//SGBM视差计算
private void ucBtnExt_sgbmDisCal_BtnClick(object sender, EventArgs e)
{
if (IS_USE_SGBM_FLAG)
{
Mat tleftImg_gray = new Mat();
Mat trightImg_gray = new Mat();
//转换灰度图
Cv2.CvtColor(leftImg_sgbm_rectify, tleftImg_gray, ColorConversionCodes.BGR2GRAY);
Cv2.CvtColor(rightImg_sgbm_rectify, trightImg_gray, ColorConversionCodes.BGR2GRAY);
//计算视差图
double min, max;
//记录SGBM算法运行时间
Stopwatch sw = new Stopwatch();
sw.Start();
sgbmMatch.Compute(tleftImg_gray, trightImg_gray, sgbmDisImg);
sw.Stop();
TimeSpan ts = sw.Elapsed; //获取SGBM代码运行时间
Cv2.MinMaxLoc(sgbmDisImg, out min, out max);
//转换为8位图
sgbmDisImg.ConvertTo(sgbm_dis8UImg, MatType.CV_8UC1, 255 / (max - min), -255 * min / (max - min));
//转换为伪彩色图
sgbm_dis24UImg = new Mat(sgbmDisImg.Size(), MatType.CV_8UC3);
Cv2.ApplyColorMap(sgbm_dis8UImg, sgbm_dis24UImg, ColormapTypes.Jet);
//计算SGBM的有效视差区域
sgbm_validDisROI = Cv2.GetValidDisparityROI(ShareData.PixROI1, ShareData.PixROI2, sgbm_minDisparity,
sgbm_numofDisparities, sgbm_blockSize);
ShareData.Log = "[msg] SGBM算法计算时间：" + ts.TotalMilliseconds.ToString() + "ms" + "\r\n"
+ "SGBM视差图计算成功，可以测量了";
}
else
{
ShareData.Log = "[warning] 请先加载图片";
}
}
public Mat mappingPalette(Mat frame)
{
lock (this.Palette)
{
var ret = new Mat();
switch (this.Palette)
{
case "Grayscale":
ret = frame;
break;
case "Default":
ret = frame.LUT(FLIR_DEFAULT_PALETTE);
break;
case "IronBlack":
ret = frame.LUT(IRON_BLACK_PALETTE);
break;
default:
var ctype = (ColormapTypes)Enum.Parse(typeof(ColormapTypes), this.Palette);
Cv2.ApplyColorMap(frame, ret, ctype);
break;
}
return(ret);
}
}
public static Mat HeatMap(this Mat mat)
{
Mat heatMap = new Mat();
Cv2.ApplyColorMap(mat, heatMap, ColormapTypes.Jet);
return(heatMap);
}
//public const long BUFFERSIZE = 640 * 480;
//public static byte[] gBuffer = new byte[BUFFERSIZE];
public void RefreshUI()
{
// 初始化gBuffer
//int index = 0;
//for (int i = 0; i < 480; i++)
//{
// for (int j = 0; j < 640; j++)
// {
// gBuffer[index++] = 1;
// }
//}
//byte[] array = binaryReader.ReadBytes(arraySize);
int arraySize = 480 * 640;
byte[,] array2 = new byte[480, 640];
//将一维数组转换为二维数组
for (int j = 0; j < arraySize; j++)
{
array2[j / 640, j % 640] = DataAcquire.gBuffer[j];
}
Mat srcG = ConvertFile.ArrayToMat(array2);
Mat dstG = new Mat();
Cv2.ApplyColorMap(srcG, dstG, ColormapTypes.Jet);
Bitmap bitmapG = ConvertFile.MatToBitmap(dstG);
pictureBox.Image = bitmapG;
}
public override object Process(object input)
{
var list = input as object[];
var net = list.First(z => z is Nnet) as Nnet;
var f1 = net.Nodes.FirstOrDefault(z => !z.IsInput);
var rets3 = net.OutputDatas[f1.Name] as float[];
InternalArray arr = new InternalArray(f1.Dims);
arr.Data = rets3.Select(z => (double)z).ToArray();
Mat mat = new Mat(f1.Dims[2],
f1.Dims[3], MatType.CV_8UC1,
arr.Data.Select(z => (byte)(z * 255)).ToArray());
Cv2.ApplyColorMap(mat, mat, ColormapTypes.Magma);
mat = mat.Resize(net.lastReadedMat.Size());
if (StackWithSourceImage)
{
Cv2.HConcat(net.lastReadedMat, mat, mat);
}
LastMat = mat;
Pbox.Invoke((Action)(() =>
{
Pbox.Image = BitmapConverter.ToBitmap(mat);
}));
return(mat);
}
/// <summary>
/// Handles the FrameReceived event
/// </summary>
/// <param name="sender">The Sender</param>
/// <param name="args">The FrameEventArgs</param>
private void OnFrameReceived(object sender, FrameEventArgs args)
{
// Start an async invoke in case this method was not
// called by the GUI thread.
