Table of Contents
Effect
Model information
project
code
download
Effect
End-to-end real-time lane line detection
Model information
lstr_360x640.onnx
Inputs
———————–
name: input_rgb
tensor: Float[1, 3, 360, 640]
name: input_mask
tensor: Float[1, 1, 360, 640]
————————————————– ————-
Outputs
———————–
name:pred_logits
tensor：Float[1, 7, 2]
name:pred_curves
tensor：Float[1, 7, 8]
name:foo_out_1
tensor：Float[1, 7, 2]
name:foo_out_2
tensor：Float[1, 7, 8]
name:weights
tensor: Float[1, 240, 240]
————————————————– ————-
project
VS2022 + .net framework 4.8
OpenCvSharp 4.8
Microsoft.ML.OnnxRuntime 1.16.2
code
Create input tensor using pointer
float[] input_tensor_data = new float[1 * 3 * inpHeight * inpWidth];
for (int c = 0; c < 3; c + + )
{
for (int i = 0; i < row; i + + )
{
for (int j = 0; j < col; j + + )
{
float pix = ((byte*)(resize_image.Ptr(i).ToPointer()))[j * 3 + c];
input_tensor_data[c * row * col + i * col + j] = (float)((pix / 255.0 – mean[c]) / std[c]);
}
}
}
input_tensor = new DenseTensor
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.IO;
using System.Text;
using System.Drawing;
namespace Onnx_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;
int inpWidth;
int inpHeight;
Mat image;
string model_path = "";
float[] factors = new float[2];
SessionOptions options;
InferenceSession onnx_session;
Tensor<float> input_tensor;
Tensor<float> mask_tensor;
List<NamedOnnxValue> input_ontainer;
IDisposableReadOnlyCollection<DisposableNamedOnnxValue> result_infer;
DisposableNamedOnnxValue[] results_onnxvalue;
Tensor<float> result_tensors;
int len_log_space = 50;
float[] log_space;
float[] mean = new float[] { 0.485f, 0.456f, 0.406f };
float[] std = new float[] { 0.229f, 0.224f, 0.225f };
Scalar[] lane_colors = new Scalar[] { new Scalar(68, 65, 249), new Scalar(44, 114, 243), new Scalar(30, 150, 248), new Scalar(74, 132, 249), new Scalar(79, 199, 249), new Scalar(109, 190, 144), new Scalar(142, 144, 77), new Scalar(161, 125, 39) };
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)
{
//Create input container
input_ontainer = new List<NamedOnnxValue>();
//Create output session
options = new SessionOptions();
options.LogSeverityLevel = OrtLoggingLevel.ORT_LOGGING_LEVEL_INFO;
options.AppendExecutionProvider_CPU(0);//Set to run on CPU
//Create an inference model class and read local model files
model_path = "model/lstr_360x640.onnx";
inpWidth = 640;
inpHeight = 360;
onnx_session = new InferenceSession(model_path, options);
//Create input container
input_ontainer = new List<NamedOnnxValue>();
FileStream fileStream = new FileStream("model/log_space.bin", FileMode.Open);
BinaryReader br = new BinaryReader(fileStream, Encoding.UTF8);
log_space = new float[len_log_space];
byte[] byteTemp;
float fTemp;
for (int i = 0; i < len_log_space; i + + )
{
byteTemp = br.ReadBytes(4);
fTemp = BitConverter.ToSingle(byteTemp, 0);
log_space[i] = fTemp;
}
br.Close();
image_path = "test_img/0.jpg";
pictureBox1.Image = new Bitmap(image_path);
}
private unsafe void button2_Click(object sender, EventArgs e)
{
if (image_path == "")
{
return;
}
textBox1.Text = "Detecting, please wait...";
pictureBox2.Image = null;
System.Windows.Forms.Application.DoEvents();
//Picture zoom
image = new Mat(image_path);
int img_height = image.Rows;
int img_width = image.Cols;
Mat resize_image = new Mat();
Cv2.Resize(image, resize_image, new OpenCvSharp.Size(inpWidth, inpHeight));
int row = resize_image.Rows;
int col = resize_image.Cols;
float[] input_tensor_data = new float[1 * 3 * inpHeight * inpWidth];
