mmdeploy/demo/csharp/object_detection/Program.cs

84 lines
2.8 KiB
C#

using System;
using System.Collections.Generic;
using OpenCvSharp;
using MMDeploy;
namespace object_detection
{
class Program
{
static void CvMatToMat(OpenCvSharp.Mat[] cvMats, out MMDeploy.Mat[] mats)
{
mats = new MMDeploy.Mat[cvMats.Length];
unsafe
{
for (int i = 0; i < cvMats.Length; i++)
{
mats[i].Data = cvMats[i].DataPointer;
mats[i].Height = cvMats[i].Height;
mats[i].Width = cvMats[i].Width;
mats[i].Channel = cvMats[i].Dims;
mats[i].Format = PixelFormat.BGR;
mats[i].Type = DataType.Int8;
mats[i].Device = null;
}
}
}
static void CvWaitKey()
{
Cv2.WaitKey();
}
static void Main(string[] args)
{
if (args.Length != 3)
{
Console.WriteLine("usage:\n object_detection deviceName modelPath imagePath\n");
Environment.Exit(1);
}
string deviceName = args[0];
string modelPath = args[1];
string imagePath = args[2];
// 1. create handle
Detector handle = new Detector(modelPath, deviceName, 0);
// 2. prepare input
OpenCvSharp.Mat[] imgs = new OpenCvSharp.Mat[1] { Cv2.ImRead(imagePath, ImreadModes.Color) };
CvMatToMat(imgs, out var mats);
// 3. process
List<DetectorOutput> output = handle.Apply(mats);
// 4. show result
foreach (var obj in output[0].Results)
{
if (obj.Score > 0.3)
{
if (obj.HasMask)
{
OpenCvSharp.Mat imgMask = new OpenCvSharp.Mat(obj.Mask.Height, obj.Mask.Width, MatType.CV_8UC1, obj.Mask.Data);
float x0 = Math.Max((float)Math.Floor(obj.BBox.Left) - 1, 0f);
float y0 = Math.Max((float)Math.Floor(obj.BBox.Top) - 1, 0f);
OpenCvSharp.Rect roi = new OpenCvSharp.Rect((int)x0, (int)y0, obj.Mask.Width, obj.Mask.Height);
Cv2.Split(imgs[0], out OpenCvSharp.Mat[] ch);
int col = 0;
Cv2.BitwiseOr(imgMask, ch[col][roi], ch[col][roi]);
Cv2.Merge(ch, imgs[0]);
}
Cv2.Rectangle(imgs[0], new Point((int)obj.BBox.Left, (int)obj.BBox.Top),
new Point((int)obj.BBox.Right, obj.BBox.Bottom), new Scalar(0, 255, 0));
}
}
Cv2.NamedWindow("mmdet", WindowFlags.GuiExpanded);
Cv2.ImShow("mmdet", imgs[0]);
CvWaitKey();
handle.Close();
}
}
}