opencvsharp图像质量评价

OpenCVSharp提供了一些图像质量评价的方法，可以用于评估图像的清晰度、对比度等指标。以下是一些常用的方法：

1. Laplacian算子评价图像清晰度

using OpenCvSharp;

Mat src = Cv2.ImRead("image.jpg", ImreadModes.Grayscale);
Mat laplacian = new Mat();
Cv2.Laplacian(src, laplacian, MatType.CV_64F);
Scalar mean, stddev;
Cv2.MeanStdDev(laplacian, out mean, out stddev);
double focusMeasure = stddev.Val0 * stddev.Val0;

2. 傅里叶变换评价图像清晰度

using OpenCvSharp;

Mat src = Cv2.ImRead("image.jpg", ImreadModes.Grayscale);
Mat padded = new Mat();
Cv2.CopyMakeBorder(src, padded, 0, src.Rows, 0, src.Cols, BorderTypes.Constant, Scalar.All(0));
padded = padded.Norm(NormTypes.MinMax);
Mat planes = new MatArray(padded, Mat.Zeros(padded.Size(), MatType.CV_32F)).GetMat(0);
Cv2.Dft(planes, planes);
Cv2.ShiftDft(planes);
Mat magnitude = new Mat();
Cv2.Split(planes, out Mat[] complex);
Cv2.Magnitude(complex[0], complex[1], magnitude);
magnitude = magnitude.Norm(NormTypes.MinMax);
double focusMeasure = Cv2.Sum(magnitude).Val0;

3. SIFT特征点匹配评价图像对比度

using OpenCvSharp;
using OpenCvSharp.XFeatures2D;

Mat src1 = Cv2.ImRead("image1.jpg", ImreadModes.Grayscale);
Mat src2 = Cv2.ImRead("image2.jpg", ImreadModes.Grayscale);
SIFT sift = SIFT.Create();
KeyPoint[] keypoints1, keypoints2;
Mat descriptors1 = new Mat(), descriptors2 = new Mat();
sift.DetectAndCompute(src1, null, out keypoints1, descriptors1);
sift.DetectAndCompute(src2, null, out keypoints2, descriptors2);
BFMatcher matcher = new BFMatcher(NormTypes.L2, false);
DMatch[] matches = matcher.Match(descriptors1, descriptors2);
double maxDist = matches.Max(m => m.Distance);
double minDist = matches.Min(m => m.Distance);
double contrastMeasure = (maxDist - minDist) / maxDist;