public ContoursFill(string imagePath, DigitsRecognitionMethod digitsRecognitionMethod)
{
this.imagePath = imagePath;
this.digitsRecognitionMethod = digitsRecognitionMethod;
if (File.Exists(imagePath))
{
//read image
image = (Bitmap)Image.FromFile(imagePath);
}
else
{
throw new FileNotFoundException("File not found", imagePath);
}
}
public EdgeContrours(string imagePath, DigitsRecognitionMethod digitsRecognitionMethod, List <TaggableFace> faces)
{
this.imagePath = imagePath;
this.digitsRecognitionMethod = digitsRecognitionMethod;
this.faces = faces;
if (File.Exists(imagePath))
{
//read image
image = (Bitmap)Image.FromFile(imagePath);
}
else
{
throw new FileNotFoundException("File not found", imagePath);
}
}
public static TaggableFace[] GetPossibleBibs(string imagePath, FaceRecognitionMethod faceRecognition, BibAreaDetectionMethod bibAreaMethod, DigitsRecognitionMethod digitsRecognition, string parentDirectory = null)
{
List <TaggableFace> faces = new List <TaggableFace>();
string pathToShow = imagePath;
if (!string.IsNullOrEmpty(parentDirectory))
{
pathToShow = FileManager.GetRelativePath(parentDirectory, imagePath);
}
LogManager.Info("Processing image " + pathToShow);
if (faceRecognition == FaceRecognitionMethod.FacePlusPlus)
{
var fppFaces = FaceManager.DetectFaceFromFile(imagePath, false, FaceAttributesValues.Gender);
if (fppFaces != null)
{
foreach (var face in fppFaces.Faces)
{
faces.Add(new TaggableFace(pathToShow, face.FaceToken, new Rectangle(face.FaceRectangle.X, face.FaceRectangle.Y, face.FaceRectangle.Width, face.FaceRectangle.Height)));
}
}
saveFaces(faces, parentDirectory);
}
else if (faceRecognition == FaceRecognitionMethod.HaarCascade)
{
var haars = getHaarFaces(imagePath);
foreach (var face in haars)
{
faces.Add(new TaggableFace(pathToShow, face));
}
saveFaces(faces, parentDirectory);
}
else
{
faces = getPreservedFaces(parentDirectory, pathToShow);
}
if (faces == null)
{
LogManager.Fatal("Fatal error during faces recognition. System halted.");
return(null);
}
if (faces.Count > 0)
{
LogManager.Info("Detected " + faces.Count + (faces.Count == 1 ? " face" : " faces"));
}
else
{
LogManager.Warning("No faces detected.");
}
if (bibAreaMethod == BibAreaDetectionMethod.SWT || bibAreaMethod == BibAreaDetectionMethod.Both)
{
faces = SWT.Process(imagePath, digitsRecognition, faces);
}
if (bibAreaMethod == BibAreaDetectionMethod.Edges || bibAreaMethod == BibAreaDetectionMethod.Both)
{
EdgeContrours contrours = new EdgeContrours(imagePath, digitsRecognition, faces);
faces = contrours.Process();
//faces = //ContoursFill.Process(imagePath, digitsRecognition, faces);
}
return(faces.ToArray());
}
public static string[] Recognize(IplImage input, TextDetectionParams _params, Chain[] chains, List <Tuple <Point2d, Point2d> > compBB, List <Tuple <CvPoint, CvPoint> > chainBB, DigitsRecognitionMethod digitsRecognition)
{
List <string> variants = new List <string>();
//convert to grayscale
IplImage grayImage = Cv.CreateImage(input.GetSize(), BitDepth.U8, 1);
Cv.CvtColor(input, grayImage, ColorConversion.RgbToGray);
for (int i = 0; i < chainBB.Count; i++)
{
Rect chainRect = new Rect(chainBB[i].Item1.X, chainBB[i].Item1.Y, chainBB[i].Item2.X - chainBB[i].Item1.X, chainBB[i].Item2.Y - chainBB[i].Item1.Y);
CvPoint center = new CvPoint((chainBB[i].Item1.X + chainBB[i].Item2.X) / 2, (chainBB[i].Item1.Y + chainBB[i].Item2.Y) / 2);
//work out if total width of chain is large enough
if (chainBB[i].Item2.X - chainBB[i].Item1.X < input.Width / _params.MaxImgWidthToTextRatio)
{
continue;
}
//eliminate chains with components of lower height than required minimum
int minHeight = chainBB[i].Item2.Y - chainBB[i].Item1.Y;
for (int j = 0; j < chains[i].components.Count; j++)
{
minHeight = Math.Min(minHeight, compBB[chains[i].components[j]].Item2.y - compBB[chains[i].components[j]].Item1.y);
}
if (minHeight < _params.MinCharacterHeight)
{
continue;
}
//invert direction if angle is in 3rd/4th quadrants
if (chains[i].direction.x < 0)
{
chains[i].direction.x = -chains[i].direction.x;
chains[i].direction.y = -chains[i].direction.y;
}
//work out chain angle
