所以总的来说，我对计算机视觉还很陌生。我目前正在尝试通过分析 2 个图像来计算单应性。我想使用单应性来校正 1 个图像的视角以匹配另一个图像。但我得到的比赛却很糟糕而且错误。所以我所做的单应性扭曲完全关闭了。
我正在使用 EmguCV 在 C# 中包装 opencv。 据我所知，我的代码似乎“正常”工作。
我加载两个图像并声明一些变量来存储计算工件。
(Image<Bgr, byte> Image, VectorOfKeyPoint Keypoints, Mat Descriptors) imgModel = (new Image<Bgr, byte>(imageFolder + "image0.jpg").Resize(0.2, Emgu.CV.CvEnum.Inter.Area), new VectorOfKeyPoint(), new Mat());
(Image<Bgr, byte> Image, VectorOfKeyPoint Keypoints, Mat Descriptors) imgTest = (new Image<Bgr, byte>(imageFolder + "image1.jpg").Resize(0.2, Emgu.CV.CvEnum.Inter.Area), new VectorOfKeyPoint(), new Mat());
Mat imgKeypointsModel = new Mat();
Mat imgKeypointsTest = new Mat();
Mat imgMatches = new Mat();
Mat imgWarped = new Mat();
VectorOfVectorOfDMatch matches = new VectorOfVectorOfDMatch();
VectorOfVectorOfDMatch filteredMatches = new VectorOfVectorOfDMatch();
List<MDMatch[]> filteredMatchesList = new List<MDMatch[]>();
请注意，我使用ValueTuple<Image,VectorOfKeyPoint,Mat>直接存储图像及其各自的关键点和描述符。
之后使用 ORB 检测器和 BruteForce 匹配器来检测、描述和匹配关键点：
ORBDetector detector = new ORBDetector();
BFMatcher matcher = new BFMatcher(DistanceType.Hamming2);
detector.DetectAndCompute(imgModel.Image, null, imgModel.Keypoints, imgModel.Descriptors, false);
detector.DetectAndCompute(imgTest.Image, null, imgTest.Keypoints, imgTest.Descriptors, false);
matcher.Add(imgTest.Descriptors);
matcher.KnnMatch(imgModel.Descriptors, matches, k: 2, mask: null);
之后我应用比率测试 https://stackoverflow.com/questions/51197091/how-does-the-lowes-ratio-test-work并使用匹配距离阈值进行进一步的过滤。
MDMatch[][] matchesArray = matches.ToArrayOfArray();
//Apply ratio test
for (int i = 0; i < matchesArray.Length; i++)
{
MDMatch first = matchesArray[i][0];
float dist1 = matchesArray[i][0].Distance;
float dist2 = matchesArray[i][1].Distance;
if (dist1 < ms_MIN_RATIO * dist2)
{
filteredMatchesList.Add(matchesArray[i]);
}
}
//Filter by threshold
MDMatch[][] defCopy = new MDMatch[filteredMatchesList.Count][];
filteredMatchesList.CopyTo(defCopy);
filteredMatchesList = new List<MDMatch[]>();
foreach (var item in defCopy)
{
if (item[0].Distance < ms_MAX_DIST)
{
filteredMatchesList.Add(item);
}
}
filteredMatches = new VectorOfVectorOfDMatch(filteredMatchesList.ToArray());
禁用任何这些过滤方法并不会真正使我的结果变得更好或更差（只是保留所有匹配项），但它们似乎有意义，所以我保留它们。
最后，我根据找到的和过滤的匹配计算我的单应性，然后用该单应性扭曲图像并绘制一些调试图像：
Mat homography = Features2DToolbox.GetHomographyMatrixFromMatchedFeatures(imgModel.Keypoints, imgTest.Keypoints, filteredMatches, null, 10);
CvInvoke.WarpPerspective(imgTest.Image, imgWarped, homography, imgTest.Image.Size);
