public Bitmap Closing(Bitmap bitmap)
{
var bmp = ReverseBitmapColors(bitmap);
Closing filter = new Closing();
filter.ApplyInPlace(bmp);
return(ReverseBitmapColors(bmp));
}
public static Bitmap ProcessImage(string imagePath)
{
var img = AForge.Imaging.Image.FromFile(imagePath);
ContrastStretch filterContrastStretch = new ContrastStretch();
filterContrastStretch.ApplyInPlace(img);
try
{
img = Grayscale.CommonAlgorithms.BT709.Apply(img);
}
catch
{
System.Windows.Forms.MessageBox.Show("The image should not be grayscale");
}
Opening filterOpening = new Opening();
filterOpening.ApplyInPlace(img);
SobelEdgeDetector sobel = new SobelEdgeDetector();
sobel.ApplyInPlace(img);
Closing filterClosing = new Closing();
filterClosing.ApplyInPlace(img);
Threshold threshold = new Threshold(100);
threshold.ApplyInPlace(img);
FillHoles fillHoles = new FillHoles();
fillHoles.MaxHoleWidth = img.Width;
fillHoles.MaxHoleHeight = img.Height;
fillHoles.CoupledSizeFiltering = false;
fillHoles.ApplyInPlace(img);
filterOpening.ApplyInPlace(img);
Erosion filterErosion = new Erosion();
filterErosion.ApplyInPlace(img);
return(img);
}
void ClosingToolStripMenuItemClick(object sender, EventArgs e)
{
//jika gambar kosong/null maka akan mengembalikan nilai kosong/null
if (gambar == null)
{
return;
}
//membuat filter dari inisiasi class Closing() pada objek closing
Closing closing = new Closing( );
//clone variable gambar pada variable gambar2
gambar2 = (Bitmap)gambar.Clone();
//aplikasikan filter objek closing pada gambar2
closing.ApplyInPlace(gambar2);
//tampilkan hasil gambar2 yang sudah diaplikasikan filter pada pictureBox2
pictureBox2.Image = gambar2;
}
/// <summary>
/// This method binarize image.
/// </summary>
/// <param name="image"></param>
/// <returns></returns>
private Bitmap Binarize(Bitmap image)
{
Median median = new Median();
median.ApplyInPlace(image);
var bmp8bpp = Grayscale.CommonAlgorithms.BT709.Apply(image);
OtsuThreshold OtsuThreshold = new OtsuThreshold();
OtsuThreshold.ApplyInPlace(bmp8bpp);
//bmp8bpp.Save(@"C:\Users\Maor\Desktop\mode\BinarizeWithoutDilation.jpeg");
Closing closing = new Closing();
closing.ApplyInPlace(bmp8bpp);
return(bmp8bpp);
}
/// <summary>
/// This method binarize dnd dilate image.
