public dynamic PreProcessImage(ref Mat image, Mat sourceImage)
{
var copy = new Mat();
try
{
Cv2.BilateralFilter(image, copy, 9, 75, 75);
Cv2.AdaptiveThreshold(copy, copy, 255, AdaptiveThresholdTypes.GaussianC, ThresholdTypes.Binary, 115, 4);
Cv2.MedianBlur(copy, copy, 11);
Cv2.CopyMakeBorder(copy, copy, 5, 5, 5, 5, BorderTypes.Constant, Scalar.Black);
// TODO: Dispose new Mat()
var otsu = Cv2.Threshold(copy, new Mat(), 0, 255, ThresholdTypes.Binary | ThresholdTypes.Otsu);
Cv2.Canny(copy, copy, otsu, otsu * 2, 3, true);
}
catch
{
copy.Dispose();
throw;
}
image.Dispose();
image = copy;
return(null);
}
static void BeautyIt(Mat src)
{
using (ResourceTracker t = new ResourceTracker())
{
Mat dst = t.NewMat();
Cv2.BilateralFilter(src, dst, 15, 35, 35);
dst.CopyTo(src);
}
}
public void Calibrate2(double planeDistance, double baseLine, double focalLength, int width, int height, Mat[] imgs)
{
var d0 = baseLine * focalLength / planeDistance;
var decoded = code.Decode(imgs)[0];
var filtered = new Mat();
Cv2.BilateralFilter(decoded, filtered, -1, 4, 4);
Calib.CalibrateCoefficient(filtered, d0);
}
private void OpenCvWorker_DoWork(object sender, DoWorkEventArgs e)
{
InitCamera();
//Load image
Mat camImage = cam.RetrieveMat();
//Convert to gray and apply bilateral filter to smooth the image
Mat gray = new Mat();
//Mat camImage = new Mat();
//Cv2.Resize(camImage, resizedCamImage, new OpenCvSharp.Size(0, 0), 0.3, 0.3);
Cv2.ImWrite("tmp.jpg", camImage);
Cv2.CvtColor(camImage, gray, ColorConversionCodes.BGR2GRAY);
Mat bilateral = new Mat();
Cv2.BilateralFilter(gray, bilateral, 25, 10, 80);
//Binarize the image and find the countours of the shape
Mat useless = new Mat();
Mat binary = new Mat();
//Cv2.Threshold(bilateral, binary, 0, 255, ThresholdTypes.BinaryInv | ThresholdTypes.Otsu);
Cv2.Threshold(bilateral, binary, 100, 255, ThresholdTypes.BinaryInv | ThresholdTypes.Otsu);
//Cv2.AdaptiveThreshold(bilateral, binary, 255, AdaptiveThresholdTypes.GaussianC, ThresholdTypes.BinaryInv, 11, 2);
Cv2.FindContours(binary.Clone(), out var contours, useless, RetrievalModes.Tree, ContourApproximationModes.ApproxSimple);
//Create a blank image and draw countours find inside
Mat background = new Mat(camImage.Rows, camImage.Cols, MatType.CV_8U);
Mat[] new_contours = new Mat[contours.Length];
for (int i = 0; i < contours.Length; i++)
{
new_contours[i] = DefinePointless(contours[i]);
}
Cv2.DrawContours(background, contours, -1, Scalar.White);
//Invert the image and convert it to RGB
Mat invertedBackground = new Mat();
Cv2.BitwiseNot(background, invertedBackground);
Mat coloredBackground = new Mat();
Cv2.CvtColor(invertedBackground, coloredBackground, ColorConversionCodes.GRAY2BGR);
this.background = coloredBackground;
this.binary = binary;
this.contours = contours.Length;
//Report progress
OpenCvWorker.ReportProgress(100, coloredBackground);
}
private void BilateralFilter()
{
WriteableBitmap wb = ShowImage.Source as WriteableBitmap;
Mat image = wb.ToMat();
if (image == null)
{
return;
}
Cv2.BilateralFilter(image, image, -1, 15, 15);
