public void Init(PictureBox pb)
{
FilterInfoCollection VideoCaptuerDevices = new FilterInfoCollection(FilterCategory.VideoInputDevice);
VideoCaptureDevice FinalVideoSource = new VideoCaptureDevice(VideoCaptuerDevices[0].MonikerString);
FinalVideoSource.NewFrame += new NewFrameEventHandler((sender, eventArgs) =>
{
Bitmap image = (Bitmap)eventArgs.Frame.Clone();
FaceHaarCascade cascade = new FaceHaarCascade();
HaarObjectDetector detector = new HaarObjectDetector(cascade, 80);
detector.UseParallelProcessing = true;
Rectangle[] faces = detector.ProcessFrame(image);
Graphics g = Graphics.FromImage(image);
foreach (var face in faces)
{
Pen p = new Pen(Color.Red, 10f);
g.DrawRectangle(p, face);
}
g.Dispose();
pb.Image = image;
});
FinalVideoSource.DesiredFrameRate = 1;
FinalVideoSource.DesiredFrameSize = new Size(1, 500);
FinalVideoSource.Start();
}
public static Rectangle detectFace(Bitmap bitmap)
{
var rectangle = new Rectangle();
var cascade = new FaceHaarCascade();
var detector = new HaarObjectDetector(cascade, 600);
detector.SearchMode = ObjectDetectorSearchMode.Default;
detector.ScalingFactor = 1.5F;
detector.ScalingMode = ObjectDetectorScalingMode.SmallerToGreater;
detector.UseParallelProcessing = true;
detector.Suppression = 3;
var faceObjects = detector.ProcessFrame(bitmap);
var possbleFaces = new List <Rectangle>();
foreach (var face in faceObjects)
{
if (face.Width > 100 && face.Height > 100)
{
possbleFaces.Add(face);
}
}
if (possbleFaces.Count > 0)
{
int x = possbleFaces.Sum((r) => r.X) / possbleFaces.Count;
int y = possbleFaces.Sum((r) => r.Y) / possbleFaces.Count;
int width = possbleFaces.Sum((r) => r.Width) / possbleFaces.Count;
int height = possbleFaces.Sum((r) => r.Width) / possbleFaces.Count;
rectangle = new Rectangle(x, y, width, height);
}
return(rectangle);
}
public void ProcessFrame3()
{
HaarCascade cascade = new FaceHaarCascade();
HaarObjectDetector target = new HaarObjectDetector(cascade,
15, ObjectDetectorSearchMode.NoOverlap);
Bitmap bmp = Properties.Resources.three;
target.ProcessFrame(bmp);
Assert.AreEqual(3, target.DetectedObjects.Length);
Assert.AreEqual(180, target.DetectedObjects[0].X);
Assert.AreEqual(275, target.DetectedObjects[0].Y);
Assert.AreEqual(41, target.DetectedObjects[0].Width);
Assert.AreEqual(41, target.DetectedObjects[0].Height);
Assert.AreEqual(168, target.DetectedObjects[1].X);
Assert.AreEqual(144, target.DetectedObjects[1].Y);
Assert.AreEqual(49, target.DetectedObjects[1].Width);
Assert.AreEqual(49, target.DetectedObjects[1].Height);
Assert.AreEqual(392, target.DetectedObjects[2].X);
Assert.AreEqual(133, target.DetectedObjects[2].Y);
Assert.AreEqual(59, target.DetectedObjects[2].Width);
Assert.AreEqual(59, target.DetectedObjects[2].Height);
target = new HaarObjectDetector(cascade,
15, ObjectDetectorSearchMode.Single);
target.ProcessFrame(bmp);
Assert.AreEqual(1, target.DetectedObjects.Length);
}
public void MaxSizeTest()
{
HaarCascade cascade = new FaceHaarCascade();
HaarObjectDetector target = new HaarObjectDetector(cascade,
50, ObjectDetectorSearchMode.Default);
Bitmap bmp = Properties.Resources.lena_color;
Rectangle[] result;
target.MaxSize = new Size(10, 60);
result = target.ProcessFrame(bmp);
Assert.AreEqual(0, result.Length);
target.MaxSize = new Size(60, 60);
result = target.ProcessFrame(bmp);
Assert.AreEqual(1, result.Length);
foreach (var r in result)
{
Assert.IsTrue(r.Width <= target.MaxSize.Width);
Assert.IsTrue(r.Height <= target.MaxSize.Height);
}
