private void OpenClicked(Object sender, EventArgs e)
{
OpenFileDialog dialog = new OpenFileDialog();
dialog.Filter =
"All image files (*.bmp, *.jpg, *.png, *.gif, *.ico, *.cur)" +
"|*.bmp;*.jpg;*.png;*.gif;*.ico;*.cur" +
"|BMP files (*.bmp)|*.bmp" +
"|JPEG files (*.jpg)|*.jpg" +
"|PNG files (*.png)|*.png" +
"|GIF files (*.gif)|*.gif" +
"|Icon files (*.ico)|*.ico" +
"|Cursor files (*.cur)|*.cur" +
"|All files (*.*)|*.*";
if(dialog.ShowDialog(this) == DialogResult.OK)
{
Bitmap image;
try
{
image = new Bitmap(dialog.FileName);
}
catch(Exception)
{
MessageBox.Show
(String.Format("Unknown image format for \"{0}\"",
dialog.FileName),
"Error", MessageBoxButtons.OK, MessageBoxIcon.Hand);
image = null;
}
if(image != null)
{
ImageWindow window = new ImageWindow
(dialog.FileName, image);
window.MdiParent = this;
window.Visible = true;
}
}
}
public static object RunViewer(Attachment a, bool localRequest)
{
if (a == null)
{
return(null);
}
if (a.Format == (int)AttachmentFormat.Pdf && a.MediaData != null)
{
string pdfPathName = Utils.RandomFilePath(".pdf");
try
{
using (var fs = new FileStream(pdfPathName, FileMode.Create))
{
fs.Write(a.MediaData.Data, 0, a.MediaData.Data.Length);
}
//Process.Start(pdfPathName);
Utils.ReportMediaOpened(StEvent.PdfOpened, a);
var pdfReader = ReaderWindow2.Instance(pdfPathName, a.Id, a.ArgPoint != null ? (int?)a.ArgPoint.Topic.Id : null, localRequest);
pdfReader.Show();
return(pdfReader);
}
catch
{
}
}
else if (MiniAttachmentManager.IsGraphicFormat(a))
{
if (a.Format == (int)AttachmentFormat.PngScreenshot)
{
Utils.ReportMediaOpened(StEvent.ScreenshotOpened, a);
}
else
{
Utils.ReportMediaOpened(StEvent.ImageOpened, a);
}
if (a.MediaData.Data != null)
{
if (!ExplanationModeMediator.Inst.ImageViewerOpen)
{
var wnd = ImageWindow.Instance(a.Id, a.ArgPoint != null ? a.ArgPoint.Topic.Id : -1, localRequest);
wnd.img.Source = LoadImageFromBlob(a.MediaData.Data);
wnd.Show();
wnd.Activate();
return(wnd);
}
}
}
else
{
//office file
var ext = Path.GetExtension(a.Link).ToLower();
string pathName = Utils.RandomFilePath(ext);
try
{
using (var fs = new FileStream(pathName, FileMode.Create))
{
fs.Write(a.MediaData.Data, 0, a.MediaData.Data.Length);
}
Process.Start(pathName);
}
catch (Exception e)
{
MessageDlg.Show(e.ToString(), "Error");
}
}
return(null);
}
private static void Main(string[] args)
{
try
{
if (args.Length == 0)
{
Console.WriteLine("Give an image dataset XML file to run this program.");
Console.WriteLine("For example, if you are running from the examples folder then run this program by typing");
Console.WriteLine(" ./RandomCropper faces/training.xml");
return;
}
// First lets load a dataset
IEnumerable <Matrix <RgbPixel> > images;
IEnumerable <IEnumerable <MModRect> > boxes;
Dlib.LoadImageDataset(args[0], out images, out boxes);
// Here we make our random_cropper. It has a number of options.
var cropper = new DlibDotNet.ImageTransforms.RandomCropper();
// We can tell it how big we want the cropped images to be.
cropper.ChipDims = new ChipDims(400, 400);
// Also, when doing cropping, it will map the object annotations from the
// dataset to the cropped image as well as perform random scale jittering.
// You can tell it how much scale jittering you would like by saying "please
// make the objects in the crops have a min and max size of such and such".
// You do that by calling these two functions. Here we are saying we want the
// objects in our crops to be no more than 0.8*400 pixels in height and width.
cropper.MaxObjectSize = 0.8;
// And also that they shouldn't be too small. Specifically, each object's smallest
// dimension (i.e. height or width) should be at least 60 pixels and at least one of
// the dimensions must be at least 80 pixels. So the smallest objects the cropper will
// output will be either 80x60 or 60x80.
cropper.MinObjectLengthLongDim = 80;
cropper.MinObjectLengthShortDim = 60;
// The cropper can also randomly mirror and rotate crops, which we ask it to
// perform as well.
cropper.RandomlyFlip = true;
cropper.MaxRotationDegrees = 50;
// This fraction of crops are from random parts of images, rather than being centered
// on some object.
cropper.BackgroundCropsFraction = 0.2;
// Now ask the cropper to generate a bunch of crops. The output is stored in
// crops and crop_boxes.
IEnumerable <Matrix <RgbPixel> > crops;
IEnumerable <IEnumerable <MModRect> > cropBoxes;
// Make 1000 crops.
cropper.Operator(1000, images, boxes, out crops, out cropBoxes);
// Finally, lets look at the results
var cropList = crops?.ToArray() ?? new Matrix <RgbPixel> [0];
var cropBoxesList = cropBoxes?.ToArray() ?? new IEnumerable <MModRect> [0];
using (var win = new ImageWindow())
for (var i = 0; i < cropList.Count(); ++i)
{
win.ClearOverlay();
win.SetImage(cropList[i]);
foreach (var b in cropBoxesList[i])
{
// Note that mmod_rect has an ignore field. If an object was labeled
// ignore in boxes then it will still be labeled as ignore in
// crop_boxes. Moreover, objects that are not well contained within
// the crop are also set to ignore.
var rect = b.Rect;
if (b.Ignore)
{
win.AddOverlay(rect, new RgbPixel {
Red = 255, Blue = 255
}); // draw ignored boxes as orange
}
else
{
win.AddOverlay(rect, new RgbPixel {
Red = 255
}); // draw other boxes as red
}
}
Console.WriteLine("Hit enter to view the next random crop.");
Console.ReadKey();
}
}
catch (Exception e)
{
Console.WriteLine(e);
}
}
static void AutoWhiteBalance(Camera camera)
{
// Check whether the Balance White Auto feature is available.
if (!camera.Parameters[PLCamera.BalanceWhiteAuto].IsWritable)
{
Console.WriteLine("The Camera does not support balance white auto.");
return;
}
// Maximize the grabbed area of interest (Image AOI).
camera.Parameters[PLCamera.OffsetX].TrySetValue(camera.Parameters[PLCamera.OffsetX].GetMinimum());
camera.Parameters[PLCamera.OffsetY].TrySetValue(camera.Parameters[PLCamera.OffsetY].GetMinimum());
camera.Parameters[PLCamera.Width].SetValue(camera.Parameters[PLCamera.Width].GetMaximum());
camera.Parameters[PLCamera.Height].SetValue(camera.Parameters[PLCamera.Height].GetMaximum());
// Set the Auto Function ROI for white balace statistics.
// We want to use ROI2 for gathering the statistics.
if (camera.Parameters [regionSelector].IsWritable)
{
camera.Parameters [regionSelector].SetValue(regionSelectorValue1);
camera.Parameters [autoFunctionAOIROIUseWhiteBalance].SetValue(false); // ROI 1 is not used for white balance control
camera.Parameters [regionSelector].SetValue(regionSelectorValue2);
camera.Parameters [autoFunctionAOIROIUseWhiteBalance].SetValue(true); // ROI 2 is used for white balance control
}
camera.Parameters[regionSelector].SetValue(regionSelectorValue2);
camera.Parameters[regionSelectorOffsetX].SetValue(camera.Parameters [PLCamera.OffsetX].GetMinimum());
camera.Parameters[regionSelectorOffsetY].SetValue(camera.Parameters [PLCamera.OffsetY].GetMinimum());
camera.Parameters[regionSelectorWidth].SetValue(camera.Parameters[PLCamera.Width].GetMaximum());
camera.Parameters[regionSelectorHeight].SetValue(camera.Parameters[PLCamera.Height].GetMaximum());
Console.WriteLine("Trying 'BalanceWhiteAuto = Once'.");
Console.WriteLine("Initial balance ratio:");
camera.Parameters[PLCamera.BalanceRatioSelector].SetValue(PLCamera.BalanceRatioSelector.Red);
Console.Write("R = {0} ", camera.Parameters[balanceRatio].GetValue());
camera.Parameters[PLCamera.BalanceRatioSelector].SetValue(PLCamera.BalanceRatioSelector.Green);
Console.Write("G = {0} ", camera.Parameters[balanceRatio].GetValue());
camera.Parameters[PLCamera.BalanceRatioSelector].SetValue(PLCamera.BalanceRatioSelector.Blue);
Console.Write("B = {0} ", camera.Parameters[balanceRatio].GetValue());
camera.Parameters[PLCamera.BalanceWhiteAuto].SetValue(PLCamera.BalanceWhiteAuto.Once);
// When the "once" mode of operation is selected,
// the parameter values are automatically adjusted until the related image property
// reaches the target value. After the automatic parameter value adjustment is complete, the auto
// function will automatically be set to "off" and the new parameter value will be applied to the
// subsequently grabbed images.
int n = 0;
while (camera.Parameters[PLCamera.BalanceWhiteAuto].GetValue() != PLCamera.BalanceWhiteAuto.Off)
{
IGrabResult result = camera.StreamGrabber.GrabOne(5000, TimeoutHandling.ThrowException);
using (result)
{
// Image grabbed successfully?
if (result.GrabSucceeded)
{
ImageWindow.DisplayImage(1, result);
}
}
n++;
//For demonstration purposes only. Wait until the image is shown.
System.Threading.Thread.Sleep(100);
//Make sure the loop is exited.
if (n > 100)
{
throw new TimeoutException("The adjustment of auto white balance did not finish.");
}
}
Console.WriteLine("BalanceWhiteAuto went back to 'Off' after {0} Frames", n);
Console.WriteLine("Final balance ratio: ");
camera.Parameters[PLCamera.BalanceRatioSelector].SetValue(PLCamera.BalanceRatioSelector.Red);
Console.Write("R = {0} ", camera.Parameters[balanceRatio].GetValue());
camera.Parameters[PLCamera.BalanceRatioSelector].SetValue(PLCamera.BalanceRatioSelector.Green);
Console.Write("G = {0} ", camera.Parameters[balanceRatio].GetValue());
camera.Parameters[PLCamera.BalanceRatioSelector].SetValue(PLCamera.BalanceRatioSelector.Blue);
Console.Write("B = {0} ", camera.Parameters[balanceRatio].GetValue());
}
private static void Main(string[] args)
{
try
{
// Make sure the user entered an argument to this program. It should be the
// filename for an image.
if (args.Length != 2)
{
Console.WriteLine("error, you have to enter a BMP file as an argument to this program.");
return;
}
// Here we declare an image object that can store color rgb_pixels.
// Now load the image file into our image. If something is wrong then
// load_image() will throw an exception. Also, if you linked with libpng
// and libjpeg then load_image() can load PNG and JPEG files in addition
// to BMP files.
using (var img = Dlib.LoadImage <RgbPixel>(args[0]))
{
// Now convert the image into a FHOG feature image. The output, hog, is a 2D array
// of 31 dimensional vectors.
using (var hog = Dlib.ExtracFHogFeatures <float>(img))
{
Console.WriteLine($"hog image has {hog.Rows} rows and {hog.Columns} columns.");
// Let's see what the image and FHOG features look like.
using (var win = new ImageWindow(img))
using (var drawhog = Dlib.DrawHog(hog))
using (var winhog = new ImageWindow(drawhog))
{
// Another thing you might want to do is map between the pixels in img and the
// cells in the hog image. dlib provides the image_to_fhog() and fhog_to_image()
// routines for this. Their use is demonstrated in the following loop which
// responds to the user clicking on pixels in the image img.
Point p; // A 2D point, used to represent pixel locations.
while (win.GetNextDoubleClick(out p))
{
using (var hp = Dlib.ImgaeToFHog(p))
{
Console.WriteLine($"The point {p} in the input image corresponds to {hp} in hog space.");
var row = hog[hp.Y];
var column = row[hp.X];
var t = Dlib.Trans(column);
Console.WriteLine($"FHOG features at this point: {t}");
}
}
// Finally, sometimes you want to get a planar representation of the HOG features
// rather than the explicit vector (i.e. interlaced) representation used above.
var planar_hog = Dlib.ExtracFHogFeaturesArray <float>(img);
// Now we have an array of 31 float valued image planes, each representing one of
// the dimensions of the HOG feature vector.
}
}
}
}
catch (Exception e)
{
Console.WriteLine($"exception thrown: {e}");
}
}
internal static void Main()
{
// The exit code of the sample application.
int exitCode = 0;
try
{
// Create a camera object selecting the first camera device found.
// More constructors are available for selecting a specific camera device.
using (Camera camera = new Camera())
{
// Change default configuration to enable software triggering.
camera.CameraOpened += Configuration.SoftwareTrigger;
// Open the camera.
camera.Open();
// Register image grabbed event to print frame info
camera.StreamGrabber.ImageGrabbed += OnImageGrabbed;
// DeviceVendorName, DeviceModelName, and DeviceFirmwareVersion are string parameters.
Console.WriteLine("Camera Device Information");
Console.WriteLine("=========================");
Console.WriteLine("Vendor : {0}", camera.Parameters[PLCamera.DeviceVendorName].GetValue());
Console.WriteLine("Model : {0}", camera.Parameters[PLCamera.DeviceModelName].GetValue());
Console.WriteLine("Firmware version : {0}", camera.Parameters[PLCamera.DeviceFirmwareVersion].GetValue());
Console.WriteLine("");
Console.WriteLine("Camera Device Settings");
Console.WriteLine("======================");
// Can the camera device be queried whether it is ready to accept the next frame trigger?
if (camera.CanWaitForFrameTriggerReady)
{
// bool for testing if sequencer is available or not
bool sequencerAvailable = false;
if (camera.GetSfncVersion() < sfnc2_0_0) // Handling for older cameras
{
if (camera.Parameters[PLCamera.SequenceEnable].IsWritable)
{
sequencerAvailable = true; //Sequencer is available that is why it is true.
// Disable the sequencer before changing parameters.
// The parameters under control of the sequencer are locked
// when the sequencer is enabled. For a list of parameters
// controlled by the sequencer, see the camera User's Manual.
camera.Parameters[PLCamera.SequenceEnable].SetValue(false);
// Turn configuration mode on
if (camera.Parameters[PLCamera.SequenceConfigurationMode].IsWritable)
{
camera.Parameters[PLCamera.SequenceConfigurationMode].SetValue(PLCamera.SequenceConfigurationMode.On);
}
// Maximize the image area of interest (Image AOI).
camera.Parameters[PLCamera.OffsetX].TrySetValue(camera.Parameters[PLCamera.OffsetX].GetMinimum());
camera.Parameters[PLCamera.OffsetY].TrySetValue(camera.Parameters[PLCamera.OffsetY].GetMinimum());
camera.Parameters[PLCamera.Width].SetValue(camera.Parameters[PLCamera.Width].GetMaximum());
camera.Parameters[PLCamera.Height].SetValue(camera.Parameters[PLCamera.Height].GetMaximum());
// Set the pixel data format.
camera.Parameters[PLCamera.PixelFormat].SetValue(PLCamera.PixelFormat.Mono8);
// Set up sequence sets.
// Configure how the sequence will advance.
// 'Auto' refers to the auto sequence advance mode.
// The advance from one sequence set to the next will occur automatically with each image acquired.