if (InvokeRequired == true)
{
BeginInvoke(new FrameReceivedHandler(this.OnFrameReceived), sender, args);
return;
}
if (true == m_Acquiring)
{
Mat img = BitmapConverter.ToMat(new Bitmap(args.Image));
img = img.CvtColor(ColorConversionCodes.RGB2GRAY);
Mat colorMapImg = new Mat();
//Cv2.ApplyColorMap(img, colorMapImg, ColormapTypes.Jet);
Cv2.ApplyColorMap(img, colorMapImg, ColormapTypes.Jet);
//Cv2.ImShow("Jet", colorMapImg);
// Display image
//Image image = args.Image;
Image image = BitmapConverter.ToBitmap(colorMapImg);
if (null != image)
{
m_PictureBox.Image = image;
}
else
{
LogMessage("An acquisition error occurred. Reason: " + args.Exception.Message);
try
{
try
{
// Start asynchronous image acquisition (grab) in selected camera
m_VimbaHelper.StopContinuousImageAcquisition();
}
finally
{
m_Acquiring = false;
UpdateControls();
m_CameraList.Enabled = true;
}
LogMessage("Asynchronous image acquisition stopped.");
}
catch (Exception exception)
{
LogError("Error while stopping asynchronous image acquisition. Reason: " + exception.Message);
}
}
}
}
public void ApplyColorMap()
{
using var src = Image("building.jpg", ImreadModes.Color);
using var dst = new Mat();
Cv2.ApplyColorMap(src, dst, ColormapTypes.Cool);
ShowImagesWhenDebugMode(src, dst);
using var userColor = new Mat(256, 1, MatType.CV_8UC3, Scalar.All(128));
Cv2.ApplyColorMap(src, dst, userColor);
ShowImagesWhenDebugMode(src, dst);
}
private void CreatImg()
{
if (Filter.SelectedIndex == 0 || Filter.SelectedIndex == -1)
{
for (int i = 0; i < 8640; i = i + 4)
{
_iniImage[i / 4] = BitConverter.ToSingle(_recvBuf, i);
}
for (int i = _imGray.Height / 2; i < _imGray.Height; i++)
{
for (int j = 0; j < _imGray.Width; j++)
{
float temp = _iniImage[(i - 27) + j * 27] * 255 / 50000;
//float temp = iniImage[i - 27 + (j + 8) * 27] * iniImage[i - 27 + (j + 8) * 27] * iniImage[i - 27 + (j + 8) * 27];
_imGray.Set <float>(i, j, temp);
}
}
Cv2.Resize(_imGray, imGrayResult2, new OpenCvSharp.Size(600, 300));
}
else
{
for (int i = 0; i < 8640; i = i + 4)
{
_iniImage[i / 4] = BitConverter.ToSingle(_recvBuf, i);
}
for (int i = _imGray.Height / 2; i < _imGray.Height; i++)
{
for (int j = 0; j < _imGray.Width; j++)
{
float temp = _iniImage[(i - 27) + j * 27] * 255 / 10000;
_imGray.Set <float>(i, j, temp);
}
}
Cv2.Resize(_imGray, imGrayResult2, new OpenCvSharp.Size(600, 300));
}
Cv2.ConvertScaleAbs(imGrayResult2, imGrayResult1);
Point2f point2F = new Point2f(imGrayResult1.Width / 2, imGrayResult1.Height);
Cv2.LinearPolar(imGrayResult1, recover, point2F, 300, InterpolationFlags.WarpInverseMap);
Cv2.ApplyColorMap(recover, dst, ColormapTypes.Jet);
Cv2.ImShow("Demo", dst);
}
//使用Winform定时器
private void timer_disparityMeasure_Tick(object sender, EventArgs e)
{
if (IS_USE_BM_FLAG)
{
if (BM_IMG_FLAG)
{
//控制是否从主窗体中读取图像
tLeftImg = ShareData.LeftImg;
tRightImg = ShareData.RightImg;
if (BM_DIS_CAL_FLAG && !IS_IMG_SHOW_PAUSE)
{
validDisROI = Cv2.GetValidDisparityROI(ShareData.PixROI1, ShareData.PixROI2, bm_minDisparity, bm_numOfDisparities, bm_SADWinSize);
//转换为灰度图
Cv2.CvtColor(tLeftImg, tLeftImg_g, ColorConversionCodes.BGR2GRAY);
Cv2.CvtColor(tRightImg, tRightImg_g, ColorConversionCodes.BGR2GRAY);
//BM计算视差图
bmMatch.Compute(tLeftImg_g, tRightImg_g, bmDisImg); //计算视差图
//转换为8位灰度图
double min, max;
Cv2.MinMaxLoc(bmDisImg, out min, out max);
bmDist8U = new Mat(bmDisImg.Size(), MatType.CV_8UC1);
