//Find outer defect
static void FindContour_and_outer_defect(Mat img, List <Point[]> contours_final, ref int nLabels, out int [,] stats, string mode)
{
// variable
OpenCvSharp.Point[][] temp = new Point[1][];
//0: 內圈 ; 1: 外圈
OpenCvSharp.Point[] contour_now;
if (mode == "inner")
{
contour_now = contours_final[0];
}
else
{
contour_now = contours_final[1];
}
// Convex hull
var ellipsecontour = Cv2.FitEllipse(contour_now);
Mat convex_mask_img = Mat.Zeros(img.Size(), MatType.CV_8UC1);
Cv2.Ellipse(convex_mask_img, ellipsecontour, 255, -1);
// Contour
temp[0] = contour_now;
Mat contour_mask_img = Mat.Zeros(img.Size(), MatType.CV_8UC1);
Cv2.DrawContours(contour_mask_img, temp, -1, 255, -1);
Mat diff_image = contour_mask_img ^ convex_mask_img;
//Opening
Mat kernel = Mat.Ones(4, 4, MatType.CV_8UC1);//改變凹角大小
diff_image = diff_image.MorphologyEx(MorphTypes.Open, kernel);
//=========================吃掉邊界=======================================
//temp[0] = contour_now;
//Cv2.DrawContours(diff_image, temp, -1, 0, 3);
//================================================================
convex_mask_img.SaveImage("./" + mode + "convex" + ".jpg");
contour_mask_img.SaveImage("./" + mode + "contour" + ".jpg");
diff_image.SaveImage("./" + mode + "mask" + ".jpg");
//Connected Component
var labelMat = new MatOfInt();
var statsMat = new MatOfInt();// Row: number of labels Column: 5
var centroidsMat = new MatOfDouble();
nLabels = Cv2.ConnectedComponentsWithStats(diff_image, labelMat, statsMat, centroidsMat);
var labels = labelMat.ToRectangularArray();
stats = statsMat.ToRectangularArray();
var centroids = centroidsMat.ToRectangularArray();
}
/// <summary>
/// computes the connected components labeled image of boolean image.
/// image with 4 or 8 way connectivity - returns N, the total number of labels [0, N-1] where 0
/// represents the background label. ltype specifies the output label image type, an important
/// consideration based on the total number of labels or alternatively the total number of
/// pixels in the source image.
/// </summary>
/// <param name="image">the image to be labeled</param>
/// <param name="connectivity">8 or 4 for 8-way or 4-way connectivity respectively</param>
/// <returns></returns>
public static ConnectedComponents ConnectedComponentsEx(
InputArray image, PixelConnectivity connectivity = PixelConnectivity.Connectivity8)
{
using (var labelsMat = new MatOfInt())
using (var statsMat = new MatOfInt())
using (var centroidsMat = new MatOfDouble())
{
int nLabels = ConnectedComponentsWithStats(
image, labelsMat, statsMat, centroidsMat, connectivity, MatType.CV_32S);
var labels = labelsMat.ToRectangularArray();
var stats = statsMat.ToRectangularArray();
var centroids = centroidsMat.ToRectangularArray();
var blobs = new ConnectedComponents.Blob[nLabels];
for (int i = 0; i < nLabels; i++)
{
blobs[i] = new ConnectedComponents.Blob
{
Label = i,
Left = stats[i, 0],
Top = stats[i, 1],
Width = stats[i, 2],
Height = stats[i, 3],
Area = stats[i, 4],
Centroid = new Point2d(centroids[i, 0], centroids[i, 1]),
};
}
return new ConnectedComponents(blobs, labels, nLabels);
}
}
/// <summary>
/// computes the connected components labeled image of boolean image.
/// image with 4 or 8 way connectivity - returns N, the total number of labels [0, N-1] where 0
/// represents the background label. ltype specifies the output label image type, an important
/// consideration based on the total number of labels or alternatively the total number of
/// pixels in the source image.
/// </summary>
/// <param name="image">the image to be labeled</param>
/// <param name="labels">destination labeled rectangular array</param>
/// <param name="connectivity">8 or 4 for 8-way or 4-way connectivity respectively</param>
/// <returns>The number of labels</returns>
public static int ConnectedComponents(InputArray image, out int[,] labels, PixelConnectivity connectivity)
{
using (var labelsMat = new MatOfInt())
{
int result = ConnectedComponents(image, labelsMat, connectivity, MatType.CV_32S);
labels = labelsMat.ToRectangularArray();
return result;
}
}