private void btnSetFilter_Click(object sender, EventArgs e)
{
Mat filtered = new Mat();
CvInvoke.BilateralFilter(imgClone.Image, filtered, 11, 17, 17);
imgClone.Image = filtered;
}
public static Image <Gray, Byte> BilateralFilter(Image <Gray, Byte> grayImage)
{
var threshImage = grayImage.CopyBlank();
CvInvoke.BilateralFilter(grayImage, threshImage, 25, 25 * 2, 25 / 2);
return(threshImage);
}
public override bool Do()
{
ClearOut();
try
{
BlurEnum blurType = (BlurEnum)EnumPropertys["Type"].Value;
foreach (var src in Sources)
{
foreach (var outData in src.GetOut())
{
Mat imgout = new Mat();
int nBlurKern = IntPropertys["Kern"].Value * 2 + 1;
switch (blurType)
{
case BlurEnum.Gaussian:
CvInvoke.GaussianBlur(outData.Image, imgout, new System.Drawing.Size(nBlurKern, nBlurKern), 0);
break;
case BlurEnum.Median:
CvInvoke.MedianBlur(outData.Image, imgout, nBlurKern);
break;
case BlurEnum.Bileteral:
CvInvoke.BilateralFilter(outData.Image, imgout, nBlurKern, nBlurKern * 2, nBlurKern * 2, 0);
break;
}
this.m_out.Add(new DataSrc(imgout, outData.Info, false));
}
}
} catch { return(false); }
return(true);
}
Mat dst = new Mat();//空白输出图像
private void BLToolStripMenuItem_Click(object sender, EventArgs e)
{
Subwindows sub = new Subwindows();
sub.ShowDialog();
CvInvoke.BilateralFilter(image, dst, sub.size, 25 * 2.0, 25 / 2.0);
resultpic.Image = dst;
}
public Image <Gray, byte> bilateralFilter(Image <Gray, byte> grayImage)
{
//bilateral filter dari EmguCV
//CvInvoke.BilateralFilter(inputArray, outputArray, int diameter of each pixel neighborhood, double sigmaColor, double sigmaSpace, opt borderType)
var threshImage = grayImage.CopyBlank();
CvInvoke.BilateralFilter(grayImage, threshImage, 7, 25, 60);
return(threshImage);
}
private void filterToolStripMenuItem_Click(object sender, EventArgs e)
{
var img = !processed ? new Image <Bgr, byte>((Bitmap)srcPicBox.Image) : new Image <Bgr, byte>((Bitmap)resPicBox.Image);
var resImage = new Image <Bgr, byte>(img.Bitmap);
CvInvoke.BilateralFilter(img, resImage, 25, 75, 75);
resPicBox.Image = resImage.ToBitmap();
processed = true;
}
// не работает, дебаг
public static Image <Gray, Byte> LineBasedDetection(Image <Gray, Byte> inputImg)
{
Image <Gray, Byte> smoothedImg = inputImg.SmoothMedian(3);
//CvInvoke.CLAHE(smoothedImg, 40, new System.Drawing.Size(16, 16), smoothedImg);
CvInvoke.BilateralFilter(inputImg.SmoothMedian(3), smoothedImg, 50, 140, -1);
smoothedImg = smoothedImg.ThresholdToZero(new Gray(30));
#region Kernels and stuff
float[,] TopKernelMatrix = new float[, ] {
{ 0, -1, 0 },
{ 0, 0, 0 },
{ 0, 1, 0, }
};
float[,] BottomKernelMatrix = new float[, ] {
{ 0, 1, 0 },
{ 0, 0, 0 },
{ 0, -1, 0, }
};
float[,] LeftKernelMatrix = new float[, ] {
{ 0, 0, 0 },
{ 1, 0, -1 },
{ 0, 0, 0, }
};
float[,] RightKernelMatrix = new float[, ] {
{ 0, 0, 0 },
{ -1, 0, 1 },
{ 0, 0, 0, }
};
ConvolutionKernelF TopKernel = new ConvolutionKernelF(TopKernelMatrix);
ConvolutionKernelF BottomKernel = new ConvolutionKernelF(BottomKernelMatrix);
ConvolutionKernelF LeftKernel = new ConvolutionKernelF(LeftKernelMatrix);
ConvolutionKernelF RightKernel = new ConvolutionKernelF(RightKernelMatrix);
#endregion
#region Craft top / bottom / left and right images with borders
Image <Gray, Byte> TopImg = new Image <Gray, byte>(inputImg.Size);
Image <Gray, Byte> BottomImg = new Image <Gray, byte>(inputImg.Size);
Image <Gray, Byte> LeftImg = new Image <Gray, byte>(inputImg.Size);
Image <Gray, Byte> RightImg = new Image <Gray, byte>(inputImg.Size);
CvInvoke.Filter2D(smoothedImg, TopImg, TopKernel, new Point(1, 1));
CvInvoke.Filter2D(smoothedImg, BottomImg, BottomKernel, new Point(1, 1));
CvInvoke.Filter2D(smoothedImg, LeftImg, LeftKernel, new Point(1, 1));
CvInvoke.Filter2D(smoothedImg, RightImg, RightKernel, new Point(1, 1));
TopImg = TopImg.ThresholdBinary(new Gray(15), new Gray(255));
BottomImg = BottomImg.ThresholdBinary(new Gray(15), new Gray(255));
LeftImg = LeftImg.ThresholdBinary(new Gray(15), new Gray(255));
RightImg = RightImg.ThresholdBinary(new Gray(15), new Gray(255));
#endregion
# region Находим контуры для всех 4х изображений
void blurFeed(Mat src, Mat dst, int size, int type)
{
switch (type)
{
case 0: CvInvoke.MedianBlur(src, dst, size); break;
case 1: CvInvoke.GaussianBlur(src, dst, new Size(size, size), 0); break;
case 2: CvInvoke.BilateralFilter(src, dst, 9, size, size); break;
default: break;
}
}
private void button6_Click(object sender, EventArgs e)
{
Image <Bgr, byte> dst = src.CopyBlank();
CvInvoke.BilateralFilter(src, dst, g_nBilateralFilterValue, g_nBilateralFilterValue * 2, g_nBilateralFilterValue / 2);
//第一个参数，InputArray类型的src，输入图像，即源图像，需要为8位或者浮点型单通道、三通道的图像。
//第二个参数，OutputArray类型的dst，即目标图像，需要和源图片有一样的尺寸和类型。
//第三个参数，int类型的d，表示在过滤过程中每个像素邻域的直径。如果这个值我们设其为非正数，那么OpenCV会从第五个参数sigmaSpace来计算出它来。
//第四个参数，double类型的sigmaColor，颜色空间滤波器的sigma值。这个参数的值越大，就表明该像素邻域内有更宽广的颜色会被混合到一起，产生较大的半相等颜色区域。
//第五个参数，double类型的sigmaSpace坐标空间中滤波器的sigma值，坐标空间的标注方差。他的数值越大，意味着越远的像素会相互影响，从而使更大的区域足够相似的颜色获取相同的颜色。当d > 0，d指定了邻域大小且与sigmaSpace无关。否则，d正比于sigmaSpace。
//第六个参数，int类型的borderType，用于推断图像外部像素的某种边界模式。注意它有默认值BORDER_DEFAULT。
imageBox2.Image = dst;
}
public Mat PreProcess(ref Mat image)
{
CvInvoke.BilateralFilter(image.Clone(), image, _configurations.BILATERAL_FILTER_DISTANCE, _configurations.BILATERAL_FILTER_SIGMA_COLOR, _configurations.BILATERAL_FILTER_SIGMA_SPACE);
var viewer = new ImageViewer(image, "Bilateral Filter Applied");
viewer.Show();
image = HistogramEqualization(image, true);
image.ConvertTo(image, DepthType.Default, (double)_configurations.CONTRAST_MULTIPLIED_BY_10 / 10.0, (double)_configurations.BRIGHTNESS_MULTIPLIED_BY_10 / 10.0);
CvInvoke.BilateralFilter(image.Clone(), image, _configurations.BILATERAL_FILTER_DISTANCE, _configurations.BILATERAL_FILTER_SIGMA_COLOR, _configurations.BILATERAL_FILTER_SIGMA_SPACE);
viewer = new ImageViewer(image, "Adjusted Contrast and Illumination");
viewer.Show();
return(image);
}
private void ProcessFrame(object sender, EventArgs arg)
{
Image <Bgr, byte> ImageFrame = capture.QueryFrame().ToImage <Bgr, Byte>();
Image <Bgr, byte> filterImage = new Image <Bgr, byte>(ImageFrame.Size);
CvInvoke.BilateralFilter(ImageFrame, filterImage, 9, 80, 150);
Image <Hsv, byte> hsvImage = filterImage.Convert <Hsv, Byte>();
// red circle
Image <Gray, byte> redImage = ThresholdImage(hsvImage, new Point[] { new Point(0, 10), new Point(163, 179) });
// blue circle
Image <Gray, byte> blueImage = ThresholdImage(hsvImage, new Point[] { new Point(100, 120) });
// green circle
Image <Gray, byte> greenImage = ThresholdImage(hsvImage, new Point[] { new Point(50, 85) });
// yellow circle
Image <Gray, byte> yellowImage = ThresholdImage(hsvImage, new Point[] { new Point(20, 40) });
var temp = DetectAndDrawCircles(redImage, ImageFrame, new MCvScalar(50, 50, 150), Color.Red);
temp = DetectAndDrawCircles(blueImage, temp, new MCvScalar(150, 50, 0), Color.Blue);
temp = DetectAndDrawCircles(yellowImage, temp, new MCvScalar(0, 200, 200), Color.Yellow);
temp = DetectAndDrawCircles(greenImage, temp, new MCvScalar(50, 150, 0), Color.Green);
CamImgBox.Image = DrawTrack(temp);
switch (filter)
{
case Color.Blue: outImgBox.Image = blueImage;
break;
case Color.Green: outImgBox.Image = greenImage;
break;
case Color.Yellow: outImgBox.Image = yellowImage;
break;
default:
break;
}
}
/// <summary>
/// Preprocess original image
/// </summary>
/// <param name="imgOriginal">Original image</param>
/// <param name="imgGrayscale">Imaginea in format grayscale</param>
/// <param name="errorCode">Error code</param>
public static void Preprocess(Mat imgOriginal, ref Mat imgGrayscale, ref Mat imgThresh, ref int errorCode)
{
try
{
// Initiation of images that will be used in this method
Mat imgBlurred = new Mat();
Mat imgBilateralFilter = new Mat();
Mat imgTophat = new Mat();
Mat imgMaxContrast = new Mat();
// Structuring element used for morphological operation
