public GetLineUseCanny(Canny parameter)//带参数的构造函数
{
this.parameter = parameter;
HOperatorSet.GenEmptyObj(out ho_SelectedRegions);
function = "边缘拟合线";
}
public GetLineUseCanny()//构造函数
{
parameter = new Canny();
HOperatorSet.GenEmptyObj(out ho_SelectedRegions);
function = "边缘拟合线";
}
private void EdgeDetect()
{
if (_image != null)
{
try
{
if (_isChecked)
{
_progressValue = 0;
base.OnPropertyChanged("ProgressValue");
Bitmap _bmpImage = BitmapConvert.BitmapImageToBitmap(_image);
Bitmap edge;
Canny.DetectCannyEdges(_bmpImage, out edge, _kernelSize, _sigma, _thrHigh, _thrLow);
_cannyImage = BitmapConvert.toBitmapImage(edge);
base.OnPropertyChanged("CannyImage");
Bitmap gf, np, se, we;
Canny.GaussianFilter(_bmpImage, out gf, _kernelSize, _sigma);
_GFImage = BitmapConvert.toBitmapImage(gf);
base.OnPropertyChanged("GFImage");
Canny.Suppression(_bmpImage, out np, _kernelSize, _sigma);
_NMSImage = BitmapConvert.toBitmapImage(np);
base.OnPropertyChanged("NMSImage");
Canny.Threshold(_bmpImage, out se, out we, _kernelSize, _sigma, _thrHigh, _thrLow);
_WEImage = BitmapConvert.toBitmapImage(we);
base.OnPropertyChanged("WEImage");
_SEImage = BitmapConvert.toBitmapImage(se);
base.OnPropertyChanged("SEImage");
}
else
{
Bitmap _bmpImage = BitmapConvert.BitmapImageToBitmap(_image);
//获得灰度图
Image <Gray, byte> grayFrame = new Image <Gray, byte>(_bmpImage);
if (_equalizeHist)
{
grayFrame._EqualizeHist();//autocontrast
}
//高斯平滑
Image <Gray, byte> smoothedGrayFrame = grayFrame.PyrDown();
smoothedGrayFrame = smoothedGrayFrame.PyrUp();
//canny
Image <Gray, byte> cannyFrame = null;
if (_smooth)
{
cannyFrame = smoothedGrayFrame.Canny(_thresh, _threshLinking);
grayFrame = smoothedGrayFrame;
}
else
{
grayFrame = grayFrame.Canny(_thresh, _threshLinking);
}
//局部自适应阈值二值化，阈值本身作为了一个变量，检测更有效
//CvInvoke.cvAdaptiveThreshold(grayFrame, grayFrame, 255, Emgu.CV.CvEnum.ADAPTIVE_THRESHOLD_TYPE.CV_ADAPTIVE_THRESH_MEAN_C, Emgu.CV.CvEnum.THRESH.CV_THRESH_BINARY, adaptiveThresholdBlockSize + adaptiveThresholdBlockSize % 2 + 1, adaptiveThresholdParameter);
CvInvoke.AdaptiveThreshold(grayFrame, grayFrame, 255, Emgu.CV.CvEnum.AdaptiveThresholdType.MeanC, Emgu.CV.CvEnum.ThresholdType.Binary, _blockSize + _blockSize % 2 + 1, _parameter);
//
grayFrame._Not();
//
if (cannyFrame != null)
{
grayFrame._Or(cannyFrame); //试验了一下，这样轮廓会更加明显
}
_cannyImage = BitmapConvert.toBitmapImage(grayFrame.ToBitmap());
base.OnPropertyChanged("CannyImage");
}
}
catch (Exception ex)
{
MessageBox.Show(ex.Message);
}
}
}
static void Main(string[] args)
{
//var readLine = Console.ReadLine();
//if (readLine == null) return;
//var args = readLine.Split(' ');
//var args = new[] { "house2.bmp", "house222.bmp", "canny", "1", "0,25", "0,1" };
//task1
//Инверсия значений пикселей изображения
if (args.Length == 3 && args[2] == "invert")
{
string inputFileName = args[0], outputFileName = args[1];
if (!File.Exists(inputFileName))
{
return;
}
var image = ImageIO.FileToGrayscaleFloatImage(inputFileName);
Inversion.InversionProcess(image);
ImageIO.ImageToFile(image, outputFileName);
}
//Отражение изображения по вертикали и по горизонтали
if (args.Length == 4 && args[2] == "mirror")
{
string inputFileName = args[0], outputFileName = args[1];
if (!File.Exists(inputFileName))
{
return;
}
var image = ImageIO.FileToGrayscaleFloatImage(inputFileName);
