我想用 openCvSharp 进行图像拼接，但出了点问题。由于我没有得到拼接在一起的 2 个图像的正确输出。
我正在使用 python链接中的教程并将其转换为 C# 代码。
这里有 2 张照片示例
应该缝合图像的输出我得到错误的结果。它看起来与图 2 相同。
我猜在排队或排队之后的某个地方出了点问题
Cv2.WarpPerspective(trainImg, result, H, new OpenCvSharp.Size(width, height));
这是完整的代码。
public partial class Form1 : Form
{
public Form1()
{
InitializeComponent();
}
private void Form1_Load(object sender, EventArgs e)
{
bool debugImages = true;
string locationFolder = "";
OpenFileDialog dlg = new OpenFileDialog();
dlg.CheckFileExists = true;
dlg.Multiselect = true;
if (dlg.ShowDialog() == System.Windows.Forms.DialogResult.OK)
{
locationFolder = Path.GetDirectoryName(dlg.FileNames[0]) + "\\output\\";
List<Mat> imagesMat = new List<Mat>();
for (var i = 0; i < dlg.FileNames.Length; i++)
{
using (Bitmap fromFile = new Bitmap(dlg.FileNames[i]))
{
Mat source = BitmapConverter.ToMat(fromFile);
imagesMat.Add(source);
}
}
if (imagesMat.Count != 2)
throw new Exception("Select only 2 images!!!");
int imageCounter = 0;
Mat trainImg = imagesMat[0];
Mat queryImg = imagesMat[1];
Mat trainImg_gray = new Mat();
Mat queryImg_gray = new Mat();
Cv2.CvtColor(trainImg, trainImg_gray, ColorConversionCodes.BGRA2GRAY);
Cv2.CvtColor(queryImg, queryImg_gray, ColorConversionCodes.BGRA2GRAY);
// detecting keypoints
// FastFeatureDetector, StarDetector, SIFT, SURF, ORB, BRISK, MSER, GFTTDetector, DenseFeatureDetector, SimpleBlobDetector
string method = "SURF";
string feature_matching = "bf"; //bf, knn
var descriptor = SURF.Create(500, 4, 2, true);
Mat descriptors1 = new Mat();
Mat descriptors2 = new Mat();
KeyPoint[] kpsA;
KeyPoint[] kpsB;
descriptor.DetectAndCompute(trainImg_gray, null, out kpsA, descriptors1);
descriptor.DetectAndCompute(queryImg_gray, null, out kpsB, descriptors2);
// Match descriptor vectors
//var flannMatcher = new FlannBasedMatcher();
DMatch[] matches;
if (feature_matching == "bf")
matches = matchKeyPointsBF(descriptors1, descriptors2, method);
else
matches = matchKeyPointsKNN(descriptors1, descriptors2, 0.75, method);
var bfView = new Mat();
Cv2.DrawMatches(trainImg, kpsA, queryImg, kpsB, matches, bfView, null, flags: DrawMatchesFlags.NotDrawSinglePoints);
if (debugImages)
{
using (Bitmap resultBitmap = BitmapConverter.ToBitmap(bfView))
resultBitmap.Save(locationFolder + (imageCounter++).ToString().PadLeft(3, '0') + ".png", ImageFormat.Png); //1
}
Mat H = getHomography(kpsA, kpsB, descriptors1, descriptors2, matches, 4);
if (H == null)
throw new Exception("No Homography!!!");
//for (var i = 0; i < H.Cols; i++)
//{
// for (var j = 0; j < H.Rows; j++)
// Console.Write(H.At<float>(i, j) + " ");
// Console.WriteLine("");
//}
double width = trainImg.Size().Width + queryImg.Size().Width;
double height = trainImg.Size().Height + queryImg.Size().Height;
Mat result = new Mat();
Cv2.WarpPerspective(trainImg, result, H, new OpenCvSharp.Size(width, height));
if (debugImages)
{
using (Bitmap resultBitmap = BitmapConverter.ToBitmap(result))
resultBitmap.Save(locationFolder + (imageCounter++).ToString().PadLeft(3, '0') + ".png", ImageFormat.Png); //1
}
result[new Rect(new OpenCvSharp.Point(0, 0), new OpenCvSharp.Size(queryImg.Size().Width, queryImg.Size().Height))] = queryImg;
if (debugImages)
{
using (Bitmap resultBitmap = BitmapConverter.ToBitmap(result))
resultBitmap.Save(locationFolder + (imageCounter++).ToString().PadLeft(3, '0') + ".png", ImageFormat.Png); //2
}
//# transform the panorama image to grayscale and threshold it
Mat gray = result.Clone();
Cv2.CvtColor(result, gray, ColorConversionCodes.BGR2GRAY);
