public static float[] ResizeIplTo(IplImage Face, int width, int height)
{
IplImage smallerFace =
new IplImage(new OpenCvSharp.CvSize(width, height),
Face.Depth, Face.NChannels);
Face.Resize(smallerFace, Interpolation.Linear);
unsafe
{
byte* smallFaceData = smallerFace.ImageDataPtr;
float[] currentFace = new float[width * height * smallerFace.NChannels * BytesPerPixel(Face.Depth)];
for (int i = 0; i < smallerFace.Height; i++)
{
for (int j = 0; j < smallerFace.Width; j++)
{
currentFace[i * smallerFace.WidthStep + j] =
(float)smallFaceData[i * smallerFace.WidthStep + j];
}
}
smallerFace.Dispose();
return currentFace;
}
}
public FindContours()
{
// cvFindContoursm cvDrawContours
// 画像中から輪郭を検出し，-1~+1までのレベルにある輪郭を描画する
const int SIZE = 500;
using (IplImage img = new IplImage(SIZE, SIZE, BitDepth.U8, 1))
{
// 画像の初期化
img.Zero();
for (int i = 0; i < 6; i++)
{
int dx = (i % 2) * 250 - 30;
int dy = (i / 2) * 150;
if (i == 0)
{
for (int j = 0; j <= 10; j++)
{
double angle = (j + 5) * Cv.PI / 21;
CvPoint p1 = new CvPoint(Cv.Round(dx + 100 + j * 10 - 80 * Math.Cos(angle)), Cv.Round(dy + 100 - 90 * Math.Sin(angle)));
CvPoint p2 = new CvPoint(Cv.Round(dx + 100 + j * 10 - 30 * Math.Cos(angle)), Cv.Round(dy + 100 - 30 * Math.Sin(angle)));
Cv.Line(img, p1, p2, CvColor.White, 1, LineType.AntiAlias, 0);
}
}
Cv.Ellipse(img, new CvPoint(dx + 150, dy + 100), new CvSize(100, 70), 0, 0, 360, CvColor.White, -1, LineType.AntiAlias, 0);
Cv.Ellipse(img, new CvPoint(dx + 115, dy + 70), new CvSize(30, 20), 0, 0, 360, CvColor.Black, -1, LineType.AntiAlias, 0);
Cv.Ellipse(img, new CvPoint(dx + 185, dy + 70), new CvSize(30, 20), 0, 0, 360, CvColor.Black, -1, LineType.AntiAlias, 0);
Cv.Ellipse(img, new CvPoint(dx + 115, dy + 70), new CvSize(15, 15), 0, 0, 360, CvColor.White, -1, LineType.AntiAlias, 0);
Cv.Ellipse(img, new CvPoint(dx + 185, dy + 70), new CvSize(15, 15), 0, 0, 360, CvColor.White, -1, LineType.AntiAlias, 0);
Cv.Ellipse(img, new CvPoint(dx + 115, dy + 70), new CvSize(5, 5), 0, 0, 360, CvColor.Black, -1, LineType.AntiAlias, 0);
Cv.Ellipse(img, new CvPoint(dx + 185, dy + 70), new CvSize(5, 5), 0, 0, 360, CvColor.Black, -1, LineType.AntiAlias, 0);
Cv.Ellipse(img, new CvPoint(dx + 150, dy + 100), new CvSize(10, 5), 0, 0, 360, CvColor.Black, -1, LineType.AntiAlias, 0);
Cv.Ellipse(img, new CvPoint(dx + 150, dy + 150), new CvSize(40, 10), 0, 0, 360, CvColor.Black, -1, LineType.AntiAlias, 0);
Cv.Ellipse(img, new CvPoint(dx + 27, dy + 100), new CvSize(20, 35), 0, 0, 360, CvColor.White, -1, LineType.AntiAlias, 0);
Cv.Ellipse(img, new CvPoint(dx + 273, dy + 100), new CvSize(20, 35), 0, 0, 360, CvColor.White, -1, LineType.AntiAlias, 0);
}
// 輪郭の検出
CvSeq<CvPoint> contours;
CvMemStorage storage = new CvMemStorage();
// native style
Cv.FindContours(img, storage, out contours, CvContour.SizeOf, ContourRetrieval.Tree, ContourChain.ApproxSimple);
contours = Cv.ApproxPoly(contours, CvContour.SizeOf, storage, ApproxPolyMethod.DP, 3, true);
// wrapper style
//img.FindContours(storage, out contours, ContourRetrieval.Tree, ContourChain.ApproxSimple);
//contours = contours.ApproxPoly(storage, ApproxPolyMethod.DP, 3, true);
