/// <summary>
/// Applies the blob extraction feature of Aforge
/// </summary>
/// <param name="Mask">Mask from the flood-fill step</param>
/// <param name="Source">Source image (full image)</param>
/// <returns>A list of tuples(blob from mask, rectangle from source)</returns>
private static List<Tuple<Bitmap, Bitmap>> ApplyBlobExtractor(Bitmap Mask, Bitmap Source)
{
List<Tuple<Bitmap, Bitmap>> BlobSrcblock = new List<Tuple<Bitmap, Bitmap>>();
log.Info("Using AForge Blob Counter to Process Mask");
AForge.Imaging.BlobCounter blobCounter = new AForge.Imaging.BlobCounter();
// Sort order
blobCounter.ObjectsOrder = AForge.Imaging.ObjectsOrder.XY;
blobCounter.ProcessImage(Mask);
AForge.Imaging.Blob[] blobs = blobCounter.GetObjects(Mask, false);
log.Info("Use the Blob Extraction Results to reverse extract blobs from images");
// Adding images into the image list
AForge.Imaging.UnmanagedImage currentImg;
foreach (AForge.Imaging.Blob blob in blobs)
{
Rectangle myRect = blob.Rectangle;
currentImg = blob.Image;
Bitmap exBlob = currentImg.ToManagedImage();
AForge.Imaging.Filters.Crop filter = new AForge.Imaging.Filters.Crop(myRect);
Bitmap exSrc = filter.Apply(Source);
BlobSrcblock.Add(new Tuple<Bitmap, Bitmap>(exBlob, exSrc));
}
log.Info("Extraction Complete: returning List of ( blob bitmap, src bitmap)");
return BlobSrcblock;
}
/// <summary>
/// Applies the blob extraction feature of Aforge
/// </summary>
/// <param name="Mask">Mask from the flood-fill step</param>
/// <param name="Source">Source image (full image)</param>
/// <returns>A list of tuples(blob from mask, rectangle from source)</returns>
private static List <Tuple <Bitmap, Bitmap> > ApplyBlobExtractor(Bitmap Mask, Bitmap Source)
{
List <Tuple <Bitmap, Bitmap> > BlobSrcblock = new List <Tuple <Bitmap, Bitmap> >();
log.Info("Using AForge Blob Counter to Process Mask");
AForge.Imaging.BlobCounter blobCounter = new AForge.Imaging.BlobCounter();
// Sort order
blobCounter.ObjectsOrder = AForge.Imaging.ObjectsOrder.XY;
blobCounter.ProcessImage(Mask);
AForge.Imaging.Blob[] blobs = blobCounter.GetObjects(Mask, false);
log.Info("Use the Blob Extraction Results to reverse extract blobs from images");
// Adding images into the image list
AForge.Imaging.UnmanagedImage currentImg;
foreach (AForge.Imaging.Blob blob in blobs)
{
Rectangle myRect = blob.Rectangle;
currentImg = blob.Image;
Bitmap exBlob = currentImg.ToManagedImage();
AForge.Imaging.Filters.Crop filter = new AForge.Imaging.Filters.Crop(myRect);
Bitmap exSrc = filter.Apply(Source);
BlobSrcblock.Add(new Tuple <Bitmap, Bitmap>(exBlob, exSrc));
}
log.Info("Extraction Complete: returning List of ( blob bitmap, src bitmap)");
return(BlobSrcblock);
}
public static Bitmap Crop(this Bitmap bitmap, Rectangle cropRect)
{
// create filter
AForge.Imaging.Filters.Crop filter = new AForge.Imaging.Filters.Crop(cropRect);
// apply the filter
return(filter.Apply(bitmap));
}
/// <summary>
/// Обработка одного сэмпла
/// </summary>
/// <param name="index"></param>
private int processSample(int r, int c)
{
/// Инвертируем изображение
AForge.Imaging.Filters.Invert InvertFilter = new AForge.Imaging.Filters.Invert();
InvertFilter.ApplyInPlace(arrayPics[r, c]);
/// Создаём BlobCounter, выдёргиваем самый большой кусок, масштабируем, пересечение и сохраняем
/// изображение в эксклюзивном использовании
AForge.Imaging.BlobCounterBase bc = new AForge.Imaging.BlobCounter();
bc.FilterBlobs = true;
bc.MinWidth = 3;
bc.MinHeight = 3;
// Упорядочиваем по размеру
bc.ObjectsOrder = AForge.Imaging.ObjectsOrder.Size;
// Обрабатываем картинку
bc.ProcessImage(arrayPics[r, c]);
Rectangle[] rects = bc.GetObjectsRectangles();
if (rects.Length == 0)
{
finalPics[r, c] = AForge.Imaging.UnmanagedImage.FromManagedImage(new Bitmap(100, 100));
return(0);
}
// К сожалению, код с использованием подсчёта blob'ов не работает, поэтому просто высчитываем максимальное покрытие
// для всех блобов - для нескольких цифр, к примеру, 16, можем получить две области - отдельно для 1, и отдельно для 6.
