呼叫影像分析 3.2 API

發行項
05/30/2024

本文示範如何呼叫影像分析 3.2 API，以傳回影像視覺功能的相關信息。此外也會說明如何使用用戶端 SDK 或 REST API 剖析傳回的資訊。

本指南假設您已建立視覺資源，並取得密鑰和端點 URL。如果您使用用戶端 SDK，您就也需要驗證用戶端物件。如果您尚未完成這些步驟，請依照快速入門的指示開始操作。

提交資料至服務

本指南中的程式碼使用 URL 所參考的遠端影像。您可以自行嘗試不同的影像，以瞭解影像分析功能的各種能力。

分析遠端影像時，您可以用下列格式輸入要求本文，以指定影像的 URL：{"url":"http://example.com/images/test.jpg"}。

若要分析本地影像，您須將二進位影像資料放入 HTTP 要求本文中。

在 main 類別中，儲存您要分析之影像的 URL 參考。

// URL image used for analyzing an image (image of puppy)
private const string ANALYZE_URL_IMAGE = "https://moderatorsampleimages.blob.core.windows.net/samples/sample16.png";

提示

您也可以分析本機影像。請參閱 ComputerVisionClient 方法，例如 AnalyzeImageInStreamAsync。或如需本機映像的相關案例，請參閱 GitHub 上的範例程式碼。

在 main 類別中，儲存您要分析之影像的 URL 參考。

String pathToRemoteImage = "https://github.com/Azure-Samples/cognitive-services-sample-data-files/raw/master/ComputerVision/Images/faces.jpg";

提示

您也可以分析本機影像。請參閱 ComputerVision 方法，例如 AnalyzeImage。或如需本機映像的相關案例，請參閱 GitHub 上的範例程式碼。

在 main 函式中，儲存您要分析之影像的 URL 參考。

const describeURL = 'https://raw.githubusercontent.com/Azure-Samples/cognitive-services-sample-data-files/master/ComputerVision/Images/celebrities.jpg';

提示

您也可以分析本機影像。請參閱 ComputerVisionClient 方法，例如 describeImageInStream。或如需本機映像的相關案例，請參閱 GitHub 上的範例程式碼。

儲存您要分析之影像的 URL 參考。

remote_image_url = "https://moderatorsampleimages.blob.core.windows.net/samples/sample16.png"

提示

您也可以分析本機影像。請參閱 ComputerVisionClientOperationsMixin 方法，例如 analyze_image_in_stream。或如需本機映像的相關案例，請參閱 GitHub 上的範例程式碼。

決定如何處理資料

選取視覺功能

分析 API 可讓您存取服務的所有影像分析功能。根據您自己的使用案例，選擇要執行的作業。如需各項功能的說明，請參閱概觀。後續小節中的範例會新增所有可用的視覺效果功能，但在實際使用時，您可能只需用到一或兩個。

您可藉由設定分析 API 的 URL 查詢參數，來指定您想要使用的功能。參數可以有多個值，並以逗號分隔。您所指定的每項特徵都需要額外的計算時間，因此請僅指定您所需要的功能。

URL 參數	值	Description
`features`	`Read`	讀取影像中的可見文字，並將其輸出為結構化 JSON 資料。
`features`	`Description`	使用支援語言的完整句子來描述影像內容。
`features`	`SmartCrops`	尋找矩形座標，將影像裁剪成所需的外觀比例，同時保留目標區域。
`features`	`Objects`	偵測影像內的各種物品，包括大約的位置。物品引數僅於英文版中提供。
`features`	`Tags`	使用與映像內容相關之字組的詳細清單標記映像。

填入的 URL 可能會如下所示：

<endpoint>/vision/v3.2/analyze?visualFeatures=Tags

定義影像分析的新方法。新增下面的程式碼，以指定您要在分析中擷取的視覺功能。如需完整清單，請參閱 VisualFeatureTypes 列舉。

/* 
 * ANALYZE IMAGE - URL IMAGE
 * Analyze URL image. Extracts captions, categories, tags, objects, faces, racy/adult/gory content,
 * brands, celebrities, landmarks, color scheme, and image types.
 */
public static async Task AnalyzeImageUrl(ComputerVisionClient client, string imageUrl)
{
    Console.WriteLine("----------------------------------------------------------");
    Console.WriteLine("ANALYZE IMAGE - URL");
    Console.WriteLine();

    // Creating a list that defines the features to be extracted from the image. 

