private Mat GetSceleton(Mat grayImage)
{
var binary = new Mat();
Cv2.Threshold(grayImage, binary, 240, 255, ThresholdType.BinaryInv);
var skel = Mat.Zeros(binary.Rows, binary.Cols, MatType.CV_8UC1).ToMat();
var temp = new Mat(binary.Cols, binary.Rows, MatType.CV_8UC1);
var elem = Cv2.GetStructuringElement(StructuringElementShape.Cross,
new OpenCvSharp.CPlusPlus.Size(3, 3));
bool done;
do
{
Cv2.MorphologyEx(binary, temp, MorphologyOperation.Open, elem);
Cv2.BitwiseNot(temp, temp);
Cv2.BitwiseAnd(binary, temp, temp);
Cv2.BitwiseOr(skel, temp, skel);
Cv2.Erode(binary, binary, elem);
double max;
double min;
Cv2.MinMaxLoc(binary, out min, out max);
done = (max == 0);
} while (!done);
return(skel);
}
void Erode(Mat image, int number)
{
for (int i = 0; i < number; i++)
{
Cv2.Erode(image, image, new Mat());
}
}
static void Main(string[] args)
{
Mat src = Cv2.ImRead("card.jpg");
Mat gray = new Mat();
Mat binary = new Mat();
Mat morp = new Mat();
Mat canny = new Mat();
Mat dst = src.Clone();
Mat kernel = Cv2.GetStructuringElement(MorphShapes.Rect, new Size(3, 3));
Cv2.CvtColor(src, gray, ColorConversionCodes.BGR2GRAY);
Cv2.Threshold(gray, binary, 150, 255, ThresholdTypes.Binary);
Cv2.Dilate(binary, morp, kernel, new Point(-1, -1));
Cv2.Erode(morp, morp, kernel, new Point(-1, -1), 3);
Cv2.Dilate(morp, morp, kernel, new Point(-1, -1), 2);
Cv2.Canny(morp, canny, 0, 0, 3);
LineSegmentPoint[] lines = Cv2.HoughLinesP(canny, 1, Cv2.PI / 180, 140, 50, 10);
for (int i = 0; i < lines.Length; i++)
{
Cv2.Line(dst, lines[i].P1, lines[i].P2, Scalar.Yellow, 2);
}
Cv2.ImShow("dst", dst);
Cv2.WaitKey(0);
Cv2.DestroyAllWindows();
}
public List <Rect> FindBounds(Mat mask)
{
List <Rect> result = new List <Rect>();
Cv2.Erode(mask, mask, new Mat());
Cv2.Dilate(mask, mask, new Mat());
Point[][] contours; //vector<vector<Point>> contours;
HierarchyIndex[] hierarchyIndexes; //vector<Vec4i> hierarchy;
Cv2.FindContours(
mask,
out contours,
out hierarchyIndexes,
ContourRetrieval.External,
ContourChain.ApproxSimple
);
var contourIndex = 0;
while ((contourIndex >= 0) && contours.Length != 0)
{
var contour = contours[contourIndex];
var boundingRect = Cv2.BoundingRect(contour); //Find bounding rect for each contour
result.Add(boundingRect);
contourIndex = hierarchyIndexes[contourIndex].Next;
}
return(result);
}
public Mat BarcodeRegion(Mat src_)
{
//Cv2.Resize(src, src, new Size(src.Size().Width / 2, src.Size().Height / 2));
Mat src = src_.Clone();
Cv2.CvtColor(src, src, ColorConversionCodes.RGB2GRAY);
Cv2.GaussianBlur(src, src, new Size(3, 3), 0);
Mat img_X = new Mat();
Mat img_Y = new Mat();
Cv2.Sobel(src, img_X, MatType.CV_16S, 1, 0);
Cv2.Sobel(src, img_Y, MatType.CV_16S, 0, 1);
Cv2.ConvertScaleAbs(img_X, img_X, 1, 0);
Cv2.ConvertScaleAbs(img_Y, img_Y, 1, 0);
Mat margin = img_X - img_Y;
//Cv2.ImShow("img_Y", margin);
//Cv2.WaitKey();
Cv2.Resize(margin, margin, new Size(margin.Width * 0.3, margin.Height * 1.5), 0, 0, InterpolationFlags.Area);
