/// <summary>
///
/// </summary>
/// <param name="img"></param>
/// <param name="templ"></param>
/// <param name="results"></param>
/// <param name="cost"></param>
/// <param name="templScale"></param>
/// <param name="maxMatches"></param>
/// <param name="minMatchDistance"></param>
/// <param name="padX"></param>
/// <param name="padY"></param>
/// <param name="scales"></param>
/// <param name="minScale"></param>
/// <param name="maxScale"></param>
/// <param name="orientationWeight"></param>
/// <param name="truncate"></param>
/// <returns></returns>
public static int ChamferMatching(
Mat img, Mat templ,
out Point[][] results, out float[] cost,
double templScale=1, int maxMatches = 20,
double minMatchDistance = 1.0, int padX = 3,
int padY = 3, int scales = 5, double minScale = 0.6, double maxScale = 1.6,
double orientationWeight = 0.5, double truncate = 20)
{
if (img == null)
throw new ArgumentNullException("img");
if (templ == null)
throw new ArgumentNullException("templ");
img.ThrowIfDisposed();
templ.ThrowIfDisposed();
using (var resultsVec = new VectorOfVectorPoint())
using (var costVec = new VectorOfFloat())
{
int ret = NativeMethods.contrib_chamerMatching(
img.CvPtr, templ.CvPtr, resultsVec.CvPtr, costVec.CvPtr,
templScale, maxMatches, minMatchDistance,
padX, padY, scales, minScale, maxScale, orientationWeight, truncate);
GC.KeepAlive(img);
GC.KeepAlive(templ);
results = resultsVec.ToArray();
cost = costVec.ToArray();
return ret;
}
}
/// <summary>
/// evaluate specified ROI and return confidence value for each location in multiple scales
/// </summary>
/// <param name="img"></param>
/// <param name="foundLocations"></param>
/// <param name="locations"></param>
/// <param name="hitThreshold"></param>
/// <param name="groupThreshold"></param>
public void DetectMultiScaleROI(
Mat img,
out Rect[] foundLocations,
out DetectionROI[] locations,
double hitThreshold = 0,
int groupThreshold = 0)
{
if (disposed)
throw new ObjectDisposedException("HOGDescriptor");
if (img == null)
throw new ArgumentNullException("img");
img.ThrowIfDisposed();
using (var flVec = new VectorOfRect())
using (var scalesVec = new VectorOfDouble())
using (var locationsVec = new VectorOfVectorPoint())
using (var confidencesVec = new VectorOfVectorDouble())
{
NativeMethods.objdetect_HOGDescriptor_detectMultiScaleROI(
ptr, img.CvPtr, flVec.CvPtr,
scalesVec.CvPtr, locationsVec.CvPtr, confidencesVec.CvPtr,
hitThreshold, groupThreshold);
foundLocations = flVec.ToArray();
double[] s = scalesVec.ToArray();
Point[][] l = locationsVec.ToArray();
double[][] c = confidencesVec.ToArray();
if(s.Length != l.Length || l.Length != c.Length)
throw new OpenCvSharpException("Invalid result data 'locations'");
locations = new DetectionROI[s.Length];
for (int i = 0; i < s.Length; i++)
{
locations[i] = new DetectionROI
{
Scale = s[i],
Locations = l[i],
Confidences = c[i]
};
}
}
}
/// <summary>
/// 2値画像中の輪郭を検出します．
/// </summary>
/// <param name="image">入力画像，8ビット，シングルチャンネル．0以外のピクセルは 1として，0のピクセルは0のまま扱われます．
/// また，この関数は，輪郭抽出処理中に入力画像 image の中身を書き換えます．</param>
/// <param name="mode">輪郭抽出モード</param>
/// <param name="method">輪郭の近似手法</param>
/// <param name="offset">オプションのオフセット．各輪郭点はこの値の分だけシフトします．これは，ROIの中で抽出された輪郭を，画像全体に対して位置づけて解析する場合に役立ちます．</param>
/// <return>検出された輪郭．各輪郭は，点のベクトルとして格納されます．</return>
#else
/// <summary>
/// Finds contours in a binary image.
/// </summary>
/// <param name="image">Source, an 8-bit single-channel image. Non-zero pixels are treated as 1’s.
/// Zero pixels remain 0’s, so the image is treated as binary.
/// The function modifies the image while extracting the contours.</param>
/// <param name="mode">Contour retrieval mode</param>
/// <param name="method">Contour approximation method</param>
/// <param name="offset"> Optional offset by which every contour point is shifted.