// ウィンドウに表示
using (CvWindow window_image = new CvWindow("image", img))
using (CvWindow window_contours = new CvWindow("contours"))
{
CvTrackbarCallback onTrackbar = delegate(int pos)
{
IplImage cnt_img = new IplImage(SIZE, SIZE, BitDepth.U8, 3);
CvSeq<CvPoint> _contours = contours;
int levels = pos - 3;
if (levels <= 0) // get to the nearest face to make it look more funny
{
//_contours = _contours.HNext.HNext.HNext;
}
cnt_img.Zero();
Cv.DrawContours(cnt_img, _contours, CvColor.Red, CvColor.Green, levels, 3, LineType.AntiAlias);
window_contours.ShowImage(cnt_img);
cnt_img.Dispose();
};
window_contours.CreateTrackbar("levels+3", 3, 7, onTrackbar);
onTrackbar(3);
Cv.WaitKey();
}
}
}
/// <summary>
/// returns sequence of squares detected on the image.
/// the sequence is stored in the specified memory storage
/// </summary>
/// <param name="img"></param>
/// <param name="storage"></param>
/// <returns></returns>
static CvPoint[] FindSquares4(IplImage img, CvMemStorage storage)
{
const int N = 11;
CvSize sz = new CvSize(img.Width & -2, img.Height & -2);
IplImage timg = img.Clone(); // make a copy of input image
IplImage gray = new IplImage(sz, BitDepth.U8, 1);
IplImage pyr = new IplImage(sz.Width / 2, sz.Height / 2, BitDepth.U8, 3);
// create empty sequence that will contain points -
// 4 points per square (the square's vertices)
CvSeq<CvPoint> squares = new CvSeq<CvPoint>(SeqType.Zero, CvSeq.SizeOf, storage);
// select the maximum ROI in the image
// with the width and height divisible by 2
timg.ROI = new CvRect(0, 0, sz.Width, sz.Height);
// down-Scale and upscale the image to filter out the noise
Cv.PyrDown(timg, pyr, CvFilter.Gaussian5x5);
Cv.PyrUp(pyr, timg, CvFilter.Gaussian5x5);
IplImage tgray = new IplImage(sz, BitDepth.U8, 1);
// find squares in every color plane of the image
for (int c = 0; c < 3; c++)
{
// extract the c-th color plane
timg.COI = c + 1;
Cv.Copy(timg, tgray, null);
// try several threshold levels
for (int l = 0; l < N; l++)
{
// hack: use Canny instead of zero threshold level.
// Canny helps to catch squares with gradient shading
if (l == 0)
{
// apply Canny. Take the upper threshold from slider
// and set the lower to 0 (which forces edges merging)
Cv.Canny(tgray, gray, 0, Thresh, ApertureSize.Size5);
// dilate canny output to remove potential
// holes between edge segments
Cv.Dilate(gray, gray, null, 1);
}
else
{
// apply threshold if l!=0:
// tgray(x,y) = gray(x,y) < (l+1)*255/N ? 255 : 0
Cv.Threshold(tgray, gray, (l + 1) * 255.0 / N, 255, ThresholdType.Binary);
}
// find contours and store them all as a list
CvSeq<CvPoint> contours;
Cv.FindContours(gray, storage, out contours, CvContour.SizeOf, ContourRetrieval.List, ContourChain.ApproxSimple, new CvPoint(0, 0));
// test each contour
while (contours != null)
{
// approximate contour with accuracy proportional
// to the contour perimeter
CvSeq<CvPoint> result = Cv.ApproxPoly(contours, CvContour.SizeOf, storage, ApproxPolyMethod.DP, contours.ContourPerimeter() * 0.02, false);
// square contours should have 4 vertices after approximation
// relatively large area (to filter out noisy contours)
// and be convex.
