/// <summary>
/// 根据图片的纵横灰度情况截取图片
/// 使用Aforge类库
/// </summary>
/// <param name="image"></param>
/// <returns></returns>
public static Rectangle GetByGrayPoint(Bitmap image)
{
//灰度化
Grayscale g = new Grayscale(0.2125, 0.7154, 0.0721);
// apply the filter
var grayImg = g.Apply(image);
//二值化
var threshold = new Threshold();
threshold.ThresholdValue = 80;
var thresholdImage = threshold.Apply(grayImg);
//找出纵横灰度值
HorizontalIntensityStatistics hs = new HorizontalIntensityStatistics(thresholdImage);
VerticalIntensityStatistics vs = new VerticalIntensityStatistics(thresholdImage);
var vThreshold = (image.Height / 4) * 255;
var hThreshold = (image.Width / 4) * 255;
var hGrays = hs.Gray.Values.ToList();
var vGrays = vs.Gray.Values.ToList();
var hmin = FindPaperBorder(hGrays, true, hThreshold, 0);
var hmax = FindPaperBorder(hGrays, false, hThreshold, 0);
var vmin = FindPaperBorder(vGrays, true, vThreshold, 0);;
var vmax = FindPaperBorder(vGrays, false, vThreshold, 0);
return(new Rectangle(hmin, vmin, hmax - hmin, vmax - vmin));
}
/// <summary>
/// Build the Histogram for Supplied Image
/// </summary>
/// <param name="image">
/// String: Path to image that histogram is to be created out of
/// </param>
public void DoHistogram(string image)
{
CurrentImage = image; // Used for re-generating the histogram
bool IsGrayScale = AForge.Imaging.Image.IsGrayscale(new Bitmap(image));
dynamic IntensityStatistics = null; // Use dynamic (a little like var) to assign this variable which maybe of different types.
swHistogram.Reset();
swHistogram.Start();
histogramStatus("Creating Histogram");
AForge.Math.Histogram grayhist;
AForge.Math.Histogram Redhist;
AForge.Math.Histogram Greenhist;
AForge.Math.Histogram Bluehist;
// collect statistics
//NOTE: We have to use the braces on these statements see: http://stackoverflow.com/questions/2496589/variable-declarations-following-if-statements
if (IsHorizontalIntensity)
{
histogramStatus("Using HorizontalIntensityStatistics");
IntensityStatistics = new HorizontalIntensityStatistics(new Bitmap(image));
}
else
{
histogramStatus("Using VerticalIntensityStatistics");
IntensityStatistics = new VerticalIntensityStatistics(new Bitmap(image));
}
// get gray histogram (for grayscale image)
if (IsGrayScale)
{
grayhist = IntensityStatistics.Gray;
//TODO: DoGrayHistogram();
histogramStatus("Grayscale Histogram");
}
else
{
Redhist = IntensityStatistics.Red;
Greenhist = IntensityStatistics.Green;
Bluehist = IntensityStatistics.Blue;
DoRGBHistogram(Redhist, Greenhist, Bluehist);
histogramStatus("RGB Histogram");
}
swHistogram.Stop();
histogramCompleted("Histogram built in " + swHistogram.Elapsed);
}
private void SaveImage(Bitmap bmp)
{
seq++;
var fname = $@"D:\tmp\images\{seq}.bmp";
bmp.Save(fname);
var s = new ImageStatistics(bmp);
var v = new VerticalIntensityStatistics(bmp);
var h = new HorizontalIntensityStatistics(bmp);
File.AppendAllText(
@"D:\tmp\images\stat.csv",
$"{seq},"
+ $"{s.Gray.Mean},{s.Gray.StdDev},"
+ $"{v.Gray.Mean},{v.Gray.StdDev},"
+ $"{h.Gray.Mean},{h.Gray.StdDev},"
+ "\r\n");
}
/// <summary>
/// Gets the content.
