本文主要是介绍C# Onnx Chinese CLIP 通过一句话从图库中搜出来符合要求的图片，希望对大家解决编程问题提供一定的参考价值，需要的开发者们随着小编来一起学习吧！
目录
效果
生成图片特征
查找踢足球的人
测试图片
模型信息
image_model.onnx
text_model.onnx
项目
代码
Form1.cs
Clip.cs
下载
C# Onnx Chinese CLIP 通过一句话从图库中搜出来符合要求的图片
效果
生成图片特征
查找踢足球的人
测试图片
模型信息
image_model.onnx
Inputs
-------------------------
name：image
tensor：Float[1, 3, 224, 224]
---------------------------------------------------------------
Outputs
-------------------------
name：unnorm_image_features
tensor：Float[1, 512]
---------------------------------------------------------------
text_model.onnx
Inputs
-------------------------
name：text
tensor：Int64[1, 52]
---------------------------------------------------------------
Outputs
-------------------------
name：unnorm_text_features
tensor：Float[1, 512]
---------------------------------------------------------------
项目
代码
Form1.cs
using System;
using System.Collections.Generic;
using System.Drawing;
using System.IO;
using System.Linq;
using System.Text;
using System.Windows.Forms;
namespace Onnx_Demo
{
public partial class Form1 : Form
{
public Form1()
{
InitializeComponent();
}
Clip mynet = new Clip("model/image_model.onnx", "model/text_model.onnx", "model/myvocab.txt");
float[] imagedir_features;
string image_dir = "test_img";
StringBuilder sb = new StringBuilder();
private void button2_Click(object sender, EventArgs e)
{
//特征向量 可以存二进制文件或者向量数据库
imagedir_features = mynet.generate_imagedir_features(image_dir);
txtInfo.Text = "生成完成！";
txtInfo.Text += "有" + mynet.imgnum + "张图片,特征向量长度=" + imagedir_features.Length;
}
private void button3_Click(object sender, EventArgs e)
{
if (imagedir_features == null)
{
MessageBox.Show("请先生成图片特征！");
return;
}
sb.Clear();
txtInfo.Text = "";
lblInfo.Text = "";
pictureBox1.Image = null;
string input_text = txt_input_text.Text;
if (string.IsNullOrEmpty(input_text))
{
return;
}
List<Dictionary<string, float>> top5imglist = mynet.input_text_search_image(input_text, imagedir_features, mynet.imglist);
sb.AppendLine("top5:");
foreach (var item in top5imglist)
{
sb.AppendLine(Path.GetFileName(item.Keys.First()) + " 相似度:" + item[item.Keys.First()].ToString("F2"));
}
txtInfo.Text = sb.ToString();
lblInfo.Text = Path.GetFileName(top5imglist[0].Keys.First());
pictureBox1.Image = new Bitmap(top5imglist[0].Keys.First());
}
private void Form1_Load(object sender, EventArgs e)
{
}
}
}
using System;
using System.Collections.Generic;
using System.Drawing;
using System.IO;
using System.Linq;
using System.Text;
using System.Windows.Forms;namespace Onnx_Demo
{public partial class Form1 : Form{public Form1(){InitializeComponent();}Clip mynet = new Clip("model/image_model.onnx", "model/text_model.onnx", "model/myvocab.txt");float[] imagedir_features;string image_dir = "test_img";StringBuilder sb = new StringBuilder();private void button2_Click(object sender, EventArgs e){//特征向量 可以存二进制文件或者向量数据库imagedir_features = mynet.generate_imagedir_features(image_dir);txtInfo.Text = "生成完成！";txtInfo.Text += "有" + mynet.imgnum + "张图片,特征向量长度=" + imagedir_features.Length;}private void button3_Click(object sender, EventArgs e){if (imagedir_features == null){MessageBox.Show("请先生成图片特征！");return;}sb.Clear();txtInfo.Text = "";lblInfo.Text = "";pictureBox1.Image = null;string input_text = txt_input_text.Text;if (string.IsNullOrEmpty(input_text)){return;}List<Dictionary<string, float>> top5imglist = mynet.input_text_search_image(input_text, imagedir_features, mynet.imglist);sb.AppendLine("top5:");foreach (var item in top5imglist){sb.AppendLine(Path.GetFileName(item.Keys.First()) + " 相似度:" + item[item.Keys.First()].ToString("F2"));}txtInfo.Text = sb.ToString();lblInfo.Text = Path.GetFileName(top5imglist[0].Keys.First());pictureBox1.Image = new Bitmap(top5imglist[0].Keys.First());}private void Form1_Load(object sender, EventArgs e){}}
}
Clip.cs
public class Clip
{
int inpWidth = 224;
int inpHeight = 224;
float[] mean = new float[] { 0.48145466f, 0.4578275f, 0.40821073f };
float[] std = new float[] { 0.26862954f, 0.26130258f, 0.27577711f };
int context_length = 52;
int len_text_feature = 512;
Net net;
float[] image_features_input;
SessionOptions options;
InferenceSession onnx_session;
Tensor<long> input_tensor;
List<NamedOnnxValue> input_container;
IDisposableReadOnlyCollection<DisposableNamedOnnxValue> result_infer;
DisposableNamedOnnxValue[] results_onnxvalue;
Tensor<float> result_tensors;
TokenizerBase tokenizer;
int[] text_tokens_input;
float[,] text_features_input;
public int imgnum = 0;
public List<string> imglist = new List<string>();
public Clip(string image_modelpath, string text_modelpath, string vocab_path)
{
net = CvDnn.ReadNetFromOnnx(image_modelpath);
// 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(text_modelpath, options);//model_path 为onnx模型文件的路径
// 创建输入容器
input_container = new List<NamedOnnxValue>();
load_tokenizer(vocab_path);
}
void load_tokenizer(string vocab_path)
{
tokenizer = new TokenizerClipChinese();
tokenizer.load_tokenize(vocab_path);
text_tokens_input = new int[1024 * context_length];
}
Mat normalize_(Mat src)
{
Cv2.CvtColor(src, src, ColorConversionCodes.BGR2RGB);
Mat[] bgr = src.Split();
for (int i = 0; i < bgr.Length; ++i)
{
bgr[i].ConvertTo(bgr[i], MatType.CV_32FC1, 1.0 / (255.0 * std[i]), (0.0 - mean[i]) / std[i]);
}
Cv2.Merge(bgr, src);
foreach (Mat channel in bgr)
{
channel.Dispose();
}
return src;
}
unsafe void generate_image_feature(Mat srcimg)
{
Mat temp_image = new Mat();
Cv2.Resize(srcimg, temp_image, new Size(inpWidth, inpHeight), 0, 0, InterpolationFlags.Cubic);
Mat normalized_mat = normalize_(temp_image);
Mat blob = CvDnn.BlobFromImage(normalized_mat);
net.SetInput(blob);
//模型推理，读取推理结果
Mat[] outs = new Mat[1] { new Mat() };
string[] outBlobNames = net.GetUnconnectedOutLayersNames().ToArray();
net.Forward(outs, outBlobNames);
float* ptr_feat = (float*)outs[0].Data;
int len_image_feature = outs[0].Size(1); //忽略第0维batchsize=1, len_image_feature是定值512,跟len_text_feature相等的, 也可以写死在类成员变量里
image_features_input = new float[len_image_feature];
float norm = 0.0f;
for (int i = 0; i < len_image_feature; i++)
{
norm += ptr_feat[i] * ptr_feat[i];
}
norm = (float)Math.Sqrt(norm);
for (int i = 0; i < len_image_feature; i++)
{
image_features_input[i] = ptr_feat[i] / norm;
}
}
unsafe void generate_text_feature(List<string> texts)
{
List<List<int>> text_token = new List<List<int>>(texts.Count);
for (int i = 0; i < texts.Count; i++)
{
text_token.Add(new List<int>());
}
for (int i = 0; i < texts.Count; i++)
{
