static void Main(string[] args)
{
using var srcA = new Mat("./input/A.jpg", ImreadModes.Color);
using var srcB = new Mat("./input/B.jpg", ImreadModes.Color);
using var maskA = new Mat("./input/A_mask.jpg", ImreadModes.Grayscale);
using var maskB = new Mat("./input/B_mask.jpg", ImreadModes.Grayscale);
var sift = OpenCvSharp.Features2D.SIFT.Create();
KeyPoint[] kp1, kp2;
Mat des1 = new Mat(), des2 = new Mat();
sift.DetectAndCompute(srcA, maskA, out kp1, des1);
sift.DetectAndCompute(srcB, maskB, out kp2, des2);
var bf = new BFMatcher();
var matches = bf.KnnMatch(des1, des2, 2);
// ratio test
double ratio = 0.8;
var good = matches.Where(match => {
if (match[0].Distance < ratio * match[1].Distance)
{
return(true);
}
return(false);
}).Select(match => new DMatch[1] {
match[0]
});
Mat result = new Mat();
Cv2.DrawMatchesKnn(srcA, kp1, srcB, kp2, good, result);
Cv2.ImWrite("result.png", result);
var ptsA = good.Select(m => kp1[m[0].QueryIdx].Pt).ToList();
var ptsB = good.Select(m => kp2[m[0].TrainIdx].Pt).ToList();
var H = Cv2.FindHomography(InputArray.Create(ptsA), InputArray.Create(ptsB), HomographyMethods.Ransac, 5.0);
Mat warped = new Mat();
Cv2.WarpPerspective(srcA, warped, H, srcA.Size());
Cv2.ImWrite("warped_0.png", warped);
Cv2.ImWrite("warped_1.png", srcB);
Console.WriteLine("Example done!");
}
