我使用Emgu CV的SURF特徵識別圖像中的相似對象。Emgu CV SURF獲取匹配的點座標
圖像繪製在右側,顯示所有關鍵點發現,在這兩個圖像,類似點(這是我想)和矩形(通常爲矩形,有時只是一條線)覆蓋類似的點。
的問題是相似點在圖像中看到的,但它們不是保存在我想要的,其實,它們存儲在VectorOfKeyPoint對象,只存儲一個指針的格式,和其他內存數據,點存儲在內存中(這就是我的想法)。意思是,我不能讓相似點像對:
((img1X,img1Y),(img2X,img2Y))
這將是我在尋找什麼的,所以我可以稍後使用這些分數。 現在,我只能看到結果圖像中的點,但我無法將它們成對地配對。
我使用的代碼是Emgu CV的示例。
//----------------------------------------------------------------------------
// Copyright (C) 2004-2016 by EMGU Corporation. All rights reserved.
//----------------------------------------------------------------------------
using System;
using System.Collections.Generic;
using System.Diagnostics;
using System.Drawing;
using System.Runtime.InteropServices;
using Emgu.CV;
using Emgu.CV.CvEnum;
using Emgu.CV.Features2D;
using Emgu.CV.Structure;
using Emgu.CV.Util;
#if !__IOS__
using Emgu.CV.Cuda;
#endif
using Emgu.CV.XFeatures2D;
namespace FirstEmgu
{
public static class DrawMatches
{
// --------------------------------
// ORIGINAL FUNCTION FROM EXAMPLE
// --------------------------------
private static void FindMatch(Mat modelImage, Mat observedImage, out long matchTime, out VectorOfKeyPoint modelKeyPoints, out VectorOfKeyPoint observedKeyPoints, VectorOfVectorOfDMatch matches, out Mat mask, out Mat homography)
{
int k = 2;
double uniquenessThreshold = 0.8;
double hessianThresh = 300;
Stopwatch watch;
homography = null;
modelKeyPoints = new VectorOfKeyPoint();
observedKeyPoints = new VectorOfKeyPoint();
#if !__IOS__
if (CudaInvoke.HasCuda)
{
CudaSURF surfCuda = new CudaSURF((float)hessianThresh);
using (GpuMat gpuModelImage = new GpuMat(modelImage))
//extract features from the object image
using (GpuMat gpuModelKeyPoints = surfCuda.DetectKeyPointsRaw(gpuModelImage, null))
using (GpuMat gpuModelDescriptors = surfCuda.ComputeDescriptorsRaw(gpuModelImage, null, gpuModelKeyPoints))
using (CudaBFMatcher matcher = new CudaBFMatcher(DistanceType.L2))
{
surfCuda.DownloadKeypoints(gpuModelKeyPoints, modelKeyPoints);
watch = Stopwatch.StartNew();
// extract features from the observed image
using (GpuMat gpuObservedImage = new GpuMat(observedImage))
using (GpuMat gpuObservedKeyPoints = surfCuda.DetectKeyPointsRaw(gpuObservedImage, null))
using (GpuMat gpuObservedDescriptors = surfCuda.ComputeDescriptorsRaw(gpuObservedImage, null, gpuObservedKeyPoints))
//using (GpuMat tmp = new GpuMat())
//using (Stream stream = new Stream())
{
matcher.KnnMatch(gpuObservedDescriptors, gpuModelDescriptors, matches, k);
surfCuda.DownloadKeypoints(gpuObservedKeyPoints, observedKeyPoints);
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);
}
}
watch.Stop();
}
}
else
#endif
{
using (UMat uModelImage = modelImage.ToUMat(AccessType.Read))
using (UMat uObservedImage = observedImage.ToUMat(AccessType.Read))
{
SURF surfCPU = new SURF(hessianThresh);
//extract features from the object image
UMat modelDescriptors = new UMat();
surfCPU.DetectAndCompute(uModelImage, null, modelKeyPoints, modelDescriptors, false);
watch = Stopwatch.StartNew();
// extract features from the observed image
UMat observedDescriptors = new UMat();
surfCPU.DetectAndCompute(uObservedImage, null, observedKeyPoints, observedDescriptors, false);
BFMatcher matcher = new BFMatcher(DistanceType.L2);
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);
}
watch.Stop();
}
}
matchTime = watch.ElapsedMilliseconds;
}
// --------------------------------
// ORIGINAL FUNCTION FROM EXAMPLE
// --------------------------------
/// <summary>
/// Draw the model image and observed image, the matched features and homography projection.
