private static Net GetEmbedderFromCaffe(string protoFileName, string modelFileName)
{
var proto = Utils.GetResourceBytes(protoFileName);
var model = Utils.GetResourceBytes(modelFileName);
return(DnnInvoke.ReadNetFromCaffe(proto, model));
}
private static Net GetDetector()
{
var proto = Utils.GetResourceBytes("Models.deploy.prototxt");
var model = Utils.GetResourceBytes("Models.res10_300x300_ssd_iter_140000.caffemodel");
return(DnnInvoke.ReadNetFromCaffe(proto, model));
}
/// <summary>
/// Download and initialize the DNN face detector
/// </summary>
/// <param name="onDownloadProgressChanged">Callback when download progress has been changed</param>
/// <returns>Async task</returns>
public async Task Init(System.Net.DownloadProgressChangedEventHandler onDownloadProgressChanged = null)
{
if (_faceDetectorNet == null)
{
FileDownloadManager manager = new FileDownloadManager();
manager.AddFile(
"https://github.com/opencv/opencv_3rdparty/raw/dnn_samples_face_detector_20170830/res10_300x300_ssd_iter_140000.caffemodel",
_modelFolderName,
"2A56A11A57A4A295956B0660B4A3D76BBDCA2206C4961CEA8EFE7D95C7CB2F2D");
manager.AddFile(
"https://raw.githubusercontent.com/opencv/opencv/4.0.1/samples/dnn/face_detector/deploy.prototxt",
_modelFolderName,
"F62621CAC923D6F37BD669298C428BB7EE72233B5F8C3389BB893E35EBBCF795");
if (onDownloadProgressChanged != null)
{
manager.OnDownloadProgressChanged += onDownloadProgressChanged;
}
await manager.Download();
if (manager.AllFilesDownloaded)
{
_faceDetectorNet = DnnInvoke.ReadNetFromCaffe(manager.Files[1].LocalFile, manager.Files[0].LocalFile);
if (Emgu.CV.Cuda.CudaInvoke.HasCuda)
{
_faceDetectorNet.SetPreferableBackend(Emgu.CV.Dnn.Backend.Cuda);
_faceDetectorNet.SetPreferableTarget(Emgu.CV.Dnn.Target.Cuda);
}
}
}
}
public DetectionModule(byte[] proto, byte[] caffeModel, string embeddingModel, double minConfidence)
{
detector = DnnInvoke.ReadNetFromCaffe(proto, caffeModel);
//todo : is there a way to read embeddingModel from bytes? it's a torch model.
embedder = DnnInvoke.ReadNet(embeddingModel);
this.minConfidence = minConfidence;
}
public async Task Init(System.Net.DownloadProgressChangedEventHandler onDownloadProgressChanged = null)
{
if (_faceDetector == null)
{
FileDownloadManager manager = new FileDownloadManager();
manager.AddFile(
"https://github.com/opencv/opencv_3rdparty/raw/dnn_samples_face_detector_20170830/res10_300x300_ssd_iter_140000.caffemodel",
_modelFolderName);
manager.AddFile(
"https://raw.githubusercontent.com/opencv/opencv/4.0.1/samples/dnn/face_detector/deploy.prototxt",
_modelFolderName);
if (onDownloadProgressChanged != null)
{
manager.OnDownloadProgressChanged += onDownloadProgressChanged;
}
await manager.Download();
_faceDetector = DnnInvoke.ReadNetFromCaffe(manager.Files[1].LocalFile, manager.Files[0].LocalFile);
if (Emgu.CV.Cuda.CudaInvoke.HasCuda)
{
_faceDetector.SetPreferableBackend(Emgu.CV.Dnn.Backend.Cuda);
_faceDetector.SetPreferableTarget(Emgu.CV.Dnn.Target.Cuda);
}
}
}
/// <summary>
/// Face detection.
