|
public Detect ( Byte>.Image |
||
| image | Byte>.Image | The image where the objects are to be detected from |
| 리턴 | Emgu.CV.Structure.MCvAvgComp[] | |
private void Window_Loaded(object sender, RoutedEventArgs e)
{
var sourceImage = new Bitmap("C:\\Steve_Wozniak.jpg");
string haarcascade = "haarcascade_frontalface_default.xml";
using (HaarCascade face = new HaarCascade(haarcascade))
{
var image = new Image<Rgb, Byte>(sourceImage);
using (var gray = image.Convert<Gray, Byte>())
{
var detectedFaces = face.Detect(
gray,
1.1,
10,
Emgu.CV.CvEnum.HAAR_DETECTION_TYPE.DO_CANNY_PRUNING,
new System.Drawing.Size(20, 20));
var firstFace = detectedFaces[0];
System.Drawing.Bitmap bmpImage = image.Bitmap;
System.Drawing.Bitmap bmpCrop = bmpImage.Clone(firstFace.rect,
bmpImage.PixelFormat);
var cropedImage = new Image<Rgb, Byte>(bmpCrop);
MainImage.Source = ToBitmapSource(sourceImage);
DetectedFaceImage.Source = ToBitmapSource(cropedImage.Bitmap);
}
}
}
public static Image<Gray, byte> DetectAndTrimFace(int[] pixels, Size initialSize, Size outputSize, String haarcascadePath)
{
var inBitmap = ConvertToBitmap(pixels, initialSize.Width, initialSize.Width);
//for testing purposes I can the picture to a folder
//inBitmap.Save(@"E:\data\phototest\received.bmp");
var grayframe = new Image<Gray, byte>(inBitmap);
var haar = new HaarCascade(haarcascadePath);
var faces = haar.Detect(grayframe,
1.2,
3,
HAAR_DETECTION_TYPE.DO_CANNY_PRUNING,
new Size(30, 30));
if (faces.Count() != 1)
{
return null;
}
var face = faces[0];
var returnImage = grayframe.Copy(face.rect).Resize(outputSize.Width, outputSize.Height, INTER.CV_INTER_CUBIC);
//cleanup managed resources
haar.Dispose();
grayframe.Dispose();
return returnImage;
}
protected virtual MCvAvgComp[] GetFacesVector(Image<Gray, byte> grayImage)
{
var haarCascade = new HaarCascade(this.HaarCascadePath);
return haarCascade.Detect(grayImage,
this.ScanFactor, //the image where the object are to be detected from
this.Neighbours, //factor by witch the window is scaled in subsequent scans
HAAR_DETECTION_TYPE.DO_CANNY_PRUNING, //min number of neighbour rectangles
Size.Empty,
Size.Empty);
}
public static Rectangle[] Detect(Image<Bgr, Byte> image, string cascadeFile,
double scaleFactor = 1.3, int minNeighbors = 10,
Emgu.CV.CvEnum.HAAR_DETECTION_TYPE detectionType = Emgu.CV.CvEnum.HAAR_DETECTION_TYPE.DO_CANNY_PRUNING,
int minSize = 20, int maxSize = 0)
{
string cascadeFilePath = CascadeManager.GetCascade(cascadeFile);
Size minimumSize;
if (minSize == 0)
{
minimumSize = Size.Empty;
}
else
{
minimumSize = new Size(minSize, minSize);
}
Size maximumSize;
if (maxSize == 0)
{
maximumSize = Size.Empty;
}
else
{
maximumSize = new Size(maxSize, maxSize);
}
if (GpuInvoke.HasCuda)
{
using (GpuCascadeClassifier cascade = new GpuCascadeClassifier(cascadeFilePath))
using (GpuImage<Bgr, Byte> gpuImage = new GpuImage<Bgr, byte>(image))
using (GpuImage<Gray, Byte> gpuGray = gpuImage.Convert<Gray, Byte>())
{
return cascade.DetectMultiScale(gpuGray, scaleFactor, minNeighbors, minimumSize);
