private List <Rect> ConditionNMS(Mat binaryImg)
{
var rectList = new List <Rect>();
var indexer = binaryImg.GetGenericIndexer <Vec3b>();
var contours = new OpenCvSharp.Point[][] { };
var hierarchy = new OpenCvSharp.HierarchyIndex[] { };
var contourAreaList = new List <double>()
{
};
var contourCenterList = new List <System.Drawing.Point>()
{
};
Cv2.FindContours(binaryImg, out contours, out hierarchy, RetrievalModes.External, ContourApproximationModes.ApproxNone);
for (int i = 0; i < hierarchy.Length; i++)
{
//contourAreaList.Add(Cv2.ContourArea(contours[i]));
//精炼出的输入为binary、一个加号还是箭头的flag，返回的是有效的rect类型的List，加号需要 rect的中心点；箭头需要rect的两边坐标以及方向
//var S = Cv2.ContourArea(contours[i]);
var M = Cv2.Moments(contours[i]);
if (M.M00 != 0)
{
var cx = (int)(M.M10 / M.M00);
var cy = (int)(M.M01 / M.M00);
if (indexer[cy, cx].Item0 == 255) //相同条件1:反向二值图的轮廓中心为0
//相同条件2：四角为0
{
var rect = Cv2.BoundingRect(contours[i]); //相同条件3：截出来的框轮廓数量为4
try
{
if (indexer[rect.Y, rect.X].Item0 == 0 && indexer[rect.Y + rect.Height, rect.X + rect.Width].Item0 == 0 &&
indexer[rect.Y + rect.Height, rect.X].Item0 == 0 && indexer[rect.Y, rect.X + rect.Width].Item0 == 0)
{
//Rect r = new Rect(new OpenCvSharp.Point(rect.X, rect.Y), new OpenCvSharp.Size(rect.Width, rect.Height));
//Cv2.Rectangle(binaryImg, r, Scalar.LimeGreen, 1);
//Cv2.DrawContours(srcImg, contours, i, Scalar.Gray, 2, LineTypes.Link8, hierarchy, 4);
var tempROI = new Mat();
binaryImg[rect].CopyTo(tempROI);//截出连通域
if (Math.Max(rect.Width, rect.Height) / Math.Min(rect.Width, rect.Height) < 1.2 && rect.Width < 200 &&
PlusCondition(tempROI)) //plus
{
rectList.Add(rect);
}
}
}
catch (Exception er)
{
//do nothing.
}
}
}
}
return(rectList);
}
private bool PlusCondition(Mat tempROI)
{
bool flag = false;
Cv2.Threshold(tempROI, tempROI, 200, 255, ThresholdTypes.BinaryInv); //反向二值化
var tempContours = new OpenCvSharp.Point[][] { };
var tempHierarchy = new OpenCvSharp.HierarchyIndex[] { };
Cv2.FindContours(tempROI, out tempContours, out tempHierarchy, RetrievalModes.Tree, ContourApproximationModes.ApproxNone);
//对于每一个tempROI，对于箭头 或者 判断是否符合条件
if (tempContours.GetLength(0) == 4)
{
var pointListX = new List <double>();//4个轮廓中心点
var pointListY = new List <double>();
var euDisList = new List <double>();
double sumX = 0, sumY = 0;
for (int i = 0; i < 4; i++)
{
var M = Cv2.Moments(tempContours[i]);
if (M.M00 != 0)
{
var cx = (M.M10 / M.M00);
var cy = (M.M01 / M.M00);
pointListX.Add(cx);
pointListY.Add(cy);
sumX += cx;
sumY += cy;
}
else
{
return(false);
}
}
var centerPointX = sumX / 4;
var centerPointY = sumY / 4;
for (int i = 0; i < 4; i++)
{
euDisList.Add(Math.Pow(Math.Pow(Math.Abs(centerPointX - pointListX[i]), 2) + Math.Pow(Math.Abs(centerPointY - pointListY[i]), 2), 0.5));
}
var stdDev = CalculateStdDev(euDisList);
if (stdDev < 0.12)
{
flag = true;
// Console.WriteLine(stdDev);
}
}
return(flag);
}
/// <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, RetrievalModes mode, ContourApproximationModes method, Point? offset = null)
{
if (image == null)
throw new ArgumentNullException(nameof(image));
image.ThrowIfNotReady();
Point 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>
/// 2値画像中の輪郭を検出します．
/// 入力画像は，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の中身を書き換えます．
/// </summary>
/// <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.