bmDisImg.ConvertTo(bmDist8U, MatType.CV_8UC1, 255 / (max - min), -255 * min / (max - min));
//转换为伪彩色图
bmDist24U = new Mat(bmDisImg.Size(), MatType.CV_8UC3);
Cv2.ApplyColorMap(bmDist8U, bmDist24U, ColormapTypes.Jet);
}
}
//控制图像显示
if (IS_SHOW_BM_DISPARITY_FLAG)
{
//标记显示图像类型
IMG_TYPE_TAG = 2;
Cv2.Rectangle(bmDist24U, validDisROI, new Scalar(0, 0, 255), 1);
this.pictureBoxIpl_Img.ImageIpl = bmDist24U;
}
else
{
//标记显示图像类型
IMG_TYPE_TAG = 1;
Cv2.Rectangle(tLeftImg, validDisROI, new Scalar(0, 0, 255), 1);
this.pictureBoxIpl_Img.ImageIpl = tLeftImg; //显示左视图
}
//validDisROI = new Rect();
}
if (IS_USE_SGBM_FLAG)
{
}
}
public byte[] Predador(byte[] file)
{
Mat input = ConvertToMat(file);
Mat grayInput;
if (input.Channels() == 1)
{
grayInput = input;
}
else
{
grayInput = new Mat(input.Rows, input.Cols, MatType.CV_8UC3);
Cv2.CvtColor(input, grayInput, ColorConversionCodes.BGR2GRAY);
}
var coloredImage = new Mat();
Cv2.ApplyColorMap(grayInput, coloredImage, ColormapTypes.Hsv);
return(ConvertToByte(coloredImage));
}
//更改SGBM参数后用于重新计算，刷新图像显示
private void ucBtnExt_sgbmrefresh_BtnClick(object sender, EventArgs e)
{
if (IS_USE_SGBM_FLAG)
{
/////////////重新计算视差图////////////////////////////
Mat tleftImg_gray = new Mat();
Mat trightImg_gray = new Mat();
Cv2.CvtColor(leftImg_sgbm, tleftImg_gray, ColorConversionCodes.BGR2GRAY);
Cv2.CvtColor(rightImg_sgbm, trightImg_gray, ColorConversionCodes.BGR2GRAY);
//计算视差图
double min, max;
//记录SGBM算法运行时间
Stopwatch sw = new Stopwatch();
sw.Start();
sgbmMatch.Compute(tleftImg_gray, trightImg_gray, sgbmDisImg);
sw.Stop();
TimeSpan ts = sw.Elapsed; //获取SGBM代码运行时间
Cv2.MinMaxLoc(sgbmDisImg, out min, out max);
//转换为8位图
sgbmDisImg.ConvertTo(sgbm_dis8UImg, MatType.CV_8UC1, 255 / (max - min), -255 * min / (max - min));
//转换为伪彩色图
sgbm_dis24UImg = new Mat(sgbmDisImg.Size(), MatType.CV_8UC3);
Cv2.ApplyColorMap(sgbm_dis8UImg, sgbm_dis24UImg, ColormapTypes.Jet);
//计算SGBM的有效视差区域
sgbm_validDisROI = Cv2.GetValidDisparityROI(ShareData.PixROI1, ShareData.PixROI2, sgbm_minDisparity,
sgbm_numofDisparities, sgbm_blockSize);
/////////////////重新显示/////////////////////
if (SGBM_IMG_TYPE_TAG == 1)
{
Cv2.Rectangle(leftImg_sgbm, sgbm_validDisROI, new Scalar(0, 0, 255), 1);
this.pictureBoxIpl_Img.ImageIpl = this.leftImg_sgbm;
}
else if (SGBM_IMG_TYPE_TAG == 2)
{
Cv2.Rectangle(sgbm_dis24UImg, sgbm_validDisROI, new Scalar(255, 255, 255), 1);
this.pictureBoxIpl_Img.ImageIpl = this.sgbm_dis24UImg;
}
ShareData.Log = "[msg] 刷新成功！" + "\r\n" +
"SGBM算法计算时间：" + ts.TotalMilliseconds.ToString() + "ms";
}
}
public bool oneShot(string path, int height = 0, int width = 0)
{
try
{
IGrabResult grabResult = grabStart(height, width);
using (grabResult)
{
if (grabResult.GrabSucceeded)
{
// convert image from basler IImage to OpenCV Mat
Mat img = convertIImage2Mat(grabResult);
// convert image from BayerBG to RGB
Cv2.CvtColor(img, img, ColorConversionCodes.BayerBG2GRAY);
Mat histo = new Mat();
Mat heatmap = new Mat();
Mat dst = img.Clone();
// Apply Histogram
histo = cvProcess.histogram(dst);
// Apply ColorMap
Cv2.ApplyColorMap(dst, heatmap, ColormapTypes.Rainbow);
// Save Original Image
if (saveOrigin)
{
Cv2.ImWrite(path + ".origin.jpg", img);
}
// Background map subtraction
Cv2.Subtract(dst, -5, dst);
// save images
if (saveTracked)
{
Cv2.ImWrite(path + ".jpg", dst);
}
if (saveHisto)
{
Cv2.ImWrite(path + ".histo.jpg", histo);
}
if (saveHeatmap)
{
Cv2.ImWrite(path + ".heatmap.jpg", heatmap);
}
