public LatentSVM()
{
using (var detector = new CvLatentSvmDetector(FilePath.Text.LatentSvmCat))
using (var imageSrc = new IplImage(FilePath.Image.Cat, LoadMode.Color))
using (var imageDst = imageSrc.Clone())
using (var storage = new CvMemStorage())
{
Console.WriteLine("Running LatentSVM...");
Stopwatch watch = Stopwatch.StartNew();
CvSeq<CvObjectDetection> result = detector.DetectObjects(imageSrc, storage, 0.5f, 2);
watch.Stop();
Console.WriteLine("Elapsed time: {0}ms", watch.ElapsedMilliseconds);
foreach (CvObjectDetection detection in result)
{
CvRect boundingBox = detection.Rect;
imageDst.Rectangle(
new CvPoint(boundingBox.X, boundingBox.Y),
new CvPoint(boundingBox.X + boundingBox.Width, boundingBox.Y + boundingBox.Height),
CvColor.Red, 3);
}
using (new CvWindow("LatentSVM result", imageDst))
{
Cv.WaitKey();
}
}
}
public Affine()
{
// cvGetAffineTransform + cvWarpAffine
// 画像上の３点対応よりアフィン変換行列を計算し，その行列を用いて画像全体のアフィン変換を行う．
// (1)画像の読み込み，出力用画像領域の確保を行なう
using (IplImage srcImg = new IplImage(Const.ImageGoryokaku, LoadMode.AnyDepth | LoadMode.AnyColor))
using (IplImage dstImg = srcImg.Clone())
{
// (2)三角形の回転前と回転後の対応する頂点をそれぞれセットし
// cvGetAffineTransformを用いてアフィン行列を求める
CvPoint2D32f[] srcPnt = new CvPoint2D32f[3];
CvPoint2D32f[] dstPnt = new CvPoint2D32f[3];
srcPnt[0] = new CvPoint2D32f(200.0f, 200.0f);
srcPnt[1] = new CvPoint2D32f(250.0f, 200.0f);
srcPnt[2] = new CvPoint2D32f(200.0f, 100.0f);
dstPnt[0] = new CvPoint2D32f(300.0f, 100.0f);
dstPnt[1] = new CvPoint2D32f(300.0f, 50.0f);
dstPnt[2] = new CvPoint2D32f(200.0f, 100.0f);
using (CvMat mapMatrix = Cv.GetAffineTransform(srcPnt, dstPnt))
{
// (3)指定されたアフィン行列により，cvWarpAffineを用いて画像を回転させる
Cv.WarpAffine(srcImg, dstImg, mapMatrix, Interpolation.Linear | Interpolation.FillOutliers, CvScalar.ScalarAll(0));
// (4)結果を表示する
using (new CvWindow("src", srcImg))
using (new CvWindow("dst", dstImg))
{
Cv.WaitKey(0);
}
}
}
}
public static OpenCvSharp.IplImage GetSub(this OpenCvSharp.IplImage ipl, OpenCvSharp.CvRect subRect)
{
if (ipl == null)
{
throw new ArgumentNullException("ipl", "ipl is null.");
}
var boundingRect = new CvRect(0, 0, ipl.Width, ipl.Height);
if (!boundingRect.Contains(subRect))
{
throw new InvalidOperationException("subRect is outside of ipl");
}
try
{
ipl.SetROI(subRect);
OpenCvSharp.IplImage sub = new IplImage(
ipl.GetSize(),
ipl.Depth,
ipl.NChannels);
ipl.Copy(sub);
return(sub);
}
finally
{
ipl.ResetROI();
}
}
public Perspective()
{
// cvGetPerspectiveTransform + cvWarpPerspective
// 画像上の４点対応より透視投影変換行列を計算し，その行列を用いて画像全体の透視投影変換を行う．
// (1)画像の読み込み，出力用画像領域の確保を行なう
using (IplImage srcImg = new IplImage(Const.ImageLenna, LoadMode.AnyDepth | LoadMode.AnyColor))
using (IplImage dstImg = srcImg.Clone())
{
// (2)四角形の変換前と変換後の対応する頂点をそれぞれセットし
// cvWarpPerspectiveを用いて透視投影変換行列を求める
CvPoint2D32f[] srcPnt = new CvPoint2D32f[4];
CvPoint2D32f[] dstPnt = new CvPoint2D32f[4];
srcPnt[0] = new CvPoint2D32f(150.0f, 150.0f);
srcPnt[1] = new CvPoint2D32f(150.0f, 300.0f);
srcPnt[2] = new CvPoint2D32f(350.0f, 300.0f);
srcPnt[3] = new CvPoint2D32f(350.0f, 150.0f);
dstPnt[0] = new CvPoint2D32f(200.0f, 200.0f);
dstPnt[1] = new CvPoint2D32f(150.0f, 300.0f);
dstPnt[2] = new CvPoint2D32f(350.0f, 300.0f);
dstPnt[3] = new CvPoint2D32f(300.0f, 200.0f);
using (CvMat mapMatrix = Cv.GetPerspectiveTransform(srcPnt, dstPnt))
{
// (3)指定されたアフィン行列により，cvWarpAffineを用いて画像を回転させる
Cv.WarpPerspective(srcImg, dstImg, mapMatrix, Interpolation.Linear | Interpolation.FillOutliers, CvScalar.ScalarAll(100));
// (4)結果を表示する
using (new CvWindow("src", srcImg))
using (new CvWindow("dst", dstImg))
{
Cv.WaitKey(0);
}
}
}
}
public int FindPlates()
{
IplImage labelImg = new IplImage(src.Size, CvBlobLib.DepthLabel, 1);
blobs = new CvBlobs();
plate.Clear();
CvBlobLib.Label(timg, labelImg, blobs);
CvBlobLib.FilterByArea(blobs, x, y);
IplImage srctemp = src.Clone();
CvBlobLib.RenderBlobs(labelImg, blobs, src, srctemp, RenderBlobsMode.BoundingBox | RenderBlobsMode.Angle);
foreach (var item in blobs)
{
item.Value.SetImageROItoBlob(pimg);