DisplayImage(image);
}
public void Calibrate(double planeDistance, double baseLine, double focalLength, int width, int height, byte[][] capturedImages)
{
var imgs = capturedImages
.Select(buff => Array.ConvertAll(buff, b => Math.Min(255, (int)b)))
.Select(arr => (new Mat(height, width, MatType.CV_32SC1, arr) * 255).ToMat())
.ToArray();
var d0 = baseLine * focalLength / planeDistance;
var decoded = code.Decode(imgs)[0];
var filtered = new Mat();
Cv2.BilateralFilter(decoded, filtered, -1, 4, 4);
Calib.CalibrateCoefficient(filtered, d0);
}
//美颜磨皮 双边滤波
private void BtnChange12_Click(object sender, RoutedEventArgs e)
{
using (var src = new Mat(@"..\..\Images\ocv02.jpg", ImreadModes.AnyDepth | ImreadModes.AnyColor))
{
using (var dst = new Mat())//复制以后处理
{
Cv2.BilateralFilter(src, dst, 15, 35d, 35d);
var mem = dst.ToMemoryStream();
BitmapImage bmp = new BitmapImage();
bmp.BeginInit();
bmp.StreamSource = mem;
bmp.EndInit();
imgOutput.Source = bmp;
}
}
SetSource(@"..\..\Images\ocv02.jpg");
}
/// <summary>
/// 双边滤波
/// </summary>
/// <param name="image">图片对象</param>
/// <returns></returns>
public static Image BilateralFilter(this Bitmap bmp)
{
try
{
using (Mat src = bmp.ToMat())
{
using (Mat result = new Mat())
{
Cv2.BilateralFilter(src, result, 25, 25 * 2, 25 / 2);
return(result.ToBitmap());
}
}
}
catch (OpenCVException)
{
return(bmp);
}
}
public IplImage cartoon(IplImage image)
{
Mat mat = new Mat(image);
Mat tmp = new Mat(mat.Size(), MatType.CV_8UC3);
int repetitions = 7;
int kernelSize = 9;
double sigmaColor = 9;
double sigmaSpace = 7;
for (int i = 0; i < repetitions; i++)
{
Cv2.BilateralFilter(mat, tmp, kernelSize, sigmaColor, sigmaSpace);
Cv2.BilateralFilter(tmp, mat, kernelSize, sigmaColor, sigmaSpace);
}
image = mat.ToIplImage();
return(image);
}
public void cuda_BilateralFilter()
{
Mat src = Mat.Zeros(128, 128, MatType.CV_8UC1);
Size size = src.Size();
int kernel_size = 5;
float sigma_color = 10f;
float sigma_spatial = 3.5f;
using (GpuMat g_src = new GpuMat(size, src.Type()))
using (GpuMat d_dst = new GpuMat()) {
g_src.Upload(src);
Cuda.cuda.bilateralFilter(g_src, d_dst, kernel_size, sigma_color, sigma_spatial);
Mat dst_gold = new Mat();
Cv2.BilateralFilter(src, dst_gold, kernel_size, sigma_color, sigma_spatial);
ImageEquals(dst_gold, d_dst);
ShowImagesWhenDebugMode(src, d_dst);
}
}
//Filter
private void comboBox1_SelectedIndexChanged(object sender, EventArgs e)
{
int index = comboBox1.SelectedIndex;
switch (index)
{
case 0:
// 단순 흐림
//입력, 출력, 커널크기, 앵커 (-1, -1)이 중심, 테두리 유형
Cv2.Blur(MyImage, Filter, new OpenCvSharp.Size(5, 5), new OpenCvSharp.Point(-1, -1),
BorderTypes.Default);
break;
case 1:
//박스 필터
Cv2.BoxFilter(MyImage, Filter, MatType.CV_8UC3, new OpenCvSharp.Size(7, 7),
new OpenCvSharp.Point(-1, -1), true, BorderTypes.Default);
break;
case 2:
//중간값 흐림
Cv2.MedianBlur(MyImage, Filter, 9);
break;
case 3:
//가우시안 흐림
Cv2.GaussianBlur(MyImage, Filter, new OpenCvSharp.Size(3, 3), 1, 0, BorderTypes.Default);
break;
case 4:
//쌍방 필터
//과도한 노이즈 필터링이 필요한 오프라인에는 d = 9를 사용하는 것이 좋습니다.