// resize image to fit the imageBox
Cv2.Resize(dst, dst, new OpenCvSharp.Size(960, 687), 0, 0, InterpolationFlags.Linear);
Cv2.Resize(heatmap, heatmap, new OpenCvSharp.Size(256, 183), 0, 0, InterpolationFlags.Linear);
// display images
BitmapToImageSource(dst);
BitmapHistoToImageSource(histo);
BitmapHeatmapToImageSource(heatmap);
}
else
{
System.Windows.MessageBox.Show("Error: {0} {1}" + grabResult.ErrorCode, grabResult.ErrorDescription);
}
}
return(true);
}
catch (Exception exception)
{
if (camera.IsOpen)
{
camera.Close();
}
System.Windows.MessageBox.Show("Exception: {0}" + exception.Message);
return(false);
}
}
private void th_grab(int height = 0, int width = 0, int snap_wait = 500)
{
try
{
// Set the acquisition mode to free running continuous acquisition when the camera is opened.
camera.CameraOpened += Configuration.AcquireContinuous;
// Open the connection to the camera device.
camera.Open();
if (width == 0 || width > camera.Parameters[PLCamera.Width].GetMaximum())
{
camera.Parameters[PLCamera.Width].SetValue(camera.Parameters[PLCamera.Width].GetMaximum());
}
else if (width < camera.Parameters[PLCamera.Width].GetMinimum())
{
camera.Parameters[PLCamera.Width].SetValue(camera.Parameters[PLCamera.Width].GetMinimum());
}
else
{
camera.Parameters[PLCamera.Width].SetValue(width);
}
if (height == 0 || width > camera.Parameters[PLCamera.Height].GetMaximum())
{
camera.Parameters[PLCamera.Height].SetValue(camera.Parameters[PLCamera.Height].GetMaximum());
}
else if (height < camera.Parameters[PLCamera.Height].GetMinimum())
{
camera.Parameters[PLCamera.Height].SetValue(camera.Parameters[PLCamera.Height].GetMinimum());
}
else
{
camera.Parameters[PLCamera.Height].SetValue(height);
}
camera.Parameters[PLCamera.CenterX].SetValue(true);
camera.Parameters[PLCamera.CenterY].SetValue(true);
camera.StreamGrabber.Start();
if (saveTracked)
{
var expected = new OpenCvSharp.Size(1920, 1374);
string filename = "D:\\save\\" + DateTime.Now.ToString("M.dd-HH.mm.ss") + ".avi";
videoWriter.Open(filename, OpenCvSharp.FourCCValues.XVID, 14, expected, false);
}
if (saveOrigin)
{
var expected = new OpenCvSharp.Size(1920, 1374);
string filename = "D:\\save\\" + DateTime.Now.ToString("M.dd-HH.mm.ss") + ".origin.avi";
originWriter.Open(filename, OpenCvSharp.FourCCValues.XVID, 14, expected, false);
}
if (saveHisto)
{
var expected = new OpenCvSharp.Size(256, 300);
string filename = "D:\\save\\" + DateTime.Now.ToString("M.dd-HH.mm.ss") + ".histo.avi";
histoWriter.Open(filename, OpenCvSharp.FourCCValues.XVID, 14, expected, false);
}
if (saveHeatmap)
{
var expected = new OpenCvSharp.Size(1920, 1374);
string filename = "D:\\save\\" + DateTime.Now.ToString("M.dd-HH.mm.ss") + ".heatmap.avi";
heatmapWriter.Open(filename, OpenCvSharp.FourCCValues.XVID, 14, expected, true);
}
while (grabbing)
{
camera.Parameters[PLCamera.Gain].SetValue(valueGain);
camera.Parameters[PLCamera.ExposureTime].SetValue(valueExpTime);
IGrabResult grabResult = camera.StreamGrabber.RetrieveResult(5000, TimeoutHandling.ThrowException);
using (grabResult)
{
if (grabResult.GrabSucceeded)
{
// convert image from basler IImage to OpenCV Mat
Mat img = convertIImage2Mat(grabResult);
// convert image from BayerBG to RGB
Cv2.CvtColor(img, img, ColorConversionCodes.BayerBG2GRAY);
Cv2.Resize(img, img, new OpenCvSharp.Size(1920, 1374), 0, 0, InterpolationFlags.Linear);
Mat histo = new Mat();
Mat heatmap = new Mat();
