C# OpenCvSharp DNN Low Light image Enhancement

目录

介绍

效果

模型信息

项目

代码

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C# OpenCvSharp DNN Low Light image Enhancement

介绍

github地址：https://github.com/zhenqifu/PairLIE

效果

模型信息

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

Inputs
-------------------------
name：input
tensor：Float[1, 3, 512, 512]
name：exposure
tensor：Float[1]
---------------------------------------------------------------

Outputs
-------------------------
name：output
tensor：Float[1, 3, 512, 512]
---------------------------------------------------------------
 

项目

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/pairlie_512x512.onnx";
 
            inpHeight = 512;
            inpWidth = 512;
 
            opencv_net = CvDnn.ReadNetFromOnnx(modelpath);
 
            image_path = "test_img/1.png";
            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);
 
            opencv_net.SetInput(BN_image, "input");
 
            Mat one = new Mat(1,1,MatType.CV_32F,new float[] { 0.5f});
            Mat exposure = CvDnn.BlobFromImage(one);
 
            opencv_net.SetInput(exposure, "exposure");
 
            //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);
 
            result_image.ConvertTo(result_image, MatType.CV_8UC3);
 
            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);
        }
    }
}

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