//시그마 값이 크면 클수록 만화 처럼 보입니다.(50 ,50)
Cv2.BilateralFilter(MyImage, Filter, 9, 50, 50, BorderTypes.Default);
break;
}
pictureBox2.Image = OpenCvSharp.Extensions.BitmapConverter.ToBitmap(Filter);
}
private List <OpenCvSharp.Rect> do_cv(Bitmap crop)
{
// convert the given image to an openCV mat(erial)
var image = OpenCvSharp.Extensions.BitmapConverter.ToMat(crop);
//new CvWindowEx(image);
// apply some image filters to the given image to improve CV operations
var gray = new Mat();
Cv2.CvtColor(image, gray, ColorConversionCodes.BGR2GRAY);
var gray2 = new Mat();
Cv2.BilateralFilter(gray, gray2, 10, 17, 17);
var edged = new Mat();
Cv2.Canny(gray, edged, 80, 200);
//new CvWindowEx(edged);
// dilate / thicken the shapes in the given image
var dilated = new Mat();
Cv2.Dilate(edged, dilated, null);
//new CvWindowEx(edged);
//
// use openCV to compute 'contours' (or boxes) around 'features' in
// in the area of interest (the cropped screenshot)
//
OpenCvSharp.Point[][] contours;
OpenCvSharp.HierarchyIndex[] hierarchy;
Cv2.FindContours(dilated, out contours, out hierarchy, RetrievalModes.Tree, ContourApproximationModes.ApproxSimple);
//----------------------------------------------------------------
// OBJECT CONTOUR / CLICK TARGET FILTRATION
//----------------------------------------------------------------
var filteredContours = new List <OpenCvSharp.Rect>();
foreach (var c in contours)
{
var contourRect = Cv2.BoundingRect(c);
double aspectRatio = contourRect.Width / contourRect.Height;
// discard contours that look like 'lines' such as the edge of a window
if ((aspectRatio > 3 || aspectRatio < 0.2) && (contourRect.Height < 8 || contourRect.Width < 8))
{
//Cv2.Rectangle(image, contourRect, new Scalar(0, 0, 255, 255));
Console.WriteLine("Bad ratio... " + aspectRatio);
continue;
}
// discard contours deemed 'too large'
if (contourRect.Width * contourRect.Height > 6000)
{
//Cv2.Rectangle(image, contourRect, new Scalar(255, 0, 0, 255));
Console.WriteLine("Bad size... " + contourRect.Width);
continue;
}
filteredContours.Add(contourRect);
//Cv2.Rectangle(image, contourRect, new Scalar(255, 255, 0, 255));
}
//new CvWindowEx(image);
//----------------------------------------------------------------
// CLICK TARGET COORDINATE TRANSLATION
//----------------------------------------------------------------
// TODO we should probably move this outside of this function
var transRects = new List <Rect>();
var goodRects = NonMaxSuppression(filteredContours, 0.3f);
Console.WriteLine(goodRects.Count);
foreach (var rect in goodRects)
{
Cv2.Rectangle(image, rect, new Scalar(0, 255, 0, 255));
transRects.Add(new Rect(ScreenGazeSnapshot.m_GazeRect.X + rect.X, ScreenGazeSnapshot.m_GazeRect.Y + rect.Y, rect.Width, rect.Height));
}
//new CvWindowEx(image);