Mat dst = img.Clone();
// Apply Histogram
histo = cvProcess.histogram(dst);
// Apply ColorMap
Cv2.ApplyColorMap(dst, heatmap, ColormapTypes.Rainbow);
// Apply Background map subtraction
Cv2.Subtract(dst, -5, dst);
if (saveOrigin)
{
originWriter.Write(img);
}
// Create Tracked Image
dst = Iso11146(img, dst);
Cv2.Resize(dst, dst, new OpenCvSharp.Size(1920, 1374), 0, 0, InterpolationFlags.Linear);
if (saveTracked)
{
videoWriter.Write(dst);
}
if (saveHisto)
{
histoWriter.Write(histo);
}
if (saveHeatmap)
{
heatmapWriter.Write(heatmap);
}
// resize image to fit the imageBox
Cv2.Resize(dst, dst, new OpenCvSharp.Size(960, 687), 0, 0, InterpolationFlags.Linear);
Cv2.Resize(heatmap, heatmap, new OpenCvSharp.Size(256, 183), 0, 0, InterpolationFlags.Linear);
Cv2.Rectangle(dst, new OpenCvSharp.Rect(axis_x, axis_y, axis_scale, axis_scale), Scalar.White, 1);
// display images
BitmapToImageSource(dst);
BitmapHistoToImageSource(histo);
BitmapHeatmapToImageSource(heatmap);
}
else
{
System.Windows.MessageBox.Show("Error: {0} {1}" + grabResult.ErrorCode, grabResult.ErrorDescription);
}
}
count++;
if (count > 500)
{
count = 0;
tracking = false;
}
Thread.Sleep(snap_wait);
}
videoWriter.Release();
originWriter.Release();
histoWriter.Release();
heatmapWriter.Release();
camera.StreamGrabber.Stop();
camera.Close();
}
catch (Exception exception)
{
if (camera.IsOpen)
{
camera.Close();
}
System.Windows.MessageBox.Show("Exception: {0}" + exception.Message);
}
}
static void Main(string[] args)
{
//Thread capturaVideoThread = new Thread(new ThreadStart(Program.CapturarVideo));
//capturaVideoThread.Start();
VideoCapture captura = new VideoCapture("D:\\Dictuc\\out1.avi");
VideoWriter salida = new VideoWriter("D:\\Dictuc\\outSegmentado.avi", FourCC.XVID, 10.0, new Size(captura.FrameWidth, captura.FrameHeight), true);
Mat imagenProcesada = new Mat();
int numImg = 0;
while (true)
{
//captura.Read(imagen);
imagen = Cv2.ImRead("D:\\uvas2.jpg");
mutex.WaitOne();
imagen.CopyTo(imagenProcesada);
mutex.ReleaseMutex();
Mat imagenRuidoFiltrado = FiltradoRuido(imagenProcesada);
Mat imagenGrisContraste = EscalaGrisesEqualizada(imagenRuidoFiltrado);
Mat imagenGrisFrecAltasProc = FrecuenciasAltasPotenciadasContraste(imagenGrisContraste);
EdgeDetector edgeDetector = new EdgeDetector()
{
Threshold = (byte)18,
SparseDistance = 3,
WeightPreviousPoint = (float)2.0,
WeightCurrentPoint = (float)1.0,
WeightAfterPoint = (float)2.0,
};
EdgeDetector edgeDetector2 = new EdgeDetector()
{
Threshold = (byte)20,
SparseDistance = 5,
WeightPreviousPoint = (float)0.5,
WeightCurrentPoint = (float)1.0,
WeightAfterPoint = (float)0.5,
};
Mat imagenBordes = edgeDetector.EdgeImage(imagenGrisContraste);
Mat imagenBordes2 = edgeDetector2.EdgeImage(imagenGrisContraste);
Mat imagenBinaria, imagenAberturaRelleno;
CalculoMatrizBinariaYRelleno(imagenBordes2, out imagenBinaria, out imagenAberturaRelleno);
Mat mascaraInv = 255 - imagenAberturaRelleno;
Mat DistSureFg = new Mat();
Mat AreasSureFg = new Mat();
Mat Unknown = new Mat();
AreasSureFg += 1;
Cv2.DistanceTransform(imagenAberturaRelleno, DistSureFg, DistanceTypes.L1, DistanceMaskSize.Mask5);
int numAreas = Cv2.ConnectedComponents(imagenAberturaRelleno, AreasSureFg, PixelConnectivity.Connectivity8);
float[,] distValues = new float[DistSureFg.Rows, DistSureFg.Cols];
for (int i = 0; i < DistSureFg.Rows; i++)
{
for (int j = 0; j < DistSureFg.Cols; j++)
{