// After the end of the sequence set cycle was reached a new sequence set cycle will start.
camera.Parameters[PLCamera.SequenceAdvanceMode].SetValue(PLCamera.SequenceAdvanceMode.Auto);
// Our sequence sets relate to three steps (0..2).
// In each step we will increase the height of the Image AOI by one increment.
camera.Parameters[PLCamera.SequenceSetTotalNumber].SetValue(3);
long increments = (camera.Parameters[PLCamera.Height].GetMaximum() - camera.Parameters[PLCamera.Height].GetMinimum()) / camera.Parameters[PLCamera.Height].GetIncrement();
// Set the parameters for step 0; quarter height image.
camera.Parameters[PLCamera.SequenceSetIndex].SetValue(0);
camera.Parameters[PLCamera.Height].SetValue(camera.Parameters[PLCamera.Height].GetIncrement() * (increments / 4) + camera.Parameters[PLCamera.Height].GetMinimum());
camera.Parameters[PLCamera.SequenceSetStore].Execute();
// Set the parameters for step 1; half height image.
camera.Parameters[PLCamera.SequenceSetIndex].SetValue(1);
camera.Parameters[PLCamera.Height].SetValue(camera.Parameters[PLCamera.Height].GetIncrement() * (increments / 2) + camera.Parameters[PLCamera.Height].GetMinimum());
camera.Parameters[PLCamera.SequenceSetStore].Execute();
// Set the parameters for step 2; full height image.
camera.Parameters[PLCamera.SequenceSetIndex].SetValue(2);
camera.Parameters[PLCamera.Height].SetValue(camera.Parameters[PLCamera.Height].GetIncrement() * (increments) + camera.Parameters[PLCamera.Height].GetMinimum());
camera.Parameters[PLCamera.SequenceSetStore].Execute();
// Finish configuration
if (camera.Parameters[PLCamera.SequenceConfigurationMode].IsWritable)
{
camera.Parameters[PLCamera.SequenceConfigurationMode].SetValue(PLCamera.SequenceConfigurationMode.Off);
}
// Enable the sequencer feature.
// From here on you cannot change the sequencer settings anymore.
camera.Parameters[PLCamera.SequenceEnable].SetValue(true);
// Start the grabbing of countOfImagesToGrab images.
camera.StreamGrabber.Start(countOfImagesToGrab);
}
else
{
sequencerAvailable = false; // Sequencer not available
}
}
else // Handling for newer cameras (using SFNC 2.0, e.g. USB3 Vision cameras)
{
if (camera.Parameters[PLCamera.SequencerMode].IsWritable)
{
sequencerAvailable = true;
// Disable the sequencer before changing parameters.
// The parameters under control of the sequencer are locked
// when the sequencer is enabled. For a list of parameters
// controlled by the sequencer, see the camera User's Manual.
camera.Parameters[PLCamera.SequencerMode].SetValue(PLCamera.SequencerMode.Off);
// Maximize the image area of interest (Image AOI).
camera.Parameters[PLCamera.OffsetX].TrySetValue(camera.Parameters[PLCamera.OffsetX].GetMinimum());
camera.Parameters[PLCamera.OffsetY].TrySetValue(camera.Parameters[PLCamera.OffsetY].GetMinimum());
camera.Parameters[PLCamera.Width].SetValue(camera.Parameters[PLCamera.Width].GetMaximum());
camera.Parameters[PLCamera.Height].SetValue(camera.Parameters[PLCamera.Height].GetMaximum());
// Set the pixel data format.
// This parameter may be locked when the sequencer is enabled.
camera.Parameters[PLCamera.PixelFormat].SetValue(PLCamera.PixelFormat.Mono8);
// Set up sequence sets and turn sequencer configuration mode on.
camera.Parameters[PLCamera.SequencerConfigurationMode].SetValue(PLCamera.SequencerConfigurationMode.On);
// Configure how the sequence will advance.
// The sequence sets relate to three steps (0..2).
// In each step, the height of the Image AOI is doubled.
long increments = (camera.Parameters[PLCamera.Height].GetMaximum() - camera.Parameters[PLCamera.Height].GetMinimum()) / camera.Parameters[PLCamera.Height].GetIncrement();
long initialSet = camera.Parameters[PLCamera.SequencerSetSelector].GetMinimum();
long incSet = camera.Parameters[PLCamera.SequencerSetSelector].GetIncrement();
long curSet = initialSet;
// Set the parameters for step 0; quarter height image.
camera.Parameters[PLCamera.SequencerSetSelector].SetValue(initialSet);
{
// valid for all sets
// reset on software signal 1;
camera.Parameters[PLCamera.SequencerPathSelector].SetValue(0);
camera.Parameters[PLCamera.SequencerSetNext].SetValue(initialSet);
camera.Parameters[PLCamera.SequencerTriggerSource].SetValue(PLCamera.SequencerTriggerSource.SoftwareSignal1);
// advance on Frame Start
camera.Parameters[PLCamera.SequencerPathSelector].SetValue(1);
camera.Parameters[PLCamera.SequencerTriggerSource].SetValue(PLCamera.SequencerTriggerSource.FrameStart);
}
camera.Parameters[PLCamera.SequencerSetNext].SetValue(curSet + incSet);
// Set the parameters for step 0; quarter height image.
camera.Parameters[PLCamera.Height].SetValue(camera.Parameters[PLCamera.Height].GetIncrement() * (increments / 4) + camera.Parameters[PLCamera.Height].GetMinimum());
camera.Parameters[PLCamera.SequencerSetSave].Execute();
// Set the parameters for step 1; half height image.
curSet += incSet;
camera.Parameters[PLCamera.SequencerSetSelector].SetValue(curSet);
// advance on Frame Start to next set
camera.Parameters[PLCamera.SequencerSetNext].SetValue(curSet + incSet);
camera.Parameters[PLCamera.Height].SetValue(camera.Parameters[PLCamera.Height].GetIncrement() * (increments / 2) + camera.Parameters[PLCamera.Height].GetMinimum());
camera.Parameters[PLCamera.SequencerSetSave].Execute();
// Set the parameters for step 2; full height image.
curSet += incSet;
camera.Parameters[PLCamera.SequencerSetSelector].SetValue(curSet);
// advance on Frame End to initial set,
camera.Parameters[PLCamera.SequencerSetNext].SetValue(initialSet); // terminates sequence definition
// full height
camera.Parameters[PLCamera.Height].SetValue(camera.Parameters[PLCamera.Height].GetIncrement() * increments + camera.Parameters[PLCamera.Height].GetMinimum());
camera.Parameters[PLCamera.SequencerSetSave].Execute();
// Enable the sequencer feature.
// From here on you cannot change the sequencer settings anymore.
camera.Parameters[PLCamera.SequencerConfigurationMode].SetValue(PLCamera.SequencerConfigurationMode.Off);
camera.Parameters[PLCamera.SequencerMode].SetValue(PLCamera.SequencerMode.On);
// Start the grabbing of countOfImagesToGrab images.
camera.StreamGrabber.Start(countOfImagesToGrab);
}
else
{
sequencerAvailable = false; // Sequencer not available
}
}
if (sequencerAvailable)
{
IGrabResult result;
// Camera.StopGrabbing() is called automatically by the RetrieveResult() method
// when countOfImagesToGrab images have been retrieved.
while (camera.StreamGrabber.IsGrabbing)
{
// Execute the software trigger. Wait up to 1000 ms for the camera to be ready for trigger.
if (camera.WaitForFrameTriggerReady(1000, TimeoutHandling.ThrowException))
{
camera.ExecuteSoftwareTrigger();
// Wait for an image and then retrieve it. A timeout of 5000 ms is used.
result = camera.StreamGrabber.RetrieveResult(5000, TimeoutHandling.ThrowException);
using (result)
{
// Image grabbed successfully?
if (result.GrabSucceeded)
{
// Display the grabbed image.
ImageWindow.DisplayImage(1, result);
}
else
{
Console.WriteLine("Error code:{0} Error description:{1}", result.ErrorCode, result.ErrorDescription);
}
}
}
// Wait for user input.
Console.WriteLine("Press Enter to continue.");
while (camera.StreamGrabber.IsGrabbing && Console.ReadKey().Key != ConsoleKey.Enter)
{
;
}
}
// Disable the sequencer.
if (camera.GetSfncVersion() < sfnc2_0_0) // Handling for older cameras
{
camera.Parameters[PLCamera.SequenceEnable].SetValue(false);
}
else // Handling for newer cameras (using SFNC 2.0, e.g. USB3 Vision cameras)
{
camera.Parameters[PLCamera.SequencerMode].SetValue(PLCamera.SequencerMode.Off);
}
}
else
{
Console.WriteLine("The sequencer feature is not available for this camera.");
}
}
else
{
Console.WriteLine("This sample can only be used with cameras that can be queried whether they are ready to accept the next frame trigger.");
}
// Close the camera.
camera.Close();
}
}
catch (Exception e)
{
// Error handling.
Console.Error.WriteLine("Exception: {0}", e.Message);
exitCode = 1;
}
// Comment the following two lines to disable waiting on exit.
Console.Error.WriteLine("\nPress enter to exit.");
Console.ReadLine();
Environment.Exit(exitCode);
}
private static void Main(string[] args)
{
if (args.Length != 1)
{
Console.WriteLine("Call this program like this: ");
Console.WriteLine("VideoTracking.exe <path of video_frames directory>");
return;
}
var path = args[0];
var files = new DirectoryInfo(path).GetFiles("*.jpg").Select(info => info.FullName).ToList();
files.Sort();
if (files.Count == 0)
{
Console.WriteLine($"No images found in {path}");
return;
}
// 定义图像捕捉方式 从摄像头 ， 注意 Windows下需要选择 VideoCaptureAPIs.DSHOW
var cap = new VideoCapture(0, VideoCaptureAPIs.DSHOW);
// 定义图像捕捉方式 从摄像头 视频文件
//var cap = new VideoCapture("video.webm");
//判断捕捉设备是否打开
if (!cap.IsOpened())
{
Console.WriteLine("Unable to connect to camera");
return;
}
Mat temp = null;
var tracker = new CorrelationTracker();
int init = 0;
//定义显示窗口
using (var win = new ImageWindow())
{
Console.WriteLine("对象追踪程序启动");
Console.WriteLine("选择命令行为当前窗口，通过按键选择需要追踪的区域Width: [A,Z] Height:[S,X] X:[right,left] Y:[up,down] ,点击Enter开始追踪");
Console.WriteLine("注意：切换命令行窗口输入法为英文输入状态");
//选择追踪对象
while (!win.IsClosed())
{
//获得1帧图片
temp = cap.RetrieveMat();// new Mat();
if (temp == null)
{
Console.WriteLine("图像获取错误!");
return;
}
var array = new byte[temp.Width * temp.Height * temp.ElemSize()];
Marshal.Copy(temp.Data, array, 0, array.Length);
using (var cimg = Dlib.LoadImageData <BgrPixel>(array, (uint)temp.Height, (uint)temp.Width, (uint)(temp.Width * temp.ElemSize())))
{
init++;
if (init > 1)
{
var KK = Console.ReadKey();
if (KK.Key == ConsoleKey.Enter)
{
Console.WriteLine("开始追踪目标!");
//确定 追踪 位置
var rect2 = DRectangle.CenteredRect(a_X, a_Y, a_W, a_H);
//开始追踪
tracker.StartTrack(cimg, rect2);
win.SetImage(cimg);
win.ClearOverlay();
win.AddOverlay(rect2);
break;
}
//选择 追踪区域
if (KK.Key == ConsoleKey.RightArrow || KK.Key == ConsoleKey.LeftArrow || KK.Key == ConsoleKey.UpArrow || KK.Key == ConsoleKey.DownArrow || KK.Key == ConsoleKey.A || KK.Key == ConsoleKey.Z || KK.Key == ConsoleKey.S || KK.Key == ConsoleKey.X)
{
if (KK.Key == ConsoleKey.RightArrow)
{
a_X++;
if (a_X > cimg.Rect.Width - a_W)
{
a_X = cimg.Rect.Width - a_W;
}
}
if (KK.Key == ConsoleKey.LeftArrow)
{
a_X--;
if (a_X < 0)
{
a_X = 0;
}
}
if (KK.Key == ConsoleKey.UpArrow)
{
a_Y--;
if (a_Y < 0)
{
a_Y = 0;
}
}
if (KK.Key == ConsoleKey.DownArrow)
{
a_Y++;
if (a_Y > cimg.Rect.Height - a_H)
{
a_Y = cimg.Rect.Height - a_H;
}
}
if (KK.Key == ConsoleKey.A)
{
a_W++;
if (a_W >= cimg.Rect.Width - a_X)
{
a_W = cimg.Rect.Width - a_X;
}
}
if (KK.Key == ConsoleKey.Z)
{
a_W--;
if (a_W < 10)
{
a_W = 10;
}
}
if (KK.Key == ConsoleKey.S)
{
a_H++;
if (a_H > cimg.Rect.Height - a_Y)
{
a_H = cimg.Rect.Height - a_Y;
}
}
if (KK.Key == ConsoleKey.X)
{
a_H--;
if (a_H < 10)
{
a_H = 10;
}
}
}
}
var rect = DRectangle.CenteredRect(a_X, a_Y, a_W, a_H);
Console.WriteLine("Set RECT:" + a_X + " " + a_Y + " " + a_W + " " + a_H);
//显示图片
win.SetImage(cimg);
win.ClearOverlay();
//显示框
win.AddOverlay(rect);
}
}
//选择追踪对象
while (!win.IsClosed())
{
//获得1帧图片
temp = cap.RetrieveMat();// new Mat();
if (temp == null)
{
Console.WriteLine("图像获取错误!");
return;
}
var array = new byte[temp.Width * temp.Height * temp.ElemSize()];
Marshal.Copy(temp.Data, array, 0, array.Length);
using (var cimg = Dlib.LoadImageData <BgrPixel>(array, (uint)temp.Height, (uint)temp.Width, (uint)(temp.Width * temp.ElemSize())))
{
//更新追踪图像
tracker.Update(cimg);
win.SetImage(cimg);
win.ClearOverlay();
//获得追踪到的目标位置
DRectangle rect = tracker.GetPosition();
win.AddOverlay(rect);
Console.WriteLine("OBJ RECT:" + (int)rect.Left + " " + (int)rect.Top + " " + (int)rect.Width + " " + (int)rect.Height);
System.Threading.Thread.Sleep(100);
}
}
}
Console.WriteLine("任意键退出");
Console.ReadKey();
}
private static void Main(string[] args)
{
try
{
// In this example we are going to train a face detector based on the
// small faces dataset in the examples/faces directory. So the first
// thing we do is load that dataset. This means you need to supply the
// path to this faces folder as a command line argument so we will know
// where it is.
if (args.Length != 1)
{
Console.WriteLine("Give the path to the examples/faces directory as the argument to this");
Console.WriteLine("program. For example, if you are in the examples folder then execute ");
Console.WriteLine("this program by running: ");
Console.WriteLine(" ./fhog_object_detector_ex faces");
Console.WriteLine();
return;
}
var facesDirectory = args[0];
// The faces directory contains a training dataset and a separate
// testing dataset. The training data consists of 4 images, each
// annotated with rectangles that bound each human face. The idea is
// to use this training data to learn to identify human faces in new
// images.
//
// Once you have trained an object detector it is always important to
// test it on data it wasn't trained on. Therefore, we will also load
// a separate testing set of 5 images. Once we have a face detector
// created from the training data we will see how well it works by
// running it on the testing images.
//
// So here we create the variables that will hold our dataset.
// images_train will hold the 4 training images and face_boxes_train
// holds the locations of the faces in the training images. So for
// example, the image images_train[0] has the faces given by the
// rectangles in face_boxes_train[0].
