C# RAM Stable Diffusion 提示词反推 Onnx Demo

介绍

效果

模型信息

项目

代码

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C# RAM Stable Diffusion 提示词反推 Onnx Demo

介绍

github地址：GitHub - xinyu1205/recognize-anything: Open-source and strong foundation image recognition models.

Open-source and strong foundation image recognition models.

效果

模型信息

Model Properties
-------------------------
---------------------------------------------------------------

Inputs
-------------------------
name：input
tensor：Float[1, 3, 384, 384]
---------------------------------------------------------------

Outputs
-------------------------
name：output
tensor：Float[1, 4585]
---------------------------------------------------------------

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.IO;
using System.Linq;
using System.Runtime.InteropServices;
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 = "";
DateTime dt1 = DateTime.Now;
DateTime dt2 = DateTime.Now;
string model_path;
Mat image;

SessionOptions options;
InferenceSession onnx_session;
Tensor<float> input_tensor;
List<NamedOnnxValue> input_container;
IDisposableReadOnlyCollection<DisposableNamedOnnxValue> result_infer;
DisposableNamedOnnxValue[] results_onnxvalue;

Tensor<float> result_tensors;

StringBuilder sbTags = new StringBuilder();
StringBuilder sbTagsCN = new StringBuilder();
StringBuilder sb = new StringBuilder();

public string[] class_names;

List<Tag> ltTag = new List<Tag>();

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);
}

float[] mean = { 0.485f, 0.456f, 0.406f };
float[] std = { 0.229f, 0.224f, 0.225f };

public void Normalize(Mat src)
{
src.ConvertTo(src, MatType.CV_32FC3, 1.0 / 255);
Mat[] bgr = src.Split();
for (int i = 0; i < bgr.Length; ++i)
{
bgr[i].ConvertTo(bgr[i], MatType.CV_32FC1, 1 / std[i], (0.0 - mean[i]) / std[i]);
}
Cv2.Merge(bgr, src);
foreach (Mat channel in bgr)
{
channel.Dispose();
}
}

public float[] ExtractMat(Mat src)
{
OpenCvSharp.Size size = src.Size();
int channels = src.Channels();
float[] result = new float[size.Width * size.Height * channels];
GCHandle resultHandle = default;
try
{
resultHandle = GCHandle.Alloc(result, GCHandleType.Pinned);
IntPtr resultPtr = resultHandle.AddrOfPinnedObject();
for (int i = 0; i < channels; ++i)
{
Mat cmat = new Mat(
src.Height, src.Width,
MatType.CV_32FC1,
resultPtr + i * size.Width * size.Height * sizeof(float));

Cv2.ExtractChannel(src, cmat, i);
cmat.Dispose();
}
}
finally
{
resultHandle.Free();
}
return result;
}

private void button2_Click(object sender, EventArgs e)
{
if (image_path == "")
{
return;
}

button2.Enabled = false;
textBox1.Text = "";
sb.Clear();
sbTagsCN.Clear();
sbTags.Clear();
Application.DoEvents();

image = new Mat(image_path);

//图片缩放
Mat resize_image = new Mat();
Cv2.Resize(image, resize_image, new OpenCvSharp.Size(384, 384));

Normalize(resize_image);

var data = ExtractMat(resize_image);

resize_image.Dispose();
image.Dispose();

// 输入Tensor
input_tensor = new DenseTensor<float>(data, new[] { 1, 3, 384, 384 });

//将 input_tensor 放入一个输入参数的容器，并指定名称
input_container.Add(NamedOnnxValue.CreateFromTensor("input", input_tensor));

dt1 = DateTime.Now;
//运行 Inference 并获取结果
result_infer = onnx_session.Run(input_container);
dt2 = DateTime.Now;

// 将输出结果转为DisposableNamedOnnxValue数组
results_onnxvalue = result_infer.ToArray();