target.MaxSize = new Size(80, 80);
result = target.ProcessFrame(bmp);
Assert.AreEqual(2, result.Length);
foreach (var r in result)
{
Assert.IsTrue(r.Width <= target.MaxSize.Width);
Assert.IsTrue(r.Height <= target.MaxSize.Height);
}
}
public void ProcessFrame2()
{
HaarCascade cascade = new FaceHaarCascade();
HaarObjectDetector target = new HaarObjectDetector(cascade,
30, ObjectDetectorSearchMode.NoOverlap);
Bitmap bmp = Properties.Resources.lena_gray;
target.ProcessFrame(bmp);
Assert.AreEqual(1, target.DetectedObjects.Length);
Assert.AreEqual(255, target.DetectedObjects[0].X);
Assert.AreEqual(225, target.DetectedObjects[0].Y);
Assert.AreEqual(123, target.DetectedObjects[0].Width);
Assert.AreEqual(123, target.DetectedObjects[0].Height);
target = new HaarObjectDetector(cascade,
30, ObjectDetectorSearchMode.Default);
target.ProcessFrame(bmp);
Assert.AreEqual(6, target.DetectedObjects.Length);
Assert.AreEqual(255, target.DetectedObjects[0].X);
Assert.AreEqual(225, target.DetectedObjects[0].Y);
Assert.AreEqual(123, target.DetectedObjects[0].Width);
Assert.AreEqual(123, target.DetectedObjects[0].Height);
}
/// <summary>
///
/// </summary>
/// <param name="sender"></param>
/// <param name="e"></param>
private void MainForm_Load(object sender, EventArgs e)
{
watch.Stop();
#region extracting icon from application to this form window
Icon = Icon.ExtractAssociatedIcon(Application.ExecutablePath);
#endregion
#region Load default Mask Image
if (File.Exists("mask.png"))
{
mask = LoadMask("mask.png");
}
else
{
mask = Icon.ToBitmap();
}
erase = new Bitmap(mask.Width, mask.Height, PixelFormat.Format32bppArgb);
#endregion
#region Add file(s) from command line args
string[] flist = Environment.GetCommandLineArgs();
lvFiles.Items.Clear();
foreach (string f in flist)
{
if (PhotoExts.Contains(Path.GetExtension(f), StringComparer.CurrentCultureIgnoreCase))
{
if (lvFiles.Items.Count == 0 || lvFiles.FindItemWithText(f, true, 0) == null)
{
ListViewItem fItem = new ListViewItem(new string[] { Path.GetFileName(f), f });
lvFiles.Items.Add(fItem);
}
}
}
#endregion
picPreview.SizeMode = PictureBoxSizeMode.Zoom;
picPreview.Image = photo;
picMask.SizeMode = PictureBoxSizeMode.Zoom;
picMask.Image = mask;
cbMode.DataSource = Enum.GetValues(typeof(ObjectDetectorSearchMode));
cbScaling.DataSource = Enum.GetValues(typeof(ObjectDetectorScalingMode));
cbMode.SelectedItem = ObjectDetectorSearchMode.NoOverlap;
cbScaling.SelectedItem = ObjectDetectorScalingMode.SmallerToGreater;
SearchMode = ObjectDetectorSearchMode.NoOverlap;
ScalingMode = ObjectDetectorScalingMode.SmallerToGreater;
OutSize = (int)numOutSize.Value;
faceSize = (int)numFaceSize.Value;
GrayFirst = chkGrayDetect.Checked;
HaarCascade cascade = new FaceHaarCascade();
detector = new HaarObjectDetector(cascade);
}
public void ParseTest3()
{
HaarCascade cascade = new FaceHaarCascade();
Assert.AreEqual(22, cascade.Stages.Length);
Assert.AreEqual(3, cascade.Stages[0].Trees.Length);
Assert.AreEqual(1, cascade.Stages[0].Trees[0].Length);
}
static void Main(string[] args)
{
////ApplyBiTonal
//Bitmap b = (Bitmap)Image.FromFile("test.png");
//Processor.ApplyBiTonal(ref b, (byte.MaxValue * 3) / 2, System.Drawing.Color.Red, System.Drawing.Color.White);
//b.Save("result.png");
//WebClient wc = new WebClient();
//byte[] bytes = wc.DownloadData("https://lpportalvhds5rnqyvf495z0.blob.core.windows.net/imagger/408fcdc5-8e91-4dc9-ada8-87287c8cf77f-orinal.jpeg");
//MemoryStream ms = new MemoryStream(bytes);
//Bitmap imageBitmap = new Bitmap(ms);