Cv2.Blur(margin, margin, new Size(3, 3));
Cv2.MedianBlur(margin, margin, 3);
Mat imgthreshold = new Mat();
Cv2.Threshold(margin, imgthreshold, 80, 255, ThresholdTypes.Binary);
//Cv2.AdaptiveThreshold(margin, imgthreshold, 255, AdaptiveThresholdTypes.GaussianC, ThresholdTypes.Binary, 3, -1);
Cv2.ImShow("thresh", imgthreshold);
Cv2.WaitKey();
//先在水平方向上膨胀，填充条码中间的空隙
Mat element = Cv2.GetStructuringElement(MorphShapes.Cross, new Size(5, 1));
Cv2.MorphologyEx(imgthreshold, imgthreshold, MorphTypes.Dilate, element);
//在垂直方向上腐蚀，分离条码和字符
element = Cv2.GetStructuringElement(MorphShapes.Cross, new Size(1, 5));
Cv2.MorphologyEx(imgthreshold, imgthreshold, MorphTypes.Erode, element);
//去除字符
element = Cv2.GetStructuringElement(MorphShapes.Cross, new Size(10, 10));
Cv2.MorphologyEx(imgthreshold, imgthreshold, MorphTypes.Open, element);
Cv2.MorphologyEx(imgthreshold, imgthreshold, MorphTypes.Close, element);
element = Cv2.GetStructuringElement(MorphShapes.Cross, new Size(10, 10));
Cv2.Erode(imgthreshold, imgthreshold, element);
Cv2.Erode(imgthreshold, imgthreshold, element);
Cv2.Dilate(imgthreshold, imgthreshold, element);
Cv2.Resize(imgthreshold, imgthreshold, new Size(src.Width, src.Height), 0, 0, InterpolationFlags.Area);
Cv2.ImShow("thresh", imgthreshold);
Cv2.WaitKey();
return(imgthreshold);
//计算每个区域的最大内接矩，然后算其包含图像的黑白区域比例
//Cv2.Dilate(imgthreshold, imgthreshold, element);
}
private static void testBuiltinFilters()
{
using (var src = new Mat(@"..\..\Images\Car.jpg", ImreadModes.AnyDepth | ImreadModes.AnyColor))
{
using (var dst = new Mat())
{
src.CopyTo(dst);
using (new Window("src", image: src))
{
Cv2.Erode(src, dst, new Mat());
using (new Window("Erode", image: dst))
{
Cv2.Dilate(src, dst, new Mat());
using (new Window("Dilate", image: dst))
{
Cv2.BitwiseNot(src, dst);
using (new Window("Invert", image: dst))
{
Cv2.WaitKey();
}
}
}
}
}
}
}
public List <Circle> FindCircleBounds(Mat mask)
{
List <Circle> result = new List <Circle>();
Cv2.Erode(mask, mask, new Mat());
// Cv2.Dilate(mask, mask, new Mat());
Point[][] contours; //vector<vector<Point>> contours;
HierarchyIndex[] hierarchyIndexes; //vector<Vec4i> hierarchy;
Cv2.FindContours(
mask,
out contours,
out hierarchyIndexes,
ContourRetrieval.External,
ContourChain.ApproxSimple
);
var contourIndex = 0;
while ((contourIndex >= 0) && contours.Length != 0)
{
var contour = contours[contourIndex];
Point2f center = new Point2f();
float radius;
Cv2.MinEnclosingCircle(contour, out center, out radius); //Find bounding rect for each contour
result.Add(new Circle(center.X, center.Y, radius));
contourIndex = hierarchyIndexes[contourIndex].Next;
}
return(result);
}
//分水岭分割函数封装
private Mat waterShed(Mat src, int MEADIANBlUR_KSIZE, Size ELEMENT_SIZE)
{
var imageGray = new Mat();
var thresh = new Mat();
var fg = new Mat();
var bgt = new Mat();
var bg = new Mat();
var marker = new Mat();
var marker32 = new Mat();