Mat structuringElement = CvInvoke.GetStructuringElement(ElementShape.Rectangle, new Size(40, 20), new Point(-1, -1));
// Extract value channel only from original image to get imgGrayscale
CvInvoke.CvtColor(imgOriginal, imgGrayscale, ColorConversion.Bgr2Gray);
//imgMaxContrast = MaximizeContrast(imgGrayscale, ref errorCode);
// Gaussian blur
CvInvoke.GaussianBlur(imgGrayscale, imgBlurred, new Size(3, 3), 3);
// Bilateral filter
CvInvoke.BilateralFilter(imgBlurred, imgBilateralFilter, 10, 15, 15);
// Morphological operation of tophap
CvInvoke.MorphologyEx(imgBilateralFilter, imgTophat, MorphOp.Tophat, structuringElement, new Point(-1, -1), 1, BorderType.Default, new MCvScalar());
// Adaptive treshold from the tophat image
CvInvoke.AdaptiveThreshold(imgTophat, imgThresh, 250, AdaptiveThresholdType.GaussianC, ThresholdType.Binary, 25, -5);
}
catch (Exception ex)
{
Console.WriteLine(ex);
errorCode = 4;
}
}
/// <summary>
///
/// </summary>
/// <param name="potentialPlates">List of potentials plates</param>
/// <param name="pass">What pass is executing</param>
/// <param name="errorCode">Error code</param>
/// <returns>Key value pair of license plate image and its treshold</returns>
private static KeyValuePair <Mat, Mat> FindPlateInPotentialPlates(List <Mat> potentialPlates, string pass, ref int errorCode)
{
// Initiate license plate variable
KeyValuePair <Mat, Mat> licensePlate = new KeyValuePair <Mat, Mat>();
// Initiate variable for if license plate was found
bool licensePlateFound = false;
try
{
// Check what pass
if (pass == "firstPass")
{
// Loop through all potential plates
foreach (var potentialPlate in potentialPlates)
{
// Initate images used
Mat imgBilateralFilter = new Mat();
Mat imgThresh = new Mat();
Mat imgOpen = new Mat();
// Initiate structuring elements used in morphological procedures
Mat structuringElement = CvInvoke.GetStructuringElement(ElementShape.Rectangle, new Size(150, 150), new Point(-1, -1));
Mat openElement = CvInvoke.GetStructuringElement(ElementShape.Rectangle, new Size(7, 7), new Point(-1, -1));
Mat closeElement = CvInvoke.GetStructuringElement(ElementShape.Rectangle, new Size(8, 15), new Point(-1, -1));
Mat erodeElement = CvInvoke.GetStructuringElement(ElementShape.Rectangle, new Size(3, 3), new Point(-1, -1));
// Initiate other variables
VectorOfVectorOfPoint contours = new VectorOfVectorOfPoint();
List <Rectangle> boundingRectangles = new List <Rectangle>();
// Maximize contrast of a potential plate
Mat imgMaxContrast = ImageProcess.MaximizeContrast(potentialPlate, ref errorCode);
// Apply bilateral filter to image with contrast maximized
CvInvoke.BilateralFilter(imgMaxContrast, imgBilateralFilter, 5, 20, 20); // 40 20 20
// Do morphological operation of tophat
CvInvoke.MorphologyEx(imgBilateralFilter, imgOpen, MorphOp.Tophat, structuringElement, new Point(-1, -1), 2, BorderType.Default, new MCvScalar());
// Do adaptive treshold to tophat image
CvInvoke.AdaptiveThreshold(imgOpen, imgThresh, 255, AdaptiveThresholdType.GaussianC, ThresholdType.BinaryInv, 295, 19); // first pass
// Do morphological operation of open to thresholded image
CvInvoke.MorphologyEx(imgThresh, imgThresh, MorphOp.Open, openElement, new Point(-1, -1), 1, BorderType.Default, new MCvScalar());
// Do morphological operation of erode to open image
CvInvoke.MorphologyEx(imgThresh, imgThresh, MorphOp.Erode, erodeElement, new Point(-1, -1), 1, BorderType.Constant, new MCvScalar());
// Do morphological operation of close to thresholded image
CvInvoke.MorphologyEx(imgThresh, imgThresh, MorphOp.Close, closeElement, new Point(-1, -1), 1, BorderType.Constant, new MCvScalar());
// Find contours in erode image
CvInvoke.FindContours(imgThresh, contours, null, RetrType.External, ChainApproxMethod.ChainApproxSimple);
Image <Bgr, byte> imgContours = imgThresh.ToImage <Bgr, byte>();
// Loop through found contours
for (int i = 0; i < contours.Size; i++)
{
// Create a bounding rectangle of the contour
var boundingRectangle = CvInvoke.BoundingRectangle(contours[i]);
// Check if contour has required dimensions
if (boundingRectangle.Height >= Properties.Settings.Default.boundingRectangleHeightMin && boundingRectangle.Height <= Properties.Settings.Default.boundingRectangleHeightMax && boundingRectangle.Width <= Properties.Settings.Default.boundingRectangleWidthMax)
{
CvInvoke.Rectangle(imgContours, boundingRectangle, new MCvScalar(0, 0, 255));
// Add bounding rectangle to the list of bounding rectangles
boundingRectangles.Add(boundingRectangle);
}
}
// Check if the list of bounding rectangles has the required number of items
if (boundingRectangles.Count >= 5 && boundingRectangles.Count <= 9)
{
// Make a key value pair with the cropped image and its threshold
licensePlate = new KeyValuePair <Mat, Mat>(potentialPlate, imgThresh);
licensePlateFound = true;
// Check if debug enabled
if (Properties.Settings.Default.debug)
{
// Show image of plate segmented
CvInvoke.Imshow("Plate segmented first pass", imgContours.Mat);
//CvInvoke.Imshow("Plate segmented first pass", potentialPlate);
}
// Stop from searching further
break;
}
}
}
else if (pass == "secondPass")
{
foreach (var potentialPlate in potentialPlates)
{
// Initate images used
Mat imgBilateralFilter = new Mat();
Mat imgThresh = new Mat();
Mat imgOpen = new Mat();
// Initiate structuring elements used in morphological procedures
Mat structuringElement = CvInvoke.GetStructuringElement(ElementShape.Rectangle, new Size(150, 150), new Point(-1, -1));
Mat openElement = CvInvoke.GetStructuringElement(ElementShape.Rectangle, new Size(7, 7), new Point(-1, -1));
Mat closeElement = CvInvoke.GetStructuringElement(ElementShape.Rectangle, new Size(5, 5), new Point(-1, -1));
Mat erodeElement = CvInvoke.GetStructuringElement(ElementShape.Ellipse, new Size(3, 3), new Point(-1, -1));
// Initiate other variables
VectorOfVectorOfPoint contours = new VectorOfVectorOfPoint();
List <Rectangle> boundingRectangles = new List <Rectangle>();
// Maximize contrast of a potential plate
Mat imgMaxContrast = ImageProcess.MaximizeContrast(potentialPlate, ref errorCode);
// Apply bilateral filter to image with contrast maximized
CvInvoke.BilateralFilter(imgMaxContrast, imgBilateralFilter, 5, 10, 10); // 20 10 10
// Do morphological operation of tophat
CvInvoke.MorphologyEx(imgBilateralFilter, imgOpen, MorphOp.Tophat, structuringElement, new Point(-1, -1), 1, BorderType.Default, new MCvScalar());
// Do adaptive treshold to tophat image
CvInvoke.AdaptiveThreshold(imgOpen, imgThresh, 255, AdaptiveThresholdType.GaussianC, ThresholdType.BinaryInv, 105, 5); // second pass
// Do morphological operation of open to thresholded image
CvInvoke.MorphologyEx(imgThresh, imgThresh, MorphOp.Open, openElement, new Point(-1, -1), 1, BorderType.Constant, new MCvScalar());
// Do morphological operation of erode to open image
CvInvoke.MorphologyEx(imgThresh, imgThresh, MorphOp.Erode, erodeElement, new Point(-1, -1), 1, BorderType.Constant, new MCvScalar());
// Do morphological operation of close to thresholded image
CvInvoke.MorphologyEx(imgThresh, imgThresh, MorphOp.Close, closeElement, new Point(-1, -1), 1, BorderType.Constant, new MCvScalar());
// Find contours in erode image
CvInvoke.FindContours(imgThresh, contours, null, RetrType.External, ChainApproxMethod.ChainApproxSimple);
Image <Bgr, byte> imgContours = imgThresh.ToImage <Bgr, byte>();
// Loop through found contours
for (int i = 0; i < contours.Size; i++)
{
// Create a bounding rectangle of the contour
var boundingRectangle = CvInvoke.BoundingRectangle(contours[i]);
// Check if contour has required dimensions
if (boundingRectangle.Height >= Properties.Settings.Default.boundingRectangleHeightMin && boundingRectangle.Height <= Properties.Settings.Default.boundingRectangleHeightMax)
{
CvInvoke.Rectangle(imgContours, boundingRectangle, new MCvScalar(0, 0, 255));
