public DnnPage()
: base()
{
var button = this.GetButton();
button.Text = "Perform Mask-rcnn Detection";
button.Clicked += OnButtonClicked;
OnImagesLoaded += async(sender, image) =>
{
if (image == null || image[0] == null)
{
return;
}
SetMessage("Please wait...");
SetImage(null);
Task <Tuple <Mat, String, long> > t = new Task <Tuple <Mat, String, long> >(
() =>
{
String configFile = "mask_rcnn_inception_v2_coco_2018_01_28.pbtxt";
#if __ANDROID__
String path = System.IO.Path.Combine(Android.OS.Environment.ExternalStorageDirectory.AbsolutePath,
Android.OS.Environment.DirectoryDownloads, "dnn_data");
FileInfo configFileInfo = AndroidFileAsset.WritePermanantFileAsset(Android.App.Application.Context, configFile, "dnn_data", AndroidFileAsset.OverwriteMethod.AlwaysOverwrite);
configFile = configFileInfo.FullName;
#else
String path = "./dnn_data/";
#endif
String graphFile = DnnDownloadFile(path, "frozen_inference_graph.pb");
String lookupFile = DnnDownloadFile(path, "coco-labels-paper.txt");
string[] labels = File.ReadAllLines(lookupFile);
Emgu.CV.Dnn.Net net = Emgu.CV.Dnn.DnnInvoke.ReadNetFromTensorflow(graphFile, configFile);
Mat blob = DnnInvoke.BlobFromImage(image[0]);
net.SetInput(blob, "image_tensor");
using (VectorOfMat tensors = new VectorOfMat())
{
net.Forward(tensors, new string[] { "detection_out_final", "detection_masks" });
using (Mat boxes = tensors[0])
using (Mat masks = tensors[1])
{
System.Drawing.Size imgSize = image[0].Size;
float[,,,] boxesData = boxes.GetData(true) as float[, , , ];
//float[,,,] masksData = masks.GetData(true) as float[,,,];
int numDetections = boxesData.GetLength(2);
for (int i = 0; i < numDetections; i++)
{
float score = boxesData[0, 0, i, 2];
if (score > 0.5)
{
int classId = (int)boxesData[0, 0, i, 1];
String label = labels[classId];
float left = boxesData[0, 0, i, 3] * imgSize.Width;
float top = boxesData[0, 0, i, 4] * imgSize.Height;
float right = boxesData[0, 0, i, 5] * imgSize.Width;
float bottom = boxesData[0, 0, i, 6] * imgSize.Height;
RectangleF rectF = new RectangleF(left, top, right - left, bottom - top);
Rectangle rect = Rectangle.Round(rectF);
rect.Intersect(new Rectangle(Point.Empty, imgSize));
CvInvoke.Rectangle(image[0], rect, new MCvScalar(0, 0, 0, 0), 1);
CvInvoke.PutText(image[0], label, rect.Location, FontFace.HersheyComplex, 1.0,
new MCvScalar(0, 0, 255), 2);
int[] masksDim = masks.SizeOfDimension;
using (Mat mask = new Mat(
masksDim[2],
masksDim[3],
DepthType.Cv32F,
1,
//masks.DataPointer +
//(i * masksDim[1] + classId )
//* masksDim[2] * masksDim[3] * masks.ElementSize,
masks.GetDataPointer(i, classId),
masksDim[3] * masks.ElementSize))
using (Mat maskLarge = new Mat())
using (Mat maskLargeInv = new Mat())
using (Mat subRegion = new Mat(image[0], rect))
using (Mat largeColor = new Mat(subRegion.Size, Emgu.CV.CvEnum.DepthType.Cv8U, 3))
{
CvInvoke.Resize(mask, maskLarge, rect.Size);
//give the mask at least 30% transparency
using (ScalarArray sa = new ScalarArray(0.7))
CvInvoke.Min(sa, maskLarge, maskLarge);
//Create the inverse mask for the original image
using (ScalarArray sa = new ScalarArray(1.0))
CvInvoke.Subtract(sa, maskLarge, maskLargeInv);
//The mask color
largeColor.SetTo(new Emgu.CV.Structure.MCvScalar(255, 0, 0));
if (subRegion.NumberOfChannels == 4)
{
using (Mat bgrSubRegion = new Mat())
{
CvInvoke.CvtColor(subRegion, bgrSubRegion, ColorConversion.Bgra2Bgr);
CvInvoke.BlendLinear(largeColor, bgrSubRegion, maskLarge, maskLargeInv, bgrSubRegion);
CvInvoke.CvtColor(bgrSubRegion, subRegion, ColorConversion.Bgr2Bgra);
}
}
else
{
CvInvoke.BlendLinear(largeColor, subRegion, maskLarge, maskLargeInv, subRegion);
}
}
}
}
}
}
long time = 0;
return(new Tuple <Mat, String, long>(image[0], null, time));
});
t.Start();
var result = await t;
SetImage(t.Result.Item1);
String computeDevice = CvInvoke.UseOpenCL ? "OpenCL: " + Ocl.Device.Default.Name : "CPU";
SetMessage(t.Result.Item2);
};
}
/// <summary>
/// Adds loaded layers into the <paramref name="net"/> and sets connetions between them.
/// </summary>
/// <param name="net">The net model</param>
public void PopulateNet(Net net)
{
ContribInvoke.cveDnnImporterPopulateNet(_ptr, net);
}
public void TestDnn()
{
Dnn.Net net = new Dnn.Net();
using (Dnn.Importer importer = Dnn.Importer.CreateCaffeImporter("bvlc_googlenet.prototxt", "bvlc_googlenet.caffemodel"))
importer.PopulateNet(net);
Mat img = EmguAssert.LoadMat("space_shuttle.jpg");
CvInvoke.Resize(img, img, new Size(224, 224));
Dnn.Blob inputBlob = new Dnn.Blob(img);
net.SetBlob(".data", inputBlob);
net.Forward();
Dnn.Blob probBlob = net.GetBlob("prob");
int classId;
double classProb;
GetMaxClass(probBlob, out classId, out classProb);
String[] classNames = ReadClassNames("synset_words.txt");
#if !NETFX_CORE
Trace.WriteLine("Best class: " + classNames[classId] + ". Probability: " + classProb);
#endif
}
