//Capture a image from webcam in Timer
private void timer1_Tick(object sender, EventArgs e)
{
/*
* if (capture_type == DO_WITH_IP_CAMERA)
* {
* //capture = CvCapture.FromFile("[IP CAMERA ADDR]");
* capture.SetCaptureProperty(CaptureProperty.FrameWidth, 680);
* capture.SetCaptureProperty(CaptureProperty.FrameHeight, 480);
* }
*/
//Retrieve image as Iplimage to resize
//이미지를 IplImage 타입으로 추출하여 가져온 후, resize한 이미지를 사용
IplImage temimg = new IplImage(680, 480, BitDepth.U8, 3);
temimg = capture.QueryFrame();
if (temimg == null)
{
return;
}
imgSrc = Cv.CreateImage(new CvSize(680, 480), BitDepth.U8, 3);
Cv.Resize(temimg, imgSrc, Interpolation.Linear);
Cv.ReleaseImage(temimg);
//Object Detection will be implmented by running thread.
//If thread is running already, this procedure is to be skipped.
//[This allows user see smooth webcam streaming]
//영역 계산 중이라면 스킵. 계산 중이 아니라면 영역 계산 스레드를 실행
if (!isComputing)
{
isComputing = true;
ComputingSURF = new Thread(() => TestSURF.setImg(tsurf, this, imgSrc));
ComputingSURF.Start();
}
//Draw Object Rectangle with cvLine
//가지고 있는 영역 값을 이용해 매 영상마다 영역은 항상 표시하도록 한다.
drawRec(rectanPts);
pictureBoxIpl1.ImageIpl = imgSrc;
}
static int DO_WITH_IP_CAMERA = 1; //IP 웹캠(안드로이드도 된다!)
public Form1()
{
InitializeComponent();
isComputing = false;
//capture_type = DO_WITH_IP_CAMERA; //웹캠 or IP웹캠
capture_type = DO_WITH_DIRECT_WEBCAM;
rectanPts = new OpenCvSharp.CPlusPlus.Point[4];
//init
for (int i = 0; i < 4; i++)
{
rectanPts[i].X = rectanPts[i].Y = 0;
}
//TestSIFT는 SURF+kdTreeMatching+RANSAC 을 구현하여 영역을 추출한다.
tsurf = new TestSURF();
tsurf.setTarget("origim.png"); //검색하려는 이미지 파일명
}
//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;
}