// save the 'rendered' cv results incase we want to dump it to disk later
m_LastScreenCrop = crop;
m_LastScreenCropCV = OpenCvSharp.Extensions.BitmapConverter.ToBitmap(image);
// return the results
return(transRects);
}
// 主要内容，图像处理方法的api
private Mat myOPENCV_run(Mat image_in, Mat image_out)
{
image_out = image_in; // 入图传给出图
for (int i = 0; i < listBox2.Items.Count; i++) //执行 列表框2内的方法
{
switch ((MyOPENCV)myOPENCV_runlist[i, 0]) // 列表框2内的运行方法
{
case MyOPENCV.cvt_color: //颜色转换 （入图，出图，颜色转换符，）
{
Cv2.CvtColor(image_out, image_out, (ColorConversionCodes)myOPENCV_runlist[i, 1], myOPENCV_runlist[i, 2]);
break;
}
case MyOPENCV.boxfilter: //方框滤波
{
OpenCvSharp.Size size;
size.Width = myOPENCV_runlist[i, 2];
size.Height = myOPENCV_runlist[i, 3];
Cv2.BoxFilter(image_out, image_out, myOPENCV_runlist[i, 1], size);
break;
}
case MyOPENCV.blur: //均值滤波
{
OpenCvSharp.Size size;
size.Width = myOPENCV_runlist[i, 1];
size.Height = myOPENCV_runlist[i, 2];
Cv2.Blur(image_out, image_out, size);
break;
}
case MyOPENCV.gaussianblur: // 高斯滤波
{
OpenCvSharp.Size size;
double sigmaX, sigmaY;
size.Width = myOPENCV_runlist[i, 1];
size.Height = myOPENCV_runlist[i, 2];
sigmaX = (double)myOPENCV_runlist[i, 3];
sigmaY = (double)myOPENCV_runlist[i, 4];
Cv2.GaussianBlur(image_out, image_out, size, sigmaX, sigmaY);
break;
}
case MyOPENCV.medianblur: //中值滤波
{
Cv2.MedianBlur(image_in, image_out, myOPENCV_runlist[i, 1]);
break;
}
case MyOPENCV.bilateralfilter: //双边滤波
{
Mat image_out2 = new Mat();
double sigmaColor, sigmaSpace;
sigmaColor = (double)myOPENCV_runlist[i, 2] * 2;
sigmaSpace = (double)myOPENCV_runlist[i, 3] / 2;
Cv2.BilateralFilter(image_out, image_out2, myOPENCV_runlist[i, 1], sigmaColor, sigmaSpace);
image_out = image_out2;
break;
}
case MyOPENCV.dilate: //膨胀
{
Mat image_element = new Mat();
OpenCvSharp.Size size;
size.Width = myOPENCV_runlist[i, 2];
size.Height = myOPENCV_runlist[i, 3];
image_element = Cv2.GetStructuringElement((MorphShapes)myOPENCV_runlist[i, 1], size);
Cv2.Dilate(image_out, image_out, image_element);
break;
}
case MyOPENCV.erode: //腐蚀
{
Mat image_element = new Mat();
OpenCvSharp.Size size;
size.Width = myOPENCV_runlist[i, 2];
size.Height = myOPENCV_runlist[i, 3];
image_element = Cv2.GetStructuringElement((MorphShapes)myOPENCV_runlist[i, 1], size);
Cv2.Erode(image_out, image_out, image_element);
break;
}
case MyOPENCV.morphologyex: //高级形态学变换
{
Mat image_element = new Mat();
OpenCvSharp.Size size;
size.Width = myOPENCV_runlist[i, 3];
size.Height = myOPENCV_runlist[i, 4];
image_element = Cv2.GetStructuringElement((MorphShapes)myOPENCV_runlist[i, 2], size);
Cv2.MorphologyEx(image_out, image_out, (MorphTypes)myOPENCV_runlist[i, 1], image_element);
break;
}
case MyOPENCV.floodfill: //漫水填充
{
OpenCvSharp.Point point;
point.X = myOPENCV_runlist[i, 1];
point.Y = myOPENCV_runlist[i, 2];
OpenCvSharp.Scalar scalar;
