// Use this for initialization
void Start()
{
Texture2D imgTexture = Resources.Load("lena") as Texture2D;
Mat img1Mat = new Mat(imgTexture.height, imgTexture.width, CvType.CV_8UC3);
Utils.texture2DToMat(imgTexture, img1Mat);
Debug.Log("img1Mat dst ToString " + img1Mat.ToString());
Mat img2Mat = new Mat(imgTexture.height, imgTexture.width, CvType.CV_8UC3);
Utils.texture2DToMat(imgTexture, img2Mat);
Debug.Log("img2Mat dst ToString " + img2Mat.ToString());
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());
FeatureDetector detector = FeatureDetector.create(FeatureDetector.ORB);
DescriptorExtractor extractor = DescriptorExtractor.create(DescriptorExtractor.ORB);
MatOfKeyPoint keypoints1 = new MatOfKeyPoint();
Mat descriptors1 = new Mat();
detector.detect(img1Mat, keypoints1);
extractor.compute(img1Mat, keypoints1, descriptors1);
MatOfKeyPoint keypoints2 = new MatOfKeyPoint();
Mat descriptors2 = new Mat();
detector.detect(img2Mat, keypoints2);
extractor.compute(img2Mat, keypoints2, descriptors2);
DescriptorMatcher matcher = DescriptorMatcher.create(DescriptorMatcher.BRUTEFORCE_HAMMINGLUT);
MatOfDMatch matches = new MatOfDMatch();
matcher.match(descriptors1, descriptors2, matches);
Mat resultImg = new Mat();
Features2d.drawMatches(img1Mat, keypoints1, img2Mat, keypoints2, matches, resultImg);
Texture2D texture = new Texture2D(resultImg.cols(), resultImg.rows(), TextureFormat.RGBA32, false);
Utils.matToTexture2D(resultImg, texture);
gameObject.GetComponent <Renderer> ().material.mainTexture = texture;
}
protected override void OnCreate(Bundle savedInstanceState)
{
base.OnCreate(savedInstanceState);
adapter = new TabsAdapter(this, SupportFragmentManager);
pager = FindViewById <ViewPager>(Resource.Id.viewpager);
var tabs = FindViewById <TabLayout>(Resource.Id.tabs);
pager.Adapter = adapter;
tabs.SetupWithViewPager(pager);
pager.OffscreenPageLimit = 3;
pager.PageSelected += (sender, args) =>
{
var fragment = adapter.InstantiateItem(pager, args.Position) as IFragmentVisible;
fragment?.BecameVisible();
};
Toolbar.MenuItemClick += (sender, e) =>
{
var intent = new Intent(this, typeof(AddItemActivity));;
StartActivity(intent);
};
SupportActionBar.SetDisplayHomeAsUpEnabled(false);
SupportActionBar.SetHomeButtonEnabled(false);
var src = new Mat[2];
var dst = new Mat[2];
var keyPoints1 = new MatOfKeyPoint();
var keyPoints2 = new MatOfKeyPoint();
var descripter1 = new Mat();
var descripter2 = new Mat();
var dmatch = new MatOfDMatch();
var output = new Mat();
src[0] = Imgcodecs.Imread("path/to/source/1.png");
src[1] = Imgcodecs.Imread("path/to/source/2.png");
dst[0] = new Mat();
dst[1] = new Mat();
Imgproc.CvtColor(src[0], dst[0], Imgproc.COLORBayerGR2GRAY);
Imgproc.CvtColor(src[1], dst[1], Imgproc.COLORBayerGR2GRAY);
var akaze = FeatureDetector.Create(FeatureDetector.Akaze);
var executor = DescriptorExtractor.Create(DescriptorExtractor.Akaze);
akaze.Detect(dst[0], keyPoints1);
akaze.Detect(dst[1], keyPoints2);
executor.Compute(dst[0], keyPoints1, descripter1);
executor.Compute(dst[1], keyPoints2, descripter2);
var matcher = DescriptorMatcher.Create(DescriptorMatcher.BruteforceHamming);
matcher.Match(descripter1, descripter2, dmatch);
Features2d.DrawMatches(src[0], keyPoints1, src[1], keyPoints2, dmatch, output);
}
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);
}
public ImageString MatchFeatures(string base64image, List <string> base64imageList)
{
List <MatOfDMatch> winnerMatches = new List <MatOfDMatch>();
MatOfKeyPoint winnerKeyPoints = new MatOfKeyPoint();
Mat winnerImage = new Mat();
int winnerIndex = -1;
int winnerValue = 0;
Texture2D imgTexture = base64ImageToTexture(base64image);
List <Texture2D> imgTextures = new List <Texture2D>();
for (int i = 0; i < base64imageList.Count; i++)
{
imgTextures.Add(base64ImageToTexture(base64imageList[i]));
}
//Create Mat from texture
Mat img1Mat = new Mat(imgTexture.height, imgTexture.width, CvType.CV_8UC3);
Utils.texture2DToMat(imgTexture, img1Mat);
MatOfKeyPoint keypoints1 = new MatOfKeyPoint();
Mat descriptors1 = new Mat();
FeatureDetector detector = FeatureDetector.create(FeatureDetector.ORB);
DescriptorExtractor extractor = DescriptorExtractor.create(DescriptorExtractor.ORB);
//Detect keypoints and compute descriptors from photo.
