public VectorOfMat Dctspy()
{
VectorOfMat vector = new VectorOfMat();
if (processMode)
{
Mat tR = new Mat();
Mat tG = new Mat();
Mat tB = new Mat();
dctR.AugmentShow().CopyTo(tR);
vector.Push(tR);
dctG.AugmentShow().CopyTo(tG);
vector.Push(tG);
dctB.AugmentShow().CopyTo(tB);
vector.Push(tB);
}
else
{
Mat temp = new Mat();
dctGray.AugmentShow().CopyTo(temp);
vector.Push(temp);
}
return(vector);
}
private void toAdaptiveYCrCbEqualization(Mat img)
{
if (img.NumberOfChannels < 3)
{
MessageBoxResult result = MessageBox.Show("흑백 영상은 변환할 수 없습니다.", "경고");
return;
}
CvInvoke.CvtColor(img, CurrentMat, ColorConversion.Bgr2YCrCb);
var channels = CurrentMat.Split(); // 이미지 채널 분리
CvInvoke.CLAHE(channels[0], 2, new System.Drawing.Size(8, 8), channels[0]);
CvInvoke.CLAHE(channels[1], 2, new System.Drawing.Size(8, 8), channels[1]);
CvInvoke.CLAHE(channels[2], 2, new System.Drawing.Size(8, 8), channels[2]);
VectorOfMat temp = new VectorOfMat();
temp.Push(channels[0]);
temp.Push(channels[1]);
temp.Push(channels[2]);
CvInvoke.Merge(temp, CurrentMat);
CvInvoke.CvtColor(CurrentMat, CurrentMat, ColorConversion.YCrCb2Bgr);
showImg(CurrentMat);
}
public VectorOfMat Dftspy()
{
VectorOfMat vector = new VectorOfMat();
if (processMode)
{
Mat tR = new Mat();
Mat tG = new Mat();
Mat tB = new Mat();
dftR.DftShift().CopyTo(tR);
vector.Push(tR);
dftG.DftShift().CopyTo(tG);
vector.Push(tG);
dftB.DftShift().CopyTo(tB);
vector.Push(tB);
}
else
{
Mat temp = new Mat();
dftGray.DftShift().CopyTo(temp);
vector.Push(temp);
}
return(vector);
}
//Канал
public Image <Bgr, byte> GetChannels(bool red, bool green, bool blue)
{
//var channel = sourceImage.Split()[0]; //[] - channel index
bool[] channels = new bool[3] {
blue, green, red
};
Image <Bgr, byte> destImage = sourceImage.CopyBlank();
VectorOfMat vm = new VectorOfMat();
for (int i = 0; i < 3; i++)
{
if (channels[i] == true)
{
vm.Push(sourceImage.Split()[i]);
}
else
{
vm.Push(sourceImage.Split()[i].CopyBlank());
}
}
CvInvoke.Merge(vm, destImage);
return(destImage);
}
private void SaveFace(object sender, RoutedEventArgs e)
{
if (SelectedFace == null)
{
MessageBox.Show("Выберите лицо для регистрации", "Ошибка регистрации", MessageBoxButton.OK, MessageBoxImage.Warning);
return;
}
FaceRecognizer = new FisherFaceRecognizer(0, 3500);
var imageList = new VectorOfMat();
var labelList = new VectorOfInt();
imageList.Push(SelectedFace.CVImage.Resize(100, 100, Inter.Cubic).Mat);
var samples = Directory.GetFiles("Face Samples");
foreach (var sample in samples)
{
imageList.Push(new Image <Gray, byte>(sample).Resize(100, 100, Inter.Cubic).Mat);
}
labelList.Push(Enumerable.Range(1, samples.Length + 1).ToArray());
FaceRecognizer.Train(imageList, labelList);
DialogResult = true;
Close();
}
public Image <Bgr, byte> channelfilter(Image <Bgr, byte> simage, int i)
{
var channel = simage.Split()[i];
Image <Bgr, byte> destImage = simage.CopyBlank();
VectorOfMat vm = new VectorOfMat();
switch (i)
{
case 0:
{
vm.Push(channel);
vm.Push(channel.CopyBlank());
vm.Push(channel.CopyBlank());
break;
}
case 1:
{
vm.Push(channel.CopyBlank());
vm.Push(channel);
vm.Push(channel.CopyBlank());
break;
}
case 2:
{
vm.Push(channel.CopyBlank());
vm.Push(channel.CopyBlank());
vm.Push(channel);
break;
}
}
CvInvoke.Merge(vm, destImage);
return(destImage);
}
public Emgu.CV.Image <Bgr, byte> chanelsChange(Image <Bgr, byte> sourceImage, int type)
{
var channel = sourceImage.Split()[type];
Image <Bgr, byte> destImage = sourceImage.CopyBlank();
VectorOfMat vm = new VectorOfMat();
if (type == 0)
{
vm.Push(channel);
vm.Push(channel.CopyBlank());
vm.Push(channel.CopyBlank());
}
else if (type == 1)
{
vm.Push(channel.CopyBlank());
vm.Push(channel.CopyBlank());
vm.Push(channel);
}
else if (type == 2)
{
vm.Push(channel.CopyBlank());
vm.Push(channel);
vm.Push(channel.CopyBlank());
}
CvInvoke.Merge(vm, destImage);
return(destImage.Resize(640, 480, Inter.Linear));
}
//Функция вывода значения канала
public static Image <Bgr, byte> Chanel(int i, Image <Bgr, byte> sourceImage)
{