// 读取第一个节点输出并转为Tensor数据
result_tensors = results_onnxvalue[0].AsTensor<float>();

var result_array = result_tensors.ToArray();

double[] scores = new double[result_array.Length];
for (int i = 0; i < result_array.Length; i++)
{
double score = 1 / (1 + Math.Exp(result_array[i] * -1));
scores[i] = score;
}
List<Tag> tags = new List<Tag>(ltTag);

List<Tag> topTags = new List<Tag>();
for (int i = 0; i < scores.Length; i++)
{
if (scores[i] > tags[i].Threshold)
{
tags[i].Score = scores[i];
topTags.Add(tags[i]);
}
}
topTags.OrderByDescending(x => x.Score).ToList();

foreach (var item in topTags)
{
sbTagsCN.Append(item.NameCN + ",");
sbTags.Append(item.Name + ",");
}
sbTagsCN.Length--;
sbTags.Length--;

sb.AppendLine("Tags:" + sbTags.ToString());
sb.AppendLine("标签:" + sbTagsCN.ToString());
sb.AppendLine("------------------");
sb.AppendLine("推理耗时:" + (dt2 - dt1).TotalMilliseconds + "ms");
textBox1.Text = sb.ToString();
button2.Enabled = true;
}

private void Form1_Load(object sender, EventArgs e)
{
model_path = "model/ram.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

// 创建输入容器
input_container = new List<NamedOnnxValue>();

image_path = "test_img/1.jpg";
pictureBox1.Image = new Bitmap(image_path);
image = new Mat(image_path);

string[] thresholdLines = File.ReadAllLines("model/ram_tag_list_threshold.txt");
string[] tagChineseLines = File.ReadAllLines("model/ram_tag_list_chinese.txt");
string[] tagLines = File.ReadAllLines("model/ram_tag_list.txt");

for (int i = 0; i < tagLines.Length; i++)
{
ltTag.Add(new Tag { NameCN = tagChineseLines[i], Name = tagLines[i], Threshold = double.Parse(thresholdLines[i]) });
}
}

}
}

using Microsoft.ML.OnnxRuntime;
using Microsoft.ML.OnnxRuntime.Tensors;
using OpenCvSharp;
using System;
using System.Collections.Generic;
using System.Drawing;
using System.IO;
using System.Linq;
using System.Runtime.InteropServices;
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 = "";
        DateTime dt1 = DateTime.Now;
        DateTime dt2 = DateTime.Now;
        string model_path;
        Mat image;

        SessionOptions options;
        InferenceSession onnx_session;
        Tensor<float> input_tensor;
        List<NamedOnnxValue> input_container;
        IDisposableReadOnlyCollection<DisposableNamedOnnxValue> result_infer;
        DisposableNamedOnnxValue[] results_onnxvalue;

        Tensor<float> result_tensors;

        StringBuilder sbTags = new StringBuilder();
        StringBuilder sbTagsCN = new StringBuilder();
        StringBuilder sb = new StringBuilder();

        public string[] class_names;

        List<Tag> ltTag = new List<Tag>();

        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);
        }

        float[] mean = { 0.485f, 0.456f, 0.406f };
        float[] std = { 0.229f, 0.224f, 0.225f };

        public void Normalize(Mat src)
        {
            src.ConvertTo(src, MatType.CV_32FC3, 1.0 / 255);
            Mat[] bgr = src.Split();
            for (int i = 0; i < bgr.Length; ++i)
            {
                bgr[i].ConvertTo(bgr[i], MatType.CV_32FC1, 1 / std[i], (0.0 - mean[i]) / std[i]);
            }
            Cv2.Merge(bgr, src);
            foreach (Mat channel in bgr)
            {
                channel.Dispose();
            }
        }

        public float[] ExtractMat(Mat src)
        {
            OpenCvSharp.Size size = src.Size();
            int channels = src.Channels();
            float[] result = new float[size.Width * size.Height * channels];
            GCHandle resultHandle = default;
            try
            {
                resultHandle = GCHandle.Alloc(result, GCHandleType.Pinned);
                IntPtr resultPtr = resultHandle.AddrOfPinnedObject();
                for (int i = 0; i < channels; ++i)
                {
                    Mat cmat = new Mat(
                       src.Height, src.Width,
                       MatType.CV_32FC1,
                       resultPtr + i * size.Width * size.Height * sizeof(float));