    List<VisualFeatureTypes?> features = new List<VisualFeatureTypes?>()
    {
        VisualFeatureTypes.Categories, VisualFeatureTypes.Description,
        VisualFeatureTypes.Faces, VisualFeatureTypes.ImageType,
        VisualFeatureTypes.Tags, VisualFeatureTypes.Adult,
        VisualFeatureTypes.Color, VisualFeatureTypes.Brands,
        VisualFeatureTypes.Objects
    };

指定要在分析中擷取的視覺功能。如需完整清單，請參閱 VisualFeatureTypes列舉。

// This list defines the features to be extracted from the image.
List<VisualFeatureTypes> featuresToExtractFromRemoteImage = new ArrayList<>();
featuresToExtractFromRemoteImage.add(VisualFeatureTypes.DESCRIPTION);
featuresToExtractFromRemoteImage.add(VisualFeatureTypes.CATEGORIES);
featuresToExtractFromRemoteImage.add(VisualFeatureTypes.TAGS);
featuresToExtractFromRemoteImage.add(VisualFeatureTypes.FACES);
featuresToExtractFromRemoteImage.add(VisualFeatureTypes.ADULT);
featuresToExtractFromRemoteImage.add(VisualFeatureTypes.COLOR);
featuresToExtractFromRemoteImage.add(VisualFeatureTypes.IMAGE_TYPE);

指定要在分析中擷取的視覺功能。如需完整清單，請參閱 VisualFeatureTypes列舉。

// Get the visual feature for analysis
const features = ['Categories','Brands','Adult','Color','Description','Faces','Image_type','Objects','Tags'];
const domainDetails = ['Celebrities','Landmarks'];

指定要在分析中擷取的視覺功能。如需完整清單，請參閱 VisualFeatureTypes列舉。

print("===== Analyze an image - remote =====")
# Select the visual feature(s) you want.
remote_image_features = [VisualFeatureTypes.categories,VisualFeatureTypes.brands,VisualFeatureTypes.adult,VisualFeatureTypes.color,VisualFeatureTypes.description,VisualFeatureTypes.faces,VisualFeatureTypes.image_type,VisualFeatureTypes.objects,VisualFeatureTypes.tags]
remote_image_details = [Details.celebrities,Details.landmarks]

指定語言

您也可以指定傳回資料的語言。

下列 URL 查詢參數會指定語言。預設值是 en。

URL 參數	值	Description
`language`	`en`	英語
`language`	`es`	西班牙文
`language`	`ja`	日文
`language`	`pt`	葡萄牙文
`language`	`zh`	簡體中文

填入的 URL 可能會如下所示：

<endpoint>/vision/v3.2/analyze?visualFeatures=Tags&language=en

使用 AnalyzeImageAsync 呼叫的 language 參數來指定語言。指定語言的方法呼叫可能會如下所示。

ImageAnalysis results = await client.AnalyzeImageAsync(imageUrl, visualFeatures: features, language: "en");

在 Analyze 呼叫中使用 AnalyzeImageOptionalParameter 輸入來指定語言。指定語言的方法呼叫可能會如下所示。

ImageAnalysis analysis = compVisClient.computerVision().analyzeImage().withUrl(pathToRemoteImage)
    .withVisualFeatures(featuresToExtractFromLocalImage)
    .language("en")
    .execute();

在 Analysis 呼叫中使用 ComputerVisionClientAnalyzeImageOptionalParams 輸入的 language 屬性來指定語言。指定語言的方法呼叫可能會如下所示。

const result = (await computerVisionClient.analyzeImage(imageURL,{visualFeatures: features, language: 'en'}));

使用 analyze_image 呼叫的 language 參數來指定語言。指定語言的方法呼叫可能會如下所示。

results_remote = computervision_client.analyze_image(remote_image_url , remote_image_features, remote_image_details, 'en')