IList <Matrix <byte> > tmpImagesTrain;
IList <Matrix <byte> > tmpImagesTest;
IList <IList <Rectangle> > tmpFaceBoxesTrain;
IList <IList <Rectangle> > tmpFaceBoxesTest;
// Now we load the data. These XML files list the images in each
// dataset and also contain the positions of the face boxes. Obviously
// you can use any kind of input format you like so long as you store
// the data into images_train and face_boxes_train. But for convenience
// dlib comes with tools for creating and loading XML image dataset
// files. Here you see how to load the data. To create the XML files
// you can use the imglab tool which can be found in the tools/imglab
// folder. It is a simple graphical tool for labeling objects in images
// with boxes. To see how to use it read the tools/imglab/README.txt
// file.
Dlib.LoadImageDataset(Path.Combine(facesDirectory, "training.xml"), out tmpImagesTrain, out tmpFaceBoxesTrain);
Dlib.LoadImageDataset(Path.Combine(facesDirectory, "testing.xml"), out tmpImagesTest, out tmpFaceBoxesTest);
// Now we do a little bit of pre-processing. This is optional but for
// this training data it improves the results. The first thing we do is
// increase the size of the images by a factor of two. We do this
// because it will allow us to detect smaller faces than otherwise would
// be practical (since the faces are all now twice as big). Note that,
// in addition to resizing the images, these functions also make the
// appropriate adjustments to the face boxes so that they still fall on
// top of the faces after the images are resized.
var imageTrain = new List <Matrix <byte> >(tmpImagesTrain);
var faceBoxesTrain = new List <IList <Rectangle> >(tmpFaceBoxesTrain);
Dlib.UpsampleImageDataset(2, imageTrain, faceBoxesTrain);
var imageTest = new List <Matrix <byte> >(tmpImagesTest);
var faceBoxesTest = new List <IList <Rectangle> >(tmpFaceBoxesTest);
Dlib.UpsampleImageDataset(2, imageTest, faceBoxesTest);
// Since human faces are generally left-right symmetric we can increase
// our training dataset by adding mirrored versions of each image back
// into images_train. So this next step doubles the size of our
// training dataset. Again, this is obviously optional but is useful in
// many object detection tasks.
Dlib.AddImageLeftRightFlips(imageTrain, faceBoxesTrain);
Console.WriteLine($"num training images: {imageTrain.Count()}");
Console.WriteLine($"num testing images: {imageTest.Count()}");
// Finally we get to the training code. dlib contains a number of
// object detectors. This typedef tells it that you want to use the one
// based on Felzenszwalb's version of the Histogram of Oriented
// Gradients (commonly called HOG) detector. The 6 means that you want
// it to use an image pyramid that downsamples the image at a ratio of
// 5/6. Recall that HOG detectors work by creating an image pyramid and
// then running the detector over each pyramid level in a sliding window
// fashion.
using (var scanner = new ScanFHogPyramid <PyramidDown, DefaultFHogFeatureExtractor>(6))
{
// The sliding window detector will be 80 pixels wide and 80 pixels tall.
scanner.SetDetectionWindowSize(80, 80);
using (var trainer = new StructuralObjectDetectionTrainer <ScanFHogPyramid <PyramidDown, DefaultFHogFeatureExtractor> >(scanner))
{
// Set this to the number of processing cores on your machine.
trainer.SetNumThreads(4);
// The trainer is a kind of support vector machine and therefore has the usual SVM
// C parameter. In general, a bigger C encourages it to fit the training data
// better but might lead to overfitting. You must find the best C value
// empirically by checking how well the trained detector works on a test set of
// images you haven't trained on. Don't just leave the value set at 1. Try a few
// different C values and see what works best for your data.
trainer.SetC(1);
// We can tell the trainer to print it's progress to the console if we want.
trainer.BeVerbose();
// The trainer will run until the "risk gap" is less than 0.01. Smaller values
// make the trainer solve the SVM optimization problem more accurately but will
// take longer to train. For most problems a value in the range of 0.1 to 0.01 is
// plenty accurate. Also, when in verbose mode the risk gap is printed on each
// iteration so you can see how close it is to finishing the training.
trainer.SetEpsilon(0.01);
// Now we run the trainer. For this example, it should take on the order of 10
// seconds to train.
var detector = trainer.Train(imageTrain, faceBoxesTrain);
// Now that we have a face detector we can test it. The first statement tests it
// on the training data. It will print the precision, recall, and then average precision.
using (var matrix = Dlib.TestObjectDetectionFunction(detector, imageTrain, faceBoxesTrain))
Console.WriteLine($"training results: {matrix}");
// However, to get an idea if it really worked without overfitting we need to run
// it on images it wasn't trained on. The next line does this. Happily, we see
// that the object detector works perfectly on the testing images.
using (var matrix = Dlib.TestObjectDetectionFunction(detector, imageTest, faceBoxesTest))
Console.WriteLine($"testing results: {matrix}");
// If you have read any papers that use HOG you have probably seen the nice looking
// "sticks" visualization of a learned HOG detector. This next line creates a
// window with such a visualization of our detector. It should look somewhat like
// a face.
using (var fhog = Dlib.DrawFHog(detector))
using (var hogwin = new ImageWindow(fhog, "Learned fHOG detector"))
{
// Now for the really fun part. Let's display the testing images on the screen and
// show the output of the face detector overlaid on each image. You will see that
// it finds all the faces without false alarming on any non-faces.
using (var win = new ImageWindow())
for (var i = 0; i < imageTest.Count; ++i)
{
// Run the detector and get the face detections.
var dets = detector.Operator(imageTest[i]);
win.ClearOverlay();
win.SetImage(imageTest[i]);
win.AddOverlay(dets, new RgbPixel(255, 0, 0));
Console.WriteLine("Hit enter to process the next image...");
Console.ReadKey();
Console.WriteLine("");
}
}
// Like everything in dlib, you can save your detector to disk using the
// serialize() function.
detector.Serialize("face_detector.svm");
// Then you can recall it using the deserialize() function.
using (var tmp = new ScanFHogPyramid <PyramidDown, DefaultFHogFeatureExtractor>(6))
using (var detector2 = new ObjectDetector <ScanFHogPyramid <PyramidDown, DefaultFHogFeatureExtractor> >(tmp))
detector2.Deserialize("face_detector.svm");
// Now let's talk about some optional features of this training tool as well as some
// important points you should understand.
//
// The first thing that should be pointed out is that, since this is a sliding
// window classifier, it can't output an arbitrary rectangle as a detection. In
// this example our sliding window is 80 by 80 pixels and is run over an image
// pyramid. This means that it can only output detections that are at least 80 by
// 80 pixels in size (recall that this is why we upsampled the images after loading
// them). It also means that the aspect ratio of the outputs is 1. So if,
// for example, you had a box in your training data that was 200 pixels by 10
// pixels then it would simply be impossible for the detector to learn to detect
// it. Similarly, if you had a really small box it would be unable to learn to
// detect it.
//
// So the training code performs an input validation check on the training data and
// will throw an exception if it detects any boxes that are impossible to detect
// given your setting of scanning window size and image pyramid resolution. You
// can use a statement like:
// remove_unobtainable_rectangles(trainer, images_train, face_boxes_train)
// to automatically discard these impossible boxes from your training dataset
// before running the trainer. This will avoid getting the "impossible box"
// exception. However, I would recommend you be careful that you are not throwing
// away truth boxes you really care about. The remove_unobtainable_rectangles()
// will return the set of removed rectangles so you can visually inspect them and
// make sure you are OK that they are being removed.
//
// Next, note that any location in the images not marked with a truth box is
// implicitly treated as a negative example. This means that when creating
// training data it is critical that you label all the objects you want to detect.
// So for example, if you are making a face detector then you must mark all the
// faces in each image. However, sometimes there are objects in images you are
// unsure about or simply don't care if the detector identifies or not. For these
// objects you can pass in a set of "ignore boxes" as a third argument to the
// trainer.train() function. The trainer will simply disregard any detections that
// happen to hit these boxes.
//
// Another useful thing you can do is evaluate multiple HOG detectors together. The
// benefit of this is increased testing speed since it avoids recomputing the HOG
// features for each run of the detector. You do this by storing your detectors
// into a std::vector and then invoking evaluate_detectors() like so:
var myDetectors = new List <ObjectDetector <ScanFHogPyramid <PyramidDown, DefaultFHogFeatureExtractor> > >();
myDetectors.Add(detector);
var dect2 = Dlib.EvaluateDetectors(myDetectors, imageTrain[0]);
//
//
// Finally, you can add a nuclear norm regularizer to the SVM trainer. Doing has
// two benefits. First, it can cause the learned HOG detector to be composed of
// separable filters and therefore makes it execute faster when detecting objects.
// It can also help with generalization since it tends to make the learned HOG
// filters smoother. To enable this option you call the following function before
// you create the trainer object:
// scanner.set_nuclear_norm_regularization_strength(1.0);
// The argument determines how important it is to have a small nuclear norm. A
// bigger regularization strength means it is more important. The smaller the
// nuclear norm the smoother and faster the learned HOG filters will be, but if the
// regularization strength value is too large then the SVM will not fit the data
// well. This is analogous to giving a C value that is too small.
//
// You can see how many separable filters are inside your detector like so:
Console.WriteLine($"num filters: {Dlib.NumSeparableFilters(detector)}");
// You can also control how many filters there are by explicitly thresholding the
// singular values of the filters like this:
using (var newDetector = Dlib.ThresholdFilterSingularValues(detector, 0.1))
{
}
// That removes filter components with singular values less than 0.1. The bigger
// this number the fewer separable filters you will have and the faster the
// detector will run. However, a large enough threshold will hurt detection
// accuracy.
}
}
}
catch (Exception e)
{
Console.WriteLine(e);
}
}
internal static void Main()
{
// Time to wait for the user to disconnect the camera device.
const int cTimeOutMs = 60000;
// The exit code of the sample application.
int exitCode = 0;
try
{
// Create a camera object that selects the first camera device found.
// More constructors are available for selecting a specific camera device.
using (Camera camera = new Camera())
{
// Print the model name of the camera.
Console.WriteLine("Using camera {0}.", camera.CameraInfo[CameraInfoKey.ModelName]);
// Set the acquisition mode to free running continuous acquisition when the camera is opened.
camera.CameraOpened += Configuration.AcquireContinuous;
// For demonstration purposes, only add an event handler for connection loss.
camera.ConnectionLost += OnConnectionLost;
// Open the connection to the camera device.
camera.Open();
///////////////// Don't single step beyond this line when using GigE cameras (see comments above) ///////////////////////////////
// Before testing the callbacks, we manually set the heartbeat timeout to a short value when using GigE cameras.
// For debug versions, the heartbeat timeout has been set to 5 minutes, so it would take up to 5 minutes
// until device removal is detected.
camera.Parameters[PLTransportLayer.HeartbeatTimeout].TrySetValue(1000, IntegerValueCorrection.Nearest); // 1000 ms timeout
// Start the grabbing.
camera.StreamGrabber.Start();
// Start the timeout timer.
Console.WriteLine("Please disconnect the device. (Timeout {0}s)", cTimeOutMs / 1000.0);
Stopwatch stopWatch = new Stopwatch();
stopWatch.Start();
// Grab and display images until timeout.
while (camera.StreamGrabber.IsGrabbing && stopWatch.ElapsedMilliseconds < cTimeOutMs)
{
try
{
// Wait for an image and then retrieve it. A timeout of 5000 ms is used.
IGrabResult grabResult = camera.StreamGrabber.RetrieveResult(5000, TimeoutHandling.ThrowException);
using (grabResult)
{
// Image grabbed successfully?
if (grabResult.GrabSucceeded)
{
// Display the grabbed image.
ImageWindow.DisplayImage(0, grabResult);
}
}
}
catch (Exception)
{
// An exception occurred. Is it because the camera device has been physically removed?
// Known issue: Wait until the system safely detects a possible removal.
System.Threading.Thread.Sleep(1000);
if (!camera.IsConnected)
{
// Yes, the camera device has been physically removed.
Console.WriteLine("The camera device has been removed. Please reconnect. (Timeout {0}s)", cTimeOutMs / 1000.0);
// Close the camera object to close underlying resources used for the previous connection.
camera.Close();
// Try to re-establish a connection to the camera device until timeout.
// Reopening the camera triggers the above registered Configuration.AcquireContinous.
// Therefore, the camera is parameterized correctly again.
camera.Open(cTimeOutMs, TimeoutHandling.ThrowException);
// Due to unplugging the camera, settings have changed, e.g. the heartbeat timeout value for GigE cameras.
// After the camera has been reconnected, all settings must be restored. This can be done in the CameraOpened
// event as shown for the Configuration.AcquireContinous.
camera.Parameters[PLTransportLayer.HeartbeatTimeout].TrySetValue(1000, IntegerValueCorrection.Nearest);
// Restart grabbing.
camera.StreamGrabber.Start();
// Restart the timeout timer.
Console.WriteLine("Camera reconnected. You may disconnect the camera device again (Timeout {0}s)", cTimeOutMs / 1000.0);
stopWatch.Restart();
}
else
{
throw;
}
}
}
}
}
catch (Exception e)
{
Console.Error.WriteLine("Exception: {0}", e.Message);
exitCode = 1;
}
finally
{
// Comment the following two lines to disable waiting on exit.
Console.Error.WriteLine("\nPress enter to exit.");
Console.ReadLine();
}
Environment.Exit(exitCode);
}
private void PositionDropWindows(Point pt)
{
//
// Find the before and after sections from the point given
HeaderColumnSection before;
HeaderColumnSection after;
GetDropBoundsSections(pt, out before, out after);
//
// Calculate the x position of the insertion arrows
int xPos;
if (before != null && after != null)
{
xPos = before.HostBasedRectangle.Right + (after.HostBasedRectangle.Left - before.HostBasedRectangle.Right) / 2;
}
else if (before == null)
{
xPos = HostBasedRectangle.Left;
if (after != null)
{
xPos += (after.HostBasedRectangle.Left - HostBasedRectangle.Left) / 2;
}
}
else
{
xPos = before.HostBasedRectangle.Right;
}
//
// Calculate the y position of the insertion arrows
int top;
int bottom;
if (after != null)
{
top = after.Rectangle.Top;
bottom = after.Rectangle.Bottom;
}
else if (before != null)
{
top = before.Rectangle.Top;
bottom = before.Rectangle.Bottom;
}
else
{
top = Rectangle.Top;
bottom = Rectangle.Bottom;
}
//
// Now we have all the info actually position them.
ImageWindow downArrowWindow = _insertionArrows.DownArrowWindow;
ImageWindow upArrowWindow = _insertionArrows.UpArrowWindow;
Point downArrowPoint = new Point(xPos - downArrowWindow.Width / 2, top - downArrowWindow.Height);
if (LayoutController != null)
{
if (xPos < 0)
{
LayoutController.CurrentHorizontalScrollPosition = xPos + GetAbsoluteScrollCoordinates().X;
}
}
downArrowWindow.Location = Host.PointToScreen(
downArrowPoint
);
Point upArrowPoint = new Point(xPos - upArrowWindow.Width / 2, bottom);
upArrowWindow.Location = Host.PointToScreen(upArrowPoint);
//
// Finally make them
if (!upArrowWindow.Visible)
{
upArrowWindow.Visible = true;
}
if (!downArrowWindow.Visible)
{
downArrowWindow.Visible = true;
}
}
private static void Main(string[] args)
{
try
{
if (args.Length != 2)
{
Console.WriteLine("Call this program like this:");
Console.WriteLine("./dnn_mmod_dog_hipsterizer mmod_dog_hipsterizer.dat faces/dogs.jpg");
Console.WriteLine("You can get the mmod_dog_hipsterizer.dat file from:");
Console.WriteLine("http://dlib.net/files/mmod_dog_hipsterizer.dat.bz2");
return;
}
// load the models as well as glasses and mustache.
using (var deserialize = new ProxyDeserialize(args[0]))
using (var net = LossMmod.Deserialize(deserialize))
using (var sp = ShapePredictor.Deserialize(deserialize))
using (var glasses = Matrix <RgbAlphaPixel> .Deserialize(deserialize))
using (var mustache = Matrix <RgbAlphaPixel> .Deserialize(deserialize))
{
Dlib.PyramidUp(glasses);
Dlib.PyramidUp(mustache);
using (var win1 = new ImageWindow(glasses))
using (var win2 = new ImageWindow(mustache))
using (var winWireframe = new ImageWindow())
using (var winHipster = new ImageWindow())
{
// Now process each image, find dogs, and hipsterize them by drawing glasses and a
// mustache on each dog :)
for (var i = 1; i < args.Length; ++i)
{
using (var img = Dlib.LoadImageAsMatrix <RgbPixel>(args[i]))
{
// Upsampling the image will allow us to find smaller dog faces but will use more
// computational resources.