                    Cv2.ExtractChannel(src, cmat, i);
                    cmat.Dispose();
                }
            }
            finally
            {
                resultHandle.Free();
            }
            return result;
        }

        private void button2_Click(object sender, EventArgs e)
        {
            if (image_path == "")
            {
                return;
            }

            button2.Enabled = false;
            textBox1.Text = "";
            sb.Clear();
            sbTagsCN.Clear();
            sbTags.Clear();
            Application.DoEvents();

            image = new Mat(image_path);

            //图片缩放
            Mat resize_image = new Mat();
            Cv2.Resize(image, resize_image, new OpenCvSharp.Size(384, 384));

            Normalize(resize_image);

            var data = ExtractMat(resize_image);

            resize_image.Dispose();
            image.Dispose();

            // 输入Tensor
            input_tensor = new DenseTensor<float>(data, new[] { 1, 3, 384, 384 });

            //将 input_tensor 放入一个输入参数的容器，并指定名称
            input_container.Add(NamedOnnxValue.CreateFromTensor("input", input_tensor));

            dt1 = DateTime.Now;
            //运行 Inference 并获取结果
            result_infer = onnx_session.Run(input_container);
            dt2 = DateTime.Now;

            // 将输出结果转为DisposableNamedOnnxValue数组
            results_onnxvalue = result_infer.ToArray();

            // 读取第一个节点输出并转为Tensor数据
            result_tensors = results_onnxvalue[0].AsTensor<float>();

            var result_array = result_tensors.ToArray();

            double[] scores = new double[result_array.Length];
            for (int i = 0; i < result_array.Length; i++)
            {
                double score = 1 / (1 + Math.Exp(result_array[i] * -1));
                scores[i] = score;
            }
            List<Tag> tags = new List<Tag>(ltTag);

            List<Tag> topTags = new List<Tag>();
            for (int i = 0; i < scores.Length; i++)
            {
                if (scores[i] > tags[i].Threshold)
                {
                    tags[i].Score = scores[i];
                    topTags.Add(tags[i]);
                }
            }
            topTags.OrderByDescending(x => x.Score).ToList();

            foreach (var item in topTags)
            {
                sbTagsCN.Append(item.NameCN + ",");
                sbTags.Append(item.Name + ",");
            }
            sbTagsCN.Length--;
            sbTags.Length--;

            sb.AppendLine("Tags:" + sbTags.ToString());
            sb.AppendLine("标签:" + sbTagsCN.ToString());
            sb.AppendLine("------------------");
            sb.AppendLine("推理耗时:" + (dt2 - dt1).TotalMilliseconds + "ms");
            textBox1.Text = sb.ToString();
            button2.Enabled = true;
        }

        private void Form1_Load(object sender, EventArgs e)
        {
            model_path = "model/ram.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

            // 创建输入容器
            input_container = new List<NamedOnnxValue>();

            image_path = "test_img/1.jpg";
            pictureBox1.Image = new Bitmap(image_path);
            image = new Mat(image_path);

            string[] thresholdLines = File.ReadAllLines("model/ram_tag_list_threshold.txt");
            string[] tagChineseLines = File.ReadAllLines("model/ram_tag_list_chinese.txt");
            string[] tagLines = File.ReadAllLines("model/ram_tag_list.txt");

            for (int i = 0; i < tagLines.Length; i++)
            {
                ltTag.Add(new Tag { NameCN = tagChineseLines[i], Name = tagLines[i], Threshold = double.Parse(thresholdLines[i]) });
            }
        }

    }
}