//Processor.ApplyBrightness(ref imageBitmap, 100);
////Processor.ApplyBiTonal(ref imageBitmap, (byte.MaxValue * 3) / 2, System.Drawing.Color.Red, System.Drawing.Color.White);
//imageBitmap.Save("result.png");
//WebClient wc = new WebClient();
//byte[] bytes = wc.DownloadData("https://lpportalvhds5rnqyvf495z0.blob.core.windows.net/imagger/157bed84-5f10-4f43-a5e5-7f0d146e3452-orinal.jpeg");
//MemoryStream ms = new MemoryStream(bytes);
//Bitmap imageBitmap = new Bitmap(ms);
//Processor.ApplyRandomJitter(ref imageBitmap, 20); //, System.Drawing.Color.Red, System.Drawing.Color.White);
//imageBitmap.Save("result.png");
//Bitmap br = (Bitmap)Image.FromFile("test.png");
//Processor.ApplyBrightness(ref br, 100);
//br.Save("result-br.png");
#region Detect faces
WebClient wc = new WebClient();
byte[] bytes = wc.DownloadData("https://scontent-fra3-1.xx.fbcdn.net/v/t1.0-1/c0.46.200.200/1724176_10151691353752537_1722497807_n.jpg?oh=19d89af364674bd704cd38613135b4e1&oe=583097F3");
MemoryStream ms = new MemoryStream(bytes);
Bitmap picture = new Bitmap(ms);
HaarObjectDetector detector;
HaarCascade cascade = new FaceHaarCascade();
detector = new HaarObjectDetector(cascade, 30);
detector.SearchMode = ObjectDetectorSearchMode.NoOverlap;
detector.ScalingMode = ObjectDetectorScalingMode.GreaterToSmaller;
detector.ScalingFactor = 1.5f;
detector.UseParallelProcessing = true;
Rectangle[] objects = detector.ProcessFrame(picture);
// RandomJitter.ApplyRandomJitterOnRectangulars(ref picture, 20, objects);
// RandomJitter.ApplyRandomJitterOnRectangulars2(ref picture, 20, objects);
RandomJitter.ApplyAddNoise(ref picture, objects);
picture.Save("result.png");
// var p = RandomJitter.ApplyColorMatrix(picture, objects);
//p.Save("result.png");
#endregion
}
// Constructor
public MainForm()
{
InitializeComponent();
HaarCascade cascade = new FaceHaarCascade();
detector = new HaarObjectDetector(cascade, 25, ObjectDetectorSearchMode.Single, 1.2f,
ObjectDetectorScalingMode.GreaterToSmaller);
}
public void ParseTest()
{
string fileName = Path.Combine(TestContext.CurrentContext.TestDirectory, "Resources", "haarcascade_frontalface_alt.xml");
HaarCascade cascade1 = HaarCascade.FromXml(new StreamReader(fileName));
Assert.AreEqual(22, cascade1.Stages.Length);
Assert.AreEqual(3, cascade1.Stages[0].Trees.Length);
Assert.AreEqual(1, cascade1.Stages[0].Trees[0].Length);
Assert.AreEqual(false, cascade1.HasTiltedFeatures);
// Load the hard coded version of the classifier
HaarCascade cascade2 = new FaceHaarCascade();
Assert.AreEqual(cascade1.Stages.Length, cascade2.Stages.Length);
Assert.AreEqual(cascade1.Height, cascade2.Height);
Assert.AreEqual(cascade1.Width, cascade2.Width);
for (int i = 0; i < 3; i++)
{
HaarCascadeStage stage1 = cascade1.Stages[i];
HaarCascadeStage stage2 = cascade2.Stages[i];
//Assert.AreEqual(stage1.NextIndex, stage2.NextIndex);
//Assert.AreEqual(stage1.ParentIndex, stage2.ParentIndex);
Assert.AreEqual(stage1.Threshold, stage2.Threshold);
Assert.AreEqual(stage1.Trees.Length, stage2.Trees.Length);
for (int j = 0; j < stage1.Trees.Length && j < stage2.Trees.Length; j++)
{
HaarFeatureNode[] tree1 = stage1.Trees[j];
HaarFeatureNode[] tree2 = stage2.Trees[j];
Assert.AreEqual(tree1.Length, tree2.Length);
for (int k = 0; k < tree1.Length; k++)
{
HaarFeatureNode node1 = tree1[k];
HaarFeatureNode node2 = tree2[k];
Assert.AreEqual(node1.LeftNodeIndex, node2.LeftNodeIndex);
Assert.AreEqual(node1.RightNodeIndex, node2.RightNodeIndex);