取得服務的結果

本節說明如何剖析 API 呼叫的結果。其中包含 API 呼叫本身。

注意

保留範圍 API 呼叫

影像分析中的某些功能可以直接呼叫，也可以透過分析 API 呼叫來呼叫。例如，您可以對 <endpoint>/vision/v3.2/tag (或對 SDK 中的對應方法) 提出要求，而僅對影像標籤執行特定範圍分析。請參閱參考文件 \(英文\) 以瞭解可個別呼叫的其他功能。

服務會傳回一份 200 HTTP 回應，而本文會以 JSON 字串的形式包含傳回的資料。以下文字是 JSON 回應的範例。

{
    "metadata":
    {
        "width": 300,
        "height": 200
    },
    "tagsResult":
    {
        "values":
        [
            {
                "name": "grass",
                "confidence": 0.9960499405860901
            },
            {
                "name": "outdoor",
                "confidence": 0.9956876635551453
            },
            {
                "name": "building",
                "confidence": 0.9893627166748047
            },
            {
                "name": "property",
                "confidence": 0.9853052496910095
            },
            {
                "name": "plant",
                "confidence": 0.9791355729103088
            }
        ]
    }
}

錯誤碼

請參閱下列可能的錯誤清單及其原因：

400
- InvalidImageUrl - 影像 URL 格式不正確或無法存取。
- InvalidImageFormat - 輸入資料不是有效影像。
- InvalidImageSize - 輸入影像太大。
- NotSupportedVisualFeature - 指定的功能類型無效。
- NotSupportedImage - 不支援的影像，例如兒童色情內容。
- InvalidDetails - 不支援的 detail 參數值。
- NotSupportedLanguage - 指定的語言不支援要求的作業。
- BadArgument - 錯誤訊息中會提供更多詳細資料。
415：不支援的媒體類型。 Content-Type 不在允許的類型中：
- 針對影像 URL，Content-Type 應為 application/json
- 針對二進位影像資料，Content-Type 應為 application/octet-stream 或 multipart/form-data
500
- FailedToProcess
- Timeout - 影像處理逾時。
- InternalServerError

下列程式碼會呼叫影像分析 API，並將結果列印至主控台。

// Analyze the URL image 
ImageAnalysis results = await client.AnalyzeImageAsync(imageUrl, visualFeatures: features);

// Summarizes the image content.
Console.WriteLine("Summary:");
foreach (var caption in results.Description.Captions)
{
    Console.WriteLine($"{caption.Text} with confidence {caption.Confidence}");
}
Console.WriteLine();

// Display categories the image is divided into.
Console.WriteLine("Categories:");
foreach (var category in results.Categories)
{
    Console.WriteLine($"{category.Name} with confidence {category.Score}");
}
Console.WriteLine();

// Image tags and their confidence score
Console.WriteLine("Tags:");
foreach (var tag in results.Tags)
{
    Console.WriteLine($"{tag.Name} {tag.Confidence}");
}
Console.WriteLine();

// Objects
Console.WriteLine("Objects:");
foreach (var obj in results.Objects)
{
    Console.WriteLine($"{obj.ObjectProperty} with confidence {obj.Confidence} at location {obj.Rectangle.X}, " +
      $"{obj.Rectangle.X + obj.Rectangle.W}, {obj.Rectangle.Y}, {obj.Rectangle.Y + obj.Rectangle.H}");
}
Console.WriteLine();

// Faces
Console.WriteLine("Faces:");
foreach (var face in results.Faces)
{
    Console.WriteLine($"A {face.Gender} of age {face.Age} at location {face.FaceRectangle.Left}, " +
      $"{face.FaceRectangle.Left}, {face.FaceRectangle.Top + face.FaceRectangle.Width}, " +
      $"{face.FaceRectangle.Top + face.FaceRectangle.Height}");
}
Console.WriteLine();

// Adult or racy content, if any.
Console.WriteLine("Adult:");
Console.WriteLine($"Has adult content: {results.Adult.IsAdultContent} with confidence {results.Adult.AdultScore}");
Console.WriteLine($"Has racy content: {results.Adult.IsRacyContent} with confidence {results.Adult.RacyScore}");
Console.WriteLine($"Has gory content: {results.Adult.IsGoryContent} with confidence {results.Adult.GoreScore}");
Console.WriteLine();