/// </summary>
/// <param name="mincross">Минимальная разница яркости ячейки с кретсиком</param>
/// <param name="maxcross">Максимальная разница яркости ячейки с кретсиком</param>
public void GetContent(double mincross, double maxcross)
{
_content = false;
_neurocontent = CellContent.Free;
List <double> _lrange = new List <double>();
BitmapData data = _parentimage.LockBits(_rect, ImageLockMode.ReadWrite, _parentimage.PixelFormat);
VerticalIntensityStatistics vis = new VerticalIntensityStatistics(data);
histogram = vis.Gray;
HorizontalIntensityStatistics his = new HorizontalIntensityStatistics(data);
Histogram hhistogram = his.Gray;
List <double> _hrange = new List <double>();
_parentimage.UnlockBits(data);
for (int i = 8; i <= 15; i++)
{
_lrange.Add((histogram.Values[i] + hhistogram.Values[i]) / 2);
// _hrange.Add(hhistogram.Values[i]);
}
// _britnessdispertion = (1 - RecogCore.Statistics.Mean(_lrange) / histogram.Values.Max()) + (1 - RecogCore.Statistics.Mean(_hrange) / hhistogram.Values.Max());
_britnessdispertion = 1 - RecogCore.Statistics.Mean(_lrange) / histogram.Values.Max();
if (_britnessdispertion <= mincross)
{
_neurocontent = CellContent.Free; _content = false;
}
if (_britnessdispertion > mincross & _britnessdispertion <= maxcross)
{
_neurocontent = CellContent.Cross; _content = true;
}
if (_britnessdispertion > maxcross)
{
_neurocontent = CellContent.Miss; _content = false;
}
}
/// <summary>
/// 开始用图像识别的办法等待上钩事件，包括了右键点击收杆
/// 计算图片的VerticalIntensityStatistics.Mean，用过去N张图片的这个值的均值作为基准
/// 当前图片的这个值离均值的偏差超过一个阈值，则认为事件发生
/// </summary>
private void WaitForFish()
{
ShowStatus("等待鱼儿上钩……");
// 保存过去N个值
const int N = 100;
var queue = new Queue <double>(N * 2);
// 不停的截图，比较指标差异，超过时间则退出
while (DateTime.Now - m_下钩时刻 < new TimeSpan(0, 0, 25))
{
var img = CaptureScreen();
// 统计表明v_mean是最有效的指标
var vs = new VerticalIntensityStatistics(img);
var val = vs.Gray.Mean;
queue.Enqueue(val);
if (queue.Count > N)
{
queue.Dequeue();
}
// 计算均值
var avg = queue.Average();
var diff = Math.Abs(val / avg - 1);
// debug输出
ShowDebugInfo(img, diff);
// 阈值判断
if (diff > m_num阈值 / 100.0)
{
ShowStatus("上钩了！");
Win32.ClickScreen(MouseButtons.Right);
Thread.Sleep(3000);
return;
}
}
}
// gr.ResetTransform();
// gr.DrawRectangle(Pens.Black, 0, 0, width - 1, height - 1);
private void DrawHistogramV(Bitmap img)
{
Color back_color = Color.White;
Invert filter = new Invert();
// apply the filter
Bitmap inv = filter.Apply(img);
VerticalIntensityStatistics his = new VerticalIntensityStatistics(inv);
AForge.Math.Histogram histogram = his.Gray;
int[] values = histogram.Values;
Color[] Colors = new Color[] {
Color.Red, Color.LightGreen, Color.Blue,
Color.Pink, Color.Green, Color.LightBlue,
Color.Orange, Color.Yellow, Color.Purple
};
gr2.Clear(back_color);
// Draw the histogram.