// Строим оболочку, включающую все блоки. Решение плохое, требуется доработка
int lx = arrayPics[r, c].Width;
int ly = arrayPics[r, c].Height;
int rx = 0;
int ry = 0;
for (int i = 0; i < rects.Length; ++i)
{
if (lx > rects[i].X)
{
lx = rects[i].X;
}
if (ly > rects[i].Y)
{
ly = rects[i].Y;
}
if (rx < rects[i].X + rects[i].Width)
{
rx = rects[i].X + rects[i].Width;
}
if (ry < rects[i].Y + rects[i].Height)
{
ry = rects[i].Y + rects[i].Height;
}
}
// Обрезаем края, оставляя только центральные блобчики
AForge.Imaging.Filters.Crop cropFilter = new AForge.Imaging.Filters.Crop(new Rectangle(lx, ly, rx - lx, ry - ly));
finalPics[r, c] = cropFilter.Apply(arrayPics[r, c]);
// Масштабируем до 100x100
AForge.Imaging.Filters.ResizeBilinear scaleFilter = new AForge.Imaging.Filters.ResizeBilinear(100, 100);
finalPics[r, c] = scaleFilter.Apply(finalPics[r, c]);
// Ну и распознаём
currentDeskState[r * 4 + c] = patternMatch(r, c);
return(0);
}
public void ProcessImage(Bitmap bitmap, bool justShow)
{
// «Распиливаем» изображение на 16 фрагментов - по отдельной плитке каждый
// Минимальная сторона изображения (обычно это высота)
if (bitmap.Height > bitmap.Width)
{
throw new Exception("К такой забавной камере меня жизнь не готовила!");
}
int side = bitmap.Height;
// Отпиливаем границы, но не более половины изображения
if (side < 4 * settings.border)
{
settings.border = side / 4;
}
side -= 2 * settings.border;
Rectangle cropRect = new Rectangle((bitmap.Width - bitmap.Height) / 2 + settings.border, settings.border, side, side);
original = new Bitmap(cropRect.Width, cropRect.Height);
// Рисуем рамки на оригинале
Graphics g = Graphics.FromImage(original);
g.DrawImage(bitmap, new Rectangle(0, 0, original.Width, original.Height), cropRect, GraphicsUnit.Pixel); Pen p = new Pen(Color.Red);
// Проверяем значение полей из settings
if (side < 10 * settings.margin)
{
settings.margin = side / 10;
}
// Высчитываем сторону блока для извлечения
int sz = side / 4 - 2 * settings.margin;
int blockSide = side / 4;
for (int r = 0; r < 4; ++r)
{
for (int c = 0; c < 4; ++c)
{
g.DrawRectangle(p, new Rectangle(settings.margin + c * blockSide, settings.margin + r * blockSide, sz, sz));
}
}
// Рисуем цифры, которые распознались на предыдущем шаге?
