C# OpenCvSharp DNN Low Light image Enhancement

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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

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Inputs

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name：input

tensor：Float[1, 3, 512, 512]

name：exposure

tensor：Float[1]

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Outputs

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name：output

tensor：Float[1, 3, 512, 512]

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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  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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