}
}
else
{
using (HaarCascade cascade = new HaarCascade(cascadeFilePath))
using (Image<Gray, Byte> gray = image.Convert<Gray, Byte>())
{
gray._EqualizeHist();
MCvAvgComp[] detected = cascade.Detect(gray,
scaleFactor, minNeighbors,
detectionType,
minimumSize, maximumSize);
return (from x in detected
select x.rect).ToArray();
}
}
}
/// <summary>
/// Run over directory, get images, detech faces, resize and storeagain
/// </summary>
/// <param name="directory"></param>
public static void CreateTrainingSet(String mainDirectory, int newSize, bool equalize, int rotation, bool flip)
{
string[] subdirEntries = Directory.GetDirectories(mainDirectory);
foreach (var directory in subdirEntries)
{
string[] fileEntries = Directory.GetFiles(directory);
foreach (var file in fileEntries.Where(x=>(!x.Contains("_"))))
{
Image<Gray, byte> image = new Image<Gray, byte>(file);
//the images are big - reduce the size to the half
image = image.Resize(0.5, INTER.CV_INTER_CUBIC);
var haar = new HaarCascade(FileAccessUtil.GetHaarCascade());
var faces = haar.Detect(image);
if (faces.Count() == 1)
{
var face = faces[0];
//resize all images to 100
var faceImg = image.Copy(face.rect).Resize(newSize, newSize, INTER.CV_INTER_CUBIC);
String imgName = file.Insert(file.IndexOf("."), "_" + newSize.ToString());
if (equalize)
{
imgName = imgName.Insert(file.IndexOf("."), "_N");
var equalized = EqualizeHist(faceImg);
faceImg = equalized;
}
faceImg.Save(imgName);
//create rotated image if it was demanded
if (rotation != 0)
{
var rotated = faceImg.Rotate(rotation, new Gray(0.3));
var rotatedName = imgName.Insert(file.IndexOf("."), "_R");
rotated.Save(rotatedName);
}
if (flip)
{
var fliped = faceImg.Flip(FLIP.HORIZONTAL);
var flipedName = imgName.Insert(file.IndexOf("."), "_F");
fliped.Save(flipedName);
}
}
}
}
}
protected MCvAvgComp[] DetectVarious(Image<Gray, byte> grayImage)
{
MCvAvgComp[] eyes = null;
foreach (var haarcascade in this.HaarCascadePaths)
{
var haarCascade = new HaarCascade(haarcascade);
eyes = haarCascade.Detect(grayImage,
this.ScanFactor, //the image where the object are to be detected from
this.Neighbours, //factor by witch the window is scaled in subsequent scans
HAAR_DETECTION_TYPE.DO_CANNY_PRUNING, //min number of neighbour rectangles
Size.Empty,
Size.Empty);
if (eyes.Any()) break;
}
return eyes;
}
public static Image<Gray, byte> DetectAndTrimFace(int[] pixels, int initialSize, int outputSize)
{
var inBitmap = ConvertToBitmap(pixels, initialSize);
//inBitmap.Save(@"E:\data\phototest\received.bmp");
var grayframe = new Image<Gray, byte>(inBitmap);
var haar = new HaarCascade(GetHaarCascade());
var faces = haar.Detect(grayframe,
1.2,
3,
HAAR_DETECTION_TYPE.DO_CANNY_PRUNING,
new Size(30, 30));
if (faces.Count() != 1)
{
return null;
}
var face = faces[0];
var returnImage = grayframe.Copy(face.rect).Resize(outputSize, outputSize, INTER.CV_INTER_CUBIC);
return returnImage;
}
/// <summary>
/// Adding to face database new faces - background worker
/// </summary>
/// <param name="sender"></param>
