private bool IsImageVisible(UiElement element, float confidence = 0.80f)
{
using var uielement = GrabElement(element);
if (Settings.SaveImages)
{
Directory.CreateDirectory("logs");
uielement.Save($"logs\\{element.Id}.bmp");
}
using var uiElementMat = uielement.ToMat();
using var templateMat = Cv2.ImDecode(element.Image, ImreadModes.AnyColor);
using var uiElementMatGray = new Mat();
using var templateMatGray = new Mat();
var size = templateMat.Size();
var targetSize = new OpenCvSharp.Size(size.Height * 2, size.Width * 2);
Cv2.CvtColor(uiElementMat, uiElementMatGray, ColorConversionCodes.BGR2GRAY);
Cv2.CvtColor(templateMat, templateMatGray, ColorConversionCodes.BGR2GRAY);
using var uiElementMatGrayScaled = new Mat();
Cv2.Resize(uiElementMatGray, uiElementMatGrayScaled, targetSize);
using var scaledTemplateMat = new Mat();
Cv2.Resize(templateMatGray, scaledTemplateMat, targetSize);
using var result = new Mat();
Cv2.MatchTemplate(uiElementMatGrayScaled, scaledTemplateMat, result, TemplateMatchModes.CCoeffNormed);
Cv2.MinMaxLoc(result, out var _, out var maxVal, out var _, out _);
Overlay?.ShowIsVisibile(element, maxVal > confidence, maxVal);
return(maxVal >= confidence);
}
//校验是否在边界
public static bool NotOnBorder(Rect rectToJudge,
OpenCvSharp.Size borderSize,
int leftLimit = 0,
int rightLimit = 0,
int topLimit = 0,
int bottomLimit = 0)
{
float leftPercent = leftLimit / 100f;
float rightPercent = rightLimit / 100f;
float topPercent = topLimit / 100f;
float bottomPercent = bottomLimit / 100f;
float widthPercent = 1f - leftPercent - rightPercent;
float heightPercent = 1f - topPercent - bottomPercent;
int xLimit = (int)(borderSize.Width * leftPercent);
int yLimit = (int)(borderSize.Height * topPercent);
int widthLimit = (int)(borderSize.Width * widthPercent);
int heightLimit = (int)(borderSize.Height * heightPercent);
Rect rectLimit = new Rect();
rectLimit.X = xLimit;
rectLimit.Y = yLimit;
rectLimit.Width = widthLimit;
rectLimit.Height = heightLimit;
return(rectLimit.Contains(rectToJudge));
}
public CharImage(string fileName, string name, PlateChar plateChar, OpenCvSharp.Size matSize)
{
this.FileName = fileName;
this.Name = name;
this.PlateChar = plateChar;
this.MatSize = matSize;
}
/// <summary>
/// 이미지가 표시될 크기를 얻습니다.
/// PictureBox의 사이즈 모드에 따라 이미지의 크기가 변경되므로 이 메소드를 이용해 정확한 크기를 얻어야합니다.
/// </summary>
/// <param name="streamingType">스트리밍 타입</param>
/// <param name="frame">표시될 이미지</param>
/// <returns>표시될 이미지의 크기</returns>
public OpenCvSharp.Size GetDisplaySize(StreamingType streamingType, Mat frame)
{
if (this.Visualizer.StreamingType == streamingType)
{
this._cachedDisplaySize = frame.Size();
}
if (this.defaultView.streamingFrameBox.SizeMode == PictureBoxSizeMode.CenterImage)
{
if (this.Blender.Enabled)
{
this._cachedDisplaySize = this.Blender.Size;
}
return(this._cachedDisplaySize);
}
else if (this.defaultView.streamingFrameBox.SizeMode == PictureBoxSizeMode.StretchImage)
{
return(new OpenCvSharp.Size(this.defaultView.streamingFrameBox.Width, this.defaultView.streamingFrameBox.Height));
}
else
{
var aspect = this._cachedDisplaySize.Width / (float)this._cachedDisplaySize.Height;
if (this._cachedDisplaySize.Width > this._cachedDisplaySize.Height)
{
return(new OpenCvSharp.Size(this.defaultView.streamingFrameBox.Width, this.defaultView.streamingFrameBox.Height / aspect));
}
else
{
return(new OpenCvSharp.Size(this.defaultView.streamingFrameBox.Width * aspect, this.defaultView.streamingFrameBox.Height));
}
}
}
public Form1()
{
InitializeComponent();
// Read an image from a file.
