C# OpenCvSharp DNN UNet 推理

目录

效果

模型

项目

代码

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效果

模型

Inputs
-------------------------
name：data
tensor：Float[1, 3, 256, 256]
---------------------------------------------------------------

Outputs
-------------------------
name：predict
tensor：Float[1, 2, 256, 256]
---------------------------------------------------------------

项目

代码

using OpenCvSharp;
using OpenCvSharp.Dnn;
using System;
using System.Drawing;
using System.Windows.Forms;

namespace OpenCvSharp_DNN_Demo
{
    public partial class frmMain : Form
    {
        public frmMain()
        {
            InitializeComponent();
        }

        string fileFilter = "*.*|*.bmp;*.jpg;*.jpeg;*.tiff;*.tiff;*.png";
        string image_path = "";

        DateTime dt1 = DateTime.Now;
        DateTime dt2 = DateTime.Now;

        string modelpath;

        int inpHeight;
        int inpWidth;

        Net opencv_net;
        Mat BN_image;

        Mat image;
        Mat result_image;

        private void button1_Click(object sender, EventArgs e)
        {
            OpenFileDialog ofd = new OpenFileDialog();
            ofd.Filter = fileFilter;
            if (ofd.ShowDialog() != DialogResult.OK) return;

            pictureBox1.Image = null;
            pictureBox2.Image = null;
            textBox1.Text = "";

            image_path = ofd.FileName;
            pictureBox1.Image = new Bitmap(image_path);
            image = new Mat(image_path);
        }

        private void Form1_Load(object sender, EventArgs e)
        {
            string modelTxt = "model/unet.prototxt";
            string modelBin = "model/unet.caffemodel";

            inpHeight = 256;
            inpWidth = 256;

            opencv_net = CvDnn.ReadNetFromCaffe(modelTxt, modelBin);

            image_path = "test_img/person.jpg";
            pictureBox1.Image = new Bitmap(image_path);

        }

        private unsafe void button2_Click(object sender, EventArgs e)
        {
            if (image_path == "")
            {
                return;
            }
            textBox1.Text = "检测中，请稍等……";
            pictureBox2.Image = null;
            Application.DoEvents();

            Mat src = new Mat(image_path);

            int max_image_length = src.Cols > src.Rows ? src.Cols : src.Rows;
            Mat max_image = Mat.Zeros(new OpenCvSharp.Size(max_image_length, max_image_length), MatType.CV_8UC3);
            Rect roi = new Rect(0, 0, src.Cols, src.Rows);
            src.CopyTo(new Mat(max_image, roi));

            Mat resize_image = max_image.Resize(new OpenCvSharp.Size(256, 256));

            BN_image = CvDnn.BlobFromImage(resize_image, 1 / 255.0, new OpenCvSharp.Size(inpWidth, inpHeight), new Scalar(127.5, 127.5, 127.5), true, false);

            //float* ptr = (float*)BN_image.Data;
            //for (int i = 0; i < 10; i++)
            //{
            //    Console.WriteLine(ptr[i]);
            //}

            opencv_net.SetInput(BN_image, "data");


            dt1 = DateTime.Now;

            Mat detection = opencv_net.Forward("predict");


            //float* ptr2 = (float*)detection.Data;
            //for (int i = 0; i < 10; i++)
            //{
            //    Console.WriteLine(ptr2[i]);
            //}

            dt2 = DateTime.Now;

            //得到的输出是一个四维的mat格式数据，大小为[1，2, 256, 256]
            //首先将他reshape，设置成一通道，512行，256列，其中前256行与后256行是互补关系，对应位置相加都为1
            //前256行为背景的概率，后256行为人像的概率
            Mat newMat = detection.Reshape(1, 512);
            //获取人像概率矩阵
            newMat = newMat.RowRange(256, 512);

            Mat result = new Mat();
            newMat.ConvertTo(result, MatType.CV_8U, 255.0);

            Cv2.Threshold(result, result, 127, 255, ThresholdTypes.Binary);

            Mat result2 = Mat.Zeros(256, 256, MatType.CV_8UC3) * 255;

            resize_image.CopyTo(result2, result);

            Cv2.ImShow("黑白", result);
            Cv2.ImShow("扣取", result2);

            pictureBox2.Image = new Bitmap(result2.ToMemoryStream());
            textBox1.Text = "推理耗时:" + (dt2 - dt1).TotalMilliseconds + "ms";
        }

        private void pictureBox2_DoubleClick(object sender, EventArgs e)
        {
            Common.ShowNormalImg(pictureBox2.Image);
        }

        private void pictureBox1_DoubleClick(object sender, EventArgs e)
        {
            Common.ShowNormalImg(pictureBox1.Image);
        }
    }
}


/*
Inputs
-------------------------
name：data
tensor：Float[1, 3, 256, 256]
---------------------------------------------------------------

Outputs
-------------------------
name：predict
tensor：Float[1, 2, 256, 256]
---------------------------------------------------------------
 */