/// </summary>
/// <param name="modelImage">The model image</param>
/// <param name="observedImage">The observed image</param>
/// <param name="matchTime">The output total time for computing the homography matrix.</param>
/// <returns>The model image and observed image, the matched features and homography projection.</returns>
public static Mat Draw(Mat modelImage, Mat observedImage, out long matchTime)
{
Mat homography;
VectorOfKeyPoint modelKeyPoints;
VectorOfKeyPoint observedKeyPoints;
using (VectorOfVectorOfDMatch matches = new VectorOfVectorOfDMatch())
{
Mat mask;
FindMatch(modelImage, observedImage, out matchTime, out modelKeyPoints, out observedKeyPoints, matches,
out mask, out homography);
//Draw the matched keypoints
Mat result = new Mat();
Features2DToolbox.DrawMatches(modelImage, modelKeyPoints, observedImage, observedKeyPoints,
matches, result, new MCvScalar(255, 255, 255), new MCvScalar(255, 255, 255), mask);
#region draw the projected region on the image
if (homography != null)
{
//draw a rectangle along the projected model
Rectangle rect = new Rectangle(Point.Empty, modelImage.Size);
PointF[] 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);
Point[] points = Array.ConvertAll<PointF, Point>(pts, Point.Round);
using (VectorOfPoint vp = new VectorOfPoint(points))
{
CvInvoke.Polylines(result, vp, true, new MCvScalar(255, 0, 0, 255), 5);
}
}
#endregion
return result;
}
}
// ----------------------------------
// WRITTEN BY MYSELF
// ----------------------------------
// Returns 4 points (usually rectangle) of similar points
// but can't be used, since sometimes this is a line (negative
// points)
public static Point[] FindPoints(Mat modelImage, Mat observedImage, out long matchTime)
{
Mat homography;
VectorOfKeyPoint modelKeyPoints;
VectorOfKeyPoint observedKeyPoints;
using (VectorOfVectorOfDMatch matches = new VectorOfVectorOfDMatch())
{
Mat mask;
FindMatch(modelImage, observedImage, out matchTime, out modelKeyPoints, out observedKeyPoints, matches,
out mask, out homography);
//Draw the matched keypoints
Mat result = new Mat();
Features2DToolbox.DrawMatches(modelImage, modelKeyPoints, observedImage, observedKeyPoints,
matches, result, new MCvScalar(255, 255, 255), new MCvScalar(255, 255, 255), mask);
Point[] points = null;
if (homography != null)
{
//draw a rectangle along the projected model
Rectangle rect = new Rectangle(Point.Empty, modelImage.Size);
PointF[] 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);
points = Array.ConvertAll<PointF, Point>(pts, Point.Round);
}
return points;
}
}
}
}
編輯
我已經設法獲得一些積分了比賽的對象是這樣的:
Features2DToolbox.DrawMatches(modelImage, modelKeyPoints, observedImage, observedKeyPoints,
matches, result, new MCvScalar(255, 255, 255), new MCvScalar(255, 255, 255), mask);
for (int i = 0; i < matches.Size; i++)
{
var a = matches[i].ToArray();
foreach (var e in a)
{
Point p = new Point(e.TrainIdx, e.QueryIdx);
Console.WriteLine(string.Format("Point: {0}", p));
}
Console.WriteLine("-----------------------");
}
我想,這應該得到我的點。我設法讓它在python中工作,並且代碼沒有什麼不同。問題是返回的點太多。事實上,這將返回箱上Y.所有點
例
(45,1),(67,1)
(656,2),(77,2)
...
儘管我可能會接近,但它並沒有讓我得到我想要的點數。任何建議表示讚賞。
編輯2 這個問題:Find interest point in surf Detector Algorithm是非常相似的東西,我需要的東西。只有一個答案,但它沒有說明如何獲得匹配的點座標。這就是我所需要的,如果兩個圖像中都有一個對象,則從兩個圖像中獲取對象點的座標。