private void button2_Click(object sender, EventArgs e)
{
//获取最佳Size，以便可以使用FFT，通常2*3*5倍数
int M = CvInvoke.GetOptimalDFTSize(image.Rows);
int N = CvInvoke.GetOptimalDFTSize(image.Cols);
//图像扩展
Mat padded = new Mat();
CvInvoke.CopyMakeBorder(image, padded, 0, M - image.Rows, 0, N - image.Cols, BorderType.Constant, new MCvScalar(1));
//创建一个2通道矩阵，0通道为源图数据，1通道为0
Mat m = new Mat(padded.Size, DepthType.Cv32F, 1);
m.SetTo(new MCvScalar(255));
CvInvoke.Divide(padded, m, padded);
m.SetTo(new MCvScalar(0));
VectorOfMat matVector = new VectorOfMat();
matVector.Push(padded);
matVector.Push(m);
Mat matComplex = new Mat(padded.Size, DepthType.Cv32F, 2);
CvInvoke.Merge(matVector, matComplex);
padded.Dispose();
m.Dispose();
matVector.Dispose();
// This will hold the DFT data，创建2通道矩阵，储存变换后结果
Matrix <float> forwardDft = new Matrix <float>(image.Rows, image.Cols, 2);
CvInvoke.Dft(matComplex, forwardDft, DxtType.Forward, 0);
// We'll display the magnitude，显示谱图像
Matrix <float> forwardDftMagnitude = GetDftMagnitude(forwardDft);
SwitchQuadrants(ref forwardDftMagnitude);
// Now compute the inverse to see if we can get back the original
//进行反变换
Matrix <float> reverseDft = new Matrix <float>(forwardDft.Rows, forwardDft.Cols, 2);
CvInvoke.Dft(forwardDft, reverseDft, DxtType.InvScale, 0);
Matrix <float> reverseDftMagnitude = GetDftMagnitude(reverseDft);
imageBox1.Image = image;
imageBox2.Image = Matrix2Image(forwardDftMagnitude);
imageBox3.Image = Matrix2Image(reverseDftMagnitude);
}
public static Task <Image <Bgr, Byte> > CorrectLightness(Image <Bgr, Byte> rgb)
{
return(Task.Run(delegate() {
Image <Lab, Byte> lab = rgb.Convert <Lab, Byte>();
Image <Gray, Byte>[] lab_planes = lab.Split();
Image <Gray, Byte> lightness = new Image <Gray, byte>(rgb.Size);
CvInvoke.CLAHE(lab_planes[0], 40, new Size(4, 4), lightness);
VectorOfMat vm = new VectorOfMat(lightness.Mat, lab_planes[1].Mat, lab_planes[2].Mat);
CvInvoke.Merge(vm, lab);
Image <Bgr, Byte> dst = lab.Convert <Bgr, Byte>();
vm.Dispose();
lab.Dispose();
lab_planes = null;
lightness.Dispose();
return dst;
}));
}
/// <summary>
/// This function compares the separte Blue, Green and Red values of each pixel and, using a clever
/// subtraction, tries to determine if it is skin-colored. The idea is taken from this paper:
/// "In-air gestures around unmodified mobile devices" by Song et al.