// Add bounding rectangle to the list of bounding rectangles
boundingRectangles.Add(boundingRectangle);
}
}
// Check if the list of bounding rectangles has the required number of items
if (boundingRectangles.Count >= 5 && boundingRectangles.Count <= 9)
{
// Make a key value pair with the cropped image and its threshold
licensePlate = new KeyValuePair <Mat, Mat>(potentialPlate, imgThresh);
licensePlateFound = true;
// Check if debug enabled
if (Properties.Settings.Default.debug)
{
// Show image of plate segmented
CvInvoke.Imshow("Plate segmented second pass", imgContours.Mat);
//CvInvoke.Imshow("Plate segmented second pass", potentialPlate);
}
// Stop from searching further
break;
}
}
}
// Check if license plate has been found
if (!licensePlateFound)
{
// Return null
return(new KeyValuePair <Mat, Mat>());
}
else
{
// Return the key value pair
return(licensePlate);
}
}
catch (Exception ex)
{
Console.WriteLine(ex);
errorCode = 8;
return(new KeyValuePair <Mat, Mat>());
}
}
private void altProcess(Bitmap bm, int level)
{
var img = new Image <Bgr, byte>(bm);
if (level == 1)
{
var resImage = new Image <Bgr, byte>(img.Bitmap);
CvInvoke.BilateralFilter(resImage, img, 30, 80, 80);
CvInvoke.MedianBlur(img, img, 5);
resImage = img;
}
else if (level == 2)
{
CvInvoke.MedianBlur(img, img, 5);
var resImage = new Image <Bgr, byte>(img.Bitmap);
CvInvoke.BilateralFilter(resImage, img, 25, 75, 75);
CvInvoke.Blur(img, img, new Size(5, 5), new Point(0, 0));
}
var grayimage = new Image <Gray, byte>(bm);
CvInvoke.CvtColor(img, grayimage, ColorConversion.Bgr2Gray);
BlackBG(grayimage);
Console.WriteLine("Filtering done");
var cannyThreshold = GetKMeansThreshold(grayimage);
label2.Text = cannyThreshold.ToString();
Thresholding(grayimage, cannyThreshold);
Console.WriteLine("Canny threshold using KMEANS found " + cannyThreshold);
//Convert the image to grayscale and filter out the noise
var cannyEdges = new UMat();
Console.WriteLine("Canny threshold using KMEANS found " + cannyThreshold);
var uimage = new UMat();
CvInvoke.CvtColor(img, uimage, ColorConversion.Bgr2Gray);
CvInvoke.Canny(uimage, cannyEdges, cannyThreshold, cannyThreshold);
BlobCounter blobCounter = new BlobCounter( );
if (level == 1)
{
blobCounter.FilterBlobs = true;
blobCounter.MinHeight = 25;
blobCounter.MinWidth = 25;
blobCounter.ProcessImage(cannyEdges.Bitmap);
}
else
{
blobCounter.ProcessImage(grayimage.ToBitmap());
}
//blobCounter.ProcessImage(grayimage.ToBitmap());
Blob[] blobs = blobCounter.GetObjectsInformation( );
SimpleShapeChecker shapeChecker = new SimpleShapeChecker();
var triangleList = new List <Triangle2DF>();
var boxList = new List <RotatedRect>();
var circleList = new List <CircleF>();
Bitmap newBM = new Bitmap(img.Bitmap);
Graphics g = Graphics.FromImage(newBM);
Pen redPen = new Pen(Color.Red, 2);
Pen yellowPen = new Pen(Color.Yellow, 2);
Pen greenPen = new Pen(Color.Green, 2);
Pen bluePen = new Pen(Color.Blue, 2);
for (int i = 0, n = blobs.Length; i < n; i++)
{
List <IntPoint> edgePoints =
blobCounter.GetBlobsEdgePoints(blobs[i]);
AForge.Point center;
float radius;
if (shapeChecker.IsCircle(edgePoints, out center, out radius))
{
//g.DrawEllipse(bluePen,
// (float)(center.X - radius), (float)(center.Y - radius),
// (float)(radius * 2), (float)(radius * 2));
circleList.Add(new CircleF(new PointF(center.X, center.Y), radius));
}
else
{
List <IntPoint> corners;
if (edgePoints.Count > 1)
{
if (shapeChecker.IsQuadrilateral(edgePoints, out corners))
{
System.Console.WriteLine(corners.Count);
if (shapeChecker.CheckPolygonSubType(corners) ==
PolygonSubType.Square || shapeChecker.CheckPolygonSubType(corners) ==
PolygonSubType.Rectangle)
{
IntPoint minXY, maxXY;
PointsCloud.GetBoundingRectangle(corners, out minXY, out maxXY);
AForge.Point c = PointsCloud.GetCenterOfGravity(corners);
//g.DrawPolygon(greenPen, ToPointsArray(corners));
boxList.Add(new RotatedRect(new PointF(c.X, c.Y), new SizeF(maxXY.X - minXY.X, maxXY.Y - minXY.Y), 0));
}
}
else
{
corners = PointsCloud.FindQuadrilateralCorners(edgePoints);
if (corners.Count == 3)
{
Triangle2DF tri = new Triangle2DF(new PointF(corners[0].X, corners[0].Y), new PointF(corners[1].X, corners[1].Y), new PointF(corners[2].X, corners[2].Y));
triangleList.Add(tri);
//g.DrawPolygon(yellowPen, ToPointsArray(corners));
}
//g.DrawPolygon(redPen, ToPointsArray(corners));
}
}
}
}
Console.WriteLine("boxes " + boxList.Count);
Console.WriteLine("triangles " + triangleList.Count);
Console.WriteLine("circles " + circleList.Count);
redPen.Dispose();
greenPen.Dispose();
bluePen.Dispose();
yellowPen.Dispose();
//g.Dispose();
resPicBox.Image = newBM;
CircleF[] circles = circleList.ToArray();
var cList = circles.ToList();
FilterSame(boxList, triangleList, cList, img.Width * img.Height);
circles = cList.ToArray();
var points = new List <PointF>();
var Image = img.CopyBlank();
foreach (var triangle in triangleList)
{
Image.Draw(triangle, new Bgr(Color.Red), 3);
points.Add(triangle.Centeroid);
}
foreach (var box in boxList)
{
Image.Draw(box, new Bgr(Color.Blue), 3);
points.Add(box.Center);
}
foreach (var circle in circles)
{
Image.Draw(circle, new Bgr(Color.DarkCyan), 3);
points.Add(circle.Center);
}
var listPoints = SortPoints(points, img);
for (var i = 0; i < listPoints.Length; i++)
{
Console.WriteLine(listPoints[i].X.ToString() + " " + listPoints[i].Y.ToString());
}
System.Console.WriteLine("Points sorted, num of objects " + listPoints.Length.ToString());
resPicBox.Image = (Image + img).ToBitmap();
if (listPoints.Length > 3)
{
var bezSegList = InterpolatePointWithBeizerCurves(listPoints.ToList <PointF>());
var gr = Graphics.FromImage(resPicBox.Image);
var p = new Pen(Color.Red);
foreach (BeizerCurveSegment seg in bezSegList)
{
var bezierList = GetBez(new PointF[]
{ seg.StartPoint, seg.FirstControlPoint, seg.SecondControlPoint, seg.EndPoint });
for (var i = 0; i < bezierList.Length - 1; i++)
{
gr.DrawLine(p, bezierList[i], bezierList[i + 1]);
}
}
}
else
{
var gr = Graphics.FromImage(resPicBox.Image);
var p = new Pen(Color.Red);
for (var i = 0; i < listPoints.Length - 1; i++)
{
gr.DrawLine(p, listPoints[i], listPoints[i + 1]);
}
}
//var bezierList = GetBezierCurve1(listPoints);
}
private void Process(Bitmap bm, int level, double circleAccumulatorThreshold = 70.0, int maxRadius = 0)
{
double cannyThreshold = 0;
var img = new Image <Bgr, byte>(bm);
if (level == 1)
{
var resImage = new Image <Bgr, byte>(img.Bitmap);
CvInvoke.BilateralFilter(resImage, img, 30, 75, 75);
CvInvoke.MedianBlur(img, img, 5);
resImage = img;
}
else if (level == 2)
{
CvInvoke.MedianBlur(img, img, 5);
var resImage = new Image <Bgr, byte>(img.Bitmap);
CvInvoke.BilateralFilter(resImage, img, 25, 75, 75);
CvInvoke.Blur(img, img, new Size(5, 5), new Point(0, 0));
}
else if (level == 3)
{
var resImage = new Image <Bgr, byte>(img.Bitmap);
CvInvoke.FastNlMeansDenoising(resImage, img);
}
var grayimage = new Image <Gray, byte>(bm);
CvInvoke.CvtColor(img, grayimage, ColorConversion.Bgr2Gray);
maxRadius = img.Width / 10;
BlackBG(grayimage);
Console.WriteLine("Filtering done");
cannyThreshold = GetKMeansThreshold(grayimage);
label2.Text = cannyThreshold.ToString();
Thresholding(grayimage, cannyThreshold);
Console.WriteLine("Canny threshold using KMEANS found " + cannyThreshold);
//Convert the image to grayscale and filter out the noise
var uimage = new UMat();
CvInvoke.CvtColor(img, uimage, ColorConversion.Bgr2Gray);
//uimage = grayimage.ToUMat();
//resPicBox.Image = grayimage.Bitmap;
var circles = CvInvoke.HoughCircles(uimage, HoughType.Gradient, 2.0, 5.0, cannyThreshold, circleAccumulatorThreshold, 1, maxRadius);
Console.WriteLine("Circles found " + circles.Length.ToString());
var cannyEdges = new UMat();
CvInvoke.Canny(uimage, cannyEdges, cannyThreshold, cannyThreshold);
var lines = CvInvoke.HoughLinesP(uimage,
1, //Distance resolution in pixel-related units
Math.PI / 180.0, //Angle resolution measured in radians.