/// </summary>
/// <param name="image"></param>
/// <returns></returns>
private Bitmap BinarizeAndDilationWithMedian(Bitmap image)
{
int[,] kernel =
{
{ -2, -1, 0 },
{ -1, 1, 1 },
{ 0, 1, 2 }
};
// create filter
Convolution Convolution = new Convolution(kernel);
// apply the filter
Convolution.ApplyInPlace(image);
var bmp8bpp = Grayscale.CommonAlgorithms.BT709.Apply(image);
Invert invert = new Invert();
invert.ApplyInPlace(bmp8bpp);
Dilatation dilatation = new Dilatation();
dilatation.ApplyInPlace(bmp8bpp);
invert.ApplyInPlace(bmp8bpp);
Median median = new Median();
median.ApplyInPlace(bmp8bpp);
Closing closing = new Closing();
closing.ApplyInPlace(bmp8bpp);
OtsuThreshold OtsuThreshold = new OtsuThreshold();
OtsuThreshold.ApplyInPlace(bmp8bpp);
//bmp8bpp.Save(@"C:\Users\Maor\Desktop\mode\BinarizeAndDilationWithMedian.jpeg");
return(bmp8bpp);
}
private void skinDetectToolStripMenuItem_Click(object sender, EventArgs e)
{
List <List <double> > Listofvectors = new List <List <double> >();
System.IO.StreamWriter file = new System.IO.StreamWriter(@"C:\Users\gsrip\Documents\MyDocuments\Saarthi AI and IP\Segmented\segmented.txt", append: true);
String alphabets = "0ABCDEFGHIJKLMNOPQRSTUVWXYZ";
for (int index = 1; index <= 26; index++)
{
//for each folder select all filenames
filenames = Directory.GetFiles(dirnames[index - 1]);
int n = 0;
foreach (string filename in filenames)
{
//load an image in a bitmap
Bitmap bmplocal = new Bitmap(filename);
int height = 300, width = 300;
bmp = new Bitmap(bmplocal, width, height);
pictureBox1.Image = new Bitmap(bmp);
pictureBox1.SizeMode = PictureBoxSizeMode.StretchImage;
using (bmp)
using (skinBmp = new Bitmap(bmp.Width, bmp.Height))
{
//skin detection
for (int x = 0; x < bmp.Width; x++)
{
for (int y = 0; y < bmp.Height; y++)
{
Color pixel = bmp.GetPixel(x, y);
int red = pixel.R;
int blue = pixel.B;
int green = pixel.G;
int max = Math.Max(red, Math.Max(green, blue));
int min = Math.Min(red, Math.Min(green, blue));
int rgdif = red - green;
int abs = Math.Abs(rgdif);
if (red > 95 && green > 40 && blue > 20 && max - min > 15 && abs > 15 && red > green && red > blue)
{
skinBmp.SetPixel(x, y, pixel);
}
}
}
pictureBox2.Image = new Bitmap(skinBmp);
//grayscale filter (BT709)
Grayscale filter1 = new Grayscale(0.2125, 0.7154, 0.0721);
Bitmap newImage = new Bitmap(bmp);
Bitmap grayImage = filter1.Apply(newImage);
Threshold filter2 = new Threshold(100);
Bitmap bwImage = filter2.Apply(grayImage);
Closing filter5 = new Closing();
filter5.ApplyInPlace(bwImage);
Opening filter3 = new Opening();
filter3.ApplyInPlace(bwImage);
ExtractBiggestBlob filter4 = new ExtractBiggestBlob();
Bitmap biggestBlobsImage = filter4.Apply(bwImage);
ExtractBiggestBlob filter6 = new ExtractBiggestBlob();
Bitmap biggestBlobsImage1 = filter6.Apply((Bitmap)pictureBox2.Image);
Bitmap orgimage = new Bitmap(biggestBlobsImage1, 300, 300);
Bitmap blobimage = new Bitmap(biggestBlobsImage, 300, 300);
Bitmap newimage = new Bitmap(300, 300);
//anding the two images
for (int x = 0; x < 300; x++)
{
for (int y = 0; y < 300; y++)
{
Color pixel1 = orgimage.GetPixel(x, y);
Color pixel2 = blobimage.GetPixel(x, y);