distValues[i, j] = DistSureFg.At <float>(i, j);
}
}
Segment[] segments = new Segment[numAreas];
for (int i = 0; i < AreasSureFg.Rows; i++)
{
for (int j = 0; j < AreasSureFg.Cols; j++)
{
int m = AreasSureFg.At <Int32>(i, j);
byte pixelSurrounding = 0;
float distance = (float)0;
//if (i >= 1)
//{
// distance = distValues[i - 1, j];
// if (distance == 2)
// {
// pixelSurrounding |= Segment.PIXEL_SURROUNDED_LEFT;
// }
//}
//if (i < AreasSureFg.Rows - 1)
//{
// distance = distValues[i + 1, j];
// if (distance == 2)
// {
// pixelSurrounding |= Segment.PIXEL_SURROUNDED_RIGHT;
// }
//}
//if (j >= 1)
//{
// distance = distValues[i, j - 1];
// if (distance == 2)
// {
// pixelSurrounding |= Segment.PIXEL_SURROUNDED_DOWN;
// }
//}
//if (j < AreasSureFg.Cols - 1)
//{
// distance = distValues[i, j + 1];
// if (distance == 2)
// {
// pixelSurrounding |= Segment.PIXEL_SURROUNDED_UP;
// }
//}
SegmentPixelData newPixel = new SegmentPixelData()
{
Distance = distValues[i, j],
CoordsXY = new int[] { i, j },
Concave = 0,
Indexes = new int[] { -1, -1 },
PixelsSurrounding = pixelSurrounding,
SubsegmentLabel = 0,
};
if (segments[m] == null)
{
segments[m] = new Segment()
{
SegmentId = m,
PixelData = new List <SegmentPixelData>(),
};
}
else
{
segments[m].MaxDistance = (segments[m].MaxDistance > newPixel.Distance) ? (int)segments[m].MaxDistance : (int)newPixel.Distance;
segments[m].PixelData.Add(newPixel);
}
}
}
Mat Centroides = new Mat();
imagenAberturaRelleno.CopyTo(Centroides);
var indexadorCentroides = Centroides.GetGenericIndexer <byte>();
var indexadorFiguras = AreasSureFg.GetGenericIndexer <Int32>();
foreach (var s in segments.Where(s => s.Circularity <= 0.9))
{
int distancia = 0;
if (s.Circularity > 0.7)
{
distancia = 5;
}
else if (s.Circularity > 0.5)
{
distancia = 5;
}
else if (s.Circularity > 0.25)
{
distancia = 6;
}
else
{
distancia = 6;
}
distancia = (distancia < s.MaxDistance) ? distancia : s.MaxDistance - 1;
foreach (var p in s.PixelData.Where(p => p.Distance <= distancia))
{
if (imagenAberturaRelleno.At <byte>(p.CoordsXY[0], p.CoordsXY[1]) != (byte)0)
{
indexadorCentroides[p.CoordsXY[0], p.CoordsXY[1]] = 0;
}
}
}
Cv2.Subtract(imagenAberturaRelleno + 255, Centroides, Unknown);
#region segmentStuff
//List<int> indexConcavos = segments.Where(s => s.Circularity > 1).Select(s => s.SegmentId).ToList();
//foreach (var s in segments.Where(s => s.Circularity < 1.1 && s.Circularity > 0.9))
//{
// foreach (var p in s.PixelData/*.Where(p => p.Distance == 1)*/)
// {
// if (imagenAberturaRelleno.At<byte>(p.CoordsXY[0], p.CoordsXY[1]) != (byte)0)
// {
// indexadorFiguras[p.CoordsXY[0], p.CoordsXY[1]] = 255;
// }
// }
//}
//foreach (var s in segments.Where(s => s.Circularity >= 1.1))
//{
// foreach (var p in s.PixelData/*.Where(p => p.Distance == 1)*/)
// {
// if (imagenAberturaRelleno.At<byte>(p.CoordsXY[0], p.CoordsXY[1]) != (byte)0)
// {
// indexadorFiguras[p.CoordsXY[0], p.CoordsXY[1]] = 255;
// }
// }
//}
//foreach (var s in segments)
//{
// s.SetPixelConcavity();
// s.Segmentation();
// foreach (var p in s.PixelData.Where(p => p.Distance == 1))
// {
// if (p.Concave == 1)
// {
// indexadorFiguras[p.CoordsXY[0], p.CoordsXY[1]] = 255;
// }
// if (p.Concave == -1)
// {
// indexadorFiguras[p.CoordsXY[0], p.CoordsXY[1]] = 255;
// }
// }
//}
//foreach (var s in segments)
//{
// //s.SetPixelConcavity();
// //s.Segmentation();
// foreach (var p in s.PixelData.Where(p => p.Distance == 2))
