快速入门:使用计算机视觉 REST API 和 C# 分析本地图像Quickstart: Analyze a local image using the Computer Vision REST API and C#

在本快速入门中,你将使用计算机视觉的 REST API 分析本地存储的图像以提取视觉特征。In this quickstart, you will analyze a locally stored image to extract visual features by using Computer Vision's REST API. 使用分析图像方法,可以根据图像内容提取视觉特征信息。With the Analyze Image method, you can extract visual feature information based on image content.

如果没有 Azure 订阅,可在开始前创建一个试用帐户If you don't have an Azure subscription, create a Trial before you begin.

先决条件Prerequisites

创建和运行示例应用程序Create and run the sample application

要在 Visual Studio 中创建示例,请执行以下步骤:To create the sample in Visual Studio, do the following steps:

  1. 使用 Visual C# 控制台应用 (.NET Framework) 模板在 Visual Studio 中创建新的 Visual Studio 解决方案。Create a new Visual Studio solution in Visual Studio, using the Visual C# Console App (.NET Framework) template.
  2. 安装 Newtonsoft.Json NuGet 包。Install the Newtonsoft.Json NuGet package.
    1. 在菜单上,单击“工具”,然后依次选择“NuGet 包管理器”、“管理解决方案的 NuGet 包” 。On the menu, click Tools, select NuGet Package Manager, then Manage NuGet Packages for Solution.
    2. 单击“浏览”选项卡,在“搜索”框中键入“Newtonsoft.Json” 。Click the Browse tab, and in the Search box type "Newtonsoft.Json".
    3. 选择显示的 Newtonsoft.Json,单击项目名称旁边的复选框,然后单击“安装” 。Select Newtonsoft.Json when it displays, then click the checkbox next to your project name, and Install.
  3. Program.cs 中的代码替换为以下代码,然后根据需要在代码中进行以下更改:Replace the code in Program.cs with the following code, and then make the following changes in code where needed:
    1. subscriptionKey 的值替换为你的订阅密钥。Replace the value of subscriptionKey with your subscription key.
    2. 如有必要,请将 uriBase 的值替换为获取的订阅密钥所在的 Azure 区域中的分析图像方法的终结点 URL。Replace the value of uriBase with the endpoint URL for the Analyze Image method from the Azure region where you obtained your subscription keys, if necessary.
  4. 运行该程序。Run the program.
  5. 在提示符处,输入本地图像的路径。At the prompt, enter the path to a local image.
using Newtonsoft.Json.Linq;
using System;
using System.IO;
using System.Net.Http;
using System.Net.Http.Headers;
using System.Threading.Tasks;

namespace CSHttpClientSample
{
    static class Program
    {
        // Replace <Subscription Key> with your valid subscription key.
        const string subscriptionKey = "<Subscription Key>";

        const string uriBase =
            "https://api.cognitive.azure.cn/vision/v2.0/analyze";

        static void Main()
        {
            // Get the path and filename to process from the user.
            Console.WriteLine("Analyze an image:");
            Console.Write(
                "Enter the path to the image you wish to analyze: ");
            string imageFilePath = Console.ReadLine();

            if (File.Exists(imageFilePath))
            {
                // Call the REST API method.
                Console.WriteLine("\nWait a moment for the results to appear.\n");
                MakeAnalysisRequest(imageFilePath).Wait();
            }
            else
            {
                Console.WriteLine("\nInvalid file path");
            }
            Console.WriteLine("\nPress Enter to exit...");
            Console.ReadLine();
        }

        /// <summary>
        /// Gets the analysis of the specified image file by using
        /// the Computer Vision REST API.
        /// </summary>
        /// <param name="imageFilePath">The image file to analyze.</param>
        static async Task MakeAnalysisRequest(string imageFilePath)
        {
            try
            {
                HttpClient client = new HttpClient();

                // Request headers.
                client.DefaultRequestHeaders.Add(
                    "Ocp-Apim-Subscription-Key", subscriptionKey);