for (int c = 0; c < 3; c + + )
{
for (int i = 0; i < row; i + + )
{
for (int j = 0; j < col; j + + )
{
float pix = ((byte*)(resize_image.Ptr(i).ToPointer()))[j * 3 + c];
input_tensor_data[c * row * col + i * col + j] = (float)((pix / 255.0 - mean[c]) / std[c]);
}
}
}
input_tensor = new DenseTensor<float>(input_tensor_data, new[] { 1, 3, inpHeight, inpWidth });
float[] input_mask_data = new float[1 * 1 * inpHeight * inpWidth];
for (int i = 0; i < input_mask_data.Length; i + + )
{
input_mask_data[i] = 0.0f;
}
mask_tensor = new DenseTensor<float>(input_mask_data, new[] { 1, 1, inpHeight, inpWidth });
//Put input_tensor into a container of input parameters and specify a name
input_ontainer.Add(NamedOnnxValue.CreateFromTensor("input_rgb", input_tensor));
input_ontainer.Add(NamedOnnxValue.CreateFromTensor("input_mask", mask_tensor));
dt1 = DateTime.Now;
//Run Inference and get the results
result_infer = onnx_session.Run(input_ontainer);
dt2 = DateTime.Now;
//Convert the output result to the DisposableNamedOnnxValue array
results_onnxvalue = result_infer.ToArray();
float[] pred_logits = results_onnxvalue[0].AsTensor<float>().ToArray();
float[] pred_curves = results_onnxvalue[1].AsTensor<float>().ToArray();
int logits_h = results_onnxvalue[0].AsTensor<float>().Dimensions[1];
int logits_w = results_onnxvalue[0].AsTensor<float>().Dimensions[2];
int curves_w = results_onnxvalue[1].AsTensor<float>().Dimensions[2];
List<int> good_detections = new List<int>();
List<List<OpenCvSharp.Point>> lanes = new List<List<OpenCvSharp.Point>>();
for (int i = 0; i < logits_h; i + + )
{
float max_logits = -10000;
int max_id = -1;
for (int j = 0; j < logits_w; j + + )
{
float data = pred_logits[i * logits_w + j];
if (data > max_logits)
{
max_logits = data;
max_id = j;
}
}
if (max_id == 1)
{
good_detections.Add(i);
int index = i * curves_w;
List<OpenCvSharp.Point> lane_points = new List<OpenCvSharp.Point>();
for (int k = 0; k < len_log_space; k + + )
{
float y = pred_curves[0 + index] + log_space[k] * (pred_curves[1 + index] - pred_curves[0 + index]);
float x = (float)(pred_curves[2 + index] / Math.Pow(y - pred_curves[3 + index], 2.0) + pred_curves[4 + index] / (y - pred_curves[3 + index]) + pred_curves[ 5 + index] + pred_curves[6 + index] * y - pred_curves[7 + index]);
lane_points.Add(new OpenCvSharp.Point(x * img_width, y * img_height));
}
lanes.Add(lane_points);
}
}
Mat result_image = image.Clone();
//draw lines
List<int> right_lane = new List<int>();
List<int> left_lane = new List<int>();
for (int i = 0; i < good_detections.Count; i + + )
{
if (good_detections[i] == 0)
{
right_lane.Add(i);
}
if (good_detections[i] == 5)
{
left_lane.Add(i);
}
}
if (right_lane.Count() == left_lane.Count())
{
Mat lane_segment_img = result_image.Clone();
List<OpenCvSharp.Point> points = new List<OpenCvSharp.Point>();
points.AddRange(lanes.First());
points.Reverse();
points.AddRange(lanes[left_lane[0]]);
Cv2.FillConvexPoly(lane_segment_img, points, new Scalar(0, 191, 255));
Cv2.AddWeighted(result_image, 0.7, lane_segment_img, 0.3, 0, result_image);
}
for (int i = 0; i < lanes.Count(); i + + )
{
for (int j = 0; j < lanes[i].Count(); j + + )
{
Cv2.Circle(result_image, lanes[i][j], 3, lane_colors[good_detections[i]], -1);
}
}
pictureBox2.Image = new System.Drawing.Bitmap(result_image.ToMemoryStream());
textBox1.Text = "Inference time consumption:" + (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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