/// <param name="e"></param>
private void addImagesWorker_DoWork( object sender, System.ComponentModel.DoWorkEventArgs e )
{
String dir = (String)e.Argument;
FileInfo [] files = new DirectoryInfo(dir).GetFiles("*.jpg");
var haar = new HaarCascade("haarcascade_frontalface_default.xml");
int count = 0;
foreach (FileInfo file in files)
{
var image = new Image<Gray, Byte>(dir + "\\" + file.Name);
Image<Gray, byte> img = image.Convert<Gray, byte>();
var faces = haar.Detect(img, 1.4, 4, HAAR_DETECTION_TYPE.DO_CANNY_PRUNING,
new Size(img.Width / 8, img.Height / 8));
Rectangle rect = new Rectangle();
if (faces.Length > 0)
{
rect = faces [0].rect;
for (int i = 1; i < faces.Length; i++)
{
if (faces [i].rect.Width > rect.Width &&
faces [i].rect.Height > rect.Height)
{
rect = faces [i].rect;
}
}//find the biggest face in detected
img.ROI = rect;
Image<Gray, Byte> face = img.Clone();
Image<Gray, Byte> faceResized = face.Resize(100, 100,
Emgu.CV.CvEnum.INTER.CV_INTER_LINEAR);
if (faceResized != null)
{
var splitted = file.Name.Substring(0, file.Name.Length - 4).Split(' ');
int tmp;
if(int.TryParse(splitted[2],out tmp)){
faceResized.Save(Learn.createFileName(splitted [0], splitted [1], int.Parse(splitted [2])));
}
}//if resized
}//if found any face
count++;
addImagesWorker.ReportProgress(count, DateTime.Now);
}//check each jpg file
}
public int LoadAndProcessImage(string FileName, Communicator comm)
{
int Vote = 0;
imgOriginal = new Image<Bgr, Byte>(FileName);
imgGray = imgOriginal.Convert<Gray, Byte>();
//BitAnalysis.StartDye(0, 0, imgGray.Height, imgGray.Width, imgGray);
hcHumerus = new HaarCascade(".\\haarHumerus_03112013_4.8_18.xml");
ibImage.Image = imgBlank;
acHumerus = hcHumerus.Detect(imgGray,
4.8,
18,
HAAR_DETECTION_TYPE.SCALE_IMAGE,
Size.Empty,
Size.Empty);
acHumerus1 = hcHumerus.Detect(imgGray,
4.8,
18,
HAAR_DETECTION_TYPE.DO_CANNY_PRUNING,
Size.Empty,
Size.Empty);
int count1 = 0, count2 = 0;
foreach (MCvAvgComp acHum in acHumerus)
{
StartDye(acHum.rect.X, acHum.rect.Y, acHum.rect.Width, acHum.rect.Height, imgGray,comm);
if (Flag) // to get coordination x,y, and with, high
{
imgOriginal.Draw(acHum.rect, new Bgr(Color.Blue), 2);
count1++;
Vote = 1;
}
imgGray.Draw(acHum.rect, new Gray(0.0), 1);
}
if (count1 ==0)
{
foreach (MCvAvgComp acHum1 in acHumerus1)
{
StartDye(acHum1.rect.X, acHum1.rect.Y, acHum1.rect.Width, acHum1.rect.Height, imgGray,comm);
if (Flag) // to get coordination x,y, and with, high
{
imgOriginal.Draw(acHum1.rect, new Bgr(Color.Red), 2);
count2++;
Vote = 1;
}
imgGray.Draw(acHum1.rect, new Gray(0.0), 1);
}
}
if (count1 == 0 && count2 == 0 )
{
imgGray = imgGray.AddWeighted(imgGray, 1.0, 0.0, 0.0);
imgGray = imgGray.ThresholdToZero(new Gray(100));
imgGray = imgGray.SmoothGaussian(9);
imgGray = imgGray.Canny(0, 80);
hcHumerus = new HaarCascade(".\\HaarHumerus_03172013_2.8_3.xml");
acHumerus = hcHumerus.Detect(imgGray,
2.8,
3,
HAAR_DETECTION_TYPE.SCALE_IMAGE,
Size.Empty,
Size.Empty);
acHumerus1 = hcHumerus.Detect(imgGray,
2.8,
3,
HAAR_DETECTION_TYPE.DO_CANNY_PRUNING,