// {
// indexadorFiguras[p.CoordsXY[0], p.CoordsXY[1]] = 230;
// }
//}
//imagenAberturaRelleno.CopyTo(SureFg);
#endregion
Mat colormap = new Mat();
Mat Marcadores = new Mat();
Cv2.ConnectedComponents(Centroides, Marcadores);
Marcadores = Marcadores + 1;
var indexador2 = Marcadores.GetGenericIndexer <Int32>();
for (int i = 0; i < Unknown.Rows; i++)
{
for (int j = 0; j < Unknown.Cols; j++)
{
if (Unknown.At <byte>(i, j) == 255)
{
indexador2[i, j] = 0;
}
}
}
Marcadores.CopyTo(colormap);
colormap.ConvertTo(colormap, MatType.CV_8UC3);
Cv2.ApplyColorMap(colormap, colormap, ColormapTypes.Rainbow);
Cv2.ImWrite("D:\\Dictuc\\marcadores.png", Marcadores);
//Mat img1 = new Mat();
//imagen.CopyTo(img1);
Mat DistColor = new Mat();
//imagenGrisContraste = 255 - imagenGrisContraste;
Cv2.CvtColor(imagenAberturaRelleno, DistColor, ColorConversionCodes.GRAY2BGR);
DistColor.ConvertTo(DistColor, MatType.CV_8U);
Cv2.Watershed(DistColor, Marcadores);
Cv2.ImWrite("D:\\Dictuc\\watersheedIn.png", DistColor);
var indexador4 = imagen.GetGenericIndexer <Vec3i>();
//for (int i = 0; i < imagen.Rows; i++)
//{
// for (int j = 0; j < imagen.Cols; j++)
// {
// //if (Centroides.At<byte>(i, j) > 0)
// // indexador4[i, j] = new Vec3i(0, 0, 255);
// if (Marcadores.At<Int32>(i, j) == -1)
// indexador4[i, j] = new Vec3i(255, 20, 20);
// }
//}
for (int i = 0; i < imagen.Rows; i++)
{
for (int j = 0; j < imagen.Cols; j++)
{
//if (Centroides.At<byte>(i, j) > 0)
// indexador4[i, j] = new Vec3i(0, 0, 255);
if (imagenBordes.At <char>(i, j) > 0)
{
indexador4[i, j] = new Vec3i(255, 20, 20);
}
}
}
Mat seg = new Mat();
Marcadores.CopyTo(seg);
var indexador5 = seg.GetGenericIndexer <int>();
for (int i = 0; i < Marcadores.Rows; i++)
{
for (int j = 0; j < Marcadores.Cols; j++)
{
indexador5[i, j] = (Math.Abs(indexador5[i, j]) > 1) ? 255 : 0;
}
}
Mat kE1 = Cv2.GetStructuringElement(MorphShapes.Rect, new Size(1, 1));
Cv2.Erode(seg, seg, kE1, iterations: 3);
int thrs1 = 1500;
int thrs2 = 1800;
Mat edge1 = new Mat();
seg.ConvertTo(seg, MatType.CV_8U);
Cv2.Canny(seg, edge1, thrs1, thrs2, apertureSize: 5);
SimpleBlobDetector.Params params1 = new SimpleBlobDetector.Params()
{
MinThreshold = 0,
MaxThreshold = 255,
FilterByArea = true,
MinArea = 15,
FilterByCircularity = false,
MinCircularity = (float)0.01,
FilterByConvexity = false,
MinConvexity = (float)0.1,
FilterByInertia = false,
MinInertiaRatio = (float)0.01,
};
SimpleBlobDetector detectorBlobs = SimpleBlobDetector.Create(params1);
KeyPoint[] segmentosBlob = detectorBlobs.Detect(edge1);
Mat segmentosBlobMat = new Mat(1, segmentosBlob.Count(), MatType.CV_32FC1);
var indexador6 = segmentosBlobMat.GetGenericIndexer <float>();
for (int i = 0; i < segmentosBlob.Count(); i++)
{
indexador6[0, i] = segmentosBlob[i].Size;
}
Mat hist = new Mat();
Rangef[] ranges = { new Rangef(0, (float)segmentosBlob.Max(x => x.Size)) };
Cv2.CalcHist(new Mat[] { segmentosBlobMat }, new int[] { 0 }, null, hist, 1, new int[] { 100 }, ranges, uniform: true, accumulate: true);
float[] histAcumulado = new float[hist.Rows];
float[] histAcumuladoPorcentaje = new float[11];
histAcumulado[0] = hist.At <float>(0, 0);
for (int i = 1; i < hist.Rows; i++)
{
histAcumulado[i] = hist.At <float>(i, 0) + histAcumulado[i - 1];
}
int k = 1;
for (int i = 1; i < histAcumuladoPorcentaje.Count(); i++)
{
for (; k < hist.Rows; k++)
{
