C# LaMa Image Inpainting 图像修复 Onnx Demo

目录

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

Inputs
-------------------------
name：image
tensor：Float[1, 3, 1000, 1504]
name：mask
tensor：Float[1, 1, 1000, 1504]
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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);
        }
    }
}

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);        }    }}

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<float> input_tensor, Tensor<float> input_tensor_mask)        {            Cv2.Resize(image, image, new OpenCvSharp.Size(1504, 1000));            // 输入Tensor            for (int y = 0; y < image.Height; y++)            {                for (int x = 0; x < image.Width; x++)                {                    input_tensor[0, 0, y, x] = image.At<Vec3b>(y, x)[0] / 255.0f;                    input_tensor[0, 1, y, x] = image.At<Vec3b>(y, x)[1] / 255.0f;                    input_tensor[0, 2, y, x] = image.At<Vec3b>(y, x)[2] / 255.0f;                }            }            Cv2.Resize(image_mask, image_mask, new OpenCvSharp.Size(1504, 1000));            //膨胀核函数            Mat element1 = new Mat();            OpenCvSharp.Size size1 = new OpenCvSharp.Size(11, 11);            element1 = Cv2.GetStructuringElement(MorphShapes.Rect, size1);            //膨胀一次，让轮廓突出            Mat dilation = new Mat();            Cv2.Dilate(image_mask, image_mask, element1);            //输入Tensor            for (int y = 0; y < image_mask.Height; y++)            {                for (int x = 0; x < image_mask.Width; x++)                {                    float v = image_mask.At<Vec3b>(y, x)[0];                    if (v > 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<DisposableNamedOnnxValue> 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<float> result_tensors = results_onnxvalue[0].AsTensor<float>();            float[] result_array = result_tensors.ToArray();            for (int i = 0; i < result_array.Length; i++)            {                result_array[i] = Math.Max(0, Math.Min(255, result_array[i]));            }            Mat result = new Mat(1000, 1504, MatType.CV_32FC3, result_array);            return result;        }    }}

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