Mat thresh = new Mat();
double thresh2 = Cv2.Threshold(gray, thresh, 0, 255, ThresholdTypes.Binary);
//# Finds contours from the binary image
OpenCvSharp.Point[][] cnts;
HierarchyIndex[] hierarchy;
Cv2.FindContours(thresh, out cnts, out hierarchy, RetrievalModes.External, ContourApproximationModes.ApproxSimple);
OpenCvSharp.Point[] cnts2 = new OpenCvSharp.Point[cnts[0].Length];
for (var k = 0; k < cnts[0].Length; k++)
cnts2[k] = cnts[0][k];
//InputArray ptsA = InputArray.Create(cnts2);
//var c = Cv2.ContourArea(ptsA, true);
OpenCvSharp.Rect xywh = Cv2.BoundingRect(cnts2);
result = result[new Rect(new OpenCvSharp.Point(xywh.X, xywh.Y), new OpenCvSharp.Size(xywh.Width, xywh.Height))];
//result = result[new Rect(new OpenCvSharp.Point(0, 0), new OpenCvSharp.Size(256, 256))];
Bitmap endResultBitmap = BitmapConverter.ToBitmap(result);
endResultBitmap.Save(locationFolder + (imageCounter++).ToString().PadLeft(3, '0') + ".png", ImageFormat.Png); //4
Environment.Exit(-1);
}
}
private BFMatcher createMatcher(string method, bool crossCheck)
{
//"Create and return a Matcher Object"
if (method == "SURF" || method == "SIFT")
return new BFMatcher(NormTypes.L2, crossCheck);
else //if (method == "ORB" || method == "BRISK")
return new BFMatcher(NormTypes.Hamming, crossCheck);
}
private DMatch[] matchKeyPointsBF(Mat featuresA, Mat featuresB, string method)
{
BFMatcher bf = createMatcher(method, crossCheck: true);
// # Match descriptors.
DMatch[] bfMatches = bf.Match(featuresA, featuresB);
//# Sort the features in order of distance.
//# The points with small distance (more similarity) are ordered first in the vector
DMatch[] rawMatches = bfMatches.OrderBy(a => a.Distance).ToArray();
if (rawMatches.Length > 100)
Array.Resize(ref rawMatches, 100);
return rawMatches;
}
private DMatch[] matchKeyPointsKNN(Mat featuresA, Mat featuresB, double ratio, string method)
{
BFMatcher bf = createMatcher(method, crossCheck: false);
// # compute the raw matches and initialize the list of actual matches
DMatch[][] rawMatches = bf.KnnMatch(featuresA, featuresB, 2);
List<DMatch> rawMatches2 = new List<DMatch>();
//# loop over the raw matches
DMatch prevmatchN = rawMatches[0][0];
rawMatches2.Add(prevmatchN);
for (int m = 0; m < rawMatches.Length; m++)
{
for (int n = 0; n < rawMatches[m].Length; n++)
{
//# ensure the distance is within a certain ratio of each
//# other (i.e. Lowe's ratio test)
DMatch matchN = rawMatches[m][n];
if (n == 0)
prevmatchN = matchN;
if (prevmatchN.Distance < matchN.Distance * (ratio))
rawMatches2.Add(matchN);
if (rawMatches2.Count >= 100)
break;
}
}
return rawMatches2.ToArray();
}
private Mat getHomography(KeyPoint[] kpsA, KeyPoint[] kpsB, Mat featuresA, Mat featuresB, DMatch[] matches, int reprojThresh)
{
//# convert the keypoints to numpy arrays
Point2f[] PtA = new Point2f[matches.Length];
Point2f[] PtB = new Point2f[matches.Length];
for (int i = 0; i < matches.Length; i++)
{
KeyPoint kpsAI = kpsA[matches[i].QueryIdx];
KeyPoint kpsBI = kpsB[matches[i].TrainIdx];
PtA[i] = new Point2f(kpsAI.Pt.X, kpsAI.Pt.Y);
PtB[i] = new Point2f(kpsBI.Pt.X, kpsBI.Pt.Y);
}
InputArray ptsA = InputArray.Create(PtA);
InputArray ptsB = InputArray.Create(PtB);
if (matches.Length > 4)
{
//You get the homography matrix usin
Mat H = Cv2.FindHomography(ptsA, ptsB, HomographyMethods.Ransac, reprojThresh);
//and then to get any point on the target picture from the original picture:
//Mat targetPoint = new Mat();
//Cv2.PerspectiveTransform(ptsA, targetPoint, H);
return H;
}
else
return null;
}
}