Mat src = Cv2.ImRead("lena.jpg");
// Prepare the processed image.
OpenCvSharp.Size size = new OpenCvSharp.Size(src.Cols, src.Rows);
Mat dst = new Mat(size, OpenCvSharp.MatType.CV_8UC3);
// Run Sobel filter.
Cv2.Sobel(src, dst, src.Depth(), 1, 1);
// Display the original image.
Cv2.ImShow("src", src);
// Display the processed image.
OpenCvSharp.Cv2.ImShow("dst", dst);
// Wait for key input.
OpenCvSharp.Cv2.WaitKey(0);
Environment.Exit(0);
}
//读入相机标定数据
private void ucBtnExt_readCamParm_BtnClick(object sender, EventArgs e)
{
rootDir = Directory.GetParent(Environment.CurrentDirectory); //定位bin目录
camParmyaml = rootDir.Parent.Parent.FullName + @"\CamParm.yaml"; //获取文件名称
using (var fs = new FileStorage(camParmyaml, FileStorage.Mode.Read))
{
ShareData.leftCamIntrinsic = fs["leftCamIntrinsic"].ReadMat();
ShareData.leftDistCoeffs = fs["leftDistCoeffs"].ReadMat();
ShareData.rightCamIntrinsic = fs["rightCamIntrinsic"].ReadMat();
ShareData.rightDistCoeffs = fs["rightDistCoeffs"].ReadMat();
ShareData.R = fs["R"].ReadMat();
ShareData.T = fs["T"].ReadMat();
ShareData.E = fs["E"].ReadMat();
ShareData.F = fs["F"].ReadMat();
}
///使用640 * 480图片的分辨率重新采集图像 计算数值
OpenCvSharp.Size imgSize = new OpenCvSharp.Size(640, 480);
//注意 这里传入的参数 必须符合规定格式
double alpha = 0; //只显示有效像素区域
Cv2.StereoRectify(ShareData.leftCamIntrinsic, ShareData.leftDistCoeffs, ShareData.rightCamIntrinsic,
ShareData.rightDistCoeffs, imgSize, ShareData.R, ShareData.T,
ShareData.R1, ShareData.R2, ShareData.P1, ShareData.P2, ShareData.Q, StereoRectificationFlags.ZeroDisparity,
alpha, imgSize, out ShareData.PixROI1, out ShareData.PixROI2);
//计算左右相机校正映射 ， 映射为定点表示形式
Cv2.InitUndistortRectifyMap(ShareData.leftCamIntrinsic, ShareData.leftDistCoeffs, ShareData.R1, ShareData.P1,
imgSize, MatType.CV_16SC2, ShareData.leftMap1, ShareData.leftMap2);
Cv2.InitUndistortRectifyMap(ShareData.rightCamIntrinsic, ShareData.rightDistCoeffs, ShareData.R2, ShareData.P2,
imgSize, MatType.CV_16SC2, ShareData.rightMap1, ShareData.rightMap2);
//标志位置1
this.IS_READPARM = true;
ShareData.Log = "[msg] 相机参数读入计算成功";
}
public static void compute_hog_test(List <Mat> img_lst, OpenCvSharp.Size size, int lables, string path)
{
HOGDescriptor hog = new HOGDescriptor();
hog.WinSize = size;
Mat gray = new Mat();
float[] descriptors;
int descriptors_size = 0;
StreamWriter sw = new StreamWriter(path, false, Encoding.UTF8);
for (int i = 0; i < img_lst.Count; i++)// vong lap duyet tung anh
{
string line = lables.ToString();
sw.Write(line);
Cv2.CvtColor(img_lst[i], gray, ColorConversionCodes.RGB2GRAY);
descriptors = hog.Compute(gray);
descriptors_size = descriptors.Length;
Mat Mat_descriptor = new Mat(descriptors_size, 1, MatType.CV_8UC1);
for (int a = 0; a < descriptors.Length; a++)
{
Mat_descriptor.Set <float>(a, 0, descriptors[a]);
float value = Mat_descriptor.Get <float>(a, 0);
string lines = " " + (a + 1) + ":" + value;
sw.Write(lines);
}
sw.WriteLine();
}
sw.Close();
}
public ImageSource ReshapePicture(string picture, int width, int lenth)
{
string str2 = System.IO.Directory.GetCurrentDirectory();
string p = str2 + "\\data.txt";
//记录
FileInfo fileInfo = new FileInfo(p);