int red1 = pixel1.R, red2 = pixel2.R;
int blue1 = pixel1.B, blue2 = pixel2.B;
int green1 = pixel1.G, green2 = pixel2.G;
int newred, newblue, newgreen;
newred = red1 & red2;
newblue = blue1 & blue2;
newgreen = green1 & green2;
Color newpixel = Color.FromArgb(newred, newgreen, newblue);
newimage.SetPixel(x, y, newpixel);
}
}
CannyEdgeDetector filter7 = new CannyEdgeDetector();
Grayscale filter = new Grayscale(0.2125, 0.7154, 0.0721);
Bitmap edges = filter.Apply(newimage);
filter7.ApplyInPlace(edges);
pictureBox3.Image = new Bitmap(edges);
String location = "C:\\Users\\gsrip\\Documents\\MyDocuments\\Saarthi AI and IP\\Segmented\\";
location = location + alphabets[index].ToString() + "\\image";
newimage.Save(@location + (n++).ToString() + ".jpg");
List <int> featureVector = new List <int>();
for (int i = 0; i < 6; i++)
{
for (int j = 0; j < 6; j++)
{
int count = 0;
for (int x = i * 50; x < (i * 50) + 50; x++)
{
for (int y = j * 50; y < (j * 50) + 50; y++)
{
Color pixel = edges.GetPixel(x, y);
if (pixel.R != 0 && pixel.G != 0 && pixel.B != 0)
{
count++;
}
}
}
featureVector.Add(count);
}
}
int sumofvector = featureVector.Sum();
List <double> featureVectorNorm = new List <double>();
foreach (var d in featureVector)
{
featureVectorNorm.Add((double)d / sumofvector);
}
Listofvectors.Add(featureVectorNorm);
}//end of using
} // end of foreach filename
foreach (var vector in Listofvectors)
{
String line = index.ToString() + ": ";
//Console.WriteLine(value);
foreach (var obj in vector)
{
line = line + obj.ToString() + " ";
//Console.Write(value);
}
file.WriteLine(line);
//Console.WriteLine();
}
} //end of foreach index
file.Close();
} //end of skindetect tool strip
private List <double> automateFeatureNormalizationExtraction(Bitmap rawBitmapData)
{
Bitmap afterSkinOnly = performSkinExtract(rawBitmapData);
Grayscale filter = new Grayscale(0.2125, 0.71254, 0.0721);
Bitmap grayImage = filter.Apply(afterSkinOnly);
Threshold filter2 = new Threshold(100);
Bitmap filteredImage = filter2.Apply(grayImage);
Closing close = new Closing();
close.ApplyInPlace(filteredImage);
Opening open = new Opening();
open.ApplyInPlace(filteredImage);
// create filter for the filtered image
ExtractBiggestBlob filter3 = new ExtractBiggestBlob();
// apply the filter
Bitmap biggestBlobsImage = filter3.Apply(filteredImage);
AForge.IntPoint a = filter3.BlobPosition;
//Console.WriteLine(a);
//Biggest blob for old extracted skin image
ExtractBiggestBlob filter4 = new ExtractBiggestBlob();
Bitmap skinBlob = new Bitmap(afterSkinOnly);
//apply filter
Bitmap biggestSkinBlob = filter4.Apply(skinBlob);
//Skin color for largest blob
Bitmap one = new Bitmap(biggestSkinBlob);
Bitmap two = new Bitmap(biggestBlobsImage);
int i, j;
for (i = 0; i < two.Width; i++)
{
for (j = 0; j < two.Height; j++)
{
Color pixelOne = one.GetPixel(i, j);
Color pixelTwo = two.GetPixel(i, j);
int redOne = pixelOne.R;
int greenOne = pixelOne.G;
int blueOne = pixelOne.B;
int redTwo = pixelTwo.R;
int greenTwo = pixelTwo.G;
int blueTwo = pixelTwo.B;
// This mask is logically AND with original image to extract only the palm which is required for feature extraction.