/// </summary>
/// <param name="inputImage">Standard BGR image.</param>
/// <returns>Grayscale image with the white pixels containing skin.</returns>
private static Image <Gray, byte> MinimumSegment(Image <Bgr, byte> inputImage)
{
Mat deltaOne = new Mat();
Mat deltaTwo = new Mat();
VectorOfMat bgrChannels = new VectorOfMat(3);
CvInvoke.Split(inputImage, bgrChannels);
CvInvoke.Subtract(bgrChannels[2], bgrChannels[1], deltaOne);
CvInvoke.Subtract(bgrChannels[2], bgrChannels[0], deltaTwo);
Mat mixedMat = new Mat();
CvInvoke.Min(deltaOne, deltaTwo, mixedMat);
Image <Gray, byte> outputImage = mixedMat.ToImage <Gray, byte>().InRange(new Gray(10), new Gray(200));
bgrChannels.Dispose();
mixedMat.Dispose();
return(outputImage);
}
private void extractFromSlides(string outdir, List <int> slideList, int maskSlide, int shadowSlide, string slidePrefix = "Slide_")
{
var mat_mask = CvInvoke.Imread(outdir + slidePrefix + maskSlide + ".png", ImreadModes.Grayscale);
Mat mat_shadow_inv = null;
if (shadowSlide > 0)
{
var mat_shadow = CvInvoke.Imread(outdir + slidePrefix + shadowSlide + ".png", ImreadModes.Grayscale);
mat_shadow_inv = 255 - mat_shadow;
mat_shadow.Dispose();
}
Mat mat_content = null;
VectorOfMat mat_content_bgr = null;
Mat mat_alpha = null;
foreach (var slide in slideList)
{
// Read current image back from File
mat_content = CvInvoke.Imread(outdir + slidePrefix + slide + ".png", ImreadModes.Color);
// Split in Blue, Green and Red channel
mat_content_bgr = new VectorOfMat(mat_content.Split());
// Mask all channel so image gets blck where mask is black
CvInvoke.Multiply(mat_content_bgr[0], mat_mask, mat_content_bgr[0], 1.0 / 255);
CvInvoke.Multiply(mat_content_bgr[1], mat_mask, mat_content_bgr[1], 1.0 / 255);
CvInvoke.Multiply(mat_content_bgr[2], mat_mask, mat_content_bgr[2], 1.0 / 255);
// Create alpha layer for image
mat_alpha = mat_mask.Clone();
if (shadowSlide > 0)
{
CvInvoke.Add(mat_mask, mat_shadow_inv, mat_alpha);
}
// Merge channels to one image again
CvInvoke.Merge(new VectorOfMat(mat_content_bgr[0], mat_content_bgr[1], mat_content_bgr[2], mat_alpha), mat_content);
// Save current extracted image with alpha layer
mat_content.Save(outdir + "Extract_" + slide + ".png");
}
// Dispose all used elements
if (mat_mask != null)
{
mat_mask.Dispose();
}
if (mat_shadow_inv != null)
{
mat_shadow_inv.Dispose();
}
if (mat_content != null)
{
mat_content.Dispose();
}
if (mat_content_bgr != null)
{
mat_content_bgr.Dispose();
}
if (mat_alpha != null)
{
mat_alpha.Dispose();
}
}
/// <summary>
/// Calculate a mask for the pieces. The function calculates a histogram to find the piece background color.
/// Everything within a specific HSV range around the piece background color is regarded as foreground. The rest is regarded as background.
/// </summary>
/// <param name="inputImg">Color input image</param>
/// <returns>Mask image</returns>
/// see: https://docs.opencv.org/2.4/modules/imgproc/doc/histograms.html?highlight=calchist
public override Image <Gray, byte> GetMask(Image <Rgba, byte> inputImg)
{
Image <Gray, byte> mask;
using (Image <Hsv, byte> hsvSourceImg = inputImg.Convert <Hsv, byte>()) //Convert input image to HSV color space
{
Mat hsvImgMat = new Mat();
hsvSourceImg.Mat.ConvertTo(hsvImgMat, DepthType.Cv32F);
VectorOfMat vm = new VectorOfMat(hsvImgMat);
// Calculate histograms for each channel of the HSV image (H, S, V)
Mat histOutH = new Mat(), histOutS = new Mat(), histOutV = new Mat();
int hbins = 32, sbins = 32, vbins = 32;
CvInvoke.CalcHist(vm, new int[] { 0 }, new Mat(), histOutH, new int[] { hbins }, new float[] { 0, 179 }, false);
CvInvoke.CalcHist(vm, new int[] { 1 }, new Mat(), histOutS, new int[] { sbins }, new float[] { 0, 255 }, false);
CvInvoke.CalcHist(vm, new int[] { 2 }, new Mat(), histOutV, new int[] { vbins }, new float[] { 0, 255 }, false);
hsvImgMat.Dispose();
vm.Dispose();
// Draw the histograms for debugging purposes
if (PluginFactory.GetGeneralSettingsPlugin().SolverShowDebugResults)
{
PluginFactory.LogHandle?.Report(new LogEventImage("Hist H", Utils.DrawHist(histOutH, hbins, 30, 1024, new MCvScalar(255, 0, 0)).Bitmap));
PluginFactory.LogHandle?.Report(new LogEventImage("Hist S", Utils.DrawHist(histOutS, sbins, 30, 1024, new MCvScalar(0, 255, 0)).Bitmap));
PluginFactory.LogHandle?.Report(new LogEventImage("Hist V", Utils.DrawHist(histOutV, vbins, 30, 1024, new MCvScalar(0, 0, 255)).Bitmap));
}
//#warning Use border color
//int borderHeight = 10;
//Image<Hsv, byte> borderImg = hsvSourceImg.Copy(new Rectangle(0, hsvSourceImg.Height - borderHeight, hsvSourceImg.Width, borderHeight));
//MCvScalar meanBorderColorScalar = CvInvoke.Mean(borderImg);
//Hsv meanBorderColor = new Hsv(meanBorderColorScalar.V0, meanBorderColorScalar.V1, meanBorderColorScalar.V2);
//if (PuzzleSolverParameters.Instance.SolverShowDebugResults)
//{
// Image<Hsv, byte> borderColorImg = new Image<Hsv, byte>(12, 12);
// borderColorImg.SetValue(meanBorderColor);
// _logHandle.Report(new LogBox.LogEventImage("HSV Border Color (" + meanBorderColor.Hue + " ; " + meanBorderColor.Satuation + "; " + meanBorderColor.Value + ")", borderColorImg.Bitmap));
//}
// Find the peaks in the histograms and use them as piece background color. Black and white areas are ignored.