// ratio values of plate between 3.5 and 5.4
double ratio = (double)item.Value.Rect.Width / item.Value.Rect.Height;
double angle = (double)item.Value.CalcAngle();
//if (ratio > 3.5 && ratio < 5.4 && angle > -15 && angle < 15)
if (ratio > 1 && ratio < 6 && angle > -15 && angle < 15)
{
// IplImage platetemp = new IplImage(new CvSize(pimg.ROI.Width, pimg.ROI.Height), pimg.Depth, pimg.NChannels);
IplImage platetemp = new IplImage(new CvSize(140, 27), pimg.Depth, pimg.NChannels);
Cv.Resize(pimg, platetemp);
// Cv.Copy(pimg, platetemp);
plate.Add(platetemp);
src.Rectangle(item.Value.Rect, new CvScalar(0, 0, 255), 2, LineType.Link4);
}
}
// CvBlobLib.RenderBlobs(labelImg, blobs, src, src, RenderBlobsMode.BoundingBox);
src.ResetROI();
return plate.Count;
}
void JpegStreamNewFrame(object sender, NewFrameEventArgs eventArgs)
{
if (!_resized)
{
if (_displayControl != null)
{
_displayControl.Invalidate();
}
this.LiveViewResized(null);
_resized = true;
}
LastImage = eventArgs.Frame;
if (_motionFramesQueue.Count > MotionQueueSize)
{
return;
}
var bmp = AForge.Imaging.Image.Clone(eventArgs.Frame);
OpenCvSharp.IplImage ipl = null;
try
{
ipl = OpenCvSharp.IplImage.FromBitmap(bmp);
}
catch (Exception)
{
return;
}
finally
{
if (bmp != null)
{
bmp.Dispose();
}
}
var frame = new Frame(ipl);
var grouped = _motionDetector.ProcessFrame(frame);
if (grouped)
{
var motionFrames = _motionDetector.GetMotionFrames();
if (motionFrames != null)
{
foreach (var motionFrame in motionFrames)
{
motionFrame.DeviceId = _cameraInfo.Id;
}
SaveMotionFrames(motionFrames);
_motionFramesQueue.Enqueue(motionFrames);
_signal.Set();
}
}
}
public HoughCircles()
{
using (IplImage imgSrc = new IplImage(Const.ImageWalkman, LoadMode.Color))
using (IplImage imgGray = new IplImage(imgSrc.Size, BitDepth.U8, 1))
using (IplImage imgHough = imgSrc.Clone())
{
Cv.CvtColor(imgSrc, imgGray, ColorConversion.BgrToGray);
Cv.Smooth(imgGray, imgGray, SmoothType.Gaussian, 9);
//Cv.Canny(imgGray, imgGray, 75, 150, ApertureSize.Size3);
using (CvMemStorage storage = new CvMemStorage())
{
CvSeq<CvCircleSegment> seq = imgGray.HoughCircles(storage, HoughCirclesMethod.Gradient, 1, 100, 150, 55, 0, 0);
foreach (CvCircleSegment item in seq)
{
imgHough.Circle(item.Center, (int)item.Radius, CvColor.Red, 3);
}
}
// (5)検出結果表示用のウィンドウを確保し表示する
using (new CvWindow("gray", WindowMode.AutoSize, imgGray))
using (new CvWindow("Hough circles", WindowMode.AutoSize, imgHough))
{
CvWindow.WaitKey(0);
}
}
}
/// <summary>
/// Находит контуры на изображении и выбирает из них самый длинный контур являющийся границей дырки
/// </summary>
/// <param name="image">Изображение на котором будем искать контуры</param>
/// <returns>Результат поиска</returns>
public CvPoint[] FindMostLengthHole(IplImage image)
{
CvMemStorage contours = new CvMemStorage();
CvSeq<CvPoint> firstContour, mostLengthContour = null;
double maxContourLength = 0, perim = 0;
// Отделяем изображение от фона
separateBackground(image, tmpImg);
// Находим все контуры на изображении
Cv.FindContours(tmpImg, contours, out firstContour, CvContour.SizeOf, ContourRetrieval.List, ContourChain.ApproxNone);
// Если не найдено ни одного контура
if (firstContour == null) return new CvPoint[0];
// Ищем самый длинный контур
for (CvSeq<CvPoint> currentContour = firstContour; currentContour.HNext != null; currentContour = currentContour.HNext)
{
if (isHole(currentContour))
{
perim = Cv.ContourPerimeter(currentContour);
if (perim >= maxContourLength)
{
maxContourLength = perim;
mostLengthContour = currentContour;
}
}
}
// Если не найдено ни одной дырки
if (mostLengthContour == null) return new CvPoint[0];
return mostLengthContour.ToArray();
}
public void SetSize(IplImage i)
{
this.Size = new Size(i.Size.Width+10, i.Size.Height+10);
this.pictureBoxIpl1.ImageIpl = i;
}
public FaceDetect()
{
CheckMemoryLeak();
// CvHaarClassifierCascade, cvHaarDetectObjects
CvColor[] colors = new CvColor[]{
new CvColor(0,0,255),
new CvColor(0,128,255),
