C# Onnx实现轻量实时的M-LSD直线检测

目录

• 介绍
• 效果
• 模型信息
• 项目
• 代码
• 其他

介绍

github地址：https://github.com/navervision/mlsd 

M-LSD: Towards Light-weight and Real-time Line Segment Detection

Official Tensorflow implementation of "M-LSD: Towards Light-weight and Real-time Line Segment Detection" (AAAI 2022 Oral session)

Geonmo Gu*, Byungsoo Ko*, seoungHyun Go, Sung-Hyun Lee, Jingeun Lee, Minchul Shin (* Authors contributed equally.)

First figure: Comparison of M-LSD and existing LSD methods on GPU. Second figure: Inference speed and memory usage on mobile devices.

We present a real-time and light-weight line segment detector for resource-constrained environments named Mobile LSD (M-LSD). M-LSD exploits extremely efficient LSD architecture and novel training schemes, including SoL augmentation and geometric learning scheme. Our model can run in real-time on GPU, CPU, and even on mobile devices.

效果

效果1

效果编程客栈2

效果3

效果4

模型信息

Inputs

-------------------------

name：input_image_with_alpha:0

tensor：Float[1, 512, 512, 4]

---------------------------------------------------------------

Outputs

---------------http://www.devze.com----------

name：Identity

tensor：Int32[1, 200, 2]

name：Identity_1

tensor：Float[1, 200]

name：Identity_2

tensor：Float[1, 256, 256, 4]

---------------------------------------------------------------

项目

VS2022

.net framework 4.8

OpenCvSharp 4.8

Microsoft.ML.OnnxRuntime 1.16.2

代码

using Microsoft.ML.OnnxRuntime.Tensors;
using Microsoft.ML.OnnxRuntime;
using OpenCvSharp;
using System;
using System.Collections.Generic;
using System.Windows.Forms;
using System.Linq;
using System.Drawing;
 
namespace Onnx_Demo
{
    public partial class frmMain : Form
    {
        public frmMain()
        {
            InitializeComponent();
        }
 
        string fileFilter = "*.*|*.bmp;*.jpg;*.jpeg;*.tiff;*.tiff;*.png";
        string imagejavascript_path = "";
 
        DateTime dt1 = DateTime.Now;
        DateTime dt2 = DateTime.Now;
 
        int inpWidth;
        int inpHeight;
 
        Mat image;
 
        string model_path = "";
 
        SessionOptions options;
        InferenceSession onnx_session;
        Tensor<float> input_tensor;
        Tensor<float> mask_tensor;
        List<NamedOnnxValue> input_ontainer;
 
        IDisposableReadOnlyCollection<DisposableNamedOnnxValue> result_infer;
        DisposableNamedOnnxValue[] results_onnxvalue;
 
        float conf_threshold = 0.5f;
        float dist_threshold = 20.0f;
 
        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 System.Drawing.Bitmap(image_path);
            image = new Mat(image_path);
        }
 
        private void Form1_Load(object sender, EventArgs e)
        {
 
            // 创建输入容器
            input_ontainer = new List<NamedOnnxValue>();
 
            // 创建输出会话
            options = new SessionOptions();
            options.LogSeverityLevel = OrtLoggingLevel.ORT_LOGGING_LEVEL_INFO;
            options.AppendExecutionProvider_CPU(0);// 设置为CPU上运行
 
            // 创建推理模型类，读取本地模型文件
            model_path = "model/model_512x512_large.onnx";
 
            inpWidth = 512;
            inpHeight = 512;
            onnx_session = new InferenceSession(model_path, options);
 
            // 创建输入容器
            input_ontainer = new List<NamedOnnxValue>();
 
            image_path = "test_img/4.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;
            System.Windows.Forms.Application.DoEvents();
 
            image = new Mat(image_path);
 
            Mat resize_image = new Mat();
            Cv2.Resize(image, resize_image, new OpenCvSharp.Size(512, 512));
 
            float h_ratio = (float)image.Rows / 512;
            float w_ratio = (float)image.Cols / 512;
 
            int row = resize_image.Rows;
            int col = resize_image.Cols;
            float[] input_tensor_data = new float[1 * 4 * row * col];
            int k = 0;
            for (int i = 0; i < row; i++)
            {
                for (int j = 0; j < col; j++)
                {
                    for (int c = 0; c < 3; c++)
                    {
                        float pix = ((byte*)(resize_image.Ptr(i).ToPointer()))[j * 3 + c];
                        input_tensor_data[k] = pix;
                     编程客栈   k++;
                    }
                    input_tensor_data[k] = 1;
                    k++;
                }
            }
 
            input_tensor = new DenseTensor<float>(input_tensor_data, new[] { 1, 512, 512, 4 });
 
            //将 input_tensor 放入一个输入参数的容器，并指定名称
            input_ontainer.Add(NamedOnnxValue.CreateFromTensor("input_image_with_alpha:0", input_tensor));
 
            dt1 = DateTime.Now;
            //运行 Inference 并获取结果
            result_infer = onnx_session.Run(input_ontainer);
            dt2 = DateTime.Now;
 
            //将输出结果转为DisposableNamedOnnxValue数组
            results_onnxvalue = result_infer.ToArray();
 
            int[] pts = results_onnxvalue[0].AsTensor<int>().ToArray();
            float[] pts_score = results_onnxvalue[1].AsTensor<float>().ToArray();
            float[] vmap = results_onnxvalue[2].AsTensor<float>().ToArray();
            List<List<int>>python segments_list = new List<List<int>>();
            int num_lines = 200;
            int map_h = 256;
            int map_w = 256;
 
            for (int i = 0; i < num_lines; i++)
            {
                int y = pts[i * 2];
                int x = pts[i * 2 + 1];
 
                float disp_x_start = vmap[0 + y * map_w * 4 + x * 4];
                float disp_y_start = vmap[1 + y * map_w * 4 + x * 4];
                float disp_x_end = vmap[2 + y * map_w * 4 + x * 4];
                float disp_y_end = vmap[3 + y * map_w * 4 + x * 4];
 
                float distance = (float)Math.Sqrt(Math.Pow(disp_x_start - disp_x_end, 2) + Math.Pow(disp_y_start - disp_y_end, 2));
 
                if (pts_score[i] > conf_threshold && distance > dist_threshold)
                {
                    float x_start = (x + disp_x_start) * 2 * w_ratio;
                    float y_start = (y + disp_y_start) * 2 * h_ratio;
                    float x_end = (x + disp_x_end) * 2 * w_ratio;
                    float y_end = (y + disp_y_end) * 2 * h_ratio;
                    List<int> line = new List<int>() { (int)x_start, (int)y_start, (int)x_end, (int)y_end };
                    segments_list.Add(line);
                }
            }
 
            Mat result_image = image.Clone();
            for (int i = 0; i < segments_list.Count; i++)
            {
                Cv2.Line(result_image, new OpenCvSharp.Point(segments_list[i][0], segments_list[i][1]), new OpenCvSharp.Point(segments_list[i][2], segments_list[i][3]), new Scalar(0, 0, 255), 3);
            }
 
            pictureBox2.Image = new System.Drawing.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);
        }
    }
}

其他

结合透视变换可实现图像校正，图像校正参考

C#使用OpenCvSharp实现图像校正

C#使用OpenCvSharp实现透视变换

以上就是C# Onnx实现轻量实时的M-LSD直线检测的详细内容，更多关于C#直线检测的资料请关注编程客栈(www.devze.com)其它相关文章！