if (justShow)
{
DrawNumbersOnOriginalBitmap(g, blockSide);
}
// Конвертируем изображение в градации серого
AForge.Imaging.Filters.Grayscale grayFilter = new AForge.Imaging.Filters.Grayscale(0.2125, 0.7154, 0.0721);
processed = grayFilter.Apply(AForge.Imaging.UnmanagedImage.FromManagedImage(original));
// Масштабируем изображение до 500x500 - этого достаточно
AForge.Imaging.Filters.ResizeBilinear scaleFilter = new AForge.Imaging.Filters.ResizeBilinear(500, 500);
original = scaleFilter.Apply(original);
// Если распознавание не планируем – просто выход
if (justShow)
{
return;
}
// Обнуляем state
for (int i = 0; i < 16; ++i)
{
currentDeskState[i] = 0;
}
// Пороговый фильтр применяем. Величина порога берётся из настроек, и меняется на форме
AForge.Imaging.Filters.BradleyLocalThresholding threshldFilter = new AForge.Imaging.Filters.BradleyLocalThresholding();
threshldFilter.PixelBrightnessDifferenceLimit = settings.differenceLim;
threshldFilter.ApplyInPlace(processed);
for (int r = 0; r < 4; ++r)
{
for (int c = 0; c < 4; ++c)
{
// Берём очередной фрагмент - с дополнительными отступами (мы же там нарисовали прямоугольники)
AForge.Imaging.Filters.Crop cropFilter = new AForge.Imaging.Filters.Crop(new Rectangle(2 + settings.margin + c * blockSide, 2 + settings.margin + r * blockSide, sz - 4, sz - 4));
arrayPics[r, c] = cropFilter.Apply(processed);
//arrayPics[r, c] = processed.Clone(new Rectangle(2+settings.margin + c * blockSide, 2+settings.margin + r * blockSide, sz-4, sz-4), processed.PixelFormat);
// И выполняем сопоставление
processSample(r, c);
}
}
DrawNumbersOnOriginalBitmap(g, blockSide);
}
public int PAConstantSet(Image <Gray, Byte> inputimg)
{
int cons = -1;
int t = 3;//tickness in pixel
Rectangle ROI;
AForge.Imaging.Filters.Crop filter;
#region 4 ROIs
ROI = new Rectangle(0, 0, inputimg.Width, t);
filter = new AForge.Imaging.Filters.Crop(ROI);
Image <Gray, Byte> up = new Image <Gray, Byte>(filter.Apply(inputimg.Bitmap));
Image <Gray, Byte> up_temp = new Image <Gray, Byte>(filter.Apply(inputimg.Bitmap));
ROI = new Rectangle(0, inputimg.Height - t, inputimg.Width, t);
filter = new AForge.Imaging.Filters.Crop(ROI);
Image <Gray, Byte> Down = new Image <Gray, Byte>(filter.Apply(inputimg.Bitmap));
Image <Gray, Byte> Down_temp = new Image <Gray, Byte>(filter.Apply(inputimg.Bitmap));
ROI = new Rectangle(inputimg.Width - t, t, t, inputimg.Height - t);
filter = new AForge.Imaging.Filters.Crop(ROI);
Image <Gray, Byte> Right = new Image <Gray, Byte>(filter.Apply(inputimg.Bitmap));
Image <Gray, Byte> Right_temp = new Image <Gray, Byte>(filter.Apply(inputimg.Bitmap));
ROI = new Rectangle(0, t, t, inputimg.Height - t);
filter = new AForge.Imaging.Filters.Crop(ROI);
Image <Gray, Byte> Left = new Image <Gray, Byte>(filter.Apply(inputimg.Bitmap));
Image <Gray, Byte> Left_temp = new Image <Gray, Byte>(filter.Apply(inputimg.Bitmap));
#endregion 4 ROIs
#region threshold
double av = 0;
for (int i = 0; i < 60; i += 2)
{
CvInvoke.cvAdaptiveThreshold(up, up_temp, 255, METState.Current.PAdaptive_type, Emgu.CV.CvEnum.THRESH.CV_THRESH_BINARY_INV, METState.Current.PAdaptive_blockSize, i);
CvInvoke.cvAdaptiveThreshold(Right, Right_temp, 255, METState.Current.PAdaptive_type, Emgu.CV.CvEnum.THRESH.CV_THRESH_BINARY_INV, METState.Current.PAdaptive_blockSize, i);