//pyramid_up(img);
var dets = net.Operator(img).First();
winWireframe.ClearOverlay();
winWireframe.SetImage(img);
// We will also draw a wireframe on each dog's face so you can see where the
// shape_predictor is identifying face landmarks.
var lines = new List <ImageWindow.OverlayLine>();
foreach (var d in dets)
{
// get the landmarks for this dog's face
var shape = sp.Detect(img, d.Rect);
var color = new RgbPixel(0, 255, 0);
var top = shape.GetPart(0);
var leftEar = shape.GetPart(1);
var leftEye = shape.GetPart(2);
var nose = shape.GetPart(3);
var rightEar = shape.GetPart(4);
var rightEye = shape.GetPart(5);
// The locations of the left and right ends of the mustache.
var leftMustache = 1.3 * (leftEye - rightEye) / 2 + nose;
var rightMustache = 1.3 * (rightEye - leftEye) / 2 + nose;
// Draw the glasses onto the image.
var from = new[]
{
2 * new Point(176, 36), 2 * new Point(59, 35)
};
var to = new[]
{
leftEye, rightEye
};
using (var transform = Dlib.FindSimilarityTransform(from, to))
for (uint r = 0, nr = (uint)glasses.Rows; r < nr; ++r)
{
for (uint c = 0, nc = (uint)glasses.Columns; c < nc; ++c)
{
var p = (Point)transform.Operator(new DPoint(c, r));
if (Dlib.GetRect(img).Contains(p))
{
var rgb = img[p.Y, p.X];
Dlib.AssignPixel(ref rgb, glasses[(int)r, (int)c]);
img[p.Y, p.X] = rgb;
}
}
}
// Draw the mustache onto the image right under the dog's nose.
var mustacheRect = Dlib.GetRect(mustache);
from = new[]
{
mustacheRect.TopLeft, mustacheRect.TopRight
};
to = new[]
{
rightMustache, leftMustache
};
using (var transform = Dlib.FindSimilarityTransform(from, to))
for (uint r = 0, nr = (uint)mustache.Rows; r < nr; ++r)
{
for (uint c = 0, nc = (uint)mustache.Columns; c < nc; ++c)
{
var p = (Point)transform.Operator(new DPoint(c, r));
if (Dlib.GetRect(img).Contains(p))
{
var rgb = img[p.Y, p.X];
Dlib.AssignPixel(ref rgb, mustache[(int)r, (int)c]);
img[p.Y, p.X] = rgb;
}
}
}
// Record the lines needed for the face wire frame.
lines.Add(new ImageWindow.OverlayLine(leftEye, nose, color));
lines.Add(new ImageWindow.OverlayLine(nose, rightEye, color));
lines.Add(new ImageWindow.OverlayLine(rightEye, leftEye, color));
lines.Add(new ImageWindow.OverlayLine(rightEye, rightEar, color));
lines.Add(new ImageWindow.OverlayLine(rightEar, top, color));
lines.Add(new ImageWindow.OverlayLine(top, leftEar, color));
lines.Add(new ImageWindow.OverlayLine(leftEar, leftEye, color));
winWireframe.AddOverlay(lines);
winHipster.SetImage(img);
}
Console.WriteLine("Hit enter to process the next image.");
Console.ReadKey();
}
}
}
}
}
catch (Exception e)
{
Console.WriteLine(e);
}
}
public void Jet2()
{
var path = this.GetDataFile($"{LoadTarget}.bmp");
var tests = new[]
{
new { Type = ImageTypes.RgbPixel, ExpectResult = false, Max = 255, Min = 0 },
new { Type = ImageTypes.RgbAlphaPixel, ExpectResult = false, Max = 255, Min = 0 },
new { Type = ImageTypes.UInt8, ExpectResult = true, Max = 255, Min = 0 },
new { Type = ImageTypes.UInt16, ExpectResult = true, Max = 255, Min = 0 },
new { Type = ImageTypes.UInt32, ExpectResult = true, Max = 255, Min = 0 },
new { Type = ImageTypes.Int8, ExpectResult = true, Max = 255, Min = 0 },
new { Type = ImageTypes.Int16, ExpectResult = true, Max = 255, Min = 0 },
new { Type = ImageTypes.Int32, ExpectResult = true, Max = 255, Min = 0 },
new { Type = ImageTypes.HsiPixel, ExpectResult = false, Max = 255, Min = 0 },
new { Type = ImageTypes.Float, ExpectResult = true, Max = 255, Min = 0 },
new { Type = ImageTypes.Double, ExpectResult = true, Max = 255, Min = 0 },
new { Type = ImageTypes.RgbPixel, ExpectResult = false, Max = 75, Min = 50 },
new { Type = ImageTypes.RgbAlphaPixel, ExpectResult = false, Max = 75, Min = 50 },
new { Type = ImageTypes.UInt8, ExpectResult = true, Max = 75, Min = 50 },
new { Type = ImageTypes.UInt16, ExpectResult = true, Max = 75, Min = 50 },
new { Type = ImageTypes.UInt32, ExpectResult = true, Max = 75, Min = 50 },
new { Type = ImageTypes.Int8, ExpectResult = true, Max = 75, Min = 50 },
new { Type = ImageTypes.Int16, ExpectResult = true, Max = 75, Min = 50 },
new { Type = ImageTypes.Int32, ExpectResult = true, Max = 75, Min = 50 },
new { Type = ImageTypes.HsiPixel, ExpectResult = false, Max = 75, Min = 50 },
new { Type = ImageTypes.Float, ExpectResult = true, Max = 75, Min = 50 },
new { Type = ImageTypes.Double, ExpectResult = true, Max = 75, Min = 50 }
};
var type = this.GetType().Name;
foreach (var test in tests)
{
Array2DBase imageObj = null;
Array2DBase horzObj = null;
Array2DBase vertObj = null;
Array2DBase outputImageObj = null;
MatrixOp matrix = null;
DlibObject windowObj = null;
try
{
const ImageTypes inputType = ImageTypes.Float;
var image = DlibTest.LoadImage(test.Type, path);
imageObj = image;
var horz = Array2DTest.CreateArray2D(inputType);
horzObj = horz;
var vert = Array2DTest.CreateArray2D(inputType);
vertObj = vert;
var outputImage = Array2DTest.CreateArray2D(test.Type);
outputImageObj = outputImage;
Dlib.SobelEdgeDetector(image, horz, vert);
Dlib.SuppressNonMaximumEdges(horz, vert, outputImage);
try
{
matrix = Jet(test.Type, outputImage, test.Max, test.Min);
if (test.ExpectResult)
{
if (this.CanGuiDebug)
{
var window = new ImageWindow(matrix, $"{test.Type} - Max: {test.Max}, Min : {test.Min}");
windowObj = window;
}
Dlib.SaveBmp(image, $"{Path.Combine(this.GetOutDir(type, "Jet2"), $"{LoadTarget}_{test.Type}_{test.Max}_{test.Min}.bmp")}");
}
else
{
Assert.Fail($"Failed to execute Jet2 to Type: {test.Type}");
}
}
catch (Exception)
{
if (!test.ExpectResult)
{
Console.WriteLine("OK");
}
else
{
throw;
}
}
}
catch (Exception e)
{
Console.WriteLine(e.StackTrace);
Console.WriteLine($"Failed to execute Jet2 to Type: {test.Type}");
throw;
}
finally
{
if (outputImageObj != null)
{
this.DisposeAndCheckDisposedState(outputImageObj);
}
if (vertObj != null)
{
this.DisposeAndCheckDisposedState(vertObj);
}
if (horzObj != null)
{
this.DisposeAndCheckDisposedState(horzObj);
}
if (windowObj != null)
{
this.DisposeAndCheckDisposedState(windowObj);
}
if (matrix != null)
{
this.DisposeAndCheckDisposedState(matrix);
}
if (imageObj != null)
{
this.DisposeAndCheckDisposedState(imageObj);
}
}
}
}
private static void Main()
{
try
{
var cap = new VideoCapture(0);
//var cap = new VideoCapture("20090124_WeeklyAddress.ogv.360p.webm");
if (!cap.IsOpened())
{
Console.WriteLine("Unable to connect to camera");
return;
}
using (var win = new ImageWindow())
{
// Load face detection and pose estimation models.
using (var detector = Dlib.GetFrontalFaceDetector())
using (var poseModel = ShapePredictor.Deserialize("shape_predictor_68_face_landmarks.dat"))
{
// Grab and process frames until the main window is closed by the user.
while (!win.IsClosed())
{
// Grab a frame
var temp = new Mat();
if (!cap.Read(temp))
{
break;
}
// Turn OpenCV's Mat into something dlib can deal with. Note that this just
// wraps the Mat object, it doesn't copy anything. So cimg is only valid as
// long as temp is valid. Also don't do anything to temp that would cause it
// to reallocate the memory which stores the image as that will make cimg
// contain dangling pointers. This basically means you shouldn't modify temp
// while using cimg.
var array = new byte[temp.Width * temp.Height * temp.ElemSize()];
Marshal.Copy(temp.Data, array, 0, array.Length);
using (var cimg = Dlib.LoadImageData <RgbPixel>(array, (uint)temp.Height, (uint)temp.Width, (uint)(temp.Width * temp.ElemSize())))
{
// Detect faces
var faces = detector.Detect(cimg);
// Find the pose of each face.
var shapes = new List <FullObjectDetection>();
for (var i = 0; i < faces.Length; ++i)
{
var det = poseModel.Detect(cimg, faces[i]);
shapes.Add(det);
}
// Display it all on the screen
win.ClearOverlay();
win.SetImage(cimg);
var lines = Dlib.RenderFaceDetections(shapes);
win.AddOverlay(lines);
foreach (var line in lines)
{
line.Dispose();
}
}
}
}
}
}
//catch (serialization_error&e)
//{
// cout << "You need dlib's default face landmarking model file to run this example." << endl;
// cout << "You can get it from the following URL: " << endl;
// cout << " http://dlib.net/files/shape_predictor_68_face_landmarks.dat.bz2" << endl;
// cout << endl << e.what() << endl;
//}
catch (Exception e)
{
Console.WriteLine(e.Message);
}
}
public void RawApiFrontalFaceDetectorUsingMemoryInput()
{
const string imagePath = "images\\lenna.bmp";
var imageBytes = File.ReadAllBytes(imagePath);
IntPtr detector = IntPtr.Zero;
IntPtr dets = IntPtr.Zero;
try
{
using (var window = new ImageWindow())
using (var image = new Array2dUchar())
{
Console.WriteLine($"Size: ({image.Width},{image.Height})");
Trace.Assert(image.Width == 0 && image.Height == 0);
window.SetImage(image);
OpenCvSharp.Cv2.WaitKey(Cv2WaitKeyDelay);
NativeMethods.dlib_load_bmp_array2d_uchar(image.DlibArray2dUchar, imageBytes, new IntPtr(imageBytes.Length));
Console.WriteLine($"Size: ({image.Width},{image.Height})");
Trace.Assert(image.Width == 512 && image.Height == 480);
window.SetImage(image);
OpenCvSharp.Cv2.WaitKey(Cv2WaitKeyDelay);
image.SetBitmap(new System.Drawing.Bitmap(imagePath));
Console.WriteLine($"Size: ({image.Width},{image.Height})");
Trace.Assert(image.Width == 512 && image.Height == 480);
window.SetImage(image);
OpenCvSharp.Cv2.WaitKey(Cv2WaitKeyDelay);
image.PyramidUp();
Console.WriteLine($"Size: ({image.Width},{image.Height})");
window.SetImage(image);
OpenCvSharp.Cv2.WaitKey(Cv2WaitKeyDelay);
detector = NativeMethods.dlib_get_frontal_face_detector();
dets = NativeMethods.vector_Rect_new1();
NativeMethods.dlib_frontal_face_detector_operator(detector, image.DlibArray2dUchar, -0.5, dets);
unsafe
{
Rect *rectangles = (Rect *)NativeMethods.vector_Rect_getPointer(dets).ToPointer();
long count = NativeMethods.vector_Rect_getSize(dets).ToInt64();
for (int i = 0; i < count; i++)
{
Console.WriteLine(rectangles[i]);
}
}
}
}
finally
{
if (detector != IntPtr.Zero)
{
NativeMethods.dlib_frontal_face_detector_delete(detector);
}
if (dets != IntPtr.Zero)
{
NativeMethods.vector_Rect_delete(dets);
}
}
}
public void RawApiFaceLandmarkDetectionUsingMemoryInput()
{
const string imagePath = "images\\lenna.bmp";
var imageBytes = File.ReadAllBytes(imagePath);
IntPtr detector = IntPtr.Zero;
IntPtr dets = IntPtr.Zero;
try
{
using (var window = new ImageWindow())
using (var image = new Array2dRgbPixel())
{
Console.WriteLine($"Size: ({image.Width},{image.Height})");
Trace.Assert(image.Width == 0 && image.Height == 0);
window.SetImage(image);
OpenCvSharp.Cv2.WaitKey(Cv2WaitKeyDelay);
NativeMethods.dlib_load_bmp_array2d_rgbpixel(image.DlibArray2dRgbPixel, imageBytes, new IntPtr(imageBytes.Length));
Console.WriteLine($"Size: ({image.Width},{image.Height})");
Trace.Assert(image.Width == 512 && image.Height == 480);
window.SetImage(image);
OpenCvSharp.Cv2.WaitKey(Cv2WaitKeyDelay);
image.SetBitmap(new System.Drawing.Bitmap(imagePath));
Console.WriteLine($"Size: ({image.Width},{image.Height})");
Trace.Assert(image.Width == 512 && image.Height == 480);
window.SetImage(image);
OpenCvSharp.Cv2.WaitKey(Cv2WaitKeyDelay);
image.PyramidUp();
Console.WriteLine($"Size: ({image.Width},{image.Height})");
window.SetImage(image);
OpenCvSharp.Cv2.WaitKey(Cv2WaitKeyDelay);
detector = NativeMethods.dlib_get_face_landmark_detection("D:/Data/Dlib/shape_predictor_68_face_landmarks.dat");
dets = NativeMethods.vector_FaceLandmarkInternal_new1();
NativeMethods.dlib_face_landmark_detection_operator(detector, image.DlibArray2dRgbPixel, -0.5, dets);
Trace.Assert(dets != null && dets != IntPtr.Zero);
long count = NativeMethods.vector_FaceLandmarkInternal_getSize(dets).ToInt64();
// If it does not return ret here, exception occurs.