scalar = myOPENCV_runlist[i, 3];
Cv2.FloodFill(image_out, point, scalar);
break;
}
case MyOPENCV.pyrup: //尺寸放大
{
OpenCvSharp.Size size;
size.Width = image_out.Cols * 2;
size.Height = image_out.Rows * 2;
Cv2.PyrUp(image_out, image_out, size);
break;
}
case MyOPENCV.pyrdown: //尺寸缩小
{
OpenCvSharp.Size size;
size.Width = image_out.Cols / 2;
size.Height = image_out.Rows / 2;
Cv2.PyrDown(image_out, image_out, size);
break;
}
case MyOPENCV.resize: //尺寸调整
{
OpenCvSharp.Size size;
InterpolationFlags interpolationFlags;
size.Width = image_out.Cols * myOPENCV_runlist[i, 1] / 10;
size.Height = image_out.Rows * myOPENCV_runlist[i, 2] / 10;
interpolationFlags = (InterpolationFlags)myOPENCV_runlist[i, 3];
Cv2.Resize(image_out, image_out, size, 0, 0, interpolationFlags);
break;
}
case MyOPENCV.threshold: //固定阈值化
{
Cv2.Threshold(image_out, image_out, myOPENCV_runlist[i, 1], myOPENCV_runlist[i, 2], (ThresholdTypes)myOPENCV_runlist[i, 3]);
break;
}
case MyOPENCV.canny: //边缘检测CANNY
{
Mat image_out2 = new Mat();
Cv2.Canny(image_out, image_out2, myOPENCV_runlist[i, 1], myOPENCV_runlist[i, 2], myOPENCV_runlist[i, 3]);
image_out = image_out2;
break;
}
case MyOPENCV.sobel: //边缘检测SOBEL
{
Cv2.Sobel(image_out, image_out, -1, myOPENCV_runlist[i, 1], myOPENCV_runlist[i, 2], myOPENCV_runlist[i, 3]);
break;
}
case MyOPENCV.laplacian: //边缘检测LAPLACIAN
{
myOPENCV_runlist[i, 1] = 0;
Cv2.Laplacian(image_out, image_out, 0, myOPENCV_runlist[i, 2], myOPENCV_runlist[i, 3]);
break;
}
case MyOPENCV.scharr: //边缘检测SCHARR
{
Cv2.Scharr(image_out, image_out, -1, myOPENCV_runlist[i, 1], myOPENCV_runlist[i, 2]);
break;
}
case MyOPENCV.convertscaleabs: //图像快速增强
{
double alpha, beta;
alpha = (double)myOPENCV_runlist[i, 1] / 10;
beta = (double)myOPENCV_runlist[i, 2] / 10;
Cv2.ConvertScaleAbs(image_out, image_out, alpha, beta);
break;
}
case MyOPENCV.addweighted: //图像融合
{
Mat image_in2 = new Mat(my_imagesource2);
double alpha, beta, gamma;
alpha = (double)myOPENCV_runlist[i, 1] / 10;
beta = (double)myOPENCV_runlist[i, 2] / 10;
gamma = (double)myOPENCV_runlist[i, 3] / 10;
Cv2.AddWeighted(image_out, alpha, image_in2, beta, gamma, image_out);
break;
}
case MyOPENCV.houghlines: //霍夫标准变换
{
Scalar scalar = new Scalar(0x00, 0xFF, 0x00); //绿色
LineSegmentPolar[] lines;
OpenCvSharp.Size size = new OpenCvSharp.Size(image_out.Width, image_out.Height);
Mat image_out3 = new Mat(size, MatType.CV_8UC3);
lines = Cv2.HoughLines(image_out, 1, Cv2.PI / 180, myOPENCV_runlist[i, 1]);
for (int ii = 0; ii < lines.Length; ii++)
{
//double rho, theta;
OpenCvSharp.Point pt1, pt2;
double a = Math.Cos(lines[ii].Theta), b = Math.Sin(lines[ii].Theta);
double x0 = a * lines[ii].Rho, y0 = b * lines[ii].Rho;
pt1.X = (int)Math.Round(x0 + 1000 * (-b));
pt1.Y = (int)Math.Round(y0 + 1000 * (a));
pt2.X = (int)Math.Round(x0 - 1000 * (-b));