var channel = sourceImage.Split()[i];
Image <Bgr, byte> destImage = sourceImage.CopyBlank();
VectorOfMat vm = new VectorOfMat();
if (i == 0)
{
vm.Push(channel);
vm.Push(channel.CopyBlank());
vm.Push(channel.CopyBlank());
}
if (i == 1)
{
vm.Push(channel.CopyBlank());
vm.Push(channel);
vm.Push(channel.CopyBlank());
}
if (i == 2)
{
vm.Push(channel.CopyBlank());
vm.Push(channel.CopyBlank());
vm.Push(channel);
}
CvInvoke.Merge(vm, destImage);
return(destImage);
}
private void comboBox1_SelectedIndexChanged(object sender, EventArgs e)
{
if (comboBox1.SelectedIndex == 0)
{
var channel = sourceImage.Split()[2];
Image <Bgr, byte> destImage = sourceImage.CopyBlank();
VectorOfMat vm = new VectorOfMat();
vm.Push(channel.CopyBlank());
vm.Push(channel.CopyBlank());
vm.Push(channel);
CvInvoke.Merge(vm, destImage);
imageBox2.Image = destImage;
}
if (comboBox1.SelectedIndex == 1)
{
var channel = sourceImage.Split()[1];
Image <Bgr, byte> destImage = sourceImage.CopyBlank();
VectorOfMat vm = new VectorOfMat();
vm.Push(channel.CopyBlank());
vm.Push(channel);
vm.Push(channel.CopyBlank());
CvInvoke.Merge(vm, destImage);
imageBox2.Image = destImage;
}
if (comboBox1.SelectedIndex == 2)
{
var channel = sourceImage.Split()[0];
Image <Bgr, byte> destImage = sourceImage.CopyBlank();
VectorOfMat vm = new VectorOfMat();
vm.Push(channel);
vm.Push(channel.CopyBlank());
vm.Push(channel.CopyBlank());
CvInvoke.Merge(vm, destImage);
imageBox2.Image = destImage;
}
}
public Image <Bgr, byte> Red()
{
var channel = sourceImage.Split()[0];
Image <Bgr, byte> destImage = sourceImage.CopyBlank();
VectorOfMat vm = new VectorOfMat();
vm.Push(channel);
vm.Push(channel.CopyBlank());
vm.Push(channel.CopyBlank());
CvInvoke.Merge(vm, destImage);
return(destImage);
}
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);
}
private void calculateToolStripMenuItem_Click(object sender, EventArgs e)
{
try
{
if (pictureBox1.Image == null)
{
return;
}
var img = new Bitmap(pictureBox1.Image)
.ToImage <Gray, byte>();
Mat hist = new Mat();
float[] ranges = new float[] { 0, 256 };
int[] channel = { 0 };
int[] histSize = { 256 };
VectorOfMat ms = new VectorOfMat();
ms.Push(img);
CvInvoke.CalcHist(ms, channel, null, hist, histSize, ranges, false);
HistogramViewer viewer = new HistogramViewer();
viewer.Text = "Image Histogram";
viewer.ShowIcon = false;
viewer.HistogramCtrl.AddHistogram("Image Histogram", Color.Blue, hist, 256, ranges);
viewer.HistogramCtrl.Refresh();
viewer.Show();
//pictureBox1.Image = CreateGraph(hist).GetImage();
}
catch (Exception ex)
{
MessageBox.Show(ex.Message);
}
}
//Faz stich de dois frames de dois video separados
private void stichFirstFrameToolStripMenuItem_Click(object sender, System.EventArgs e)
{
try
{
using (Stitcher stitcher = new Stitcher(true))
{
using (VectorOfMat vm = new VectorOfMat())
{
vm.Push(sourceImages);
var stitchStatus = stitcher.Stitch(vm, result);
if (stitchStatus)
{
Bitmap bt = new Bitmap(result.Bitmap);
//por algum motivo a imagem fica rodada :(
bt.RotateFlip(RotateFlipType.RotateNoneFlipXY);
pictureBox1.Image = bt;
// pictureBox1.Image.Save(@"path", ImageFormat.Jpeg);
}
else
{
MessageBox.Show(this, String.Format("Stiching Error: {0}", stitchStatus));
pictureBox1.Image = null;
}
}
}
}
catch
{
}
}
public Image <Gray, byte> GetChannel(int channelIndex)
{
if (channelIndex != 3)
{
var channel = sourceImage.Split()[channelIndex];
return(channel);
}
else
{
Mat destImage = new Mat();
VectorOfMat vm = new VectorOfMat();
vm.Push(sourceImage.Split()[0]); vm.Push(sourceImage.Split()[1]); vm.Push(sourceImage.Split()[2]);
CvInvoke.Merge(vm, destImage);
return(destImage.ToImage <Gray, byte>());
}
}
/*
* /// <summary>
* /// Create a LevMarqSparse solver
* /// </summary>
* public LevMarqSparse()
* {
* _ptr = CvInvoke.CvCreateLevMarqSparse();
* }*/
/// <summary>
/// Useful function to do simple bundle adjustment tasks
/// </summary>
/// <param name="points">Positions of points in global coordinate system (input and output), values will be modified by bundle adjustment</param>