  1. using OpenCvSharp;
  2. using OpenCvSharp.Dnn;
  3. using System;
  4. using System.Drawing;
  5. using System.Windows.Forms;
  6.  
  7. namespace OpenCvSharp_DNN_Demo
  8. {
  9.     public partial class frmMain : Form
  10.     {
  11.         public frmMain()
  12.         {
  13.             InitializeComponent();
  14.         }
  15.  
  16.         string fileFilter = "*.*|*.bmp;*.jpg;*.jpeg;*.tiff;*.tiff;*.png";
  17.         string image_path = "";
  18.  
  19.         DateTime dt1 = DateTime.Now;
  20.         DateTime dt2 = DateTime.Now;
  21.  
  22.         string modelpath;
  23.  
  24.         int inpHeight;
  25.         int inpWidth;
  26.  
  27.         Net opencv_net;
  28.         Mat BN_image;
  29.  
  30.         Mat image;
  31.         Mat result_image;
  32.  
  33.         private void button1_Click(object sender, EventArgs e)
  34.         {
  35.             OpenFileDialog ofd = new OpenFileDialog();
  36.             ofd.Filter = fileFilter;
  37.             if (ofd.ShowDialog() != DialogResult.OK) return;
  38.  
  39.             pictureBox1.Image = null;
  40.             pictureBox2.Image = null;
  41.             textBox1.Text = "";
  42.  
  43.             image_path = ofd.FileName;
  44.             pictureBox1.Image = new Bitmap(image_path);
  45.             image = new Mat(image_path);
  46.         }
  47.  
  48.         private void Form1_Load(object sender, EventArgs e)
  49.         {
  50.             string modelTxt = "model/unet.prototxt";
  51.             string modelBin = "model/unet.caffemodel";
  52.  
  53.             inpHeight = 256;
  54.             inpWidth = 256;
  55.  
  56.             opencv_net = CvDnn.ReadNetFromCaffe(modelTxt, modelBin);
  57.  
  58.             image_path = "test_img/person.jpg";
  59.             pictureBox1.Image = new Bitmap(image_path);
  60.  
  61.         }
  62.  
  63.         private unsafe void button2_Click(object sender, EventArgs e)
  64.         {
  65.             if (image_path == "")
  66.             {
  67.                 return;
  68.             }
  69.             textBox1.Text = "检测中，请稍等……";
  70.             pictureBox2.Image = null;
  71.             Application.DoEvents();
  72.  
  73.             Mat src = new Mat(image_path);
  74.  
  75.             int max_image_length = src.Cols > src.Rows ? src.Cols : src.Rows;
  76.             Mat max_image = Mat.Zeros(new OpenCvSharp.Size(max_image_length, max_image_length), MatType.CV_8UC3);
  77.             Rect roi = new Rect(0, 0, src.Cols, src.Rows);
  78.             src.CopyTo(new Mat(max_image, roi));
  79.  
  80.             Mat resize_image = max_image.Resize(new OpenCvSharp.Size(256, 256));
  81.  
  82.             BN_image = CvDnn.BlobFromImage(resize_image, 1 / 255.0, new OpenCvSharp.Size(inpWidth, inpHeight), new Scalar(127.5, 127.5, 127.5), true, false);
  83.  
  84.             //float* ptr = (float*)BN_image.Data;
  85.             //for (int i = 0; i < 10; i++)
  86.             //{
  87.             //    Console.WriteLine(ptr[i]);
  88.             //}
  89.  
  90.             opencv_net.SetInput(BN_image, "data");
  91.  
  92.  
  93.             dt1 = DateTime.Now;
  94.  
  95.             Mat detection = opencv_net.Forward("predict");
  96.  
  97.  
  98.             //float* ptr2 = (float*)detection.Data;
  99.             //for (int i = 0; i < 10; i++)
  100.             //{
  101.             //    Console.WriteLine(ptr2[i]);
  102.             //}
  103.  
  104.             dt2 = DateTime.Now;
  105.  
  106.             //得到的输出是一个四维的mat格式数据，大小为[1，2, 256, 256]
  107.             //首先将他reshape，设置成一通道，512行，256列，其中前256行与后256行是互补关系，对应位置相加都为1
  108.             //前256行为背景的概率，后256行为人像的概率
  109.             Mat newMat = detection.Reshape(1, 512);
  110.             //获取人像概率矩阵
  111.             newMat = newMat.RowRange(256, 512);
  112.  
  113.             Mat result = new Mat();
  114.             newMat.ConvertTo(result, MatType.CV_8U, 255.0);
  115.  
  116.             Cv2.Threshold(result, result, 127, 255, ThresholdTypes.Binary);
  117.  
  118.             Mat result2 = Mat.Zeros(256, 256, MatType.CV_8UC3) * 255;
  119.  
  120.             resize_image.CopyTo(result2, result);
  121.  
  122.             Cv2.ImShow("黑白", result);
  123.             Cv2.ImShow("扣取", result2);
  124.  
  125.             pictureBox2.Image = new Bitmap(result2.ToMemoryStream());
  126.             textBox1.Text = "推理耗时:" + (dt2 - dt1).TotalMilliseconds + "ms";
  127.         }
  128.  
  129.         private void pictureBox2_DoubleClick(object sender, EventArgs e)
  130.         {
  131.             Common.ShowNormalImg(pictureBox2.Image);
  132.         }
  133.  
  134.         private void pictureBox1_DoubleClick(object sender, EventArgs e)
  135.         {
  136.             Common.ShowNormalImg(pictureBox1.Image);
  137.         }
  138.     }
  139. }
  140.  
  141.  
  142. /*
  143. Inputs
  144. -------------------------
  145. name：data
  146. tensor：Float[1, 3, 256, 256]
  147. ---------------------------------------------------------------
  148.  
  149. Outputs
  150. -------------------------
  151. name：predict
  152. tensor：Float[1, 2, 256, 256]
  153. ---------------------------------------------------------------
  154.  */

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