CvInvoke.cvAdaptiveThreshold(Down, Down_temp, 255, METState.Current.PAdaptive_type, Emgu.CV.CvEnum.THRESH.CV_THRESH_BINARY_INV, METState.Current.PAdaptive_blockSize, i);
CvInvoke.cvAdaptiveThreshold(Left, Left_temp, 255, METState.Current.PAdaptive_type, Emgu.CV.CvEnum.THRESH.CV_THRESH_BINARY_INV, METState.Current.PAdaptive_blockSize, i);
av = up_temp.GetAverage().Intensity + Right_temp.GetAverage().Intensity + Down_temp.GetAverage().Intensity + Left_temp.GetAverage().Intensity;
Debug.WriteLine(av + " up=" + up_temp.GetAverage().Intensity + " right=" + Right_temp.GetAverage().Intensity + " down=" + Down_temp.GetAverage().Intensity + " left=" + Left_temp.GetAverage().Intensity);
// METState.Current.EyeImageForShow = new Image<Bgr,byte>(EmgImgProcssing.Filter_PupilAdaptiveThreshold(inputimg , 255, true, i).Bitmap );
// METState.Current.EyeImageTest = up_temp.ConcateVertical(Left_temp).ConcateVertical(Down_temp).ConcateHorizontal(Right_temp);
// METState.Current.METCoreObject.SendToForm("", "EyeImage");
if (av < 5)//METState.Current.glintThreshold
{
cons = i;
break;
}
}
#endregion threshold
Debug.WriteLine(av);
return(cons);
}
public Bitmap CreateMosaic(int grid_width, int grid_height, string sourceFolder, Bitmap original_img, bool ColourMosaic)
{
int original_width = original_img.Width;
int original_height = original_img.Height;
//int grid_side = 30;
int sub_image_width = (int)(original_width / (double)grid_width);
int sub_image_height = (int)(original_height / (double)grid_height);
Console.WriteLine($"Sub image: {sub_image_width} x {sub_image_height}");
Console.WriteLine($"Sub image pixels: {grid_width * sub_image_width} x {grid_height * sub_image_height} == {original_width} x {original_height}");
AForge.Imaging.Filters.ResizeNearestNeighbor resize = new AForge.Imaging.Filters.ResizeNearestNeighbor(sub_image_width, sub_image_height);
AForge.Imaging.Filters.Grayscale grayscale = new AForge.Imaging.Filters.Grayscale(1 / 3.0, 1 / 3.0, 1 / 3.0);
string[] files = Directory.GetFiles(sourceFolder);
Bitmap combined_image = new Bitmap(original_width, original_height);
Graphics g = Graphics.FromImage(combined_image);
Bitmap original_gray = grayscale.Apply(original_img);
Bitmap[] original_slices = new Bitmap[grid_width * grid_height];
for (int i = 0; i < grid_width; i++)
{
for (int j = 0; j < grid_height; j++)
{
if (CancelProcessing)
{
return(null);
}
Rectangle rect = new Rectangle(i * sub_image_width, j * sub_image_height, sub_image_width, sub_image_height);
AForge.Imaging.Filters.Crop crop_region = new AForge.Imaging.Filters.Crop(rect);
Bitmap slice = (ColourMosaic == true) ? crop_region.Apply(original_img) : crop_region.Apply(original_gray);
original_slices[i * grid_width + j] = slice;
}
}
Bitmap[] candidates = new Bitmap[files.Length];
for (int i = 0; i < candidates.Length; i++)
{
if (i % 100 == 0 || i + 100 > candidates.Length)
{
int PercentComplete = (int)((100.0 * i) / candidates.Length);
Console.WriteLine($"Candidate preprocessing progress: {PercentComplete}%");
OnProcessingProgressChanged(new ProcessingProgressEventArgs()
{
PercentComplete = PercentComplete, ProcessingTask = "Preprocessing..."