/// <param name="imagePoints">Projections of 3d points for every camera</param>
/// <param name="visibility">Visibility of 3d points for every camera</param>
/// <param name="cameraMatrix">Intrinsic matrices of all cameras (input and output), values will be modified by bundle adjustment</param>
/// <param name="R">rotation matrices of all cameras (input and output), values will be modified by bundle adjustment</param>
/// <param name="T">translation vector of all cameras (input and output), values will be modified by bundle adjustment</param>
/// <param name="distCoeffcients">distortion coefficients of all cameras (input and output), values will be modified by bundle adjustment</param>
/// <param name="termCrit">Termination criteria, a reasonable value will be (30, 1.0e-12) </param>
public static void BundleAdjust(
MCvPoint3D64f[] points, MCvPoint2D64f[][] imagePoints, int[][] visibility,
Matrix <double>[] cameraMatrix, Matrix <double>[] R, Matrix <double>[] T, Matrix <double>[] distCoeffcients, MCvTermCriteria termCrit)
{
using (Matrix <double> imagePointsMat = CvToolbox.GetMatrixFromPoints(imagePoints))
using (Matrix <int> visibilityMat = CvToolbox.GetMatrixFromArrays(visibility))
using (VectorOfMat cameraMatVec = new VectorOfMat())
using (VectorOfMat rMatVec = new VectorOfMat())
using (VectorOfMat tMatVec = new VectorOfMat())
using (VectorOfMat distorMatVec = new VectorOfMat())
{
cameraMatVec.Push(cameraMatrix);
rMatVec.Push(R);
tMatVec.Push(T);
distorMatVec.Push(distCoeffcients);
GCHandle handlePoints = GCHandle.Alloc(points, GCHandleType.Pinned);
CvInvoke.CvLevMarqSparseAdjustBundle(
cameraMatrix.Length,
points.Length, handlePoints.AddrOfPinnedObject(),
imagePointsMat, visibilityMat, cameraMatVec, rMatVec, tMatVec, distorMatVec, ref termCrit);
handlePoints.Free();
}
}
/*
/// <summary>
/// Create a LevMarqSparse solver
/// </summary>
public LevMarqSparse()
{
_ptr = CvInvoke.CvCreateLevMarqSparse();
}*/
/// <summary>
/// Useful function to do simple bundle adjustment tasks
/// </summary>
/// <param name="points">Positions of points in global coordinate system (input and output), values will be modified by bundle adjustment</param>
/// <param name="imagePoints">Projections of 3d points for every camera</param>
/// <param name="visibility">Visibility of 3d points for every camera</param>
/// <param name="cameraMatrix">Intrinsic matrices of all cameras (input and output), values will be modified by bundle adjustment</param>
/// <param name="R">rotation matrices of all cameras (input and output), values will be modified by bundle adjustment</param>
/// <param name="T">translation vector of all cameras (input and output), values will be modified by bundle adjustment</param>
/// <param name="distCoeffcients">distortion coefficients of all cameras (input and output), values will be modified by bundle adjustment</param>
/// <param name="termCrit">Termination criteria, a reasonable value will be (30, 1.0e-12) </param>
public static void BundleAdjust(
MCvPoint3D64f[] points, MCvPoint2D64f[][] imagePoints, int[][] visibility,
Matrix<double>[] cameraMatrix, Matrix<double>[] R, Matrix<double>[] T, Matrix<double>[] distCoeffcients, MCvTermCriteria termCrit)
{
using (Matrix<double> imagePointsMat = CvToolbox.GetMatrixFromPoints(imagePoints))
using (Matrix<int> visibilityMat = CvToolbox.GetMatrixFromArrays(visibility))
using (VectorOfMat cameraMatVec = new VectorOfMat())
using (VectorOfMat rMatVec = new VectorOfMat())
using (VectorOfMat tMatVec = new VectorOfMat())
using (VectorOfMat distorMatVec = new VectorOfMat())
{
cameraMatVec.Push(cameraMatrix);
rMatVec.Push(R);
tMatVec.Push(T);
distorMatVec.Push(distCoeffcients);
GCHandle handlePoints = GCHandle.Alloc(points, GCHandleType.Pinned);
CvInvoke.CvLevMarqSparseAdjustBundle(
cameraMatrix.Length,
points.Length, handlePoints.AddrOfPinnedObject(),
imagePointsMat, visibilityMat, cameraMatVec, rMatVec, tMatVec, distorMatVec, ref termCrit);
handlePoints.Free();
}
}
private void timer1_Tick(object sender, EventArgs e)
{