two.SetPixel(i, j, Color.FromArgb(redOne & redTwo, greenOne & greenTwo, blueOne & blueTwo));
}
}
//Getting a grayscae image from the recolored image
Bitmap getGrayImage = filter.Apply(two);
// create filter
CannyEdgeDetector filter1 = new CannyEdgeDetector();
filter1.LowThreshold = 0;
filter1.HighThreshold = 0;
filter1.GaussianSigma = 1.4;
// apply the filter
Bitmap cannyEdgeImage = filter1.Apply(getGrayImage);
Bitmap resizeImage = new Bitmap(360, 360);
using (var graphics = Graphics.FromImage(resizeImage))
graphics.DrawImage(cannyEdgeImage, 0, 0, 360, 360);
pictureBox3.Image = new Bitmap(resizeImage);
pictureBox3.SizeMode = PictureBoxSizeMode.StretchImage;
int x, y;
//Image to obtain blocks for
Bitmap imageWithBlock = new Bitmap(resizeImage);
//Console.WriteLine("Width = " + resizeImage.Width + " Height = " + resizeImage.Height);
int imageHeightSize = resizeImage.Height / blockSize;
int imageWidthSize = resizeImage.Width / blockSize;
//Console.WriteLine("Width = " + imageWidthSize + " Height = " + imageHeightSize);
List <int> featureVector = new List <int>();
double totalPixelCount = 0;
for (i = 0; i < blockSize; i++)
{
for (j = 0; j < blockSize; j++)
{
int whiteEdgeCount = 0, blackEdgeCount = 0;
for (x = i * imageWidthSize; x < (i * imageWidthSize) + imageWidthSize; x++)
{
for (y = j * imageHeightSize; y < (j * imageHeightSize) + imageHeightSize; y++)
{
// To count the edges in the range
Color singlePixel = imageWithBlock.GetPixel(x, y);
int red = singlePixel.R;
int green = singlePixel.G;
int blue = singlePixel.B;
if (singlePixel != Color.FromArgb(Color.Black.ToArgb()))
{
whiteEdgeCount++;
}
else
{
blackEdgeCount++;
}
}
}
//Console.WriteLine("White = " + whiteEdgeCount + " Black = " + blackEdgeCount);
//Add value to total count
totalPixelCount += whiteEdgeCount;
// whiteCount = edges in range
featureVector.Add(whiteEdgeCount);
}
}
//Calculate Normalization and add the value to the featureNormVector
List <double> featureNormVector = new List <double>();
//Total Pixel Count
//Console.WriteLine(totalPixelCount);
//Normalization
for (i = 0; i < featureVector.Count; i++)
{
double normalizedValue = featureVector[i] / totalPixelCount;
Console.WriteLine(normalizedValue);
featureNormVector.Add(normalizedValue);
}
Console.WriteLine("Total count of norm(individual)=" + i);
return(featureNormVector);
}
private void skinColorToolStripMenuItem_Click(object sender, EventArgs e)
{
//Extracting RGBs
Bitmap hand = new Bitmap(pictureBox1.Image, newSize);
Bitmap skinDetect = new Bitmap(hand.Width, hand.Height);
//Bitmap blackWhite = new Bitmap(hand.Width, hand.Height);
Color black = Color.Black;
//Color white = Color.White;
int i, j;
for (i = 0; i < hand.Width; i++)
{
for (j = 0; j < hand.Height; j++)
{
Color pixel = hand.GetPixel(i, j);
int red = pixel.R;
int green = pixel.G;
int blue = pixel.B;
/* (R, G, B) is classified as skin if:
* R > 95 and G > 40 and B > 20 and
* max {R, G, B} – min{R, G, B} > 15 and
|R – G| > 15 and R > G and R > B
*/
if ((red > 95 && green > 40 && blue > 20) && (max(red, green, blue) - min(red, green, blue) > 15) &&
Math.Abs(red - green) > 15 && red > green && red > blue)
{
//Console.WriteLine("Success");
skinDetect.SetPixel(i, j, pixel);
}
}
}
pictureBox2.Image = new Bitmap(skinDetect);
pictureBox2.SizeMode = PictureBoxSizeMode.StretchImage;
Grayscale filter = new Grayscale(0.2125, 0.71254, 0.0721);
Bitmap grayImage = filter.Apply(skinDetect);
Threshold filter2 = new Threshold(100);
Bitmap filteredImage = filter2.Apply(grayImage);
Closing close = new Closing();
close.ApplyInPlace(filteredImage);
Opening open = new Opening();
open.ApplyInPlace(filteredImage);
// create filter for the filtered image
ExtractBiggestBlob filter3 = new ExtractBiggestBlob();
// apply the filter
Bitmap biggestBlobsImage = filter3.Apply(filteredImage);
AForge.IntPoint a = filter3.BlobPosition;
Console.WriteLine(a);
//Biggest blob for old extracted skin image
ExtractBiggestBlob filter4 = new ExtractBiggestBlob();
Bitmap skinBlob = new Bitmap(skinDetect);
//apply filter
Bitmap biggestSkinBlob = filter4.Apply(skinBlob);
//Skin color for largest blob
Bitmap one = new Bitmap(biggestSkinBlob);
Bitmap two = new Bitmap(biggestBlobsImage);
for (i = 0; i < two.Width; i++)
{
for (j = 0; j < two.Height; j++)
{
Color pixelOne = one.GetPixel(i, j);
Color pixelTwo = two.GetPixel(i, j);
int redOne = pixelOne.R;
int greenOne = pixelOne.G;
int blueOne = pixelOne.B;
int redTwo = pixelTwo.R;
int greenTwo = pixelTwo.G;
int blueTwo = pixelTwo.B;
// This mask is logically AND with original image to extract only the palm which is required for feature extraction.