using (Pen thin_pen = new Pen(Color.Black, 0))
{
for (int i = 0; i < values.Length; i++)
{
float f2 = 420F;
float fl = (float)values[i] / f2;
gr2.DrawLine(thin_pen, 0, i, fl, i);
}
}
}
/// <summary>
/// Performs hand detection calculations
/// </summary>
/// <param name="bmp">The camera frame to be processed</param>
/// <returns>A bitmap representation of the detected hands</returns>
public static Bitmap Findhands(Bitmap bmp)
{
Bitmap test = new Bitmap(bmp.Width, bmp.Height);
Graphics g = Graphics.FromImage(test);
for (int j = 0; j < 2; j++)
{
Bitmap partial = bmp.Clone(new System.Drawing.Rectangle(j * bmp.Width / 2, 0, bmp.Width / 2, bmp.Height), bmp.PixelFormat);
HorizontalIntensityStatistics his = new HorizontalIntensityStatistics(partial);
VerticalIntensityStatistics vis = new VerticalIntensityStatistics(partial);
Histogram gray = his.Gray;
Histogram GrayY = vis.Gray;
double meanX;
double meanY;
int[] hisValues = (int[])his.Gray.Values.Clone();
int[] visValues = (int[])vis.Gray.Values.Clone();
List <int> lX = new List <int>();
List <int> lY = new List <int>();
for (int i = 0; i < hisValues.Length; i++)
{
if (hisValues[i] != 0)
{
hisValues[i] = (hisValues[i] / hisValues.Max()) * 255;
}
if (hisValues[i] == 0)
{
lX.Add(i + (j * test.Width / 2));
}
}
for (int i = 0; i < visValues.Length; i++)
{
if (visValues[i] != 0)
{
visValues[i] = (visValues[i] / visValues.Max()) * 255;
}
if (visValues[i] == 0)
{
lY.Add(i);
}
}
if (lX.Count != 0)
{
meanX = lX.Average();
}
else
{
meanX = 0;
}
if (lY.Count != 0)
{
meanY = lY.Average();
}
else
{
meanY = 0;
}
Rectangle r = new Rectangle((int)meanX, (int)meanY, 10, 10);
g.DrawRectangle(new Pen(Color.Aqua), r);
}
return(test);
}
/// <summary>
/// Performs hand detection calculations within the given areas.
/// </summary>
/// <param name="bmp">The camera frame to be processed</param>
/// <param name="areas">A list of rectangles in which the hand detection is to be calculated</param>
/// <returns>A list of coordinates representing the location of each hand</returns>
public static List <Point> GetHandLocations(Bitmap bmp, List <Rectangle> areas)
{
List <Point> hands = new List <Point>();
foreach (Rectangle rect in areas)
{
Bitmap partial = bmp.Clone(rect, bmp.PixelFormat);
HorizontalIntensityStatistics his = new HorizontalIntensityStatistics(partial);
VerticalIntensityStatistics vis = new VerticalIntensityStatistics(partial);
Histogram gray = his.Gray;
Histogram GrayY = vis.Gray;
double meanX;
double meanY;
int[] hisValues = (int[])his.Gray.Values.Clone();
int[] visValues = (int[])vis.Gray.Values.Clone();
List <int> lX = new List <int>();
List <int> lY = new List <int>();
for (int i = 0; i < hisValues.Length; i++)
{
if (hisValues[i] != 0)
{
hisValues[i] = (hisValues[i] / hisValues.Max()) * 255;
}
if (hisValues[i] == 0)
{
lX.Add(i + rect.X);
}
}
for (int i = 0; i < visValues.Length; i++)
{
if (visValues[i] != 0)
{
visValues[i] = (visValues[i] / visValues.Max()) * 255;
}
if (visValues[i] == 0)
{
lY.Add(i + rect.Y);
}
}
if (lX.Count != 0)
{
meanX = lX.Average();
}
else
{
meanX = 0;
}
if (lY.Count != 0)
{
meanY = lY.Average();
}
else
{
meanY = 0;
}
hands.Add(new Point((int)meanX, (int)meanY));