Mat cap = new Mat();
Mat[] imgs = new Mat[3];
Mat result = new Mat();
cap = c1.QueryFrame();
imageBox1.Image = cap;
imgs[0] = cap;
cap = c2.QueryFrame();
imageBox2.Image = cap;
imgs[1] = cap;
cap = c3.QueryFrame();
imageBox3.Image = cap;
imgs[2] = cap;
Emgu.CV.Stitching.Stitcher stitcher = new Emgu.CV.Stitching.Stitcher(true);
using (VectorOfMat vms = new VectorOfMat())
{
vms.Push(imgs);
bool stitchStatus = stitcher.Stitch(vms, result);
if (stitchStatus)
{
imageBox4.Image = result;
toolStripStatusLabel1.Text = "Stitch OK";
}
else
{
toolStripStatusLabel1.Text = "Stitch error";
}
}
}
public bool ConfigRecognitionImageTrain(Mat imageTrain, Mat roiTrain, bool useGlobalMatch)
{
_trainsImage.Push(imageTrain);
_keypointsImageTrain.Add(new VectorOfKeyPoint());
_descriptorsImageTrain.Push(new Mat());
_LODIndex = _trainsImage.Size - 1;
SIFT sift = new SIFT();
//Insere os pontos chaves da imagem alvo na lista de pontos chaves
_keypointsImageTrain.Insert(_LODIndex, new VectorOfKeyPoint(sift.Detect(_trainsImage[_LODIndex], roiTrain)));
if (_keypointsImageTrain[_LODIndex] != null && _keypointsImageTrain[_LODIndex].Size < 4)
{
return(false);
}
//Calcula os descritores dos pontos chaves extraidos, no caso se extrair poucos descritores ele return false = não reconhecido
sift.Compute(_trainsImage[_LODIndex], _keypointsImageTrain[_LODIndex], _descriptorsImageTrain[_LODIndex]);
if (_descriptorsImageTrain[_LODIndex].Rows < 4)
{
return(false);
}
if (useGlobalMatch)
{
return(true);
}
else
{
return(ConfigureImageTrainROI(_keypointsImageTrain[_LODIndex], roiTrain));
}
}
/// <summary>
/// Stitch images together
/// </summary>
/// <param name="images">The list of images to stitch</param>
/// <returns>A final stitched image</returns>
public static Mat StichImages(List <Mat> images)
{
//Declare the Mat object that will store the final output
Mat output = new Mat();
//Declare a vector to store all images from the list
VectorOfMat matVector = new VectorOfMat();
//Push all images in the list into a vector
foreach (Mat img in images)
{
matVector.Push(img);
}
//Declare a new stitcher
Stitcher stitcher = new Stitcher();
//Declare the type of detector that will be used to detect keypoints
Brisk detector = new Brisk();
//Here are some other detectors that you can try
//ORBDetector detector = new ORBDetector();
//KAZE detector = new KAZE();
//AKAZE detector = new AKAZE();
//Set the stitcher class to use the specified detector declared above
stitcher.SetFeaturesFinder(detector);
//Stitch the images together
stitcher.Stitch(matVector, output);
//Return the final stiched image
return(output);
}
private void selectImagesButton_Click(object sender, EventArgs e)
{
OpenFileDialog dlg = new OpenFileDialog();
dlg.CheckFileExists = true;
dlg.Multiselect = true;
if (dlg.ShowDialog() == System.Windows.Forms.DialogResult.OK)
{
sourceImageDataGridView.Rows.Clear();
Image <Bgr, byte>[] sourceImages = new Image <Bgr, byte> [dlg.FileNames.Length];
for (int i = 0; i < sourceImages.Length; i++)
{
sourceImages[i] = new Image <Bgr, byte>(dlg.FileNames[i]);
using (Image <Bgr, byte> thumbnail = sourceImages[i].Resize(200, 200, Emgu.CV.CvEnum.Inter.Cubic, true))
{
DataGridViewRow row = sourceImageDataGridView.Rows[sourceImageDataGridView.Rows.Add()];
row.Cells["FileNameColumn"].Value = dlg.FileNames[i];
row.Cells["ThumbnailColumn"].Value = thumbnail.ToBitmap();
row.Height = 200;
}
}
try
{
//only use GPU if you have build the native binary from code and enabled "NON_FREE"
using (Stitcher stitcher = new Stitcher(false))
using (AKAZEFeaturesFinder finder = new AKAZEFeaturesFinder())
{
stitcher.SetFeaturesFinder(finder);
using (VectorOfMat vm = new VectorOfMat())
{
Mat result = new Mat();
vm.Push(sourceImages);
Stitcher.Status stitchStatus = stitcher.Stitch(vm, result);
if (stitchStatus == Stitcher.Status.Ok)
{
resultImageBox.Image = result;
}
else
{
MessageBox.Show(this, String.Format("Stiching Error: {0}", stitchStatus));
resultImageBox.Image = null;
}
}
}
}
finally
{
foreach (Image <Bgr, Byte> img in sourceImages)