two.SetPixel(i, j, Color.FromArgb(redOne & redTwo, greenOne & greenTwo, blueOne & blueTwo));
}
}
//Getting a grayscae image from the recolored image
Bitmap getGrayImage = filter.Apply(two);
// create filter
CannyEdgeDetector filter1 = new CannyEdgeDetector();
filter1.LowThreshold = 0;
filter1.HighThreshold = 0;
filter1.GaussianSigma = 1.4;
// apply the filter
Bitmap cannyEdgeImage = filter1.Apply(getGrayImage);
Bitmap resizeImage = new Bitmap(360, 360);
using (var graphics = Graphics.FromImage(resizeImage))
graphics.DrawImage(cannyEdgeImage, 0, 0, 360, 360);
pictureBox3.Image = new Bitmap(resizeImage);
pictureBox3.SizeMode = PictureBoxSizeMode.StretchImage;
int x, y;
//Image to obtain blocks for
Bitmap imageWithBlock = new Bitmap(resizeImage);
Console.WriteLine("Width = " + resizeImage.Width + " Height = " + resizeImage.Height);
int imageHeightSize = resizeImage.Height / blockSize;
int imageWidthSize = resizeImage.Width / blockSize;
Console.WriteLine("Width = " + imageWidthSize + " Height = " + imageHeightSize);
List <int> featureVector = new List <int>();
double totalPixelCount = 0;
for (i = 0; i < blockSize; i++)
{
for (j = 0; j < blockSize; j++)
{
int whiteEdgeCount = 0, blackEdgeCount = 0;
for (x = i * imageWidthSize; x < (i * imageWidthSize) + imageWidthSize; x++)
{
for (y = j * imageHeightSize; y < (j * imageHeightSize) + imageHeightSize; y++)
{
// To count the edges in the range
Color singlePixel = imageWithBlock.GetPixel(x, y);
int red = singlePixel.R;
int green = singlePixel.G;
int blue = singlePixel.B;
if (singlePixel != Color.FromArgb(Color.Black.ToArgb()))
{
whiteEdgeCount++;
}
else
{
blackEdgeCount++;
}
}
}
//Console.WriteLine("White = " + whiteEdgeCount + " Black = " + blackEdgeCount);
//Add value to total count
totalPixelCount += whiteEdgeCount;
// whiteCount = edges in range
featureVector.Add(whiteEdgeCount);
}
}
//Calculate Normalization and add the value to the featureNormVector
List <double> featureNormVector = new List <double>();
//Total Pixel Count
//Console.WriteLine(totalPixelCount);
//Normalization
for (i = 0; i < featureVector.Count; i++)
{
double normalizedValue = featureVector[i] / totalPixelCount;
Console.WriteLine(normalizedValue);
featureNormVector.Add(normalizedValue);
}
}
protected override string ProcessInternal(Bitmap bitmap)
{
using (var grayScaleImage = GrayScaleImageHelper.ToGrayScale(bitmap))
{
var bounds = new Rectangle(0, 0, grayScaleImage.Width, grayScaleImage.Height);
BitmapData bitmapData = grayScaleImage.LockBits(bounds, ImageLockMode.ReadOnly, grayScaleImage.PixelFormat);
var grayScaleHW = new byte[grayScaleImage.Height * bitmapData.Stride];
Marshal.Copy(bitmapData.Scan0, grayScaleHW, 0, grayScaleImage.Height * bitmapData.Stride);