FileStream fs = new FileStream(fileInfo.Name, FileMode.OpenOrCreate);
StreamWriter sw = new StreamWriter(fs);
//开始写入
sw.Write(width);
sw.Write('\n');
sw.Write(lenth);
//清空缓冲区
sw.Flush();
//关闭流
sw.Close();
fs.Close();
//修改图片大小
var img_origin = Cv2.ImRead(picture, ImreadModes.Color);
//Mat img_origin = new Mat(picture, ImreadModes.Color);
Mat dst = new Mat();
OpenCvSharp.Size size = new OpenCvSharp.Size(lenth, width);
Cv2.Resize(img_origin, dst, size);
Bitmap bitmap = new Bitmap(dst.Cols, dst.Rows, (int)dst.Step(), System.Drawing.Imaging.PixelFormat.Format32bppArgb, dst.Data);
bitmap.Save(str2 + "data.bmp");
ImageSourceConverter imageSourceConverter = new ImageSourceConverter();
ImageSource imageSource = (ImageSource)imageSourceConverter.ConvertFrom(bitmap);
//ImageSource imageSource = (ImageSource)
return(imageSource);
}
private void PicZoom()
{
Rectangle cropRect1 = new Rectangle(xOffset, yOffset, imageWidth, imageHeight);
Bitmap src1 = image_in as Bitmap;
picIn.Image = src1.Clone(cropRect1, src1.PixelFormat);
Mat image_opencv = BitmapConverter.ToMat(new Bitmap(image_in));
Size dsize = new Size(0, 0);
int fx = zoom;
int fy = zoom;
Mat image_filtered = image_opencv.Resize(dsize, fx, fy, interpolation);
Mat blur = new Mat();
Mat image_usm = new Mat();
Cv2.GaussianBlur(image_filtered, blur, new Size(0, 0), 25);
Cv2.AddWeighted(image_filtered, alpha, blur, -1 * beta, gamma, image_usm);
MemoryStream ms_out = new MemoryStream(image_filtered.ToBytes());
image_out = Image.FromStream(ms_out);
Rectangle cropRect2 = new Rectangle(xOffset * zoom, yOffset * zoom, image_out.Width / zoom, image_out.Height / zoom);
Bitmap src2 = image_out as Bitmap;
picOut.Image = src2.Clone(cropRect2, src2.PixelFormat);
}
public static void MeanValueCompute_test(List <Mat> img_lst, OpenCvSharp.Size size, int lables, string path)
{
Mat[] RGB = new Mat[3];
StreamWriter sw2 = new StreamWriter(path, false, Encoding.UTF8);
for (int i = 0; i < img_lst.Count; i++)// vong lap duyet tung anh
{
RGB = Cv2.Split(img_lst[i]);
string line = lables.ToString();
sw2.Write(line);
//if (InforPublic.canny == true)
//{
// Cv2.Canny(gray, gray, InforPublic.Th1, InforPublic.Th2);
//}
//if (InforPublic.median == true)
//{
// Cv2.MedianBlur(gray, gray, InforPublic.Kernel_median);
// // ListImage.Add(img);
//}
for (int a = 0; a < RGB.Length; a++)
{
double mean = CalMeanValue(RGB[a]);
string lines = " " + (a + 1) + ":" + mean;
sw2.Write(lines);
}
sw2.WriteLine();
}
sw2.Close();
}
private void imageshow_MouseLeftButtonDown(object sender, MouseButtonEventArgs e)
{
CoordinateTransformation dd = new CoordinateTransformation();
Mat rgb = null;
string rgbpath = System.IO.Path.Combine(Global.ImageDataPath, "rgb_" + pCurrentImage.ToString("D5") + ".jpg");
rgb = Cv2.ImRead(rgbpath);
OpenCvSharp.Size imagesize = rgb.Size();
int inputx = ((int)e.GetPosition(imageshow).X *imagesize.Width) / ((int)imageshow.ActualWidth);
int inputy = ((int)e.GetPosition(imageshow).Y *imagesize.Height) / ((int)imageshow.ActualHeight);
pt = dd.getvalue(pCurrentImage, inputx, inputy);
// Console.WriteLine(pt);
if (pt != new Point3D(0, 0, 0))
{
VIewer3D.Instance.ClickedCoordinate();
}
}