1, //threshold
5, //min Line length
5); //gap between lines
Console.WriteLine("Lines detected");
var triangleList = new List <Triangle2DF>();
var boxList = new List <RotatedRect>();
using (var contours = new VectorOfVectorOfPoint())
{
CvInvoke.FindContours(cannyEdges, contours, null, RetrType.List, ChainApproxMethod.ChainApproxSimple);
var count = contours.Size;
for (var i = 0; i < count; i++)
{
using (var contour = contours[i])
using (var approxContour = new VectorOfPoint())
{
CvInvoke.ApproxPolyDP(contour, approxContour, CvInvoke.ArcLength(contour, true) * 0.05, true);
if (!(CvInvoke.ContourArea(approxContour, false) > 10))
{
continue;
}
if (approxContour.Size == 3)
{
var pts = approxContour.ToArray();
triangleList.Add(new Triangle2DF(
pts[0],
pts[1],
pts[2]
));
}
else if (approxContour.Size == 4)
{
var pts = approxContour.ToArray();
var edges = PointCollection.PolyLine(pts, true);
var isRectangle = edges
.Select((t, j) => Math.Abs(edges[(j + 1) % edges.Length].GetExteriorAngleDegree(t)))
.All(angle => !(angle < 80) && !(angle > 100));
if (isRectangle)
{
boxList.Add(CvInvoke.MinAreaRect(approxContour));
}
}
}
}
}
System.Console.WriteLine("Boxes found " + boxList.Count.ToString());
System.Console.WriteLine("Triangles found " + triangleList.Count.ToString());
var cList = circles.ToList();
FilterSame(boxList, triangleList, cList, img.Width * img.Height);
circles = cList.ToArray();
var points = new List <PointF>();
var Image = img.CopyBlank();
foreach (var triangle in triangleList)
{
Image.Draw(triangle, new Bgr(Color.Red), 3);
points.Add(triangle.Centeroid);
}
foreach (var box in boxList)
{
Image.Draw(box, new Bgr(Color.Blue), 3);
points.Add(box.Center);
}
foreach (var circle in circles)
{
Image.Draw(circle, new Bgr(Color.DarkCyan), 3);
points.Add(circle.Center);
}
var listPoints = SortPoints(points, img);
System.Console.WriteLine("Points sorted, num of objects " + listPoints.Length.ToString());
resPicBox.Image = (Image + img).ToBitmap();
if (listPoints.Length > 3)
{
var bezSegList = InterpolatePointWithBeizerCurves(listPoints.ToList <PointF>());
var gr = Graphics.FromImage(resPicBox.Image);
var p = new Pen(Color.Red);
foreach (BeizerCurveSegment seg in bezSegList)
{
var bezierList = GetBez(new PointF[]
{ seg.StartPoint, seg.FirstControlPoint, seg.SecondControlPoint, seg.EndPoint });
for (var i = 0; i < bezierList.Length - 1; i++)
{
gr.DrawLine(p, bezierList[i], bezierList[i + 1]);
}
}
}
else
{
var gr = Graphics.FromImage(resPicBox.Image);
var p = new Pen(Color.Red);
for (var i = 0; i < listPoints.Length - 1; i++)
{
gr.DrawLine(p, listPoints[i], listPoints[i + 1]);
}
}
}
public string DetectLicencePlate(Bitmap image)
{
Image <Bgr, Byte> originalImage = new Image <Bgr, byte>(image);
//originalImage = originalImage.Resize(500, 500, Emgu.CV.CvEnum.Inter.Linear, true);
//Convert the image to grayscale and filter out the noise
Image <Gray, Byte> grayImage = originalImage.Convert <Gray, Byte>();
//use image pyr to remove noise
UMat pyrDown = new UMat();
CvInvoke.PyrDown(grayImage, pyrDown);
CvInvoke.PyrUp(pyrDown, grayImage);
// Noise removal with iterative bilateral filter(removes noise while preserving edges)
Mat filteredImage = new Mat();
CvInvoke.BilateralFilter(grayImage, filteredImage, 11, 17, 17);
///Find Edges of the grayscale image
Mat edges = new Mat();
CvInvoke.Canny(filteredImage, edges, 120, 200);
filteredImage.Save("medziproj.jpg");
// Find contours
Mat hierarchy = new Mat();
var edgesCopy = edges.Clone();
var contours = new VectorOfVectorOfPoint();
CvInvoke.FindContours(edgesCopy, contours, hierarchy, Emgu.CV.CvEnum.RetrType.List, Emgu.CV.CvEnum.ChainApproxMethod.ChainApproxSimple);
//Create copy of original image to draw all contours
var copyOriginalImage = originalImage.Clone();
CvInvoke.DrawContours(copyOriginalImage, contours, -1, new MCvScalar(0, 255, 0, 255), 3);
var newContoursArray = new VectorOfVectorOfPoint();
int count = contours.Size;
for (int i = 1; i < count; i++)
{
using (VectorOfPoint contour = contours[i])
{
if (CvInvoke.ContourArea(contour, false) > 60)
{
newContoursArray.Push(contour);
}
}
}
//Create copy of original image to draw all contours
var copyOriginalImage2 = originalImage.Clone();
CvInvoke.DrawContours(copyOriginalImage2, newContoursArray, -1, new MCvScalar(0, 255, 0, 255), 3);
var numberPlateVectorArray = new VectorOfVectorOfPoint();
var resultRectangle = new Rectangle();
for (int i = 0; i < newContoursArray.Size; i++)
{
var c = newContoursArray[i];
var peri = CvInvoke.ArcLength(c, true);
var approx = new VectorOfPoint();
CvInvoke.ApproxPolyDP(c, approx, (0.02 * peri), true);
////mame 4hranu
if (approx.Size == 4)
{
//determine if all the angles in the contour are within [80, 100] degree
bool isRectangle = true;
LineSegment2D[] edgesArr = PointCollection.PolyLine(approx.ToArray(), true);
for (int j = 0; j < edgesArr.Length; j++)
{
double angle = Math.Abs(
edgesArr[(j + 1) % edgesArr.Length].GetExteriorAngleDegree(edgesArr[j]));
if (angle < 80 || angle > 100)
{
isRectangle = false;
break;
}
}
if (isRectangle)
{
numberPlateVectorArray.Push(approx);
resultRectangle = CvInvoke.BoundingRectangle(c);
break;
}
}
}
var originalImage3 = originalImage.Clone();
//# Drawing the selected contour on the original image
CvInvoke.DrawContours(originalImage3, numberPlateVectorArray, -1, new MCvScalar(0, 255, 0), 3);
//// save cropped image
originalImage.ROI = resultRectangle;
originalImage = originalImage.Copy();
CvInvoke.cvResetImageROI(originalImage);
//originalImage = originalImage.Resize(200, 200, Emgu.CV.CvEnum.Inter.Linear, true);
originalImage.Save(RESULT_PLATE);
return(this.RecognizeText(RESULT_PLATE));
}
public CircleF FindCircle(Image <Gray, Byte> image, int estimatedRadius, int patternType, int error = 30)
{
circles.Clear();
Image <Gray, Byte> bilateralFilteredImage, edgeDetectedImage, eroded, img;
img = image.Clone();
bilateralFilteredImage = new Mat().ToImage <Gray, byte>();
edgeDetectedImage = new Mat().ToImage <Gray, byte>();
eroded = new Mat().ToImage <Gray, byte>();
Mat hierarchy = new Mat();
VectorOfVectorOfPoint contours = new VectorOfVectorOfPoint();
//Mat
CvInvoke.MorphologyEx(img, img, MorphOp.Close, GenerateEllipseKernel(13), new Point(-1, -1), 1, BorderType.Default, new MCvScalar());
CvInvoke.BilateralFilter(img, bilateralFilteredImage, 9, 30, 30);
CvInvoke.Canny(bilateralFilteredImage, edgeDetectedImage, 25, 25);
CvInvoke.MorphologyEx(edgeDetectedImage, eroded, MorphOp.Close, GenerateEllipseKernel(11), new Point(-1, -1), 1, BorderType.Default, new MCvScalar());
CvInvoke.FindContours(eroded, contours, hierarchy, RetrType.List, ChainApproxMethod.ChainApproxSimple);
for (int i = 0; i < contours.Size; i++)
{
using (VectorOfPoint contour = contours[i])
{
Rectangle r = CvInvoke.BoundingRectangle(contour);
double w, h;
if (IsSquare(r.Width, r.Height))
{
w = r.Width;
h = r.Height;
double rect_area = ((w * w) / 4) * Math.PI;
CircleF circle = CvInvoke.MinEnclosingCircle(contour);
double circle_area = circle.Radius * circle.Radius * Math.PI;
if ((Math.Abs(rect_area - circle_area) < rect_area / 10) &&
(Math.Abs(Math.Sqrt(circle_area / 3.14) - estimatedRadius) < error) && (w > 21) && (h > 21))
{
CircleFWithScore temp = new CircleFWithScore(circle, CvInvoke.ContourArea(contour) / circle.Area);
circles.Add(temp);
}
}
}
}
//CvInvoke.MatchTemplate(img,templ:templ,)
//CvInvoke.Imshow("edge", eroded);
//var watch = System.Diagnostics.Stopwatch.StartNew();
CircleF result = FindHighestScoreCircle();
if (MatchPattern(image, result, patternType))
{
//watch.Stop();
//var elapsedMs = watch.ElapsedMilliseconds;
//Console.WriteLine("\nFinished pattern matching in " + elapsedMs + "ms");
return(result);
}
else
{
//watch.Stop();
//var elapsedMs = watch.ElapsedMilliseconds;
//Console.WriteLine("\nFinished pattern matching in " + elapsedMs + "ms");
throw new IndexOutOfRangeException();
}
}
private void btnOpenFile_Click(object sender, EventArgs e)
{
DialogResult drChosenFile;
drChosenFile = ofdOpenFile.ShowDialog();
if (drChosenFile != DialogResult.OK || ofdOpenFile.FileName == "")
{
lblChosenFile.Text = "file not chosen";
return;
}
Mat inputFormColor;
try
{
inputFormColor = new Mat(ofdOpenFile.FileName, ImreadModes.Color);
}
catch (Exception ex)
{
lblChosenFile.Text = "unable to open image, error: " + ex.Message;
return;
}
if (inputFormColor == null)
{
lblChosenFile.Text = "unable to open image";
return;
}
CvInvoke.Resize(inputFormColor, inputFormColor, new Size(0, 0), 0.25, 0.25, Inter.Cubic);
Mat inputFormGray = new Mat(inputFormColor.Size, DepthType.Cv8U, 1);
Mat inputFormGrayFiltered = new Mat(inputFormColor.Size, DepthType.Cv8U, 1);
Mat inputFormComplement = new Mat(inputFormColor.Size, DepthType.Cv8U, 1);
inputFormComplement.SetTo(new MCvScalar(255));
CvInvoke.CvtColor(inputFormColor, inputFormGray, ColorConversion.Bgr2Gray);
CvInvoke.BilateralFilter(inputFormGray, inputFormGrayFiltered, 9, 80.0, 80.0);
CvInvoke.AdaptiveThreshold(inputFormGrayFiltered, inputFormGrayFiltered, 255, AdaptiveThresholdType.GaussianC, ThresholdType.Binary, 11, 2);
CvInvoke.Subtract(inputFormComplement, inputFormGrayFiltered, inputFormComplement);
Size inputFormSize = new Size(450, 650);
Point inputFormCenter = new Point(inputFormGray.Size.Width / 2, inputFormGray.Size.Height / 2);
CvInvoke.GetRectSubPix(inputFormComplement, inputFormSize, inputFormCenter, inputFormComplement);
Size sampleSize = new Size(45, 65);
int[,] histogram = new int[10, 10];
for (int i = 0; i < 10; i++)
{
for (int j = 0; j < 10; j++)
{
int Histogram = 0;
Mat sampleForm = new Mat(sampleSize, DepthType.Cv8U, 1);
Point sampleSizeCenter = new Point(45 * i + 22, 65 * j + 32);
CvInvoke.GetRectSubPix(inputFormComplement, sampleSize, sampleSizeCenter, sampleForm);
for (int height = 0; height < sampleForm.Rows; height++)
{
for (int width = 0; width < sampleForm.Cols; width++)
{
if (Convert.ToInt16(sampleForm.GetData(height, width)[0]) != 0)
{
Histogram++;
}
}
}
histogram[i, j] = Histogram;
}
}
string fileName = System.IO.Path.GetFileNameWithoutExtension(ofdOpenFile.FileName);
string arrayFileName = "C:\\CsharpTemp\\" + fileName + "_Array.txt";
using (StreamWriter sw = new StreamWriter(arrayFileName))
{
//sw.WriteLine("HistogramArray");
for (int i = 0; i < 10; i++)
{
for (int j = 0; j < 10; j++)
{
sw.Write(histogram[j, i] + " ");
}
sw.WriteLine("");
}
sw.Flush();
sw.Close();
}
ibOriginal.Image = inputFormColor;
ibFiltered.Image = inputFormComplement;
////Converting Emgu.CV.Image to Bitmap
////The Image class has a ToBitmap() function that return a Bitmap object, which can easily be displayed on a PictureBox control using Windows Form.