detector.detect(img1Mat, keypoints1);
extractor.compute(img1Mat, keypoints1, descriptors1);
Debug.Log("Billede features: " + descriptors1.rows());
if (descriptors1.rows() < 10)
{
Debug.Log("ARRRRRRGH der er ikke mange descripters i mit original-billede");
return(new ImageString(base64image, winnerIndex));
}
//Run through each image in list
for (int i = 0; i < imgTextures.Count; i++)
{
Texture2D imgTexture2 = imgTextures[i];
//Create Mat from texture
Mat img2Mat = new Mat(imgTexture2.height, imgTexture2.width, CvType.CV_8UC3);
Utils.texture2DToMat(imgTexture2, img2Mat);
//Find keypoints and descriptors from image in list
MatOfKeyPoint keypoints2 = new MatOfKeyPoint();
Mat descriptors2 = new Mat();
detector.detect(img2Mat, keypoints2);
extractor.compute(img2Mat, keypoints2, descriptors2);
//Match photo with image from list
DescriptorMatcher matcher = DescriptorMatcher.create(DescriptorMatcher.BRUTEFORCE_HAMMINGLUT);
Debug.Log("Billede2 features: " + descriptors2.rows());
if (descriptors2.rows() < 10)
{
Debug.Log("ARRRRRRGH der er ikke mange descripters i mit test billede: " + i);
continue;
}
List <MatOfDMatch> matchList = new List <MatOfDMatch>();
matcher.knnMatch(descriptors1, descriptors2, matchList, 2);
//Find the good matches and put them ind a list
List <MatOfDMatch> good = new List <MatOfDMatch>();
foreach (MatOfDMatch match in matchList)
{
DMatch[] arrayDmatch = match.toArray();
if (arrayDmatch[0].distance < 0.7f * arrayDmatch[1].distance)
{
good.Add(match);
}
}
//Find the best match image based on the good lists
if (good.Count > winnerThreshold && good.Count > winnerValue)
{
winnerImage = img2Mat;
winnerMatches = good;
winnerKeyPoints = keypoints2;
winnerIndex = i;
winnerValue = good.Count;
}
}
Debug.Log("The winner is image: " + winnerIndex + " with a value of: " + winnerValue);
//If no winner just return the original image
if (winnerIndex == -1)
{
Debug.Log("No winner");
return(new ImageString(base64image, winnerIndex));
}
Debug.Log("No winner");
//Find the matching keypoints from the winner list.