// Well-known (or custom, if set) brands.
Console.WriteLine("Brands:");
foreach (var brand in results.Brands)
{
    Console.WriteLine($"Logo of {brand.Name} with confidence {brand.Confidence} at location {brand.Rectangle.X}, " +
      $"{brand.Rectangle.X + brand.Rectangle.W}, {brand.Rectangle.Y}, {brand.Rectangle.Y + brand.Rectangle.H}");
}
Console.WriteLine();

// Celebrities in image, if any.
Console.WriteLine("Celebrities:");
foreach (var category in results.Categories)
{
    if (category.Detail?.Celebrities != null)
    {
        foreach (var celeb in category.Detail.Celebrities)
        {
            Console.WriteLine($"{celeb.Name} with confidence {celeb.Confidence} at location {celeb.FaceRectangle.Left}, " +
              $"{celeb.FaceRectangle.Top}, {celeb.FaceRectangle.Height}, {celeb.FaceRectangle.Width}");
        }
    }
}
Console.WriteLine();

// Popular landmarks in image, if any.
Console.WriteLine("Landmarks:");
foreach (var category in results.Categories)
{
    if (category.Detail?.Landmarks != null)
    {
        foreach (var landmark in category.Detail.Landmarks)
        {
            Console.WriteLine($"{landmark.Name} with confidence {landmark.Confidence}");
        }
    }
}
Console.WriteLine();

// Identifies the color scheme.
Console.WriteLine("Color Scheme:");
Console.WriteLine("Is black and white?: " + results.Color.IsBWImg);
Console.WriteLine("Accent color: " + results.Color.AccentColor);
Console.WriteLine("Dominant background color: " + results.Color.DominantColorBackground);
Console.WriteLine("Dominant foreground color: " + results.Color.DominantColorForeground);
Console.WriteLine("Dominant colors: " + string.Join(",", results.Color.DominantColors));
Console.WriteLine();

// Detects the image types.
Console.WriteLine("Image Type:");
Console.WriteLine("Clip Art Type: " + results.ImageType.ClipArtType);
Console.WriteLine("Line Drawing Type: " + results.ImageType.LineDrawingType);
Console.WriteLine();

下列程式碼會呼叫影像分析 API，並將結果列印至主控台。

// Call the Computer Vision service and tell it to analyze the loaded image.
ImageAnalysis analysis = compVisClient.computerVision().analyzeImage().withUrl(pathToRemoteImage)
        .withVisualFeatures(featuresToExtractFromRemoteImage).execute();

// Display image captions and confidence values.
System.out.println("\nCaptions: ");
for (ImageCaption caption : analysis.description().captions()) {
    System.out.printf("\'%s\' with confidence %f\n", caption.text(), caption.confidence());
}

// Display image category names and confidence values.
System.out.println("\nCategories: ");
for (Category category : analysis.categories()) {
    System.out.printf("\'%s\' with confidence %f\n", category.name(), category.score());
}

// Display image tags and confidence values.
System.out.println("\nTags: ");
for (ImageTag tag : analysis.tags()) {
    System.out.printf("\'%s\' with confidence %f\n", tag.name(), tag.confidence());
}

// Display any faces found in the image and their location.
System.out.println("\nFaces: ");
for (FaceDescription face : analysis.faces()) {
    System.out.printf("\'%s\' of age %d at location (%d, %d), (%d, %d)\n", face.gender(), face.age(),
            face.faceRectangle().left(), face.faceRectangle().top(),
            face.faceRectangle().left() + face.faceRectangle().width(),
            face.faceRectangle().top() + face.faceRectangle().height());
}

// Display whether any adult or racy content was detected and the confidence
// values.
System.out.println("\nAdult: ");
System.out.printf("Is adult content: %b with confidence %f\n", analysis.adult().isAdultContent(),
        analysis.adult().adultScore());
System.out.printf("Has racy content: %b with confidence %f\n", analysis.adult().isRacyContent(),
        analysis.adult().racyScore());