Assert.AreEqual(node1.LeftValue, node2.LeftValue);
Assert.AreEqual(node1.RightValue, node2.RightValue);
Assert.AreEqual(node1.Feature.Tilted, node2.Feature.Tilted);
Assert.AreEqual(node1.Threshold, node2.Threshold, 0.000000001);
}
}
}
}
public FaceDetector()
{
HaarCascade cascade = new FaceHaarCascade();
detector = new HaarObjectDetector(cascade, 30);
detector.SearchMode = ObjectDetectorSearchMode.Default;
detector.ScalingMode = ObjectDetectorScalingMode.GreaterToSmaller;
detector.ScalingFactor = 1.1f;
detector.UseParallelProcessing = true;
detector.Suppression = 2;
}
public void ProcessFrame()
{
HaarCascade cascade = new FaceHaarCascade();
HaarObjectDetector target = new HaarObjectDetector(cascade,
50, ObjectDetectorSearchMode.NoOverlap);
Bitmap bmp = Properties.Resources.lena_color;
target.ProcessFrame(bmp);
Assert.AreEqual(1, target.DetectedObjects.Length);
Assert.AreEqual(126, target.DetectedObjects[0].X);
Assert.AreEqual(112, target.DetectedObjects[0].Y);
Assert.AreEqual(59, target.DetectedObjects[0].Width);
Assert.AreEqual(59, target.DetectedObjects[0].Height);
}
public ImageController(PictureBox image)
{
this.image = image;
/*
* cbMode.DataSource = Enum.GetValues(typeof(ObjectDetectorSearchMode));
* cbScaling.DataSource = Enum.GetValues(typeof(ObjectDetectorScalingMode));
*
* cbMode.SelectedItem = ObjectDetectorSearchMode.NoOverlap;
* cbScaling.SelectedItem = ObjectDetectorScalingMode.SmallerToGreater;
*/
//toolStripStatusLabel1.Text = "Please select the detector options and click Detect to begin.";
HaarCascade cascade = new FaceHaarCascade();
detector = new HaarObjectDetector(cascade, 30);
}
public MainForm()
{
InitializeComponent();
pictureBox1.Image = picture;
cbMode.DataSource = Enum.GetValues(typeof(ObjectDetectorSearchMode));
cbScaling.DataSource = Enum.GetValues(typeof(ObjectDetectorScalingMode));
cbMode.SelectedItem = ObjectDetectorSearchMode.NoOverlap;
cbScaling.SelectedItem = ObjectDetectorScalingMode.SmallerToGreater;
toolStripStatusLabel1.Text = "Please select the detector options and click Detect to begin.";
HaarCascade cascade = new FaceHaarCascade();
detector = new HaarObjectDetector(cascade, 30);
}
public void CloneTest()
{
HaarCascade expected = new FaceHaarCascade();
HaarCascade actual = (HaarCascade)expected.Clone();
Assert.AreNotEqual(expected, actual);
Assert.AreEqual(expected.HasTiltedFeatures, actual.HasTiltedFeatures);
Assert.AreEqual(expected.Height, actual.Height);
Assert.AreEqual(expected.Width, actual.Width);
Assert.AreNotEqual(expected.Stages, actual.Stages);
Assert.AreEqual(expected.Stages.Length, actual.Stages.Length);
for (int i = 0; i < expected.Stages.Length; i++)
{
equals(expected.Stages[i], actual.Stages[i]);
}
}
public Tracking_face()
{
InitializeComponent();
try
{
// enumerate video devices
videoDevices = new FilterInfoCollection(FilterCategory.VideoInputDevice);
if (videoDevices.Count == 0)
{
throw new ApplicationException();
}
// add all devices to combo
foreach (FilterInfo device in videoDevices)
{
devicesCombo.Items.Add(device.Name);
}
HaarCascade cascade = new FaceHaarCascade();
detector = new HaarObjectDetector(cascade,
25, ObjectDetectorSearchMode.Single, 1.2f,
ObjectDetectorScalingMode.GreaterToSmaller);
}
catch (ApplicationException)
{
devicesCombo.Items.Add("No local capture devices");
devicesCombo.Enabled = false;
}
devicesCombo.SelectedIndex = 1;
// create video source
VideoCaptureDevice videoSource = new VideoCaptureDevice(videoDevices[devicesCombo.SelectedIndex].MonikerString);
