComp> HaarDetectObjects(CvArr image, CvHaarClassifierCascade cascade, CvMemStorage storage, double scaleFactor, int minNeighbors)
{
return HaarDetectObjects(image, cascade, storage, scaleFactor, minNeighbors, HaarDetectionType.Zero, new CvSize(0, 0));
}
/// <summary>
/// 与えられた画像からオブジェクトを含む様な矩形領域を検出し，それらの領域を矩形の列として返す．
/// </summary>
/// <param name="image">この画像の中からオブジェクトを検出する</param>
/// <param name="cascade">Haar 分類器カスケード の内部表現</param>
/// <param name="storage">オブジェクト候補の矩形が得られた場合に，その矩形列を保存するメモリストレージ</param>
/// <returns>CvAvgCompを要素とするCvSeq</returns>
#else
/// <summary>
/// Finds rectangular regions in the given image that are likely to contain objects the cascade has been trained for and returns those regions as a sequence of rectangles.
/// </summary>
/// <param name="image">Image to detect objects in. </param>
/// <param name="cascade">Haar classifier cascade in internal representation. </param>
/// <param name="storage">Memory storage to store the resultant sequence of the object candidate rectangles. </param>
/// <returns></returns>
#endif
public static CvSeq<CvAvgComp> HaarDetectObjects(CvArr image, CvHaarClassifierCascade cascade, CvMemStorage storage)
{
return HaarDetectObjects(image, cascade, storage, 1.1, 3, HaarDetectionType.Zero, new CvSize(0, 0));
}
/// <summary>
/// 与えられた画像からオブジェクトを含む様な矩形領域を検出し，それらの領域を矩形の列として返す．
/// </summary>
/// <param name="image">この画像の中からオブジェクトを検出する</param>
/// <param name="cascade">Haar 分類器カスケード の内部表現</param>
/// <param name="storage">オブジェクト候補の矩形が得られた場合に，その矩形列を保存するメモリストレージ</param>
/// <param name="scaleFactor">スキャン毎に探索ウィンドウがスケーリングされる際のスケールファクタ． 例えばこの値が 1.1 ならば，ウィンドウが 10% 大きくなる</param>
/// <param name="minNeighbors">（これから 1 を引いた値が）オブジェクトを構成する近傍矩形の最小数となる． min_neighbors-1 よりも少ない矩形しか含まないようなグループは全て棄却される． もし min_neighbors が 0 である場合，この関数はグループを一つも生成せず，候補となる矩形を全て返す．これはユーザがカスタマイズしたグループ化処理を適用したい場合に有用である． </param>
/// <param name="flags">処理モード</param>
/// <param name="minSize">最小ウィンドウサイズ．デフォルトでは分類器の学習に用いられたサンプルのサイズが設定される（顔検出の場合は，~20×20）.</param>
/// <returns>CvAvgCompを要素とするCvSeq</returns>
#else
/// <summary>
/// Finds rectangular regions in the given image that are likely to contain objects the cascade has been trained for and returns those regions as a sequence of rectangles.
/// </summary>
/// <param name="image">Image to detect objects in. </param>
/// <param name="cascade">Haar classifier cascade in internal representation. </param>
/// <param name="storage">Memory storage to store the resultant sequence of the object candidate rectangles. </param>
/// <param name="scaleFactor">The factor by which the search window is scaled between the subsequent scans, for example, 1.1 means increasing window by 10%. </param>
/// <param name="minNeighbors">Minimum number (minus 1) of neighbor rectangles that makes up an object. All the groups of a smaller number of rectangles than min_neighbors-1 are rejected. If min_neighbors is 0, the function does not any grouping at all and returns all the detected candidate rectangles, which may be useful if the user wants to apply a customized grouping procedure. </param>
/// <param name="flags">Mode of operation. Currently the only flag that may be specified is CV_HAAR_DO_CANNY_PRUNING. If it is set, the function uses Canny edge detector to reject some image regions that contain too few or too much edges and thus can not contain the searched object. The particular threshold values are tuned for face detection and in this case the pruning speeds up the processing. </param>
/// <param name="minSize">Minimum window size. By default, it is set to the size of samples the classifier has been trained on (~20×20 for face detection). </param>
/// <returns></returns>
#endif
public static CvSeq<CvAvgComp> HaarDetectObjects(CvArr image, CvHaarClassifierCascade cascade, CvMemStorage storage, double scaleFactor, int minNeighbors, HaarDetectionType flags, CvSize minSize)
{
if (image == null)
throw new ArgumentNullException("image");
if (cascade == null)
throw new ArgumentNullException("cascade");
if (storage == null)
throw new ArgumentNullException("storage");
IntPtr result = NativeMethods.cvHaarDetectObjects(image.CvPtr, cascade.CvPtr, storage.CvPtr, scaleFactor, minNeighbors, flags, minSize);
if (result == IntPtr.Zero)
return null;
else
return new CvSeq<CvAvgComp>(result);
}
/// <summary>
/// OpenCV関連の変数初期化
/// </summary>
private void InitOpenCV()
{
openCVImage = new IplImage(
kinect.ColorStream.FrameWidth,
kinect.ColorStream.FrameHeight,
BitDepth.U8, 3 );
openCVGrayImage = new IplImage( kinect.ColorStream.FrameWidth, kinect.ColorStream.FrameHeight, BitDepth.U8, 1 );
storage = new CvMemStorage();
cascade = CvHaarClassifierCascade.FromFile( "../../haarcascade_frontalface_alt2.xml" );
}