new CvColor(0,255,255),
new CvColor(0,255,0),
new CvColor(255,128,0),
new CvColor(255,255,0),
new CvColor(255,0,0),
new CvColor(255,0,255),
};
const double Scale = 1.14;
const double ScaleFactor = 1.0850;
const int MinNeighbors = 2;
using (IplImage img = new IplImage(FilePath.Image.Yalta, LoadMode.Color))
using (IplImage smallImg = new IplImage(new CvSize(Cv.Round(img.Width / Scale), Cv.Round(img.Height / Scale)), BitDepth.U8, 1))
{
using (IplImage gray = new IplImage(img.Size, BitDepth.U8, 1))
{
Cv.CvtColor(img, gray, ColorConversion.BgrToGray);
Cv.Resize(gray, smallImg, Interpolation.Linear);
Cv.EqualizeHist(smallImg, smallImg);
}
using (var cascade = CvHaarClassifierCascade.FromFile(FilePath.Text.HaarCascade))
using (var storage = new CvMemStorage())
{
storage.Clear();
// 顔の検出
Stopwatch watch = Stopwatch.StartNew();
CvSeq<CvAvgComp> faces = Cv.HaarDetectObjects(smallImg, cascade, storage, ScaleFactor, MinNeighbors, 0, new CvSize(30, 30));
watch.Stop();
Console.WriteLine("detection time = {0}ms\n", watch.ElapsedMilliseconds);
// 検出した箇所にまるをつける
for (int i = 0; i < faces.Total; i++)
{
CvRect r = faces[i].Value.Rect;
CvPoint center = new CvPoint
{
X = Cv.Round((r.X + r.Width * 0.5) * Scale),
Y = Cv.Round((r.Y + r.Height * 0.5) * Scale)
};
int radius = Cv.Round((r.Width + r.Height) * 0.25 * Scale);
img.Circle(center, radius, colors[i % 8], 3, LineType.AntiAlias, 0);
}
}
// ウィンドウに表示
CvWindow.ShowImages(img);
}
}
private static void usingCInterface()
{
using (var src = new IplImage(@"..\..\Images\Penguin.Png", LoadMode.AnyDepth | LoadMode.AnyColor))
using (var dst = new IplImage(src.Size, src.Depth, src.NChannels))
{
for (var y = 0; y < src.Height; y++)
{
for (var x = 0; x < src.Width; x++)
{
CvColor pixel = src[y, x];
dst[y, x] = new CvColor
{
B = (byte)(255 - pixel.B),
G = (byte)(255 - pixel.G),
R = (byte)(255 - pixel.R)
};
}
}
// [C] Accessing Pixel
// https://github.com/shimat/opencvsharp/wiki/%5BC%5D-Accessing-Pixel
using (new CvWindow("C Interface: Src", image: src))
using (new CvWindow("C Interface: Dst", image: dst))
{
Cv.WaitKey(0);
}
}
}
public CornerDetect()
{
int cornerCount = 150;
using (IplImage dstImg1 = new IplImage(FilePath.Image.Lenna, LoadMode.AnyColor | LoadMode.AnyDepth))
using (IplImage dstImg2 = dstImg1.Clone())
using (IplImage srcImgGray = new IplImage(FilePath.Image.Lenna, LoadMode.GrayScale))
using (IplImage eigImg = new IplImage(srcImgGray.GetSize(), BitDepth.F32, 1))
using (IplImage tempImg = new IplImage(srcImgGray.GetSize(), BitDepth.F32, 1))
{
CvPoint2D32f[] corners;
Cv.GoodFeaturesToTrack(srcImgGray, eigImg, tempImg, out corners, ref cornerCount, 0.1, 15);
Cv.FindCornerSubPix(srcImgGray, corners, cornerCount, new CvSize(3, 3), new CvSize(-1, -1), new CvTermCriteria(20, 0.03));
for (int i = 0; i < cornerCount; i++)
Cv.Circle(dstImg1, corners[i], 3, new CvColor(255, 0, 0), 2);
cornerCount = 150;
Cv.GoodFeaturesToTrack(srcImgGray, eigImg, tempImg, out corners, ref cornerCount, 0.1, 15, null, 3, true, 0.01);
Cv.FindCornerSubPix(srcImgGray, corners, cornerCount, new CvSize(3, 3), new CvSize(-1, -1), new CvTermCriteria(20, 0.03));
for (int i = 0; i < cornerCount; i++)
Cv.Circle(dstImg2, corners[i], 3, new CvColor(0, 0, 255), 2);
using (new CvWindow("EigenVal", WindowMode.AutoSize, dstImg1))
using (new CvWindow("Harris", WindowMode.AutoSize, dstImg2))
{
Cv.WaitKey(0);
}
}
}
public Squares()
{
// create memory storage that will contain all the dynamic data
CvMemStorage storage = new CvMemStorage(0);
for (int i = 0; i < _names.Length; i++)
{
// load i-th image
using (IplImage img = new IplImage(_names[i], LoadMode.Color))
{
// create window and a trackbar (slider) with parent "image" and set callback
// (the slider regulates upper threshold, passed to Canny edge detector)
Cv.NamedWindow(WindowName, WindowMode.AutoSize);
// find and draw the squares
DrawSquares(img, FindSquares4(img, storage));
}
// clear memory storage - reset free space position
storage.Clear();
// wait for key.
