C# LaMa Image Inpainting 图像修复 Onnx Demo

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介绍

效果 

模型信息

项目

代码

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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

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Inputs

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name：image

tensor：Float[1, 3, 1000, 1504]

name：mask

tensor：Float[1, 1, 1000, 1504]

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Outputs

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name：inpainted

tensor：Float[1, 1000, 1504, 3]

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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 input_tensor;

        Tensor input_tensor_mask;

        List input_container;

        IDisposableReadOnlyCollection 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(new[] { 1, 3, 1000, 1504 });

            input_tensor_mask = new DenseTensor(new[] { 1, 1, 1000, 1504 });

            // 创建输入容器

            input_container = new List();

            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);

        }

    }

}

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 input_tensor;        Tensor input_tensor_mask;        List input_container;        IDisposableReadOnlyCollection 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(new[] { 1, 3, 1000, 1504 });            input_tensor_mask = new DenseTensor(new[] { 1, 1, 1000, 1504 });            // 创建输入容器            input_container = new List();            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);        }    }}

Common.cs

using Microsoft.ML.OnnxRuntime;using Microsoft.ML.OnnxRuntime.Tensors;using OpenCvSharp;using System;using System.Collections.Generic;using System.Linq;using System.Text;using System.Threading.Tasks;namespace Onnx_Demo{    internal class Common    {        public static void Preprocess(Mat image, Mat image_mask,  Tensor input_tensor, Tensor input_tensor_mask)        {            Cv2.Resize(image, image, new OpenCvSharp.Size(1504, 1000));            // 输入Tensor            for (int y = 0; y  127)                    {                        input_tensor_mask[0, 0, y, x] = 1.0f;                    }                    else                    {                        input_tensor_mask[0, 0, y, x] = 0.0f;                    }                }            }        }        public static Mat Postprocess(IDisposableReadOnlyCollection result_infer)        {            // 将输出结果转为DisposableNamedOnnxValue数组            DisposableNamedOnnxValue[] results_onnxvalue = result_infer.ToArray();            // 读取第一个节点输出并转为Tensor数据            Tensor result_tensors = results_onnxvalue[0].AsTensor();            float[] result_array = result_tensors.ToArray();            for (int i = 0; i  
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