private static Mat DetectFace(CascadeClassifier cascade, Mat photo)
{
Mat result;
//Mat src = photo;
using (var src = photo)
using (var gray = new Mat())
{
result = src.Clone();
Cv2.CvtColor(src, gray, ColorConversionCodes.BGR2GRAY);
// Detect faces
Rect[] faces = cascade.DetectMultiScale(
gray, 1.08, 2, HaarDetectionType.ScaleImage, new OpenCvSharp.Size(30, 30));
// Render all detected faces
foreach (Rect face in faces)
{
var center = new OpenCvSharp.Point
{
X = (int)(face.X + face.Width * 0.5),
Y = (int)(face.Y + face.Height * 0.5)
};
var axes = new OpenCvSharp.Size
{
Width = (int)(face.Width * 0.5),
Height = (int)(face.Height * 0.5)
};
Cv2.Ellipse(result, center, axes, 0, 0, 360, new Scalar(255, 0, 255), 4);
}
return(result);
}
}
private CvSize GetScaledCvSize(CvSize size, double scale)
{
var wid = size.Width * scale;
var hei = size.Height * scale;
return(new CvSize(wid, hei));
}
private Mat findFace(Mat srcMat)
{
using (CascadeClassifier detectFace = new CascadeClassifier(System.Windows.Forms.Application.StartupPath + "\\opencv_data\\resource\\opencv_data\\haarcascade_frontalface_alt.xml"))
{
Mat result = new Mat();
Mat output = new Mat();
Cv2.CvtColor(srcMat, result, ColorConversionCodes.BGR2GRAY);
int gr_thr = 4;
double scale_step = 1.1;
OpenCvSharp.Size min_obj_sz = new OpenCvSharp.Size(48, 96);
OpenCvSharp.Size max_obj_sz = new OpenCvSharp.Size(100, 200);
int x1, x2;
int y1, y2;
// detect
Rect[] found;
found = detectFace.DetectMultiScale(result, scale_step, gr_thr);
if (found.Length == 1)
{
output = srcMat.Clone();
srcMat.Rectangle(found[0], Scalar.Red, 5);
OpenCvSharp.Cv2.ImWrite(System.Windows.Forms.Application.StartupPath + "\\opencv_data\\test.jpg", output); //jpg파일 저장
x1 = found[0].X;
y1 = found[0].Y;
x2 = found[0].X + found[0].Width;
y2 = found[0].Y + found[0].Height;
/*
* var json = new JObject();
* json.Add("x1", x1);
* json.Add("y1", y1);
* json.Add("x2", x2);
* json.Add("y2", y2);
*/
//Console.WriteLine(json.ToString());
using (StreamWriter wr = new StreamWriter(System.Windows.Forms.Application.StartupPath + "\\opencv_data\\data.txt"))
{
wr.WriteLine(x1);
wr.WriteLine(y1);
wr.WriteLine(x2);
wr.WriteLine(y2);
}
}
output.Dispose();
found = null;
detectFace.Dispose();
result.Dispose();
found = null;
}
return(srcMat);
}
public StructuredLight(System.Drawing.Size size, bool xaxis = false, bool yaxis = true, bool inversePattern = true)//horizonal codes for disparity-gating
{
var cvSize = new OpenCvSharp.Size(size.Width, size.Height);
code = new XorCode(cvSize, xaxis, yaxis, inversePattern);
Calib = new DisparityCalibration();
}
public F_Main()
{
InitializeComponent();
//Recording BackgroundWorker
bw_cap = new BackgroundWorker();
bw_cap.DoWork += new DoWorkEventHandler(doWork_cap);
bw_cap.ProgressChanged += new ProgressChangedEventHandler(progressChanged_cap); //Cancellation is not implimented yet.
bw_cap.WorkerSupportsCancellation = true;
bw_cap.WorkerReportsProgress = true;
bw_chScan = new BackgroundWorker();
bw_chScan.DoWork += new DoWorkEventHandler(doWork_chScan);
bw_chScan.ProgressChanged += new ProgressChangedEventHandler(progressChanged_chScan); //Cancellation is not implimented yet.