// Note: absolute value of an area is used because
// area may be positive or negative - in accordance with the
// contour orientation
if (result.Total == 4 && Math.Abs(result.ContourArea(CvSlice.WholeSeq)) > 1000 && result.CheckContourConvexity())
{
double s = 0;
for (int i = 0; i < 5; i++)
{
// find minimum Angle between joint
// edges (maximum of cosine)
if (i >= 2)
{
double t = Math.Abs(Angle(result[i].Value, result[i - 2].Value, result[i - 1].Value));
s = s > t ? s : t;
}
}
// if cosines of all angles are small
// (all angles are ~90 degree) then write quandrange
// vertices to resultant sequence
if (s < 0.3)
{
for (int i = 0; i < 4; i++)
{
//Console.WriteLine(result[i]);
squares.Push(result[i].Value);
}
}
}
// take the next contour
contours = contours.HNext;
}
}
}
// release all the temporary images
gray.Dispose();
pyr.Dispose();
tgray.Dispose();
timg.Dispose();
return squares.ToArray();
}
static void Main(string[] args)
{
// CreateCameraCaptureの引数はカメラのIndex(通常は0から始まる)
using (var capture = Cv.CreateCameraCapture(0))
{
Console.WriteLine("Hit any key to quit");
/*
double fps=12.0;
int interval=1;
double zoom=1.0;
string OutputFile;
*/
double fps ;
int interval ;
double zoom=1.0 ;
string OutputFile;
var opts = new Options();
bool isSuccess = CommandLine.Parser.Default.ParseArguments(args, opts);
if(!isSuccess)
{
opts.GetUsage();
Console.WriteLine(Environment.GetCommandLineArgs()[0] + " -o Outputfilename(string) -f fps(double) -i CaptureInterval(int)");
Environment.Exit(0);
}
fps = opts.fps;
interval = opts.interval;
zoom = opts.zoom;
OutputFile = opts.OutputFile;
Console.WriteLine(OutputFile);
if (fps > 30 | interval < 0.1)
{
Console.WriteLine(" :-p");
Environment.Exit(1);
}
Int32 codec = 0; // コーデック(AVI)
IplImage frame = new IplImage();
/*
double width = capture.FrameWidth/2;
double height = capture.FrameHeight/2;
//double width = 640, height = 240;
Cv.SetCaptureProperty(capture, CaptureProperty.FrameWidth, width);
Cv.SetCaptureProperty(capture, CaptureProperty.FrameHeight, height);
CvSize size = new CvSize((int)width, (int)height);
CvVideoWriter vw = new CvVideoWriter(OutputFile, codec, fps, size, true);
*/
int width = (int)(Cv.GetCaptureProperty(capture, CaptureProperty.FrameWidth)*zoom);
int height = (int)(Cv.GetCaptureProperty(capture, CaptureProperty.FrameHeight)*zoom);
//Cv.SetCaptureProperty(capture, CaptureProperty.FrameWidth, width);
//Cv.SetCaptureProperty(capture, CaptureProperty.FrameWidth, height);
//Bitmap bitmap = new Bitmap(width, height);
CvSize size = new CvSize(width, height);
CvVideoWriter vw = new CvVideoWriter(OutputFile, codec, fps, size, true);
//CvFont font = new CvFont(FontFace.HersheyTriplex, 0.7, 0.7);
//(FontFace.HersheyPlain, 1.0, 1.0, 0, 2);
double fontSize;
if(width>600)
fontSize=1.0;
else
fontSize=0.5;
CvFont font = new CvFont(FontFace.HersheyPlain,fontSize,fontSize);
// 何かキーを押すまでは、Webカメラの画像を表示し続ける
while (Cv.WaitKey(1) == -1)
{
System.Threading.Thread.Sleep(1000*interval);
// カメラからフレームを取得
frame = Cv.QueryFrame(capture);
string str = DateTime.Now.ToString();
// Window「Capture」を作って、Webカメラの画像を表示
if (frame != null)
{
frame.PutText(str, new CvPoint(10, 20), font, new CvColor(200,100,50));
Cv.ShowImage("Timelapse", frame);
//frame.SaveImage("result.bmp");
//bitmap = BitmapConverter.ToBitmap(frame);
//OpenCvSharp.IplImage ipl2 = (OpenCvSharp.IplImage)BitmapConverter.ToIplImage(bitmap);
vw.WriteFrame(frame);
// vw.WriteFrame(ipl2);
frame.Dispose();
}
}
Cv.DestroyWindow("Capture");
vw.Dispose();
}
}
/// <summary>
/// 認識処理を行う
/// </summary>
/// <param name="imagePath">認識対象の画像パス</param>
/// <param name="isDebug">デバッグモード</param>
/// <returns></returns>
public static String Recognize(String imagePath, bool isDebug = false)
{
List<String> results = new List<string>();
// 検出対象の画像を読み込み
IplImage src = new IplImage(imagePath, LoadMode.GrayScale);
using (IplImage tmpImage = new IplImage(src.Size, BitDepth.U8, 1))
{
// 1)検出前処理
// エッジ強調
src.UnsharpMasking(src, 3);
// 大津の手法による二値化処理
// 大津, "判別および最小２乗基準に基づく自動しきい値選定法", 電子通信学会論文誌, Vol.J63-D, No.4, pp.349-356, 1980.