// set frame size
videoSource.VideoResolution = selectResolution(videoSource);
// open it
OpenVideoSource(videoSource);
detecting = true;
}
public static string DetectFace(string path)
{
GetPicture(path);
// Process frame to detect objects
HaarCascade cascade = new FaceHaarCascade();
HaarObjectDetector detector = new HaarObjectDetector(cascade, 30);
detector.SearchMode = (ObjectDetectorSearchMode)cbMode;
detector.ScalingMode = (ObjectDetectorScalingMode)cbScaling;
detector.ScalingFactor = 1.5f;
Stopwatch sw = Stopwatch.StartNew();
Rectangle[] objects = detector.ProcessFrame(picture);
sw.Stop();
if (objects.Length > 0)
{
Console.WriteLine("here");
RectanglesMarker marker = new RectanglesMarker(objects, Color.Fuchsia);
picture = marker.Apply(picture);
}
if (picture != null)
{
Console.WriteLine("trying to print picture");
/*ImageCodecInfo myici = ImageCodecInfo.GetImageEncoders();
* int numCodecs = myici.GetLength(0);
* Encoder myEncoder = Encoder.Quality;
* EncoderParameters param = new EncoderParameters(1);
* EncoderParameter param1 = new EncoderParameter(myEncoder, 25L);
*
* picture.Save(@"output.jpg", myici, param);
*/
picture.Save("file.png", ImageFormat.Png);;
}
return(path);
}
public static void Main(string[] args)
{
var picture = Resource.lena_color;
HaarCascade cascade = new FaceHaarCascade();
var detector = new HaarObjectDetector(cascade, 30);
//detector.SearchMode = ObjectDetectorSearchMode.Average;
//detector.ScalingMode = ObjectDetectorScalingMode.GreaterToSmaller;
//detector.ScalingFactor = 1.5f;
//detector.UseParallelProcessing = false;
//detector.Suppression = 2;
Rectangle[] objects = detector.ProcessFrame(picture);
if (objects.Length > 0)
{
RectanglesMarker marker = new RectanglesMarker(objects, Color.Fuchsia);
var markedup = marker.Apply(picture);
markedup.Save($"{basedir}{sep}{sep}markedup.jpg");
}
}
//+mainWinForm(): Constructor, gets parts initialized
public mainWinForm()
{
SetStyle(ControlStyles.SupportsTransparentBackColor, true);
data = new FileHandler();
InitializeComponent();
this.bntStart.Enabled = false;
this.bntStop.Enabled = false;
this.recordBtn.Enabled = false;
this.capture.Enabled = false;
this.send.Enabled = false;
this.bntVideoSource.Enabled = false;
this.userInput.Enabled = false;
this.mic.SelectedIndexChanged += mic_SelectedIndexChanged;
Log("[Software Started]");
Log("[Components initialized]");
this.cascade = new FaceHaarCascade();
this.detector = new HaarObjectDetector(cascade, minSize: 50, searchMode: ObjectDetectorSearchMode.NoOverlap);
Log(">Please choose an audio source.");
}
public void MinSizeTest()
{
HaarCascade cascade = new FaceHaarCascade();
HaarObjectDetector target = new HaarObjectDetector(cascade,
50, ObjectDetectorSearchMode.Default);
Bitmap bmp = Accord.Imaging.Image.Clone(Resources.lena_color);
Rectangle[] result;
target.MinSize = new Size(10, 60);
result = target.ProcessFrame(bmp);
Assert.AreEqual(3, result.Length);
foreach (var r in result)
{
Assert.IsTrue(r.Width >= target.MinSize.Width);
Assert.IsTrue(r.Height >= target.MinSize.Height);
}
target.MinSize = new Size(85, 85);
result = target.ProcessFrame(bmp);
Assert.AreEqual(2, result.Length);
foreach (var r in result)
{
Assert.IsTrue(r.Width >= target.MinSize.Width);
Assert.IsTrue(r.Height >= target.MinSize.Height);
}
target.MinSize = new Size(1, 1);
result = target.ProcessFrame(bmp);
Assert.AreEqual(4, result.Length);
foreach (var r in result)
{
Assert.IsTrue(r.Width >= target.MinSize.Width);
