// Draw matches between two images
public static Mat getMatchesImage(Mat query, Mat pattern, MatOfKeyPoint queryKp, MatOfKeyPoint trainKp, MatOfDMatch matches, int maxMatchesDrawn)
{
Mat outImg = new Mat();
List <DMatch> matchesList = matches.toList();
if (matchesList.Count > maxMatchesDrawn)
{
matchesList.RemoveRange(maxMatchesDrawn, matchesList.Count - maxMatchesDrawn);
}
MatOfDMatch tmpMatches = new MatOfDMatch();
tmpMatches.fromList(matchesList);
Features2d.drawMatches
(
query,
queryKp,
pattern,
trainKp,
tmpMatches,
outImg,
new Scalar(0, 200, 0, 255),
Scalar.all(-1),
new MatOfByte()
);
return(outImg);
}
/// <summary>
/// Gets the matches.
/// </summary>
/// <param name="queryDescriptors">Query descriptors.</param>
/// <param name="matches">Matches.</param>
void getMatches(Mat queryDescriptors, MatOfDMatch matches)
{
List <DMatch> matchesList = new List <DMatch>();
//matches.clear();
if (enableRatioTest)
{
// To avoid NaN's when best match has zero distance we will use inversed ratio.
float minRatio = 1.0f / 1.5f;
// KNN match will return 2 nearest matches for each query descriptor
m_matcher.knnMatch(queryDescriptors, m_knnMatches, 2);
for (int i = 0; i < m_knnMatches.Count; i++)
{
List <DMatch> m_knnMatchesList = m_knnMatches[i].toList();
DMatch bestMatch = m_knnMatchesList[0];
DMatch betterMatch = m_knnMatchesList[1];
float distanceRatio = bestMatch.distance / betterMatch.distance;
// Pass only matches where distance ratio between
// nearest matches is greater than 1.5 (distinct criteria)
if (distanceRatio < minRatio)
{
matchesList.Add(bestMatch);
}
}
matches.fromList(matchesList);
}
else
{
matches.fromList(matchesList);
// Perform regular match
m_matcher.match(queryDescriptors, matches);
}
//Debug.Log ("getMatches " + matches.ToString ());
}
/// <summary>
/// Refines the matches with homography.
/// </summary>
/// <returns><c>true</c>, if matches with homography was refined, <c>false</c> otherwise.</returns>
/// <param name="queryKeypoints">Query keypoints.</param>
/// <param name="trainKeypoints">Train keypoints.</param>
/// <param name="reprojectionThreshold">Reprojection threshold.</param>
/// <param name="matches">Matches.</param>
/// <param name="homography">Homography.</param>
static bool refineMatchesWithHomography
(
MatOfKeyPoint queryKeypoints,
MatOfKeyPoint trainKeypoints,
float reprojectionThreshold,
MatOfDMatch matches,
Mat homography
)
{
//Debug.Log ("matches " + matches.ToString ());
int minNumberMatchesAllowed = 8;
List <KeyPoint> queryKeypointsList = queryKeypoints.toList();
List <KeyPoint> trainKeypointsList = trainKeypoints.toList();
List <DMatch> matchesList = matches.toList();
if (matchesList.Count < minNumberMatchesAllowed)
{
return(false);
}
// Prepare data for cv::findHomography
List <Point> srcPointsList = new List <Point>(matchesList.Count);
List <Point> dstPointsList = new List <Point>(matchesList.Count);
for (int i = 0; i < matchesList.Count; i++)
