目录
介绍
效果
模型信息
项目
代码
下载
LaMa Image Inpainting 图像修复 Onnx Demo
介绍
gihub地址:https://github.com/advimman/lama
🦙 LaMa Image Inpainting, Resolution-robust Large Mask Inpainting with Fourier Convolutions, WACV 2022
效果
模型信息
Model Properties
-------------------------
---------------------------------------------------------------
Inputs
-------------------------
name:image
tensor:Float[1, 3, 1000, 1504]
name:mask
tensor:Float[1, 1, 1000, 1504]
---------------------------------------------------------------
Outputs
-------------------------
name:inpainted
tensor:Float[1, 1000, 1504, 3]
---------------------------------------------------------------
项目
代码
using Microsoft.ML.OnnxRuntime;
using Microsoft.ML.OnnxRuntime.Tensors;
using OpenCvSharp;
using System;
using System.Collections.Generic;
using System.Drawing;
using System.Linq;
using System.Text;
using System.Windows.Forms;
namespace Onnx_Demo
{
public partial class Form1 : Form
{
public Form1()
{
InitializeComponent();
}
string fileFilter = "*.*|*.bmp;*.jpg;*.jpeg;*.tiff;*.tiff;*.png";
string image_path = "";
string image_path_mask = "";
DateTime dt1 = DateTime.Now;
DateTime dt2 = DateTime.Now;
string model_path;
Mat image;
Mat image_mask;
SessionOptions options;
InferenceSession onnx_session;
Tensor<float> input_tensor;
Tensor<float> input_tensor_mask;
List<NamedOnnxValue> input_container;
IDisposableReadOnlyCollection<DisposableNamedOnnxValue> result_infer;
StringBuilder sb = new StringBuilder();
private void button1_Click(object sender, EventArgs e)
{
OpenFileDialog ofd = new OpenFileDialog();
ofd.Filter = fileFilter;
if (ofd.ShowDialog() != DialogResult.OK) return;
pictureBox1.Image = null;
image_path = ofd.FileName;
pictureBox1.Image = new Bitmap(image_path);
textBox1.Text = "";
image = new Mat(image_path);
pictureBox2.Image = null;
}
private void button2_Click(object sender, EventArgs e)
{
if (image_path == "")
{
return;
}
button2.Enabled = false;
pictureBox2.Image = null;
textBox1.Text = "";
image = new Mat(image_path);
int w = image.Width;
int h = image.Height;
image_mask = new Mat(image_path_mask);
Common.Preprocess(image, image_mask, input_tensor, input_tensor_mask);
//将 input_tensor 放入一个输入参数的容器,并指定名称
input_container.Add(NamedOnnxValue.CreateFromTensor("image", input_tensor));
//将 input_tensor_mask 放入一个输入参数的容器,并指定名称
input_container.Add(NamedOnnxValue.CreateFromTensor("mask", input_tensor_mask));
dt1 = DateTime.Now;
//运行 Inference 并获取结果
result_infer = onnx_session.Run(input_container);
dt2 = DateTime.Now;
Mat result = Common.Postprocess(result_infer);
Cv2.Resize(result, result, new OpenCvSharp.Size(w, h));
sb.AppendLine("推理耗时:" + (dt2 - dt1).TotalMilliseconds + "ms");
pictureBox2.Image = new Bitmap(result.ToMemoryStream());
textBox1.Text = sb.ToString();
button2.Enabled = true;
}
private void Form1_Load(object sender, EventArgs e)
{
model_path = "model/big_lama_regular_inpaint.onnx";
// 创建输出会话,用于输出模型读取信息
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
onnx_session = new InferenceSession(model_path, options);//model_path 为onnx模型文件的路径
// 输入Tensor
input_tensor = new DenseTensor<float>(new[] { 1, 3, 1000, 1504 });
input_tensor_mask = new DenseTensor<float>(new[] { 1, 1, 1000, 1504 });
// 创建输入容器
input_container = new List<NamedOnnxValue>();
image_path = "test_img/test.jpg";
pictureBox1.Image = new Bitmap(image_path);
image_path_mask = "test_img/mask.jpg";
pictureBox3.Image = new Bitmap(image_path_mask);
}
}
}
下载
源码下载