pt2.Y = (int)Math.Round(y0 - 1000 * (a));
Cv2.Line(image_out3, pt1, pt2, scalar, 1, LineTypes.AntiAlias);
}
if (myOPENCV_runlist[i, 2] == 0)
{
Cv2.AddWeighted(image_out3, (double)myOPENCV_runlist[i, 3] / 10, image_in, (double)myOPENCV_runlist[i, 4] / 10, 0, image_out);
}
else
{
image_out = image_out3;
}
break;
}
case MyOPENCV.houghlinep: //霍夫累计概率变换
{
Scalar scalar = new Scalar(0x00, 0xFF, 0x00); //绿色
LineSegmentPoint[] lines; // 线段检索
OpenCvSharp.Size size = new OpenCvSharp.Size(image_out.Width, image_out.Height);
Mat image_out3 = new Mat(size, MatType.CV_8UC3);
lines = Cv2.HoughLinesP(image_out, 1, Cv2.PI / 180, myOPENCV_runlist[i, 1], myOPENCV_runlist[i, 3], myOPENCV_runlist[i, 4]);
for (int ii = 0; ii < lines.Length; ii++)
{
OpenCvSharp.Point point1, point2;
point1.X = lines[i].P1.X;
point1.Y = lines[i].P1.Y;
point2.X = lines[i].P2.X;
point2.Y = lines[i].P2.Y;
Cv2.Line(image_out3, point1, point2, scalar, 1, LineTypes.AntiAlias);
}
if (myOPENCV_runlist[i, 2] == 0)
{
Cv2.AddWeighted(image_out3, 1, image_in, 0.8, 0, image_out);
}
else
{
image_out = image_out3;
}
break;
}
case MyOPENCV.houghcircles: //霍夫圆变换
{
Scalar scalar = new Scalar(0x00, 0xFF, 0x00); //绿色
CircleSegment[] circles;
OpenCvSharp.Size size = new OpenCvSharp.Size(image_out.Width, image_out.Height);
Mat image_out3 = new Mat(size, MatType.CV_8UC3);
circles = Cv2.HoughCircles(image_out, HoughMethods.Gradient, 1, myOPENCV_runlist[i, 1], myOPENCV_runlist[i, 2], myOPENCV_runlist[i, 3], 0, myOPENCV_runlist[i, 4]);
for (int ii = 0; ii < circles.Length; ii++)
{
OpenCvSharp.Point center;
center.X = (int)Math.Round(circles[ii].Center.X);
center.Y = (int)Math.Round(circles[ii].Center.Y);
int radius = (int)Math.Round(circles[ii].Radius);
Cv2.Circle(image_out3, center.X, center.Y, radius, scalar);
Cv2.Circle(image_out3, center, radius, scalar);
}
Cv2.AddWeighted(image_out3, 1, image_in, 0.6, 0, image_out);
break;
}
case MyOPENCV.remap: //重映射
{
OpenCvSharp.Size size = new OpenCvSharp.Size(image_out.Width, image_out.Height);
Mat map_x = new Mat(size, MatType.CV_32FC1), map_y = new Mat(size, MatType.CV_32FC1);
for (int ii = 0; ii < image_out.Rows; ii++)
{
for (int jj = 0; jj < image_out.Cols; jj++)
{
if (myOPENCV_runlist[i, 1] == 0)
{
map_x.Set <float>(ii, jj, jj); //上下翻转
map_y.Set <float>(ii, jj, image_out.Rows - ii); //上下翻转
}
else if (myOPENCV_runlist[i, 1] == 1)
{
map_x.Set <float>(ii, jj, image_out.Cols - jj); //左右翻转
map_y.Set <float>(ii, jj, ii); //左右翻转
}
else if (myOPENCV_runlist[i, 1] == 2)
{
map_x.Set <float>(ii, jj, image_out.Cols - jj); //上下左右翻转
map_y.Set <float>(ii, jj, image_out.Rows - ii); //上下左右翻转
}
else if (myOPENCV_runlist[i, 1] == 3)
{
map_x.Set <float>(ii, jj, (float)myOPENCV_runlist[i, 2] / 10 * jj); //放大缩小
map_y.Set <float>(ii, jj, (float)myOPENCV_runlist[i, 2] / 10 * ii); //放大缩小
