//---------------------------------------------------------
// 関数名 : Update
// 機能 : 1フレーム毎に呼び出される
// 引数 : なし
// 戻り値 : なし
//---------------------------------------------------------
void Update()
{
using (Mat m_img = new Mat())
{
// Webカメラから画像を取得する
video.Read(m_img);
// Mat から IplImage に変換
using (var i_img = m_img.ToIplImage())
{
// OpenCVのデータがBGRなのでHSVに変換
Cv.CvtColor(i_img, i_img, ColorConversion.BgrToHsv);
// 画像のヒストグラム平滑化
Cv.Smooth(i_img, i_img, SmoothType.Median, 5, 0, 0, 0);
// 画像の任意の点からデータ取得
getPointData(i_img, pointResult);
// 点情報から画像を生成（デバッグ用）
getPointImage(i_img, pointResult);
// 画像を画面に表示
using (var r_img = Cv2.CvArrToMat(i_img))
{
// 画像を jpeg にエンコード
texture.LoadImage(r_img.ImEncode(ext));
texture.Apply();
}
}
}
}
/// <summary>
/// Create new generation.
/// </summary>
/// <param name="image"></param>
private void Generation(IplImage image)
{
for (int i = 0; i < GenerationNo; i++)
{
// random no of vertixes
int vertixes = (int)((4.0 * random.NextDouble()) + 1);
// genrate points
OpenCvSharp.CPlusPlus.Point[] points = new OpenCvSharp.CPlusPlus.Point[vertixes];
for (int j = 0; j < vertixes; j++)
{
int y = (int)(image.Height * random.NextDouble());
int x = (int)(image.Width * random.NextDouble());
points[j] = new OpenCvSharp.CPlusPlus.Point(x, y);
}
// color scale
byte colorInt = (byte)(255.0 * random.NextDouble());
Scalar color = new Scalar(colorInt);
List <List <OpenCvSharp.CPlusPlus.Point> > polygon = new List <List <OpenCvSharp.CPlusPlus.Point> >();
polygon.Add(points.ToList());
// draw polygon
children[i] = image.Clone();
Cv2.FillPoly(Cv2.CvArrToMat(children[i]), polygon, color);
}
}
/// <summary>
/// Find best fit in the generation.
/// </summary>
/// <param name="parent"></param>
/// <param name="children"></param>
/// <returns></returns>
private IplImage BestChild(IplImage parent, IplImage[] children)
{
double[] distances = new double[GenerationNo];
for (int i = 0; i < GenerationNo; i++)
{
distances[i] = Cv2.Norm(Cv2.CvArrToMat(parent), Cv2.CvArrToMat(children[i]));
}
int index = Array.IndexOf(distances, distances.Min());
return(children[index]);
}
// Update is called once per frame
void Update()
{
// カメラ画像の取得
i_img = cam.getCameraImage();
// カメラ画像をBGRからHSVへ変換
g.convertBgrToHsv(i_img, h_img);
// 平滑化
g.convertSmooothing(h_img);
// カメラ画像の任意の点からデータ取得
g.getPointData(h_img, ps_arr);
/* デバッグ用（HSV画像表示表示） */
/*---------------------------------------*/
//using (var r_img = Cv2.CvArrToMat(h_img))
//{
// texture.LoadImage(r_img.ImEncode(".jpeg"));
// texture.Apply();
//}
/*---------------------------------------*/
/* デバッグ用（点情報表示） */
/*----------------------------------*/
d_img = g.getPointImage1(d_img, ps_arr);
using (var r_img = Cv2.CvArrToMat(d_img))
{
texture.LoadImage(r_img.ImEncode(".jpeg"));
texture.Apply();
}
/*----------------------------------*/
/* テスト */
//display3Ddot(ps_arr);
/* ------------------ */
}
// Update is called once per frame
void Update()
{
/* デバッグ用（FPS） */
frameCount++;
float time = Time.realtimeSinceStartup - prevTime;
/* ------------------------- */
// 変数初期化
f_vec = new ArrayList();
d_vec = new ArrayList();
// カメラ画像の取得
i_img = cam.getCameraImage();
// カメラ画像をBGRからHSVへ変換
g.convertBgrToHsv(i_img, h_img);
// 平滑化
g.convertSmooothing(h_img);
// カメラ画像の任意の点からデータ取得
g.getPointData(h_img, ps_arr);
// 図形の座標を配列に格納
insertDataToVector2(xdata, ydata, f_vec);
// 画像を初期化（真っ白に）
initImg(d_img);
// 内部に点がある場合
if (isInsideOrOutside(io_flag, ps_arr))
{
// 図形を移動
overrideXYData(xdata, ydata, io_flag);
// 観測点データ初期化
initMFlag(io_flag);
// 図形と観測点の内部外部判定を行う
getIODMonitoringPoint(f_vec, m_vec, io_flag);
}
// 図形を描画
printFigureData(d_img, f_vec);
// 観測点の内部外部判定結果を描写
printIODMonitoringPoint(d_img, io_flag);
