public Mat OtsuThresh(Mat mat)
{
Mat output = new Mat();
Imgproc.Threshold(mat, output, 0, 255, Imgproc.ThreshBinary + Imgproc.ThreshOtsu);
return(output);
}
public IEnumerable <Match> FindMatches(IPattern Template, double Similarity)
{
using var result = new DisposableMat();
// max is used for tmccoeff
Imgproc.MatchTemplate(Mat, (Template as DroidCvPattern)?.Mat, result.Mat, Imgproc.TmCcoeffNormed);
Imgproc.Threshold(result.Mat, result.Mat, 0.1, 1, Imgproc.ThreshTozero);
while (true)
{
using var minMaxLocResult = Core.MinMaxLoc(result.Mat);
var score = minMaxLocResult.MaxVal;
if (score >= Similarity)
{
var loc = minMaxLocResult.MaxLoc;
var region = new Region((int)loc.X, (int)loc.Y, Template.Width, Template.Height);
yield return(new Match(region, score));
using var mask = new DisposableMat();
// Flood fill eliminates the problem of nearby points to a high similarity point also having high similarity
const double floodFillDiff = 0.05;
Imgproc.FloodFill(result.Mat, mask.Mat, loc, new Scalar(0),
new Rect(),
new Scalar(floodFillDiff), new Scalar(floodFillDiff),
0);
}
else
{
break;
}
}
}
public DroidCvPattern(Stream Stream, bool MakeMask)
{
var buffer = new byte[Stream.Length];
using var ms = new MemoryStream(buffer);
Stream.CopyTo(ms);
using var raw = new DisposableMat(new MatOfByte(buffer));
if (MakeMask)
{
using var rgbaMat = new DisposableMat(Imgcodecs.Imdecode(raw.Mat, Imgcodecs.CvLoadImageUnchanged));
Mat = new Mat();
Imgproc.CvtColor(rgbaMat.Mat, Mat, Imgproc.ColorRgba2gray);
Mask = new Mat();
// Extract alpha channel
Core.ExtractChannel(rgbaMat.Mat, Mask, 3);
// Mask containing 0 or 255
Imgproc.Threshold(Mask, Mask, 0, 255, Imgproc.ThreshBinary);
}
else
{
Mat = Imgcodecs.Imdecode(raw.Mat, Imgcodecs.CvLoadImageGrayscale);
}
}
/// <summary>
/// Equalizes the histogram of an image and thresholds the result.
/// </summary>
/// <param name="src"> Image to eq & thresh </param>
/// <param name="thresh"> thresh level </param>
/// <param name="max"> max value to use </param>
/// <returns> binary image (high pixels are in range) </returns>
private Mat GetWhiteFromHistogramEq(Mat src, byte thresh, byte max)
{
Mat gray = new Mat(src.Size(), src.Type());
Imgproc.CvtColor(src, gray, Imgproc.ColorBgr2gray);
Imgproc.EqualizeHist(gray, gray);
Imgproc.Threshold(gray, gray, thresh, max, Imgproc.ThreshBinary);
return(gray);
}
/// <summary>
///
/// </summary>
/// <param name="img"></param>
/// <returns></returns>
public static Bitmap greyImg(Bitmap img, double threshValue1, double blurValue)
{
//Matrix für das Bild
Mat imgMat = new Mat();
//Bild zu Matrix umwandeln
Utils.BitmapToMat(img, imgMat);
//-----------------Bild bearbeiten---------------------
//Variablen
//Size s = new Size(10.0, 10.0);
Size s = new Size(blurValue, blurValue);
OpenCV.Core.Point p = new OpenCV.Core.Point(0, 0);
//TODO Matrix größe beachten?
Bitmap bmp = null;
Mat tmpgrey = new Mat(10, 10, CvType.Cv8uc1, new Scalar(4));
Mat tmpblur = new Mat(10, 10, CvType.Cv8uc1, new Scalar(4));
Mat tmpthresh = new Mat(10, 10, CvType.Cv8uc1, new Scalar(4));
Mat imgresult = new Mat(10, 10, CvType.Cv8uc1, new Scalar(4));
try
{
//Grau
Imgproc.CvtColor(imgMat, tmpgrey, Imgproc.ColorBgr2gray, 4);
//Blur
Imgproc.Blur(tmpgrey, tmpblur, s, p);
//Thresh
//Orginal
//Imgproc.Threshold(tmpblur, tmpthresh, 90, 255, Imgproc.ThreshBinary);
Imgproc.Threshold(tmpblur, tmpthresh, threshValue1, 255, Imgproc.ThreshBinary);
//Kontrast
//tmpthresh.ConvertTo(imgresult, -1, 9.0, 10);
bmp = Bitmap.CreateBitmap(tmpthresh.Cols(), tmpthresh.Rows(), Bitmap.Config.Argb8888);
Utils.MatToBitmap(tmpthresh, bmp);
}
catch (CvException e) { System.Console.WriteLine(e.Message); }
return(bmp);
}
public override bool OnOptionsItemSelected(IMenuItem item)
{
Log.Info(Tag, "Menu Item selected " + item);
if (item == _itemPickPhoto)
{
var imageIntent = new Intent();
imageIntent.SetType("image/*");
imageIntent.SetAction(Intent.ActionGetContent);
StartActivityForResult(Intent.CreateChooser(imageIntent, "Select photo"), 0);
}
else if (item == _itemGray)
{
// 灰度图
_gray = new Mat(_raw.Width(), _raw.Height(), CvType.Cv8uc1);
Imgproc.CvtColor(_raw, _gray, Imgproc.ColorRgb2gray);