double theta_deg = 180 * Math.Atan2(chains[i].direction.y, chains[i].direction.x) / Math.PI;
if (Math.Abs(theta_deg) > _params.MaxAngle)
{
continue;
}
if ((chainBB.Count == 2) && (Math.Abs(theta_deg) > 5))
{
continue;
}
//Console.WriteLine("Chain #" + i + " angle: " + theta_deg + " degress");
//create copy of input image including only the selected components
Mat inputMat = new Mat(input);
Mat grayMat = new Mat(grayImage);
Mat componentsImg = Mat.Zeros(new Size(grayMat.Cols, grayMat.Rows), grayMat.Type());
//CvMat componentsImg = _componentsImg.ToCvMat();
Mat componentsImgRoi = null;
List <CvPoint> compCoords = new List <CvPoint>();
chains[i].components = chains[i].components.Distinct().ToList();
int order = 0;
//ordering components bounding boxes by x coord
var ordCompBB = compBB.OrderBy(x => x.Item1.x).ToList();
List <string> digits = new List <string>();
for (int j = 0; j < ordCompBB.Count; j++)
{
Rect roi = new Rect(ordCompBB[j].Item1.x, ordCompBB[j].Item1.y, ordCompBB[j].Item2.x - ordCompBB[j].Item1.x, ordCompBB[j].Item2.y - ordCompBB[j].Item1.y);
if (!chainRect.Contains(roi))
{
continue;
}
Mat componentRoi = new Mat(grayMat, roi);
compCoords.Add(new CvPoint(ordCompBB[j].Item1.x, ordCompBB[j].Item1.y));
compCoords.Add(new CvPoint(ordCompBB[j].Item2.x, ordCompBB[j].Item2.y));
compCoords.Add(new CvPoint(ordCompBB[j].Item1.x, ordCompBB[j].Item2.y));
compCoords.Add(new CvPoint(ordCompBB[j].Item2.x, ordCompBB[j].Item1.y));
Mat thresholded = new Mat(grayMat, roi);
Cv2.Threshold(componentRoi, thresholded, 0, 255, ThresholdType.Otsu | ThresholdType.BinaryInv);
componentsImgRoi = new Mat(componentsImg, roi);
Cv2.Threshold(componentRoi, componentsImgRoi, 0, 255, ThresholdType.Otsu | ThresholdType.BinaryInv);
//var size = thresholded.Size();
//digits.Add(new Bitmap(size.Width, size.Height, (int)thresholded.Step1(), System.Drawing.Imaging.PixelFormat.Format24bppRgb, thresholded.Data));
if (digitsRecognition == DigitsRecognitionMethod.Neural || digitsRecognition == DigitsRecognitionMethod.Both)
{
string file = FileManager.TempBitmap;
Cv2.ImWrite(file, thresholded);
try
{
digits.Add(file);
}
catch
{
GC.Collect();
GC.WaitForFullGCComplete();
}
//digits.Last().Save("test" + order + ".bmp");
order++;
}
//else if (digitsRecognition == DigitsRecognitionMethod.Tesseract || digitsRecognition == DigitsRecognitionMethod.Both)
//{
// DO NOTHING
//}
}
if (digitsRecognition == DigitsRecognitionMethod.Neural || digitsRecognition == DigitsRecognitionMethod.Both)
{
//TODO: neural recognition
var result = OCRParser.ParseNeural(digits.ToArray());
variants.Add(result.Value);
//variants.AddRange(OCRParser.ParseNeural(digits.ToArray()));
//variants.Add(BibOCR.OCRParser.ParseBib(digits.ToArray()));
}
if (digitsRecognition == DigitsRecognitionMethod.Tesseract || digitsRecognition == DigitsRecognitionMethod.Both)
{
CvRect _roi = GetBoundingBox(compCoords, new CvSize(input.Width, input.Height));
//ROI area can be null if outside of clipping area
if ((_roi.Width == 0) || (_roi.Height == 0))
{
continue;
}
//rotate each component coordinates
const int border = 3;
Mat _mat = new Mat(_roi.Height + 2 * border, _roi.Width + 2 * border, grayMat.Type());
Mat tmp = new Mat(grayMat, _roi);
//copy bounded box from rotated mat to new mat with borders - borders are needed to improve OCR success rate
Mat mat = new Mat(_mat, new Rect(border, border, _roi.Width, _roi.Height));
tmp.CopyTo(mat);
//resize image to improve OCR success rate
float upscale = 5.0f;
Cv2.Resize(mat, mat, new Size(0, 0), upscale, upscale);
//erode text to get rid of thin joints
int s = (int)(0.05 * mat.Rows); // 5% of up-scaled size
Mat elem = Cv2.GetStructuringElement(StructuringElementShape.Ellipse, new Size(2 * s + 1, 2 * s + 1), new Point(s, s));
//Cv2.Erode(mat, mat, elem);
//Cv2.Threshold(mat, mat, 0, 255, ThresholdType.Otsu | ThresholdType.BinaryInv);
string file = FileManager.TempPng;
Cv2.ImWrite(file, mat);
// TODO: Pass it to Tesseract API
variants.Add(OCRParser.ParseTesseract(file));
}
//for (int j = 0; j < digits.Count; j++)
// digits[j].Dispose();
digits.Clear();
GC.Collect();
GC.WaitForFullGCComplete(5000);
}
Cv.ReleaseImage(grayImage);
return(variants.Distinct().ToArray());
}