}
}
}
Cv2.Remap(image_out, image_out, map_x, map_y);
break;
}
case MyOPENCV.warpaffine: //仿射变换
{
if (0 == myOPENCV_runlist[i, 1])
{
Mat rot_mat = new Mat(2, 3, MatType.CV_32FC1);
OpenCvSharp.Point center = new OpenCvSharp.Point(image_out.Cols / 2, image_out.Rows / 2);
double angle = myOPENCV_runlist[i, 2];
double scale = (double)myOPENCV_runlist[i, 3] / 10;
///// 通过上面的旋转细节信息求得旋转矩阵
rot_mat = Cv2.GetRotationMatrix2D(center, angle, scale);
///// 旋转已扭曲图像
Cv2.WarpAffine(image_out, image_out, rot_mat, image_out.Size());
}
else
{
Point2f[] srcTri = new Point2f[3];
Point2f[] dstTri = new Point2f[3];
Mat warp_mat = new Mat(2, 3, MatType.CV_32FC1);
Mat warp_dst;
warp_dst = Mat.Zeros(image_out.Rows, image_out.Cols, image_out.Type());
srcTri[0] = new Point2f(0, 0);
srcTri[1] = new Point2f(image_out.Cols, 0);
srcTri[2] = new Point2f(0, image_out.Rows);
dstTri[0] = new Point2f((float)(image_out.Cols * myOPENCV_runlist[i, 2] / 100), (float)(image_out.Rows * myOPENCV_runlist[i, 2] / 100));
dstTri[1] = new Point2f((float)(image_out.Cols * (1 - (float)myOPENCV_runlist[i, 3] / 100)), (float)(image_out.Rows * myOPENCV_runlist[i, 3] / 100));
dstTri[2] = new Point2f((float)(image_out.Cols * myOPENCV_runlist[i, 4] / 100), (float)(image_out.Rows * (1 - (float)myOPENCV_runlist[i, 4] / 100)));
warp_mat = Cv2.GetAffineTransform(srcTri, dstTri);
Cv2.WarpAffine(image_out, image_out, warp_mat, image_out.Size());
}
break;
}
case MyOPENCV.equalizehist: //直方图均衡化
{
Cv2.EqualizeHist(image_out, image_out);
break;
}
case MyOPENCV.facedetection: //人脸识别
{
if (0 == myOPENCV_runlist[i, 1]) // 参数一为0 调用haar，其余数字调用lbp
{
var haarCascade = new CascadeClassifier(@"haarcascade_frontalface_alt.xml");
Mat haarResult = DetectFace(image_out, haarCascade);
image_out = haarResult;
}
else
{
var lbpCascade = new CascadeClassifier(@"lbpcascade_frontalface.xml");
Mat lbpResult = DetectFace(image_out, lbpCascade);
image_out = lbpResult;
}
break;
}
case MyOPENCV.matchtemplate: // 模板匹配
{
Mat originalMat = Cv2.ImRead(my_imagesource, ImreadModes.AnyColor); //母图
Mat modelMat = Cv2.ImRead(my_imagesource2, ImreadModes.AnyColor); //模板
Mat resultMat = new Mat(); // 匹配结果
//resultMat.Create(mat1.Cols - modelMat.Cols + 1, mat1.Rows - modelMat.Cols + 1, MatType.CV_32FC1);//创建result的模板，就是MatchTemplate里的第三个参数
if (0 == myOPENCV_runlist[i, 1])
{
Cv2.MatchTemplate(originalMat, modelMat, resultMat, TemplateMatchModes.SqDiff); //进行匹配(1母图,2模版子图,3返回的result，4匹配模式)
}
else if (1 == myOPENCV_runlist[i, 1])
{
Cv2.MatchTemplate(originalMat, modelMat, resultMat, TemplateMatchModes.SqDiffNormed);
}
else if (2 == myOPENCV_runlist[i, 1])
{
Cv2.MatchTemplate(originalMat, modelMat, resultMat, TemplateMatchModes.CCorr);
}
else if (3 == myOPENCV_runlist[i, 1])
{
Cv2.MatchTemplate(originalMat, modelMat, resultMat, TemplateMatchModes.CCorrNormed);