{
img.Dispose();
}
}
}
}
private void selectImagesButton_Click(object sender, EventArgs e)
{
OpenFileDialog dlg = new OpenFileDialog();
dlg.CheckFileExists = true;
dlg.Multiselect = true;
if (dlg.ShowDialog() == System.Windows.Forms.DialogResult.OK)
{
sourceImageDataGridView.Rows.Clear();
Image <Bgr, Byte>[] sourceImages = new Image <Bgr, byte> [dlg.FileNames.Length];
for (int i = 0; i < sourceImages.Length; i++)
{
sourceImages[i] = new Image <Bgr, byte>(dlg.FileNames[i]);
using (Image <Bgr, byte> thumbnail = sourceImages[i].Resize(200, 200, Emgu.CV.CvEnum.Inter.Cubic, true))
{
DataGridViewRow row = sourceImageDataGridView.Rows[sourceImageDataGridView.Rows.Add()];
row.Cells["FileNameColumn"].Value = dlg.FileNames[i];
row.Cells["ThumbnailColumn"].Value = thumbnail.ToBitmap();
row.Height = 200;
}
}
try
{
//using (Stitcher stitcher = new Stitcher(true))
//CUDA bruteforce matcher seems to cause issue in this release, not using CUDA for matching for this reason
using (Stitcher stitcher = new Stitcher(false))
{
using (VectorOfMat vm = new VectorOfMat())
{
Mat result = new Mat();
vm.Push(sourceImages);
Stitcher.Status stitchStatus = stitcher.Stitch(vm, result);
if (stitchStatus == Stitcher.Status.Ok)
{
resultImageBox.Image = result;
}
else
{
MessageBox.Show(this, String.Format("Stiching Error: {0}", stitchStatus));
resultImageBox.Image = null;
}
}
}
}
finally
{
foreach (Image <Bgr, Byte> img in sourceImages)
{
img.Dispose();
}
}
}
}
/// <summary>
/// Train the face recognizer with the specific images and labels
/// </summary>
/// <param name="images">The images used in the training.</param>
/// <param name="labels">The labels of the images.</param>
public void Train(Mat[] images, int[] labels)
{
using (VectorOfMat imgVec = new VectorOfMat())
using (VectorOfInt labelVec = new VectorOfInt(labels))
{
imgVec.Push(images);
Train(imgVec, labelVec);
}
}
/// <summary>
/// Update the face recognizer with the specific images and labels
/// </summary>
/// <param name="images">The images used for updating the face recognizer</param>
/// <param name="labels">The labels of the images</param>
public void Update(Mat[] images, int[] labels)
{
Debug.Assert(images.Length == labels.Length, "The number of labels must equals the number of images");
using (VectorOfMat imgVec = new VectorOfMat())
using (VectorOfInt labelVec = new VectorOfInt(labels))
{
imgVec.Push(images);
Update(imgVec, labelVec);
}
}
/// <summary>
/// Train the face recognizer with the specific images and labels
/// </summary>
/// <param name="images">The images used in the training.</param>
/// <param name="labels">The labels of the images.</param>
public void Train <TColor, TDepth>(Image <TColor, TDepth>[] images, int[] labels)
where TColor : struct, IColor
where TDepth : new()
{
using (VectorOfMat imgVec = new VectorOfMat())
using (VectorOfInt labelVec = new VectorOfInt(labels))
{
imgVec.Push <TDepth>(images);
Train(imgVec, labelVec);
}
}
// Equalization
private void equalization_Click(object sender, EventArgs e)
{
maskDraw.Enabled = true;
Image <Ycc, Byte> temp = new Image <Ycc, Byte>(img.Width, img.Height);
CvInvoke.CvtColor(img, temp, ColorConversion.Rgb2YCrCb);
Image <Gray, Byte>[] channels = temp.Split();
channels[0]._EqualizeHist();
VectorOfMat c = new VectorOfMat();
c.Push(channels[0]);
c.Push(channels[1]);
c.Push(channels[2]);
CvInvoke.Merge(c, temp);
CvInvoke.CvtColor(temp, img, ColorConversion.YCrCb2Rgb);
imgBackUp = img.Clone();
mainImage2.Image = img.ToBitmap();
label1.Text = "Status: Histogram equalization";
}
public void TestVectorOfVectorOfMat()
{
using (Mat m = new Mat())
using (VectorOfMat vm = new VectorOfMat())
{
vm.Push(m);
using (VectorOfVectorOfMat vvm = new VectorOfVectorOfMat())
{
vvm.Push(vm);
}
}
}
/// <summary>
/// Update the face recognizer with the specific images and labels
/// </summary>
/// <param name="images">The images used for updating the face recognizer</param>
/// <param name="labels">The labels of the images</param>
public void Update <TColor, TDepth>(Image <TColor, TDepth>[] images, int[] labels)