ShowImage(_gray);
}
else if (item == _itemThreshold)
{
// 二值化
_threshold = new Mat(_image.Width, _image.Height, CvType.Cv8uc1);
Imgproc.Threshold(_gray, _threshold, 168, 255, Imgproc.ThreshBinary);
ShowImage(_threshold);
}
else if (item == _itemFindContours)
{
// 查找最大连通区域
IList <MatOfPoint> contours = new JavaList <MatOfPoint>();
Mat hierarchy = new Mat();
var target = _threshold.Clone();
Imgproc.FindContours(target, contours, hierarchy, Imgproc.RetrExternal, Imgproc.ChainApproxNone);
MatOfPoint max = new MatOfPoint();
double contour_area_max = 0;
if (contours.Any())
{
foreach (var contour in contours)
{
var contour_area_temp = Math.Abs(Imgproc.ContourArea(contour));
if (contour_area_temp > contour_area_max)
{
contour_area_max = contour_area_temp;
max = contour;
}
}
}
var last = new JavaList <MatOfPoint>();
last.Add(max);
Imgproc.DrawContours(_raw, last, -1, new Scalar(255, 0, 0));
ShowImage(_raw);
}
else if (item == _itemCreateTrimap)
{
// 生成三元图 暂时先用生成的图替代
var imageIntent = new Intent();
imageIntent.SetType("image/*");
imageIntent.SetAction(Intent.ActionGetContent);
StartActivityForResult(Intent.CreateChooser(imageIntent, "Select photo"), 1);
}
else if (item == _itemSharedMatting)
{
// 扣图
var sharedMatting = new SharedMatting();
sharedMatting.SetImage(_raw);
sharedMatting.SetTrimap(_trimap);
sharedMatting.SolveAlpha();
}
return(base.OnOptionsItemSelected(item));
}
public static async Task <string> detectAndExtractText(Bitmap img)
{
//Matrix für die Bilder
Mat large = new Mat();
Mat small = new Mat();
Mat rgb = new Mat();
//Bild zu Matrix umwandeln
Utils.BitmapToMat(img, large);
// downsample and use it for processing
Imgproc.PyrDown(large, rgb);
//Grey
Imgproc.CvtColor(rgb, small, Imgproc.ColorBgr2gray);
//Gradiant
Mat grad = new Mat();
Size morphsize = new Size(3.0, 3.0);
Mat morphKernel = Imgproc.GetStructuringElement(Imgproc.MorphEllipse, morphsize);
Imgproc.MorphologyEx(small, grad, Imgproc.MorphGradient, morphKernel);
//Binarize
Mat bw = new Mat();
Imgproc.Threshold(grad, bw, 0.0, 255.0, Imgproc.ThreshBinary | Imgproc.ThreshOtsu);
// connect horizontally oriented regions
Mat connected = new Mat();
Size connectsize = new Size(9.0, 1.0);
morphKernel = Imgproc.GetStructuringElement(Imgproc.MorphRect, connectsize);
Imgproc.MorphologyEx(bw, connected, Imgproc.MorphClose, morphKernel);
// find contours
Mat mask = Mat.Zeros(bw.Size(), CvType.Cv8uc1);
JavaList <MatOfPoint> contours = new JavaList <MatOfPoint>();
Mat hierarchy = new Mat();
OpenCV.Core.Point contourPoint = new OpenCV.Core.Point(0, 0);
Imgproc.FindContours(connected, contours, hierarchy, Imgproc.RetrCcomp, Imgproc.ChainApproxSimple, contourPoint);
Scalar zero = new Scalar(0, 0, 0);
Scalar contourscal = new Scalar(255, 255, 255);
Scalar rectScalar = new Scalar(0, 255, 0);
OpenCV.Core.Rect rect;
Mat maskROI;
double r;
double[] contourInfo;
string resulttext = "";
string part;
Bitmap bmpOcr;
Mat croppedPart;
for (int i = 0; i >= 0;)
{
rect = Imgproc.BoundingRect(contours[i]);
maskROI = new Mat(mask, rect);
maskROI.SetTo(zero);
//fill the contour
Imgproc.DrawContours(mask, contours, i, contourscal, Core.Filled);
// ratio of non-zero pixels in the filled region
r = (double)Core.CountNonZero(maskROI) / (rect.Width * rect.Height);
/* assume at least 45% of the area is filled if it contains text */
/* constraints on region size */
/* these two conditions alone are not very robust. better to use something
* like the number of significant peaks in a horizontal projection as a third condition */
if (r > .45 && (rect.Height > 8 && rect.Width > 8))
{
//Imgproc.Rectangle(rgb, rect.Br(), rect.Tl(), rectScalar, 2);
try
{
croppedPart = rgb.Submat(rect);
bmpOcr = Bitmap.CreateBitmap(croppedPart.Width(), croppedPart.Height(), Bitmap.Config.Argb8888);
Utils.MatToBitmap(croppedPart, bmpOcr);
part = await OCR.getText(bmpOcr);
resulttext = resulttext + part;
Console.WriteLine("------------------Durchlauf-------------");
}
catch (Exception e)
{
Android.Util.Log.Debug("Fehler", "cropped part data error " + e.Message);
}
}
//Nächste Element bestimmen
contourInfo = hierarchy.Get(0, i);
i = (int)contourInfo[0];
}
return(resulttext);
}
