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