where TColor : struct, IColor
where TDepth : new()
{
Debug.Assert(images.Length == labels.Length, "The number of labels must equals the number of images");
using (VectorOfMat imgVec = new VectorOfMat())
using (VectorOfInt labelVec = new VectorOfInt(labels))
{
imgVec.Push(images);
Update(imgVec, labelVec);
}
}
private void button5_Click(object sender, EventArgs e)
{
Stitcher _sticher = new Stitcher(Stitcher.Mode.Scans); //创建一个 Sticher 类。
Mat result_image = new Mat(); //创建 Mat 存储输出全景图
VectorOfMat sti_image = new VectorOfMat(); //创建 VectorOfMat 类型， 输入图像拼接数组
// 添加图像到 sti_image 中， 不按照循序进行添加， 说明拼接图像与顺序无关*//
sti_image.Push(image1);
sti_image.Push(image2);
sti_image.Push(image3);
sti_image.Push(image4);
Stitcher.Status status = _sticher.Stitch(sti_image, result_image);//进行图像拼接， 返回 bool 类型， 是否拼接成功。
if (status == Stitcher.Status.Ok)
{
imageBox5.Image = result_image;//显示图像。
}
else
{
MessageBox.Show("拼接失败", "提示");
}
}
////////////////////////////////////////////////////////////////////////////////////////////
private void btnOpenFile_Click(object sender, EventArgs e)
{
OpenFileDialog ofdOpenfilesD = new OpenFileDialog();
ofdOpenfilesD.CheckFileExists = true;
ofdOpenfilesD.Multiselect = true;
if (ofdOpenfilesD.ShowDialog() != DialogResult.OK || ofdOpenfilesD.FileName == "")
{
MessageBox.Show("Can not read image");
}
else if (ofdOpenfilesD.ShowDialog() == DialogResult.OK)
{
dgvSourceImage.Rows.Clear();
Image <Bgr, Byte>[] originalImagesU = new Image <Bgr, byte> [ofdOpenfilesD.FileNames.Length];
for (int i = 0; i < originalImagesU.Length; i++)
{
originalImagesU[i] = new Image <Bgr, byte>(ofdOpenfilesD.FileNames[i]);
using (Image <Bgr, byte> firstImage = originalImagesU[i].Resize(200, 200, Inter.Cubic, true))
{
DataGridViewRow row = dgvSourceImage.Rows[dgvSourceImage.Rows.Add()];
row.Cells["fileNameColumn"].Value = ofdOpenfilesD.FileNames[i];
row.Cells["samplePictureColumn"].Value = firstImage.ToBitmap();
row.Height = 200;
}
}
try
{
using (Stitcher stiching = new Stitcher(false))
{
using (VectorOfMat matVector = new VectorOfMat())
{
Mat finalImageN = new Mat();
matVector.Push(originalImagesU);
stiching.Stitch(matVector, finalImageN);
ibFinalImage.Image = finalImageN;
}
}
}
finally
{
foreach (Image <Bgr, Byte> image in originalImagesU)
{
image.Dispose();
}
}
}
}
private void selectImagesButton_Click(object sender, EventArgs e)
{
OpenFileDialog dlg = new OpenFileDialog();
dlg.CheckFileExists = true;
dlg.Multiselect = true;
if (dlg.ShowDialog() == System.Windows.Forms.DialogResult.OK)
{
sourceImageDataGridView.Rows.Clear();
Image<Bgr, byte>[] sourceImages = new Image<Bgr, byte>[dlg.FileNames.Length];
for (int i = 0; i < sourceImages.Length; i++)
{
sourceImages[i] = new Image<Bgr, byte>(dlg.FileNames[i]);
using (Image<Bgr, byte> thumbnail = sourceImages[i].Resize(200, 200, Emgu.CV.CvEnum.Inter.Cubic, true))
{
DataGridViewRow row = sourceImageDataGridView.Rows[sourceImageDataGridView.Rows.Add()];
row.Cells["FileNameColumn"].Value = dlg.FileNames[i];
row.Cells["ThumbnailColumn"].Value = thumbnail.ToBitmap();
row.Height = 200;
}
}
try
{
using (Stitcher stitcher = new Stitcher(true))
{
using (VectorOfMat vm = new VectorOfMat())
{
Mat result = new Mat();
vm.Push(sourceImages);
Stitcher.Status stitchStatus = stitcher.Stitch(vm, result);
if (stitchStatus == Stitcher.Status.Ok)
resultImageBox.Image = result;
else
{
MessageBox.Show(this, String.Format("Stiching Error: {0}", stitchStatus));
resultImageBox.Image = null;
}
}
}
}
finally
{
foreach (Image<Bgr, Byte> img in sourceImages)
{
img.Dispose();
}
}
}
}
private void button_hist_Click(object sender, EventArgs e)
{
if (Image_Target == null || Image_Texture == null)
{
return;
}
double temp_weight = (double)trackBar_hist.Value / 10.0;
Image <Gray, Byte> target_gray = Image_Target.Clone().Convert <Gray, Byte>();
Image <Gray, Byte> texture_gray = Image_Texture.Clone().Convert <Gray, Byte>();
Image <Gray, Byte> target_hist_matched = new Image <Gray, Byte>(Image_Target.Size);