});
}
if (CancelProcessing)
{
return(null);
}
if (!IsImage(files[i]))
{
continue;
}
Bitmap candidate_image = AForge.Imaging.Image.FromFile(files[i]);
Bitmap candidate_gray;
Bitmap resized_image;
if (IsGrayScale(candidate_image) && false)
{
if (ColourMosaic)
{
continue;
}
resized_image = resize.Apply(candidate_image);
}
else
{
if (ColourMosaic)
{
resized_image = resize.Apply(candidate_image);
}
else
{
candidate_gray = grayscale.Apply(candidate_image);
resized_image = resize.Apply(candidate_gray);
}
}
candidates[i] = resized_image;
}
List <int> used_indices = new List <int>();
int step = 0;
for (int i = 0; i < grid_width; i++)
{
for (int j = 0; j < grid_height; j++)
{
if (CancelProcessing)
{
return(null);
}
int PercentComplete = (int)((100.0 * step) / (grid_width * grid_height - 1));
OnProcessingProgressChanged(new ProcessingProgressEventArgs()
{
PercentComplete = PercentComplete, ProcessingTask = "Creating mosaic..."
});
Console.WriteLine($"Finding best match to slice {step}/{grid_width * grid_height - 1}...");
int best_match_index = FindBestMatch(original_slices[step], candidates, used_indices);
used_indices.Add(best_match_index);
Bitmap sub_image = candidates[best_match_index];
int cornerX = i * sub_image_width;
int cornerY = j * sub_image_height;
g.DrawImage(sub_image, new Point(cornerX, cornerY));
step++;
}
}
combined_image.Save("combined_image.jpg");
return(combined_image);
}
private Bitmap resize(Bitmap image, int newWidth, int newHeight)
{
if (KeepAspectRatio)
{
double ratio = (double)newHeight / (double)image.Height;
newWidth = (int)((double)image.Width * ratio);
}
AForge.Imaging.Filters.Crop cropper = new AForge.Imaging.Filters.Crop(new Rectangle(0, 0, newWidth, newHeight));
if (ScalingMethod == ScalingMethods.Nearest_Neighbor || ScalingMethod == ScalingMethods.Bicubic)
{
AForge.Imaging.Filters.ResizeNearestNeighbor resizer = new AForge.Imaging.Filters.ResizeNearestNeighbor(newWidth, newHeight);
image = cropper.Apply(resizer.Apply((Bitmap)image));
}
if (ScalingMethod == ScalingMethods.Bicubic)
{
MessageBox.Show("Bicubic resize is not implimented for now.\nNReverting to nearest neighbor...");
//AForge.Imaging.Filters.ResizeBicubic resizer = new AForge.Imaging.Filters.ResizeBicubic(newWidth, newHeight);
//image = cropper.Apply(resizer.Apply((Bitmap)image));
}
if (ScalingMethod == ScalingMethods.Bilinear)
{
AForge.Imaging.Filters.ResizeBilinear resizer = new AForge.Imaging.Filters.ResizeBilinear(newWidth, newHeight);
image = cropper.Apply(resizer.Apply((Bitmap)image));
}
return image;
}
private Bitmap resize(Bitmap image, int newWidth, int newHeight)
{
if (keepAspectRatio)
{
double ratio = (double)newHeight / (double)image.Height;
newWidth = (int)((double)image.Width * ratio);
}
AForge.Imaging.Filters.Crop cropper = new AForge.Imaging.Filters.Crop(new Rectangle(0, 0, newWidth, newHeight));
if (cmbScalingMethod.SelectedIndex == 0)
{
AForge.Imaging.Filters.ResizeNearestNeighbor resizer = new AForge.Imaging.Filters.ResizeNearestNeighbor(newWidth, newHeight);
image = cropper.Apply(resizer.Apply((Bitmap)image));
}
if (cmbScalingMethod.SelectedIndex == 1)
{
