public Rectangle[] Detect(Mat mat, byte[] buffer)
{
var frameWidth = mat.Width;
var frameHeight = mat.Height;
//using var blob = CvDnn.BlobFromImage(mat, 1.0, new OpenCvSharp.Size(300, 300), new OpenCvSharp.Scalar(104, 117, 123), false, false);
using var blob = CvDnn.BlobFromImage(mat, 1.0 / Mean, new OpenCvSharp.Size(300, 300), new OpenCvSharp.Scalar(Mean), true, false);
Net.SetInput(blob);
using var detections = Net.Forward();
using var detectionMat = new Mat(detections.Size(2), detections.Size(3), MatType.CV_32F, detections.Ptr(0));
var bestConfidence = 0.0;
Rectangle?candidate = null;
for (var i = 0; i < detectionMat.Rows; i++)
{
var confidence = detectionMat.At <float>(i, 2);
if ((confidence > 0.5) && (confidence > bestConfidence))
{
var left = (int)(detectionMat.At <float>(i, 3) * frameWidth);
var top = (int)(detectionMat.At <float>(i, 4) * frameHeight);
var right = (int)(detectionMat.At <float>(i, 5) * frameWidth);
var bottom = (int)(detectionMat.At <float>(i, 6) * frameHeight);
candidate = new Rectangle(left, top, right - left, bottom - top);
}
}
if (candidate != null)
{
return(new Rectangle[] { candidate.Value });
}
else
{
return(Array.Empty <Rectangle>());
}
}
/// <summary>
/// Find the divided index for a histogram, used in the dynamic threshiolding algorithms
/// </summary>
/// <param name="histogram">input histogram, of type OpenCvSharp.Mat</param>
/// <param name="histCalculation">histogram calculation direction, of type Entities.Enums.HistogramCalculation</param>
/// <param name="maxHistValue">maximum value in histogram, of type double</param>
/// <param name="maxHistIndex">index of maximum value in histogram, of type int</param>
/// <returns>division value, of type int</returns>
public int FindHistogramDividedIndex(Mat histogram, Enums.HistogramCalculation histCalculation, double maxHistValue, int maxHistIndex)
{
if (histCalculation == Enums.HistogramCalculation.Normal)
{
// Divided threshold value will me maximum threshold value divided by predefined value (THRESH_DIVIDE_VAL)
for (int dividedValIdx = maxHistIndex; dividedValIdx < TASettings.HIST_BINS_COUNT; dividedValIdx++)
{
if (histogram.At <float>(dividedValIdx) <= ((float)maxHistValue / TASettings.THRESH_DIVIDE_VAL))
{
return(dividedValIdx);
}
}
// Return maximum histogram value if no other value was found
return((int)maxHistValue);
}
else
{
for (int dividedValIdx = maxHistIndex; dividedValIdx >= 0; dividedValIdx--)
{
if (histogram.At <float>(dividedValIdx) <= ((float)maxHistValue / TASettings.THRESH_DIVIDE_VAL))
{
return(dividedValIdx);
}
}
// Return maximum histogram value if no other value was found
return((int)maxHistValue);
}
}
/// <summary>
/// center image
/// </summary>
/// <param name="img"></param>
static void MoveToCenter(Mat img)
{
int left = img.Rows - 1, right = 0, top = img.Rows - 1, buttom = 0;
for (var i = 0; i < img.Rows; i++)
{
for (var j = 0; j < img.Cols; j++)
{
if (img.At <byte>(i, j) != 0)
{
left = Math.Min(left, j);
right = Math.Max(right, j);
top = Math.Min(top, i);
buttom = Math.Max(buttom, i);
}
}
}
int dx = (right + left - img.Rows) / 2, dy = (buttom + top - img.Cols) / 2;
Mat dst = Mat.Zeros(img.Rows, img.Cols, img.Type());
for (var i = 0; i < img.Rows; i++)
{
for (var j = 0; j < img.Cols; j++)
{
if (i + dx < img.Rows && i + dx >= 0 && j + dy < img.Cols && j + dy >= 0)
{
dst.Set(i + dx, j + dy, img.At <byte>(i, j));
}
}
}
dst.CopyTo(img);
}
private Mat mergeImageLayer(Mat buttomLayer, Mat upperLayer)
{
Mat result = new Mat(350, 450, MatType.CV_8UC3, Scalar.LightGray);
Cv2.CopyTo(upperLayer, result);
for (int row = 0; row < buttomLayer.Rows; row++)
{
for (int col = 0; col < buttomLayer.Cols; col++)
{
Debug.Log(row + ": " + col);
int blue = buttomLayer.At <Vec3b>(row, col).Item0;
int green = buttomLayer.At <Vec3b>(row, col).Item1;
int red = buttomLayer.At <Vec3b>(row, col).Item2; // RGB value
if (blue == 255 && green == 255 && red == 255) // white
{
int blue2 = upperLayer.At <Vec3b>(row, col).Item0;
int green2 = upperLayer.At <Vec3b>(row, col).Item1;
int red2 = upperLayer.At <Vec3b>(row, col).Item2;
if (blue2 == 211 && green2 == 211 && red2 == 211) // light-grey
{
result.Set <Vec3b>(row, col, new Vec3b(255, 255, 255));
}
}
else if (blue == 144 && green == 238 && red == 144) // light-green
{
result.Set <Vec3b>(row, col, new Vec3b(144, 238, 144));
}
}
}
return(result);
}
private Mat mergeImageLayer(Mat buttomLayer, Mat upperLayer)
{
//Cv2.ImWrite(Application.persistentDataPath + "/buttom.png", buttomLayer);
Cv2.CopyTo(upperLayer, result);
Parallel.For(0, buttomLayer.Rows, row =>
{
for (int col = 0; col < buttomLayer.Cols; col++)
{
int blue = buttomLayer.At <Vec3b>(row, col).Item0;
int green = buttomLayer.At <Vec3b>(row, col).Item1;
int red = buttomLayer.At <Vec3b>(row, col).Item2; // RGB value
if (blue == 255 && green == 255 && red == 255) // white
{
int blue2 = upperLayer.At <Vec3b>(row, col).Item0;