if (count > 0)
{
unsafe
{
FaceLandmarkInternal *faceLandmarkInternals = (FaceLandmarkInternal *)NativeMethods.vector_FaceLandmarkInternal_getPointer(dets).ToPointer();
for (int i = 0; i < count; i++)
{
Console.WriteLine(new FaceLandmark(faceLandmarkInternals[i]));
}
}
}
}
}
finally
{
if (detector != IntPtr.Zero)
{
NativeMethods.dlib_face_landmark_detection_delete(detector);
}
if (dets != IntPtr.Zero)
{
NativeMethods.vector_FaceLandmarkInternal_delete(dets);
}
}
}
private static void Main()
{
try
{
//var cap = new VideoCapture(0);
//var cap = new VideoCapture("https://js.live-play.acgvideo.com/live-js/890069/live_30947419_1716018.flv?wsSecret=2cee8a379a871fa8dbf714ba9d16e8a4&wsTime=1548240723&trid=4f64a0ae5e2444938cfdd109a54c6e1c&sig=no&platform=web&pSession=yR3bsQk1-SCY4-4QGi-K7EG-AsbTiwbX7tZF");
var cap = new VideoCapture(0);
if (!cap.IsOpened())
{
Console.WriteLine("Unable to connect to camera");
return;
}
using (var win = new ImageWindow())
{
// Load face detection and pose estimation models.
using (var detector = Dlib.GetFrontalFaceDetector())
using (var poseModel = ShapePredictor.Deserialize("shape_predictor_68_face_landmarks.dat"))
{
//抓取和处理帧，直到用户关闭主窗口。
while (!win.IsClosed())
{
// Grab a frame
var temp = new Mat();
if (!cap.Read(temp))
{
break;
}
//把OpenCV的Mat变成dlib可以处理的东西。注意
//包装Mat对象，它不复制任何东西。所以cimg只对as有效
//只要温度是有效的。也不要做任何可能导致它的临时工作
//重新分配存储图像的内存，因为这将使cimg
//包含悬空指针。这基本上意味着您不应该修改temp
//使用cimg时。
var array = new byte[temp.Width * temp.Height * temp.ElemSize()];
Marshal.Copy(temp.Data, array, 0, array.Length);
using (var cimg = Dlib.LoadImageData <RgbPixel>(array, (uint)temp.Height, (uint)temp.Width, (uint)(temp.Width * temp.ElemSize())))
{
// Detect faces
var faces = detector.Operator(cimg);
// Find the pose of each face.
var shapes = new List <FullObjectDetection>();
for (var i = 0; i < faces.Length; ++i)
{
var det = poseModel.Detect(cimg, faces[i]);
Console.WriteLine(faces[i].Left);
shapes.Add(det);
}
//在屏幕上显示
win.ClearOverlay();
win.SetImage(cimg);
var lines = Dlib.RenderFaceDetections(shapes);
win.AddOverlay(faces, new RgbPixel {
Red = 255
});
win.AddOverlay(lines);
foreach (var line in lines)
{
line.Dispose();
}
}
}
}
}
}
//catch (serialization_error&e)
//{
// cout << "You need dlib's default face landmarking model file to run this example." << endl;
// cout << "You can get it from the following URL: " << endl;
// cout << " http://dlib.net/files/shape_predictor_68_face_landmarks.dat.bz2" << endl;
// cout << endl << e.what() << endl;
//}
catch (Exception e)
{
Console.WriteLine(e.Message);
}
}
private bool ParseUrlParameters(ActivityWindow activityWindow)
{
if (IsRunningOutOfBrowser)
{
return(false);
}
bool foundParam = false;
if (queryString.ContainsKey(PARAMETER_ACTIVITY))
{
activityWindow.LoadOptions(makeActivityUri(queryString[PARAMETER_ACTIVITY]));
foundParam = true;
}
if (queryString.ContainsKey(PARAMETER_MEDIA))
{
WaitTillNotBusy(activityWindow); //TODO: doesn't work (should wait for any activity to load first)
MediaPlayerWindow w = new MediaPlayerWindow();
w.Width = activityWindow.Width;
w.Height = activityWindow.Height - (activityWindow.ActivityView.ToolbarVisible ? 80 : 0); //TODO: should change this if/when activity toolbar is made vertical (have option to get ActualWidth/ActualHeight of activity toolbar)
activityWindow.Container.AddWindowInViewCenter(w);
w.MediaPlayerView.Source = makeClipUri(CLIPFLAIR_GALLERY_VIDEO, queryString[PARAMETER_MEDIA]);
foundParam = true;
}
if (queryString.ContainsKey(PARAMETER_VIDEO))
{
WaitTillNotBusy(activityWindow); //TODO: doesn't work (should wait for any activity to load first)
MediaPlayerWindow w = new MediaPlayerWindow();
w.Width = activityWindow.Width;
w.Height = activityWindow.Height - (activityWindow.ActivityView.ToolbarVisible ? 80 : 0); //TODO: should change this if/when activity toolbar is made vertical (have option to get ActualWidth/ActualHeight of activity toolbar)
activityWindow.Container.AddWindowInViewCenter(w);
w.MediaPlayerView.Source = makeClipUri(CLIPFLAIR_GALLERY_VIDEO, queryString[PARAMETER_VIDEO]);
foundParam = true;
}
if (queryString.ContainsKey(PARAMETER_AUDIO))
{
WaitTillNotBusy(activityWindow); //TODO: doesn't work (should wait for any activity to load first)
MediaPlayerWindow w = new MediaPlayerWindow();
w.Width = activityWindow.Width;
w.Height = 69; //Using small height since there's only audio //NOTE: if SMF skin changes this may not look ok
activityWindow.Container.AddWindowInViewCenter(w);
w.MediaPlayerView.VideoVisible = false;
w.MediaPlayerView.Source = new Uri(new Uri(CLIPFLAIR_GALLERY_AUDIO), queryString[PARAMETER_AUDIO]);
foundParam = true;
}
if (queryString.ContainsKey(PARAMETER_IMAGE))
{
WaitTillNotBusy(activityWindow); //TODO: doesn't work (should wait for any activity to load first)
ImageWindow w = new ImageWindow();
w.Width = activityWindow.Width;
w.Height = activityWindow.Height - (activityWindow.ActivityView.ToolbarVisible ? 80 : 0); //TODO: should change this if/when activity toolbar is made vertical (have option to get ActualWidth/ActualHeight of activity toolbar)
activityWindow.Container.AddWindowInViewCenter(w);
w.ImageView.Source = new Uri(new Uri(CLIPFLAIR_GALLERY_IMAGE), queryString[PARAMETER_IMAGE]);
foundParam = true;
}
if (queryString.ContainsKey(PARAMETER_GALLERY))
{
WaitTillNotBusy(activityWindow); //TODO: doesn't work (should wait for any activity to load first)
GalleryWindow w = new GalleryWindow();
w.Width = activityWindow.Width;
w.Height = activityWindow.Height - (activityWindow.ActivityView.ToolbarVisible ? 80 : 0); //TODO: should change this if/when activity toolbar is made vertical (have option to get ActualWidth/ActualHeight of activity toolbar)
activityWindow.Container.AddWindowInViewCenter(w);
w.GalleryView.Source = makeGalleryUri(queryString[PARAMETER_GALLERY]);
foundParam = true;
}
if (queryString.ContainsKey(PARAMETER_COLLECTION))
{
WaitTillNotBusy(activityWindow); //TODO: doesn't work (should wait for any activity to load first)
GalleryWindow w = new GalleryWindow();
w.Width = activityWindow.Width;
w.Height = activityWindow.Height - (activityWindow.ActivityView.ToolbarVisible ? 80 : 0); //TODO: should change this if/when activity toolbar is made vertical (have option to get ActualWidth/ActualHeight of activity toolbar)
activityWindow.Container.AddWindowInViewCenter(w);
w.GalleryView.Source = makeGalleryUri(queryString[PARAMETER_COLLECTION]);
foundParam = true;
}
//TODO: add ...PARAMETER_CAPTIONS, PARAMETER_COMPONENT, TEXT, MAP etc.
return(foundParam);
} //TODO: add CAPTIONS parameter to load .SRT/.TTS/.SSA and show it
public void LoadImageData()
{
const int cols = 512;
const int rows = 512;
var path = this.GetDataFile($"lena_gray.raw");
var data = File.ReadAllBytes(path.FullName);
var tests = new[]
{
new { Type = ImageTypes.UInt8, ExpectResult = true },
//new { Type = ImageTypes.RgbPixel, ExpectResult = true},
//new { Type = ImageTypes.RgbAlphaPixel, ExpectResult = true},
//new { Type = ImageTypes.UInt16, ExpectResult = true},
//new { Type = ImageTypes.HsiPixel, ExpectResult = true},
//new { Type = ImageTypes.Float, ExpectResult = true},
//new { Type = ImageTypes.Double, ExpectResult = true}
};
foreach (var test in tests)
{
TwoDimentionObjectBase image;
using (var win = new ImageWindow())
{
switch (test.Type)
{
case ImageTypes.UInt8:
image = Dlib.LoadImageData <byte>(data, 512, 512, 512);
if (this.CanGuiDebug)
{
win.SetImage((Array2D <byte>)image);
win.WaitUntilClosed();
}
break;
//case ImageTypes.UInt16:
// image = Dlib.LoadBmp<ushort>(path.FullName);
// break;
//case ImageTypes.HsiPixel:
// image = Dlib.LoadBmp<HsiPixel>(path.FullName);
// break;
//case ImageTypes.Float:
// image = Dlib.LoadBmp<float>(path.FullName);
// break;
//case ImageTypes.Double:
// image = Dlib.LoadBmp<double>(path.FullName);
// break;
//case ImageTypes.RgbPixel:
// image = Dlib.LoadBmp<RgbPixel>(path.FullName);
// break;
//case ImageTypes.RgbAlphaPixel:
// image = Dlib.LoadBmp<RgbAlphaPixel>(path.FullName);
// break;
default:
throw new ArgumentOutOfRangeException();
}
}
Assert.AreEqual(image.Columns, cols, $"Failed to load {test.Type}.");
Assert.AreEqual(image.Rows, rows, $"Failed to load {test.Type}.");
this.DisposeAndCheckDisposedState(image);
}
}
public async Task <ActionResult> Login([FromBody] InputFaceModel model)
{
RequestFaceModel request = new RequestFaceModel()
{
Status = 500,
Message = null
};
var filePath = Path.Combine(AppDomain.CurrentDomain.BaseDirectory, "FaceImages", model.user_name);
if (!Directory.Exists(filePath))
{
request.Enum = RequestEnum.Failed;
Console.WriteLine(request.Message);
Thread.Sleep(5000);
return(Ok(request));
}
FaceContrast faceContrast = new FaceContrast(filePath);
VideoCapture cap = null;
try
{
if (model.rmtp_url == "0")
{
cap = new VideoCapture(0);
}
else
{
cap = new VideoCapture(model.rmtp_url);
}
var flag = false;
var faceFlag = false;
var bioFlag = false;
QueueFixedLength <double> leftEarQueue = new QueueFixedLength <double>(10);
QueueFixedLength <double> rightEarQueue = new QueueFixedLength <double>(10);
QueueFixedLength <double> mouthQueue = new QueueFixedLength <double>(20);
bool leftEarFlag = false;
bool rightEarFlag = false;
bool mouthFlag = false;
using (var sp = ShapePredictor.Deserialize(Path.Combine(AppDomain.CurrentDomain.BaseDirectory, "ShapeModel", "shape_predictor_5_face_landmarks.dat")))
using (var win = new ImageWindow())
{
// Load face detection and pose estimation models.
using (var detector = Dlib.GetFrontalFaceDetector())
using (var net = LossMetric.Deserialize(Path.Combine(AppDomain.CurrentDomain.BaseDirectory, "ShapeModel", "dlib_face_recognition_resnet_model_v1.dat")))
using (var poseModel = ShapePredictor.Deserialize(Path.Combine(AppDomain.CurrentDomain.BaseDirectory, "ShapeModel", "shape_predictor_68_face_landmarks.dat")))
{
var ti = true;
System.Timers.Timer t = new System.Timers.Timer(30000);
t.Elapsed += new System.Timers.ElapsedEventHandler((object source, System.Timers.ElapsedEventArgs e) =>
{
ti = false;
});
t.AutoReset = false;
t.Enabled = true;
//抓取和处理帧，直到用户关闭主窗口。
while (/*!win.IsClosed() &&*/ ti)
{
try
{
// Grab a frame
var temp = new Mat();
if (!cap.Read(temp))
{
break;
}
//把OpenCV的Mat变成dlib可以处理的东西。注意
//包装Mat对象，它不复制任何东西。所以cimg只对as有效
//只要温度是有效的。也不要做任何可能导致它的临时工作
//重新分配存储图像的内存，因为这将使cimg
//包含悬空指针。这基本上意味着您不应该修改temp
//使用cimg时。
var array = new byte[temp.Width * temp.Height * temp.ElemSize()];
Marshal.Copy(temp.Data, array, 0, array.Length);
using (var cimg = Dlib.LoadImageData <RgbPixel>(array, (uint)temp.Height, (uint)temp.Width, (uint)(temp.Width * temp.ElemSize())))
{
// Detect faces
var faces = detector.Operator(cimg);
// Find the pose of each face.