bw_chScan.WorkerSupportsCancellation = true;
bw_chScan.WorkerReportsProgress = true;
this.vids = new List <int>();
if (this.scanCamera() == 0)
{
currentch = 0;
this.vcap = new VideoCapture(this.vids[currentch]);
this.vcap.Set(VideoCaptureProperties.Focus, 0.01);
this.vcap.Set(VideoCaptureProperties.Gain, 0.0);
FPS = this.vcap.Fps;
this.w = this.vcap.FrameWidth; this.h = this.vcap.FrameHeight;
this.size = new OpenCvSharp.Size(this.w, this.h);
this.mat_cap = new Mat(this.h, this.w, MatType.CV_8UC3);
}
bw_cap.RunWorkerAsync();
bw_chScan.RunWorkerAsync();
}
static Mat GetCorrectedRectangle(Mat sourceImage, OpenCvSharp.Point[] contour)
{
var rotatedRectangle = Cv2.MinAreaRect(contour);
var rectangleAngle = rotatedRectangle.Angle;
var rectangleSize = ToSize(rotatedRectangle.Size);
if (rectangleAngle < -45f)
{
rectangleAngle += 90f;
rectangleSize = new OpenCvSharp.Size(rectangleSize.Height, rectangleSize.Width);
}
using (var rotationMatrix = Cv2.GetRotationMatrix2D(rotatedRectangle.Center, rectangleAngle, 1d))
{
using (var rotatedImage = new Mat())
{
Cv2.WarpAffine(sourceImage, rotatedImage, rotationMatrix, sourceImage.Size(), InterpolationFlags.Lanczos4);
var croppedImage = new Mat();
Cv2.GetRectSubPix(rotatedImage, rectangleSize, rotatedRectangle.Center, croppedImage);
return(croppedImage);
}
}
}
/// <summary>
/// Render detected faces via OpenCV.
/// </summary>
/// <param name="state">Current frame state.</param>
/// <param name="image">Web cam or video frame.</param>
/// <returns>Returns new image frame.</returns>
private static Mat RenderFaces(FrameState state, Mat image)
{
Mat result = image.Clone();
Cv2.CvtColor(image, image, ColorConversionCodes.BGR2GRAY);
// Render all detected faces
foreach (var face in state.Faces)
{
var center = new OpenCvSharp.Point
{
X = face.Center.X,
Y = face.Center.Y
};
var axes = new OpenCvSharp.Size
{
Width = (int)(face.Size.Width * 0.5) + 10,
Height = (int)(face.Size.Height * 0.5) + 10
};
Cv2.Ellipse(result, center, axes, 0, 0, 360, _faceColorBrush, 4);
}
return(result);
}
public int checkAppearObject(Rect before, Rect after, OpenCvSharp.Size matSz)
/// <summary>
/// check the object rect which is disappearing or appearing on edge location
/// </summary>
/// <param name="after">objecct rect current position</param>
/// <param name="before">object rect previews position</param>
/// <param name="matSz"> size of video frame </param>
{
var bef = this.isBorderRect(before, matSz);
var aft = this.isBorderRect(after, matSz);
if (bef && !aft)
{
return(0); // appear
}
else if (!bef && aft)
{
this.isBorderRect(before, matSz);
this.isBorderRect(after, matSz);
return(1); // disappear
}
else
{
return(2); // track
}
}
public BitmapSource WeatheringNG(BitmapSource SourceImage, WeatheringParam param, bool Watermark)
{
Mat SourceMat = SourceImage.ToMat();
if (SourceMat.Type() == MatType.CV_8UC1)
{
SourceMat = SourceMat.CvtColor(ColorConversionCodes.GRAY2BGRA);
}
if (Watermark)
{
AddWatermark(ref SourceMat, Watermark_Zhihu);
AddWatermark(ref SourceMat, Watermark_Sina);
AddWatermark(ref SourceMat, Watermark_Toutiao);
AddWatermark(ref SourceMat, Watermark_Tieba);
}
if (param.Noise != 0.0)
{
Noising(ref SourceMat, param.Noise);
}
if (param.Green != 0.0)
{
Greening(ref SourceMat, param.Green);
}
if (param.AspectRatio != 1.0)
{
OpenCvSharp.Size size = new OpenCvSharp.Size(0, 0);
SourceMat = SourceMat.Resize(size, param.AspectRatio, param.AspectRatio, InterpolationFlags.Area);