/// </summary>
public FaceDetectionWindow()
{
InitializeComponent();
frame = new Image <Bgr, byte>(resolutionX, resolutionY);
xRate = resolutionX / (float)detectionSize;
yRate = resolutionY / (float)detectionSize;
net = DnnInvoke.ReadNetFromCaffe(protoPath, caffemodelPath);
}
private void InitFaceDetector()
{
if (_faceDetector == null)
{
InitPath();
String ssdFileLocal = DnnPage.DnnDownloadFile(
"https://github.com/opencv/opencv_3rdparty/raw/dnn_samples_face_detector_20170830/",
"res10_300x300_ssd_iter_140000.caffemodel",
_path);
String ssdProtoFileLocal = DnnPage.DnnDownloadFile(
"https://raw.githubusercontent.com/opencv/opencv/4.0.1/samples/dnn/face_detector/",
"deploy.prototxt",
_path);
_faceDetector = DnnInvoke.ReadNetFromCaffe(ssdProtoFileLocal, ssdFileLocal);
}
}
public Form1()
{
InitializeComponent();
try
{
eyes_detect = new CascadeClassifier(Path.GetFullPath("Models/haarcascade_eye.xml"));
xRate = resolutionX / (float)detectionSize;
yRate = resolutionY / (float)detectionSize;
net = DnnInvoke.ReadNetFromCaffe(protoPath, caffemodelPath);
this.Width = resolutionX;
this.Height = resolutionY;
camera = new VideoCapture(cameraIndex);
camera.SetCaptureProperty(Emgu.CV.CvEnum.CapProp.FrameWidth, resolutionX);
camera.SetCaptureProperty(Emgu.CV.CvEnum.CapProp.FrameHeight, resolutionY);
//camera.FlipHorizontal = true;
}
catch (NullReferenceException ex)
{
throw;
}
}
private void PoseEstimationBody_25_Click(object sender, EventArgs e)
{
try
{
if (!IMGDict.ContainsKey("input"))
{
throw new Exception("Please read in image first.");
}
// for openopse
int inWidth = 368;
int inHeight = 368;
float threshold = 0.1f;
int nPoints = 25;
var BODY_PARTS = new Dictionary <string, int>()
{
{ "Nose", 0 },
{ "Neck", 1 },
{ "RShoulder", 2 },
{ "RElbow", 3 },
{ "RWrist", 4 },
{ "LShoulder", 5 },
{ "LElbow", 6 },
{ "LWrist", 7 },
{ "MidHip", 8 },
{ "RHip", 9 },
{ "RKnee", 10 },
{ "RAnkle", 11 },
{ "LHip", 12 },
{ "LKnee", 13 },
{ "LAnkle", 14 },
{ "REye", 15 },
{ "LEye", 16 },
{ "REar", 17 },
{ "LEar", 18 },
{ "LBigToe", 19 },
{ "LSmallToe", 20 },
{ "LHeel", 21 },
{ "RBigToe", 22 },
{ "RSmallToe", 23 },
{ "RHeel", 24 },
{ "Background", 25 }
};
int[,] point_pairs = new int[, ] {
{ 1, 0 }, { 1, 2 }, { 1, 5 },
{ 2, 3 }, { 3, 4 }, { 5, 6 },
{ 6, 7 }, { 0, 15 }, { 15, 17 },
{ 0, 16 }, { 16, 18 }, { 1, 8 },
{ 8, 9 }, { 9, 10 }, { 10, 11 },
{ 11, 22 }, { 22, 23 }, { 11, 24 },
{ 8, 12 }, { 12, 13 }, { 13, 14 },
{ 14, 19 }, { 19, 20 }, { 14, 21 }
};
// Load the caffe Model
string prototxt = @"F:\openpose\models\pose\body_25\pose_deploy.prototxt";
string modelPath = @"F:\openpose\models\pose\body_25\pose_iter_584000.caffemodel";
var net = DnnInvoke.ReadNetFromCaffe(prototxt, modelPath);
var img = IMGDict["input"].Clone();
var imgHeight = img.Height;
var imgWidth = img.Width;
var blob = DnnInvoke.BlobFromImage(img, 1.0 / 255.0, new Size(inWidth, inHeight), new MCvScalar(0, 0, 0));
net.SetInput(blob);
net.SetPreferableBackend(Emgu.CV.Dnn.Backend.OpenCV);
var output = net.Forward();
var H = output.SizeOfDimension[2];
var W = output.SizeOfDimension[3];
var HeatMap = output.GetData();
var points = new List <Point>();
for (int i = 0; i < nPoints; i++)
{
Matrix <float> matrix = new Matrix <float>(H, W);
for (int row = 0; row < H; row++)
{
for (int col = 0; col < W; col++)
{
matrix[row, col] = (float)HeatMap.GetValue(0, i, row, col);
}
}
double minVal = 0, maxVal = 0;
Point minLoc = default, maxLoc = default;