Image <Gray, Byte> target_hist_matched_weighted = new Image <Gray, Byte>(Image_Texture.Size);
Matrix <byte> histLUT = new Matrix <byte>(1, 256);
Mat hist_target = new Mat();
Mat hist_texture = new Mat();
VectorOfMat vm_target = new VectorOfMat();
VectorOfMat vm_texture = new VectorOfMat();
vm_target.Push(target_gray);
vm_texture.Push(texture_gray);
CvInvoke.CalcHist(vm_target, new int[] { 0 }, null, hist_target, new int[] { 256 }, new float[] { 0, 255 }, false);
CvInvoke.CalcHist(vm_texture, new int[] { 0 }, null, hist_texture, new int[] { 256 }, new float[] { 0, 255 }, false);
float[] CDF_hist_target = new float[256];
float[] CDF_hist_texture = new float[256];
Marshal.Copy(hist_target.DataPointer, CDF_hist_target, 0, 256);
Marshal.Copy(hist_texture.DataPointer, CDF_hist_texture, 0, 256);
for (int i = 1; i < 256; i++)
{
CDF_hist_target[i] += CDF_hist_target[i - 1];
CDF_hist_texture[i] += CDF_hist_texture[i - 1];
}
for (int i = 0; i < 256; i++)
{
histLUT.Data[0, i] = 0;
for (int j = 0; j < 256; j++)
{
if (CDF_hist_texture[j] >= CDF_hist_target[i])
{
histLUT.Data[0, i] = (byte)j;
break;
}
}
}
CvInvoke.LUT(target_gray, histLUT, target_hist_matched);
target_hist_matched_weighted = target_hist_matched * temp_weight + target_gray * (1.0 - temp_weight);
imageBox_hist.Image = target_hist_matched_weighted;
}
public Image <Bgr, byte> editColourChanel(Image <Bgr, byte> sourceImage, CheckedListBox checkedListBox)
{
var channel = sourceImage.Split()[checkedListBox.SelectedIndex];
Image <Bgr, byte> destImage = sourceImage.CopyBlank();
VectorOfMat vm = new VectorOfMat();
for (int i = 0; i < 3; i++)
{
if (i == checkedListBox.SelectedIndex)
{
vm.Push(channel);
}
else
{
vm.Push(channel.CopyBlank());
}
}
CvInvoke.Merge(vm, destImage);
return(destImage);
}
public async Task<Stream> StitchImages(List<string> imageUrls)
{
if (imageUrls == null || !imageUrls.Any())
{
return null;
}
var httpClient = new HttpClient();
var imageStreams = new List<Stream>();
foreach (var imageUrl in imageUrls)
{
var imageStream = await httpClient.GetStreamAsync(imageUrl);
imageStreams.Add(imageStream);
}
var imageBitmaps = new List<Bitmap>();
foreach (var imageStream in imageStreams)
{
var imageBitmap = new Bitmap(imageStream);
imageBitmaps.Add(imageBitmap);
}
var emguImages = new List<Image<Bgr, byte>>();
foreach (var imageBitmap in imageBitmaps)
{
var image = new Image<Bgr, byte>(imageBitmap);
emguImages.Add(image);
}
var arr = new VectorOfMat();
foreach (var emguImage in emguImages)
{
arr.Push(emguImage.Mat);
}
var stitchedImage = new Mat();
using (var stitcher = new Stitcher(false))
{
stitcher.Stitch(arr, stitchedImage);
}
var resultMemStream = new MemoryStream();
stitchedImage.Bitmap.Save(resultMemStream, ImageFormat.Jpeg);
resultMemStream.Position = 0;
return resultMemStream;
}
private void selectImagesButton_Click(object sender, EventArgs e)
{
OpenFileDialog dlg = new OpenFileDialog();
dlg.CheckFileExists = true;
dlg.Multiselect = true;
if (dlg.ShowDialog() == System.Windows.Forms.DialogResult.OK)
{
sourceImageDataGridView.Rows.Clear();
Image<Bgr, Byte>[] sourceImages = new Image<Bgr, byte>[dlg.FileNames.Length];
for (int i = 0; i < sourceImages.Length; i++)
{
sourceImages[i] = new Image<Bgr, byte>(dlg.FileNames[i]);
using (Image<Bgr, byte> thumbnail = sourceImages[i].Resize(200, 200, Emgu.CV.CvEnum.Inter.Cubic, true))
{
DataGridViewRow row = sourceImageDataGridView.Rows[sourceImageDataGridView.Rows.Add()];
row.Cells["FileNameColumn"].Value = dlg.FileNames[i];
row.Cells["ThumbnailColumn"].Value = thumbnail.ToBitmap();
row.Height = 200;
}
}
try
{
//using (Stitcher stitcher = new Stitcher(true))
//CUDA bruteforce matcher seems to cause issue in this release, not using CUDA for matching for this reason
using (Stitcher stitcher = new Stitcher(false))
{
using (VectorOfMat vm = new VectorOfMat())
{
Mat result = new Mat();
vm.Push(sourceImages);
stitcher.Stitch(vm, result);
resultImageBox.Image = result;
}
}
}
finally
{
foreach (Image<Bgr, Byte> img in sourceImages)
{
img.Dispose();
}
}
}
}