Size.Empty,
Size.Empty);
foreach (MCvAvgComp acHum in acHumerus)
{
StartDye(acHum.rect.X, acHum.rect.Y, acHum.rect.Width, acHum.rect.Height, imgGray,comm);
if (Flag) // to get coordination x,y, and with, high
{
imgOriginal.Draw(acHum.rect, new Bgr(Color.Orange), 2);
Vote = 1;
}
imgGray.Draw(acHum.rect, new Gray(0.0), 1);
}
foreach (MCvAvgComp acHum1 in acHumerus1)
{
StartDye(acHum1.rect.X, acHum1.rect.Y, acHum1.rect.Width, acHum1.rect.Height, imgGray,comm);
if (Flag) // to get coordination x,y, and with, high
{
imgOriginal.Draw(acHum1.rect, new Bgr(Color.Green), 2);
Vote = 1;
}
imgGray.Draw(acHum1.rect, new Gray(), 1);
}
}
return Vote;
}
public bool LoadAndProcessImage(string FileName)
{
bool Broken = false;
imgOriginal = new Image<Bgr, Byte>(FileName);
imgGray = imgOriginal.Convert<Gray, Byte>();
//BitAnalysis.StartDye(0, 0, imgGray.Height, imgGray.Width, imgGray);
hcHumerus = new HaarCascade(@"c:\haarHumerus_03112013_4.8_18.xml"); //haarHumerus_03112013_4.8_18
ibImage.Image = imgBlank;
acHumerus = hcHumerus.Detect(imgGray,
4.8,
18,
HAAR_DETECTION_TYPE.SCALE_IMAGE,
Size.Empty,
Size.Empty);
acHumerus1 = hcHumerus.Detect(imgGray,
4.8,
18,
HAAR_DETECTION_TYPE.DO_CANNY_PRUNING,
Size.Empty,
Size.Empty);
int count1 = 0, count2 = 0;
PB.Value += 10;
foreach (MCvAvgComp acHum in acHumerus)
{
if (GetCoordination(acHum)) // to get coordination x,y, and with, high
{
imgOriginal.Draw(acHum.rect, new Bgr(Color.Blue), 2);
count1++;
Broken = true;
}
imgGray.Draw(acHum.rect, new Gray(0.0), 1);
}
PB.Value += 10;
if (count1 == 0)
{
foreach (MCvAvgComp acHum1 in acHumerus1)
{
if (GetCoordination(acHum1)) // to get coordination x,y, and with, high
{
imgOriginal.Draw(acHum1.rect, new Bgr(Color.Red), 2);
count2++;
Broken = true;
}
imgGray.Draw(acHum1.rect, new Gray(0.0), 1);
}
}
if (count1 == 0 && count2 == 0 )
{
imgGray = imgGray.AddWeighted(imgGray, 1.0, 0.0, 0.0);
imgGray = imgGray.ThresholdToZero(new Gray(100));
imgGray = imgGray.SmoothGaussian(9);
imgGray = imgGray.Canny(0, 80);
hcHumerus = new HaarCascade(@"c:\HaarHumerus_03172013_2.8_3.xml");
acHumerus = hcHumerus.Detect(imgGray,
2.8,
3,
HAAR_DETECTION_TYPE.SCALE_IMAGE,
Size.Empty,
Size.Empty);
acHumerus1 = hcHumerus.Detect(imgGray,
2.8,
3,
HAAR_DETECTION_TYPE.DO_CANNY_PRUNING,
Size.Empty,
Size.Empty);
foreach (MCvAvgComp acHum in acHumerus)
{
if (GetCoordination(acHum)) // to get coordination x,y, and with, high
{
imgOriginal.Draw(acHum.rect, new Bgr(Color.Orange), 2);
Broken = true;
}
imgGray.Draw(acHum.rect, new Gray(0.0), 1);
}
foreach (MCvAvgComp acHum1 in acHumerus1)
{
if (GetCoordination(acHum1)) // to get coordination x,y, and with, high
{
imgOriginal.Draw(acHum1.rect, new Bgr(Color.Green), 2);
Broken = true;
}
imgGray.Draw(acHum1.rect, new Gray(), 1);
}
}
PB.Value = +20;
return Broken;
}
//cannyEdgeDetect ends
//Face Detection in image
private MCvAvgComp[] faceDetection(Image<Bgr, Byte> image)
{
Console.Out.WriteLine("Finding faces in the images...");
grayScale_Image = convertToGrayScale(image);
//HaarCascade for face detection.