MatOfPoint2f queryPoints = new MatOfPoint2f();
MatOfPoint2f matchPoints = new MatOfPoint2f();
List <Point> queryPointsList = new List <Point>();
List <Point> matchPointsList = new List <Point>();
foreach (MatOfDMatch match in winnerMatches)
{
DMatch[] arrayDmatch = match.toArray();
queryPointsList.Add(keypoints1.toList()[arrayDmatch[0].queryIdx].pt);
matchPointsList.Add(winnerKeyPoints.toList()[arrayDmatch[0].trainIdx].pt);
}
queryPoints.fromList(queryPointsList);
matchPoints.fromList(matchPointsList);
//Calculate the homography of the best matching image
Mat homography = Calib3d.findHomography(queryPoints, matchPoints, Calib3d.RANSAC, 5.0);
Mat resultImg = new Mat();
Imgproc.warpPerspective(img1Mat, resultImg, homography, winnerImage.size());
//Show image
Texture2D texture = new Texture2D(winnerImage.cols(), winnerImage.rows(), TextureFormat.RGBA32, false);
Utils.matToTexture2D(resultImg, texture);
return(new ImageString(Convert.ToBase64String(texture.EncodeToPNG()), winnerIndex));
}
public List <ImageObject> MatchFeatures(string base64image, List <string> base64imageList)
{
ImageObject myImage = new ImageObject();
ImageObject winnerImage = new ImageObject();
List <ImageObject> returnImageList = new List <ImageObject>();
Texture2D imgTexture = base64ImageToTexture(base64image);
List <Texture2D> imgTextures = new List <Texture2D>();
for (int i = 0; i < base64imageList.Count; i++)
{
imgTextures.Add(base64ImageToTexture(base64imageList[i]));
}
//Create Mat from texture
Mat img1Mat = new Mat(imgTexture.height, imgTexture.width, CvType.CV_8UC3);
Utils.texture2DToMat(imgTexture, img1Mat);
MatOfKeyPoint keypoints1 = new MatOfKeyPoint();
Mat descriptors1 = new Mat();
FeatureDetector detector = FeatureDetector.create(FeatureDetector.ORB);
DescriptorExtractor extractor = DescriptorExtractor.create(DescriptorExtractor.ORB);
//Detect keypoints and compute descriptors from photo.
detector.detect(img1Mat, keypoints1);
extractor.compute(img1Mat, keypoints1, descriptors1);
//Debug.Log("Billede features: " + descriptors1.rows());
myImage.image = base64image;
myImage.keyPoints = keypoints1;
myImage.imageMat = img1Mat;
if (descriptors1.rows() < 10)
{
Debug.Log("ARRRRRRGH der er ikke mange descripters i mit original-billede");
//No winner as there is to few descriptors.
return(returnImageList);
}
//Run through each image in list
//-------------------------------------------------------------
for (int i = 0; i < imgTextures.Count; i++)
{
Texture2D imgTexture2 = imgTextures[i];
//Create Mat from texture
Mat img2Mat = new Mat(imgTexture2.height, imgTexture2.width, CvType.CV_8UC3);
Utils.texture2DToMat(imgTexture2, img2Mat);
//Find keypoints and descriptors from image in list
MatOfKeyPoint keypoints2 = new MatOfKeyPoint();
Mat descriptors2 = new Mat();
detector.detect(img2Mat, keypoints2);
extractor.compute(img2Mat, keypoints2, descriptors2);
//Match photo with image from list
DescriptorMatcher matcher = DescriptorMatcher.create(DescriptorMatcher.BRUTEFORCE_HAMMINGLUT);
//Debug.Log("Billede2 features: " + descriptors2.rows());
if (descriptors2.rows() < 10)
{
Debug.Log("ARRRRRRGH der er ikke mange descripters i mit test billede: " + i);
continue;
}
List <MatOfDMatch> matchList = new List <MatOfDMatch>();
matcher.knnMatch(descriptors1, descriptors2, matchList, 2);
//Find the good matches and put them ind a list
List <MatOfDMatch> good = new List <MatOfDMatch>();
foreach (MatOfDMatch match in matchList)
{
DMatch[] arrayDmatch = match.toArray();
if (arrayDmatch[0].distance < 0.7f * arrayDmatch[1].distance)
{
good.Add(match);
}
}
//Find the best match image based on the good lists
if (good.Count > winnerThreshold && good.Count > winnerImage.value)
{
winnerImage.index = i;
winnerImage.imageMat = img2Mat;
winnerImage.keyPoints = keypoints2;
winnerImage.value = good.Count;
winnerImage.matches = good;
}
}
// Run through done
//-------------------------------------------------------------
Debug.Log("The winner is image: " + winnerImage.index + " with a value of: " + winnerImage.value);
//If no winner just return the original image
if (winnerImage.index == -1)
{
Debug.Log("No winner");
return(returnImageList);
}
Texture2D imageTexture = new Texture2D(winnerImage.imageMat.cols(), winnerImage.imageMat.rows(), TextureFormat.RGBA32, false);
winnerImage.image = Convert.ToBase64String(imageTexture.EncodeToPNG());
returnImageList.Add(myImage);
returnImageList.Add(winnerImage);
return(returnImageList);
}