// Also the function cvWaitKey takes care of event processing
int c = Cv.WaitKey(0);
if ((char)c == 27)
break;
}
Cv.DestroyWindow(WindowName);
}
public Undistort()
{
using (IplImage srcImg = new IplImage(FilePath.Image.Distortion, LoadMode.Color))
using (IplImage dstImg = srcImg.Clone())
{
CvMat intrinsic, distortion;
using (CvFileStorage fs = new CvFileStorage(FilePath.Text.Camera, null, FileStorageMode.Read))
{
CvFileNode param = fs.GetFileNodeByName(null, "intrinsic");
intrinsic = fs.Read<CvMat>(param);
param = fs.GetFileNodeByName(null, "distortion");
distortion = fs.Read<CvMat>(param);
}
Cv.Undistort2(srcImg, dstImg, intrinsic, distortion);
using (new CvWindow("Distortion", WindowMode.AutoSize, srcImg))
using (new CvWindow("Undistortion", WindowMode.AutoSize, dstImg))
{
CvWindow.WaitKey(0);
}
intrinsic.Dispose();
distortion.Dispose();
}
}
public 描画画面()
{
InitializeComponent();
dis_height= System.Windows.Forms.Screen.PrimaryScreen.Bounds.Height;
dis_width=System.Windows.Forms.Screen.PrimaryScreen.Bounds.Width;
pos_max = Tobii.pos_max;
while (Tobii. 眼球位置_L[0] == 0 || Tobii. 眼球位置_R[0] == 100) { }//両目とれるまでここにとどまる
diff_in = Tobii. 眼球位置_R[0]-Tobii. 眼球位置_L[0];
posY_in = (Tobii.眼球位置_L[1] + Tobii.眼球位置_R[1] )/ 2;
pictureBoxIpl1.Width = dis_width;
pictureBoxIpl1.Height = dis_height;
frame = Cv.CreateImage(new CvSize(dis_width, dis_height), BitDepth.U8, 3);
background = Cv.CreateImage(new CvSize(dis_width, dis_height), BitDepth.U8, 3);
background=メイン画面.background;
pictureBoxIpl1.ImageIpl = background;
window_size = new CvSize(メイン画面.window[0], メイン画面.window[1]);
point_old = new CvPoint(window_size.Width / 2, window_size.Height / 2);
許容半径 = メイン画面.radius;
PC=new System.Diagnostics.PerformanceCounter[3];
タイマー開始();
}
private static void ShowCvWindow(IplImage image)
{
Cv.NamedWindow("window");
Cv.ShowImage("window", image);
Cv.WaitKey();
Cv.DestroyWindow("window");
}
public Undistort()
{
// cvUndistort2
// キャリブレーションデータを利用して，歪みを補正する
// (1)補正対象となる画像の読み込み
using (IplImage srcImg = new IplImage(Const.ImageDistortion, LoadMode.Color))
using (IplImage dstImg = srcImg.Clone())
{
// (2)パラメータファイルの読み込み
CvMat intrinsic, distortion;
using (CvFileStorage fs = new CvFileStorage(Const.XmlCamera, null, FileStorageMode.Read))
{
CvFileNode param = fs.GetFileNodeByName(null, "intrinsic");
intrinsic = fs.Read<CvMat>(param);
param = fs.GetFileNodeByName(null, "distortion");
distortion = fs.Read<CvMat>(param);
}
// (3)歪み補正
Cv.Undistort2(srcImg, dstImg, intrinsic, distortion);
// (4)画像を表示，キーが押されたときに終了
using (CvWindow w1 = new CvWindow("Distortion", WindowMode.AutoSize, srcImg))
using (CvWindow w2 = new CvWindow("Undistortion", WindowMode.AutoSize, dstImg))
{
CvWindow.WaitKey(0);
}
intrinsic.Dispose();
distortion.Dispose();
}
}
public Filter2D()
{
// cvFilter2D
// ユーザが定義したカーネルによるフィルタリング
// (1)画像の読み込み
using (IplImage srcImg = new IplImage(Const.ImageFruits, LoadMode.AnyDepth | LoadMode.AnyColor))
using (IplImage dstImg = new IplImage(srcImg.Size, srcImg.Depth, srcImg.NChannels))
{
// (2)カーネルの正規化と，フィルタ処理
float[] data = { 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1
};
CvMat kernel = new CvMat(1, 21, MatrixType.F32C1, data);
Cv.Normalize(kernel, kernel, 1.0, 0, NormType.L1);
Cv.Filter2D(srcImg, dstImg, kernel, new CvPoint(0, 0));
// (3)結果を表示する
using (CvWindow window = new CvWindow("Filter2D", dstImg))
{
Cv.WaitKey(0);
}
}
}
public static OpenCvSharp.IplImage GetSub(this OpenCvSharp.IplImage ipl, OpenCvSharp.CvRect subRect)
{
if (ipl == null)
throw new ArgumentNullException("ipl", "ipl is null.");
var boundingRect = new CvRect(0, 0, ipl.Width, ipl.Height);
if (!boundingRect.Contains(subRect))
throw new InvalidOperationException("subRect is outside of ipl");
try
{
ipl.SetROI(subRect);
OpenCvSharp.IplImage sub = new IplImage(
ipl.GetSize(),
ipl.Depth,
ipl.NChannels);
ipl.Copy(sub);
return sub;
}
finally
{
ipl.ResetROI();
}
}
public Snake()
{
using (IplImage src = new IplImage(Const.ImageCake, LoadMode.GrayScale))
using (IplImage dst = new IplImage(src.Size, BitDepth.U8, 3))
{
CvPoint[] contour = new CvPoint[100];
CvPoint center = new CvPoint(src.Width / 2, src.Height / 2);