{
srcPointsList.Add(trainKeypointsList[matchesList[i].trainIdx].pt);
dstPointsList.Add(queryKeypointsList[matchesList[i].queryIdx].pt);
}
// Find homography matrix and get inliers mask
using (MatOfPoint2f srcPoints = new MatOfPoint2f())
using (MatOfPoint2f dstPoints = new MatOfPoint2f())
using (MatOfByte inliersMask = new MatOfByte(new byte[srcPointsList.Count]))
{
srcPoints.fromList(srcPointsList);
dstPoints.fromList(dstPointsList);
//Debug.Log ("srcPoints " + srcPoints.ToString ());
//Debug.Log ("dstPoints " + dstPoints.ToString ());
Calib3d.findHomography(srcPoints,
dstPoints,
Calib3d.FM_RANSAC,
reprojectionThreshold,
inliersMask, 2000, 0.955).copyTo(homography);
if (homography.rows() != 3 || homography.cols() != 3)
{
return(false);
}
//Debug.Log ("homography " + homography.ToString ());
//Debug.Log ("inliersMask " + inliersMask.dump ());
List <byte> inliersMaskList = inliersMask.toList();
List <DMatch> inliers = new List <DMatch>();
for (int i = 0; i < inliersMaskList.Count; i++)
{
if (inliersMaskList[i] == 1)
{
inliers.Add(matchesList[i]);
}
}
matches.fromList(inliers);
//Debug.Log ("matches " + matches.ToString ());
}
return(matchesList.Count > minNumberMatchesAllowed);
}
public bool descriptorsORB_Old(Mat RGB, Mat cameraFeed, string targetName)//找出特徵的顏色方法三(可運行但效率不佳放棄)
{
if (RGB == null)
{
Debug.Log("RGB Mat is Null");
return(false);
}
//將傳入的RGB存入Src
Mat SrcMat = new Mat();
RGB.copyTo(SrcMat);
//比對樣本
Texture2D imgTexture = Resources.Load(targetName) as Texture2D;
// Texture2D imgTexture2 = Resources.Load("lenaK") as Texture2D;
//Texture2D轉Mat
Mat img1Mat = new Mat(imgTexture.height, imgTexture.width, CvType.CV_8UC3);
Utils.texture2DToMat(imgTexture, img1Mat);
//創建 ORB的特徵點裝置
FeatureDetector detector = FeatureDetector.create(FeatureDetector.ORB);
DescriptorExtractor extractor = DescriptorExtractor.create(DescriptorExtractor.ORB);
//產生存放特徵點Mat
MatOfKeyPoint keypoints1 = new MatOfKeyPoint();
Mat descriptors1 = new Mat();
MatOfKeyPoint keypointsSrc = new MatOfKeyPoint();
Mat descriptorsSrc = new Mat();
//找特徵點圖1
detector.detect(img1Mat, keypoints1);
extractor.compute(img1Mat, keypoints1, descriptors1);
//找特徵點圖Src
detector.detect(SrcMat, keypointsSrc);
extractor.compute(SrcMat, keypointsSrc, descriptorsSrc);
DescriptorMatcher matcher = DescriptorMatcher.create(DescriptorMatcher.BRUTEFORCE_HAMMINGLUT);
MatOfDMatch matches = new MatOfDMatch();
matcher.match(descriptors1, descriptorsSrc, matches);
DMatch[] arrayDmatch = matches.toArray();
for (int i = arrayDmatch.Length - 1; i >= 0; i--)
{
// Debug.Log("match " + i + ": " + arrayDmatch[i].distance);
}
//做篩選
double max_dist = 0;
double min_dist = 100;
//-- Quick calculation of max and min distances between keypoints
double dist = new double();
for (int i = 0; i < matches.rows(); i++)
{
dist = arrayDmatch[i].distance;
if (dist < min_dist)
{
min_dist = dist;
}
if (dist > max_dist)
{
max_dist = dist;
}
}