Features2DToolbox.DrawKeypoints(imgModel.Image, imgModel.Keypoints, imgKeypointsModel, new Bgr(0, 0, 255));
Features2DToolbox.DrawKeypoints(imgTest.Image, imgTest.Keypoints, imgKeypointsTest, new Bgr(0, 0, 255));
Features2DToolbox.DrawMatches(imgModel.Image, imgModel.Keypoints, imgTest.Image, imgTest.Keypoints, filteredMatches, imgMatches, new MCvScalar(0, 255, 0), new MCvScalar(0, 0, 255));
//Task.Factory.StartNew(() => ImageViewer.Show(imgKeypointsModel, "Keypoints Model"));
//Task.Factory.StartNew(() => ImageViewer.Show(imgKeypointsTest, "Keypoints Test"));
Task.Factory.StartNew(() => ImageViewer.Show(imgMatches, "Matches"));
Task.Factory.StartNew(() => ImageViewer.Show(imgWarped, "Warp"));
tl;dr：ORBDetector->BFMatcher->FilterMatches->GetHomography->WarpPerspective
Example for the algorithm
Test whether projection is going wrong
Using crosscheck when matching
原始图像均为 2448x3264，缩放 0.2before对它们进行任何计算。
基本上它既简单又复杂：我做错了什么？ 正如您从上面的示例中看到的，我检测特征并匹配它们的方法似乎效果非常差。所以我问是否有人可以发现我的代码中的错误。或者给出建议，说明为什么我的结果如此糟糕，因为互联网上有数百个示例展示了它的工作原理以及它是多么“简单”。
到目前为止我尝试过的：
我使用的例子：
https://opencv-python-tutroals.readthedocs.io/en/latest/py_tutorials/py_feature2d/py_matcher/py_matcher.html#matcher https://opencv-python-tutroals.readthedocs.io/en/latest/py_tutorials/py_feature2d/py_matcher/py_matcher.html#matcher
https://opencv-python-tutroals.readthedocs.io/en/latest/py_tutorials/py_feature2d/py_feature_homography/py_feature_homography.html https://opencv-python-tutroals.readthedocs.io/en/latest/py_tutorials/py_feature2d/py_feature_homography/py_feature_homography.html
https://www.learnopencv.com/image-alignment-feature-based-using-opencv-c-python/ https://www.learnopencv.com/image-alignment-feature-based-using-opencv-c-python/（我在那里得到了代码的主要结构）
原始图片：原始图像可以从这里下载：https://drive.google.com/open?id=1Nlqv_0sH8t1wiH5PG-ndMxoYhsUbFfkC https://drive.google.com/open?id=1Nlqv_0sH8t1wiH5PG-ndMxoYhsUbFfkC
So I did some further research after asking. Most changes are already included above but I wanted to make a separate section for this one.
So after running into so many problems and seemingly nowhere to start I decided to google up the original paper on ORB http://www.willowgarage.com/sites/default/files/orb_final.pdf. After this I decided to try and replicate some of their results. Upon trying this I realised that even I try to match the match image rotate by a degree the matches seem to look fine but the transformation completely breaks down.