int green2 = upperLayer.At <Vec3b>(row, col).Item1;
int red2 = upperLayer.At <Vec3b>(row, col).Item2;
if (blue2 == 211 && green2 == 211 && red2 == 211) // light-grey
{
result.Set <Vec3b>(row, col, new Vec3b(255, 255, 255));
}
}
else if (blue == 144 && green == 238 && red == 144) // light-green
{
int blue2 = upperLayer.At <Vec3b>(row, col).Item0;
int green2 = upperLayer.At <Vec3b>(row, col).Item1;
int red2 = upperLayer.At <Vec3b>(row, col).Item2;
result.Set <Vec3b>(row, col, new Vec3b(144, 238, 144));
}
}
});
return(result);
}
private void Divide(Mat img, List <Point> points, out Mat left, out Mat right)
{
var pointsL = new List <Point>(new Point[] { new Point(0, img.Height) });
var pointsR = new List <Point>(new Point[] { new Point(img.Width, img.Height) });
foreach (var p in points)
{
if (p.X < img.Width / 2 && p.Y > img.Height / 4)
{
pointsL.Add(p);
}
else if (p.X > img.Width / 2 && p.Y > img.Height / 4)
{
pointsR.Add(p);
}
}
left = new Mat(pointsL.Count, 2, MatType.CV_32F);
right = new Mat(pointsR.Count, 2, MatType.CV_32F);
for (int i = 0; i < pointsL.Count; i++)
{
left.At <float>(i, 0) = pointsL[i].X;
left.At <float>(i, 1) = pointsL[i].Y;
//Cv2.Circle(img, pointsL[i], 3, green, 4);
}
for (int i = 0; i < pointsR.Count; i++)
{
right.At <float>(i, 0) = pointsR[i].X;
right.At <float>(i, 1) = pointsR[i].Y;
//Cv2.Circle(img, pointsR[i], 3, green, 4);
}
}
//去边框
public static Mat ClearBorder(Mat threshold)
{
int rows = threshold.Rows;
int cols = threshold.Cols;
int noJumpCountThresh = (int)(0.15f * cols);
Mat border = new Mat(rows, 1, MatType.CV_8UC1);
for (int rowIndex = 0; rowIndex < rows; rowIndex++)
{
int noJumpCount = 0;
byte isBorder = 0;
for (int colIndex = 1; colIndex < cols - 7; colIndex++)
{
if (threshold.At <byte>(rowIndex, colIndex) == threshold.At <byte>(rowIndex, colIndex + 1))
{
noJumpCount++;
}
if (noJumpCount > noJumpCountThresh)
{
noJumpCount = 0;
isBorder = 1;
break;
}
}
border.Set <byte>(rowIndex, 0, isBorder);
}
int minTop = (int)(0.1f * rows);
int maxTop = (int)(0.9f * rows);
Mat result = threshold.Clone();
for (int rowIndex = 0; rowIndex < minTop; rowIndex++)
{
if (border.At <byte>(rowIndex, 0) == 1)
{
for (int colIndex = 0; colIndex < cols; colIndex++)
{
result.Set <byte>(rowIndex, colIndex, 0);
}
}
}
for (int rowIndex = rows - 1; rowIndex > maxTop; rowIndex--)
{
if (border.At <byte>(rowIndex, 0) == 1)
{
for (int colIndex = 0; colIndex < cols; colIndex++)
{
result.Set <byte>(rowIndex, colIndex, 0);
}
}
}
return(result);
}
public override void RunTest()
{
// Read sample image
using var frame = Cv2.ImRead(image);
int frameHeight = frame.Rows;
int frameWidth = frame.Cols;
using var faceNet = CvDnn.ReadNetFromCaffe(configFile, faceModel);
using var blob = CvDnn.BlobFromImage(frame, 1.0, new Size(300, 300), new Scalar(104, 117, 123), false, false);
faceNet.SetInput(blob, "data");
using var detection = faceNet.Forward("detection_out");
using var detectionMat = new Mat(detection.Size(2), detection.Size(3), MatType.CV_32F,
detection.Ptr(0));
for (int i = 0; i < detectionMat.Rows; i++)
{
float confidence = detectionMat.At <float>(i, 2);
if (confidence > 0.7)
{
int x1 = (int)(detectionMat.At <float>(i, 3) * frameWidth);
int y1 = (int)(detectionMat.At <float>(i, 4) * frameHeight);
int x2 = (int)(detectionMat.At <float>(i, 5) * frameWidth);
int y2 = (int)(detectionMat.At <float>(i, 6) * frameHeight);
Cv2.Rectangle(frame, new Point(x1, y1), new Point(x2, y2), new Scalar(0, 255, 0), 2, LineTypes.Link4);
}
}
Window.ShowImages(frame);
}
private static Point3f MatrixToEulerAngles(Mat mHomography)
{
float num = (float)Math.Sqrt(mHomography.At <double>(0, 0) * mHomography.At <double>(0, 0) + mHomography.At <double>(1, 0) * mHomography.At <double>(1, 0));
float num2;
float num3;
float num4;
if (!((double)num < 1E-06))
{
num2 = (float)Math.Atan2(mHomography.At <double>(2, 1), mHomography.At <double>(2, 2));
num3 = (float)Math.Atan2(0.0 - mHomography.At <double>(2, 0), num);
num4 = (float)Math.Atan2(mHomography.At <double>(1, 0), mHomography.At <double>(0, 0));
}
else
{
num2 = (float)Math.Atan2(0.0 - mHomography.At <double>(1, 2), mHomography.At <double>(1, 1));
num3 = (float)Math.Atan2(0.0 - mHomography.At <double>(2, 0), num);
num4 = 0f;
}
Point3f result = default(Point3f);
result.X = (float)((double)num2 * (180.0 / Math.PI));
result.Y = (float)((double)num3 * (180.0 / Math.PI));
result.Z = (float)((double)num4 * (180.0 / Math.PI));
return(result);
}
void DetectLaser()
{
Mat[] bgrChannels = new Mat[3];
Cv2.Split(videoSourceImage, out bgrChannels);
Mat rt = new Mat();
Cv2.Threshold(bgrChannels[2], rt, 240.0, 255.0, ThresholdTypes.Binary);
Cv2.GaussianBlur(rt, rt, new Size(5, 5), 1);
rt *= 255;
Mat labels = new Mat();
Mat stats = new Mat();
Mat centroids = new Mat();
Cv2.ConnectedComponentsWithStats(rt, labels, stats, centroids);
if (centroids.Rows > 1)
{