Debug.Log("Max dist :" + max_dist);
Debug.Log("Min dist :" + min_dist);
//只畫好的點
List <DMatch> matchesGoodList = new List <DMatch>();
for (int i = 0; i < matches.rows(); i++)
{
//if (arrayDmatch[i].distance < RateDist.value * min_dist)
//{
// //Debug.Log("match " + i + ": " + arrayDmatch[i].distance);
// matchesGoodList.Add(arrayDmatch[i]);
//}
}
MatOfDMatch matchesGood = new MatOfDMatch();
matchesGood.fromList(matchesGoodList);
//Draw Keypoints
Features2d.drawKeypoints(SrcMat, keypointsSrc, SrcMat);
//做輸出的轉換予宣告
Mat resultImg = new Mat();
// Features2d.drawMatches(img1Mat, keypoints1, SrcMat, keypointsSrc, matchesGood, resultImg);
List <Point> P1 = new List <Point>();
// List<Point> P2 = new List<Point>();
List <Point> pSrc = new List <Point>();
Debug.Log("MatchCount" + matchesGoodList.Count);
for (int i = 0; i < matchesGoodList.Count; i++)
{
P1.Add(new Point(keypoints1.toArray()[matchesGoodList[i].queryIdx].pt.x, keypoints1.toArray()[matchesGoodList[i].queryIdx].pt.y));
pSrc.Add(new Point(keypointsSrc.toArray()[matchesGoodList[i].trainIdx].pt.x, keypointsSrc.toArray()[matchesGoodList[i].trainIdx].pt.y));
//Debug.Log("ID = " + matchesGoodList[i].queryIdx );
//Debug.Log("x,y =" + (int)keypoints1.toArray()[matchesGoodList[i].queryIdx].pt.x + "," + (int)keypoints1.toArray()[matchesGoodList[i].queryIdx].pt.y);
//Debug.Log("x,y =" + (int)keypoints2.toArray()[matchesGoodList[i].trainIdx].pt.x + "," + (int)keypoints2.toArray()[matchesGoodList[i].trainIdx].pt.y);
}
MatOfPoint2f p2fTarget = new MatOfPoint2f(P1.ToArray());
MatOfPoint2f p2fSrc = new MatOfPoint2f(pSrc.ToArray());
Mat matrixH = Calib3d.findHomography(p2fTarget, p2fSrc, Calib3d.RANSAC, 3);
List <Point> srcPointCorners = new List <Point>();
srcPointCorners.Add(new Point(0, 0));
srcPointCorners.Add(new Point(img1Mat.width(), 0));
srcPointCorners.Add(new Point(img1Mat.width(), img1Mat.height()));
srcPointCorners.Add(new Point(0, img1Mat.height()));
Mat originalRect = Converters.vector_Point2f_to_Mat(srcPointCorners);
List <Point> srcPointCornersEnd = new List <Point>();
srcPointCornersEnd.Add(new Point(0, img1Mat.height()));
srcPointCornersEnd.Add(new Point(0, 0));
srcPointCornersEnd.Add(new Point(img1Mat.width(), 0));
srcPointCornersEnd.Add(new Point(img1Mat.width(), img1Mat.height()));
Mat changeRect = Converters.vector_Point2f_to_Mat(srcPointCornersEnd);
Core.perspectiveTransform(originalRect, changeRect, matrixH);
List <Point> srcPointCornersSave = new List <Point>();
Converters.Mat_to_vector_Point(changeRect, srcPointCornersSave);
if ((srcPointCornersSave[2].x - srcPointCornersSave[0].x) < 5 || (srcPointCornersSave[2].y - srcPointCornersSave[0].y) < 5)
{
Debug.Log("Match Out Put image is to small");
SrcMat.copyTo(cameraFeed);
SrcMat.release();
Imgproc.putText(cameraFeed, "X-S", new Point(10, 50), 0, 1, new Scalar(255, 255, 255), 2);
return(false);
}
// Features2d.drawMatches(img1Mat, keypoints1, SrcMat, keypointsSrc, matchesGood, resultImg);
