• 如何利用AI判断上传的图片是否正确
  • 发布于 1个月前
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    0 评论
本篇看一下ImageToText,获取图片内的信息,加以利用。全例是用户上传图片,利用AI来判断上传的图片是否正确。
  <ItemGroup>
    <PackageReference Include="Microsoft.SemanticKernel" Version="1.6.2" />
  </ItemGroup>
下面是识别图片,之前一直是把问题和图片作为入参,效果不稳定,这次做了调整,先询问图片有哪些特征,让模型回答,等回来特征后,二次通过文本询问,进行判断,效果要好一些,看来该花的token省不了。

后端cs代码如下:
using Microsoft.SemanticKernel.ChatCompletion;
using Microsoft.SemanticKernel.Connectors.OpenAI;
using Microsoft.SemanticKernel;
// 堆代码 duidaima.com
var builder = WebApplication.CreateBuilder(args);
var app = builder.Build();
app.UseStaticFiles();
var key = File.ReadAllText(@"C:\GPT\key.txt");

var kernel = Kernel.CreateBuilder()
    .AddOpenAIChatCompletion("gpt-4-vision-preview", key)
    .Build();
var chatGPT = kernel.GetRequiredService<IChatCompletionService>();
var systemMessage = @"你是一个很很认真的助手,能很仔细出色的识别图片。";

var chatHistory = new ChatHistory(systemMessage);
var prompt = "请简单描述图片的特征";
app.MapGet("/iscard", async (string name) =>
{
    app.Logger.LogInformation("图片名称:{0}", name);
    chatHistory.Clear();
    var ImageUri = $"https://github.com/axzxs2001/Asp.NetCoreExperiment/blob/master/Asp.NetCoreExperiment/SemanticKernel/GPTVision/{name}.png?raw=true";
    chatHistory.AddUserMessage(new ChatMessageContentItemCollection
        {
            new TextContent(prompt),
            new ImageContent(new Uri(ImageUri))
        });
    var reply = await chatGPT.GetChatMessageContentAsync(chatHistory, new OpenAIPromptExecutionSettings() { MaxTokens = 1000 });
    var message = reply.Content;
    chatHistory.Clear();
    app.Logger.LogInformation(message);
    var newprompt = @$"根据特征下面的特征:
--------------------------
{message},
--------------------------
回答下面的问题,请用“Yes”或“No”回答,如果不能识别,请用“No”回答。
问题:这张图片是“Residence Card”吗?
“Residence Card”有如下特征:
包含有“Residence Card”字样
包含有“GOVERNMENT OF JAPAN”字样
包含有“DATE OF BIRTH”字样
包含有“ADDRESS”字样
包含有“STATUS”字样
";
    chatHistory.AddUserMessage(new ChatMessageContentItemCollection
        {
            new TextContent(newprompt),
        }); 
    reply = await chatGPT.GetChatMessageContentAsync(chatHistory, new OpenAIPromptExecutionSettings() { MaxTokens = 100 });
    app.Logger.LogInformation(reply.Content);
    return reply.Content;
});

app.Run();
前端代码如下:
<!DOCTYPE html>
<html>
<head>
    <meta charset="utf-8" />
    <title>堆代码 duidaima.com</title>
    <script src="https://cdn.jsdelivr.net/npm/bootstrap@5.1.3/dist/js/bootstrap.bundle.min.js" integrity="sha384-ka7Sk0Gln4gmtz2MlQnikT1wXgYsOg+OMhuP+IlRH9sENBO0LRn5q+8nbTov4+1p" crossorigin="anonymous"></script>
    <link href="https://cdn.jsdelivr.net/npm/bootstrap@5.1.3/dist/css/bootstrap.min.css" rel="stylesheet" integrity="sha384-1BmE4kWBq78iYhFldvKuhfTAU6auU8tT94WrHftjDbrCEXSU1oBoqyl2QvZ6jIW3" crossorigin="anonymous">
    <script src="https://code.jquery.com/jquery-3.7.1.min.js"
            integrity="sha256-/JqT3SQfawRcv/BIHPThkBvs0OEvtFFmqPF/lYI/Cxo="
            crossorigin="anonymous"></script>
</head>
<body>
    <div class="container">
        <div class="row">
            <div class="col">
                <img onclick="isziliucard('a')" src="https://github.com/axzxs2001/Asp.NetCoreExperiment/blob/master/Asp.NetCoreExperiment/SemanticKernel/GPTVision/a.png?raw=true" width="330" />
            </div>
            <div class="col">
                <img onclick="isziliucard('zlk')" src="https://github.com/axzxs2001/Asp.NetCoreExperiment/blob/master/Asp.NetCoreExperiment/SemanticKernel/GPTVision/zlk.png?raw=true" width="330" />
            </div>
            <div class="col">
                <img onclick="isziliucard('c')" src="https://github.com/axzxs2001/Asp.NetCoreExperiment/blob/master/Asp.NetCoreExperiment/SemanticKernel/GPTVision/c.png?raw=true" width="330" />
            </div>
        </div>
        <div class="row">
            <span id="result"></span>
        </div>
    </div>
    <script>
        function isziliucard(name) {
            $('#result').css('color', 'black');
            $("#result").html("判断中……");
            $.ajax({
                url: '/iscard?name=' + name,
                type: 'GET',
                success: function (data) {
                    if (data.includes('No')) {
                        $('#result').css('color', 'red');
                    } else {
                        $('#result').css('color', 'green');
                    }
                    $("#result").html('判断结果:' + data);
                },
                error: function (xhr, status, error) {
                    alter(error)
                }
            });
        }
</script>
</body>
</html>
支行结果:

下面是询问过程中的后台返回,a是第一张图片的名字,zlk是第二张图片的名字,c是第三张图片的名字。

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