}
if (param.Quality != 0.0)
{
SourceMat = Compressing(SourceMat, param.Quality);
}
return(SourceMat.ToBitmapSource());
}
protected override void ProcessRecord()
{
// File existence check
if (!File.Exists(@Target))
{
WriteError(new ErrorRecord((new FileNotFoundException()), "FileNotFound", ErrorCategory.ObjectNotFound, Target));
return;
}
if (!File.Exists(@Template))
{
WriteError(new ErrorRecord((new FileNotFoundException()), "FileNotFound", ErrorCategory.ObjectNotFound, Template));
return;
}
try
{
//Load taraget and template image file
using (var matTarget = new Mat(@Target))
using (var matTemplate = new Mat(@Template))
//Prepare result image
using (var result = new Mat(matTarget.Height - matTemplate.Height + 1, matTarget.Width - matTemplate.Width + 1, MatType.CV_8UC1))
{
var templateSize = new OpenCvSharp.Size(matTemplate.Width, matTemplate.Height);
do
{
// Invoke template matching
Cv2.MatchTemplate(matTarget, matTemplate, result, TemplateMatchModes.CCoeffNormed);
// Extract most similar point
Cv2.MinMaxLoc(result, out double minVal, out double maxVal, out Point minPoint, out Point maxPoint);
if (maxVal < Threshold)
{
break;
}
else
{
var matchedRect = new MatchedRect();
matchedRect.Rect = new Rect(maxPoint, templateSize);
matchedRect.Similarity = (float)maxVal;
// Return matched rectangle
WriteObject(matchedRect);
// Fill matched rectangle as random
Rect fillRect = matchedRect.Rect;
fillRect.Inflate(Convert.ToInt32(fillRect.Width * -0.2), Convert.ToInt32(fillRect.Height * -0.2));
using (var matRandom = new Mat(fillRect.Size, matTarget.Type()))
{
Cv2.Randu(matRandom, new Scalar(0, 0, 0), new Scalar(255, 255, 255));
matTarget[fillRect] = matRandom;
}
}
} while (true);
}
}
catch (Exception e) {
WriteError(new ErrorRecord(e, "Template Matching Failed", ErrorCategory.OperationStopped, null));
}
}
private (string iconKey, float conf, Vector2 pos) ScanStaticAndDynamic(Size iconSlotSize, Mat source)
{
var matchResultStatic = MatchIcon(iconSlotSize, source, true);
var matchResultDynamic = MatchIcon(iconSlotSize, source, false);
return(matchResultStatic.conf > matchResultDynamic.conf ? matchResultStatic : matchResultDynamic);
}
private void timer1_Tick(object sender, EventArgs e)
{
try
{
Console.WriteLine("Tick");
cap.Read(frame);
OpenCvSharp.Size newsize = new OpenCvSharp.Size(pictureBox1.Width, pictureBox1.Height);
frame = frame.Resize(newsize);
Cv2.Canny(frame, dstframe, 20, 100);
Bitmap tempimage1 = BitmapConverter.ToBitmap(frame);
Bitmap tempimage2 = BitmapConverter.ToBitmap(dstframe);
pictureBox1.Image = tempimage1;
pictureBox2.Image = tempimage2;
//Cv2.WaitKey(10);
}
catch (Exception extdf)
{
Console.WriteLine(extdf.ToString());
}
}
private void bStart_MouseClick(object sender, MouseEventArgs e)
{
//CvCapture camera = new CvCapture("C:\\rosbank\\facedetect\\test\\media\\test.avi");
var capture = new VideoCapture("C:\\rosbank\\facedetect\\test\\media\\test.avi");
//capture.Set(CaptureProperty.FrameWidth, 320);
//capture.Set(CaptureProperty.FrameHeight, 240);
int sleepTime = (int)Math.Round(1000 / capture.Fps);
Mat image = new Mat();
Rect[] faces = null;
int i = 0;
while (true)
{
i++;
capture.Read(image);
if (image.Empty())
{
break;
}
Mat small = new Mat();
Cv2.Resize(image, small, new OpenCvSharp.Size(320, 240), 0, 0, InterpolationFlags.Lanczos4);
if (i % 5 == 0)
{