Imgproc.line(SrcMat, srcPointCornersSave[0], srcPointCornersSave[1], new Scalar(255, 0, 0), 3);
Imgproc.line(SrcMat, srcPointCornersSave[1], srcPointCornersSave[2], new Scalar(255, 0, 0), 3);
Imgproc.line(SrcMat, srcPointCornersSave[2], srcPointCornersSave[3], new Scalar(255, 0, 0), 3);
Imgproc.line(SrcMat, srcPointCornersSave[3], srcPointCornersSave[0], new Scalar(255, 0, 0), 3);
SrcMat.copyTo(cameraFeed);
keypoints1.release();
img1Mat.release();
SrcMat.release();
return(true);
}
//============================================================
//=================以下為沒有再使用的函式=====================
//============================================================
//找出特徵的顏色方法三(ORB特徵點比對)
public bool descriptorsORB(Mat RGB, Mat cameraFeed, string targetName)
{
if (RGB == null)
{
Debug.Log("RGB Mat is Null");
return(false);
}
//將傳入的RGB存入Src
Mat SrcMat = new Mat();
RGB.copyTo(SrcMat);
//比對樣本載入
Texture2D imgTexture = Resources.Load(targetName) as Texture2D;
//Texture2D轉Mat
Mat targetMat = new Mat(imgTexture.height, imgTexture.width, CvType.CV_8UC3);
Utils.texture2DToMat(imgTexture, targetMat);
//創建 ORB的特徵點裝置
FeatureDetector detector = FeatureDetector.create(FeatureDetector.ORB);
DescriptorExtractor extractor = DescriptorExtractor.create(DescriptorExtractor.ORB);
//產生存放特徵點Mat
MatOfKeyPoint keypointsTarget = new MatOfKeyPoint();
Mat descriptorsTarget = new Mat();
MatOfKeyPoint keypointsSrc = new MatOfKeyPoint();
Mat descriptorsSrc = new Mat();
//找特徵點圖Target
detector.detect(targetMat, keypointsTarget);
extractor.compute(targetMat, keypointsTarget, descriptorsTarget);
//找特徵點圖Src
detector.detect(SrcMat, keypointsSrc);
extractor.compute(SrcMat, keypointsSrc, descriptorsSrc);
//創建特徵點比對物件
DescriptorMatcher matcher = DescriptorMatcher.create(DescriptorMatcher.BRUTEFORCE_HAMMINGLUT);
MatOfDMatch matches = new MatOfDMatch();
//丟入兩影像的特徵點
matcher.match(descriptorsTarget, descriptorsSrc, matches);
DMatch[] arrayDmatch = matches.toArray();
//做篩選
double max_dist = 0;
double min_dist = 100;
//-- Quick calculation of max and min distances between keypoints
double dist = new double();
for (int i = 0; i < matches.rows(); i++)
{
dist = arrayDmatch[i].distance;
if (dist < min_dist)
{
min_dist = dist;
}
if (dist > max_dist)
{
max_dist = dist;
}
}
Debug.Log("Max dist :" + max_dist);
Debug.Log("Min dist :" + min_dist);
List <DMatch> matchesGoodList = new List <DMatch>();
MatOfDMatch matchesGood = new MatOfDMatch();
matchesGood.fromList(matchesGoodList);
//Draw Keypoints
Features2d.drawKeypoints(SrcMat, keypointsSrc, SrcMat);
List <Point> pTarget = new List <Point>();
List <Point> pSrc = new List <Point>();
Debug.Log("MatchCount" + matchesGoodList.Count);
for (int i = 0; i < matchesGoodList.Count; i++)
{
pTarget.Add(new Point(keypointsTarget.toArray()[matchesGoodList[i].queryIdx].pt.x, keypointsTarget.toArray()[matchesGoodList[i].queryIdx].pt.y));