Assert.IsTrue(r.Height >= target.MinSize.Height);
}
}
public void ProcessFrame3()
{
HaarCascade cascade = new FaceHaarCascade();
HaarObjectDetector target = new HaarObjectDetector(cascade,
15, ObjectDetectorSearchMode.NoOverlap);
Bitmap bmp = Accord.Imaging.Image.Clone(Resources.three);
target.ProcessFrame(bmp);
Assert.AreEqual(2, target.DetectedObjects.Length);
int i = 0;
/* Assert.AreEqual(180, target.DetectedObjects[0].X);
* Assert.AreEqual(275, target.DetectedObjects[0].Y);
* Assert.AreEqual(41, target.DetectedObjects[0].Width);
* Assert.AreEqual(41, target.DetectedObjects[0].Height);
*/
Assert.AreEqual(168, target.DetectedObjects[i].X);
Assert.AreEqual(144, target.DetectedObjects[i].Y);
Assert.AreEqual(49, target.DetectedObjects[i].Width);
Assert.AreEqual(49, target.DetectedObjects[i].Height);
i++;
Assert.AreEqual(392, target.DetectedObjects[i].X);
Assert.AreEqual(133, target.DetectedObjects[i].Y);
Assert.AreEqual(59, target.DetectedObjects[i].Width);
Assert.AreEqual(59, target.DetectedObjects[i].Height);
target = new HaarObjectDetector(cascade,
15, ObjectDetectorSearchMode.Single);
target.ProcessFrame(bmp);
Assert.AreEqual(1, target.DetectedObjects.Length);
}
private async void MainWindow_Loaded(object sender, RoutedEventArgs e)
{
// Wait for the client to get setup
await Task.Delay(5000);
// Setup Socket Client
Client = new TcpClient();
Client.Connect(IPAddress.Parse("10.10.10.100"), 1911);
ClientStream = Client.GetStream();
// enumerate video devices
videoDevices = new FilterInfoCollection(FilterCategory.VideoInputDevice);
// create video source
videoSource = new VideoCaptureDevice(videoDevices[1].MonikerString);
videoSource.SnapshotResolution = videoSource.VideoCapabilities[7];
// set NewFrame event handler
videoSource.NewFrame += new NewFrameEventHandler(video_NewFrame);
// Setup Face Detection
Cascade = new FaceHaarCascade();
Detector = new HaarObjectDetector(Cascade, 30);
Detector.SearchMode = ObjectDetectorSearchMode.Average;
//Detector.ScalingFactor = 1f;
Detector.ScalingMode = ObjectDetectorScalingMode.GreaterToSmaller;
Detector.UseParallelProcessing = true;
Detector.Suppression = 3;
// Setup Tracking Data
CameraVector.X = 90;
CameraVector.Y = 90;
//ClientStream.Write(Encoding.UTF8.GetBytes("090,090"), 0, 7);
//await Task.Delay(3000);
// start the video source
videoSource.Start();
}
static void Main(string[] args)
{
String outputDir = "H:\\Temp\\";
UserCredential credential;
FaceHaarCascade cascade = new FaceHaarCascade();
HaarObjectDetector detector = new HaarObjectDetector(cascade);
detector.SearchMode = ObjectDetectorSearchMode.Average;
detector.ScalingFactor = 1.5F;
detector.ScalingMode = ObjectDetectorScalingMode.GreaterToSmaller;;
detector.UseParallelProcessing = true;
detector.Suppression = 3;
using (var stream = new FileStream("client_secret.json", FileMode.Open, FileAccess.Read))
{
string credPath = System.Environment.GetFolderPath(
System.Environment.SpecialFolder.Personal);
credPath = Path.Combine(credPath, ".credentials/gmail-dotnet-quickstart.json");
credential = GoogleWebAuthorizationBroker.AuthorizeAsync(
GoogleClientSecrets.Load(stream).Secrets,
Scopes,
"user",
CancellationToken.None,
new FileDataStore(credPath, true)).Result;
Console.WriteLine("Credential file saved to: " + credPath);
}
// Create Gmail API service.
var service = new GmailService(new BaseClientService.Initializer()
{
HttpClientInitializer = credential,
ApplicationName = ApplicationName,
});
// Define parameters of request.