HaarCascade facehaar;
try
{
//Face is detected by using haar-like structure training. The trained data set is already defined in the XML file
facehaar = new HaarCascade(@"haarcascade_frontalface_default.xml");
//Use Canny edge to filter the images.
return facehaar.Detect(grayScale_Image, 1.1, 4, HAAR_DETECTION_TYPE.DO_CANNY_PRUNING, new Size(grayScale_Image.Width / 12, grayScale_Image.Height / 12), new Size(grayScale_Image.Width / 2, grayScale_Image.Height / 2));
}
catch (Exception ex) {
Console.Out.WriteLine("File Not Found for FaceHaarCascade: "+ex.Message);
}
return null;
}
static void Run()
{
Image<Bgr, Byte> image = new Image<Bgr, byte>("lena.jpg"); //Read the files as an 8-bit Bgr image
Stopwatch watch;
String faceFileName = "haarcascade_frontalface_default.xml";
String eyeFileName = "haarcascade_eye.xml";
if (GpuInvoke.HasCuda)
{
using (GpuCascadeClassifier face = new GpuCascadeClassifier(faceFileName))
using (GpuCascadeClassifier eye = new GpuCascadeClassifier(eyeFileName))
{
watch = Stopwatch.StartNew();
using (GpuImage<Bgr, Byte> gpuImage = new GpuImage<Bgr, byte>(image))
using (GpuImage<Gray, Byte> gpuGray = gpuImage.Convert<Gray, Byte>())
{
Rectangle[] faceRegion = face.DetectMultiScale(gpuGray, 1.1, 10, Size.Empty);
foreach (Rectangle f in faceRegion)
{
//draw the face detected in the 0th (gray) channel with blue color
image.Draw(f, new Bgr(Color.Blue), 2);
using (GpuImage<Gray, Byte> faceImg = gpuGray.GetSubRect(f))
{
//For some reason a clone is required.
//Might be a bug of GpuCascadeClassifier in opencv
using (GpuImage<Gray, Byte> clone = faceImg.Clone())
{
Rectangle[] eyeRegion = eye.DetectMultiScale(clone, 1.1, 10, Size.Empty);
foreach (Rectangle e in eyeRegion)
{
Rectangle eyeRect = e;
eyeRect.Offset(f.X, f.Y);
image.Draw(eyeRect, new Bgr(Color.Red), 2);
}
}
}
}
}
watch.Stop();
}
}
else
{
//Read the HaarCascade objects
using(HaarCascade face = new HaarCascade(faceFileName))
using(HaarCascade eye = new HaarCascade(eyeFileName))
{
watch = Stopwatch.StartNew();
using (Image<Gray, Byte> gray = image.Convert<Gray, Byte>()) //Convert it to Grayscale
{
//normalizes brightness and increases contrast of the image
gray._EqualizeHist();
//Detect the faces from the gray scale image and store the locations as rectangle
//The first dimensional is the channel
//The second dimension is the index of the rectangle in the specific channel
MCvAvgComp[] facesDetected = face.Detect(
gray,
1.1,
10,
Emgu.CV.CvEnum.HAAR_DETECTION_TYPE.DO_CANNY_PRUNING,
new Size(20, 20));
foreach (MCvAvgComp f in facesDetected)
{
//draw the face detected in the 0th (gray) channel with blue color
image.Draw(f.rect, new Bgr(Color.Blue), 2);
//Set the region of interest on the faces
gray.ROI = f.rect;
MCvAvgComp[] eyesDetected = eye.Detect(
gray,
1.1,
10,
Emgu.CV.CvEnum.HAAR_DETECTION_TYPE.DO_CANNY_PRUNING,
new Size(20, 20));
gray.ROI = Rectangle.Empty;
foreach (MCvAvgComp e in eyesDetected)
{
Rectangle eyeRect = e.rect;
eyeRect.Offset(f.rect.X, f.rect.Y);