public async Task<HttpResponseMessage> GetPanoDemo()
{
try
{
var imgUrl1 = @"https://cs.brown.edu/courses/csci1950-g/results/proj6/edwallac/source001_01.jpg";
var imgUrl2 = @"https://cs.brown.edu/courses/csci1950-g/results/proj6/edwallac/source001_02.jpg";
var img1Stream = await(new HttpClient()).GetStreamAsync(imgUrl1);
var img2Stream = await(new HttpClient()).GetStreamAsync(imgUrl2);
var bitmap1 = new Bitmap(img1Stream);
var bitmap2 = new Bitmap(img2Stream);
var img1 = new Image<Bgr, byte>(bitmap1);
var img2 = new Image<Bgr, byte>(bitmap2);
var arr = new VectorOfMat();
arr.Push(new[] { img1, img2 });
var stitchedImage = new Mat();
using (var stitcher = new Stitcher(false))
{
stitcher.Stitch(arr, stitchedImage);
}
var resultMemStream = new MemoryStream();
stitchedImage.Bitmap.Save(resultMemStream, ImageFormat.Jpeg);
resultMemStream.Position = 0;
var responseMessage = new HttpResponseMessage
{
Content = new StreamContent(resultMemStream)
{
Headers =
{
ContentLength = resultMemStream.Length,
ContentType = new MediaTypeHeaderValue("image/jpeg"),
ContentDisposition = new ContentDispositionHeaderValue("attachment")
{
FileName = HttpUtility.UrlDecode("result.jpg"),
Size = resultMemStream.Length
}
}
}
};
return responseMessage;
}
catch (Exception e)
{
return new HttpResponseMessage(HttpStatusCode.InternalServerError)
{
ReasonPhrase = e.Message
};
}
}
public void TestStitching4()
{
Mat[] images = new Mat[1];
images[0] = EmguAssert.LoadMat("stitch1.jpg");
using (Stitcher stitcher = new Stitcher(false))
//using (OrbFeaturesFinder finder = new OrbFeaturesFinder(new Size(3, 1)))
{
//stitcher.SetFeaturesFinder(finder);
Mat result = new Mat();
using (VectorOfMat vm = new VectorOfMat())
{
vm.Push(images);
stitcher.Stitch(vm, result);
}
//Emgu.CV.UI.ImageViewer.Show(result);
}
}
public void TestHistogram()
{
using (Image<Bgr, Byte> img = EmguAssert.LoadImage<Bgr, Byte>("stuff.jpg"))
using (Image<Hsv, Byte> img2 = img.Convert<Hsv, Byte>())
{
Image<Gray, Byte>[] HSVs = img2.Split();
using (Mat h = new Mat())
using (Mat bpj = new Mat())
using (VectorOfMat vm = new VectorOfMat())
{
vm.Push(HSVs[0]);
CvInvoke.CalcHist(vm, new int[] { 0 }, null, h, new int[] { 20 }, new float[] { 0, 180 }, false);
CvInvoke.CalcBackProject(vm, new int[] { 0 }, h, bpj, new float[] { 0, 180 }, 0.1);
//Emgu.CV.UI.HistogramViewer.Show(bpj);
//Emgu.CV.UI.ImageViewer.Show(bpj);
//h.Calculate(new Image<Gray, Byte>[1] { HSVs[0] }, true, null);
//using (Image<Gray, Byte> bpj = h.BackProject(new Image<Gray, Byte>[1] { HSVs[0] }))
//{
// Size sz = bpj.Size;
//}
//using (Image<Gray, Single> patchBpj = h.BackProjectPatch(
// new Image<Gray, Byte>[1] { HSVs[0] },
// new Size(5, 5),
// Emgu.CV.CvEnum.HISTOGRAM_COMP_METHOD.CV_COMP_CHISQR,
// 1.0))
//{
// Size sz = patchBpj.Size;
//}
}
foreach (Image<Gray, Byte> i in HSVs)
i.Dispose();
}
}
public void TestStitching2()
{
Image<Bgr, Byte>[] images = new Image<Bgr, byte>[4];
images[0] = EmguAssert.LoadImage<Bgr, Byte>("stitch1.jpg");
images[1] = EmguAssert.LoadImage<Bgr, Byte>("stitch2.jpg");
images[2] = EmguAssert.LoadImage<Bgr, Byte>("stitch3.jpg");
images[3] = EmguAssert.LoadImage<Bgr, Byte>("stitch4.jpg");
using (Stitcher stitcher = new Stitcher(false))
using (OrbFeaturesFinder finder = new OrbFeaturesFinder(new Size(3, 1)))
{
stitcher.SetFeaturesFinder(finder);
Mat result = new Mat();
using (VectorOfMat vm = new VectorOfMat())
{
vm.Push(images);
stitcher.Stitch(vm, result);
}
//Emgu.CV.UI.ImageViewer.Show(result);
}
}
public static bool Sticher(IEnumerable<string> fileList, string saveFileLocation)
{
var imageArray = from fileName in fileList
select new Image<Bgr, byte>(fileName);
try
{
using (var stitcher = new Stitcher(false))
{
using (var vm = new VectorOfMat())
{
var result = new Mat();
vm.Push(imageArray.ToArray());
stitcher.Stitch(vm, result);
result.Save(saveFileLocation);
}
}
return true;
}
catch (Exception ex)
{
Logger.Error("Failed to stich !!", ex);
return false;
}
finally
{
foreach (Image<Bgr, Byte> img in imageArray)
{
img.Dispose();
}
}
}