var cascade = new CascadeClassifier(@"C:\opencv3\opencv\sources\data\haarcascades\haarcascade_frontalface_alt.xml");
faces = cascade.DetectMultiScale(small, 1.08, 2, HaarDetectionType.ScaleImage, new OpenCvSharp.Size(30, 30));
}
if (faces != null && faces.Length > 0)
{
foreach (Rect face in faces)
{
var center = new OpenCvSharp.Point
{
X = (int)(face.X + face.Width * 0.5),
Y = (int)(face.Y + face.Height * 0.5)
};
var axes = new OpenCvSharp.Size
{
Width = (int)(face.Width * 0.5),
Height = (int)(face.Height * 0.5)
};
Mat f = new Mat(small, face);
pb1.Image = OpenCvSharp.Extensions.BitmapConverter.ToBitmap(f);
pb1.Refresh();
Cv2.Ellipse(small, center, axes, 0, 0, 360, new Scalar(255, 0, 255), 2);
}
}
bpMain.Image = OpenCvSharp.Extensions.BitmapConverter.ToBitmap(small);
bpMain.Refresh();
Cv2.WaitKey(100);
}
}
public static void transImage(Mat src, Mat dst, double[,] positon)
{
//变换前的四点
Point2f[] srcPoints = new Point2f[]
{
new Point2f((float)positon[0, 0], (float)positon[0, 1]),
new Point2f((float)positon[1, 0], (float)positon[1, 1]),
new Point2f((float)positon[2, 0], (float)positon[2, 1]),
new Point2f((float)positon[3, 0], (float)positon[3, 1]),
};
float[] size = setSize(positon);
//变换后的四点
Point2f[] dstPoints = new Point2f[]
{
new Point2f(0, 0),
new Point2f(0, size[1]),
new Point2f(size[0], size[1]),
new Point2f(size[0], 0),
};
//根据变换前后四个点坐标,获取变换矩阵
Mat mm = Cv2.GetPerspectiveTransform(srcPoints, dstPoints);
Size size1 = new Size(size[0], size[1]);
//进行透视变换
Cv2.WarpPerspective(src, dst, mm, size1);
}
private OpenCvSharp.Size GetGmapSize(Mat frame)
{
var minimum = frame.Width > frame.Height ? frame.Height : frame.Width;
var size = new OpenCvSharp.Size();
if (this.State == GmapState.Collapsed)
{
size.Width = Math.Min((int)(minimum / 10.0f), this._icon.Width);
size.Height = Math.Min((int)(minimum / 10.0f), this._icon.Height);
}
else if (this.State == GmapState.Expanded)
{
if (this._owner.SizeMode == PictureBoxSizeMode.CenterImage)
{
if (frame.Width > this._owner.Width)
{
size.Width = (int)(this._owner.Width * EXPANDED_WIDTH_RATIO);
}
else
{
size.Width = (int)(frame.Width * EXPANDED_HEIGHT_RATIO);
}
if (frame.Height > this._owner.Height)
{
size.Height = (int)(this._owner.Height * EXPANDED_WIDTH_RATIO);
}
else
{
size.Height = (int)(frame.Height * EXPANDED_HEIGHT_RATIO);
}
}
else if (this._owner.SizeMode == PictureBoxSizeMode.Zoom)
{
if (frame.Width > frame.Height)
{
size.Width = (int)(this._owner.Width * EXPANDED_WIDTH_RATIO);
size.Height = (int)(frame.Height * EXPANDED_HEIGHT_RATIO);
}
else
{
size.Width = (int)(frame.Width * EXPANDED_WIDTH_RATIO);
size.Height = (int)(this._owner.Height * EXPANDED_HEIGHT_RATIO);
}
}
else
{
size.Width = (int)(this._owner.Width * EXPANDED_WIDTH_RATIO);
size.Height = (int)(this._owner.Height * EXPANDED_HEIGHT_RATIO);
}
}
else
{
size.Width = frame.Width;
size.Height = frame.Height;
}
return(size);
}
/// <summary>
/// カメラ画像を取得して次々に表示を切り替える
/// </summary>
public virtual void Capture(object state)
{
var haarCascade = new CascadeClassifier("data/haarcascades/haarcascade_frontalface_default.xml");
var camera = new VideoCapture(0 /*0番目のデバイスを指定*/)
{
// キャプチャする画像のサイズフレームレートの指定
FrameWidth = 480,
FrameHeight = 270,
// Fps = 60
};
using (var img = new Mat()) // 撮影した画像を受ける変数
using (camera) {
while (true)
{
if (this.IsExitCapture)
{
this.Dispatcher.Invoke(() => this._Image.Source = null);
break;
}
camera.Read(img); // Webカメラの読み取り（バッファに入までブロックされる
if (img.Empty())
{
break;