float porcentajeActual = histAcumulado[k] / segmentosBlob.Count() * 100;
float porcentajeAnterior = histAcumulado[k - 1] / segmentosBlob.Count() * 100;
float porcentajeRequerido = (float)((i < 10) ? i * 10 : 99.3);
if (porcentajeRequerido <= porcentajeActual)
{
float tamañoPorcentajeActual = (float)(k * (float)segmentosBlob.Max(x => x.Size) / 100.0);
float tamañoPorcentajeAnterior = (float)((k - 1) * (float)segmentosBlob.Max(x => x.Size) / 100.0);
float tasaVariacionTamañoPorcentaje = (tamañoPorcentajeActual - tamañoPorcentajeAnterior) / (porcentajeActual - porcentajeAnterior);
histAcumuladoPorcentaje[i] = tamañoPorcentajeAnterior + tasaVariacionTamañoPorcentaje * (i * 10 - porcentajeAnterior);
break;
}
}
}
for (int i = 0; i < histAcumuladoPorcentaje.Count(); i++)
{
Console.Write(histAcumuladoPorcentaje[i] + ",");
}
Console.WriteLine("");
// data1 = [];
// for i in range(0, len(keypoints1)):
// data1.append(keypoints1[i].size * coefTamano)
// #tamano.write(str(i)+'\t'+str(keypoints1[i].size*2*0.3)+'\n')
// cv2.line(im_with_keypoints1, (int(float(keypoints1[i].pt[0] - keypoints1[i].size)), int(float(keypoints1[i].pt[1]))), (int(float(keypoints1[i].pt[0] + keypoints1[i].size)), int(float(keypoints1[i].pt[1]))), (255, 0, 0), 1)
// cv2.line(im_with_keypoints1, (int(float(keypoints1[i].pt[0])), int(float(keypoints1[i].pt[1] - keypoints1[i].size))), (int(float(keypoints1[i].pt[0])), int(float(keypoints1[i].pt[1] + keypoints1[i].size))), (255, 0, 0), 1)
//# print(data1)
//n1, bins1, patches1 = hist(data1, 200,[0, max(data1)], normed = 100, cumulative = True, bottom = True, histtype = 'stepfilled', align = 'mid', orientation = 'vertical', rwidth = 1, log = False, color = "r")
// tamano = open(temp + "instancia_" + instancia + ".txt", "w")
// x = np.array(bins1)
// y = np.append([0], n1)
// xnew = [x[1], x[21], x[36], x[45], x[53], x[60], x[69], x[78], x[88], x[97], x[200]]
//ynew = [y[1], y[21], y[36], y[45], y[53], y[60], y[69], y[78], y[88], y[97], y[200]]
//tamano.write('INSERT INTO [dbo].[Granulometria](Cod_Instancia,Fecha,P_10,P_20,P_30,P_40,P_50,P_60,P_70,P_80,P_90,P_100, Filename) values (')
//tamano.write(instancia + ",CONVERT(datetime, '" + sys.argv[1][0:4] + "-" + sys.argv[1][4:6] + "-" + sys.argv[1][6:8] + ' ' + sys.argv[1][9:11] + ':' + sys.argv[1][11:13] + ':' + sys.argv[1][13:15] + "', 120)")
//for j in range(1, len(xnew)):
// #tamano.write (str(j)+'\t'+str(round(xnew[j],1))+'\t'+str(round(ynew[j]*100,2))+'\n')
// tamano.write(',' + str(round(xnew[j], 1)))
//tamano.write(",'" + sys.argv[1] + " - Resultado.jpg'")
//tamano.write(')')
//CvXImgProc.Thinning(mascaraInv, mascaraInv, ThinningTypes.ZHANGSUEN);
Mat imWithKeypoints1 = new Mat();
Cv2.DrawKeypoints(imagen, segmentosBlob, imWithKeypoints1, new Scalar(0, 0, 255), DrawMatchesFlags.DrawRichKeypoints);
var dataTamaños = segmentosBlob.Select(s => s.Size).ToArray();
Cv2.ImWrite("D:\\Dictuc\\output0" + numImg + ".png", imagen);
Cv2.ImWrite("D:\\Dictuc\\output1" + numImg++ + ".png", imWithKeypoints1);
Cv2.ImShow("Segmentado", imagen);
Cv2.ImShow("GrisContraste", imagenGrisContraste);
Cv2.ImShow("bordes90", imagenBordes);
Cv2.ImShow("bordes50", imagenBordes2);
salida.Write(imagen);
//System.Threading.Thread.Sleep(10);
Cv2.WaitKey(10);
imagenRuidoFiltrado.Release();
imagenGrisContraste.Release();
imagenGrisFrecAltasProc.Release();
imagenBordes.Release();
imagenBinaria.Release();
imagenAberturaRelleno.Release();
}
}