for (int i = 0; i < contour.Length; i++)
{
contour[i].X = (int)(center.X * Math.Cos(2 * Math.PI * i / contour.Length) + center.X);
contour[i].Y = (int)(center.Y * Math.Sin(2 * Math.PI * i / contour.Length) + center.Y);
}
Console.WriteLine("Press any key to snake\nEsc - quit");
using (CvWindow w = new CvWindow())
{
while (true)
{
src.SnakeImage(contour, 0.45f, 0.35f, 0.2f, new CvSize(15, 15), new CvTermCriteria(1), true);
src.CvtColor(dst, ColorConversion.GrayToRgb);
for (int i = 0; i < contour.Length - 1; i++)
{
dst.Line(contour[i], contour[i + 1], new CvColor(255, 0, 0), 2);
}
dst.Line(contour[contour.Length - 1], contour[0], new CvColor(255, 0, 0), 2);
w.Image = dst;
int key = CvWindow.WaitKey();
if (key == 27)
{
break;
}
}
}
}
}
public CornerDetect()
{
// cvGoodFeaturesToTrack, cvFindCornerSubPix
// 画像中のコーナー（特徴点）検出
int cornerCount = 150;
using (IplImage dstImg1 = new IplImage(Const.ImageLenna, LoadMode.AnyColor | LoadMode.AnyDepth))
using (IplImage dstImg2 = dstImg1.Clone())
using (IplImage srcImgGray = new IplImage(Const.ImageLenna, LoadMode.GrayScale))
using (IplImage eigImg = new IplImage(srcImgGray.GetSize(), BitDepth.F32, 1))
using (IplImage tempImg = new IplImage(srcImgGray.GetSize(), BitDepth.F32, 1))
{
CvPoint2D32f[] corners;
// (1)cvCornerMinEigenValを利用したコーナー検出
Cv.GoodFeaturesToTrack(srcImgGray, eigImg, tempImg, out corners, ref cornerCount, 0.1, 15);
Cv.FindCornerSubPix(srcImgGray, corners, cornerCount, new CvSize(3, 3), new CvSize(-1, -1), new CvTermCriteria(20, 0.03));
// (2)コーナーの描画
for (int i = 0; i < cornerCount; i++)
Cv.Circle(dstImg1, corners[i], 3, new CvColor(255, 0, 0), 2);
// (3)cvCornerHarrisを利用したコーナー検出
cornerCount = 150;
Cv.GoodFeaturesToTrack(srcImgGray, eigImg, tempImg, out corners, ref cornerCount, 0.1, 15, null, 3, true, 0.01);
Cv.FindCornerSubPix(srcImgGray, corners, cornerCount, new CvSize(3, 3), new CvSize(-1, -1), new CvTermCriteria(20, 0.03));
// (4)コーナーの描画
for (int i = 0; i < cornerCount; i++)
Cv.Circle(dstImg2, corners[i], 3, new CvColor(0, 0, 255), 2);
// (5)画像の表示
using (new CvWindow("EigenVal", WindowMode.AutoSize, dstImg1))
using (new CvWindow("Harris", WindowMode.AutoSize, dstImg2))
{
Cv.WaitKey(0);
}
}
}
public ActionResult Canny(HttpPostedFileBase imageData)
{
if (imageData != null)
{
using (var image = IplImage.FromStream(imageData.InputStream, LoadMode.Color))
{
using (var grayImage = new IplImage(image.Size, BitDepth.U8, 1))
using (var cannyImage = new IplImage(image.Size, BitDepth.U8, 1))
{
Cv.CvtColor(image, grayImage, ColorConversion.BgrToGray);
Cv.Canny(grayImage, cannyImage, 60, 180);
byte[] cannyBytes = cannyImage.ToBytes(".png");
string base64 = Convert.ToBase64String(cannyBytes);
ViewBag.Base64Image = base64;
byte[] originalBytes = image.ToBytes(".png");
string base64Org = Convert.ToBase64String(originalBytes);
ViewBag.Base64OrgImage = base64Org;
byte[] grayBytes = grayImage.ToBytes(".png");
string base64Gray = Convert.ToBase64String(grayBytes);
ViewBag.Base64GrayImage = base64Gray;
}
}
}
return View();
}
public ConvertToWriteableBitmap()
{
WriteableBitmap wb = null;
// OpenCVによる画像処理 (Threshold)
using (IplImage src = new IplImage(Const.ImageLenna, LoadMode.GrayScale))
using (IplImage dst = new IplImage(src.Size, BitDepth.U8, 1))
{
src.Smooth(src, SmoothType.Gaussian, 5);
src.Threshold(dst, 0, 255, ThresholdType.Otsu);
// IplImage -> WriteableBitmap
wb = dst.ToWriteableBitmap(PixelFormats.BlackWhite);
//wb = WriteableBitmapConverter.ToWriteableBitmap(dst, PixelFormats.BlackWhite);
}
// WPFのWindowに表示してみる
Image image = new Image { Source = wb };
Window window = new Window
{
Title = "from IplImage to WriteableBitmap",
Width = wb.PixelWidth,
Height = wb.PixelHeight,
Content = image
};
Application app = new Application();
app.Run(window);
}
public void PreProcess()
{
//Cv.NamedWindow("anhthoai", WindowMode.AutoSize);
IplConvKernel element = Cv.CreateStructuringElementEx(21, 3, 10, 2, ElementShape.Rect, null);
timg = new IplImage(src.Size, BitDepth.U8, 1);
IplImage temp = timg.Clone();
IplImage dest = timg.Clone();
src.CvtColor(timg, ColorConversion.RgbaToGray);
pimg = timg.Clone();