src.Threshold(tmpImage, 200, 250, ThresholdType.Otsu);
src.Dispose();
Dictionary<int, List<double>> shapeMatchResults = new Dictionary<int, List<double>>();
List<string> answerFileNames = washTagDictionary.Keys.ToList();
foreach (var answerFileName in answerFileNames)
{
var washTagInfo = washTagDictionary[answerFileName];
var answerImagePath = String.Format(@"answer\{0}.png", answerFileName);
// 2) 検出処理
var resultSURF = SURF(tmpImage, answerImagePath, isDebug);
// 3) 検出候補の評価
string result = null;
// その１：頂点がある場合
if (resultSURF.dstCorners != null)
{
// TODO:平面評価
//result = fileBaseName + " : " + washTagDictionary[fileBaseName];
}
// その２：形状マッチング
if (result == null && resultSURF.findPointList.Count > 0)
{
// ROIの1辺は、横に4つ位入る大きさで（何となくｗ）
CvSize roiSize = new CvSize(tmpImage.Width / 4, tmpImage.Width / 4);
List<double> matchResults = new List<double>();
foreach (var findPoint in resultSURF.findPointList)
{
// ROIを設定
tmpImage.SetROI(
(int)findPoint.Pt.X - roiSize.Width / 2,
(int)findPoint.Pt.Y - roiSize.Height / 2,
roiSize.Width, roiSize.Height
);
// Huモーメントによる形状マッチング [回転・スケーリング・反転に強い]
matchResults.Add(
CompareShapeMoment(tmpImage, answerImagePath, MatchShapesMethod.I1)
);
// ROIをリセット
tmpImage.ResetROI();
}
// 閾値以下だった場合に検出と見なす
if (matchResults.Min() < 0.005)
{
// カテゴリに値が無ければ確保
if (shapeMatchResults.ContainsKey(washTagInfo.CategoryNo) == false)
{
shapeMatchResults.Add(washTagInfo.CategoryNo, new List<double>());
}
shapeMatchResults[washTagInfo.CategoryNo].Add(matchResults.Min());
}
}
}
// 4)認識結果の整理
foreach (var categoryNo in shapeMatchResults.Keys)
{
var matchResult = shapeMatchResults[categoryNo];
var min = matchResult.Min();
var index = matchResult.FindIndex((x) =>
{
return x == min;
});
var id = String.Format("{0:0}{1:00}", categoryNo, index + 1);
var recognitionWashTag = washTagDictionary[id];
// 結果を格納
results.Add(
String.Format(isDebug ? "{0} : {1} ({2})" : "{0} : {1}", id, recognitionWashTag.Description, min)
);
}
// デバッグ表示
if (isDebug)
{
using (CvWindow win = new CvWindow("image", tmpImage))
{
CvWindow.WaitKey();
}
}
}
return results.Count > 0
? String.Join("\n", results.ToArray())
: "検出する事が出来ませんでした。";
}
public static IplImage GetTrainingExample(System.Drawing.Size size, string fileName)
{
IplImage src = new IplImage(fileName);
IplImage dst = new IplImage(src.Size, src.Depth, src.NChannels);
Cv.Resize(src, dst, Interpolation.Linear);
src.Dispose();
return dst;
}