pSrc.Add(new Point(keypointsSrc.toArray()[matchesGoodList[i].trainIdx].pt.x, keypointsSrc.toArray()[matchesGoodList[i].trainIdx].pt.y));
}
MatOfPoint2f p2fTarget = new MatOfPoint2f(pTarget.ToArray());
MatOfPoint2f p2fSrc = new MatOfPoint2f(pSrc.ToArray());
Mat matrixH = Calib3d.findHomography(p2fTarget, p2fSrc, Calib3d.RANSAC, 3);
List <Point> srcPointCorners = new List <Point>();
srcPointCorners.Add(new Point(0, 0));
srcPointCorners.Add(new Point(targetMat.width(), 0));
srcPointCorners.Add(new Point(targetMat.width(), targetMat.height()));
srcPointCorners.Add(new Point(0, targetMat.height()));
Mat originalRect = Converters.vector_Point2f_to_Mat(srcPointCorners);
List <Point> srcPointCornersEnd = new List <Point>();
srcPointCornersEnd.Add(new Point(0, targetMat.height()));
srcPointCornersEnd.Add(new Point(0, 0));
srcPointCornersEnd.Add(new Point(targetMat.width(), 0));
srcPointCornersEnd.Add(new Point(targetMat.width(), targetMat.height()));
Mat changeRect = Converters.vector_Point2f_to_Mat(srcPointCornersEnd);
Core.perspectiveTransform(originalRect, changeRect, matrixH);
List <Point> srcPointCornersSave = new List <Point>();
Converters.Mat_to_vector_Point(changeRect, srcPointCornersSave);
if ((srcPointCornersSave[2].x - srcPointCornersSave[0].x) < 5 || (srcPointCornersSave[2].y - srcPointCornersSave[0].y) < 5)
{
Debug.Log("Match Out Put image is to small");
SrcMat.copyTo(cameraFeed);
SrcMat.release();
Imgproc.putText(cameraFeed, targetName, srcPointCornersSave[0], 0, 1, new Scalar(255, 255, 255), 2);
return(false);
}
//畫出框框
Imgproc.line(SrcMat, srcPointCornersSave[0], srcPointCornersSave[1], new Scalar(255, 0, 0), 3);
Imgproc.line(SrcMat, srcPointCornersSave[1], srcPointCornersSave[2], new Scalar(255, 0, 0), 3);
Imgproc.line(SrcMat, srcPointCornersSave[2], srcPointCornersSave[3], new Scalar(255, 0, 0), 3);
Imgproc.line(SrcMat, srcPointCornersSave[3], srcPointCornersSave[0], new Scalar(255, 0, 0), 3);
//畫中心
Point middlePoint = new Point((srcPointCornersSave[0].x + srcPointCornersSave[2].x) / 2, (srcPointCornersSave[0].y + srcPointCornersSave[2].y) / 2);
Imgproc.line(SrcMat, middlePoint, middlePoint, new Scalar(0, 0, 255), 10);
SrcMat.copyTo(cameraFeed);
keypointsTarget.release();
targetMat.release();
SrcMat.release();
return(true);
}
void Orb()
{
p1Mat = Imgcodecs.imread(Application.dataPath + "/Textures/1.jpg", 1);
p2Mat = Imgcodecs.imread(Application.dataPath + "/Textures/3.jpg", 1);
Imgproc.cvtColor(p1Mat, p1Mat, Imgproc.COLOR_BGR2RGB);
Imgproc.cvtColor(p2Mat, p2Mat, Imgproc.COLOR_BGR2RGB);
Imgproc.resize(p2Mat, p2Mat, new Size(p1Mat.width(), p1Mat.height()));
Debug.Log(p2Mat);
/*
* //仿射变换（矩阵旋转）
* float angle = UnityEngine.Random.Range(0, 360), scale = 1.0f;
* Point center = new Point(img2Mat.cols() * 0.5f, img2Mat.rows() * 0.5f);
*
* Mat affine_matrix = Imgproc.getRotationMatrix2D(center, angle, scale);
* Imgproc.warpAffine(img1Mat, img2Mat, affine_matrix, img2Mat.size());
*