//Cv.Threshold(pimg, pimg, 128, 255, ThresholdType.Binary | ThresholdType.Otsu);
Cv.Smooth(timg, timg, SmoothType.Gaussian);
Cv.MorphologyEx(timg, dest, temp, element, MorphologyOperation.TopHat, 1);
Cv.Threshold(dest, timg, 180, 255, ThresholdType.Binary | ThresholdType.Otsu);
//Cv.AdaptiveThreshold(dest, timg, 255, AdaptiveThresholdType.MeanC, ThresholdType.Binary,75, 0);
Cv.Smooth(timg, dest, SmoothType.Median);
Cv.Dilate(dest, dest, element, 2);
/*using (CvWindow window = new CvWindow("BoundingRect", WindowMode.AutoSize))
{
window.Image = dest;
CvWindow.WaitKey(0);
}*/
//Cv.ShowImage("anhthoai", dest);
Cv.ReleaseImage(temp);
Cv.ReleaseImage(dest);
}
public DrawToHdc()
{
CvRect roi = new CvRect(320, 260, 100, 100); // region of roosevelt's face
using (IplImage src = new IplImage(Const.ImageYalta, LoadMode.Color))
using (IplImage dst = new IplImage(roi.Size, BitDepth.U8, 3))
{
src.ROI = roi;
using (Bitmap bitmap = new Bitmap(roi.Width, roi.Height, PixelFormat.Format32bppArgb))
using (Graphics g = Graphics.FromImage(bitmap))
{
//BitmapConverter.DrawToGraphics(src, g, new CvRect(new CvPoint(0, 0), roi.Size));
IntPtr hdc = g.GetHdc();
BitmapConverter.DrawToHdc(src, hdc, new CvRect(new CvPoint(0,0), roi.Size));
g.ReleaseHdc(hdc);
g.DrawString("Roosevelt", new Font(FontFamily.GenericSerif, 12), Brushes.Red, 20, 0);
g.DrawEllipse(new Pen(Color.Red, 4), new Rectangle(20, 20, roi.Width/2, roi.Height/2));
dst.CopyFrom(bitmap);
}
src.ResetROI();
using (new CvWindow("src", src))
using (new CvWindow("dst", dst))
{
Cv.WaitKey();
}
}
}
public ConvertToBitmapSource()
{
BitmapSource bs = null;
// OpenCVによる画像処理 (Threshold)
using (IplImage src = new IplImage(Const.ImageLenna, LoadMode.GrayScale))
using (IplImage dst = new IplImage(src.Size, BitDepth.U8, 1))
{
src.Smooth(src, SmoothType.Gaussian, 5);
src.Threshold(dst, 0, 255, ThresholdType.Otsu);
// IplImage -> BitmapSource
bs = dst.ToBitmapSource();
//bs = BitmapSourceConverter.ToBitmapSource(dst);
}
// WPFのWindowに表示してみる
Image image = new Image { Source = bs };
Window window = new Window
{
Title = "from IplImage to BitmapSource",
Width = bs.PixelWidth,
Height = bs.PixelHeight,
Content = image
};
Application app = new Application();
app.Run(window);
}
public Serialization()
{
const string FileName = "serialization.dat";
IplImage imgWrite = new IplImage(Const.ImageFruits, LoadMode.Color);
IplImage imgRead;
using (FileStream fs = new FileStream(FileName, FileMode.Create))
{
BinaryFormatter bf = new BinaryFormatter();
bf.Serialize(fs, imgWrite);
}
FileInfo info = new FileInfo(FileName);
Console.WriteLine("{0} filesize:{1}bytes", info.Name, info.Length);
using (FileStream fs = new FileStream(FileName, FileMode.Open))
{
BinaryFormatter bf = new BinaryFormatter();
imgRead = (IplImage)bf.Deserialize(fs);
}
Console.WriteLine("Source: width:{0} height:{1} depth:{2} channels:{3} imagesize:{4}",
imgWrite.Width, imgWrite.Height, imgWrite.Depth, imgWrite.NChannels, imgWrite.ImageSize);
Console.WriteLine("Deserialize: width:{0} height:{1} depth:{2} channels:{3} imagesize:{4}",
imgRead.Width, imgRead.Height, imgRead.Depth, imgRead.NChannels, imgRead.ImageSize);
using (new CvWindow("Source Image", imgWrite))
using (new CvWindow("Deserialized Image", imgRead))
{
Cv.WaitKey();
}
}
public SaveImage()
{
using (IplImage img = new IplImage(Const.Image16bit, LoadMode.Color))
{
// JPEG quality test
img.SaveImage("q000.jpg", new JpegEncodingParam(0));
img.SaveImage("q025.jpg", new JpegEncodingParam(25));
img.SaveImage("q050.jpg", new JpegEncodingParam(50));
img.SaveImage("q075.jpg", new JpegEncodingParam(75));
img.SaveImage("q100.jpg", new JpegEncodingParam(100));
using (IplImage q000 = new IplImage("q000.jpg", LoadMode.Color))
using (IplImage q025 = new IplImage("q025.jpg", LoadMode.Color))
using (IplImage q050 = new IplImage("q050.jpg", LoadMode.Color))
using (IplImage q075 = new IplImage("q075.jpg", LoadMode.Color))
using (IplImage q100 = new IplImage("q100.jpg", LoadMode.Color))
using (CvWindow w000 = new CvWindow("quality 0", q000))
using (CvWindow w025 = new CvWindow("quality 25", q025))
using (CvWindow w050 = new CvWindow("quality 50", q050))