Cv2.Circle(videoSourceImage, (int)centroids.At <double>(1, 0), (int)centroids.At <double>(1, 1), 20, new Scalar(255, 255, 255));
Cv2.Circle(cannyImage, (int)centroids.At <double>(1, 0), (int)centroids.At <double>(1, 1), 20, new Scalar(255, 255, 255));
}
Cv2.Flip(videoSourceImage, videoSourceImage, FlipMode.XY);
Cv2.Flip(cannyImage, cannyImage, FlipMode.XY);
Cv2.ImShow("SuperLaserDetect", videoSourceImage);
Cv2.ImShow("Processed WebCam", cannyImage);
}
public void AddPRNUNoise()
{
var precision = 10000;
for (var y = 0; y < 600; y++)
{
for (var x = 0; x < 800; x++)
{
var value = Image.At <ushort>(y, x);
if (value > 0)
{
var performance = 1.0 + ((_rand.Next(0, precision) / ((double)precision)) - 0.5) / 100;
var noised = value * performance;
if (noised > ushort.MaxValue)
{
noised = ushort.MaxValue;
}
else if (noised < 0)
{
noised = 0;
}
Image.Set(y, x, (ushort)noised);
}
}
}
}
public static Mat ToSDFRender(Mat sdf)
{
var drawing = new Mat(sdf.Rows, sdf.Cols, MatType.CV_8UC3);
double minVal, maxVal;
sdf.MinMaxIdx(out minVal, out maxVal);
for (int j = 0; j < sdf.Rows; j++)
{
for (int i = 0; i < sdf.Cols; i++)
{
if (sdf.At <float>(j, i) < 0)
{
drawing.Set(j, i, new Vec3b((byte)(255 - (int)Math.Abs((sdf.At <float>(j, i)) * 255 / minVal)), 0, 0));
}
else if (sdf.At <float>(j, i) > 0)
{
drawing.Set(j, i, new Vec3b(0, 0, (byte)(255 - (int)sdf.At <float>(j, i) * 255 / maxVal)));
}
else
{
drawing.Set(j, i, new Vec3b(255, 255, 255));
}
}
}
return(drawing);
}
////// <summary>
/// Combines a 3x3 matrix with a 1x3 translation vector into a 4x4 transformation matrix
/// </summary>
/// <param name="matrix">3x3 matrix</param>
/// <param name="translationVector">1x3 translation vector</param>
/// <param name="specials">Set of values to be appended to the last column of the resulting matrix</param>
/// <returns>4x4 transformation matrix in the correct order for vvvv</returns>
private static Matrix ToTransformationMatrix(Mat matrix, Mat translationVector, float[] specials)
{
Matrix result = Matrix.Identity;
if (matrix != null && translationVector != null)
{
if ((matrix.Width == 3 && matrix.Height == 3) &&
(translationVector.Width == 1 && translationVector.Height == 3))
{
Mat rm = matrix.Clone();
Mat tv = translationVector.Clone();
rm.ConvertTo(rm, OpenCvSharp.MatType.CV_32FC1);
tv.ConvertTo(tv, OpenCvSharp.MatType.CV_32FC1);
result = new Matrix(
rm.At <float>(0, 0),
rm.At <float>(1, 0),
rm.At <float>(2, 0),
specials[0],
rm.At <float>(0, 1),
rm.At <float>(1, 1),
rm.At <float>(2, 1),
specials[1],
rm.At <float>(0, 2),
rm.At <float>(1, 2),
rm.At <float>(2, 2),
specials[2],
tv.At <float>(0),
tv.At <float>(1),
tv.At <float>(2),
specials[3]);
}
}
return(result);
}
public Angles GetAnglesAndPoints(Mat <Point2d> points, int width, int height)
{
using (var objPtsMat = InputArray.Create(Model_points, MatType.CV_32FC3)) //new Mat(objPts.Count, 1, MatType.CV_32FC3, objPts))
using (var imgPtsMat = InputArray.Create(points, MatType.CV_32FC2)) //new Mat(imgPts.Length, 1, MatType.CV_32FC2, imgPts))
using (var cameraMatrixMat = Mat.Eye(3, 3, MatType.CV_64FC1))
using (var distMat = Mat.Zeros(5, 0, MatType.CV_64FC1))
using (var rvecMat = new Mat())
using (var tvecMat = new Mat())
{
Cv2.SolvePnP(objPtsMat, imgPtsMat, cameraMatrixMat, distMat, rvecMat, tvecMat);
using (Mat resultPoints = new Mat())
{
Cv2.ProjectPoints(objPtsMat, rvecMat, tvecMat, cameraMatrixMat, distMat, resultPoints);
}
// 根据旋转矩阵求解坐标旋转角
double theta_x = Math.Atan2((float)rvecMat.At <double>(2, 1), (float)rvecMat.At <double>(2, 2));
double theta_y = Math.Atan2((float)-rvecMat.At <double>(2, 0),
Math.Sqrt((float)((rvecMat.At <double>(2, 1) * rvecMat.At <double>(2, 1)) + ((float)rvecMat.At <double>(2, 2) * rvecMat.At <double>(2, 2)))));
double theta_z = Math.Atan2((float)rvecMat.At <double>(1, 0), (float)rvecMat.At <double>(0, 0));
// 将弧度转为角度
Angles angles = new Angles();
angles.Roll = theta_x * (180 / Math.PI);
angles.Pitch = theta_y * (180 / Math.PI);
angles.Yaw = theta_z * (180 / Math.PI);
// 将映射的点的坐标保存下来
// outarray类型的resultpoints如何转存到list中？
return(angles);
}
}
/// <summary>
/// 輝度値をステータスバーへ表示
/// </summary>
/// <param name="mat"></param>
/// <param name="X">画像のX座標</param>
/// <param name="Y">画像のY座標</param>
private void DrawBrightValue(Mat mat, int X, int Y)
{
// 画像データが無い場合は何もしない
if (mat == null)
{
return;
}
if (mat.Data == IntPtr.Zero)
{
return;
}
string lblText;
lblText = "(" + X.ToString() + ", " + Y.ToString() + ") = ";
if (mat.Channels() == 1)
{
// モノクロの場合
lblText += mat.At <byte>(Y, X).ToString();
}
else
{
// カラーの場合
lblText += "(" +
mat.At <OpenCvSharp.CPlusPlus.Vec3b>(Y, X)[2].ToString() + ", " +
mat.At <OpenCvSharp.CPlusPlus.Vec3b>(Y, X)[1].ToString() + ", " +
mat.At <OpenCvSharp.CPlusPlus.Vec3b>(Y, X)[0].ToString() + ")";
}
lblBright.Text = lblText;
}
//Поиск крайних точек объекта сверху и снизу
Point[] FindShape(Rect rect)
{
bool topFound = false, bottomFound = false;