var shapes = new List <FullObjectDetection>();
for (var i = 0; i < faces.Length; ++i)
{
var det = poseModel.Detect(cimg, faces[i]);
shapes.Add(det);
}
if (shapes.Count > 0)
{
// 活体检测
if (!bioFlag)
{
bioFlag = BioAssay(shapes[0], ref leftEarQueue, ref rightEarQueue, ref mouthQueue, ref leftEarFlag, ref rightEarFlag, ref mouthFlag);
}
}
if (!faceFlag)
{
foreach (var face in faces)
{
var shape = sp.Detect(cimg, face);
var faceChipDetail = Dlib.GetFaceChipDetails(shape, 150, 0.25);
Matrix <RgbPixel> rgbPixels = new Matrix <RgbPixel>(cimg);
var faceChip = Dlib.ExtractImageChip <RgbPixel>(rgbPixels, faceChipDetail);
var faceDescriptors = net.Operator(faceChip);
faceFlag = faceContrast.Contrast(faceDescriptors);
}
}
Console.WriteLine(model.user_name + ":" + faceFlag);
if (bioFlag && faceFlag)
{
flag = bioFlag && faceFlag;
if (flag)
{
break;
}
}
//在屏幕上显示
win.ClearOverlay();
win.SetImage(cimg);
var lines = Dlib.RenderFaceDetections(shapes);
win.AddOverlay(faces, new RgbPixel {
Red = 72, Green = 118, Blue = 255
});
win.AddOverlay(lines);
foreach (var line in lines)
{
line.Dispose();
}
}
}
catch (Exception ex)
{
request.Message = ex.ToString();
break;
}
}
}
}
if (flag)
{
request.Enum = RequestEnum.Succeed;
}
else
{
request.Enum = RequestEnum.Failed;
}
}
catch (Exception ex)
{
request.Message = ex.ToString();
}
finally
{
if (cap != null)
{
cap.Dispose();
}
}
Console.WriteLine(request.Message);
return(Ok(request));
}
public void LoadImageData2()
{
const int cols = 512;
const int rows = 512;
const int steps = 512;
var tests = new[]
{
new { Type = ImageTypes.UInt8, ExpectResult = true },
new { Type = ImageTypes.UInt16, ExpectResult = true },
new { Type = ImageTypes.Int16, ExpectResult = true },
new { Type = ImageTypes.Int32, ExpectResult = true },
new { Type = ImageTypes.HsiPixel, ExpectResult = true },
new { Type = ImageTypes.RgbPixel, ExpectResult = true },
new { Type = ImageTypes.RgbAlphaPixel, ExpectResult = true },
new { Type = ImageTypes.Float, ExpectResult = true },
new { Type = ImageTypes.Double, ExpectResult = true }
};
var random = new Random(0);
foreach (var test in tests)
{
TwoDimentionObjectBase image;
using (var win = new ImageWindow())
{
switch (test.Type)
{
case ImageTypes.UInt8:
{
var data = new byte[rows * cols];
for (var r = 0; r < rows; r++)
{
for (var c = 0; c < cols; c++)
{
data[steps * r + c] = (byte)random.Next(0, 255);
}
}
image = Dlib.LoadImageData <byte>(data, rows, cols, steps);
if (this.CanGuiDebug)
{
win.SetImage((Array2D <byte>)image);
win.WaitUntilClosed();
}
}
break;
case ImageTypes.UInt16:
{
var data = new ushort[rows * cols];
for (var r = 0; r < rows; r++)
{
for (var c = 0; c < cols; c++)
{
data[steps * r + c] = (ushort)random.Next(0, 255);
}
}
image = Dlib.LoadImageData <ushort>(data, rows, cols, steps);
if (this.CanGuiDebug)
{
win.SetImage((Array2D <ushort>)image);
win.WaitUntilClosed();
}
}
break;
case ImageTypes.Int16:
{
var data = new short[rows * cols];
for (var r = 0; r < rows; r++)
{
for (var c = 0; c < cols; c++)
{
data[steps * r + c] = (short)random.Next(0, 255);
}
}
image = Dlib.LoadImageData <short>(data, rows, cols, steps);
if (this.CanGuiDebug)
{
win.SetImage((Array2D <short>)image);
win.WaitUntilClosed();
}
}
break;
case ImageTypes.Int32:
{
var data = new int[rows * cols];
for (var r = 0; r < rows; r++)
{
for (var c = 0; c < cols; c++)
{
data[steps * r + c] = random.Next(0, 255);
}
}
image = Dlib.LoadImageData <int>(data, rows, cols, steps);
if (this.CanGuiDebug)
{
win.SetImage((Array2D <int>)image);
win.WaitUntilClosed();
}
}
break;
case ImageTypes.Float:
{
var data = new float[rows * cols];
for (var r = 0; r < rows; r++)
{
for (var c = 0; c < cols; c++)
{
data[steps * r + c] = (float)random.NextDouble();
}
}
image = Dlib.LoadImageData <float>(data, rows, cols, steps);
if (this.CanGuiDebug)
{
win.SetImage((Array2D <float>)image);
win.WaitUntilClosed();
}
}
break;
case ImageTypes.Double:
{
var data = new double[rows * cols];
for (var r = 0; r < rows; r++)
{
for (var c = 0; c < cols; c++)
{
data[steps * r + c] = (double)random.NextDouble();
}
}
image = Dlib.LoadImageData <double>(data, rows, cols, steps);
if (this.CanGuiDebug)
{
win.SetImage((Array2D <double>)image);
win.WaitUntilClosed();
}
}
break;
case ImageTypes.HsiPixel:
{
var data = new HsiPixel[rows * cols];
for (var r = 0; r < rows; r++)
{
for (var c = 0; c < cols; c++)
{
data[steps * r + c] = new HsiPixel
{
H = (byte)random.Next(0, 255),
S = (byte)random.Next(0, 255),
I = (byte)random.Next(0, 255)
}
}
}
;
image = Dlib.LoadImageData <HsiPixel>(data, rows, cols, steps);
if (this.CanGuiDebug)
{
win.SetImage((Array2D <HsiPixel>)image);
win.WaitUntilClosed();
}
}
break;
case ImageTypes.RgbPixel:
{
var data = new RgbPixel[rows * cols];
for (var r = 0; r < rows; r++)
{
for (var c = 0; c < cols; c++)
{
data[steps * r + c] = new RgbPixel
{
Red = (byte)random.Next(0, 255),
Green = (byte)random.Next(0, 255),
Blue = (byte)random.Next(0, 255)
}
}
}
;
image = Dlib.LoadImageData <RgbPixel>(data, rows, cols, steps);
if (this.CanGuiDebug)
{
win.SetImage((Array2D <RgbPixel>)image);
win.WaitUntilClosed();
}
}
break;
case ImageTypes.RgbAlphaPixel:
{
var data = new RgbAlphaPixel[rows * cols];
for (var r = 0; r < rows; r++)
{
for (var c = 0; c < cols; c++)
{
data[steps * r + c] = new RgbAlphaPixel
{
Red = (byte)random.Next(0, 255),
Green = (byte)random.Next(0, 255),
Blue = (byte)random.Next(0, 255),
Alpha = (byte)random.Next(0, 255)
}
}
}
;
image = Dlib.LoadImageData <RgbAlphaPixel>(data, rows, cols, steps);
if (this.CanGuiDebug)
{
win.SetImage((Array2D <RgbAlphaPixel>)image);
win.WaitUntilClosed();
}
}
break;
default:
throw new ArgumentOutOfRangeException();
}
}
Assert.AreEqual(image.Columns, cols, $"Failed to load {test.Type}.");
Assert.AreEqual(image.Rows, rows, $"Failed to load {test.Type}.");
this.DisposeAndCheckDisposedState(image);
}
}
internal static void Main()
{
// The exit code of the sample application.
int exitCode = 0;
try
{
// Create a camera object that selects the first camera device found.
// More constructors are available for selecting a specific camera device.
// For multicast only look for GigE cameras here.
using (Camera camera = new Camera(DeviceType.GigE, CameraSelectionStrategy.FirstFound))
{
// Print the model name of the camera.
Console.WriteLine("Using camera {0}.", camera.CameraInfo[CameraInfoKey.ModelName]);
String deviceType = camera.CameraInfo[CameraInfoKey.DeviceType];
Console.WriteLine("==========");
Console.WriteLine("{0} Camera", deviceType);
Console.WriteLine("==========");
camera.StreamGrabber.ImageGrabbed += OnImageGrabbed;
camera.StreamGrabber.ImageGrabbed += OnImageSkipped;
// Get the Key from the user for selecting the mode
Console.Write("Start multicast sample in (c)ontrol or in (m)onitor mode? (c/m) ");
ConsoleKeyInfo keyPressed = Console.ReadKey();
switch (keyPressed.KeyChar)
{
// The default configuration must be removed when monitor mode is selected
// because the monitoring application is not allowed to modify any parameter settings.
case 'm':
case 'M':
// Monitor mode selected.
Console.WriteLine("\nIn Monitor mode");
// Set MonitorModeActive to true to act as monitor
camera.Parameters [PLCameraInstance.MonitorModeActive].SetValue(true); // Set monitor mode
// Open the camera.
camera.Open();
// Select transmission type. If the camera is already controlled by another application
// and configured for multicast, the active camera configuration can be used
// (IP Address and Port will be set automatically).
camera.Parameters[PLGigEStream.TransmissionType].TrySetValue(PLGigEStream.TransmissionType.UseCameraConfig);
// Alternatively, the stream grabber could be explicitly set to "multicast"...
// In this case, the IP Address and the IP port must also be set.
//
//camera.Parameters[PLGigEStream.TransmissionType].SetValue(PLGigEStream.TransmissionType.Multicast);
//camera.Parameters[PLGigEStream.DestinationAddr].SetValue("239.0.0.1");
//camera.Parameters[PLGigEStream.DestinationPort].SetValue(49152);
if ((camera.Parameters[PLGigEStream.DestinationAddr].GetValue() != "0.0.0.0") &&
(camera.Parameters[PLGigEStream.DestinationPort].GetValue() != 0))
{
camera.StreamGrabber.Start(countOfImagesToGrab);
}
else
{
throw new Exception("Failed to open stream grabber (monitor mode): The acquisition is not yet started by the controlling application. Start the controlling application before starting the monitor application.");
}
break;
case 'c':
case 'C':
// Controlling mode selected.
Console.WriteLine("\nIn Control mode");
// Open the camera.
camera.Open();
// Set transmission type to "multicast"...
// In this case, the IP Address and the IP port must also be set.
camera.Parameters[PLGigEStream.TransmissionType].SetValue(PLGigEStream.TransmissionType.Multicast);
//camera.Parameters[PLGigEStream.DestinationAddr].SetValue("239.0.0.1");
//camera.Parameters[PLGigEStream.DestinationPort].SetValue(49152);
// Maximize the image area of interest (Image AOI).
camera.Parameters[PLGigECamera.OffsetX].TrySetValue(camera.Parameters[PLGigECamera.OffsetX].GetMinimum());
camera.Parameters[PLGigECamera.OffsetY].TrySetValue(camera.Parameters[PLGigECamera.OffsetY].GetMinimum());
camera.Parameters[PLGigECamera.Width].SetValue(camera.Parameters[PLGigECamera.Width].GetMaximum());
camera.Parameters[PLGigECamera.Height].SetValue(camera.Parameters[PLGigECamera.Height].GetMaximum());
// Set the pixel data format.
camera.Parameters[PLGigECamera.PixelFormat].SetValue(PLGigECamera.PixelFormat.Mono8);
camera.StreamGrabber.Start();
break;
default:
throw new NotSupportedException("Invalid mode selected.");
}
IGrabResult grabResult;
// Camera.StopGrabbing() is called automatically by the RetrieveResult() method
// when countOfImagesToGrab images have been retrieved in monitor mode
// or when a key is pressed and the camera object is destroyed.
Console.WriteLine("Press any key to quit FrameGrabber...");
while (!Console.KeyAvailable && camera.StreamGrabber.IsGrabbing)
{
grabResult = camera.StreamGrabber.RetrieveResult(5000, TimeoutHandling.ThrowException);
using (grabResult)
{
// Image grabbed successfully?
if (grabResult.GrabSucceeded)
{
// Display the image
ImageWindow.DisplayImage(1, grabResult);
// The grab result could now be processed here.
}
else
{
Console.WriteLine("Error: {0} {1}", grabResult.ErrorCode, grabResult.ErrorDescription);
}
}
}
camera.Close();
}
}
catch (Exception e)
{
// Error handling
Console.Error.WriteLine("\nException: {0}", e.Message);
exitCode = 1;
}
finally
{
// Comment the following two lines to disable waiting on exit.
Console.Error.WriteLine("\nPress enter to exit.");
Console.ReadLine();
}
Environment.Exit(exitCode);
}
private static void Main(string[] args)
{
//if (args.Length != 1)
//{
// Console.WriteLine("Run this example by invoking it like this: ");
// Console.WriteLine(" ./DnnFaceRecognition faces/bald_guys.jpg");
// Console.WriteLine("You will also need to get the face landmarking model file as well as ");
// Console.WriteLine("the face recognition model file. Download and then decompress these files from: ");
// Console.WriteLine("http://dlib.net/files/shape_predictor_5_face_landmarks.dat.bz2");
// Console.WriteLine("http://dlib.net/files/dlib_face_recognition_resnet_model_v1.dat.bz2");
// return;
//}
// The first thing we are going to do is load all our models. First, since we need to
// find faces in the image we will need a face detector:
using (var detector = Dlib.GetFrontalFaceDetector())
// We will also use a face landmarking model to align faces to a standard pose: (see face_landmark_detection_ex.cpp for an introduction)
using (var sp = ShapePredictor.Deserialize("shape_predictor_5_face_landmarks.dat"))
// And finally we load the DNN responsible for face recognition.
using (var net = DlibDotNet.Dnn.LossMetric.Deserialize("dlib_face_recognition_resnet_model_v1.dat"))
using (var img = Dlib.LoadImage <RgbPixel>(@"C:\Users\Chris\Desktop\test3\me pics\IMG_20170520_202221.jpg"))
using (var mat = new Matrix <RgbPixel>(img))
// Display the raw image on the screen
using (var win = new ImageWindow(img))
{
// Run the face detector on the image of our action heroes, and for each face extract a
// copy that has been normalized to 150x150 pixels in size and appropriately rotated
// and centered.
var faces = new List <Matrix <RgbPixel> >();
foreach (var face in detector.Detect(img))
{
var shape = sp.Detect(img, face);
var faceChipDetail = Dlib.GetFaceChipDetails(shape, 150, 0.25);
var faceChip = Dlib.ExtractImageChip <RgbPixel>(mat, faceChipDetail);
//faces.Add(move(face_chip));
faces.Add(faceChip);
// Also put some boxes on the faces so we can see that the detector is finding
// them.
win.AddOverlay(face);
}
if (!faces.Any())
{
Console.WriteLine("No faces found in image!");
return;
}
// This call asks the DNN to convert each face image in faces into a 128D vector.
// In this 128D vector space, images from the same person will be close to each other
// but vectors from different people will be far apart. So we can use these vectors to
// identify if a pair of images are from the same person or from different people.
var faceDescriptors = net.Operator(faces);
// In particular, one simple thing we can do is face clustering. This next bit of code
// creates a graph of connected faces and then uses the Chinese whispers graph clustering
// algorithm to identify how many people there are and which faces belong to whom.
var edges = new List <SamplePair>();
for (uint i = 0; i < faceDescriptors.Count; ++i)
{
for (var j = i; j < faceDescriptors.Count; ++j)
{
// Faces are connected in the graph if they are close enough. Here we check if
// the distance between two face descriptors is less than 0.6, which is the
// decision threshold the network was trained to use. Although you can
// certainly use any other threshold you find useful.
var diff = faceDescriptors[i] - faceDescriptors[j];
if (Dlib.Length(diff) < 0.6)
{
edges.Add(new SamplePair(i, j));
}
}
}
Dlib.ChineseWhispers(edges, 100, out var numClusters, out var labels);
// This will correctly indicate that there are 4 people in the image.
Console.WriteLine($"number of people found in the image: {numClusters}");
// Now let's display the face clustering results on the screen. You will see that it
// correctly grouped all the faces.
var winClusters = new List <ImageWindow>();
for (var i = 0; i < numClusters; i++)
{
winClusters.Add(new ImageWindow());
}
var tileImages = new List <Matrix <RgbPixel> >();
for (var clusterId = 0ul; clusterId < numClusters; ++clusterId)
{
var temp = new List <Matrix <RgbPixel> >();
for (var j = 0; j < labels.Length; ++j)
{
if (clusterId == labels[j])
{
temp.Add(faces[j]);
}
}
winClusters[(int)clusterId].Title = $"face cluster {clusterId}";
var tileImage = Dlib.TileImages(temp);
tileImages.Add(tileImage);
winClusters[(int)clusterId].SetImage(tileImage);
}
// Finally, let's print one of the face descriptors to the screen.
using (var trans = Dlib.Trans(faceDescriptors[0]))
{
Console.WriteLine($"face descriptor for one face: {trans}");
// It should also be noted that face recognition accuracy can be improved if jittering
// is used when creating face descriptors. In particular, to get 99.38% on the LFW
// benchmark you need to use the jitter_image() routine to compute the descriptors,
// like so:
var jitterImages = JitterImage(faces[0]).ToArray();
var ret = net.Operator(jitterImages);
using (var m = Dlib.Mat(ret))
using (var faceDescriptor = Dlib.Mean <float>(m))
using (var t = Dlib.Trans(faceDescriptor))
{
Console.WriteLine($"jittered face descriptor for one face: {t}");
// If you use the model without jittering, as we did when clustering the bald guys, it
// gets an accuracy of 99.13% on the LFW benchmark. So jittering makes the whole
// procedure a little more accurate but makes face descriptor calculation slower.