var m = new Mat();
var res = new Mat();
var threshOpen = new Mat();
var threshClose = new Mat();
Cv2.CvtColor(src, imageGray, ColorConversionCodes.BGR2GRAY);
Cv2.EqualizeHist(imageGray, imageGray); //直方图均衡化
Cv2.MedianBlur(imageGray, imageGray, MEADIANBlUR_KSIZE); //中值滤波
Cv2.Threshold(imageGray, thresh, 0, 255, ThresholdTypes.Otsu);
Cv2.Erode(thresh, fg, 0, null, 2);
Cv2.Dilate(thresh, bgt, 0, null, 3);
Cv2.Threshold(bgt, bg, 1, 128, ThresholdTypes.BinaryInv);
marker = fg + bg;
marker.ConvertTo(marker32, MatType.CV_32SC1);
Cv2.Watershed(src, marker32);
Cv2.ConvertScaleAbs(marker32, m);
Cv2.Threshold(m, thresh, 0, 255, ThresholdTypes.Otsu);
var element = Cv2.GetStructuringElement(MorphShapes.Rect, ELEMENT_SIZE); //获取自定义核
Cv2.MorphologyEx(thresh, threshOpen, MorphTypes.Open, element); //开运算
Cv2.MorphologyEx(threshOpen, threshClose, MorphTypes.Close, element); //闭运算;
Cv2.BitwiseAnd(src, src, res, threshClose);
return(res);
}
private void mnuFilterMorphologyErode_Click(object sender, EventArgs e)
{
// 収縮
Cv2.Erode(_matDisp, _matDisp, null);
// 画像の描画
DrawMatImage(_matDisp);
}
static void Main(string[] args)
{
Mat src = Cv2.ImRead("colorball.png");
Mat image = new Mat();
Mat dst = src.Clone();
Mat kernel = Cv2.GetStructuringElement(MorphShapes.Rect, new Size(3, 3));
Cv2.CvtColor(src, image, ColorConversionCodes.BGR2GRAY);
Cv2.Dilate(image, image, kernel, new Point(-1, -1), 3);
Cv2.GaussianBlur(image, image, new Size(13, 13), 3, 3, BorderTypes.Reflect101);
Cv2.Erode(image, image, kernel, new Point(-1, -1), 3);
CircleSegment[] circles = Cv2.HoughCircles(image, HoughMethods.Gradient, 1, 100, 100, 35, 0, 0);
for (int i = 0; i < circles.Length; i++)
{
Point center = new Point(circles[i].Center.X, circles[i].Center.Y);
Cv2.Circle(dst, center, (int)circles[i].Radius, Scalar.White, 3);
Cv2.Circle(dst, center, 5, Scalar.AntiqueWhite, Cv2.FILLED);
}
Cv2.ImShow("dst", dst);
Cv2.WaitKey(0);
Cv2.DestroyAllWindows();
}
/// <summary>
/// 腐蚀
/// </summary>
/// <param name="image">图片对象</param>
/// <returns></returns>
public static Image Corrosion(Image image)
{
using (Mat src = new Bitmap(image).ToMat())
{
Cv2.Erode(src, src, new Mat());
return(src.ToBitmap());
}
}
/// <summary>
/// Called each time the Vuforia state is updated
/// Tracks the given color and sets the position and rotation of the spraycan
/// </summary>
void OnTrackablesUpdated()
{
if (mFormatRegistered)
{
if (mAccessCameraImage)
{
Vuforia.Image image = CameraDevice.Instance.GetCameraImage(mPixelFormat); // get the current camera image in the given pixel format
if (image != null)
{
#if UNITY_EDITOR
inputMat = new Mat(image.Height, image.Width, MatType.CV_8UC1, image.Pixels);
#else
inputMat = new Mat(image.Height, image.Width, MatType.CV_8UC3, image.Pixels); // store the image's pixels in an OpenCV mat
#endif
Cv2.Resize(inputMat, smallMat, new Size(480, 270)); // resizing for performance reasons (keep aspect ratio!)