var stride = bitmapData.Stride;
grayScaleImage.UnlockBits(bitmapData);
if (!_backgroundModel.IsOperational())
{
_backgroundModel.Train(grayScaleHW, grayScaleImage.Width, grayScaleImage.Height, stride);
if (_backgroundModel.IsOperational())
{
_interceptor.Intercept(MinIntensityBackgroundDebugView, GrayScaleImageHelper.FromData2(_backgroundModel._width, _backgroundModel._height, _backgroundModel._stride, _backgroundModel._minIntensity));
_interceptor.Intercept(MaxIntensityBackgroundDebugView, GrayScaleImageHelper.FromData2(_backgroundModel._width, _backgroundModel._height, _backgroundModel._stride, _backgroundModel._maxIntensity));
_interceptor.Intercept(MaxPerFrameDifferenceDebugView, GrayScaleImageHelper.FromData2(_backgroundModel._width, _backgroundModel._height, _backgroundModel._stride, _backgroundModel._maxPerFrameDifference));
}
return(null);
}
var foreground = GetForefround(grayScaleHW, grayScaleImage.Width, grayScaleImage.Height, stride);
//excluded, because does not work.
//ExcludeShadows(grayScaleHW, foreground, grayScaleImage.Width, grayScaleImage.Height, stride);
var temp = GrayScaleImageHelper.FromData(
_backgroundModel._width,
_backgroundModel._height,
_backgroundModel._stride,
foreground);
int countDetected = 0;
// create filter
var diamondMask = CreateClosingOpeningFileter();
Closing filter = new Closing(diamondMask);
Opening filter2 = new Opening(diamondMask);
// apply the filter
filter.ApplyInPlace(temp);
filter2.ApplyInPlace(temp);
_interceptor.Intercept(
XXX,
ImageHelper.ToBytes(temp));
// blobs!
var bc = new BlobCounter {
BackgroundThreshold = Color.FromArgb(254, 254, 254)
};
bc.ProcessImage(temp);
Rectangle[] rects = bc.GetObjectsRectangles();
var rectanglesToDraw = new List <Rectangle>();
foreach (Rectangle rect in rects)
{
if (rect.Width < _maximumDetectionWidthPixels &&
rect.Width > _minimumDetectionWidthPixels &&
rect.Height < _maximumDetectionHeightPixels &&
rect.Height > _minimumDetectionHeightPixels)
{
countDetected++;
rectanglesToDraw.Add(rect);
}
}
temp.Dispose();
_interceptor.Intercept(InputFrameDebugView, GrayScaleImageHelper.FromData2(_backgroundModel._width, _backgroundModel._height, _backgroundModel._stride, grayScaleHW));
_interceptor.Intercept(DifferenceDebugView, GrayScaleImageHelper.FromData2(_backgroundModel._width, _backgroundModel._height, _backgroundModel._stride, foreground));
using (Graphics graphics = Graphics.FromImage(bitmap))
using (var brush = new SolidBrush(Color.Red))
using (var pen = new Pen(brush, 3))
{
if (rectanglesToDraw.Any())
{
graphics.DrawRectangles(pen, rectanglesToDraw.ToArray());
}
_interceptor.Intercept(DetectedBlobDebugView, ImageHelper.ToBytes(bitmap));
}
if (countDetected > 0)
{
return("Alarm: " + countDetected);
}
}
return(null);
}