using (CvWindow w075 = new CvWindow("quality 75", q075))
using (CvWindow w100 = new CvWindow("quality 100", q100))
{
Cv.WaitKey();
}
}
}
public IplImage Init(string fileName1)
{
_Obj = Cv.LoadImage(fileName1, LoadMode.GrayScale);
_ObjColor = Cv.CreateImage(_Obj.Size, BitDepth.U8, 3);
using (CvMemStorage storage = Cv.CreateMemStorage(0))
{
Cv.CvtColor(_Obj, _ObjColor, ColorConversion.GrayToBgr);
Stopwatch watch = Stopwatch.StartNew();
{
CvSURFParams param = new CvSURFParams(500, true);
Cv.ExtractSURF(_Obj, null, out _ObjectKeypoints, out _ObjectDescriptors, storage, param);
Console.WriteLine("Object Descriptors: {0}", _ObjectDescriptors.Total);
}
watch.Stop();
}
return _Obj;
}
public Resize()
{
// cvResize
// 指定した出力画像サイズに合うように、入力画像のサイズを変更し出力する．
// (1)画像を読み込む
using (IplImage src = new IplImage(Const.ImageSquare5, LoadMode.AnyColor | LoadMode.AnyDepth))
{
// (2)出力用画像領域の確保を行なう
CvSize size = new CvSize(src.Width * 2, src.Height * 2);
using (IplImage dstNN = new IplImage(size, src.Depth, src.NChannels))
using (IplImage dstCubic = new IplImage(size, src.Depth, src.NChannels))
using (IplImage dstLinear = new IplImage(size, src.Depth, src.NChannels))
using (IplImage dstLanczos = new IplImage(size, src.Depth, src.NChannels))
{
// (3)画像のサイズ変更を行う
Cv.Resize(src, dstNN, Interpolation.NearestNeighbor);
Cv.Resize(src, dstCubic, Interpolation.Cubic);
Cv.Resize(src, dstLinear, Interpolation.Linear);
Cv.Resize(src, dstLanczos, Interpolation.Lanczos4);
// (4)結果を表示する
using (new CvWindow("src", src))
using (new CvWindow("dst NearestNeighbor", dstNN))
using (new CvWindow("dst Cubic", dstCubic))
using (new CvWindow("dst Linear", dstLinear))
using (new CvWindow("dst Lanczos4", dstLanczos))
{
Cv.WaitKey();
}
}
}
}
public FitLine()
{
CvSize imageSize = new CvSize(500, 500);
// cvFitLine
CvPoint2D32f[] points = GetRandomPoints(20, imageSize);
CvLine2D line = Cv.FitLine2D(points, DistanceType.L2, 0, 0.01, 0.01);
using (IplImage img = new IplImage(imageSize, BitDepth.U8, 3))
{
img.Zero();
// draw line
{
CvPoint pt1, pt2;
line.FitSize(img.Width, img.Height, out pt1, out pt2);
img.Line(pt1, pt2, CvColor.Green, 1, LineType.Link8);
}
// draw points and distances
using (CvFont font = new CvFont(FontFace.HersheySimplex, 0.33, 0.33))
{
foreach (CvPoint2D32f p in points)
{
double d = line.Distance(p);
img.Circle(p, 2, CvColor.White, -1, LineType.AntiAlias);
img.PutText(string.Format("{0:F1}", d), new CvPoint((int) (p.X + 3), (int) (p.Y + 3)), font, CvColor.Green);
}
}
CvWindow.ShowImages(img);
}
}
/// <summary>
///
/// </summary>
public SeqPartition()
{
CvMemStorage storage = new CvMemStorage(0);
pointSeq = new CvSeq<CvPoint>(SeqType.EltypeS32C2, CvSeq.SizeOf, storage);
Random rand = new Random();
canvas = new IplImage(Width, Height, BitDepth.U8, 3);
colors = new CvScalar[Count];
for (int i = 0; i < Count; i++)
{
CvPoint pt = new CvPoint
{
X = rand.Next(Width),
Y = rand.Next(Height)
};
pointSeq.Push(pt);
int icolor = rand.Next() | 0x00404040;
colors[i] = Cv.RGB(icolor & 255, (icolor >> 8) & 255, (icolor >> 16) & 255);
}
using (window = new CvWindowEx() { Text = "points" })
{
window.CreateTrackbar("threshold", 10, 50, OnTrack);
OnTrack(10);
CvWindowEx.WaitKey();
}
}
public IplImage GetIpl()
{
if (_ipl == null)
{
_ipl = IplImage.FromFile(ImageFilePath);
_ipl.ROI = FaceRect;
}
return(_ipl);
}
public static bool PreProcessFrame(Frame frame, ref Frame lastFrame)
{
OpenCvSharp.IplImage ipl = new OpenCvSharp.IplImage(frame.image);
ipl.IsEnabledDispose = false;
lastFrame = frame;
lastFrame.searchRect = new CvRect(0, 0, ipl.Width, ipl.Height);
return(true);
}
public Frame(OpenCvSharp.IplImage ipl)
{
this.iplImage = ipl;
this.InitializeFields();
}
public PersonOfInterest(IplImage iplImage, IplImage alignedImage)
: this()
{
_ipl = iplImage;
_alignedImage = alignedImage;
}
/// <summary>
///
/// </summary>
/// <param name="imgGray"></param>
/// <param name="pRects"></param>
/// <param name="ptRotate"></param>
/// <param name="dbAngleRotate"></param>
/// <returns></returns>
public bool TrackFace(IplImage imgGray, CvRect[] pRects, out CvPoint ptRotate, out double dbAngleRotate)
{