AForge.Imaging.Filters.ResizeBicubic resizer = new AForge.Imaging.Filters.ResizeBicubic(newWidth, newHeight);
image = cropper.Apply(resizer.Apply((Bitmap)image));
}
if (cmbScalingMethod.SelectedIndex == 2)
{
AForge.Imaging.Filters.ResizeBilinear resizer = new AForge.Imaging.Filters.ResizeBilinear(newWidth, newHeight);
image = cropper.Apply(resizer.Apply((Bitmap)image));
}
return image;
}
/// <summary>
/// Обработка одного сэмпла
/// </summary>
/// <param name="index"></param>
public static string processSample(ref Imaging.UnmanagedImage unmanaged)
{
string rez = "Обработка";
/// Инвертируем изображение
AForge.Imaging.Filters.Invert InvertFilter = new AForge.Imaging.Filters.Invert();
InvertFilter.ApplyInPlace(unmanaged);
/// Создаём BlobCounter, выдёргиваем самый большой кусок, масштабируем, пересечение и сохраняем
/// изображение в эксклюзивном использовании
AForge.Imaging.BlobCounterBase bc = new AForge.Imaging.BlobCounter();
bc.FilterBlobs = true;
bc.MinWidth = 10;
bc.MinHeight = 10;
// Упорядочиваем по размеру
bc.ObjectsOrder = AForge.Imaging.ObjectsOrder.Size;
// Обрабатываем картинку
bc.ProcessImage(unmanaged);
Rectangle[] rects = bc.GetObjectsRectangles();
rez = "Насчитали " + rects.Length.ToString() + " прямоугольников!";
//if (rects.Length == 0)
//{
// finalPics[r, c] = AForge.Imaging.UnmanagedImage.FromManagedImage(new Bitmap(100, 100));
// return 0;
//}
// К сожалению, код с использованием подсчёта blob'ов не работает, поэтому просто высчитываем максимальное покрытие
// для всех блобов - для нескольких цифр, к примеру, 16, можем получить две области - отдельно для 1, и отдельно для 6.
// Строим оболочку, включающую все блоки. Решение плохое, требуется доработка
int lx = unmanaged.Width;
int ly = unmanaged.Height;
int rx = 0;
int ry = 0;
for (int i = 0; i < rects.Length; ++i)
{
if (lx > rects[i].X)
{
lx = rects[i].X;
}
if (ly > rects[i].Y)
{
ly = rects[i].Y;
}
if (rx < rects[i].X + rects[i].Width)
{
rx = rects[i].X + rects[i].Width;
}
if (ry < rects[i].Y + rects[i].Height)
{
ry = rects[i].Y + rects[i].Height;
}
}
// Обрезаем края, оставляя только центральные блобчики
AForge.Imaging.Filters.Crop cropFilter = new AForge.Imaging.Filters.Crop(new Rectangle(lx, ly, rx - lx, ry - ly));
unmanaged = cropFilter.Apply(unmanaged);
// Масштабируем до 100x100
AForge.Imaging.Filters.ResizeBilinear scaleFilter = new AForge.Imaging.Filters.ResizeBilinear(100, 100);
unmanaged = scaleFilter.Apply(unmanaged);
return(rez);
}
/// <summary>
/// Image processor and the heart of sensor
/// </summary>
/// <param name="sender">object - owner</param>
/// <param name="eventArgs">NewFrameEventArgs - args, contains frame from camera</param>
private void processFrame(object sender, NewFrameEventArgs eventArgs)
{
Rectangle rect = eventArgs.Frame.Bounds();
PixelFormat pf = eventArgs.Frame.PixelFormat;
Bitmap frame = eventArgs.Frame.Clone(rect, pf);
scaleX = frame.Width / processWidth;
scaleY = frame.Height / processHeight;
Bitmap frameFace = eventArgs.Frame.Clone(rect, pf);
if (OnMotionDetected != null)
{
var dataMotion = frame.GetDirectAccess();
var frameUI = dataMotion.GetUnmanaged();