UsersResource.MessagesResource.ListRequest request = service.Users.Messages.List("me");
request.Q = "has:attachment";
// List labels.
IList <Message> messages = request.Execute().Messages;
Console.WriteLine("Messages:");
if (messages != null && messages.Count > 0)
{
foreach (var messageItem in messages)
{
//Console.WriteLine("{0}", messageItem.Id);
Message message = service.Users.Messages.Get("me", messageItem.Id).Execute();
Console.WriteLine(message.Payload.MimeType.ToString());
IList <MessagePart> parts = message.Payload.Parts;
foreach (MessagePart part in parts)
{
if (!String.IsNullOrEmpty(part.Filename))
{
String attId = part.Body.AttachmentId;
MessagePartBody attachPart = service.Users.Messages.Attachments.Get("me", messageItem.Id, attId).Execute();
Console.WriteLine(part.Filename);
// Converting from RFC 4648 base64 to base64url encoding
// see http://en.wikipedia.org/wiki/Base64#Implementations_and_history
String attachData = attachPart.Data.Replace('-', '+');
attachData = attachData.Replace('_', '/');
byte[] data = Convert.FromBase64String(attachData);
MemoryStream ms = new MemoryStream(data);
Bitmap img = new Bitmap(Image.FromStream(ms));
Rectangle[] rects = detector.ProcessFrame(img);
if (rects.Count() > 0)
{
Console.WriteLine("Face detected!!!!");
File.WriteAllBytes(Path.Combine(outputDir, part.Filename), data);
}
}
}
}
}
else
{
Console.WriteLine("No messages found.");
}
Console.Read();
}
public AccordFaceDetector(Bitmap image1)
{
Accord.Vision.Detection.HaarCascade cascade = new FaceHaarCascade();
_detector = new HaarObjectDetector(cascade, 30);
image = image1;
}
public ViolaJonesFaceDetector(Profile profile, params Type[] clients) : base(profile, clients)
{
Cascade = new FaceHaarCascade();
Detector = new HaarObjectDetector(Cascade, 30);
Status = ApiStatus.Initializing;
}
private static void Process(string imagePath)
{
try
{
var cascade = new FaceHaarCascade();
var detector = new HaarObjectDetector(cascade, _varHaarMinSize);
detector.SearchMode = (ObjectDetectorSearchMode)_varHaarSearchMode;
detector.ScalingFactor = _varHaarScalingFact;
detector.ScalingMode = (ObjectDetectorScalingMode)_varHaarScalingMode;
detector.UseParallelProcessing = _varHaarParallel;
detector.Suppression = _varHaarSuppression;
using (var img = Image.FromFile(imagePath))
{
using (var bmp = new Bitmap(img)) {
var sw = Stopwatch.StartNew();
var faceObjects = detector.ProcessFrame(bmp);
sw.Stop();
LogMessage(string.Format("Detected {0} faces", faceObjects.Length));
foreach (var f in faceObjects)
{
Bitmap bmpImage = new Bitmap(img);
bmpImage = bmp.Clone(new Rectangle(f.Location, f.Size), bmpImage.PixelFormat);
string outPath = Path.Combine(_varDestFolder, _varDestPrefix + (++numImages).ToString("00000") + "." + _varDestFileType);
LogMessage("Output file " + outPath);
ImageFormat imageFormat = ImageFormat.Png;
switch (_varDestFileType.ToLower())
{
case "png":
imageFormat = ImageFormat.Png;
break;
case "jpg":
case "jpeg":
imageFormat = ImageFormat.Jpeg;
break;
case "bmp":
imageFormat = ImageFormat.Bmp;
break;
}
using (var resized = ResizeImage(bmpImage, _varDestSize, _varDestSize))
{
resized.Save(outPath, imageFormat);
}
bmpImage.Dispose();
}
}
}
}
catch (Exception ex)
{
LogMessage("Error processing file " + imagePath + "\n" + ex.Message);
}
}