/// This is useful if the contours are extracted from the image ROI and then they should be analyzed in the whole image context.</param>
/// <returns>Detected contours. Each contour is stored as a vector of points.</returns>
#endif
public static Point[][] FindContoursAsArray(InputOutputArray image,
ContourRetrieval mode, ContourChain method, Point? offset = null)
{
if (image == null)
throw new ArgumentNullException("image");
image.ThrowIfNotReady();
CvPoint offset0 = offset.GetValueOrDefault(new Point());
IntPtr contoursPtr;
NativeMethods.imgproc_findContours2_vector(image.CvPtr, out contoursPtr, (int)mode, (int)method, offset0);
image.Fix();
using (var contoursVec = new VectorOfVectorPoint(contoursPtr))
{
return contoursVec.ToArray();
}
}
/// <summary>
/// 2値画像中の輪郭を検出します．
/// </summary>
/// <param name="image">入力画像，8ビット，シングルチャンネル．0以外のピクセルは 1として，0のピクセルは0のまま扱われます．
/// また，この関数は，輪郭抽出処理中に入力画像 image の中身を書き換えます．</param>
/// <param name="contours">検出された輪郭．各輪郭は，点のベクトルとして格納されます．</param>
/// <param name="hierarchy">画像のトポロジーに関する情報を含む出力ベクトル．これは，輪郭数と同じ数の要素を持ちます．各輪郭 contours[i] に対して，
/// 要素 hierarchy[i]のメンバにはそれぞれ，同じ階層レベルに存在する前後の輪郭，最初の子輪郭，および親輪郭の
/// contours インデックス（0 基準）がセットされます．また，輪郭 i において，前後，親，子の輪郭が存在しない場合，
/// それに対応する hierarchy[i] の要素は，負の値になります．</param>
/// <param name="mode">輪郭抽出モード</param>
/// <param name="method">輪郭の近似手法</param>
/// <param name="offset">オプションのオフセット．各輪郭点はこの値の分だけシフトします．これは，ROIの中で抽出された輪郭を，画像全体に対して位置づけて解析する場合に役立ちます．</param>
#else
/// <summary>
/// Finds contours in a binary image.
/// </summary>
/// <param name="image">Source, an 8-bit single-channel image. Non-zero pixels are treated as 1’s.
/// Zero pixels remain 0’s, so the image is treated as binary.
/// The function modifies the image while extracting the contours.</param>
/// <param name="contours">Detected contours. Each contour is stored as a vector of points.</param>
/// <param name="hierarchy">Optional output vector, containing information about the image topology.
/// It has as many elements as the number of contours. For each i-th contour contours[i],
/// the members of the elements hierarchy[i] are set to 0-based indices in contours of the next
/// and previous contours at the same hierarchical level, the first child contour and the parent contour, respectively.
/// If for the contour i there are no next, previous, parent, or nested contours, the corresponding elements of hierarchy[i] will be negative.</param>
/// <param name="mode">Contour retrieval mode</param>
/// <param name="method">Contour approximation method</param>
/// <param name="offset"> Optional offset by which every contour point is shifted.
/// This is useful if the contours are extracted from the image ROI and then they should be analyzed in the whole image context.</param>
#endif
public static void FindContours(InputOutputArray image, out Point[][] contours,
out HierarchyIndex[] hierarchy, ContourRetrieval mode, ContourChain method, Point? offset = null)
{
if (image == null)
throw new ArgumentNullException("image");
image.ThrowIfNotReady();
CvPoint offset0 = offset.GetValueOrDefault(new Point());
IntPtr contoursPtr, hierarchyPtr;
NativeMethods.imgproc_findContours1_vector(image.CvPtr, out contoursPtr, out hierarchyPtr, (int)mode, (int)method, offset0);
using (var contoursVec = new VectorOfVectorPoint(contoursPtr))
using (var hierarchyVec = new VectorOfVec4i(hierarchyPtr))
{
contours = contoursVec.ToArray();
Vec4i[] hierarchyOrg = hierarchyVec.ToArray();
hierarchy = EnumerableEx.SelectToArray(hierarchyOrg, HierarchyIndex.FromVec4i);
}
image.Fix();
}
/// <summary>
///
/// </summary>
/// <param name="image"></param>
/// <param name="msers"></param>
/// <param name="bboxes"></param>
public virtual void DetectRegions(
InputArray image, out Point[][] msers, out Rect[] bboxes)
{
if (disposed)
throw new ObjectDisposedException(GetType().Name);
if (image == null)
throw new ArgumentNullException("image");
image.ThrowIfDisposed();
using (var msersVec = new VectorOfVectorPoint())
using (var bboxesVec = new VectorOfRect())
{
NativeMethods.features2d_MSER_detectRegions(
ptr, image.CvPtr, msersVec.CvPtr, bboxesVec.CvPtr);
msers = msersVec.ToArray();
bboxes = bboxesVec.ToArray();
}
GC.KeepAlive(image);
}
/// <summary>
/// MSERのすべての輪郭情報を抽出する
/// </summary>
/// <param name="image"></param>
/// <param name="mask"></param>
/// <returns></returns>
#else
/// <summary>
/// Extracts the contours of Maximally Stable Extremal Regions
/// </summary>
/// <param name="image"></param>
/// <param name="mask"></param>
/// <returns></returns>
#endif
public Point[][] Run(Mat image, Mat mask)
{
ThrowIfDisposed();
if (image == null)
throw new ArgumentNullException("image");
image.ThrowIfDisposed();
IntPtr msers;
NativeMethods.features2d_MSER_detect(ptr, image.CvPtr, out msers, Cv2.ToPtr(mask));
using (VectorOfVectorPoint msersVec = new VectorOfVectorPoint(msers))
{
return msersVec.ToArray();
}
}
