C# OpenCvSharp DNN Image Retouching

目录

介绍

模型

项目

效果

代码

下载

C# OpenCvSharp DNN Image Retouching

介绍

github地址：https://github.com/hejingwenhejingwen/CSRNet

(ECCV 2020) Conditional Sequential Modulation for Efficient Global Image Retouching

模型

Model Properties
-------------------------
---------------------------------------------------------------

Inputs
-------------------------
name：input
tensor：Float[1, 3, 360, 640]
---------------------------------------------------------------

Outputs
-------------------------
name：output
tensor：Float[1, 3, 360, 640]
---------------------------------------------------------------

项目

效果

代码

using OpenCvSharp;
using OpenCvSharp.Dnn;
using System;
using System.Collections.Generic;
using System.Drawing;
using System.IO;
using System.Linq;
using System.Linq.Expressions;
using System.Numerics;
using System.Reflection;
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)
        {
            modelpath = "model/csrnet_360x640.onnx";

            inpHeight = 360;
            inpWidth = 640;

            opencv_net = CvDnn.ReadNetFromOnnx(modelpath);

            image_path = "test_img/0014.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();

            image = new Mat(image_path);

            int srch = image.Rows;
            int srcw = image.Cols;


            BN_image = CvDnn.BlobFromImage(image, 1 / 255.0, new OpenCvSharp.Size(inpWidth, inpHeight), new Scalar(0, 0, 0), true, false);

            //配置图片输入数据
            opencv_net.SetInput(BN_image);

            //模型推理，读取推理结果
            Mat[] outs = new Mat[1] { new Mat() };
            string[] outBlobNames = opencv_net.GetUnconnectedOutLayersNames().ToArray();

            dt1 = DateTime.Now;

            opencv_net.Forward(outs, outBlobNames);

            dt2 = DateTime.Now;

            float* pdata = (float*)outs[0].Data;
            int out_h = outs[0].Size(2);
            int out_w = outs[0].Size(3);
            int channel_step = out_h * out_w;
            float[] data = new float[channel_step * 3];
            for (int i = 0; i < data.Length; i++)
            {
                data[i] = pdata[i] * 255;

                if (data[i] < 0)
                {
                    data[i] = 0;
                }
                else if (data[i] > 255)
                {
                    data[i] = 255;
                }
            }

            float[] temp_r = new float[out_h * out_w];
            float[] temp_g = new float[out_h * out_w];
            float[] temp_b = new float[out_h * out_w];

            Array.Copy(data, temp_r, out_h * out_w);
            Array.Copy(data, out_h * out_w, temp_g, 0, out_h * out_w);
            Array.Copy(data, out_h * out_w * 2, temp_b, 0, out_h * out_w);

            Mat rmat = new Mat(out_h, out_w, MatType.CV_32F, temp_r);
            Mat gmat = new Mat(out_h, out_w, MatType.CV_32F, temp_g);
            Mat bmat = new Mat(out_h, out_w, MatType.CV_32F, temp_b);

            result_image = new Mat();
            Cv2.Merge(new Mat[] { bmat, gmat, rmat }, result_image);

            Cv2.Resize(result_image, result_image, new OpenCvSharp.Size(srcw, srch));

            pictureBox2.Image = new Bitmap(result_image.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);
        }
    }
}
 