if (args[3] == "x")
{
Reflection.FlipHorizontal(image);
}
if (args[3] == "y")
{
Reflection.FlipVertical(image);
}
ImageIO.ImageToFile(image, outputFileName);
}
//Поворот изображений по и против часовой стрелки на 90, 180 и 270 градусов(на произвольный угол)
if (args.Length == 5 && args[2] == "rotate")
{
string inputFileName = args[0], outputFileName = args[1];
if (!File.Exists(inputFileName))
{
return;
}
var image = ImageIO.FileToGrayscaleFloatImage(inputFileName);
if (args[3] == "cw")
{
image = Rotate.ClockWiseRotate(image, angle: int.Parse(args[4]));
}
if (args[3] == "ccw")
{
image = Rotate.CounterClockWiseRotate(image, angle: int.Parse(args[4]));
}
ImageIO.ImageToFile(image, outputFileName);
}
//фильтр Превитта
if (args.Length == 4 && args[2] == "prewitt")
{
string inputFileName = args[0], outputFileName = args[1];
if (!File.Exists(inputFileName))
{
return;
}
var image = ImageIO.FileToGrayscaleFloatImage(inputFileName);
if (args[3] == "x")
{
image = MedianFiltering.Convolution(image, Filters.PrewittHorizontal(), 3, 2);
}
if (args[3] == "y")
{
image = MedianFiltering.Convolution(image, Filters.PrewittVertical(), 3, 2);
}
ImageIO.ImageToFile(image, outputFileName);
}
//фильтр Собеля
if (args.Length == 4 && args[2] == "sobel")
{
string inputFileName = args[0], outputFileName = args[1];
if (!File.Exists(inputFileName))
{
return;
}
var image = ImageIO.FileToGrayscaleFloatImage(inputFileName);
if (args[3] == "x")
{
image = MedianFiltering.Convolution(image, Filters.SobelHorizontal(), 3);
}
if (args[3] == "y")
{
image = MedianFiltering.Convolution(image, Filters.SobelVertical(), 3);
}
ImageIO.ImageToFile(image, outputFileName);
}
//фильтр Робертса
if (args.Length == 4 && args[2] == "roberts")
{
string inputFileName = args[0], outputFileName = args[1];
if (!File.Exists(inputFileName))
{
return;
}
var image = ImageIO.FileToGrayscaleFloatImage(inputFileName);
if (args[3] == "1")
{
image = MedianFiltering.Convolution(image, Filters.RobertsMainDiagonal(), 2, 2);
}
if (args[3] == "2")
{
image = MedianFiltering.Convolution(image, Filters.RobertsAdditionalDiagonal(), 2, 2);
}
ImageIO.ImageToFile(image, outputFileName);
}
//Медианная фильтрация с квадратным окном произвольного размера
if (args.Length == 4 && args[2] == "median")
{
string inputFileName = args[0], outputFileName = args[1];
if (!File.Exists(inputFileName))
{
return;
}
var image = ImageIO.FileToGrayscaleFloatImage(inputFileName);
image = MedianFiltering.MedianFilter(image, kernelSize: int.Parse(args[3]));
ImageIO.ImageToFile(image, outputFileName);
}
//Свёртка с фильтром Гаусса с произвольным выбором параметра — радиуса σ
if (args.Length == 4 && args[2] == "gauss")
{
string inputFileName = args[0], outputFileName = args[1];
if (!File.Exists(inputFileName))
{
return;
}
var image = ImageIO.FileToGrayscaleFloatImage(inputFileName);
var data = new double[image.rawdata.Length];
var template = new double[image.rawdata.Length];
for (var i = 0; i < image.rawdata.Length; i++)
{
data[i] = Convert.ToDouble(image.rawdata[i]);
}
ConvolutionGauss.GaussProcess(data, image.Width, image.Height, sigma: double.Parse(args[3]), windowSize: 9, temp: template, dest: data);
for (var i = 0; i < image.rawdata.Length; i++)
{
image.rawdata[i] = Convert.ToSingle(data[i]);
}
ImageIO.ImageToFile(image, outputFileName);
}
//Вычисление модуля градиента как корень из суммы квадратов свёрток с первой производной фильтра Гаусса по горизонтали и вертикали
if (args.Length == 4 && args[2] == "gradient")
{
string inputFileName = args[0], outputFileName = args[1];
if (!File.Exists(inputFileName))
{
return;
}
var image = ImageIO.FileToGrayscaleFloatImage(inputFileName);
ConvolutionGauss.GradientProcess(image.rawdata, image.Width, image.Height, double.Parse(args[3]), (int)(double.Parse(args[3]) * 6), image.rawdata);
ImageIO.ImageToFile(image, outputFileName);
}
//Фильтр Габора с произвольными параметрами
if (args.Length == 8 && args[2] == "gabor")
{
string inputFileName = args[0], outputFileName = args[1];
if (!File.Exists(inputFileName))
{
return;
}
var image = ImageIO.FileToGrayscaleFloatImage(inputFileName);
image = ConvolutionGauss.Gabor(image, Filters.Gabor((int)(6 * double.Parse(args[3])), double.Parse(args[3]), double.Parse(args[6]), double.Parse(args[5]), double.Parse(args[4]), double.Parse(args[6])), (int)(6 * double.Parse(args[3])), (int)(6 * double.Parse(args[3])), int.Parse(args[7]));
ImageIO.ImageToFile(image, outputFileName);
}
//Обнаружение сосудов на изображениях глазного дна с помощью фильтров Габора
if (args.Length == 4 && args[2] == "vessels")
{
string inputFileName = args[0], outputFileName = args[1];
if (!File.Exists(inputFileName))
{
return;
}
var image = ImageIO.FileToGrayscaleFloatImage(inputFileName);
image = ConvolutionGauss.Vessels(image, (int)(6 * Convert.ToDouble(args[3])), Convert.ToDouble(args[3]));
ImageIO.ImageToFile(image, outputFileName);
}
//task2
//Увеличение изображений в вещественное число раз с помощью билинейной интерполяции
if (args.Length == 4 && args[2] == "up_bilinear")
{
string inputFileName = args[0], outputFileName = args[1];
if (!File.Exists(inputFileName))
{
return;
}
var image = ImageIO.FileToGrayscaleFloatImage(inputFileName);
image = ImageResolution.Bilinear(image, Convert.ToDouble(args[3]));
ImageIO.ImageToFile(image, outputFileName);
}
//Увеличение изображений в вещественное число раз с помощью бикубической интерполяции
if (args.Length == 4 && args[2] == "up_bicubic")
{
string inputFileName = args[0], outputFileName = args[1];
if (!File.Exists(inputFileName))
{
return;
}
var image = ImageIO.FileToGrayscaleFloatImage(inputFileName);
var resultImage = ImageResolution.Bicubic(image, Convert.ToDouble(args[3]));
ImageIO.ImageToFile(resultImage, outputFileName);
}
//Понижение разрешения изображений в вещественное число раз
if (args.Length == 4 && args[2] == "downsample")
{
string inputFileName = args[0], outputFileName = args[1];
if (!File.Exists(inputFileName))
{
return;
}
var image = ImageIO.FileToGrayscaleFloatImage(inputFileName);
image = ImageResolution.DownBilinear(image, Convert.ToDouble(args[3]));
ImageIO.ImageToFile(image, outputFileName);
}
//Вычисление метрик сравнения изображений(MSE и PSNR, SSIM и MSSIM)
if (args.Length == 4 && args[2] == "metric")
{
string inputFileName = args[0], inputFileName2 = args[1];
if (!File.Exists(inputFileName) || !File.Exists(inputFileName2))
{
return;
}
var image = ImageIO.FileToGrayscaleFloatImage(inputFileName);
var image2 = ImageIO.FileToGrayscaleFloatImage(inputFileName2);
double result;
switch (args[3])
{
case "mse":
result = Metrics.Mse(image, image2);
Console.WriteLine(result);
Console.ReadKey();
break;
case "psnr":