Cv2.GaussianBlur(smallMat, blurredMat, new Size(11, 11), 0); // blur image to reduce noise
Cv2.CvtColor(blurredMat, hsvMat, ColorConversionCodes.RGB2HSV); // convert to HSV colors
Cv2.InRange(hsvMat, lowerHSVColor, upperHSVColor, thresholdMat); // filter out all pixels matching the given HSV range
Cv2.Erode(thresholdMat, thresholdMat, Cv2.GetStructuringElement(MorphShapes.Ellipse, new Size(3, 3)), null, 2); // shave off pixels from blobs to eliminate small blobs
Cv2.Dilate(thresholdMat, thresholdMat, Cv2.GetStructuringElement(MorphShapes.Ellipse, new Size(3, 3)), null, 2); // strengthen the remaining blobs
Cv2.FindContours(thresholdMat, out contours, hierarchy, RetrievalModes.External, ContourApproximationModes.ApproxSimple); // detect the blobs and save them as contours
if (contours.Length > 0)
{
Mat contour = contours.Aggregate((i, j) => i.ContourArea() > j.ContourArea() ? i : j); // find the blob with the biggest ContourArea/Size
Point2f point;
float radius;
Cv2.MinEnclosingCircle(contour, out point, out radius); // get the radius for passing a final threshold
if (radius > 5)
{
Moments moments = Cv2.Moments(contour); // use moments to calculate the center point of the biggest blob
double area = moments.M00;
double m01 = moments.M01;
double m10 = moments.M10;
double posX = m10 / area;
double posY = m01 / area;
double rotX = MapValue(posX, 0, 480, -31.5, 31.5); // map the values to match coordinates usable in Unity
double rotY = MapValue(posY, 0, 270, -19.75, 19.75);
posX = MapValue(posX, 0, 480, -6, 6);
posY = MapValue(posY, 0, 270, 3.5, -3.5);
this.transform.localPosition = new Vector3((float)posX, (float)posY, 10); // apply the changes to position and rotation
this.transform.localEulerAngles = new Vector3((float)rotY, (float)rotX, 0);
}
}
}
}
}
}
private static void PreProcessing()
{
//Cv2.GaussianBlur(_mask, _mask, new Size(4, 4), 8, 6);
Cv2.Erode(_mask, _mask, null);
//Cv2.Erode(_mask, _mask, null);
Cv2.Dilate(_mask, _mask, null);
//Cv2.Dilate(_mask, _mask, null);
}
// PreProcessing Part
private Bitmap PreProcessing(Mat img)
{
var element = Cv2.GetStructuringElement(MorphShapes.Rect, new OpenCvSharp.Size(5, 5), new OpenCvSharp.Point(0, 0));
Cv2.Threshold(img, img, 100, 255, ThresholdTypes.Binary);
Cv2.Erode(img, img, element);
Cv2.Dilate(img, img, element);
return(OpenCvSharp.Extensions.BitmapConverter.ToBitmap(img));
}
static OpenCvSharp.Point[][] GetCountours(Mat thresholdedImage, int erodeIterations)
{
using (var erodedImage = new Mat())
{
Cv2.Erode(thresholdedImage, erodedImage, new Mat(), iterations: erodeIterations);
Cv2.FindContours(erodedImage, out var contours, out var hierarchyIndices, RetrievalModes.List, ContourApproximationModes.ApproxSimple);
return(contours);
}
}
public static void soften(ref Mat input_image)
{
Cv2.MedianBlur(input_image, input_image, 7);
OpenCvSharp.Point morph_point = new OpenCvSharp.Point(1, 1);
Mat erodeElement = Cv2.GetStructuringElement(shape: MorphShapes.Rect, new OpenCvSharp.Size(3, 3));
Mat dilateElement = Cv2.GetStructuringElement(shape: MorphShapes.Rect, new OpenCvSharp.Size(3, 3));
Cv2.Erode(input_image, input_image, erodeElement, morph_point, 5, borderType: BorderTypes.Reflect);
Cv2.Dilate(input_image, input_image, dilateElement, morph_point, 5, borderType: BorderTypes.Reflect);