if (motion.ProcessFrame(frameUI) > 0.15)
{
updateMotion(true);
}
else
{
updateMotion(false);
}
frame.UnlockBits(dataMotion);
}
if (OnFaceDetected != null)
{
var dataFace = frameFace.GetDirectAccess();
var faceUI = dataFace.GetUnmanaged();
var downsample = faceUI.ResizeTo(processWidth, processHeight);
var faceDetections = detectorFace.ProcessFrame(downsample);
var faceDetections2 = detectorFaceProfile.ProcessFrame(downsample);
if (isPreview)
{
if (faceDetections.Length > 0)
{
marker = new Accord.Imaging.Filters.RectanglesMarker(faceDetections.Scale(scaleX, scaleY));
marker.MarkerColor = Color.Yellow;
frame = marker.Apply(frame);
}
if (faceDetections2.Length > 0)
{
marker = new Accord.Imaging.Filters.RectanglesMarker(faceDetections2.Scale(scaleX, scaleY));
marker.MarkerColor = Color.Yellow;
frame = marker.Apply(frame);
}
}
frameFace.UnlockBits(dataFace);
if (detectorFace.DetectedObjects != null && detectorFace.DetectedObjects.Length > 0)
{
var faces = detectorFace.DetectedObjects.ToFaces((int)scaleX, (int)scaleY);
for (int i = 0; i < faces.Length; i++)
{
var cutter = new AForge.Imaging.Filters.Crop(faces[i].Bounds);
faces[i].FaceImage = cutter.Apply(frameFace);
if (searchForFaceDirection)
{
detectorNose.ProcessFrame(faces[i].FaceImage);
if (detectorNose.DetectedObjects.Length > 0)
{
faces[i].Direction = FaceDirection.Frontal;
}
}
var eyeDetections = detectorEye.ProcessFrame(faces[i].FaceImage);
if (eyeDetections.Length > 0)
{
if (eyeDetections.Length >= 1)
{
faces[i].Direction = FaceDirection.Frontal;
}
Eye[] eyes = new Eye[eyeDetections.Length];
for (int ie = 0; ie < eyes.Length; ie++)
{
eyes[ie] = new Eye();
eyes[ie].Left = faces[i].Left + eyeDetections[ie].X;
eyes[ie].Top = faces[i].Top + eyeDetections[ie].Y;
eyes[ie].Width = eyeDetections[ie].Width;
eyes[ie].Height = eyeDetections[ie].Height;
var cutter2 = new AForge.Imaging.Filters.Crop(eyes[ie].Bounds);
eyes[ie].EyeImage = cutter.Apply(frameFace);
}
if (isPreview)
{
marker = new Accord.Imaging.Filters.RectanglesMarker(eyes.toRects());
marker.MarkerColor = Color.Orange;
frame = marker.Apply(frame);
}
updateEyeDetected(eyes);
}
}
updateFaceDetected(faces);
}
else if (detectorFaceProfile.DetectedObjects != null && detectorFaceProfile.DetectedObjects.Length > 0)
{
var faces = detectorFaceProfile.DetectedObjects.ToFaces((int)scaleX, (int)scaleY);
for (int i = 0; i < faces.Length; i++)
{
var cutter = new AForge.Imaging.Filters.Crop(faces[i].Bounds);
faces[i].FaceImage = cutter.Apply(frameFace);
if (searchForFaceDirection)
{
detectorEarLeft.ProcessFrame(faces[i].FaceImage);
if (detectorEarLeft.DetectedObjects.Length > 0)
{
faces[i].Direction = FaceDirection.TurnedRight;
}
else
{
detectorEarRight.ProcessFrame(faces[i].FaceImage);
if (detectorEarRight.DetectedObjects.Length > 0)
{
faces[i].Direction = FaceDirection.TurnedLeft;
}
else
{
faces[i].Direction = FaceDirection.NoInfo;
}
}
}
}
updateFaceDetected(faces);
}
}
updateFrameReceived(frame);
}
public static Dictionary <Rectangle, Bitmap> Split(KeyValuePair <Rectangle, Bitmap> originalImage, int maxSize, byte quality)
{
var result = new Dictionary <Rectangle, Bitmap>();