image.Draw(eyeRect, new Bgr(Color.Red), 2);
}
}
}
watch.Stop();
}
}
//display the image
ImageViewer.Show(image, String.Format(
"Completed face and eye detection using {0} in {1} milliseconds",
GpuInvoke.HasCuda ? "GPU": "CPU",
watch.ElapsedMilliseconds));
}
public void ProcessFaces()
{
this._stopwatch.Reset();
this._stopwatch.Start();
// get the current frame from our webcam
Image<Bgr, Byte> frame = _capture.QueryFrame();
Image<Gray, Byte> gray;
//Read the HaarCascade objects
using (HaarCascade face = new HaarCascade(FACEFILENAME)) {
//Convert it to Grayscale
gray = frame.Convert<Gray, Byte>();
//normalizes brightness and increases contrast of the image
gray._EqualizeHist();
//Detect the faces from the gray scale image and store the locations as rectangle
//The first dimensional is the channel
//The second dimension is the index of the rectangle in the specific channel
MCvAvgComp[] facesDetected = face.Detect(
gray,
1.1,
10,
Emgu.CV.CvEnum.HAAR_DETECTION_TYPE.DO_CANNY_PRUNING,
new System.Drawing.Size(20, 20));
if (facesDetected != null && facesDetected.Length > 0) {
this.notifyIcon.ShowBalloonTip(5000, "Alert!", "Shoulder surfer detected!",ToolTipIcon.Warning);
Console.WriteLine("Shoulder surfer detected!");
}
}
this._stopwatch.Stop();
Console.WriteLine("Elapsed {0}s", this._stopwatch.Elapsed.TotalSeconds);
}
private void ProcessFrame(object sender, EventArgs e)
{
if(viewer!=null && capture != null)
{
img = capture.QueryFrame();
HaarCascade haar = new HaarCascade("../../haarcascade_frontalface_default.xml");
Image<Gray, byte> grayframe = img.Convert<Gray, byte>();
var faces = haar.Detect(grayframe, 1.2, 3, HAAR_DETECTION_TYPE.DO_ROUGH_SEARCH, new Size(20, 20), new Size(img.Width / 2, img.Height / 2));
foreach (var face in faces)
{
img.Draw(face.rect, new Bgr(0, double.MaxValue, 0), 3);
}
viewer.Image = img;
}
}
public static void detectFaceHaar(Image<Bgr, Byte> image, String faceFileName, String eyesFileName, List<MCvAvgComp> facesList, List<MCvAvgComp> eyesList, out long detectionTime)
{
Stopwatch watch;
using(HaarCascade faceCascade = new HaarCascade(faceFileName))
using (HaarCascade eyesCascade = new HaarCascade(eyesFileName))
{
watch = Stopwatch.StartNew();
using (Image<Gray, Byte> grayImage = image.Convert<Gray, Byte>())
{
//grayImage._EqualizeHist();
MCvAvgComp[] facesRegion = faceCascade.Detect(grayImage, 1.4, 3, Emgu.CV.CvEnum.HAAR_DETECTION_TYPE.FIND_BIGGEST_OBJECT, new Size(image.Width / 8, image.Height / 8) /*new Size(24, 24)*/, Size.Empty);
facesList.AddRange(facesRegion);
foreach (MCvAvgComp f in facesList)
{
grayImage.ROI = f.rect;
MCvAvgComp[] eyesDetected = eyesCascade.Detect(grayImage, 1.15, 3, Emgu.CV.CvEnum.HAAR_DETECTION_TYPE.DO_CANNY_PRUNING, new Size(20, 20) /*new Size(24, 24)*/, Size.Empty);
grayImage.ROI = Rectangle.Empty;
foreach (MCvAvgComp e in eyesDetected)
{
MCvAvgComp eyeRect = e;
eyeRect.rect.Offset(f.rect.X, f.rect.Y);
eyesList.Add(eyeRect);
}
}
}
watch.Stop();
}
detectionTime = watch.ElapsedMilliseconds;
}