Point top = new Point(), bottom = new Point();
int i = 0;
while (!topFound || !bottomFound)
{
if (depthMap.At <byte>(rect.Top + i, rect.Left + rect.Width / 2) > 0 && !topFound)
{
top = new Point(rect.Left + rect.Width / 2, rect.Top + i);
topFound = true;
}
if (depthMap.At <byte>(rect.Bottom - i, rect.Left + rect.Width / 2) > 0 && !bottomFound)
{
bottom = new Point(rect.Left + rect.Width / 2, rect.Bottom - i);
bottomFound = true;
}
i++;
}
return(new Point[2] {
top, bottom
});
}
Mat TransfLogaritimica()
{
Mat logImage = greyImage.Clone();
byte fmax = 0; byte fmin = 255;
for (int x = 0; x < logImage.Rows; x++)
{
for (int y = 0; y < logImage.Cols; y++)
{
byte pixel = logImage.At <byte>(x, y);
if (pixel > fmax)
{
fmax = pixel;
}
if (pixel < fmin)
{
fmin = pixel;
}
}
}
for (int x = 0; x < logImage.Rows; x++)
{
for (int y = 0; y < logImage.Cols; y++)
{
byte f = logImage.At <byte>(x, y);
double alpha = 255 / Math.Log(1 + fmax);
double resultado = alpha * Math.Log(f + 1);
logImage.Set <byte>(x, y, (byte)resultado);
}
}
return(logImage);
}
Mat AjustarContraste()
{
Mat cont = greyImage.Clone();
byte fmax = 0; byte fmin = 255;
for (int x = 0; x < cont.Rows; x++)
{
for (int y = 0; y < cont.Cols; y++)
{
byte pixel = cont.At <byte>(x, y);
if (pixel > fmax)
{
fmax = pixel;
}
if (pixel < fmin)
{
fmin = pixel;
}
}
}
byte gmax = 255; byte gmin = 0;
for (int x = 0; x < cont.Rows; x++)
{
for (int y = 0; y < cont.Cols; y++)
{
byte f = cont.At <byte>(x, y);
cont.Set <byte>(x, y, (byte)(((gmax - gmin) / (fmax - fmin)) * (f - fmin) + gmin));
}
}
return(cont);
}
private (double, double) findMatchPoint(Mat templateFromLeftImg, Mat epliline)
{
Mat rightImg_roi = new Mat(templateFromLeftImg.Rows, SCREEN_WIDTH, MatType.CV_8UC3);
double a = epliline.At <Point3d>(0).X;
double b = epliline.At <Point3d>(0).Y;
double c = epliline.At <Point3d>(0).Z; // ax+by+cz=0 (homogeneous coordinates)
for (int col = 0; col < SCREEN_WIDTH; col++) // affine transformation
{
double y = (-c - a * col) / b;
for (int row = (int)(y - rightImg_roi.Rows / 2); row < (int)(y + rightImg_roi.Rows / 2); row++)
{
rightImg_roi.Set <Vec3b>(row - (int)(y - rightImg_roi.Rows / 2), col, rightImg.Get <Vec3b>(row, col));
}
}
Mat result = new Mat();
double maxVal;
double minVal;
Point minLoc = new Point();
Point maxLoc = new Point();
Cv2.MatchTemplate(rightImg_roi, templateFromLeftImg, result, TemplateMatchModes.SqDiffNormed);
Cv2.MinMaxLoc(result, out minVal, out maxVal, out minLoc, out maxLoc);
/*
* if(templateFromLeftImg == part1)
* {
* Cv2.Circle(rightImg_roi, minLoc.X + templateFromLeftImg.Cols / 2, minLoc.Y + templateFromLeftImg.Rows / 2, 2, Scalar.Red, -1, LineTypes.Link8);
* Cv2.ImWrite(Application.persistentDataPath + "/test output.png", rightImg_roi);
* }
*/
return(minLoc.X + templateFromLeftImg.Cols / 2, minLoc.Y + templateFromLeftImg.Rows / 2); // center point of matcing rectangle
}
public double[,] captureRGBCloud()
{
Mmind.Response reply = sendRequest(CaptureGratingImage, 4, "");
Mat depthC3 = read32FC3Mat(reply.ImageGrating);
Mat color = captureColorImg();
int nums = color.Rows * color.Cols;
double[,] xyzbgr = new double[nums, 6];
int count = 0;
for (int i = 0; i < depthC3.Rows; i++)
{
for (int j = 0; j < depthC3.Cols; j++)
{
xyzbgr[count, 0] = (double)depthC3.At <Vec3f>(i, j)[0] * 0.001;
xyzbgr[count, 1] = (double)depthC3.At <Vec3f>(i, j)[1] * 0.001;
xyzbgr[count, 2] = (double)depthC3.At <Vec3f>(i, j)[2] * 0.001;
xyzbgr[count, 3] = color.At <Vec3b>(i, j)[0];
xyzbgr[count, 4] = color.At <Vec3b>(i, j)[1];
xyzbgr[count, 5] = color.At <Vec3b>(i, j)[2];
count++;
}
}
return(xyzbgr);
}
// 去柳钉
public static Mat ClearMaoding(Mat threshold, int num)
{
List <float> jumps = new List <float>();
Mat jump = new Mat(threshold.Rows, 1, MatType.CV_32F);
for (int rowIndex = num; rowIndex < threshold.Rows - num; rowIndex++)
{
int jumpCount = 0;
for (int colIndex = num; colIndex < threshold.Cols - num; colIndex++)
{
if (threshold.At <byte>(rowIndex, colIndex) != threshold.At <byte>(rowIndex, colIndex + 1))
{
jumpCount++;
}
}
jump.Set <float>(rowIndex, 0, (float)jumpCount);
}
int x = 7;
Mat result = threshold.Clone();
for (int rowIndex = 0; rowIndex < threshold.Rows; rowIndex++)
{
if (jump.At <float>(rowIndex) <= x)
{
for (int colIndex = 0; colIndex < threshold.Cols; colIndex++)
{
result.Set <byte>(rowIndex, colIndex, 0);
}
}
}
return(result);
}
public double[,] captureRGBCloud()
{
byte[] response = sendRequest(Command.CaptureImage, 0, "", MatXYZ);
int jsonSize = readInt(response, 0);
double scale = readDouble(response, jsonSize + SIZE_OF_JSON);
int imageSize = readInt(response, SIZE_OF_JSON + jsonSize + SIZE_OF_SCALE);
int imageBegin = SIZE_OF_JSON + jsonSize + SIZE_OF_SCALE + sizeof(Int32);
byte[] imageDepth = response.Skip(imageBegin).Take(imageSize).ToArray();
Mat depthC3 = read32FC3Mat(imageDepth, scale);
Mat color = captureColorImg();