}
else if (4 == myOPENCV_runlist[i, 1])
{
Cv2.MatchTemplate(originalMat, modelMat, resultMat, TemplateMatchModes.CCoeff);
}
else if (5 == myOPENCV_runlist[i, 1])
{
Cv2.MatchTemplate(originalMat, modelMat, resultMat, TemplateMatchModes.CCoeffNormed);
}
OpenCvSharp.Point minLocation, maxLocation, matchLocation;
Cv2.MinMaxLoc(resultMat, out minLocation, out maxLocation);
matchLocation = maxLocation;
Mat mask = originalMat.Clone();
Cv2.Rectangle(mask, minLocation, new OpenCvSharp.Point(minLocation.X + modelMat.Cols, minLocation.Y + modelMat.Rows), Scalar.Green, 2); //画出匹配的矩 （图像，最小点，最大点，颜色，线宽）
image_out = mask;
break;
}
case MyOPENCV.find_draw_contours: // 找出并绘制轮廓
{
Cv2.CvtColor(image_out, image_out, ColorConversionCodes.RGB2GRAY); //转换为灰度图
//Cv2.Blur(image_out, image_out, new OpenCvSharp.Size(2, 2)); //滤波
Cv2.Canny(image_out, image_out, 100, 200); //Canny边缘检测
OpenCvSharp.Point[][] contours;
HierarchyIndex[] hierarchly;
Cv2.FindContours(image_out, out contours, out hierarchly, RetrievalModes.Tree, ContourApproximationModes.ApproxSimple, new OpenCvSharp.Point(0, 0)); //获得轮廓
Mat dst_Image = Mat.Zeros(image_out.Size(), image_out.Type()); // 图片像素值归零
Random rnd = new Random();
for (int j = 0; j < contours.Length; j++)
{
Scalar color = new Scalar(myOPENCV_runlist[i, 1], myOPENCV_runlist[i, 2], myOPENCV_runlist[i, 3]);
//Scalar color = new Scalar(rnd.Next(0, 255), rnd.Next(0, 255), rnd.Next(0, 255));
Cv2.DrawContours(dst_Image, contours, j, color, myOPENCV_runlist[i, 4], LineTypes.Link8, hierarchly); //画出轮廓
}
image_out = dst_Image;
break;
}
case MyOPENCV.componentdefectdetecting: // 零件缺陷检测
{
Cv2.CvtColor(image_out, image_out, ColorConversionCodes.RGB2GRAY); //转换为灰度图
//Cv2.Blur(image_out, image_out, new OpenCvSharp.Size(2, 2)); //滤波
Cv2.Canny(image_out, image_out, 100, 200); //Canny边缘检测
OpenCvSharp.Point[][] contours;
HierarchyIndex[] hierarchly;
Cv2.FindContours(image_out, out contours, out hierarchly, RetrievalModes.Tree, ContourApproximationModes.ApproxSimple, new OpenCvSharp.Point(0, 0)); //获得轮廓
Mat dst_Image = Mat.Zeros(image_out.Size(), image_out.Type()); // 图片像素值归零
Random rnd = new Random();
for (int j = 0; j < contours.Length; j++)
{
Scalar color = new Scalar(myOPENCV_runlist[i, 1], myOPENCV_runlist[i, 2], myOPENCV_runlist[i, 3]);
//Scalar color = new Scalar(rnd.Next(0, 255), rnd.Next(0, 255), rnd.Next(0, 255));
Cv2.DrawContours(dst_Image, contours, j, color, myOPENCV_runlist[i, 4], LineTypes.Link8, hierarchly); //画出轮廓
}
Mat cnt = new Mat();
Cv2.ConvexHull(image_out, cnt);
break;
}
default: break;
}
}
return(image_out);
}
//---------------------------------------------------------------
public static void bilateral_filter(Mat src, Mat dst, int d, double sigma_color, double sigma_space)
{
Cv2.BilateralFilter(src, dst, d, sigma_color, sigma_space);
}