}
public List <Mat> ProcessPuzzle(Mat unwarpedPuzzle)
{
Cv2.Erode(unwarpedPuzzle, horzSubtract, kernelHorz);
Cv2.Dilate(horzSubtract, horzSubtract, kernelHorz);
Cv2.Erode(unwarpedPuzzle, vertSubtract, kernelVert);
Cv2.Dilate(vertSubtract, vertSubtract, kernelVert);
unwarpedSudoku = unwarpedSudoku - (horzSubtract + vertSubtract);
return(getIndividualBoxes(unwarpedSudoku));
}
private static Bitmap PrepareImage(Bitmap image)
{
//Mat src = new Mat(@"D:\tesseract4\docs\tables\balans_1kv_2013_21.jpg", ImreadModes.GrayScale);
//var src = Cv2.ImRead(@"D:\tesseract4\docs\tables\balans_1kv_2013_21.jpg");
var gray = image.ToGrayscaleMat();
var bw = new Mat();
Cv2.AdaptiveThreshold(~gray, bw, 256, AdaptiveThresholdTypes.MeanC, ThresholdTypes.Binary, 25, -2);
var horizontal = bw.Clone();
var vertical = bw.Clone();
var scale = 15;
var horizontalSize = horizontal.Cols / scale;
var verticalSize = vertical.Rows / scale;
var horizontalStructure = Cv2.GetStructuringElement(MorphShapes.Rect, new OpenCvSharp.Size(horizontalSize, 1));
var verticalStructure = Cv2.GetStructuringElement(MorphShapes.Rect, new OpenCvSharp.Size(1, verticalSize));
Cv2.Erode(horizontal, horizontal, horizontalStructure, new OpenCvSharp.Point(-1, -1));
Cv2.Dilate(horizontal, horizontal, horizontalStructure, new OpenCvSharp.Point(-1, -1));
Cv2.Erode(vertical, vertical, verticalStructure, new OpenCvSharp.Point(-1, -1));
Cv2.Dilate(vertical, vertical, verticalStructure, new OpenCvSharp.Point(-1, -1));
//SaveImage(vertical, "vertical");
//Cv2.Canny(src, dst, 50, 200);
//using (new Window(horizontal))
//{
// Cv2.WaitKey();
//}
var mask = horizontal + vertical;
//SaveImage(mask, "mask");
var newMask = new Mat();
Cv2.AdaptiveThreshold(~mask, newMask, 256, AdaptiveThresholdTypes.MeanC, ThresholdTypes.Binary, 7, -2);
//SaveImage(newMask, "newMask");
newMask = mask + newMask;
SaveImage(newMask, "hyperMask");
//var withOutTable = gray + newMask;
//SaveImage(withOutTable, "withOutTable");
return(BitmapConverter.ToBitmap(mask));
}
private static void MorphOps(Mat thresh)
{
//create structuring element that will be used to "dilate" and "erode" image.
//the element chosen here is a 3px by 3px rectangle
var erodeElement = Cv2.GetStructuringElement(MorphShapes.Rect, new Size(3, 3));
//dilate with larger element so make sure object is nicely visible
var dilateElement = Cv2.GetStructuringElement(MorphShapes.Rect, new Size(8, 8));
Cv2.Erode(thresh, thresh, erodeElement);
Cv2.Erode(thresh, thresh, erodeElement);
Cv2.Dilate(thresh, thresh, dilateElement);
Cv2.Dilate(thresh, thresh, dilateElement);
}
//腐蚀
private void BtnChange4_Click(object sender, RoutedEventArgs e)
{
using (var src = new Mat(@"..\..\Images\ocv02.jpg", ImreadModes.AnyDepth | ImreadModes.AnyColor))
{
Cv2.Erode(src, src, new Mat());
var mem = src.ToMemoryStream();
BitmapImage bmp = new BitmapImage();
bmp.BeginInit();
bmp.StreamSource = mem;
bmp.EndInit();
imgOutput.Source = bmp;
}
SetSource(@"..\..\Images\ocv02.jpg");
}
private void GlareDetection(ref System.Drawing.Bitmap image, out System.Drawing.Bitmap mask)
{
mask = null;
Mat hsv = new Mat();
Mat src = BitmapConverter.ToMat(image);
Cv2.CvtColor(src, hsv, ColorConversionCodes.BGR2HSV);
Mat[] planes;
Cv2.Split(hsv, out planes);