int nums = color.Rows * color.Cols;
double[,] xyzbgr = new double[nums, 6];
int count = 0;
for (int i = 0; i < depthC3.Rows; i++)
{
for (int j = 0; j < depthC3.Cols; j++)
{
xyzbgr[count, 0] = (double)depthC3.At <Vec3f>(i, j)[0] * 0.001;
xyzbgr[count, 1] = (double)depthC3.At <Vec3f>(i, j)[1] * 0.001;
xyzbgr[count, 2] = (double)depthC3.At <Vec3f>(i, j)[2] * 0.001;
xyzbgr[count, 3] = color.At <Vec3b>(i, j)[0];
xyzbgr[count, 4] = color.At <Vec3b>(i, j)[1];
xyzbgr[count, 5] = color.At <Vec3b>(i, j)[2];
count++;
}
}
return(xyzbgr);
}
//清除铆钉
public static Mat ClearMaoding(Mat threshold)
{
//List<float> jumps = new List<float>();
Mat jump = new Mat(threshold.Rows, 1, MatType.CV_32F);
for (int rowIndex = 0; rowIndex < threshold.Rows; rowIndex++)
{
int jumpCount = 0;
for (int colIndex = 0; colIndex < threshold.Cols - 1; colIndex++)
{
if (threshold.At <byte>(rowIndex, colIndex) != threshold.At <byte>(rowIndex, colIndex + 1))
{
jumpCount++;
}
}
jump.Set <float>(rowIndex, 0, (float)jumpCount);
}
//设置阈值，检测过程中黑白像素点变化的次数，并记录下每一排的变化次数，从而将铆钉去除
int x = 7;
Mat result = threshold.Clone();
for (int rowIndex = 0; rowIndex < threshold.Rows; rowIndex++)
{
if (jump.At <float>(rowIndex) <= x)
{
for (int colIndex = 0; colIndex < threshold.Cols; colIndex++)
{
result.Set <byte>(rowIndex, colIndex, 0);
}
}
}
return(result);
}
public void CalcHist()
{
using var src = new Mat(@"_data/image/mandrill.png", ImreadModes.Grayscale);
using var hist = new Mat();
Cv2.CalcHist(
images: new[] { src },
channels: new[] { 0 },
mask: null,
hist: hist,
dims: 1,
histSize: new[] { 256 },
ranges: new[] { new Rangef(0, 256) });
if (Debugger.IsAttached)
{
const int histW = 512;
const int histH = 400;
var binW = Math.Round((double)histW / 256);
using var histImage = new Mat(histH, histW, MatType.CV_8UC3, Scalar.All(0));
Cv2.Normalize(hist, hist, 0, histImage.Rows, NormTypes.MinMax, -1);
for (int i = 0; i < 256; i++)
{
var pt1 = new Point2d(binW * (i - 1), histH - Math.Round(hist.At <float>(i - 1)));
var pt2 = new Point2d(binW * (i), histH - Math.Round(hist.At <float>(i)));
Cv2.Line(
histImage, (Point)pt1, (Point)pt2,
Scalar.Red, 1, LineTypes.Link8);
}
Window.ShowImages(src, histImage);
}
}
Vector3 project3DPoints(Vector3 obj)
{
Mat point2d_vec = new Mat(4, 1, MatType.CV_64FC1);
Mat cameraMatrixMat = new Mat(3, 3, MatType.CV_64FC1, cameraMatrix);
Mat point3d_vec = new Mat(4, 1, MatType.CV_64FC1);
point3d_vec.Set <double>(0, obj.x);
point3d_vec.Set <double>(1, obj.y);
point3d_vec.Set <double>(2, obj.z);
point3d_vec.Set <double>(3, 1);
point2d_vec = cameraMatrixMat * projMatrix * point3d_vec;
var X2D = point2d_vec.At <double>(0) / point2d_vec.At <double>(2);
var Y2D = point2d_vec.At <double>(1) / point2d_vec.At <double>(2);
Debug.Log(" 3d Points " + obj.x + " " + obj.y + " " + obj.z);
Debug.Log(" 2d point x : " + X2D + " y " + Y2D);
//Debug.Log(" float 2d point x : " + (float)X2D + " y " + (float)Y2D);
Vector3 pnpPoints = new Vector3((float)X2D, (float)Y2D, 0.0f);
return(pnpPoints);
}
public Rectangle[] Detect(Mat mat, byte[] buffer)
{
var frameWidth = mat.Width;
var frameHeight = mat.Height;
using var blob = CvDnn.BlobFromImage(mat, 1.0, new OpenCvSharp.Size(300, 300), new OpenCvSharp.Scalar(104, 117, 123), false, false);
Net.SetInput(blob);
using var detections = Net.Forward();
using var detectionMat = new Mat(detections.Size(2), detections.Size(3), MatType.CV_32F, detections.Ptr(0));
var rectangles = new List <Rectangle>();
for (var i = 0; i < detectionMat.Rows; i++)
{
var confidence = detectionMat.At <float>(i, 2);
if (confidence > 0.7)
{
var left = (int)(detectionMat.At <float>(i, 3) * frameWidth);
var top = (int)(detectionMat.At <float>(i, 4) * frameHeight);
var right = (int)(detectionMat.At <float>(i, 5) * frameWidth);
var bottom = (int)(detectionMat.At <float>(i, 6) * frameHeight);
rectangles.Add(new Rectangle(left, top, right - left, bottom - top));
}
}
return(rectangles.ToArray());
}
PointF GetEdgePoint(Mat imgGray)
{
PointF __vector = __end.Location - new System.Drawing.Size(System.Drawing.Point.Round(__start.Location));
float __distance = (float)Math.Sqrt(Math.Pow(__vector.X, 2) + Math.Pow(__vector.Y, 2));
///turns into unit vector
__vector.X = __vector.X / __distance;
__vector.Y = __vector.Y / __distance;
if (__distance <= 0)
{
return(PointF.Empty);
}
PointF __accumulation = __start.Location;
double __acuumulatedLength = 0;
// interpolate start-end , load into table
// access each point's gray value
// post handling
Dictionary <PointF, MatType> __mattypeTable = new Dictionary <PointF, MatType>();
Dictionary <PointF, byte> __grayValueTable = new Dictionary <PointF, byte>();
while (__acuumulatedLength <= __distance)
{
__accumulation += new SizeF(__vector);
//establish pair of coordinate , gray value
__mattypeTable.Add(__accumulation,
imgGray.At <MatType>(
Convert.ToInt32(__accumulation.Y),
Convert.ToInt32(__accumulation.X)
)
);