//Image<Gray, Byte> imageInputFormComplement = inputFormComplement.ToImage<Gray, Byte>();
//Bitmap newImage = new Bitmap(ofdOpenFile.FileName);
//newImage = imageInputFormComplement.ToBitmap();
////Create a 3 channel image of 400x200
////A "Hello World" Example
//using (Mat img = new Mat(200, 400, DepthType.Cv8U, 3))
//{
// img.SetTo(new Bgr(255, 0, 0).MCvScalar); // set it to Blue color
// //Draw "Hello, world." on the image using the specific font
// CvInvoke.PutText(
// img,
// "Hello, world",
// new System.Drawing.Point(10, 80),
// FontFace.HersheyComplex,
// 1.0,
// new Bgr(0, 255, 0).MCvScalar);
// //Show the image using ImageViewer from Emgu.CV.UI
// ImageViewer.Show(img, "Test Window");
//}
}
private void Processing(Image <Bgr, byte> ImgSource, TextColor Color = TextColor.White)
{
Rectangle ROICode = new Rectangle();
mImgDetected = ImgSource.Copy();
// create and ROI image
Rectangle ROI = new Rectangle(ImgSource.Width / 2, ImgSource.Height / 10, ImgSource.Width, ImgSource.Height / 4);
mImgDetected.ROI = ROI;
// filter noise
//detect code
using (Image <Gray, byte> imgGray = mImgDetected.Convert <Gray, byte>())
{
using (Image <Gray, byte> imgFilter = new Image <Gray, byte>(imgGray.Size))
{
CvInvoke.BilateralFilter(imgGray, imgFilter, 9, 49, 49);
using (Mat k = CvInvoke.GetStructuringElement(Emgu.CV.CvEnum.ElementShape.Rectangle, new Size(5, 5), new Point(-1, -1)))
{
if (Color == TextColor.White)
{
CvInvoke.Erode(imgFilter, imgFilter, k, new Point(-1, -1), 1, Emgu.CV.CvEnum.BorderType.Default, new MCvScalar());
}
else
{
CvInvoke.Dilate(imgFilter, imgFilter, k, new Point(-1, -1), 1, Emgu.CV.CvEnum.BorderType.Default, new MCvScalar());
}
}
using (Image <Gray, double> ImgSobel = new Image <Gray, double>(imgFilter.Size))
{
CvInvoke.Sobel(imgFilter, ImgSobel, Emgu.CV.CvEnum.DepthType.Cv64F, 1, 0, kSize: 1);
CvInvoke.ConvertScaleAbs(ImgSobel, imgFilter, 2, 0);
CvInvoke.Threshold(imgFilter, imgFilter, 20, 255, Emgu.CV.CvEnum.ThresholdType.Binary);
using (VectorOfVectorOfPoint contours = new VectorOfVectorOfPoint())
{
CvInvoke.FindContours(imgFilter, contours, null, Emgu.CV.CvEnum.RetrType.External, Emgu.CV.CvEnum.ChainApproxMethod.ChainApproxSimple);
for (int i = 0; i < contours.Size; i++)
{
double s = CvInvoke.ContourArea(contours[i]);
Rectangle bound = CvInvoke.BoundingRectangle(contours[i]);
if (bound.Height > 65 || s < 10)
{
CvInvoke.DrawContours(imgFilter, contours, i, new MCvScalar(0), -1);
}
}
}
using (Mat k = CvInvoke.GetStructuringElement(Emgu.CV.CvEnum.ElementShape.Rectangle, new Size(107, 1), new Point(-1, -1)))
{
CvInvoke.MorphologyEx(imgFilter, imgFilter, Emgu.CV.CvEnum.MorphOp.Close, k, new Point(-1, -1), 1, Emgu.CV.CvEnum.BorderType.Default, new MCvScalar());
}
using (VectorOfVectorOfPoint contours = new VectorOfVectorOfPoint())
{
CvInvoke.FindContours(imgFilter, contours, null, Emgu.CV.CvEnum.RetrType.External, Emgu.CV.CvEnum.ChainApproxMethod.ChainApproxSimple);
double large_area = 0;
int index_large = 0;
for (int i = 0; i < contours.Size; i++)
{
double s = CvInvoke.ContourArea(contours[i]);
if (large_area < s)
{
large_area = s;
index_large = i;
}
}
Rectangle boxFirstLine = CvInvoke.BoundingRectangle(contours[index_large]);
Rectangle boxSecondLine = new Rectangle();
for (int i = 0; i < contours.Size; i++)
{
Rectangle b = CvInvoke.BoundingRectangle(contours[i]);
if (b.Y - boxFirstLine.Y < 120 && b.Y - boxFirstLine.Y > 0 && b.Width > 30)
{
boxSecondLine = CvInvoke.BoundingRectangle(contours[i]);
break;
}
}
ROICode = new Rectangle(boxFirstLine.X - 20, boxFirstLine.Y - 20, boxFirstLine.Width + 40, boxSecondLine.Y + boxSecondLine.Height + 60 - boxFirstLine.X);
ROICode.X = ROICode.X < 0 ? 0: ROICode.X;
ROICode.Y = ROICode.Y < 0 ? 0 : ROICode.Y;
ROICode.Width = ROICode.X + ROICode.Width > mImgDetected.Width ? mImgDetected.Width - ROICode.X : ROICode.Width;
ROICode.Height = ROICode.Y + ROICode.Height > mImgDetected.Height ? mImgDetected.Height - ROICode.Y : ROICode.Height;
mImgCroped = mImgDetected.Copy();
mImgCroped.ROI = ROICode;
CvInvoke.Rectangle(mImgDetected, ROICode, new MCvScalar(255, 0, 0), 3);
mImgDetected.ROI = new Rectangle();
imb3.Image = mImgCroped.Bitmap;
}
}
}
}
// segment char text
mImgSegment = new Image <Gray, byte>(mImgCroped.Size);
mImgCharSegment = mImgCroped.Copy();
CvInvoke.CvtColor(mImgCroped, mImgSegment, Emgu.CV.CvEnum.ColorConversion.Bgr2Gray);
using (Mat k = CvInvoke.GetStructuringElement(Emgu.CV.CvEnum.ElementShape.Rectangle, new Size(5, 5), new Point(-1, -1)))
{
CvInvoke.MorphologyEx(mImgSegment, mImgSegment, Emgu.CV.CvEnum.MorphOp.Open, k, new Point(-1, -1), 1, Emgu.CV.CvEnum.BorderType.Default, new MCvScalar());
}
Image <Gray, byte> img_decode = mImgSegment.Copy();
CvInvoke.BitwiseNot(img_decode, img_decode);
CvInvoke.Imwrite("test.png", img_decode);
CvInvoke.Threshold(mImgSegment, mImgSegment, 127, 255, Emgu.CV.CvEnum.ThresholdType.Binary);
using (Mat k = CvInvoke.GetStructuringElement(Emgu.CV.CvEnum.ElementShape.Rectangle, new Size(3, 3), new Point(-1, -1)))
{
CvInvoke.MorphologyEx(mImgSegment, mImgSegment, Emgu.CV.CvEnum.MorphOp.Open, k, new Point(-1, -1), 1, Emgu.CV.CvEnum.BorderType.Default, new MCvScalar());
}
using (VectorOfVectorOfPoint contours = new VectorOfVectorOfPoint())
{
CvInvoke.FindContours(mImgSegment, contours, null, Emgu.CV.CvEnum.RetrType.External, Emgu.CV.CvEnum.ChainApproxMethod.ChainApproxSimple);
for (int i = 0; i < contours.Size; i++)
{
Rectangle bound = CvInvoke.BoundingRectangle(contours[i]);
if (bound.Height > 60 || bound.Height < 30 || bound.Width > 35)
{
CvInvoke.DrawContours(mImgSegment, contours, i, new MCvScalar(0), -1);
}
}
}
using (VectorOfVectorOfPoint contours = new VectorOfVectorOfPoint())
{
CvInvoke.FindContours(mImgSegment, contours, null, Emgu.CV.CvEnum.RetrType.External, Emgu.CV.CvEnum.ChainApproxMethod.ChainApproxSimple);
for (int i = 0; i < contours.Size; i++)
{
Rectangle bound = CvInvoke.BoundingRectangle(contours[i]);
CvInvoke.Rectangle(mImgCharSegment, bound, new MCvScalar(0, 255, 0), 2);
}
}
CvInvoke.Threshold(mImgSegment, mImgSegment, 127, 255, Emgu.CV.CvEnum.ThresholdType.BinaryInv);
imb4.Image = mImgSegment.Bitmap;
imb5.Image = mImgCharSegment.Bitmap;
string code = Read(img_decode);
Console.WriteLine(code);
imb2.Image = mImgDetected.Bitmap;
}
void FilterChoice(Image <Bgr, Byte> imgToFilter, Image <Bgr, Byte> imgReference, string numPictureGood, string numDistortionGood, string numIntensityGood)
{
Image <Bgr, Byte> imgFiltered = new Image <Bgr, byte>(imgToFilter.Size);
string name = numPictureGood + numDistortionGood + "_" + numIntensityGood;
int sizeMask = (int)numericMinMask.Value;
if (sizeMask % 2 == 0)
{
sizeMask++;
}
if (radioAvg.Checked)
{
for (int i = sizeMask; i <= (int)numericMaxMask.Value; i = i + 2)
{
timer.Reset();
timer.Start();
CvInvoke.Blur(imgToFilter, imgFiltered, new Size(i, i), new Point(-1, -1));
timer.Stop();
EvaluationOfFilter(imgReference, imgFiltered);
row[0] = "Filtr uśredniający";
row[1] = i.ToString();
row[2] = "-";
row[3] = "-";
row[4] = "-";
row[5] = "-";
row[6] = PSNRMSE.ToString();
row[7] = PSNRMSD.ToString();
row[8] = PSNRMED.ToString();
row[9] = Marziliano.ToString();
row[10] = timer.Elapsed.TotalMilliseconds.ToString();
ListViewItem listViewItem = new ListViewItem(row);
listViewEval.Items.Add(listViewItem);
SaveResults(imgFiltered, "Avg", i, 0, 0, 0, 0, row, name);
}
}
if (radioGauss.Checked)
{
for (int i = sizeMask; i <= (int)numericMaxMask.Value; i = i + 2)
{
for (int j = (int)numericMinSigmaX.Value; j <= (int)numericMaxSigmaX.Value; j = j + 5)
{
timer.Reset();
timer.Start();
CvInvoke.GaussianBlur(imgToFilter, imgFiltered, new Size(i, i), j);
timer.Stop();
EvaluationOfFilter(imgReference, imgFiltered);
row[0] = "Filtr Gaussa";
row[1] = i.ToString();
row[2] = j.ToString();
row[3] = "-";
row[4] = "-";
row[5] = "-";
row[6] = PSNRMSE.ToString();
row[7] = PSNRMSD.ToString();
row[8] = PSNRMED.ToString();