Cv2.ImWrite(@"C:\temp\hImage.png", planes[0]);
Cv2.ImWrite(@"C:\temp\sImage.png", planes[1]);
Cv2.ImWrite(@"C:\temp\vImage.png", planes[2]);
mask = BitmapConverter.ToBitmap(planes[2]);
Mat nonSat = new Mat(new OpenCvSharp.Size(src.Width, src.Height), MatType.CV_8UC1, 0);
//nonSat = Cv2.Threshold(planes[1], nonSat, 180, 255, ThresholdTypes.Binary);
for (int i = 0; i < planes[1].Cols; i++)
{
for (int j = 0; j < planes[1].Rows; j++)
{
byte e = planes[1].Get <byte>(j, i);
byte v = planes[2].Get <byte>(j, i);
if (e < 150 && v > 220)
{
nonSat.Set <byte>(j, i, 255);
}
else
{
nonSat.Set <byte>(j, i, 0);
}
}
}
var disk = Cv2.GetStructuringElement(
MorphShapes.Rect,
new OpenCvSharp.Size(5, 5),
new OpenCvSharp.Point(2, 2));
Mat m = new Mat();
Cv2.Erode(nonSat, m, disk);
Cv2.ImWrite(@"C:\temp\maskImage.png", nonSat);
Mat dst = new Mat();
src.CopyTo(dst, m);
mask = BitmapConverter.ToBitmap(dst);
}
private Mat morphOps(Mat thresh, int x)
{
//create structuring element that will be used to "dilate"
Mat erodeElement = Cv2.GetStructuringElement(MorphShapes.Rect, new OpenCvSharp.Size(1, 1));
Mat dilateElement = Cv2.GetStructuringElement(MorphShapes.Rect, new OpenCvSharp.Size(x, x));
Cv2.Erode(thresh, thresh, erodeElement);
Cv2.Dilate(thresh, thresh, dilateElement);
Cv2.Dilate(thresh, thresh, dilateElement);
Cv2.Erode(thresh, thresh, dilateElement);
Cv2.Erode(thresh, thresh, dilateElement);
return(thresh);
}
private static Point[][] GetContoursForParagraph(Mat imageToProcesing)
{
Point[][] wordsContours;
HierarchyIndex[] hierarchyIndexes;
var kernelH = Cv2.GetStructuringElement(MorphShapes.Rect, new Size(1, 1));
Cv2.Erode(imageToProcesing, imageToProcesing, kernelH, null, 4);
Cv2.Dilate(imageToProcesing, imageToProcesing, kernelH, null, 4);
FindContours(imageToProcesing, out wordsContours, out hierarchyIndexes);
Mat newOutput = new Mat();
newOutput = CreateMaskParagraph(imageToProcesing);
Point[][] contoursMask; //vector<vector<Point>> contours;
FindContours(newOutput, out contoursMask, out hierarchyIndexes);
return(contoursMask);
}
private Mat histMasking(Mat cameraMat)
{
Mat hsvMat = new Mat();
Mat dstMat = new Mat();
Cv2.CvtColor(cameraMat, hsvMat, ColorConversionCodes.BGR2HSV);
Cv2.CalcBackProject(new[] { hsvMat }, new[] { 0, 1 }, handHist, dstMat, new[] { new Rangef(0, 180), new Rangef(0, 255) });
if (filter2D)
{
Mat filter2DMat = Cv2.GetStructuringElement(filter2DShape, (new Size(filter2DSize.x, filter2DSize.y)));
Cv2.Filter2D(dstMat, dstMat, -1, filter2DMat);
}
if (thresholdFilter)
{
Cv2.Threshold(dstMat, dstMat, thresholdLow, thresholdHigh, ThresholdTypes.Binary);
}
if (morphologyTransformation)
{
Mat morphologyTransformationMat =
Cv2.GetStructuringElement(morphologyTransformationShape, (new Size(morphologyTransformationSize.x, morphologyTransformationSize.y)));
Cv2.MorphologyEx(dstMat, dstMat, morphologyTransformationType, morphologyTransformationMat, null, morphologyTransformationIterations);
}
if (dilatateFilter)
{
Mat dilatateShapeMat = Cv2.GetStructuringElement(this.dilatateShape, (new Size(dilatateSize.x, dilatateSize.y)));
Cv2.Dilate(dstMat, dstMat, dilatateShapeMat, null, dilatateIterations);
}
if (erodeFilter)
{
Mat erodeShapeMat = Cv2.GetStructuringElement(this.erodeShape, (new Size(erodeSize.x, erodeSize.y)));
