Quickstart: Analyze a local image using the Computer Vision REST API and C#

In this quickstart, you will analyze a locally stored image to extract visual features by using Computer Vision's REST API. With the Analyze Image method, you can extract visual feature information based on image content.

If you don't have an Azure subscription, create a free account before you begin.

Prerequisites

Create and run the sample application

To create the sample in Visual Studio, do the following steps:

  1. Create a new Visual Studio solution in Visual Studio, using the Visual C# Console App (.NET Framework) template.
  2. Install the Newtonsoft.Json NuGet package.
    1. On the menu, click Tools, select NuGet Package Manager, then Manage NuGet Packages for Solution.
    2. Click the Browse tab, and in the Search box type "Newtonsoft.Json".
    3. Select Newtonsoft.Json when it displays, then click the checkbox next to your project name, and Install.
  3. Run the program.
  4. 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
    {
        // Add your Computer Vision subscription key and endpoint to your environment variables.
        static string subscriptionKey = Environment.GetEnvironmentVariable("COMPUTER_VISION_SUBSCRIPTION_KEY");

        static string endpoint = Environment.GetEnvironmentVariable("COMPUTER_VISION_ENDPOINT");
        
        // the Analyze method endpoint
        const string uriBase = endpoint + "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);
            }
        }
    }
}

Examine the response

A successful response is returned in JSON. 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
    }
}

Next steps

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.