                // Request parameters. A third optional parameter is "details".
                // The Analyze Image method returns information about the following
                // visual features:
                // Categories:  categorizes image content according to a
                //              taxonomy defined in documentation.
                // Description: describes the image content with a complete
                //              sentence in supported languages.
                // Color:       determines the accent color, dominant color, 
                //              and whether an image is black & white.
                string requestParameters =
                    "visualFeatures=Categories,Description,Color";

                // Assemble the URI for the REST API method.
                string uri = uriBase + "?" + requestParameters;

                HttpResponseMessage response;

                // Read the contents of the specified local image
                // into a byte array.
                byte[] byteData = GetImageAsByteArray(imageFilePath);

                // Add the byte array as an octet stream to the request body.
                using (ByteArrayContent content = new ByteArrayContent(byteData))
                {
                    // This example uses the "application/octet-stream" content type.
                    // The other content types you can use are "application/json"
                    // and "multipart/form-data".
                    content.Headers.ContentType =
                        new MediaTypeHeaderValue("application/octet-stream");

                    // Asynchronously call the REST API method.
                    response = await client.PostAsync(uri, content);
                }

                // Asynchronously get the JSON response.
                string contentString = await response.Content.ReadAsStringAsync();

                // Display the JSON response.
                Console.WriteLine("\nResponse:\n\n{0}\n",
                    JToken.Parse(contentString).ToString());
            }
            catch (Exception e)
            {
                Console.WriteLine("\n" + e.Message);
            }
        }

        /// <summary>
        /// Returns the contents of the specified file as a byte array.
        /// </summary>
        /// <param name="imageFilePath">The image file to read.</param>
        /// <returns>The byte array of the image data.</returns>
        static byte[] GetImageAsByteArray(string imageFilePath)
        {
            // Open a read-only file stream for the specified file.
            using (FileStream fileStream =
                new FileStream(imageFilePath, FileMode.Open, FileAccess.Read))
            {
                // Read the file's contents into a byte array.
                BinaryReader binaryReader = new BinaryReader(fileStream);
                return binaryReader.ReadBytes((int)fileStream.Length);
            }
        }
    }
}

检查响应Examine the response

成功的响应以 JSON 格式返回。A successful response is returned in JSON. 示例应用程序会在控制台窗口中分析和显示成功响应,如下例所示:The sample application parses and displays a successful response in the console window, similar to the following example:

{
    "categories": [
        {
            "name": "abstract_",
            "score": 0.00390625
        },
        {
            "name": "others_",
            "score": 0.0234375
        },
        {
            "name": "outdoor_",
            "score": 0.00390625
        }
    ],
    "description": {
        "tags": [
            "road",
            "building",
            "outdoor",
            "street",
            "night",
            "black",
            "city",
            "white",
            "light",
            "sitting",
            "riding",
            "man",
            "side",
            "empty",
            "rain",
            "corner",
            "traffic",
            "lit",
            "hydrant",
            "stop",
            "board",
            "parked",
            "bus",
            "tall"
        ],
        "captions": [
            {
                "text": "a close up of an empty city street at night",
                "confidence": 0.7965622853462756
            }
        ]
    },
    "requestId": "dddf1ac9-7e66-4c47-bdef-222f3fe5aa23",
    "metadata": {
        "width": 3733,
        "height": 1986,
        "format": "Jpeg"
    },
    "color": {
        "dominantColorForeground": "Black",
        "dominantColorBackground": "Black",
        "dominantColors": [
            "Black",
            "Grey"
        ],
        "accentColor": "666666",
        "isBWImg": true
    }
}

后续步骤Next steps

浏览一款基本 Windows 应用程序,该应用程序使用计算机视觉执行光学字符识别 (OCR)、创建智能裁剪缩略图,并对图像中的视觉特征(包括人脸)进行检测、分类、标记和描述。Explore a basic Windows application that uses Computer Vision to perform optical character recognition (OCR); create smart-cropped thumbnails; plus detect, categorize, tag, and describe visual features, including faces, in an image.