Cv2.Erode(dstMat, dstMat, erodeShapeMat, null, erodeIterations);
}
handMask = dstMat;
if (bitwiseFilter)
{
// Cv2.BitwiseAnd(cameraMat, dstMat, dstMat); //TODO: we need it to mask our hand, in this form it return exception
}
return(dstMat);
}
private void bn_Erode_Click(object sender, RoutedEventArgs e)
{
if (listImage.Count > 0)
{
string strTitle = listImage[_nSelWin].Title;
Mat matSrc = listImage[_nSelWin].fn_GetImage();
Mat matDst = new Mat();
int width = matSrc.Cols;
int height = matSrc.Rows;
timeStart = DateTime.Now;
Cv2.Erode(matSrc, matDst, new Mat());
fn_WriteLog($"[Erode] {strTitle} ({(DateTime.Now - timeStart).TotalMilliseconds} ms)");
fn_NewImage(matDst, $"{strTitle}_Erode");
}
}
public static void cv_15() // 形态学系列之 腐蚀/膨胀
{
//膨胀原理：跟卷积操作相似，假设有图像A和结构元素B，结构元素B在A上面移动，其中B定义其中心点为锚点，计算B覆盖下A的最大像素值用来替换锚点的像素，其中结构体B可以是任意形状。
//腐蚀原理：腐蚀与膨胀的操作的过程一样，不同的是以最小值替换锚点重叠下的像素值。
//Cv2.GetStructuringElement(): 获取结构元素
//Cv2.Dilate(): 膨胀，通过使用特定的结构元素来扩展图像。
//Cv2.Erode(): 腐蚀，通过使用特定的结构元素来侵蚀图像。
Mat src = new Mat(@"G:\\pics\7.jpg", ImreadModes.Color);
Cv2.ImShow("src", src);
int size = 5; //要为奇数
Mat structuringElement = Cv2.GetStructuringElement(MorphShapes.Rect, new OpenCvSharp.Size(size, size), new OpenCvSharp.Point(-1, -1));
/* 参数 1，MorphShapes shape 结果元素的形状
* 2，Size ksize 结构元素的大小
* 3，Point anchor 结构元素的锚点（中心点）
*/
Mat DilateImg = new Mat();
Cv2.Dilate(src, DilateImg, structuringElement, new OpenCvSharp.Point(-1, -1), 1); //膨胀
/* 参数：1， 源图像
* 2， 输出图像
* 3， 结构元素,奇数
* 4， 锚点位置，默认是null
* 5， 应用膨胀的次数。[默认情况下这是1]
*/
Mat ErodeImg = new Mat();
Cv2.Erode(src, ErodeImg, structuringElement, new OpenCvSharp.Point(-1, -1), 1); //腐蚀
/* 参数：1，源图像
* 2，输出图像
* 3，结构元素,奇数
* 4， 锚点位置，默认是null
* 5，应用膨胀的次数。[默认情况下这是1]
*/
Cv2.ImShow("ErodeImg", ErodeImg);
Cv2.ImShow("DilateImg", DilateImg);
Cv2.WaitKey();
}
private IEnumerable <Rect> DetectMotion(Mat frame)
{
Mat fgmask = new Mat();
_segmentor.Apply(frame, fgmask);
if (fgmask.Empty())
{
yield break;
}
Cv2.Threshold(fgmask, fgmask, 25, 255, ThresholdTypes.Binary);
int noiseSize = 9;
Mat kernel = Cv2.GetStructuringElement(MorphShapes.Rect, new Size(noiseSize, noiseSize));
Cv2.Erode(fgmask, fgmask, kernel);
kernel = Cv2.GetStructuringElement(MorphShapes.Rect, new Size(noiseSize, noiseSize));
Cv2.Dilate(fgmask, fgmask, kernel, new Point(-1, -1), 3);
Cv2.FindContours(fgmask, out Point[][] contours, out HierarchyIndex[] hierarchies, RetrievalModes.Tree, ContourApproximationModes.ApproxSimple);
public void MotionDetector(SoftwareBitmap input, SoftwareBitmap output, Algorithm algorithm)
{
if (algorithm.AlgorithmName == "MotionDetector")
{
using Mat mInput = SoftwareBitmap2Mat(input);
using Mat mOutput = new Mat(mInput.Rows, mInput.Cols, MatType.CV_8UC4);
using Mat fgMaskMOG2 = new Mat(mInput.Rows, mInput.Cols, MatType.CV_8UC4);
using Mat temp = new Mat(mInput.Rows, mInput.Cols, MatType.CV_8UC4);
this.mog2.Apply(mInput, fgMaskMOG2, (double)algorithm.AlgorithmProperties[0].CurrentValue);
Cv2.CvtColor(fgMaskMOG2, temp, ColorConversionCodes.GRAY2BGRA);
using Mat element = Cv2.GetStructuringElement(MorphShapes.Rect, new Size(3, 3));
Cv2.Erode(temp, temp, element);
temp.CopyTo(mOutput);
Mat2SoftwareBitmap(mOutput, output);
}
}
/// <summary>
/// 提前图片里的表格
/// </summary>
/// <param name="mat"></param>
/// <returns></returns>
public Mat To_extract_form(Mat mat)