__grayValueTable.Add(__accumulation,
imgGray.At <byte>(
Convert.ToInt32(__accumulation.Y),
Convert.ToInt32(__accumulation.X)
)
);
//unit length added one
__acuumulatedLength++;
}
//diff and find maximum
List <byte> __grayValueList = __grayValueTable.Values.ToList();
List <int> __diffValueList = new List <int>();
for (int i = 0; i < __grayValueList.Count - 1; i++)
{
__diffValueList.Add(Math.Abs(__grayValueList[i + 1] - __grayValueList[i]));
}
int edgeIndex = __diffValueList.IndexOf(__diffValueList.Max());
return(__grayValueTable.Keys.ElementAt(edgeIndex));
}
public void pnp(Point3f[] objPts, Point2f[] imgPts, Camera cam, SolvePnPFlags type, string filename)
{
Debug.Log($" PNP : {objPts.Length} : {imgPts.Length}");
TextWriter rotFile = new StreamWriter(filename + "-RotPnP.txt");
TextWriter transFile = new StreamWriter(filename + "-TransPnP.txt");
using (var objPtsMat = new Mat(objPts.Length, 1, MatType.CV_32FC3, objPts))
using (var imgPtsMat = new Mat(imgPts.Length, 1, MatType.CV_32FC2, imgPts))
using (var cameraMatrixMat = new Mat(3, 3, MatType.CV_64FC1, getCameraMatrix(cam)))
using (var distMat = Mat.Zeros(5, 0, MatType.CV_64FC1))
using (var rvecMat = new Mat())
using (var tvecMat = new Mat())
{
Cv2.SolvePnP(objPtsMat, imgPtsMat, cameraMatrixMat, distMat, rvecMat, tvecMat, flags: type);
//Debug.Log("Solved " + rvecMat + " : "+tvecMat);
Mat rot_matrix = Mat.Zeros(3, 3, MatType.CV_64FC1);
Mat tr_matrix = Mat.Zeros(3, 1, MatType.CV_64FC1);
Mat proj_matrix = Mat.Zeros(3, 4, MatType.CV_64FC1);
Cv2.Rodrigues(rvecMat, rot_matrix);
tr_matrix = tvecMat;
rotFile.WriteLine(rot_matrix.At <double>(0, 0) + " " + rot_matrix.At <double>(0, 1) + " " + rot_matrix.At <double>(0, 2));
rotFile.WriteLine(rot_matrix.At <double>(1, 0) + " " + rot_matrix.At <double>(1, 1) + " " + rot_matrix.At <double>(1, 2));
rotFile.WriteLine(rot_matrix.At <double>(2, 0) + " " + rot_matrix.At <double>(2, 1) + " " + rot_matrix.At <double>(2, 2));
transFile.WriteLine(tr_matrix.At <double>(0) + " " + tr_matrix.At <double>(1) + " " + tr_matrix.At <double>(2));
proj_matrix.Set <double>(0, 0, rot_matrix.At <double>(0, 0));
proj_matrix.Set <double>(0, 1, rot_matrix.At <double>(0, 1));
proj_matrix.Set <double>(0, 2, rot_matrix.At <double>(0, 2));
proj_matrix.Set <double>(1, 0, rot_matrix.At <double>(1, 0));
proj_matrix.Set <double>(1, 1, rot_matrix.At <double>(1, 1));
proj_matrix.Set <double>(1, 2, rot_matrix.At <double>(1, 2));
proj_matrix.Set <double>(2, 0, rot_matrix.At <double>(2, 0));
proj_matrix.Set <double>(2, 1, rot_matrix.At <double>(2, 1));
proj_matrix.Set <double>(2, 2, rot_matrix.At <double>(2, 2));
proj_matrix.Set <double>(0, 3, tr_matrix.At <double>(0));
proj_matrix.Set <double>(1, 3, tr_matrix.At <double>(1));
proj_matrix.Set <double>(2, 3, tr_matrix.At <double>(2));
//rot = eulerAngle(rot_matrix);
// Debug.Log($"1 : Matrix4x4.TRS(translation, rotation, scale) {Matrix4x4.TRS(cam.transform.position, cam.transform.rotation, new Vector3(10, 10, 10))}");
// Debug.Log($"1 : Matrix4x4.TRS(translation, rotation, scale) {Matrix4x4.TRS(cam.transform.position, cam.transform.rotation, new Vector3(10, 10, 10))}");
//Debug.Log($"rot1 {cam.transform.eulerAngles} rot2 { rot * Mathf.Rad2Deg}");
//Debug.Log($"tr1: {cam.transform.position} tr2: {tr_matrix.At<double>(0)}, {tr_matrix.At<double>(1)}, {tr_matrix.At<double>(2)}");
projectionMatrix = proj_matrix;
}
rotFile.Flush();
transFile.Flush();
}
private void drawEpipolarLine(Mat plate, Mat epliline)// use for testing only
{
double x = epliline.At <Point3d>(0).X;
double y = epliline.At <Point3d>(0).Y;
double z = epliline.At <Point3d>(0).Z;
Cv2.Line(plate, new Point(0, -z / y), new Point(SCREEN_WIDTH - 1, (-x * (SCREEN_WIDTH - 1) - z) / y), Scalar.Green);
Cv2.ImWrite(Application.persistentDataPath + "/test output.png", plate);
}
private static Mat BGRToHSV(Mat img)
{
int width = img.Cols;
int height = img.Rows;
float r, g, b;
float h = 0f, s, v;
float _max, _min;
var hsv = Mat.Zeros(height, width, MatType.CV_32FC3).ToMat();
// each y, x
for (int y = 0; y < height; y++)
{
for (int x = 0; x < width; x++)
{
// BGR -> HSV
r = (float)img.At <Vec3b>(y, x)[2] / 255;
g = (float)img.At <Vec3b>(y, x)[1] / 255;
b = (float)img.At <Vec3b>(y, x)[0] / 255;
_max = Math.Max(r, Math.Max(g, b));
_min = Math.Min(r, Math.Min(g, b));
// get Hue
if (_max == _min)
{
h = 0;
}
else if (_min == b)
{
h = 60 * (g - r) / (_max - _min) + 60;
}
else if (_min == r)
{
h = 60 * (b - g) / (_max - _min) + 180;
}
else if (_min == g)
{
h = 60 * (r - b) / (_max - _min) + 300;
}
// get Saturation
s = _max - _min;
// get Value
v = _max;
var pixel = hsv.At <Vec3f>(y, x);
pixel.Item0 = h;
pixel.Item1 = s;
pixel.Item2 = v;
hsv.Set(y, x, pixel);
}
}
return(hsv);
}
private static void HDR()
{
var hdr = CalibrateDebevec.Create();
Mat[] images = new Mat[3];
images[0] = Cv2.ImRead(@"data\lenna.png", ImreadModes.AnyColor);