Console.WriteLine("hit enter to terminate");
Console.ReadKey();
foreach (var jitterImage in jitterImages)
{
jitterImage.Dispose();
}
foreach (var tileImage in tileImages)
{
tileImage.Dispose();
}
foreach (var edge in edges)
{
edge.Dispose();
}
foreach (var descriptor in faceDescriptors)
{
descriptor.Dispose();
}
foreach (var face in faces)
{
face.Dispose();
}
}
}
}
}
private static void Main(string[] args)
{
if (args.Length != 1)
{
Console.WriteLine("You call this program like this: ");
Console.WriteLine("./dnn_instance_segmentation_train_ex /path/to/images");
Console.WriteLine();
Console.WriteLine($"You will also need a trained '{InstanceSegmentationNetFilename}' file.");
Console.WriteLine("You can either train it yourself (see example program");
Console.WriteLine("dnn_instance_segmentation_train_ex), or download a");
Console.WriteLine($"copy from here: http://dlib.net/files/{InstanceSegmentationNetFilename}");
return;
}
try
{
// Read the file containing the trained network from the working directory.
using (var deserialize = new ProxyDeserialize(InstanceSegmentationNetFilename))
using (var detNet = LossMmod.Deserialize(deserialize, 4))
{
var segNetsByClass = new Dictionary <string, LossMulticlassLogPerPixel>();
segNetsByClass.Deserialize(deserialize, 4);
// Show inference results in a window.
using (var win = new ImageWindow())
{
// Find supported image files.
var files = Directory.GetFiles(args[0])
.Where(s => s.EndsWith(".jpeg") || s.EndsWith(".jpg") || s.EndsWith(".png")).ToArray();
using (var rnd = new Rand())
{
Console.WriteLine($"Found {files.Length} images, processing...");
foreach (var file in files.Select(s => new FileInfo(s)))
{
// Load the input image.
using (var inputImage = Dlib.LoadImageAsMatrix <RgbPixel>(file.FullName))
{
// Create predictions for each pixel. At this point, the type of each prediction
// is an index (a value between 0 and 20). Note that the net may return an image
// that is not exactly the same size as the input.
using (var output = detNet.Operator(inputImage))
{
var instances = output.First().ToList();
instances.Sort((lhs, rhs) => (int)lhs.Rect.Area - (int)rhs.Rect.Area);
using (var rgbLabelImage = new Matrix <RgbPixel>())
{
rgbLabelImage.SetSize(inputImage.Rows, inputImage.Columns);
rgbLabelImage.Assign(Enumerable.Range(0, rgbLabelImage.Size).Select(i => new RgbPixel(0, 0, 0)).ToArray());
var foundSomething = false;
foreach (var instance in instances)
{
if (!foundSomething)
{
Console.Write("Found ");
foundSomething = true;
}
else
{
Console.Write(", ");
}
Console.Write(instance.Label);
var croppingRect = GetCroppingRect(instance.Rect);
using (var dims = new ChipDims(SegDim, SegDim))
using (var chipDetails = new ChipDetails(croppingRect, dims))
using (var inputChip = Dlib.ExtractImageChip <RgbPixel>(inputImage, chipDetails, InterpolationTypes.Bilinear))
{
if (!segNetsByClass.TryGetValue(instance.Label, out var i))
{
// per-class segmentation net not found, so we must be using the same net for all classes
// (see bool separate_seg_net_for_each_class in dnn_instance_segmentation_train_ex.cpp)
if (segNetsByClass.Count == 1)
{
throw new ApplicationException();
}
if (string.IsNullOrEmpty(segNetsByClass.First().Key))
{
throw new ApplicationException();
}
}
var segNet = i != null
? i // use the segmentation net trained for this class
: segNetsByClass.First().Value; // use the same segmentation net for all classes
using (var mask = segNet.Operator(inputChip))
{
var randomColor = new RgbPixel(
rnd.GetRandom8BitNumber(),
rnd.GetRandom8BitNumber(),
rnd.GetRandom8BitNumber()
);
using (var resizedMask = new Matrix <ushort>((int)chipDetails.Rect.Height, (int)chipDetails.Rect.Width))
{
Dlib.ResizeImage(mask.First(), resizedMask);
for (int r = 0, nr = resizedMask.Rows; r < nr; ++r)
{
for (int c = 0, nc = resizedMask.Columns; c < nc; ++c)
{
if (resizedMask[r, c] != 0)
{
var y = (int)(chipDetails.Rect.Top + r);
var x = (int)(chipDetails.Rect.Left + c);
if (y >= 0 && y < rgbLabelImage.Rows && x >= 0 && x < rgbLabelImage.Columns)
{
rgbLabelImage[y, x] = randomColor;
}
}
}
}
}
var voc2012Class = PascalVOC2012.FindVoc2012Class(instance.Label);
Dlib.DrawRectangle(rgbLabelImage, instance.Rect, voc2012Class.RgbLabel, 1u);
}
}
}
instances.DisposeElement();
using (var tmp = Dlib.JoinRows(inputImage, rgbLabelImage))
{
// Show the input image on the left, and the predicted RGB labels on the right.
win.SetImage(tmp);
if (instances.Any())
{
Console.Write($" in {file.Name} - hit enter to process the next image");
Console.ReadKey();
}
}
}
}
}
}
}
}
foreach (var kvp in segNetsByClass)
{
kvp.Value.Dispose();
}
}
}
catch (Exception e)
{
Console.WriteLine(e);
}
}
private static void Main()
{
using (var img = new Array2D <byte>(400, 400))
using (var ht = new DlibDotNet.HoughTransform(300))
using (var win = new ImageWindow())
using (var win2 = new ImageWindow())
{
var angle1 = 0d;
var angle2 = 0d;
while (true)
{
angle1 += Math.PI / 130;
angle2 += Math.PI / 400;
var rect = img.Rect;
var cent = rect.Center;
var arc = Point.Rotate(cent, cent + new Point(90, 0), angle1 * 180 / Math.PI);
var tmp2 = arc + new Point(500, 0);
var tmp3 = arc - new Point(500, 0);
var l = Point.Rotate(arc, tmp2, angle2 * 180 / Math.PI);
var r = Point.Rotate(arc, tmp3, angle2 * 180 / Math.PI);
Dlib.AssignAllPixels(img, 0);
Dlib.DrawLine(img, l, r, 255);
using (var himg = new Array2D <int>())
{
var offset = new Point(50, 50);
var hrect = Dlib.GetRect(ht);
var box = Rectangle.Translate(hrect, offset);
// Now let's compute the hough transform for a subwindow in the image. In
// particular, we run it on the 300x300 subwindow with an upper left corner at the
// pixel point(50,50). The output is stored in himg.
ht.Operator(img, box, himg);
// Now that we have the transformed image, the Hough image pixel with the largest
// value should indicate where the line is. So we find the coordinates of the
// largest pixel:
using (var mat = Dlib.Mat(himg))
{
var p = Dlib.MaxPoint(mat);
// And then ask the ht object for the line segment in the original image that
// corresponds to this point in Hough transform space.
var line = ht.GetLine(p);
// Finally, let's display all these things on the screen. We copy the original
// input image into a color image and then draw the detected line on top in red.
using (var temp = new Array2D <RgbPixel>())
{
Dlib.AssignImage(img, temp);
var p1 = line.Item1 + offset;
var p2 = line.Item2 + offset;
Dlib.DrawLine(temp, p1, p2, new RgbPixel
{
Red = 255
});
win.ClearOverlay();
win.SetImage(temp);
// Also show the subwindow we ran the Hough transform on as a green box. You will
// see that the detected line is exactly contained within this box and also
// overlaps the original line.
win.AddOverlay(box, new RgbPixel
{
Green = 255
});
using (var jet = Dlib.Jet(himg))
win2.SetImage(jet);
}
}
}
}
}
}
static void AutoExposureContinuous(Camera camera)
{
// Check whether the Exposure Auto feature is available.
if (!camera.Parameters[PLCamera.ExposureAuto].IsWritable)
{
Console.WriteLine("The camera does not support Exposure Auto.");
return;
}
// Maximize the grabbed image area of interest (Image AOI).
camera.Parameters[PLCamera.OffsetX].TrySetValue(camera.Parameters[PLCamera.OffsetX].GetMinimum());
camera.Parameters[PLCamera.OffsetY].TrySetValue(camera.Parameters[PLCamera.OffsetY].GetMinimum());
camera.Parameters[PLCamera.Width].SetValue(camera.Parameters[PLCamera.Width].GetMaximum());
camera.Parameters[PLCamera.Height].SetValue(camera.Parameters[PLCamera.Height].GetMaximum());
// Set the Auto Function ROI for luminance statistics.
// We want to use ROI1 for gathering the statistics.
if (camera.Parameters[autoFunctionAOIROIUseBrightness].IsWritable)
{
camera.Parameters[regionSelector].SetValue(regionSelectorValue1);
camera.Parameters[autoFunctionAOIROIUseBrightness].SetValue(true); // ROI 1 is used for brightness control
camera.Parameters[regionSelector].SetValue(regionSelectorValue2);
camera.Parameters[autoFunctionAOIROIUseBrightness].SetValue(false); // ROI 2 is not used for brightness control
}
camera.Parameters[regionSelector].SetValue(regionSelectorValue1);
camera.Parameters[regionSelectorOffsetX].SetValue(camera.Parameters [PLCamera.OffsetX].GetMinimum());
camera.Parameters[regionSelectorOffsetY].SetValue(camera.Parameters [PLCamera.OffsetY].GetMinimum());
camera.Parameters[regionSelectorWidth].SetValue(camera.Parameters[PLCamera.Width].GetMaximum());
camera.Parameters[regionSelectorHeight].SetValue(camera.Parameters[PLCamera.Height].GetMaximum());
if (camera.GetSfncVersion() < sfnc2_0_0) // Handling for older cameras
{
// Set the target value for luminance control. The value is always expressed
// as an 8 bit value regardless of the current pixel data output format,
// i.e., 0 -> black, 255 -> white.
camera.Parameters[PLCamera.AutoTargetValue].SetValue(80);
}
else // Handling for newer cameras (using SFNC 2.0, e.g. USB3 Vision cameras)
{
// Set the target value for luminance control.
// A value of 0.3 means that the target brightness is 30 % of the maximum brightness of the raw pixel value read out from the sensor.
// A value of 0.4 means 40 % and so forth.
camera.Parameters[PLCamera.AutoTargetBrightness].SetValue(0.3);
}
// Try ExposureAuto = Continuous.
Console.WriteLine("Trying 'ExposureAuto = Continuous'.");
Console.WriteLine("Initial Exposure time = {0} us", camera.Parameters[exposureTime].GetValue());
camera.Parameters[PLCamera.ExposureAuto].SetValue(PLCamera.ExposureAuto.Continuous);
// When "continuous" mode is selected, the parameter value is adjusted repeatedly while images are acquired.
// Depending on the current frame rate, the automatic adjustments will usually be carried out for
// every or every other image, unless the camera's microcontroller is kept busy by other tasks.
// The repeated automatic adjustment will proceed until the "once" mode of operation is used or
// until the auto function is set to "off", in which case the parameter value resulting from the latest
// automatic adjustment will operate unless the value is manually adjusted.
for (int n = 0; n < 20; n++) // For demonstration purposes, we will use only 20 images.
{
IGrabResult result = camera.StreamGrabber.GrabOne(5000, TimeoutHandling.ThrowException);
using (result)
{
// Image grabbed successfully?
if (result.GrabSucceeded)
{
ImageWindow.DisplayImage(1, result);
}
}
//For demonstration purposes only. Wait until the image is shown.
System.Threading.Thread.Sleep(100);
}
camera.Parameters[PLCamera.ExposureAuto].SetValue(PLCamera.ExposureAuto.Off); // Switch off Exposure Auto.
Console.WriteLine("Final Exposure Time = {0} us", camera.Parameters[exposureTime].GetValue());
}
static void Main(string[] args)
{
/// FaceDetectionWith_API
Location[] coord = TestImage(fileName, Model.Hog);
/// Face DetectionWith_DLIB
using (var fd = Dlib.GetFrontalFaceDetector())
{
var img = Dlib.LoadImage <RgbPixel>(fileName);
// find all faces in the image
var faces = fd.Operator(img);
foreach (var face in faces)
{
// draw a rectangle for each face
Dlib.DrawRectangle(img, face, color: new RgbPixel(0, 255, 255), thickness: 4);
}
Dlib.SaveJpeg(img, outputName);
}
// The first thing we are going to do is load all our models. First, since we need to
// find faces in the image we will need a face detector:
using (var detector = Dlib.GetFrontalFaceDetector())
// We will also use a face landmarking model to align faces to a standard pose: (see face_landmark_detection_ex.cpp for an introduction)
using (var sp = ShapePredictor.Deserialize("shape_predictor_68_face_landmarks.dat"))
// And finally we load the DNN responsible for face recognition.
using (var net = DlibDotNet.Dnn.LossMetric.Deserialize("dlib_face_recognition_resnet_model_v1.dat"))
using (var img = Dlib.LoadImageAsMatrix <RgbPixel>(fileName))
using (var win = new ImageWindow(img))
{
var faces = new List <Matrix <RgbPixel> >();
foreach (var face in detector.Operator(img))
{
var shape = sp.Detect(img, face);
var faceChipDetail = Dlib.GetFaceChipDetails(shape, 150, 0.25);
var faceChip = Dlib.ExtractImageChip <RgbPixel>(img, faceChipDetail);
//faces.Add(move(face_chip));
faces.Add(faceChip);
win.AddOverlay(face);
}
if (!faces.Any())
{
Console.WriteLine("No faces found in image!");
return;
}
// This call asks the DNN to convert each face image in faces into a 128D vector.
// In this 128D vector space, images from the same person will be close to each other
// but vectors from different people will be far apart. So we can use these vectors to
// identify if a pair of images are from the same person or from different people.
var faceDescriptors = net.Operator(faces);
// In particular, one simple thing we can do is face clustering. This next bit of code
// creates a graph of connected faces and then uses the Chinese whispers graph clustering
// algorithm to identify how many people there are and which faces belong to whom.
var edges = new List <SamplePair>();
for (uint i = 0; i < faceDescriptors.Count; ++i)
{
for (var j = i; j < faceDescriptors.Count; ++j)
{
// Faces are connected in the graph if they are close enough. Here we check if
// the distance between two face descriptors is less than 0.6, which is the
// decision threshold the network was trained to use. Although you can
// certainly use any other threshold you find useful.
var diff = faceDescriptors[i] - faceDescriptors[j];
if (Dlib.Length(diff) < 0.6)
{
edges.Add(new SamplePair(i, j));
}
}
}
Dlib.ChineseWhispers(edges, 100, out var numClusters, out var labels);
// This will correctly indicate that there are 4 people in the image.
Console.WriteLine($"number of people found in the image: {numClusters}");
// Отобразим результат в ImageList
var winClusters = new List <ImageWindow>();
for (var i = 0; i < numClusters; i++)
{
winClusters.Add(new ImageWindow());
}
var tileImages = new List <Matrix <RgbPixel> >();
for (var clusterId = 0ul; clusterId < numClusters; ++clusterId)
{
var temp = new List <Matrix <RgbPixel> >();
for (var j = 0; j < labels.Length; ++j)
{
if (clusterId == labels[j])
{
temp.Add(faces[j]);
}
}
winClusters[(int)clusterId].Title = $"face cluster {clusterId}";
var tileImage = Dlib.TileImages(temp);
tileImages.Add(tileImage);
winClusters[(int)clusterId].SetImage(tileImage);
}
// Finally, let's print one of the face descriptors to the screen.
using (var trans = Dlib.Trans(faceDescriptors[0]))
{
Console.WriteLine($"face descriptor for one face: {trans}");
// It should also be noted that face recognition accuracy can be improved if jittering
// is used when creating face descriptors. In particular, to get 99.38% on the LFW
// benchmark you need to use the jitter_image() routine to compute the descriptors,
// like so:
var jitterImages = JitterImage(faces[0]).ToArray();
var ret = net.Operator(jitterImages);
using (var m = Dlib.Mat(ret))
using (var faceDescriptor = Dlib.Mean <float>(m))
using (var t = Dlib.Trans(faceDescriptor))
{
Console.WriteLine($"jittered face descriptor for one face: {t}");
// If you use the model without jittering, as we did when clustering the bald guys, it
// gets an accuracy of 99.13% on the LFW benchmark. So jittering makes the whole
// procedure a little more accurate but makes face descriptor calculation slower.