row[9] = Marziliano.ToString();
row[10] = timer.Elapsed.TotalMilliseconds.ToString();
ListViewItem listViewItem = new ListViewItem(row);
listViewEval.Items.Add(listViewItem);
SaveResults(imgFiltered, "Gauss", i, j, 0, 0, 0, row, name);
}
}
}
if (radioMedian.Checked)
{
for (int i = sizeMask; i <= (int)numericMaxMask.Value; i = i + 2)
{
timer.Reset();
timer.Start();
CvInvoke.MedianBlur(imgToFilter, imgFiltered, i);
timer.Stop();
EvaluationOfFilter(imgReference, imgFiltered);
row[0] = "Filtr medianowy";
row[1] = i.ToString();
row[2] = "-";
row[3] = "-";
row[4] = "-";
row[5] = "-";
row[6] = PSNRMSE.ToString();
row[7] = PSNRMSD.ToString();
row[8] = PSNRMED.ToString();
row[9] = Marziliano.ToString();
row[10] = timer.Elapsed.TotalMilliseconds.ToString();
ListViewItem listViewItem = new ListViewItem(row);
listViewEval.Items.Add(listViewItem);
SaveResults(imgFiltered, "Median", i, 0, 0, 0, 0, row, name);
}
}
if (radioBilateral.Checked)
{
for (double j = (double)numericMinSigmaColor.Value; j <= (double)numericMaxSigmaColor.Value; j = j + 10)
{
for (double k = (double)numericMinSigmaSpace.Value; k <= (double)numericMaxSigmaSpace.Value; k = k + 10)
{
timer.Reset();
timer.Start();
CvInvoke.BilateralFilter(imgToFilter, imgFiltered, -1, j, k);
timer.Stop();
EvaluationOfFilter(imgReference, imgFiltered);
row[0] = "Filtr bilateralny";
row[1] = "-";
row[2] = "-";
row[3] = j.ToString();
row[4] = k.ToString();
row[5] = "-";
row[6] = PSNRMSE.ToString();
row[7] = PSNRMSD.ToString();
row[8] = PSNRMED.ToString();
row[9] = Marziliano.ToString();
row[10] = timer.Elapsed.TotalMilliseconds.ToString();
ListViewItem listViewItem = new ListViewItem(row);
listViewEval.Items.Add(listViewItem);
SaveResults(imgFiltered, "Bilateral", -1, 0, j, k, 0, row, name);
}
}
}
if (radioKuwahara.Checked)
{
for (int i = sizeMask; i <= (int)numericMaxMask.Value; i = i + 2)
{
timer.Reset();
timer.Start();
imgFiltered = KuwaharaFilter(imgToFilter, i);
timer.Stop();
EvaluationOfFilter(imgReference, imgFiltered);
row[0] = "Filtr Kuwahara";
row[1] = i.ToString();
row[2] = "-";
row[3] = "-";
row[4] = "-";
row[5] = "-";
row[6] = PSNRMSE.ToString();
row[7] = PSNRMSD.ToString();
row[8] = PSNRMED.ToString();
row[9] = Marziliano.ToString();
row[10] = timer.Elapsed.TotalMilliseconds.ToString();
ListViewItem listViewItem = new ListViewItem(row);
listViewEval.Items.Add(listViewItem);
SaveResults(imgFiltered, "Kuwahara", i, 0, 0, 0, 0, row, name);
}
}
if (radioUnsharp.Checked)
{
for (int i = sizeMask; i <= (int)numericMaxMask.Value; i = i + 2)
{
float j = (float)numericUnsharpMaskMin.Value;
for (; j <= (float)numericUnsharpMaskMax.Value; j = (float)(j + 0.5))
{
timer.Reset();
timer.Start();
imgFiltered = UnsharpMasking(imgToFilter, i, j);
timer.Stop();
EvaluationOfFilter(imgReference, imgFiltered);
row[0] = "Unsharp masking";
row[1] = i.ToString();
row[2] = "-";
row[3] = "-";
row[4] = "-";
row[5] = "-";
row[6] = PSNRMSE.ToString();
row[7] = PSNRMSD.ToString();
row[8] = PSNRMED.ToString();
row[9] = Marziliano.ToString();
row[10] = timer.Elapsed.TotalMilliseconds.ToString();
ListViewItem listViewItem = new ListViewItem(row);
listViewEval.Items.Add(listViewItem);
SaveResults(imgFiltered, "Unsharp", i, 0, 0, 0, j, row, name);
}
}
}
if (radioEqualize.Checked)
{
timer.Reset();
timer.Start();
imgFiltered = EqualizeHistogram(imgToFilter);
timer.Stop();
EvaluationOfFilter(imgReference, imgFiltered);
row[0] = "Wyrownanie";
row[1] = "-";
row[2] = "-";
row[3] = "-";
row[4] = "-";
row[5] = "-";
row[6] = PSNRMSE.ToString();
row[7] = PSNRMSD.ToString();
row[8] = PSNRMED.ToString();
row[9] = Marziliano.ToString();
row[10] = timer.Elapsed.TotalMilliseconds.ToString();
ListViewItem listViewItem = new ListViewItem(row);
listViewEval.Items.Add(listViewItem);
SaveResults(imgFiltered, "Eq", 0, 0, 0, 0, 0, row, name);
}
if (radioStretch.Checked)
{
timer.Reset();
timer.Start();
imgFiltered = StretchHistogram(imgToFilter);
timer.Stop();
EvaluationOfFilter(imgReference, imgFiltered);
row[0] = "Rozciągniecie";
row[1] = "-";
row[2] = "-";
row[3] = "-";
row[4] = "-";
row[5] = "-";
row[6] = PSNRMSE.ToString();
row[7] = PSNRMSD.ToString();
row[8] = PSNRMED.ToString();
row[9] = Marziliano.ToString();
row[10] = timer.Elapsed.TotalMilliseconds.ToString();
ListViewItem listViewItem = new ListViewItem(row);
listViewEval.Items.Add(listViewItem);
SaveResults(imgFiltered, "Str", 0, 0, 0, 0, 0, row, name);
}
MessageBox.Show("Ukończono operacje");
}
void AllFilters(string path, string numPicture, string extension, Image <Bgr, Byte> imgReference)
{
Image <Bgr, Byte> imgFiltered;
List <int> rejectedDistortions = new List <int> {
2, 3, 5, 8, 9, 11, 13, 15, 16, 18, 19, 21, 24
};
int sizeMask = (int)numericMinMask.Value;
if (sizeMask % 2 == 0)
{
sizeMask++;
}
for (int numImage = 1; numImage <= 24; numImage++)
{
while (rejectedDistortions.Contains(numImage))
{
numImage++;
}
for (int distortionLvl = 1; distortionLvl <= 5; distortionLvl = distortionLvl + 4)
{
string numPath;
if (numImage < 10)
{
numPath = "0" + numImage.ToString() + "_" + distortionLvl.ToString();
}
else
{
numPath = numImage.ToString() + "_" + distortionLvl.ToString();
}
Image <Bgr, Byte> imgToFilter = new Image <Bgr, byte>(path + numPath + extension);
imgFiltered = new Image <Bgr, byte>(imgToFilter.Size);
// uśredniający
{
for (int i = sizeMask; i <= (int)numericMaxMask.Value; i = i + 2)
{
timer.Reset();
timer.Start();
CvInvoke.Blur(imgToFilter, imgFiltered, new Size(i, i), new Point(-1, -1));
timer.Stop();
EvaluationOfFilter(imgReference, imgFiltered);
row[0] = "Filtr uśredniający";
row[1] = i.ToString();
row[2] = "-";
row[3] = "-";
row[4] = "-";
row[5] = "-";
row[6] = PSNRMSE.ToString();
row[7] = PSNRMSD.ToString();
row[8] = PSNRMED.ToString();
row[9] = Marziliano.ToString();
row[10] = timer.Elapsed.TotalMilliseconds.ToString();
ListViewItem listViewItem = new ListViewItem(row);
listViewEval.Items.Add(listViewItem);
SaveResults(imgFiltered, "Avg", i, 0, 0, 0, 0, row, numPicture + numPath);
}
}
// Gaussa
{
for (int i = sizeMask; i <= (int)numericMaxMask.Value; i = i + 2)
{
for (int j = (int)numericMinSigmaX.Value; j <= (int)numericMaxSigmaX.Value; j = j + 5)
{
timer.Reset();
timer.Start();
CvInvoke.GaussianBlur(imgToFilter, imgFiltered, new Size(i, i), j);
timer.Stop();
EvaluationOfFilter(imgReference, imgFiltered);
row[0] = "Filtr Gaussa";
row[1] = i.ToString();
row[2] = j.ToString();
row[3] = "-";
row[4] = "-";
row[5] = "-";
row[6] = PSNRMSE.ToString();
row[7] = PSNRMSD.ToString();
row[8] = PSNRMED.ToString();
row[9] = Marziliano.ToString();
row[10] = timer.Elapsed.TotalMilliseconds.ToString();
ListViewItem listViewItem = new ListViewItem(row);
listViewEval.Items.Add(listViewItem);
SaveResults(imgFiltered, "Gauss", i, j, 0, 0, 0, row, numPicture + numPath);
}
}
}
// medianowy
{
for (int i = sizeMask; i <= (int)numericMaxMask.Value; i = i + 2)
{
timer.Reset();
timer.Start();
CvInvoke.MedianBlur(imgToFilter, imgFiltered, i);
timer.Stop();
EvaluationOfFilter(imgReference, imgFiltered);
row[0] = "Filtr medianowy";
row[1] = i.ToString();
row[2] = "-";
row[3] = "-";
row[4] = "-";
row[5] = "-";
row[6] = PSNRMSE.ToString();
row[7] = PSNRMSD.ToString();
row[8] = PSNRMED.ToString();
row[9] = Marziliano.ToString();
row[10] = timer.Elapsed.TotalMilliseconds.ToString();
ListViewItem listViewItem = new ListViewItem(row);
listViewEval.Items.Add(listViewItem);
SaveResults(imgFiltered, "Median", i, 0, 0, 0, 0, row, numPicture + numPath);
}
}
// bilateralny
{
for (double j = (double)numericMinSigmaColor.Value; j <= (double)numericMaxSigmaColor.Value; j = j + 10)
{
for (double k = (double)numericMinSigmaSpace.Value; k <= (double)numericMaxSigmaSpace.Value; k = k + 10)
{
timer.Reset();
timer.Start();
CvInvoke.BilateralFilter(imgToFilter, imgFiltered, -1, j, k);
timer.Stop();
EvaluationOfFilter(imgReference, imgFiltered);
row[0] = "Filtr bilateralny";
row[1] = "-";
row[2] = "-";