* Texture2D texture = new Texture2D(img2Mat.cols(), img2Mat.rows());
* Utils.matToTexture2D(img2Mat, texture);
* outputRawImage.texture = texture;
*/
ORB detector = ORB.create();
ORB extractor = ORB.create();
//提取图一特征点
MatOfKeyPoint keypoints1 = new MatOfKeyPoint();
Mat descriptors1 = new Mat();
detector.detect(p1Mat, keypoints1);
extractor.compute(p1Mat, keypoints1, descriptors1);
//提取图二特征点
MatOfKeyPoint keypoints2 = new MatOfKeyPoint();
Mat descriptors2 = new Mat();
detector.detect(p2Mat, keypoints2);
extractor.compute(p2Mat, keypoints2, descriptors2);
//第一次匹配结果（密密麻麻）
DescriptorMatcher matcher = DescriptorMatcher.create(DescriptorMatcher.BRUTEFORCE_HAMMINGLUT);
MatOfDMatch matches = new MatOfDMatch();
matcher.match(descriptors1, descriptors2, matches);
//筛选（非官方）
//计算向量距离的最大值/最小值
double max_dist = 0;
double min_dist = 15; //通过距离控制需要的特征。
//（设到10，最终只有2个耳朵匹配。。。）
//（设到15，尾巴也开始匹配。。。。。。）
//新建两个容器存放筛选样本
List <DMatch> matchesArray = matches.toList(); //用Unity版API多转一步
//Debug.Log(matchesArray.Count); //500
List <DMatch> goodmatchesArray = new List <DMatch>();
//Debug.Log(img1Mat.rows()); //512
for (int i = 0; i < matchesArray.Count; i++)
{
Debug.Log("[" + i + "]" + matchesArray[i].distance);
if (matchesArray[i].distance > max_dist)
{
//max_dist = matchesArray[i].distance;
}
if (matchesArray[i].distance < min_dist)
{
min_dist = matchesArray[i].distance;
}
}
//Debug.Log("The max distance is: " + max_dist);
Debug.Log("The min distance is: " + min_dist);
for (int i = 0; i < matchesArray.Count; i++)
{
if (matchesArray[i].distance < 2 * min_dist) //
{
goodmatchesArray.Add(matchesArray[i]);
}
}
MatOfDMatch newMatches = new MatOfDMatch();
newMatches.fromList(goodmatchesArray);
Debug.Log(newMatches.toList().Count); //第二次筛选后符合的
//绘制第二次筛选结果
dstMat = new Mat();
Features2d.drawMatches(p1Mat, keypoints1, p2Mat, keypoints2, newMatches, dstMat);
Texture2D t2d = new Texture2D(dstMat.width(), dstMat.height());
Utils.matToTexture2D(dstMat, t2d);
Sprite sp = Sprite.Create(t2d, new UnityEngine.Rect(0, 0, t2d.width, t2d.height), Vector2.zero);
m_dstImage.sprite = sp;
m_dstImage.preserveAspect = true;
}
private void Update()
{
inputMat = webCamTextureToMatHelper.GetMat();
MatOfKeyPoint camKeyPoints = new MatOfKeyPoint();
Mat camDescriptors = new Mat();
Imgproc.cvtColor(inputMat, grayMat, Imgproc.COLOR_BGR2GRAY);
detector.detect(grayMat, camKeyPoints);
extractor.compute(grayMat, camKeyPoints, camDescriptors);
if (camKeyPoints.toList().Count < 1)
{
return;
}
List <MatOfDMatch> matches = new List <MatOfDMatch>();
matcher.knnMatch(makerDescriptors, camDescriptors, matches, 2);
//-- Filter matches using the Lowe's ratio test
float ratioThresh = 0.75f;
List <DMatch> listOfGoodMatches = new List <DMatch>();
for (int i = 0; i < matches.Count; i++)
{
if (matches[i].rows() > 1)
{
DMatch[] dMatches = matches[i].toArray();
if (dMatches[0].distance < ratioThresh * dMatches[1].distance)
{
listOfGoodMatches.Add(dMatches[0]);
