• 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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  • 原文地址：https://blog.csdn.net/lw112190/article/details/136193012