return(Cv.TrackFace(this, imgGray, pRects, out ptRotate, out dbAngleRotate));
}
public void PyrSegmentation(IplImage dst, CvMemStorage storage, out CvSeq comp, int level, double threshold1,
double threshold2);
private void btnOk_Click(object sender, EventArgs e)
{
if (this.picTargetPerson.Image == null)
{
MessageBox.Show("请选定一张人脸图片");
return;
}
if (drawRectangle == Rectangle.Empty)
{
MessageBox.Show("请定位人脸");
return;
}
String oldFileName = this.picTargetPerson.Image.Tag as string;
String fileName = System.Guid.NewGuid().ToString().ToUpper() + System.IO.Path.GetExtension(oldFileName);
//搜索人脸
OpenCvSharp.IplImage iplFace = BitmapConverter.ToIplImage((Bitmap)this.picTargetPerson.Image);
string savePath = Path.Combine(FileSavePath, fileName);
iplFace.SaveImage(savePath);
//归一化
OpenCvSharp.CvRect rect = new OpenCvSharp.CvRect(
this.drawRectangle.X,
this.drawRectangle.Y,
this.drawRectangle.Width,
this.drawRectangle.Height);
OpenCvSharp.IplImage[] normalizedImages =
Program.searcher.NormalizeImageForTraining(iplFace, rect);
for (int i = 0; i < normalizedImages.Length; ++i)
{
string normalizedFaceName = string.Format("{0}_{1:d4}.jpg",
System.IO.Path.GetFileNameWithoutExtension(fileName), i);
string fullPath = System.IO.Path.Combine(faceFeatureImagePath,
normalizedFaceName);
normalizedImages[i].SaveImage(fullPath);
}
string id = txtId.Text.ToString();
string name = txtName.Text.ToString();
string sex = rabMan.Checked ? "男" : "女";
int age = 0;
int.TryParse(txtAge.Text, out age);
string card = txtCard.Text.ToString();
PersonInfo info = new PersonInfo();
info.ID = id;
info.Name = name;
info.Sex = sex;
info.Age = age;
info.CardId = card;
info.FileName = fileName;
info.Similarity = 0;
perinfo.WriteInfo(info);
MessageBox.Show("添加成功");
Array.ForEach(normalizedImages, ipl => ipl.Dispose());
}
/// <summary>
/// find rectangular regions in the given image that are likely
/// to contain objects and corresponding confidence levels
/// </summary>
/// <param name="image"></param>
/// <param name="storage"></param>
/// <returns></returns>
#else
/// <summary>
/// find rectangular regions in the given image that are likely
/// to contain objects and corresponding confidence levels
/// </summary>
/// <param name="image">image to detect objects in</param>
/// <param name="storage">memory storage to store the resultant sequence of the object candidate rectangles</param>
/// <returns></returns>
#endif
public CvSeq <CvObjectDetection> DetectObjects(IplImage image, CvMemStorage storage)
{
return(Cv.LatentSvmDetectObjects(image, this, storage));
}
public void PyrSegmentation(IplImage dst, int level, double threshold1, double threshold2);
/// <summary>
/// 一つのフレームをビデオファイルに書き込む/追加する
/// </summary>
/// <param name="image">書き込まれるフレーム</param>
/// <returns></returns>
#else
/// <summary>
/// Writes/appends one frame to video file.
/// </summary>
/// <param name="image">the written frame.</param>
/// <returns></returns>
#endif
public int WriteFrame(IplImage image)
{
return(Cv.WriteFrame(this, image));
}
/// <summary>
/// find rectangular regions in the given image that are likely
/// to contain objects and corresponding confidence levels
/// </summary>
/// <param name="image"></param>
/// <param name="storage"></param>
/// <param name="overlap_threshold"></param>
/// <param name="numThreads"></param>
/// <returns></returns>
#else
/// <summary>
/// find rectangular regions in the given image that are likely
/// to contain objects and corresponding confidence levels
/// </summary>
/// <param name="image">image to detect objects in</param>
/// <param name="storage">memory storage to store the resultant sequence of the object candidate rectangles</param>
/// <param name="overlap_threshold">threshold for the non-maximum suppression algorithm
/// = 0.5f [here will be the reference to original paper]</param>
/// <param name="numThreads"></param>
/// <returns></returns>
#endif
public CvSeq <CvObjectDetection> DetectObjects(IplImage image, CvMemStorage storage, float overlap_threshold, int numThreads)
{
return(Cv.LatentSvmDetectObjects(image, this, storage, overlap_threshold, numThreads));
}