  1. using OpenCvSharp;
  2. using OpenCvSharp.Dnn;
  3. using System;
  4. using System.Collections.Generic;
  5. using System.Drawing;
  6. using System.IO;
  7. using System.Linq;
  8. using System.Linq.Expressions;
  9. using System.Numerics;
  10. using System.Reflection;
  11. using System.Windows.Forms;
  12.  
  13. namespace OpenCvSharp_DNN_Demo
  14. {
  15.     public partial class frmMain : Form
  16.     {
  17.         public frmMain()
  18.         {
  19.             InitializeComponent();
  20.         }
  21.  
  22.         string fileFilter = "*.*|*.bmp;*.jpg;*.jpeg;*.tiff;*.tiff;*.png";
  23.         string image_path = "";
  24.  
  25.         DateTime dt1 = DateTime.Now;
  26.         DateTime dt2 = DateTime.Now;
  27.  
  28.         string modelpath;
  29.  
  30.         int inpHeight;
  31.         int inpWidth;
  32.  
  33.         Net opencv_net;
  34.         Mat BN_image;
  35.  
  36.         Mat image;
  37.         Mat result_image;
  38.  
  39.         private void button1_Click(object sender, EventArgs e)
  40.         {
  41.             OpenFileDialog ofd = new OpenFileDialog();
  42.             ofd.Filter = fileFilter;
  43.             if (ofd.ShowDialog() != DialogResult.OK) return;
  44.  
  45.             pictureBox1.Image = null;
  46.             pictureBox2.Image = null;
  47.             textBox1.Text = "";
  48.  
  49.             image_path = ofd.FileName;
  50.             pictureBox1.Image = new Bitmap(image_path);
  51.             image = new Mat(image_path);
  52.         }
  53.  
  54.         private void Form1_Load(object sender, EventArgs e)
  55.         {
  56.             modelpath = "model/csrnet_360x640.onnx";
  57.  
  58.             inpHeight = 360;
  59.             inpWidth = 640;
  60.  
  61.             opencv_net = CvDnn.ReadNetFromOnnx(modelpath);
  62.  
  63.             image_path = "test_img/0014.jpg";
  64.             pictureBox1.Image = new Bitmap(image_path);
  65.  
  66.         }
  67.  
  68.         private unsafe void button2_Click(object sender, EventArgs e)
  69.         {
  70.             if (image_path == "")
  71.             {
  72.                 return;
  73.             }
  74.             textBox1.Text = "检测中，请稍等……";
  75.             pictureBox2.Image = null;
  76.             Application.DoEvents();
  77.  
  78.             image = new Mat(image_path);
  79.  
  80.             int srch = image.Rows;
  81.             int srcw = image.Cols;
  82.  
  83.  
  84.             BN_image = CvDnn.BlobFromImage(image, 1 / 255.0, new OpenCvSharp.Size(inpWidth, inpHeight), new Scalar(0, 0, 0), true, false);
  85.  
  86.             //配置图片输入数据
  87.             opencv_net.SetInput(BN_image);
  88.  
  89.             //模型推理，读取推理结果
  90.             Mat[] outs = new Mat[1] { new Mat() };
  91.             string[] outBlobNames = opencv_net.GetUnconnectedOutLayersNames().ToArray();
  92.  
  93.             dt1 = DateTime.Now;
  94.  
  95.             opencv_net.Forward(outs, outBlobNames);
  96.  
  97.             dt2 = DateTime.Now;
  98.  
  99.             float* pdata = (float*)outs[0].Data;
  100.             int out_h = outs[0].Size(2);
  101.             int out_w = outs[0].Size(3);
  102.             int channel_step = out_h * out_w;
  103.             float[] data = new float[channel_step * 3];
  104.             for (int i = 0; i < data.Length; i++)
  105.             {
  106.                 data[i] = pdata[i] * 255;
  107.  
  108.                 if (data[i] < 0)
  109.                 {
  110.                     data[i] = 0;
  111.                 }
  112.                 else if (data[i] > 255)
  113.                 {
  114.                     data[i] = 255;
  115.                 }
  116.             }
  117.  
  118.             float[] temp_r = new float[out_h * out_w];
  119.             float[] temp_g = new float[out_h * out_w];
  120.             float[] temp_b = new float[out_h * out_w];
  121.  
  122.             Array.Copy(data, temp_r, out_h * out_w);
  123.             Array.Copy(data, out_h * out_w, temp_g, 0, out_h * out_w);
  124.             Array.Copy(data, out_h * out_w * 2, temp_b, 0, out_h * out_w);
  125.  
  126.             Mat rmat = new Mat(out_h, out_w, MatType.CV_32F, temp_r);
  127.             Mat gmat = new Mat(out_h, out_w, MatType.CV_32F, temp_g);
  128.             Mat bmat = new Mat(out_h, out_w, MatType.CV_32F, temp_b);
  129.  
  130.             result_image = new Mat();
  131.             Cv2.Merge(new Mat[] { bmat, gmat, rmat }, result_image);
  132.  
  133.             Cv2.Resize(result_image, result_image, new OpenCvSharp.Size(srcw, srch));
  134.  
  135.             pictureBox2.Image = new Bitmap(result_image.ToMemoryStream());
  136.             textBox1.Text = "推理耗时:" + (dt2 - dt1).TotalMilliseconds + "ms";
  137.         }
  138.  
  139.         private void pictureBox2_DoubleClick(object sender, EventArgs e)
  140.         {
  141.             Common.ShowNormalImg(pictureBox2.Image);
  142.         }
  143.  
  144.         private void pictureBox1_DoubleClick(object sender, EventArgs e)
  145.         {
  146.             Common.ShowNormalImg(pictureBox1.Image);
  147.         }
  148.     }
  149. }

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