}
return(hands);
}
public void test_creditcardcover()
{
try
{
const string FILE1 = @"C:\IMAGES\PB\PB742030.jpg";
const string FILE2 = @"C:\IMAGES\PB\PB742030_done2.jpg";
const string FILE3 = @"C:\IMAGES\PB\PB742030_done3.jpg";
const string FILE4 = @"C:\IMAGES\PB\PB742030_done4.jpg";
//using (var image = (Bitmap)Bitmap.FromFile(FILE1))
//{
// //Invert filter = new Invert();
// //filter.ApplyInPlace(image);
// using (var img = image.ConvertToBitonal())
// {
// img.Save(FILE2, ImageFormat.Jpeg);
// }
//}
using (var image = (Bitmap)Bitmap.FromFile(FILE1))
{
EuclideanColorFiltering filter = new EuclideanColorFiltering();
filter.CenterColor = new AForge.Imaging.RGB(Color.BlueViolet); //Pure White
filter.Radius = 0; //Increase this to allow off-whites
filter.FillColor = new AForge.Imaging.RGB(Color.Red); //Replacement Colour
filter.ApplyInPlace(image);
}
return;
//To gray scale
using (var image = (Bitmap)Bitmap.FromFile(FILE1))
{
FiltersSequence seq = new FiltersSequence();
seq.Add(Grayscale.CommonAlgorithms.BT709); //First add GrayScaling filter
seq.Add(new OtsuThreshold()); //Then add binarization(thresholding) filter
var img = seq.Apply(image); // Apply filters on source image
img.Save(FILE2, ImageFormat.Jpeg);
return;
}
using (var image = (Bitmap)Bitmap.FromFile(FILE1))
{
Grayscale filter = new Grayscale(0.2125, 0.7154, 0.0721);
// apply the filter
using (Bitmap grayImage = filter.Apply(image))
{
VerticalIntensityStatistics vis = new VerticalIntensityStatistics(grayImage);
// get gray histogram (for grayscale image)
var histogram = vis.Gray;
// output some histogram's information
System.Diagnostics.Debug.WriteLine("Mean = " + histogram.Mean);
System.Diagnostics.Debug.WriteLine("Min = " + histogram.Min);
System.Diagnostics.Debug.WriteLine("Max = " + histogram.Max);
}
}
using (var image = (Bitmap)Bitmap.FromFile(FILE2))
{
VerticalIntensityStatistics vis = new VerticalIntensityStatistics(image);
// get gray histogram (for grayscale image)
var histogram = vis.Gray;
// output some histogram's information
System.Diagnostics.Debug.WriteLine("Mean = " + histogram.Mean);
System.Diagnostics.Debug.WriteLine("Min = " + histogram.Min);
System.Diagnostics.Debug.WriteLine("Max = " + histogram.Max);
}
//using (var image = (Bitmap)Bitmap.FromFile(FILE2))
//{
// //EuclideanColorFiltering filter = new EuclideanColorFiltering();
// //// set center colol and radius
// //filter.CenterColor = new RGB(82, 26, 39);
// //filter.Radius = 100;
// //// apply the filter
// //filter.ApplyInPlace(image);
// Median filter2 = new Median();
// // apply the filter
// filter2.ApplyInPlace(image);
// image.Save(FILE3, ImageFormat.Jpeg);
//}
using (var image = (Bitmap)Bitmap.FromFile(FILE2))
{
//Median filter2 = new Median();
//// apply the filter
//filter2.ApplyInPlace(image);
//ConservativeSmoothing filter = new ConservativeSmoothing();
//filter.ApplyInPlace(image);
//BilateralSmoothing filter = new BilateralSmoothing();
//filter.KernelSize = 25;
//filter.SpatialFactor = 10;
//filter.ColorFactor = 60;
//filter.ColorPower = 0.5;
//// apply the filter
//filter.ApplyInPlace(image);
///////////////////////////////////////////