// 手情報を描画
printPointData(d_img, ps_arr);
// テクスチャ表示
using (var r_img = Cv2.CvArrToMat(d_img))
{
texture.LoadImage(r_img.ImEncode(".jpeg"));
texture.Apply();
}
/*---------------------------------------*/
/* デバッグ用（FPS） */
/*---------------------------------------*/
//if (time >= 0.5f)
//{
// Debug.LogFormat("{0}fps", frameCount/time);
// frameCount = 0;
// prevTime = Time.realtimeSinceStartup;
//}
/* ------------------------- */
}
//Set Frame image from Webcam to start matching&detection
//img에 Mat 하나를 등록. 본격적인 매칭을 시작하도록 한다
public static void setImg(TestSURF thisobj, Form1 mainform, IplImage imgFromCam)
{
if (thisobj.isComputing)
{
return;
}
thisobj.isComputing = true;
//---frame 특징점, 디스크립터
//---frame image's keypoints and descriptor
KeyPoint[] f_keypoints;
Mat f_descriptor;
Mat imgOrig; //캠 영상 (Original)
Mat img; //캠 영상 (Grayscale)
//Convert to GrayScale Mat
imgOrig = Cv2.CvArrToMat(imgFromCam);
img = new Mat();
Cv2.CvtColor(imgOrig, img, ColorConversion.BgrToGray);
//---------------------1. 디스크립터 추출 (keypoint & descriptor retrieval)
f_keypoints = new KeyPoint[10000];
f_descriptor = new Mat();
f_keypoints = thisobj.surfobj.Detect(img); //SIFT keypoint
thisobj.surfobj.Compute(img, ref f_keypoints, f_descriptor); //SIFT descriptor
//---------------------2. 매칭 (descriptor Matching)
DMatch[] matches = new DMatch[10000];
try
{
matches = thisobj.fm.Match(thisobj.t_descriptor, f_descriptor); //MATCHING
//matching error will be caught in this block
}
catch { return; }
//record proper distances for choosing Good Matches
//좋은 매치를 찾기 위해 디스크립터 간 매칭 거리를 기록한다
double max_dist = 0;
double min_dist = 100;
for (int i = 0; i < thisobj.t_descriptor.Rows; i++)
{
double dist = matches[i].Distance;
if (dist < min_dist)
{
min_dist = dist;
}
if (dist > max_dist)
{
max_dist = dist;
}
}
//---------------------3. gootmatch 탐색 (calculating goodMatches)
List <DMatch> good_matches = new List <DMatch>();
for (int i = 0; i < thisobj.t_descriptor.Rows; i++)
{
if (matches[i].Distance < 3 * min_dist)
{
good_matches.Add(matches[i]);
}
}
/*
* KeyPoint[] goodkey = new KeyPoint[good_matches.Count];
* for(int goodidx = 0; goodidx < good_matches.Count; goodidx++)
* {
* goodkey[goodidx] = new KeyPoint((f_keypoints[good_matches.ElementAt(goodidx).TrainIdx].Pt.X), (f_keypoints[good_matches.ElementAt(goodidx).TrainIdx].Pt.Y), f_keypoints[good_matches.ElementAt(goodidx).TrainIdx].Size);
* }
*/
//Goodmatches의 keypoint 중, target과 frame 이미지에 해당하는 keypoint 정리
Point2d[] target_lo = new Point2d[good_matches.Count];
Point2d[] frame_lo = new Point2d[good_matches.Count];
for (int i = 0; i < good_matches.Count; i++)
{
target_lo[i] = new Point2d(thisobj.t_keypoints[good_matches.ElementAt(i).QueryIdx].Pt.X,
thisobj.t_keypoints[good_matches.ElementAt(i).QueryIdx].Pt.Y);
frame_lo[i] = new Point2d(f_keypoints[good_matches.ElementAt(i).TrainIdx].Pt.X,
f_keypoints[good_matches.ElementAt(i).TrainIdx].Pt.Y);
}
//Homography for RANSAC
Mat hom = new Mat();
//-------------------------------4. RANSAC
hom = Cv2.FindHomography(target_lo, frame_lo, HomographyMethod.Ransac);
Point2d[] frame_corners;
frame_corners = Cv2.PerspectiveTransform(thisobj.obj_corners, hom);
//Mat -> iplimage
//IplImage returnimg = (IplImage)imgOrig;
mainform.setDetectionRec((int)frame_corners[0].X, (int)frame_corners[0].Y,
(int)frame_corners[1].X, (int)frame_corners[1].Y,
(int)frame_corners[2].X, (int)frame_corners[2].Y,
(int)frame_corners[3].X, (int)frame_corners[3].Y);
mainform.isComputing = false;
thisobj.isComputing = false;
//Cv2.DrawKeypoints(imgOrig, goodkey, imgOrig);
//Cv2.DrawKeypoints(img, f_keypoints, img);
//Cv2.ImWrite("temtem.png", img);
return;
}