Hsv pieceBackgroundColor = new Hsv
{
Hue = Utils.HighestBinValInRange(histOutH, MainHueSegment - HueDiffHist, MainHueSegment + HueDiffHist, 179), //25, 179, 179);
Satuation = Utils.HighestBinValInRange(histOutS, 50, 205, 255), //50, 255, 255);
Value = Utils.HighestBinValInRange(histOutV, 75, 205, 255) //75, 255, 255);
};
histOutH.Dispose();
histOutS.Dispose();
histOutV.Dispose();
// Show the found piece background color
if (PluginFactory.GetGeneralSettingsPlugin().SolverShowDebugResults)
{
Image <Hsv, byte> pieceBgColorImg = new Image <Hsv, byte>(4, 12);
Image <Hsv, byte> lowPieceBgColorImg = new Image <Hsv, byte>(4, 12);
Image <Hsv, byte> highPieceBgColorImg = new Image <Hsv, byte>(4, 12);
pieceBgColorImg.SetValue(pieceBackgroundColor);
lowPieceBgColorImg.SetValue(new Hsv(pieceBackgroundColor.Hue - HueDiff, pieceBackgroundColor.Satuation - SaturationDiff, pieceBackgroundColor.Value - ValueDiff));
highPieceBgColorImg.SetValue(new Hsv(pieceBackgroundColor.Hue + HueDiff, pieceBackgroundColor.Satuation + SaturationDiff, pieceBackgroundColor.Value + ValueDiff));
PluginFactory.LogHandle?.Report(new LogEventImage("HSV Piece Bg Color (" + pieceBackgroundColor.Hue + " ; " + pieceBackgroundColor.Satuation + "; " + pieceBackgroundColor.Value + ")", Utils.Combine2ImagesHorizontal(Utils.Combine2ImagesHorizontal(lowPieceBgColorImg.Convert <Rgb, byte>(), pieceBgColorImg.Convert <Rgb, byte>(), 0), highPieceBgColorImg.Convert <Rgb, byte>(), 0).Bitmap));
pieceBgColorImg.Dispose();
lowPieceBgColorImg.Dispose();
highPieceBgColorImg.Dispose();
}
// do HSV segmentation and keep only the meanColor areas with some hysteresis as pieces
mask = hsvSourceImg.InRange(new Hsv(pieceBackgroundColor.Hue - HueDiff, pieceBackgroundColor.Satuation - SaturationDiff, pieceBackgroundColor.Value - ValueDiff), new Hsv(pieceBackgroundColor.Hue + HueDiff, pieceBackgroundColor.Satuation + SaturationDiff, pieceBackgroundColor.Value + ValueDiff));
// close small black gaps with morphological closing operation
Mat kernel = CvInvoke.GetStructuringElement(ElementShape.Rectangle, new Size(5, 5), new Point(-1, -1));
CvInvoke.MorphologyEx(mask, mask, MorphOp.Close, kernel, new Point(-1, -1), 5, BorderType.Default, new MCvScalar(0));
}
return(mask);
}