images[1] = Cv2.ImRead(@"data\lenna.png", ImreadModes.AnyColor);
images[2] = Cv2.ImRead(@"data\lenna.png", ImreadModes.AnyColor);
float[] speeds = new float[3];
speeds[0] = 1;
speeds[1] = 1;
speeds[2] = 1;
Mat dst = new Mat();
hdr.Process(images, dst, speeds);
dst.ToString();
for (int i = 0; i < Math.Max(dst.Rows, dst.Cols); i++)
{
Console.WriteLine(dst.At<float>(i));
}
}
/// <summary>
/// Solve equation AX = Y
/// </summary>
private void ByMat()
{
// x + y = 10
// 2x + 3y = 26
// (x=4, y=6)
double[,] av = {{1, 1},
{2, 3}};
double[] yv = {10, 26};
Mat a = new Mat(2, 2, MatType.CV_64FC1, av);
Mat y = new Mat(2, 1, MatType.CV_64FC1, yv);
Mat x = new Mat();
Cv2.Solve(a, y, x, MatrixDecomposition.LU);
Console.WriteLine("ByMat:");
Console.WriteLine("X1 = {0}, X2 = {1}", x.At<double>(0), x.At<double>(1));
}
public void ByMat()
{
// x + y = 10
// 2x + 3y = 26
// (x=4, y=6)
double[,] av = {{1, 1},
{2, 3}};
double[] yv = { 10, 26 };
Mat a = new Mat(2, 2, MatType.CV_64FC1, av);
Mat y = new Mat(2, 1, MatType.CV_64FC1, yv);
Mat x = new Mat();
Cv2.Solve(a, y, x, DecompTypes.LU);
Console.WriteLine("X1 = {0}, X2 = {1}", x.At<double>(0), x.At<double>(1));
Assert.That(x.At<double>(0), Is.EqualTo(4).Within(1e-6));
Assert.That(x.At<double>(1), Is.EqualTo(6).Within(1e-6));
}
private static void example02()
{
var src = new Mat(@"..\..\Images\fruits.jpg", LoadMode.AnyDepth | LoadMode.AnyColor);
Cv2.ImShow("Source", src);
Cv2.WaitKey(1); // do events
Cv2.Blur(src, src, new Size(15, 15));
Cv2.ImShow("Blurred Image", src);
Cv2.WaitKey(1); // do events
// Converts the MxNx3 image into a Kx3 matrix where K=MxN and
// each row is now a vector in the 3-D space of RGB.
// change to a Mx3 column vector (M is number of pixels in image)
var columnVector = src.Reshape(cn: 3, rows: src.Rows * src.Cols);
// convert to floating point, it is a requirement of the k-means method of OpenCV.
var samples = new Mat();
columnVector.ConvertTo(samples, MatType.CV_32FC3);
for (var clustersCount = 2; clustersCount <= 8; clustersCount += 2)
{
var bestLabels = new Mat();
var centers = new Mat();
Cv2.Kmeans(
data: samples,
k: clustersCount,
bestLabels: bestLabels,
criteria:
new TermCriteria(type: CriteriaType.Epsilon | CriteriaType.Iteration, maxCount: 10, epsilon: 1.0),
attempts: 3,
flags: KMeansFlag.PpCenters,
centers: centers);
var clusteredImage = new Mat(src.Rows, src.Cols, src.Type());
for (var size = 0; size < src.Cols * src.Rows; size++)
{
var clusterIndex = bestLabels.At<int>(0, size);
var newPixel = new Vec3b
{
Item0 = (byte)(centers.At<float>(clusterIndex, 0)), // B
Item1 = (byte)(centers.At<float>(clusterIndex, 1)), // G
Item2 = (byte)(centers.At<float>(clusterIndex, 2)) // R
};
clusteredImage.Set(size / src.Cols, size % src.Cols, newPixel);
}
Cv2.ImShow(string.Format("Clustered Image [k:{0}]", clustersCount), clusteredImage);
Cv2.WaitKey(1); // do events
}
Cv2.WaitKey();
Cv2.DestroyAllWindows();
}
/// <summary>
/// https://github.com/Itseez/opencv_extra/blob/master/learning_opencv_v2/ch13_ex13_1.cpp
/// </summary>
private static void example01()
{
using (var window = new Window("Clusters", flags: WindowMode.AutoSize | WindowMode.FreeRatio))
{
const int maxClusters = 5;
var rng = new RNG(state: (ulong)DateTime.Now.Ticks);
for (; ; )
{
var clustersCount = rng.Uniform(a: 2, b: maxClusters + 1);
var samplesCount = rng.Uniform(a: 1, b: 1001);
var points = new Mat(rows: samplesCount, cols: 1, type: MatType.CV_32FC2);
clustersCount = Math.Min(clustersCount, samplesCount);
var img = new Mat(rows: 500, cols: 500, type: MatType.CV_8UC3, s: Scalar.All(0));
// generate random sample from multi-gaussian distribution
for (var k = 0; k < clustersCount; k++)
{
var pointChunk = points.RowRange(
startRow: k * samplesCount / clustersCount,
endRow: (k == clustersCount - 1)
? samplesCount
: (k + 1) * samplesCount / clustersCount);
var center = new Point
{
X = rng.Uniform(a: 0, b: img.Cols),
Y = rng.Uniform(a: 0, b: img.Rows)
};
rng.Fill(
mat: pointChunk,
distType: DistributionType.Normal,
a: new Scalar(center.X, center.Y),
b: new Scalar(img.Cols * 0.05f, img.Rows * 0.05f));
}
Cv2.RandShuffle(dst: points, iterFactor: 1, rng: rng);
var labels = new Mat();
var centers = new Mat(rows: clustersCount, cols: 1, type: points.Type());
Cv2.Kmeans(
data: points,
k: clustersCount,
bestLabels: labels,
criteria: new TermCriteria(CriteriaType.Epsilon | CriteriaType.Iteration, 10, 1.0),
attempts: 3,
flags: KMeansFlag.PpCenters,
centers: centers);
Scalar[] colors =
{
new Scalar(0, 0, 255),
new Scalar(0, 255, 0),
new Scalar(255, 100, 100),
new Scalar(255, 0, 255),
new Scalar(0, 255, 255)
};
for (var i = 0; i < samplesCount; i++)
{
var clusterIdx = labels.At<int>(i);
Point ipt = points.At<Point2f>(i);
Cv2.Circle(
img: img,
center: ipt,
radius: 2,
color: colors[clusterIdx],
lineType: LineType.AntiAlias,
thickness: Cv.FILLED);
}
window.Image = img;
var key = (char)Cv2.WaitKey();