{
using (Mat horizontal横线图 = mat.Clone())
using (Mat vertical竖线图 = mat.Clone())
//为了获取横向的表格线，设置腐蚀和膨胀的操作区域为一个比较大的横向直条
//横核,函数
using (Mat horizontalStructure = Cv2.GetStructuringElement(MorphShapes.Rect, new OpenCvSharp.Size(horizontal横线图.Cols / 20, 1)))
//竖核,函数
using (Mat verticalStructure = Cv2.GetStructuringElement(MorphShapes.Rect, new OpenCvSharp.Size(1, vertical竖线图.Rows / 30)))
{
//横腐蚀
Cv2.Erode(~horizontal横线图, horizontal横线图, horizontalStructure, new OpenCvSharp.Point(-1, -1));
//横膨胀
Cv2.Dilate(horizontal横线图, horizontal横线图, horizontalStructure, new OpenCvSharp.Point(-1, -1));
//竖腐蚀
Cv2.Erode(~vertical竖线图, vertical竖线图, verticalStructure, new OpenCvSharp.Point(-1, -1));
//竖膨胀
Cv2.Dilate(vertical竖线图, vertical竖线图, verticalStructure, new OpenCvSharp.Point(-1, -1));
Mat Image纯表格 = horizontal横线图 + vertical竖线图;
Cv2.CvtColor(~Image纯表格, Image纯表格, ColorConversionCodes.BGR2GRAY);
//var window先腐蚀再膨胀纵向 = new Window("先腐蚀再膨胀纵向");
//window先腐蚀再膨胀纵向.Image = vertical竖线图;
//using (new Window("horizontal横线图", WindowMode.AutoSize, horizontal横线图))
//using (new Window("vertical竖线图", WindowMode.AutoSize, vertical竖线图))
//using (new Window("Image纯表格", WindowMode.AutoSize, Image纯表格))
//{
// Window.WaitKey(0);
// //Image阈值化.SaveImage(@"D:\new1\langyp.fontyp.exp0.tif");
//}
//var window横纵向 = new Window("横纵向");
//window横纵向.Image = Image纯表格;
//Image纯表格.SaveImage(@"D:\new1\纯表格.tif");
//Cv2.ImWrite("横纵向.jpg", Image纯表格);
return(Image纯表格);
}
}
//图像分割
private void ucBtnExt_A1_BtnClick(object sender, EventArgs e)
{
Scalar apple_hsv_min_1 = new Scalar(apple_h_min_1, apple_s_min, apple_v_min);
Scalar apple_hsv_max_1 = new Scalar(apple_h_max_1, apple_s_max, apple_v_max);
Scalar apple_hsv_min_2 = new Scalar(apple_h_min_2, apple_s_min, apple_v_min);
Scalar apple_hsv_max_2 = new Scalar(apple_h_max_2, apple_s_max, apple_v_max);
Scalar pear_hsv_min = new Scalar(pear_h_min, pear_s_min, pear_v_min);
Scalar pear_hsv_max = new Scalar(pear_h_max, pear_s_max, pear_v_max);
Cv2.CvtColor(srcImg, hsvImg, ColorConversionCodes.BGR2HSV);
this.pictureBoxIpl_img.ImageIpl = hsvImg;
//输出Log
newLog = "[msg] 显示HSV图像";
//等待1s
Cv2.WaitKey(1000);
//计算苹果掩膜
Cv2.InRange(hsvImg, apple_hsv_min_1, apple_hsv_max_1, apple_1);
Cv2.InRange(hsvImg, apple_hsv_min_2, apple_hsv_max_2, apple_2);
//合并掩膜
Cv2.Add(apple_1, apple_2, apple_mask);
//膨胀腐蚀
Mat apple_element = Cv2.GetStructuringElement(MorphShapes.Rect,
new OpenCvSharp.Size(3, 3), new OpenCvSharp.Point(-1, -1));
Cv2.Dilate(apple_mask, apple_mask, apple_element, new OpenCvSharp.Point(-1, -1), 1);
Cv2.Erode(apple_mask, apple_mask, apple_element, new OpenCvSharp.Point(-1, -1), 1);
//计算梨子掩膜
Cv2.InRange(hsvImg, pear_hsv_min, pear_hsv_max, pear_mask);
//膨胀腐蚀 注意核大小取值
Mat pear_element_erode = Cv2.GetStructuringElement(MorphShapes.Rect,
new OpenCvSharp.Size(7, 7), new OpenCvSharp.Point(-1, -1));
Mat pear_element_dilate = Cv2.GetStructuringElement(MorphShapes.Rect,
new OpenCvSharp.Size(7, 7), new OpenCvSharp.Point(-1, -1));
Cv2.Erode(pear_mask, pear_mask, pear_element_erode, new OpenCvSharp.Point(-1, -1), 1);
Cv2.Dilate(pear_mask, pear_mask, pear_element_dilate, new OpenCvSharp.Point(-1, -1), 1);
Cv2.Add(apple_mask, pear_mask, fruit_mask);
//显示图像分割效果
this.pictureBoxIpl_img.ImageIpl = fruit_mask;
//输出日志
newLog = "[msg] 图像分割完毕";
}