/// The source is 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 this image while extracting the contours.
/// </summary>
/// <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 void FindContours(out Point[][] contours, out HierarchyIndex[] hierarchy,
RetrievalModes mode, ContourApproximationModes method, Point? offset = null)
{
Cv2.FindContours(this, out contours, out hierarchy, mode, method, offset);
}
private List <Rect> ConditionNMS(Mat binaryImg, ref List <int> orientationList)
{
var rectList = new List <Rect>();
var indexer = binaryImg.GetGenericIndexer <Vec3b>();
var contours = new OpenCvSharp.Point[][] { };
var hierarchy = new OpenCvSharp.HierarchyIndex[] { };
var contourAreaList = new List <double>()
{
};
var contourCenterList = new List <System.Drawing.Point>()
{
};
Cv2.FindContours(binaryImg, out contours, out hierarchy, RetrievalModes.External, ContourApproximationModes.ApproxNone);
for (int i = 0; i < hierarchy.Length; i++)
{
var M = Cv2.Moments(contours[i]);
if (M.M00 != 0)
{
var cx = (int)(M.M10 / M.M00);
var cy = (int)(M.M01 / M.M00);
if (indexer[cy, cx].Item0 == 255)
{
var rect = Cv2.BoundingRect(contours[i]);
if (indexer[rect.Y, rect.X].Item0 == 0 && indexer[rect.Y + rect.Height, rect.X + rect.Width].Item0 == 0 &&
indexer[rect.Y + rect.Height, rect.X].Item0 == 0 && indexer[rect.Y, rect.X + rect.Width].Item0 == 0 &&
rect.Width / rect.Height > 4 && rect.Width > 40)
{
//Rect r = new Rect(new OpenCvSharp.Point(rect.X, rect.Y), new OpenCvSharp.Size(rect.Width, rect.Height));
//Cv2.Rectangle(binaryImg, r, Scalar.LimeGreen, 1);
//Cv2.DrawContours(srcImg, contours, i, Scalar.Gray, 2, LineTypes.Link8, hierarchy, 4);
var point1 = new OpenCvSharp.Point(cx, cy);
Cv2.FloodFill(binaryImg, point1, new Scalar(155, 155, 155));
var tempROI = new Mat();
binaryImg[rect].CopyTo(tempROI);//截出连通域, 按中心点做漫水填充
int orientation = 0;
///////////////
Vec3b color = new Vec3b(0, 0, 0);
int sLeft = 0, sRight = 0;
var ROIIndexer = tempROI.GetGenericIndexer <Vec3b>();
for (int k = 0; k < tempROI.Rows; k++)
{
for (int j = 0; j < tempROI.Cols; j++)
{
if (ROIIndexer[k, j].Item0 == 255)
{
ROIIndexer[k, j] = color;
continue;
}
else if (ROIIndexer[k, j].Item0 == 155)
{
if (j < tempROI.Cols / 2)
{
sLeft++;
}
else
{
sRight++;
}
}
}
}
Cv2.Threshold(tempROI, tempROI, 154, 255, ThresholdTypes.BinaryInv);
if (sLeft < sRight)
{
orientation = 1; //正向箭头
}
//////////////////
var tempContours = new OpenCvSharp.Point[][] { };
var tempHierarchy = new OpenCvSharp.HierarchyIndex[] { };
Cv2.FindContours(tempROI, out tempContours, out tempHierarchy, RetrievalModes.Tree, ContourApproximationModes.ApproxNone);
if (tempContours.GetLength(0) == 4 && Math.Abs(sLeft - sRight) > 10)
{
rectList.Add(rect);
orientationList.Add(orientation);
}
}
}
}
}
return(rectList);
}