tokenizer.encode_text(texts[i], text_token[i]);
}
if (text_token.Count * context_length > text_tokens_input.Length)
{
text_tokens_input = new int[text_token.Count * context_length];
}
foreach (int i in text_tokens_input) { text_tokens_input[i] = 0; }
for (int i = 0; i < text_token.Count; i++)
{
if (text_token[i].Count > context_length)
{
Console.WriteLine("text_features index " + i + " ,bigger than " + context_length + "\n");
continue;
}
for (int j = 0; j < text_token[i].Count; j++)
{
text_tokens_input[i * context_length + j] = text_token[i][j];
}
}
int[] text_token_shape = new int[] { 1, context_length };
text_features_input = new float[text_token.Count, len_text_feature];
long[] text_tokens_input_64 = new long[texts.Count * context_length];
for (int i = 0; i < text_tokens_input_64.Length; i++)
{
text_tokens_input_64[i] = text_tokens_input[i];
}
for (int i = 0; i < text_token.Count; i++)
{
input_tensor = new DenseTensor<long>(text_tokens_input_64, new[] { 1, 52 });
input_container.Clear();
input_container.Add(NamedOnnxValue.CreateFromTensor("text", input_tensor));
//运行 Inference 并获取结果
result_infer = onnx_session.Run(input_container);
// 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>();
float[] text_feature_ptr = results_onnxvalue[0].AsTensor<float>().ToArray();
float norm = 0.0f;
for (int j = 0; j < len_text_feature; j++)
{
norm += text_feature_ptr[j] * text_feature_ptr[j];
}
norm = (float)Math.Sqrt(norm);
for (int j = 0; j < len_text_feature; j++)
{
text_features_input[i, j] = text_feature_ptr[j] / norm;
}
}
}
void softmax(float[] input)
{
int length = input.Length;
float[] exp_x = new float[length];
float maxVal = input.Max();
float sum = 0;
for (int i = 0; i < length; i++)
{
float expval = (float)Math.Exp(input[i] - maxVal);
exp_x[i] = expval;
sum += expval;
}
for (int i = 0; i < length; i++)
{
input[i] = exp_x[i] / sum;
}
}
int[] argsort_ascend(float[] array)
{
int array_len = array.Length;
int[] array_index = new int[array_len];
for (int i = 0; i < array_len; ++i)
{
array_index[i] = i;
}
var temp = array_index.ToList();
temp.Sort((pos1, pos2) =>
{
if (array[pos1] < array[pos2])
{
return -1;
}
else if (array[pos1] == array[pos2])
{
return 0;
}
else
{
return 0;
}
});
return temp.ToArray();
}
public List<Dictionary<string, float>> input_text_search_image(string text, float[] image_features, List<string> imglist)
{
int imgnum = imglist.Count;
List<string> texts = new List<string> { text };
generate_text_feature(texts);
float[] logits_per_image = new float[imgnum];
for (int i = 0; i < imgnum; i++)
{
float sum = 0;
for (int j = 0; j < len_text_feature; j++)
{
sum += image_features[i * len_text_feature + j] * text_features_input[0, j]; //图片特征向量跟文本特征向量做内积
}
logits_per_image[i] = 100 * sum;
}
softmax(logits_per_image);
int[] index = argsort_ascend(logits_per_image);
List<Dictionary<string, float>> top5imglist = new List<Dictionary<string, float>>(5);
for (int i = 0; i < 5; i++)
{
int ind = index[imgnum - 1 - i];
Dictionary<string, float> result = new Dictionary<string, float>();
result.Add(imglist[ind], logits_per_image[ind]);
top5imglist.Add(result);
}
return top5imglist;
}
public float[] generate_imagedir_features(string image_dir)
{
imglist = Common.listdir(image_dir);
imgnum = imglist.Count;