if (ImageToByteArray(originalImage.Value, quality).Length <= maxSize)
{
result.Add(originalImage.Key, originalImage.Value);
return(result);
}
Bitmap firstBitmap;
Bitmap secondBitmap;
Rectangle r1;
Rectangle r2;
if (originalImage.Value.Width > originalImage.Value.Height)
{
//split vertically
AForge.Imaging.Filters.Crop crop1 = new AForge.Imaging.Filters.Crop(new Rectangle(0, 0, originalImage.Value.Width / 2, originalImage.Value.Height));
AForge.Imaging.Filters.Crop crop2 = new AForge.Imaging.Filters.Crop(new Rectangle(originalImage.Value.Width / 2, 0, originalImage.Value.Width / 2, originalImage.Value.Height));
firstBitmap = crop1.Apply(originalImage.Value);
secondBitmap = crop2.Apply(originalImage.Value);
//firstBitmap = new Bitmap(originalImage.Value.Width/2, originalImage.Value.Height);
//secondBitmap = new Bitmap(originalImage.Value.Width/2, originalImage.Value.Height);
//Graphics g1 = Graphics.FromImage(firstBitmap);
//g1.DrawImage(originalImage.Value, 0, 0,
// new Rectangle(0, 0, originalImage.Value.Width/2, originalImage.Value.Height),
// GraphicsUnit.Pixel);
//Graphics g2 = Graphics.FromImage(secondBitmap);
//g2.DrawImage(originalImage.Value, 0, 0,
// new Rectangle(originalImage.Value.Width / 2, 0, originalImage.Value.Width / 2, originalImage.Value.Height),
// GraphicsUnit.Pixel);
r1 = new Rectangle(originalImage.Key.X, originalImage.Key.Y, originalImage.Key.Width / 2,
originalImage.Key.Height);
r2 = new Rectangle(originalImage.Key.X + originalImage.Key.Width / 2, originalImage.Key.Y, originalImage.Key.Width / 2,
originalImage.Key.Height);
}
else
{
//split horizontally
AForge.Imaging.Filters.Crop crop1 = new AForge.Imaging.Filters.Crop(new Rectangle(0, 0, originalImage.Value.Width, originalImage.Value.Height / 2));
AForge.Imaging.Filters.Crop crop2 = new AForge.Imaging.Filters.Crop(new Rectangle(0, originalImage.Value.Height / 2, originalImage.Value.Width, originalImage.Value.Height / 2));
firstBitmap = crop1.Apply(originalImage.Value);
secondBitmap = crop2.Apply(originalImage.Value);
//firstBitmap = new Bitmap(originalImage.Value.Width, originalImage.Value.Height/2);
//secondBitmap = new Bitmap(originalImage.Value.Width, originalImage.Value.Height/2);
//Graphics g1 = Graphics.FromImage(firstBitmap);
//g1.DrawImage(originalImage.Value, 0, 0,
// new Rectangle(0, 0, originalImage.Value.Width, originalImage.Value.Height/2),
// GraphicsUnit.Pixel);
//Graphics g2 = Graphics.FromImage(secondBitmap);
//g2.DrawImage(originalImage.Value, 0, 0,
// new Rectangle(0, originalImage.Value.Height/2, originalImage.Value.Width, originalImage.Value.Height/2),
// GraphicsUnit.Pixel);
r1 = new Rectangle(originalImage.Key.X, originalImage.Key.Y, originalImage.Key.Width,
originalImage.Key.Height / 2);
r2 = new Rectangle(originalImage.Key.X, originalImage.Key.Y + originalImage.Key.Height / 2, originalImage.Key.Width,
originalImage.Key.Height / 2);
}
return(Split(new KeyValuePair <Rectangle, Bitmap>(r1, firstBitmap), maxSize, quality).Concat(
Split(new KeyValuePair <Rectangle, Bitmap>(r2, secondBitmap), maxSize, quality)).ToDictionary(p => p.Key, p => p.Value));
}