row[3] = j.ToString();
row[4] = k.ToString();
row[5] = "-";
row[6] = PSNRMSE.ToString();
row[7] = PSNRMSD.ToString();
row[8] = PSNRMED.ToString();
row[9] = Marziliano.ToString();
row[10] = timer.Elapsed.TotalMilliseconds.ToString();
ListViewItem listViewItem = new ListViewItem(row);
listViewEval.Items.Add(listViewItem);
SaveResults(imgFiltered, "Bilateral", -1, 0, j, k, 0, row, numPicture + numPath);
}
}
}
// Kuwahara
{
for (int i = sizeMask; i <= (int)numericMaxMask.Value; i = i + 2)
{
timer.Reset();
timer.Start();
imgFiltered = KuwaharaFilter(imgToFilter, i);
timer.Stop();
EvaluationOfFilter(imgReference, imgFiltered);
row[0] = "Filtr Kuwahara";
row[1] = i.ToString();
row[2] = "-";
row[3] = "-";
row[4] = "-";
row[5] = "-";
row[6] = PSNRMSE.ToString();
row[7] = PSNRMSD.ToString();
row[8] = PSNRMED.ToString();
row[9] = Marziliano.ToString();
row[10] = timer.Elapsed.TotalMilliseconds.ToString();
ListViewItem listViewItem = new ListViewItem(row);
listViewEval.Items.Add(listViewItem);
SaveResults(imgFiltered, "Kuwahara", i, 0, 0, 0, 0, row, numPicture + numPath);
}
}
// unsharp masking
{
for (int i = sizeMask; i <= (int)numericMaxMask.Value; i = i + 2)
{
float j = (float)numericUnsharpMaskMin.Value;
for (; j <= (float)numericUnsharpMaskMax.Value; j = (float)(j + 0.5))
{
timer.Reset();
timer.Start();
imgFiltered = UnsharpMasking(imgToFilter, i, j);
timer.Stop();
EvaluationOfFilter(imgReference, imgFiltered);
row[0] = "Unsharp masking";
row[1] = i.ToString();
row[2] = "-";
row[3] = "-";
row[4] = "-";
row[5] = "-";
row[6] = PSNRMSE.ToString();
row[7] = PSNRMSD.ToString();
row[8] = PSNRMED.ToString();
row[9] = Marziliano.ToString();
row[10] = timer.Elapsed.TotalMilliseconds.ToString();
ListViewItem listViewItem = new ListViewItem(row);
listViewEval.Items.Add(listViewItem);
SaveResults(imgFiltered, "Unsharp", i, 0, 0, 0, j, row, numPicture + numPath);
}
}
}
// wyrówanie histogramu
{
timer.Reset();
timer.Start();
imgFiltered = EqualizeHistogram(imgToFilter);
timer.Stop();
EvaluationOfFilter(imgReference, imgFiltered);
row[0] = "Wyrownanie";
row[1] = "-";
row[2] = "-";
row[3] = "-";
row[4] = "-";
row[5] = "-";
row[6] = PSNRMSE.ToString();
row[7] = PSNRMSD.ToString();
row[8] = PSNRMED.ToString();
row[9] = Marziliano.ToString();
row[10] = timer.Elapsed.TotalMilliseconds.ToString();
ListViewItem listViewItem = new ListViewItem(row);
listViewEval.Items.Add(listViewItem);
SaveResults(imgFiltered, "Eq", 0, 0, 0, 0, 0, row, numPicture + numPath);
}
// rozciągnięcie histogramu
{
timer.Reset();
timer.Start();
imgFiltered = StretchHistogram(imgToFilter);
timer.Stop();
EvaluationOfFilter(imgReference, imgFiltered);
row[0] = "Rozciągniecie";
row[1] = "-";
row[2] = "-";
row[3] = "-";
row[4] = "-";
row[5] = "-";
row[6] = PSNRMSE.ToString();
row[7] = PSNRMSD.ToString();
row[8] = PSNRMED.ToString();
row[9] = Marziliano.ToString();
row[10] = timer.Elapsed.TotalMilliseconds.ToString();
ListViewItem listViewItem = new ListViewItem(row);
listViewEval.Items.Add(listViewItem);
SaveResults(imgFiltered, "Str", 0, 0, 0, 0, 0, row, numPicture + numPath);
}
}
}
}
public static Bitmap ExtractDocumentFromBitmap(Bitmap bitmap)
{
if (bitmap.Width > bitmap.Height)
{
bitmap.RotateFlip(RotateFlipType.Rotate90FlipNone);
}
using (var image = new Image <Bgr, byte>(bitmap))
using (var imageGray = image.Convert <Gray, byte>())
using (var filteredImage = new Image <Bgr, byte>(bitmap))
using (var cannyEdges = new UMat())
using (var contours = new VectorOfVectorOfPoint())
{
CvInvoke.BilateralFilter(imageGray, filteredImage, 9, 75, 75);
CvInvoke.AdaptiveThreshold(filteredImage, filteredImage, 255, AdaptiveThresholdType.GaussianC, ThresholdType.Binary, 115, 4);
CvInvoke.MedianBlur(filteredImage, filteredImage, 11);
CvInvoke.CopyMakeBorder(filteredImage, filteredImage, 5, 5, 5, 5, BorderType.Constant, new MCvScalar(0, 0, 0));
CvInvoke.Canny(filteredImage, cannyEdges, 200, 250);
CvInvoke.FindContours(cannyEdges, contours, null, RetrType.List, ChainApproxMethod.ChainApproxSimple);
var cannyEdgesHeight = cannyEdges.Bitmap.Height;
var cannyEdgesWidth = cannyEdges.Bitmap.Width;
var areaContour = (cannyEdgesHeight - 10) * (cannyEdgesWidth - 10);
var areaCount = areaContour * 0.5;
double areaContour2;
var sourcePointsVector = new VectorOfPoint();
for (int i = 0; i < contours.Size; i++)
{
using (var cont = contours[i])
{
CvInvoke.ApproxPolyDP(cont, cont, CvInvoke.ArcLength(cont, true) * 0.05, true);
if (cont.Size == 4 && CvInvoke.IsContourConvex(cont) &&
areaCount < CvInvoke.ContourArea(cont) &&
CvInvoke.ContourArea(cont) < areaContour)
{
sourcePointsVector = cont;
areaContour2 = CvInvoke.ContourArea(cont);
sortVector(sourcePointsVector);
break;
}
}
}
var sortedVector = sortVector(sourcePointsVector);
var vectorWithOffset = addOffsetToVector(sourcePointsVector, -5);
var euclideanHeight = new int[] { getEuclideanDistance(vectorWithOffset[0], vectorWithOffset[1]), getEuclideanDistance(vectorWithOffset[2], vectorWithOffset[3]) }.Max();
var euclideanWidth = new int[] { getEuclideanDistance(vectorWithOffset[0], vectorWithOffset[2]), getEuclideanDistance(vectorWithOffset[1], vectorWithOffset[3]) }.Max();
VectorOfPoint targetPoints = new VectorOfPoint(new Point[]
{
new Point(0, 0),
new Point(0, euclideanWidth),
new Point(euclideanHeight, euclideanWidth),
new Point(euclideanHeight, 0)
}.ToArray());
var source = sortVector(vectorWithOffset).ToArray().Select(x => new PointF(x.X, x.Y)).ToArray();
var target = sortVector(targetPoints).ToArray().Select(x => new PointF(x.X, x.Y)).ToArray();
var tran = CvInvoke.GetPerspectiveTransform(source, target);
CvInvoke.WarpPerspective(image, image, tran, new Size(euclideanHeight, euclideanWidth));
return(image.ToBitmap((int)standardDocumentWidth * 4, (int)standardDocumentHeight * 4));
}
}
public Mat[] GetPreprocessedFace(Mat srcImg,
int desiredFaceWidth, int desiredFaceHeight,
CascadeClassifier faceCascade,
CascadeClassifier eyeCascade1, CascadeClassifier eyeCascade2,
bool doLeftAndRightSeparately, bool isCamera)
{
faceRect = null;
faceRect = odh.DetectManyObjects(srcImg, faceCascade, HelperFeature.DESIRED_CAMERA_WIDTH, isCamera);
List <Mat> faceData = new List <Mat>();
if (faceRect == null)
{
return(faceData.ToArray());
}
for (int i = 0; i < faceRect.Length; i++)
{
InitVarible();
if (faceRect[i].Width > 0)
{
storeFaceRect = faceRect[i];
Mat faceImg = new Mat(srcImg, faceRect[i]);
Mat gray = new Mat();
if (faceImg.NumberOfChannels == 3)
{
CvInvoke.CvtColor(faceImg, gray, ColorConversion.Bgr2Gray);
}
else if (faceImg.NumberOfChannels == 4)
{
CvInvoke.CvtColor(faceImg, gray, ColorConversion.Bgr2Gray);
}
else
{
gray = faceImg;
}
bool isDefaut = DetectBothEyes(gray, eyeCascade1, eyeCascade2, isCamera);
storeLeftEye = leftEye;
storeRightEye = rightEye;
if (leftEye.X >= 0 && rightEye.X >= 0)
{
PointF eyesCenter = new PointF((leftEye.X + rightEye.X) * 0.5f, (leftEye.Y + rightEye.Y) * 0.5f);
double dy = (rightEye.Y - leftEye.Y);
double dx = Math.Abs(rightEye.X - leftEye.X);
double len = Math.Sqrt(dx * dx + dy * dy);
double angle = Math.Atan2(dy, dx) * 180.0 / 3.14159265359;
if (isDefaut)
{
angle = 0;
}
const double DESIRED_RIGHT_EYE_X = (1.0f - HelperFeature.DESIRED_LEFT_EYE_X);
double desiredLen = (DESIRED_RIGHT_EYE_X - HelperFeature.DESIRED_LEFT_EYE_X) * desiredFaceWidth;
double scale = desiredLen / len;
Mat rot_mat = new Mat();
CvInvoke.GetRotationMatrix2D(eyesCenter, angle, scale, rot_mat);
Image <Gray, double> temp_rot_mat = rot_mat.ToImage <Gray, double>();
temp_rot_mat.Data[0, 2, 0] += desiredFaceWidth * 0.5f - eyesCenter.X;
temp_rot_mat.Data[1, 2, 0] += desiredFaceHeight * HelperFeature.DESIRED_LEFT_EYE_Y - eyesCenter.Y;
rot_mat = temp_rot_mat.Mat;
Mat warped = new Mat(desiredFaceHeight, desiredFaceWidth, DepthType.Cv8U, 128);
CvInvoke.WarpAffine(gray, warped, rot_mat, warped.Size);
if (!doLeftAndRightSeparately)
{
CvInvoke.EqualizeHist(warped, warped);