}
}
}
MatOfDMatch goodMatches = new MatOfDMatch();
goodMatches.fromList(listOfGoodMatches);
//-- Draw matches
Mat resultImg = new Mat();
Features2d.drawMatches(makerMat, makerKeyPoints, grayMat, camKeyPoints, goodMatches, resultImg);
//listOfGoodMatches = goodMatches.toList();
////-- Localize the object
//List<Point> obj = new List<Point>();
//List<Point> scene = new List<Point>();
//List<KeyPoint> listOfKeypointsObject = makerKeyPoints.toList();
//List<KeyPoint> listOfKeypointsScene = camKeyPoints.toList();
//for (int i = 0; i < listOfGoodMatches.Count(); i++)
//{
// //-- Get the keypoints from the good matches
// obj.Add(listOfKeypointsObject[listOfGoodMatches[i].queryIdx].pt);
// scene.Add(listOfKeypointsScene[listOfGoodMatches[i].trainIdx].pt);
//}
//MatOfPoint2f objMat = new MatOfPoint2f();
//MatOfPoint2f sceneMat = new MatOfPoint2f();
//objMat.fromList(obj);
//sceneMat.fromList(scene);
//double ransacReprojThreshold = 3.0;
//Mat H = Calib3d.findHomography(objMat, sceneMat, Calib3d.RANSAC, ransacReprojThreshold);
////-- Get the corners from the image_1 ( the object to be "detected" )
//Mat objCorners = new Mat(4, 1, CvType.CV_32FC2);
//Mat sceneCorners = new Mat();
//float[] objCornersData = new float[(int)(objCorners.total() * objCorners.channels())];
//objCorners.get(0, 0, objCornersData);
//objCornersData[0] = 0;
//objCornersData[1] = 0;
//objCornersData[2] = makerMat.cols();
//objCornersData[3] = 0;
//objCornersData[4] = makerMat.cols();
//objCornersData[5] = makerMat.rows();
//objCornersData[6] = 0;
//objCornersData[7] = makerMat.rows();
//objCorners.put(0, 0, objCornersData);
//Core.perspectiveTransform(objCorners, sceneCorners, H);
//byte[] sceneCornersData = new byte[(int)(sceneCorners.total() * sceneCorners.channels())];
//sceneCorners.get(0, 0, sceneCornersData);
////-- Draw lines between the corners (the mapped object in the scene - image_2 )
//Imgproc.line(resultImg, new Point(sceneCornersData[0] + makerMat.cols(), sceneCornersData[1]),
// new Point(sceneCornersData[2] + makerMat.cols(), sceneCornersData[3]), new Scalar(0, 255, 0), 4);
//Imgproc.line(resultImg, new Point(sceneCornersData[2] + makerMat.cols(), sceneCornersData[3]),
// new Point(sceneCornersData[4] + makerMat.cols(), sceneCornersData[5]), new Scalar(0, 255, 0), 4);
//Imgproc.line(resultImg, new Point(sceneCornersData[4] + makerMat.cols(), sceneCornersData[5]),
// new Point(sceneCornersData[6] + makerMat.cols(), sceneCornersData[7]), new Scalar(0, 255, 0), 4);
//Imgproc.line(resultImg, new Point(sceneCornersData[6] + makerMat.cols(), sceneCornersData[7]),
// new Point(sceneCornersData[0] + makerMat.cols(), sceneCornersData[1]), new Scalar(0, 255, 0), 4);
if (!first)
{
texture = new Texture2D(resultImg.cols(), resultImg.rows(), TextureFormat.RGBA32, false);
dstQuad.GetComponent <Renderer>().material.mainTexture = texture;
first = true;
}
Utils.matToTexture2D(resultImg, texture);
}