Console.WriteLine("遍历到" + imgnum + "张图片");
float[] imagedir_features = new float[0];
for (int i = 0; i < imgnum; i++)
{
string imgpath = imglist[i];
Mat srcimg = Cv2.ImRead(imgpath);
generate_image_feature(srcimg);
imagedir_features = imagedir_features.Concat(image_features_input).ToArray();
srcimg.Dispose();
}
return imagedir_features;
}
}
public class Clip{int inpWidth = 224;int inpHeight = 224;float[] mean = new float[] { 0.48145466f, 0.4578275f, 0.40821073f };float[] std = new float[] { 0.26862954f, 0.26130258f, 0.27577711f };int context_length = 52;int len_text_feature = 512;Net net;float[] image_features_input;SessionOptions options;InferenceSession onnx_session;Tensor<long> input_tensor;List<NamedOnnxValue> input_container;IDisposableReadOnlyCollection<DisposableNamedOnnxValue> result_infer;DisposableNamedOnnxValue[] results_onnxvalue;Tensor<float> result_tensors;TokenizerBase tokenizer;int[] text_tokens_input;float[,] text_features_input;public int imgnum = 0;public List<string> imglist = new List<string>();public Clip(string image_modelpath, string text_modelpath, string vocab_path){net = CvDnn.ReadNetFromOnnx(image_modelpath);// 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(text_modelpath, options);//model_path 为onnx模型文件的路径// 创建输入容器input_container = new List<NamedOnnxValue>();load_tokenizer(vocab_path);}void load_tokenizer(string vocab_path){tokenizer = new TokenizerClipChinese();tokenizer.load_tokenize(vocab_path);text_tokens_input = new int[1024 * context_length];}Mat normalize_(Mat src){Cv2.CvtColor(src, src, ColorConversionCodes.BGR2RGB);Mat[] bgr = src.Split();for (int i = 0; i < bgr.Length; ++i){bgr[i].ConvertTo(bgr[i], MatType.CV_32FC1, 1.0 / (255.0 * std[i]), (0.0 - mean[i]) / std[i]);}Cv2.Merge(bgr, src);foreach (Mat channel in bgr){channel.Dispose();}return src;}unsafe void generate_image_feature(Mat srcimg){Mat temp_image = new Mat();Cv2.Resize(srcimg, temp_image, new Size(inpWidth, inpHeight), 0, 0, InterpolationFlags.Cubic);Mat normalized_mat = normalize_(temp_image);Mat blob = CvDnn.BlobFromImage(normalized_mat);net.SetInput(blob);//模型推理，读取推理结果Mat[] outs = new Mat[1] { new Mat() };string[] outBlobNames = net.GetUnconnectedOutLayersNames().ToArray();net.Forward(outs, outBlobNames);float* ptr_feat = (float*)outs[0].Data;int len_image_feature = outs[0].Size(1); //忽略第0维batchsize=1, len_image_feature是定值512,跟len_text_feature相等的, 也可以写死在类成员变量里image_features_input = new float[len_image_feature];float norm = 0.0f;for (int i = 0; i < len_image_feature; i++){norm += ptr_feat[i] * ptr_feat[i];}norm = (float)Math.Sqrt(norm);for (int i = 0; i < len_image_feature; i++){image_features_input[i] = ptr_feat[i] / norm;}}unsafe void generate_text_feature(List<string> texts){List<List<int>> text_token = new List<List<int>>(texts.Count);for (int i = 0; i < texts.Count; i++){text_token.Add(new List<int>());}for (int i = 0; i < texts.Count; i++){tokenizer.encode_text(texts[i], text_token[i]);}if (text_token.Count * context_length > text_tokens_input.Length){text_tokens_input = new int[text_token.Count * context_length];}foreach (int i in text_tokens_input) { text_tokens_input[i] = 0; }for (int i = 0; i < text_token.Count; i++){if (text_token[i].Count > context_length){Console.WriteLine("text_features index " + i + " ,bigger than " + context_length + "\n");continue;}for (int j = 0; j < text_token[i].Count; j++){text_tokens_input[i * context_length + j] = text_token[i][j];}}int[] text_token_shape = new int[] { 1, context_length };text_features_input = new float[text_token.Count, len_text_feature];long[] text_tokens_input_64 = new long[texts.Count * context_length];for (int i = 0; i < text_tokens_input_64.Length; i++){text_tokens_input_64[i] = text_tokens_input[i];}for (int i = 0; i < text_token.Count; i++){input_tensor = new DenseTensor<long>(text_tokens_input_64, new[] { 1, 52 });input_container.Clear();input_container.Add(NamedOnnxValue.CreateFromTensor("text", input_tensor));//运行 Inference 并获取结果result_infer = onnx_session.Run(input_container);// 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>();float[] text_feature_ptr = results_onnxvalue[0].AsTensor<float>().ToArray();float norm = 0.0f;for (int j = 0; j < len_text_feature; j++){norm += text_feature_ptr[j] * text_feature_ptr[j];}norm = (float)Math.Sqrt(norm);for (int j = 0; j < len_text_feature; j++){text_features_input[i, j] = text_feature_ptr[j] / norm;}}}void softmax(float[] input){int length = input.Length;float[] exp_x = new float[length];float maxVal = input.Max();float sum = 0;for (int i = 0; i < length; i++){float expval = (float)Math.Exp(input[i] - maxVal);exp_x[i] = expval;sum += expval;}for (int i = 0; i < length; i++){input[i] = exp_x[i] / sum;}}int[] argsort_ascend(float[] array){int array_len = array.Length;int[] array_index = new int[array_len];for (int i = 0; i < array_len; ++i){array_index[i] = i;}var temp = array_index.ToList();temp.Sort((pos1, pos2) =>{if (array[pos1] < array[pos2]){return -1;}else if (array[pos1] == array[pos2]){return 0;}else{return 0;}});return temp.ToArray();}public List<Dictionary<string, float>> input_text_search_image(string text, float[] image_features, List<string> imglist){int imgnum = imglist.Count;List<string> texts = new List<string> { text };generate_text_feature(texts);float[] logits_per_image = new float[imgnum];for (int i = 0; i < imgnum; i++){float sum = 0;for (int j = 0; j < len_text_feature; j++){sum += image_features[i * len_text_feature + j] * text_features_input[0, j]; //图片特征向量跟文本特征向量做内积}logits_per_image[i] = 100 * sum;}softmax(logits_per_image);int[] index = argsort_ascend(logits_per_image);List<Dictionary<string, float>> top5imglist = new List<Dictionary<string, float>>(5);for (int i = 0; i < 5; i++){int ind = index[imgnum - 1 - i];Dictionary<string, float> result = new Dictionary<string, float>();result.Add(imglist[ind], logits_per_image[ind]);top5imglist.Add(result);}return top5imglist;}public float[] generate_imagedir_features(string image_dir){imglist = Common.listdir(image_dir);imgnum = imglist.Count;Console.WriteLine("遍历到" + imgnum + "张图片");float[] imagedir_features = new float[0];for (int i = 0; i < imgnum; i++){string imgpath = imglist[i];Mat srcimg = Cv2.ImRead(imgpath);generate_image_feature(srcimg);imagedir_features = imagedir_features.Concat(image_features_input).ToArray();srcimg.Dispose();}return imagedir_features;}}下载
源码下载
这篇关于C# Onnx Chinese CLIP 通过一句话从图库中搜出来符合要求的图片的文章就介绍到这儿，希望我们推荐的文章对编程师们有所帮助！