}
var result = img.Clone();
using (var gray = new Mat()) {
Cv2.CvtColor(img, gray, ColorConversionCodes.BGR2GRAY);
var faces = haarCascade.DetectMultiScale(
gray,
1.08,
2,
HaarDetectionType.FindBiggestObject,
new OpenCvSharp.Size(50, 50)
);
foreach (var face in faces)
{
var center = new OpenCvSharp.Point {
X = (int)(face.X + face.Width * 0.5),
Y = (int)(face.Y + face.Height * 0.5)
};
var axes = new OpenCvSharp.Size {
Width = (int)(face.Width * 0.5),
Height = (int)(face.Height * 0.5)
};
Cv2.Ellipse(result, center, axes, 0, 0, 360, new Scalar(255, 0, 255), 4);
}
}
this.Dispatcher.Invoke(() => {
this._Image.Source = result.ToWriteableBitmap(); // WPFに画像を表示
});
}
}
}
private int OpenCVDeepLearningDetector(string path)
{
// uses emugu library
//https://medium.com/@vinuvish/face-detection-with-opencv-and-deep-learning-90bff9028fa8
string prototextPath = @"./Dnn/deploy.prototxt";
string caffeModelPath = @"./Dnn/res10_300x300_ssd_iter_140000.caffemodel";
//// load the model;
using (var net = OpenCvSharp.Dnn.CvDnn.ReadNetFromCaffe(prototxt: prototextPath, caffeModel: caffeModelPath))
using (OpenCvSharp.Mat image = Cv2.ImRead(path))
{
// get the original image size
OpenCvSharp.Size imageSize = image.Size();
// the dnn detector works on a 300x300 image;
// now resize the image for the Dnn dector;
OpenCvSharp.Size size = new OpenCvSharp.Size(299, 299);
// set the scalar property to RGB colors, don't know what these values represent.
OpenCvSharp.Scalar mcvScalar = new OpenCvSharp.Scalar(104.0, 177.0, 123.0);
using (var blob = OpenCvSharp.Dnn.CvDnn.BlobFromImage(image: image, scaleFactor: 1, size: size, mean: mcvScalar, swapRB: true))
{
net.SetInput(blob, "data");
using (OpenCvSharp.Mat detections = net.Forward())
{
// convert the detected values to a faces object that we can use to
// draw rectangles.
List <ConfidenceRect> Faces = new List <ConfidenceRect>();
//var rows = detections.SizeOfDimension[2];
//Array ans = detections.GetData();
//for (int n = 0; n < rows; n++)
//{
// object confidence = ans.GetValue(0, 0, n, 2);
// object x1 = ans.GetValue(0, 0, n, 3);
// object y1 = ans.GetValue(0, 0, n, 4);
// object x2 = ans.GetValue(0, 0, n, 5);
// object y2 = ans.GetValue(0, 0, n, 6);
// ConfidenceRect cr = new ConfidenceRect(confidence, x1, y1, x2, y2, imageSize);
// if (cr.Confidence > 0)
// {
// Debug.WriteLine($"Confidence {cr.Confidence}");
// }
// if (cr.Confidence > Confidence)
// {
// Faces.Add(cr);
// }
//}
//// convert to a writeableBitmap
//WriteableBitmap writeableBitmap = new WriteableBitmap(ImageSource);
//ImageSource = ConvertWriteableBitmapToBitmapImage(writeableBitmap);
//OnPropertyChanged("ImageSource");
//DrawDnnOnImage?.Invoke(Faces, imageSize);
//return Faces.Count.ToString();
}
}
}
return(0);
}
public PlateImage(string fileName, string name, PlateCategory plateCategory, OpenCvSharp.Size
matSize)
{
this.FileName = fileName;
this.Name = name;
this.PlateCategory = plateCategory;
this.MatSize = matSize;
}
/// <summary>
/// Get dynamic icons with matching size
/// </summary>
/// <seealso cref="StaticIcons"/>
/// <seealso cref="DynamicIcons"/>
/// <param name="size">Size of the icon in cells</param>
/// <returns>Dictionary with the key being the uid and the value being the icon</returns>
internal static Dictionary <string, Mat> GetDynamicIcons(Size size)
{
if (!DynamicIcons.ContainsKey(size))
{
return(null);
}
return(DynamicIcons[size]);
}