我尝试复制物体视角的方法是否有可能是错误的？
https://drive.google.com/open?id=17DwFoSmco9UezHkON5prk8OsPalmp2MX https://drive.google.com/open?id=17DwFoSmco9UezHkON5prk8OsPalmp2MX（没有包，但 nuget Restore 足以让它编译）
我遇到了同样的问题并找到了合适的解决方案：github Emgu.CV.Example DrawMatches.cs https://github.com/emgucv/emgucv/blob/master/Emgu.CV.Example/FeatureMatching/DrawMatches.cs一切正常。
我修改了代码和方法FindMatch
看起来像这样：
public static void FindMatch(Mat modelImage, Mat observedImage, out VectorOfKeyPoint modelKeyPoints, out VectorOfKeyPoint observedKeyPoints, VectorOfVectorOfDMatch matches, out Mat mask, out Mat homography)
{
int k = 2;
double uniquenessThreshold = 0.80;
homography = null;
modelKeyPoints = new VectorOfKeyPoint();
observedKeyPoints = new VectorOfKeyPoint();
using (UMat uModelImage = modelImage.GetUMat(AccessType.Read))
using (UMat uObservedImage = observedImage.GetUMat(AccessType.Read))
{
var featureDetector = new ORBDetector(9000);
Mat modelDescriptors = new Mat();
featureDetector.DetectAndCompute(uModelImage, null, modelKeyPoints, modelDescriptors, false);
Mat observedDescriptors = new Mat();
featureDetector.DetectAndCompute(uObservedImage, null, observedKeyPoints, observedDescriptors, false);
using (var matcher = new BFMatcher(DistanceType.Hamming, false))
{
matcher.Add(modelDescriptors);
matcher.KnnMatch(observedDescriptors, matches, k, null);
mask = new Mat(matches.Size, 1, DepthType.Cv8U, 1);
mask.SetTo(new MCvScalar(255));
Features2DToolbox.VoteForUniqueness(matches, uniquenessThreshold, mask);
int nonZeroCount = CvInvoke.CountNonZero(mask);
if (nonZeroCount >= 4)
{
nonZeroCount = Features2DToolbox.VoteForSizeAndOrientation(modelKeyPoints, observedKeyPoints,
matches, mask, 1.5, 20);
if (nonZeroCount >= 4)
homography = Features2DToolbox.GetHomographyMatrixFromMatchedFeatures(modelKeyPoints,
observedKeyPoints, matches, mask, 2);
}
}
}
}
Using:
var model = new Mat(@"image0.jpg");
var scene = new Mat(@"image1.jpg");
Mat result = new Mat();
VectorOfKeyPoint modelKeyPoints;
VectorOfKeyPoint observedKeyPoints;
var matches = new VectorOfVectorOfDMatch();
Mat mask;
Mat homography;
FindMatch(model, scene, out modelKeyPoints, out observedKeyPoints, matches, out mask, out homography);
CvInvoke.WarpPerspective(scene, result, homography, model.Size, Inter.Linear, Warp.InverseMap);
Result:
如果你想看过程使用下一个代码：
public static Mat Draw(Mat modelImage, Mat observedImage)
{
Mat homography;
VectorOfKeyPoint modelKeyPoints;
VectorOfKeyPoint observedKeyPoints;
using (VectorOfVectorOfDMatch matches = new VectorOfVectorOfDMatch())
{
Mat mask;
FindMatch(modelImage, observedImage, out modelKeyPoints, out observedKeyPoints, matches, out mask, out homography);
Mat result = new Mat();
Features2DToolbox.DrawMatches(modelImage, modelKeyPoints, observedImage, observedKeyPoints,
matches, result, new MCvScalar(255, 0, 0), new MCvScalar(0, 0, 255), mask);
if (homography != null)
{
var imgWarped = new Mat();
CvInvoke.WarpPerspective(observedImage, imgWarped, homography, modelImage.Size, Inter.Linear, Warp.InverseMap);
Rectangle rect = new Rectangle(Point.Empty, modelImage.Size);
var pts = new PointF[]
{
new PointF(rect.Left, rect.Bottom),
new PointF(rect.Right, rect.Bottom),
new PointF(rect.Right, rect.Top),
new PointF(rect.Left, rect.Top)
};
pts = CvInvoke.PerspectiveTransform(pts, homography);
var points = new Point[pts.Length];
for (int i = 0; i < points.Length; i++)
points[i] = Point.Round(pts[i]);
using (var vp = new VectorOfPoint(points))
{
CvInvoke.Polylines(result, vp, true, new MCvScalar(255, 0, 0, 255), 5);
}
}
return result;
}
}
Using:
var model = new Mat(@"image0.jpg");
var scene = new Mat(@"image1.jpg");
var result = Draw(model, scene);
Result: