//Grabcut分割函数封装
private Mat grabCut(Mat image, int ITER_COUNT, GrabCutModes GRABCUTMODE, int x, int y, int w, int h)
{
var bgModel = new Mat();
var fgdModel = new Mat();
var bgModel1 = new Mat();
var fgdModel1 = new Mat();
var mask = new Mat();
const int GC_PR_FGD = 3;
Rect rect = new Rect();
rect.X = x;
rect.Y = y;
rect.Width = w;
rect.Height = h;
Cv2.GrabCut(image, mask, rect, bgModel, fgdModel, ITER_COUNT, GRABCUTMODE);
Cv2.Compare(mask, GC_PR_FGD, mask, CmpTypes.EQ);
Mat foreground = new Mat(image.Size(), MatType.CV_8UC3, new Scalar(0, 0, 0));
image.CopyTo(foreground, mask);
showRect(rect.X, rect.Y, rect.Width, rect.Height);
showImagePara(image);
return(foreground);
}
//更改参数再次分割图片
private void button4_Click(object sender, EventArgs e)
{
if (comboBox1.Text == "grabcut" && value1.Text == "迭代次数：" && value2.Text == "分割算子：")
{
try
{
int iter_count = int.Parse(textBox5.Text);
GrabCutModes grabcutmode = (GrabCutModes)(Enum.Parse(typeof(GrabCutModes), comboBox4.Text));
int x = int.Parse(textBox7.Text);
int y = int.Parse(textBox8.Text);
int w = int.Parse(textBox9.Text);
int h = int.Parse(textBox10.Text);
if (iter_count > 20 || iter_count < 0)
{
MessageBox.Show("迭代次数于0-20次之内较为合适");
}
else
{
System.Drawing.Bitmap bitmap = (System.Drawing.Bitmap)pictureBox1.Image;
Mat image = BitmapConverter.ToMat(bitmap);
double startTime = Cv2.GetTickCount();
Mat res = grabCut(image, iter_count, grabcutmode, x, y, w, h);
double duration = (Cv2.GetTickCount() - startTime) / (Cv2.GetTickFrequency());
showImage(res, duration);
showParameters("迭代次数：", iter_count.ToString(), "分割算子：", grabcutmode.ToString());
textBox6.Hide();
showRect(x, y, w, h);
}
}
catch (Exception err)
{
if (err.ToString().Contains("totalSampleCount"))
{
MessageBox.Show("您选择的区域无法分割出前景，无法分割，请重新选择区域大小");
}
else if (err.ToString().Contains("mask"))
{
MessageBox.Show("掩模mask为空，无法分割");
}
else if (err.ToString().Contains("Format"))
{
MessageBox.Show("输入的字符串格式不正确，请重新输入");
}
}
}
if (comboBox1.Text == "watershed" && value1.Text == "中值滤波内核：" && value2.Text == "形态学卷积核：")
{
try
{
if (int.Parse(textBox5.Text) % 2 == 0)
{
MessageBox.Show("中值滤波内核应为奇数，请重新输入。");
}
else
{
int meadianblur_ksize = int.Parse(textBox5.Text);
if (meadianblur_ksize > 1000 || meadianblur_ksize < 0)
{
MessageBox.Show("数值不合理，请重新输入适当的数值");
}
else
{
string text = textBox6.Text;
string diff11 = text.Replace("(", string.Empty);
string diff12 = diff11.Replace(")", string.Empty);
string[] str = diff12.Split(',');
Size element_size = new Size(int.Parse(str[0]), int.Parse(str[1]));
System.Drawing.Bitmap bitmap = (System.Drawing.Bitmap)pictureBox1.Image;
Mat image = BitmapConverter.ToMat(bitmap);
double startTime = Cv2.GetTickCount();
Mat result = waterShed(image, meadianblur_ksize, element_size);
double duration = (Cv2.GetTickCount() - startTime) / (Cv2.GetTickFrequency());
showImage(result, duration);
showParameters("中值滤波内核：", meadianblur_ksize.ToString(), "形态学卷积核：", "(" + element_size.Width.ToString() + "," + element_size.Height.ToString() + ")");
}
}
}
catch (Exception err)
{
if (err.ToString().Contains("Format"))
{
MessageBox.Show("输入的字符串格式不正确，请重新输入");
}
else if (err.ToString().Contains("索引超出了数组界限"))
{
MessageBox.Show("形态学卷积内核应用英文括号隔开");
}
else
{
MessageBox.Show(err.ToString());
}
}
}
if (comboBox1.Text == "meanshift" && value1.Text == "颜色域半径：" && value2.Text == "空间域半径：")
{
try
{
int meanshift_sp = int.Parse(textBox5.Text);
int meanshift_sr = int.Parse(textBox6.Text);
if (meanshift_sp < 0 || meanshift_sr < 0 || meanshift_sp > 1000 || meanshift_sr > 1000)
{
MessageBox.Show("参数有误或数值过大，请重新输入数据");
}
else
{
System.Drawing.Bitmap bitmap = (System.Drawing.Bitmap)pictureBox1.Image;
Mat image = BitmapConverter.ToMat(bitmap);
double startTime = Cv2.GetTickCount();
Mat res = meanShift(image, meanshift_sp, meanshift_sr);
double duration = (Cv2.GetTickCount() - startTime) / (Cv2.GetTickFrequency());
showImage(res, duration);
showParameters("颜色域半径：", meanshift_sp.ToString(), "空间域半径：", meanshift_sr.ToString());
}
}
catch (Exception err)
{
if (err.ToString().Contains("Format"))
{
MessageBox.Show("输入的字符串格式不正确，请重新输入");
}
else
{
MessageBox.Show(err.ToString());
}
}
}
if (comboBox1.Text == "floodfill" && value1.Text == "像素最大下行差异值：" && value2.Text == "像素最大上行差异值：")
{
try
{
string diff1 = textBox5.Text;
string diff11 = diff1.Replace("[", string.Empty);
string diff12 = diff11.Replace("]", string.Empty);
string[] str1 = diff12.Split(',');
Scalar lodiff = new Scalar(int.Parse(str1[0]), int.Parse(str1[1]), int.Parse(str1[2]), int.Parse(str1[3]));
string diff2 = textBox6.Text;
string diff21 = diff2.Replace("[", string.Empty);
string diff22 = diff21.Replace("]", string.Empty);
string[] str2 = diff22.Split(',');
int x = int.Parse(textBox7.Text);
int y = int.Parse(textBox8.Text);
int w = int.Parse(textBox9.Text);
int h = int.Parse(textBox10.Text);
Scalar updiff = new Scalar(int.Parse(str2[0]), int.Parse(str2[1]), int.Parse(str2[2]), int.Parse(str2[3]));
System.Drawing.Bitmap bitmap = (System.Drawing.Bitmap)pictureBox1.Image;
Mat image = BitmapConverter.ToMat(bitmap);
double startTime = Cv2.GetTickCount();
Mat res = floodFill(image, lodiff, updiff, x, y, w, h);
double duration = (Cv2.GetTickCount() - startTime) / (Cv2.GetTickFrequency());
showImage(res, duration);
showRect(x, y, w, h);
showParameters("像素最大下行差异值：", lodiff.ToString(), "像素最大上行差异值：", updiff.ToString());
}
catch (Exception err)
{
if (err.ToString().Contains("Format"))
{
MessageBox.Show("输入的字符串格式不正确，请重新输入(应输入英文逗号)");
}
else if (err.ToString().Contains("索引超出了数组界限"))
{
MessageBox.Show("像素最大下(上)行差异值应用英文括号隔开");
}
else
{
MessageBox.Show(err.ToString());
}
}
}
if (comboBox1.Text == "contour" && value1.Text == "高斯核x方向标准差:" && value2.Text == "二值化算子:")
{
try
{
ThresholdTypes contour_type = (ThresholdTypes)(Enum.Parse(typeof(ThresholdTypes), comboBox4.Text));
int sigmax = int.Parse(textBox5.Text);
System.Drawing.Bitmap bitmap = (System.Drawing.Bitmap)pictureBox1.Image;
Mat image = BitmapConverter.ToMat(bitmap);
double startTime = Cv2.GetTickCount();
Mat res = contourSeg(image, contour_type, sigmax);
double duration = (Cv2.GetTickCount() - startTime) / (Cv2.GetTickFrequency());
showImage(res, duration);
showParameters("高斯核x方向标准差:", sigmax.ToString(), "二值化算子:", contour_type.ToString());
textBox6.Hide();
}
catch (Exception err)
{
if (err.ToString().Contains("Format"))
{
MessageBox.Show("输入的字符串格式不正确，请重新输入");
}
else if (err.ToString().Contains("索引超出了数组界限"))
{
MessageBox.Show("索引超出了数组界限，无法分割");
}
else
{
MessageBox.Show(err.ToString());
}
}
}
}
/// <summary>
/// Segments the image using GrabCut algorithm
/// </summary>
/// <param name="img">Input 8-bit 3-channel image.</param>
/// <param name="mask">Input/output 8-bit single-channel mask.
/// The mask is initialized by the function when mode is set to GC_INIT_WITH_RECT.
/// Its elements may have Cv2.GC_BGD / Cv2.GC_FGD / Cv2.GC_PR_BGD / Cv2.GC_PR_FGD</param>
/// <param name="rect">ROI containing a segmented object. The pixels outside of the ROI are
/// marked as "obvious background". The parameter is only used when mode==GC_INIT_WITH_RECT.</param>
/// <param name="bgdModel">Temporary array for the background model. Do not modify it while you are processing the same image.</param>
/// <param name="fgdModel">Temporary arrays for the foreground model. Do not modify it while you are processing the same image.</param>
/// <param name="iterCount">Number of iterations the algorithm should make before returning the result.
/// Note that the result can be refined with further calls with mode==GC_INIT_WITH_MASK or mode==GC_EVAL .</param>
/// <param name="mode">Operation mode that could be one of GrabCutFlag value.</param>
public static void GrabCut(InputArray img, InputOutputArray mask, Rect rect,
InputOutputArray bgdModel, InputOutputArray fgdModel,
int iterCount, GrabCutModes mode)
{
if (img == null)
throw new ArgumentNullException(nameof(img));
if (mask == null)
throw new ArgumentNullException(nameof(mask));
if (bgdModel == null)
throw new ArgumentNullException(nameof(bgdModel));
if (fgdModel == null)
throw new ArgumentNullException(nameof(fgdModel));
img.ThrowIfDisposed();
mask.ThrowIfNotReady();
bgdModel.ThrowIfNotReady();
fgdModel.ThrowIfNotReady();
NativeMethods.imgproc_grabCut(img.CvPtr, mask.CvPtr, rect,
bgdModel.CvPtr, fgdModel.CvPtr, iterCount, (int)mode);
GC.KeepAlive(img);
mask.Fix();
bgdModel.Fix();
fgdModel.Fix();
}
/// <summary>
/// Segments the image using GrabCut algorithm.
/// The input is 8-bit 3-channel image.
/// </summary>
/// <param name="mask">Input/output 8-bit single-channel mask.
/// The mask is initialized by the function when mode is set to GC_INIT_WITH_RECT.
/// Its elements may have Cv2.GC_BGD / Cv2.GC_FGD / Cv2.GC_PR_BGD / Cv2.GC_PR_FGD</param>
/// <param name="rect">ROI containing a segmented object. The pixels outside of the ROI are
/// marked as "obvious background". The parameter is only used when mode==GC_INIT_WITH_RECT.</param>
/// <param name="bgdModel">Temporary array for the background model. Do not modify it while you are processing the same image.</param>
/// <param name="fgdModel">Temporary arrays for the foreground model. Do not modify it while you are processing the same image.</param>
/// <param name="iterCount">Number of iterations the algorithm should make before returning the result.
/// Note that the result can be refined with further calls with mode==GC_INIT_WITH_MASK or mode==GC_EVAL .</param>
/// <param name="mode">Operation mode that could be one of GrabCutFlag value.</param>
public void GrabCut(InputOutputArray mask, Rect rect,
InputOutputArray bgdModel, InputOutputArray fgdModel,
int iterCount, GrabCutModes mode)
{
Cv2.GrabCut(this, mask, rect, bgdModel, fgdModel, iterCount, mode);
}