result = Metrics.Psnr(image, image2);
Console.WriteLine(result);
Console.ReadKey();
break;
case "ssim":
result = Metrics.Ssim(image.rawdata, image2.rawdata, image.Width, image.Height);
Console.WriteLine(result);
Console.ReadKey();
break;
case "mssim":
result = Metrics.Mssim(image.rawdata, image2.rawdata, image.Width, image.Height);
Console.WriteLine(result);
Console.ReadKey();
break;
}
}
//task3
//Алгоритм детектирования границ Канни
if (args.Length == 6 && args[2] == "canny")
{
string inputFileName = args[0], outputFileName = args[1];
if (!File.Exists(inputFileName))
{
return;
}
var image = ImageIO.FileToGrayscaleFloatImage(inputFileName);
var image2 = Canny.Process(image, Convert.ToSingle(args[3]), Convert.ToSingle(args[4]), Convert.ToSingle(args[5]));
ImageIO.ImageToFile(image2, outputFileName);
}
//Алгоритм Харриса для детектирования углов
if (args.Length == 4 && args[2] == "harris")
{
string inputFileName = args[0], outputFileName = args[1];
if (!File.Exists(inputFileName))
{
return;
}
var image = ImageIO.FileToGrayscaleFloatImage(inputFileName);
image = Harris.Process(image, double.Parse(args[3]));
ImageIO.ImageToFile(image, outputFileName);
}
//Билатеральная фильтрация изображений
if (args.Length == 5 && args[2] == "bilateral")
{
string inputFileName = args[0], outputFileName = args[1];
if (!File.Exists(inputFileName))
{
return;
}
var image = ImageIO.FileToGrayscaleFloatImage(inputFileName);
image = Bilateral.Process(image, double.Parse(args[3]), double.Parse(args[3]));
ImageIO.ImageToFile(image, outputFileName);
}
}
/// <summary>
/// This is the method that actually does the work.
/// </summary>
/// <param name="DA">The DA object is used to retrieve from inputs and store in outputs.</param>
protected override void SolveInstance(IGH_DataAccess DA)
{
IGH_Goo goo = null;
Image image = new Image();
if (!DA.GetData(0, ref goo))
{
return;
}
if (!goo.TryGetImage(ref image))
{
return;
}
int mode = 0;
DA.GetData(1, ref mode);
double numValA = 1.0;
DA.GetData(2, ref numValA);
int numValB = 1;
DA.GetData(3, ref numValB);
int numValC = 1;
DA.GetData(4, ref numValC);
int numValD = 1;
DA.GetData(5, ref numValD);
Filter filter = new Filter();
int[] indices = new int[] { 2, 3, 4, 5 };
switch ((FilterModes)mode)
{
case FilterModes.Canny:
SetParameter(2, "X", "Sigma", "Gaussian sigma");
SetParameter(3, "S", "Size", "Gaussian size");
SetParameter(4, "L", "Low Threshold", "High threshold value used for hysteresis");
SetParameter(5, "H", "High Threshold", "Low threshold value used for hysteresis");
filter = new Canny(numValA, numValB, numValC, numValD);
image.Filters.Add(new Canny());
break;
case FilterModes.Difference:
ClearParameter(indices);
filter = new Difference();
image.Filters.Add(new Difference());
break;
case FilterModes.Homogeneity:
ClearParameter(indices);
filter = new Homogeneity();
image.Filters.Add(new Homogeneity());
break;
case FilterModes.Kirsch:
ClearParameter(indices);
filter = new Kirsch();
image.Filters.Add(new Kirsch());
break;
case FilterModes.Robinson:
ClearParameter(indices);
filter = new Robinson();
image.Filters.Add(new Robinson());
break;
case FilterModes.Sobel:
ClearParameter(indices);
filter = new Sobel();
image.Filters.Add(new Sobel());
break;
}
message = ((FilterModes)mode).ToString();
UpdateMessage();
DA.SetData(0, image);
DA.SetData(1, filter);
}