if (key == 27 || key == 'q' || key == 'Q') // 'ESC'
{
break;
}
}
}
}
/// <summary>
/// https://github.com/Itseez/opencv_extra/blob/master/learning_opencv_v2/ch9_watershed.cpp
/// </summary>
private static void watershedExample()
{
var src = new Mat(@"..\..\Images\corridor.jpg", LoadMode.AnyDepth | LoadMode.AnyColor);
var srcCopy = new Mat();
src.CopyTo(srcCopy);
var markerMask = new Mat();
Cv2.CvtColor(srcCopy, markerMask, ColorConversion.BgrToGray);
var imgGray = new Mat();
Cv2.CvtColor(markerMask, imgGray, ColorConversion.GrayToBgr);
markerMask = new Mat(markerMask.Size(), markerMask.Type(), s: Scalar.All(0));
var sourceWindow = new Window("Source (Select areas by mouse and then press space)")
{
Image = srcCopy
};
var previousPoint = new Point(-1, -1);
sourceWindow.OnMouseCallback += (@event, x, y, flags) =>
{
if (x < 0 || x >= srcCopy.Cols || y < 0 || y >= srcCopy.Rows)
{
return;
}
if (@event == MouseEvent.LButtonUp || !flags.HasFlag(MouseEvent.FlagLButton))
{
previousPoint = new Point(-1, -1);
}
else if (@event == MouseEvent.LButtonDown)
{
previousPoint = new Point(x, y);
}
else if (@event == MouseEvent.MouseMove && flags.HasFlag(MouseEvent.FlagLButton))
{
var pt = new Point(x, y);
if (previousPoint.X < 0)
{
previousPoint = pt;
}
Cv2.Line(img: markerMask, pt1: previousPoint, pt2: pt, color: Scalar.All(255), thickness: 5);
Cv2.Line(img: srcCopy, pt1: previousPoint, pt2: pt, color: Scalar.All(255), thickness: 5);
previousPoint = pt;
sourceWindow.Image = srcCopy;
}
};
var rnd = new Random();
for (; ; )
{
var key = Cv2.WaitKey(0);
if ((char)key == 27) // ESC
{
break;
}
if ((char)key == 'r') // Reset
{
markerMask = new Mat(markerMask.Size(), markerMask.Type(), s: Scalar.All(0));
src.CopyTo(srcCopy);
sourceWindow.Image = srcCopy;
}
if ((char)key == 'w' || (char)key == ' ') // Apply watershed
{
Point[][] contours; //vector<vector<Point>> contours;
HiearchyIndex[] hierarchyIndexes; //vector<Vec4i> hierarchy;
Cv2.FindContours(
markerMask,
out contours,
out hierarchyIndexes,
mode: ContourRetrieval.CComp,
method: ContourChain.ApproxSimple);
if (contours.Length == 0)
{
continue;
}
var markers = new Mat(markerMask.Size(), MatType.CV_32S, s: Scalar.All(0));
var componentCount = 0;
var contourIndex = 0;
while ((contourIndex >= 0))
{
Cv2.DrawContours(
markers,
contours,
contourIndex,
color: Scalar.All(componentCount+1),
thickness: -1,
lineType: LineType.Link8,
hierarchy: hierarchyIndexes,
maxLevel: int.MaxValue);
componentCount++;
contourIndex = hierarchyIndexes[contourIndex].Next;
}
if (componentCount == 0)
{
continue;
}
var colorTable = new List<Vec3b>();
for (var i = 0; i < componentCount; i++)
{
var b = rnd.Next(0, 255); //Cv2.TheRNG().Uniform(0, 255);
var g = rnd.Next(0, 255); //Cv2.TheRNG().Uniform(0, 255);
var r = rnd.Next(0, 255); //Cv2.TheRNG().Uniform(0, 255);
colorTable.Add(new Vec3b((byte)b, (byte)g, (byte)r));
}
Cv2.Watershed(src, markers);
var watershedImage = new Mat(markers.Size(), MatType.CV_8UC3);
// paint the watershed image
for (var i = 0; i < markers.Rows; i++)
{
for (var j = 0; j < markers.Cols; j++)
{
var idx = markers.At<int>(i, j);
if (idx == -1)
{
watershedImage.Set(i, j, new Vec3b(255, 255, 255));
}
else if (idx <= 0 || idx > componentCount)
{
watershedImage.Set(i, j, new Vec3b(0, 0, 0));
}
else
{
watershedImage.Set(i, j, colorTable[idx - 1]);
}
}
}
watershedImage = watershedImage * 0.5 + imgGray * 0.5;
Cv2.ImShow("Watershed Transform", watershedImage);
Cv2.WaitKey(1); //do events
}
}
sourceWindow.Dispose();
Cv2.DestroyAllWindows();
src.Dispose();
}
public ActionResult Histgram(HttpPostedFileBase imageData)
{
try
{
if (imageData == null) { throw new ArgumentException("File is not exist."); }
using (var img = Mat.FromStream(imageData.InputStream, LoadMode.Color))
{
int chWidth = 260;
int chHeight = 200;
using (var mask = new Mat())
using (var hist = new Mat())
using (var histImg = new Mat(new Size(chWidth, chHeight), MatType.CV_8UC3, Scalar.All(255)))
{
Mat[] images = new Mat[] { img };
int[] channels = new int[] { 0 };
int[] hdims = new int[] { 256 };
float[] hranges = new float[] { 0, 256 };
float[][] ranges = new float[][] { hranges };
Cv2.CalcHist(images, channels, mask, hist, 1, hdims, ranges);
double minVal, maxVal;
Cv2.MinMaxLoc(hist, out minVal, out maxVal);
for (int j = 0; j < hdims[0]; ++j)
{
int binW = (int)((double)chWidth / hdims[0]);
Cv2.Rectangle(histImg, new Point(j * binW, histImg.Rows), new Point((j + 1) * binW, histImg.Rows - (int)(hist.At<float>(j) * (maxVal != 0 ? chHeight/ maxVal : 0.0))), Scalar.All(100));
}
byte[] imgBytes = img.ToBytes(".png");
string base64Img = Convert.ToBase64String(imgBytes);
ViewBag.Base64Img = base64Img;
byte[] result = histImg.ToBytes(".png");
string base64Result = Convert.ToBase64String(result);
ViewBag.Base64Result = base64Result;
}
}
}
catch (Exception ex)
{
Console.WriteLine(ex.ToString());
}
return View();
}