}
else
{
warped = EqualizeLeftAndRightHalves(warped);
}
Mat filtered = new Mat(warped.Size, DepthType.Cv8U, 0);
CvInvoke.BilateralFilter(warped, filtered, 0, 10, 2.0);
Mat mask = warped;
mask = FilterMatContructor(mask);
Point faceCenter = new Point(desiredFaceWidth / 2, (int)Math.Round(desiredFaceHeight * HelperFeature.FACE_ELLIPSE_CY));
Size size = new Size((int)Math.Round(desiredFaceWidth * HelperFeature.FACE_ELLIPSE_W),
(int)Math.Round(desiredFaceHeight * HelperFeature.FACE_ELLIPSE_H));
CvInvoke.Ellipse(mask, faceCenter, size, 0, 0, 360, new MCvScalar(0));
mask = FilterEllipse(mask);
Mat dstImg = new Mat();
filtered.CopyTo(dstImg, mask);
faceData.Add(dstImg);
CvInvoke.Rectangle(srcImg, faceRect[i], new MCvScalar(0, 255, 0), 1);
//CvInvoke.Circle(face, leftEye, 2, new MCvScalar(0, 255, 0), 2);
//CvInvoke.Circle(face, rightEye, 2, new MCvScalar(0, 255, 0), 2);
}
}
}
return(faceData.ToArray());
}
private VectorOfPointF FindTarget(Mat input)
{
var cannyEdges = new Mat();
var uImage = new Mat();
var gray = new Mat();
var blurred = new Mat();
// Convert to greyscale
CvInvoke.CvtColor(input, uImage, ColorConversion.Bgr2Gray);
CvInvoke.BilateralFilter(uImage, gray, 11, 17, 17);
uImage.Dispose();
CvInvoke.MedianBlur(gray, blurred, 11);
gray.Dispose();
// Get edged version
const double cannyThreshold = 0.0;
const double cannyThresholdLinking = 200.0;
CvInvoke.Canny(blurred, cannyEdges, cannyThreshold, cannyThresholdLinking);
blurred.Dispose();
if (_showEdged)
{
CvInvoke.Imshow("Source", cannyEdges);
}
// Get contours
using (var contours = new VectorOfVectorOfPoint()) {
CvInvoke.FindContours(cannyEdges, contours, null, RetrType.List,
ChainApproxMethod.ChainApproxSimple);
var count = contours.Size;
// Looping contours
for (var i = 0; i < count; i++)
{
var approxContour = new VectorOfPoint();
using var contour = contours[i];
CvInvoke.ApproxPolyDP(contour, approxContour, CvInvoke.ArcLength(contour, true) * 0.02,
true);
if (approxContour.Size != 4)
{
continue;
}
var cntArea = CvInvoke.ContourArea(approxContour);
if (!(cntArea / _srcArea > .15))
{
continue;
}
var pointOut = new VectorOfPointF(SortPoints(approxContour));
_targets.Add(VPointFToVPoint(pointOut));
}
if (_showEdged)
{
var color = new MCvScalar(255, 255, 0);
CvInvoke.DrawContours(input, contours, -1, color);
CvInvoke.Imshow("Edged", cannyEdges);
}
}
var output = CountTargets(_targets);
cannyEdges.Dispose();
return(output);
}
public EdgeAlogorithm(string ImageFilePath)
{
Mat src = CvInvoke.Imread(ImageFilePath, ImreadModes.Color);
Mat dst = CvInvoke.Imread(ImageFilePath, ImreadModes.Color);
Mat OtsuImage = CvInvoke.Imread(ImageFilePath, ImreadModes.Color);
CvInvoke.GaussianBlur(src, src, new System.Drawing.Size(3, 3), 0, 0); // 平滑濾波
CvInvoke.BilateralFilter(src, dst, 9, 30, 30); // 雙邊濾波
Mat dst1 = CvInvoke.Imread(ImageFilePath, ImreadModes.Color);
Mat dst2 = CvInvoke.Imread(ImageFilePath, ImreadModes.Color);
CvInvoke.Canny(dst, dst1, 10, 100, 3); // Canny Edge Decteion
CvInvoke.Threshold(dst1, dst2, 128, 255, ThresholdType.BinaryInv); //反轉影像，讓邊緣呈現黑線
Mat GraylevelImage = CvInvoke.Imread(ImageFilePath, ImreadModes.Color);
CvInvoke.CvtColor(src, GraylevelImage, ColorConversion.Rgb2Gray);
CvInvoke.Threshold(GraylevelImage, OtsuImage, 0, 255, ThresholdType.Otsu);
Mat labels = CvInvoke.Imread(ImageFilePath, ImreadModes.Color), stats = CvInvoke.Imread(ImageFilePath, ImreadModes.Color), centroids = CvInvoke.Imread(ImageFilePath, ImreadModes.Color);
int nccomps = CvInvoke.ConnectedComponentsWithStats(OtsuImage, labels, stats, centroids);
List <uint[]> colors;
colors = new List <uint[]>();
colors.Add(new uint[] { 0, 0, 0 });
for (int loopnum = 1; loopnum < nccomps; loopnum++)
{
Random r = new Random();
if (stats.GetData(loopnum, 4)[0] < 200)
{
colors.Add(new uint[3] {
0, 0, 0
});
}
else
{
colors.Add(new uint[3] {
(uint)r.Next(0, 256), (uint)r.Next(0, 256), (uint)r.Next(0, 256)
});
}
}
//var mat = new Mat(200, 200, DepthType.Cv64F, 3);
for (int row = 0; row < src.Rows; row++)
{
for (int col = 0; col < src.Cols; col++)
{
src.SetValue(row, col, (byte)col);
var value = src.GetValue(row, col);
//Console.WriteLine("Value = " + value);
/*if (value != 255 && Test < 50)
* {
* OtsuImage.SetValue(row, col, (byte)255);
* Test++;
* //System.Console.ReadKey();
* }*/
}
}
ImageContour imagecontour = new ImageContour();
Mat ImageContourImage = imagecontour.ImageContourMethod(OtsuImage);
Step1 = dst;
Step2 = dst2;
Step3 = OtsuImage;
ContourImage = ImageContourImage;
/*
* String win1 = "Canny_1"; //The name of the window
* CvInvoke.NamedWindow(win1); //Create the window using the specific name
*
* String win2 = "Canny_2"; //The name of the window
* CvInvoke.NamedWindow(win2); //Create the window using the specific name
*
* String win3 = "OtsuImage"; //The name of the window
* CvInvoke.NamedWindow(win3); //Create the window using the specific name
*
* String win4 = "OtsuImage_ImageContourMethod"; //The name of the window
* CvInvoke.NamedWindow(win4); //Create the window using the specific name
*
* CvInvoke.Imshow(win1, dst); //Show the image
* CvInvoke.Imshow(win2, dst2); //Show the image
* CvInvoke.Imshow(win3, OtsuImage); //Show the image
* CvInvoke.Imshow(win4, ImageContourImage); //Show the image
* CvInvoke.WaitKey(0); //Wait for the key pressing event
* CvInvoke.DestroyWindow(win1); //Destroy the window if key is pressed
* CvInvoke.DestroyWindow(win2); //Destroy the window if key is pressed
* CvInvoke.DestroyWindow(win3); //Destroy the window if key is pressed
* CvInvoke.DestroyWindow(win4); //Destroy the window if key is pressed
*/
}
/// <summary>
/// From a given cropped image looks for single characters contours.
/// </summary>
/// <remarks>
/// After applying basic bilateral and canny filter it searches the contours of single characters.
/// Each contour is being validated whether it is a contour of a possible character or not. It uses some
/// basic total area and ratio (width/height) comparison. If contour met the requiremenents,
/// it is then marked with a bounding rectangular to simplify the further recognition.
/// </remarks>
/// <param name="croppedImage">Cropped image on which we want to search characters' contours</param>
/// <param name="split">Whether the plate should be splitted into single characters array</param>
/// <returns></returns>
private Mat FindPlateContours(Image <Hsv, byte> croppedImage, bool split = false)
{
using (var bilateralMat = new Mat())
using (var cannyMat = new Mat())
using (var contours = new VectorOfVectorOfPoint())
{
var treshold = ImageConverter.GetAutomatedTreshold(bilateralMat.ToImage <Gray, byte>());
var cleanMat = new Mat(croppedImage.Rows, croppedImage.Cols, DepthType.Cv8U, 1);
cleanMat.SetTo(new MCvScalar(255));
CvInvoke.BilateralFilter(
croppedImage,
bilateralMat,
20, 20, 10);
CvInvoke.Canny(
croppedImage,
cannyMat,
treshold.Lower,
treshold.Upper);
CvInvoke.FindContours(
cannyMat.ToImage <Gray, byte>(),
contours,
null,
RetrType.External,
ChainApproxMethod.ChainApproxSimple);
for (var i = 0; i < contours.Size; i++)
{
var contour = contours[i];
var smoothContour = new VectorOfPoint(contour.Size);
CvInvoke.ApproxPolyDP(
contour,
smoothContour,
0.01 * CvInvoke.ArcLength(contour, true), true);
if (smoothContour.Size >= 4)
{
var rect = CvInvoke.BoundingRectangle(smoothContour);
double ratio = (double)rect.Width / rect.Height;
double area = rect.Width * (double)rect.Height;
if (ratio <= 1.5 &&
ratio >= 0.1 &&
area >= 400)
{
Mat ROI = new Mat(cleanMat, rect);
Mat potentialCharArea = new Mat(croppedImage.Convert <Gray, byte>().Mat, rect);
potentialCharArea.CopyTo(ROI);
// w celach analizy dokładamy wartości ratio
CvInvoke.Rectangle(croppedImage, rect, new MCvScalar(0, 250, 0));
CvInvoke.PutText(croppedImage, Math.Round(ratio, 3).ToString(), rect.Location, FontFace.HersheyDuplex, fontScale: 0.5d, new MCvScalar(0, 250, 0));
}
}
}
return(cleanMat);
}
}