// create filter
BlobsFiltering filter3 = new BlobsFiltering();
// configure filter
//filter.CoupledSizeFiltering = true;
filter3.MinWidth = 20;
filter3.MinHeight = 20;
filter3.MaxWidth = 150;
filter3.MaxHeight = 150;
filter3.ApplyInPlace(image); //apply the filter
////////////////////////////
//ExtractBiggestBlob filter = new ExtractBiggestBlob();
//// apply the filter
//Bitmap biggestBlobsImage = filter.Apply(image);
image.Save(FILE3, ImageFormat.Jpeg);
}
}
catch (Exception ex)
{
Debug.WriteLine(ex);
}
}
// New frame received by the player
private void videoSourcePlayer_NewFrame(object sender, ref Bitmap image)
{
BitmapData imageData = image.LockBits(new Rectangle(0, 0, image.Width, image.Height), ImageLockMode.ReadWrite, image.PixelFormat);
try
{
UnmanagedImage unmanagedImg = new UnmanagedImage(imageData);
int x = (int)(unmanagedImg.Width / 3.19); // 443
int y = (int)(unmanagedImg.Height / 2.2); // 490
int w = (int)(unmanagedImg.Width / 2.7); // 553
int h = (int)(unmanagedImg.Height / 4); // 270
int s = (int)(unmanagedImg.Width / 10.2); // 141
// Crop the player scroll window. Speeds up the next couple operations
Crop onePlayer = new Crop(new Rectangle(x, y, w, h));
UnmanagedImage img = onePlayer.Apply(unmanagedImg);
// Use a quadrilateral transformation to make the scroller a big square.
List <IntPoint> corners = new List <IntPoint>();
corners.Add(new IntPoint(s, 0));
corners.Add(new IntPoint(img.Width - s, 0));;
corners.Add(new IntPoint(img.Width, img.Height));
corners.Add(new IntPoint(0, img.Height));
QuadrilateralTransformation filter =
new QuadrilateralTransformation(corners, img.Width, img.Height);
img = filter.Apply(img);
// Crop the bottom half since it appears to have the best imagery
Crop bottom = new Crop(new Rectangle(0, img.Height / 2, img.Width, img.Height / 2));
img = bottom.Apply(img);
UnmanagedImage grayImg = UnmanagedImage.Create(img.Width, img.Height, PixelFormat.Format8bppIndexed);
Grayscale.CommonAlgorithms.BT709.Apply(img, grayImg);
OtsuThreshold threshold = new OtsuThreshold();
threshold.ApplyInPlace(grayImg);
// Divide the square into 5 peices. One for each color.
UnmanagedImage[] colorImg = new UnmanagedImage[5];
for (int i = 0; i < 5; i++)
{
int colorWidth = grayImg.Width / 5;
int colorHeight = grayImg.Height;
Crop colorCrop = new Crop(new Rectangle(colorWidth * i, 0, colorWidth, colorHeight));
colorImg[i] = colorCrop.Apply(grayImg);
}
greenCol.Image = colorImg[GREEN].ToManagedImage();
redCol.Image = colorImg[RED].ToManagedImage();
yellowCol.Image = colorImg[YELLOW].ToManagedImage();
blueCol.Image = colorImg[BLUE].ToManagedImage();
orangeCol.Image = colorImg[ORANGE].ToManagedImage();
VerticalIntensityStatistics[] hist = new VerticalIntensityStatistics[5];
for (int i = 0; i < 5; i++)
{
hist[i] = new VerticalIntensityStatistics(colorImg[i]);
}
findPucks(hist);
greenHist.Values = hist[GREEN].Gray.Values;
redHist.Values = hist[RED].Gray.Values;
yellowHist.Values = hist[YELLOW].Gray.Values;
blueHist.Values = hist[BLUE].Gray.Values;
orangeHist.Values = hist[ORANGE].Gray.Values;
pictureBox1.Image = img.ToManagedImage();
}
finally
{
image.UnlockBits(imageData);
}
}