Console.WriteLine("hit enter to terminate");
Console.ReadKey();
foreach (var jitterImage in jitterImages)
{
jitterImage.Dispose();
}
foreach (var tileImage in tileImages)
{
tileImage.Dispose();
}
foreach (var edge in edges)
{
edge.Dispose();
}
foreach (var descriptor in faceDescriptors)
{
descriptor.Dispose();
}
foreach (var face in faces)
{
face.Dispose();
}
}
}
}
System.Console.ReadLine();
}
static void AutoGainOnce(Camera camera)
{
// Check whether the gain auto function is available.
if (!camera.Parameters[PLCamera.GainAuto].IsWritable)
{
Console.WriteLine("The camera does not support Gain Auto.");
return;
}
// Maximize the grabbed image area of interest (Image AOI).
camera.Parameters[PLCamera.OffsetX].TrySetValue(camera.Parameters[PLCamera.OffsetX].GetMinimum());
camera.Parameters[PLCamera.OffsetX].TrySetValue(camera.Parameters[PLCamera.OffsetY].GetMinimum());
camera.Parameters[PLCamera.Width].SetValue(camera.Parameters[PLCamera.Width].GetMaximum());
camera.Parameters[PLCamera.Height].SetValue(camera.Parameters[PLCamera.Height].GetMaximum());
// Set the Auto Function ROI for luminance statistics.
// We want to use ROI1 for gathering the statistics.
if (camera.Parameters[autoFunctionAOIROIUseBrightness].IsWritable)
{
camera.Parameters[regionSelector].SetValue(regionSelectorValue1);
camera.Parameters[autoFunctionAOIROIUseBrightness].SetValue(true); // ROI 1 is used for brightness control
camera.Parameters[regionSelector].SetValue(regionSelectorValue2);
camera.Parameters[autoFunctionAOIROIUseBrightness].SetValue(false); // ROI 2 is not used for brightness control
}
camera.Parameters[regionSelector].SetValue(regionSelectorValue1);
camera.Parameters[regionSelectorOffsetX].SetValue(camera.Parameters [PLCamera.OffsetX].GetMinimum());
camera.Parameters[regionSelectorOffsetY].SetValue(camera.Parameters [PLCamera.OffsetY].GetMinimum());
camera.Parameters[regionSelectorWidth].SetValue(camera.Parameters[PLCamera.Width].GetMaximum());
camera.Parameters[regionSelectorHeight].SetValue(camera.Parameters[PLCamera.Height].GetMaximum());
// We are going to try GainAuto = Once.
Console.WriteLine("Trying 'GainAuto = Once'.");
if (camera.GetSfncVersion() < sfnc2_0_0) // Handling for older cameras
{
// Set the target value for luminance control. The value is always expressed
// as an 8 bit value regardless of the current pixel data output format,
// i.e., 0 -> black, 255 -> white.
camera.Parameters[PLCamera.AutoTargetValue].SetValue(80);
Console.WriteLine("Initial Gain = {0}", camera.Parameters[PLCamera.GainRaw].GetValue());
// Set the gain ranges for luminance control.
camera.Parameters[PLCamera.AutoGainRawLowerLimit].SetValue(camera.Parameters[PLCamera.GainRaw].GetMinimum());
camera.Parameters[PLCamera.AutoGainRawUpperLimit].SetValue(camera.Parameters[PLCamera.GainRaw].GetMaximum());
}
else // Handling for newer cameras (using SFNC 2.0, e.g. USB3 Vision cameras)
{
// Set the target value for luminance control.
// A value of 0.3 means that the target brightness is 30 % of the maximum brightness of the raw pixel value read out from the sensor.
// A value of 0.4 means 40 % and so forth.
camera.Parameters[PLCamera.AutoTargetBrightness].SetValue(0.3);
Console.WriteLine("Initial Gain = {0}", camera.Parameters[PLCamera.Gain].GetValue());
// Set the gain ranges for luminance control.
camera.Parameters[PLCamera.AutoGainLowerLimit].SetValue(camera.Parameters[PLCamera.Gain].GetMinimum());
camera.Parameters[PLCamera.AutoGainUpperLimit].SetValue(camera.Parameters[PLCamera.Gain].GetMaximum());
}
camera.Parameters[PLCamera.GainAuto].SetValue(PLCamera.GainAuto.Once);
// When the "once" mode of operation is selected,
// the parameter values are automatically adjusted until the related image property
// reaches the target value. After the automatic parameter value adjustment is complete, the auto
// function will automatically be set to "off" and the new parameter value will be applied to the
// subsequently grabbed images.
int n = 0;
while (camera.Parameters[PLCamera.GainAuto].GetValue() != PLCamera.GainAuto.Off)
{
IGrabResult result = camera.StreamGrabber.GrabOne(5000, TimeoutHandling.ThrowException);
using (result)
{
// Image grabbed successfully?
if (result.GrabSucceeded)
{
ImageWindow.DisplayImage(1, result);
}
}
n++;
//For demonstration purposes only. Wait until the image is shown.
System.Threading.Thread.Sleep(100);
//Make sure the loop is exited.
if (n > 100)
{
throw new TimeoutException("The adjustment of auto gain did not finish.");
}
}
Console.WriteLine("GainAuto went back to 'Off' after {0} frames", n);
if (camera.GetSfncVersion() < sfnc2_0_0) // Handling for older cameras
{
Console.WriteLine("Final Gain = {0}", camera.Parameters[PLCamera.GainRaw].GetValue());
}
else // Handling for newer cameras (using SFNC 2.0, e.g. USB3 Vision cameras)
{
Console.WriteLine("Final Gain = {0}", camera.Parameters[PLCamera.Gain].GetValue());
}
}
public void AddOverlay()
{
if (!this.CanGuiDebug)
{
Console.WriteLine("Build and run as Release mode if you wanna show Gui!!");
return;
}
var path = this.GetDataFile("Lenna.bmp");
var tests = new[]
{
new { Type = ImageTypes.HsiPixel, ExpectResult = true },
new { Type = ImageTypes.LabPixel, ExpectResult = true },
new { Type = ImageTypes.BgrPixel, ExpectResult = true },
new { Type = ImageTypes.RgbPixel, ExpectResult = true },
new { Type = ImageTypes.RgbAlphaPixel, ExpectResult = true },
new { Type = ImageTypes.UInt8, ExpectResult = true },
new { Type = ImageTypes.UInt16, ExpectResult = true },
new { Type = ImageTypes.UInt32, ExpectResult = true },
new { Type = ImageTypes.Int8, ExpectResult = true },
new { Type = ImageTypes.Int16, ExpectResult = true },
new { Type = ImageTypes.Int32, ExpectResult = true },
new { Type = ImageTypes.Float, ExpectResult = true },
new { Type = ImageTypes.Double, ExpectResult = true }
};
foreach (var test in tests)
{
try
{
var rect = new Rectangle(10, 10, 100, 100);
var array = Array2D.Array2DTest.CreateArray2DHelp(test.Type, path.FullName);
using (var window = new ImageWindow(array))
{
switch (test.Type)
{
case ImageTypes.UInt8:
window.AddOverlay(rect, (byte)0, test.Type.ToString());
break;
case ImageTypes.UInt16:
window.AddOverlay(rect, (ushort)0, test.Type.ToString());
break;
case ImageTypes.UInt32:
window.AddOverlay(rect, 0u, test.Type.ToString());
break;
case ImageTypes.Int8:
window.AddOverlay(rect, (sbyte)0, test.Type.ToString());
break;
case ImageTypes.Int16:
window.AddOverlay(rect, (short)0, test.Type.ToString());
break;
case ImageTypes.Int32:
window.AddOverlay(rect, 0, test.Type.ToString());
break;
case ImageTypes.Float:
window.AddOverlay(rect, (short)0f, test.Type.ToString());
break;
case ImageTypes.Double:
window.AddOverlay(rect, 0d, test.Type.ToString());
break;
case ImageTypes.RgbAlphaPixel:
window.AddOverlay(rect, new RgbAlphaPixel(127, 0, 0, 0), test.Type.ToString());
break;
case ImageTypes.RgbPixel:
window.AddOverlay(rect, new RgbPixel(0, 0, 0), test.Type.ToString());
break;
case ImageTypes.HsiPixel:
window.AddOverlay(rect, new HsiPixel(0, 0, 0), test.Type.ToString());
break;
case ImageTypes.LabPixel:
window.AddOverlay(rect, new LabPixel(0, 0, 0), test.Type.ToString());
break;
}
window.WaitUntilClosed();
}
}
catch (Exception e)
{
Console.WriteLine(e.StackTrace);
Console.WriteLine($"Failed to create ImageWindow from Array2D Type: {test.Type}");
throw;
}
}
}
private static void Main(string[] args)
{
if (args.Length != 1)
{
Console.WriteLine("You call this program like this: ");
Console.WriteLine("./dnn_semantic_segmentation_train_ex /path/to/images");
Console.WriteLine();
Console.WriteLine("You will also need a trained 'semantic_segmentation_voc2012net.dnn' file.");
Console.WriteLine("You can either train it yourself (see example program");
Console.WriteLine("dnn_semantic_segmentation_train_ex), or download a");
Console.WriteLine("copy from here: http://dlib.net/files/semantic_segmentation_voc2012net.dnn");
return;
}
try
{
// Read the file containing the trained network from the working directory.
using (var net = LossMulticlassLogPerPixel.Deserialize("semantic_segmentation_voc2012net.dnn"))
{
// Show inference results in a window.
using (var win = new ImageWindow())
{
// Find supported image files.
var files = Directory.GetFiles(args[0])
.Where(s => s.EndsWith(".jpeg") || s.EndsWith(".jpg") || s.EndsWith(".png")).ToArray();
Console.WriteLine($"Found {files.Length} images, processing...");
foreach (var file in files)
{
// Load the input image.
using (var inputImage = Dlib.LoadImageAsMatrix <RgbPixel>(file))
{
// Create predictions for each pixel. At this point, the type of each prediction
// is an index (a value between 0 and 20). Note that the net may return an image
// that is not exactly the same size as the input.
using (var output = net.Operator(inputImage))
using (var temp = output.First())
{
// Crop the returned image to be exactly the same size as the input.
var rect = Rectangle.CenteredRect((int)(temp.Columns / 2d), (int)(temp.Rows / 2d), (uint)inputImage.Columns, (uint)inputImage.Rows);
using (var dims = new ChipDims((uint)inputImage.Rows, (uint)inputImage.Columns))
using (var chipDetails = new ChipDetails(rect, dims))
using (var indexLabelImage = Dlib.ExtractImageChip <ushort>(temp, chipDetails, InterpolationTypes.NearestNeighbor))
{
// Convert the indexes to RGB values.
using (var rgbLabelImage = IndexLabelImageToRgbLabelImage(indexLabelImage))
{
// Show the input image on the left, and the predicted RGB labels on the right.
using (var joinedRow = Dlib.JoinRows(inputImage, rgbLabelImage))
{
win.SetImage(joinedRow);
// Find the most prominent class label from amongst the per-pixel predictions.
var classLabel = GetMostProminentNonBackgroundClassLabel(indexLabelImage);
Console.WriteLine($"{file} : {classLabel} - hit enter to process the next image");
Console.ReadKey();
}
}
}
}
}
}
}
}
}
catch (Exception e)
{
Console.WriteLine(e);
}
}
internal static void Main()
{
// The exit code of the sample application.
int exitCode = 0;
try
{
// Create a camera object that selects the first camera device found.
// More constructors are available for selecting a specific camera device.
using (Camera camera = new Camera())
{
// Print the model name of the camera.
Console.WriteLine("Using camera {0}.", camera.CameraInfo[CameraInfoKey.ModelName]);
// Set the acquisition mode to free running continuous acquisition when the camera is opened.
camera.CameraOpened += Configuration.AcquireContinuous;
// Open the connection to the camera device.
camera.Open();
// Set buffer factory before starting the stream grabber because allocation
// happens there.
MyBufferFactory myFactory = new MyBufferFactory();
camera.StreamGrabber.BufferFactory = myFactory;
// Start grabbing.
camera.StreamGrabber.Start();
// Grab a number of images.
for (int i = 0; i < 10; ++i)
{
// Wait for an image and then retrieve it. A timeout of 5000 ms is used.
IGrabResult grabResult = camera.StreamGrabber.RetrieveResult(5000, TimeoutHandling.ThrowException);
using (grabResult)
{
// Image grabbed successfully?
if (grabResult.GrabSucceeded)
{
// Access the image data.
Console.WriteLine("SizeX: {0}", grabResult.Width);
Console.WriteLine("SizeY: {0}", grabResult.Height);
// Normally we would have a byte array in the pixel data.
// However we are using the buffer factory here which allocates
// ushort arrays.
ushort[] buffer = grabResult.PixelData as ushort[];
Console.WriteLine("First value of pixel data: {0}", buffer[0]);
Console.WriteLine("");
// Display the grabbed image.
ImageWindow.DisplayImage(0, grabResult);
}
else
{
Console.WriteLine("Error: {0} {1}", grabResult.ErrorCode, grabResult.ErrorDescription);
}
}
}
// Stop grabbing.
camera.StreamGrabber.Stop();
// Close the connection to the camera device.
camera.Close();
}
}
catch (Exception e)
{
Console.Error.WriteLine("Exception: {0}", e.Message);
exitCode = 1;
}
finally
{
// Comment the following two lines to disable waiting on exit.
Console.Error.WriteLine("\nPress enter to exit.");
Console.ReadLine();
}
Environment.Exit(exitCode);
}
private static void Main(string[] args)
{
if (args.Length == 0)
{
Console.WriteLine("Give some image files as arguments to this program.");
Console.WriteLine("Call this program like this:");
Console.WriteLine("./face_landmark_detection_ex shape_predictor_68_face_landmarks.dat faces/*.jpg");
Console.WriteLine("You can get the shape_predictor_68_face_landmarks.dat file from:");
Console.WriteLine("http://dlib.net/files/shape_predictor_68_face_landmarks.dat.bz2");
return;
}
using (var win = new ImageWindow())
using (var winFaces = new ImageWindow())
using (var detector = FrontalFaceDetector.GetFrontalFaceDetector())
using (var sp = new ShapePredictor(args[0]))
foreach (var file in args.ToList().GetRange(1, args.Length - 1))
{
Console.WriteLine($"processing image {file}");
using (var img = Dlib.LoadImage <RgbPixel>(file))
{
Dlib.PyramidUp(img);
var dets = detector.Detect(img);
Console.WriteLine($"Number of faces detected: {dets.Length}");
var shapes = new List <FullObjectDetection>();
foreach (var rect in dets)
{
var shape = sp.Detect(img, rect);
Console.WriteLine($"number of parts: {shape.Parts}");
if (shape.Parts > 2)
{
Console.WriteLine($"pixel position of first part: {shape.GetPart(0)}");
Console.WriteLine($"pixel position of second part: {shape.GetPart(1)}");
shapes.Add(shape);
}
}
win.ClearOverlay();
win.SetImage(img);
if (shapes.Any())
{
var lines = Dlib.RenderFaceDetections(shapes);
win.AddOverlay(lines);
foreach (var l in lines)
{
l.Dispose();
}
var chipLocations = Dlib.GetFaceChipDetails(shapes);
using (var faceChips = Dlib.ExtractImageChips <RgbPixel>(img, chipLocations))
using (var tileImage = Dlib.TileImages(faceChips))
winFaces.SetImage(tileImage);
foreach (var c in chipLocations)
{
c.Dispose();
}
}
Console.WriteLine("hit enter to process next frame");
Console.ReadKey();
foreach (var s in shapes)
{
s.Dispose();
}
foreach (var r in dets)
{
r.Dispose();
}
}
}
}
void buttonClick(object sender, MouseButtonEventArgs e)
{
Button clicked = (Button)sender;
ImageWindow imageWindow = new ImageWindow();
imageWindow.Content = clicked.Content;
imageWindow.Topmost = true;
imageWindow.Show();
}