// Display the image color scheme.
System.out.println("\nColor scheme: ");
System.out.println("Is black and white: " + analysis.color().isBWImg());
System.out.println("Accent color: " + analysis.color().accentColor());
System.out.println("Dominant background color: " + analysis.color().dominantColorBackground());
System.out.println("Dominant foreground color: " + analysis.color().dominantColorForeground());
System.out.println("Dominant colors: " + String.join(", ", analysis.color().dominantColors()));

// Display any celebrities detected in the image and their locations.
System.out.println("\nCelebrities: ");
for (Category category : analysis.categories()) {
    if (category.detail() != null && category.detail().celebrities() != null) {
        for (CelebritiesModel celeb : category.detail().celebrities()) {
            System.out.printf("\'%s\' with confidence %f at location (%d, %d), (%d, %d)\n", celeb.name(),
                    celeb.confidence(), celeb.faceRectangle().left(), celeb.faceRectangle().top(),
                    celeb.faceRectangle().left() + celeb.faceRectangle().width(),
                    celeb.faceRectangle().top() + celeb.faceRectangle().height());
        }
    }
}

// Display any landmarks detected in the image and their locations.
System.out.println("\nLandmarks: ");
for (Category category : analysis.categories()) {
    if (category.detail() != null && category.detail().landmarks() != null) {
        for (LandmarksModel landmark : category.detail().landmarks()) {
            System.out.printf("\'%s\' with confidence %f\n", landmark.name(), landmark.confidence());
        }
    }
}

// Display what type of clip art or line drawing the image is.
System.out.println("\nImage type:");
System.out.println("Clip art type: " + analysis.imageType().clipArtType());
System.out.println("Line drawing type: " + analysis.imageType().lineDrawingType());

下列程式碼會呼叫影像分析 API，並將結果列印至主控台。

     const result = (await computerVisionClient.analyzeImage(facesImageURL,{visualFeatures: features},{details: domainDetails}));

     // Detect faces
     // Print the bounding box, gender, and age from the faces.
     const faces = result.faces
     if (faces.length) {
       console.log(`${faces.length} face${faces.length == 1 ? '' : 's'} found:`);
       for (const face of faces) {
         console.log(`    Gender: ${face.gender}`.padEnd(20)
           + ` Age: ${face.age}`.padEnd(10) + `at ${formatRectFaces(face.faceRectangle)}`);
       }
     } else { console.log('No faces found.'); }

     // Formats the bounding box
     function formatRectFaces(rect) {
       return `top=${rect.top}`.padEnd(10) + `left=${rect.left}`.padEnd(10) + `bottom=${rect.top + rect.height}`.padEnd(12)
         + `right=${rect.left + rect.width}`.padEnd(10) + `(${rect.width}x${rect.height})`;
     }

     // Detect Objects
     const objects = result.objects;
     console.log();
     // Print objects bounding box and confidence
     if (objects.length) {
       console.log(`${objects.length} object${objects.length == 1 ? '' : 's'} found:`);
       for (const obj of objects) { console.log(`    ${obj.object} (${obj.confidence.toFixed(2)}) at ${formatRectObjects(obj.rectangle)}`); }
     } else { console.log('No objects found.'); }

     // Formats the bounding box
     function formatRectObjects(rect) {
       return `top=${rect.y}`.padEnd(10) + `left=${rect.x}`.padEnd(10) + `bottom=${rect.y + rect.h}`.padEnd(12)
         + `right=${rect.x + rect.w}`.padEnd(10) + `(${rect.w}x${rect.h})`;
     }
     console.log();

     // Detect tags
     const tags = result.tags;
     console.log(`Tags: ${formatTags(tags)}`);

     // Format tags for display
     function formatTags(tags) {
       return tags.map(tag => (`${tag.name} (${tag.confidence.toFixed(2)})`)).join(', ');
     }
     console.log();

     // Detect image type
     const types = result.imageType;
     console.log(`Image appears to be ${describeType(types)}`);

     function describeType(imageType) {
       if (imageType.clipArtType && imageType.clipArtType > imageType.lineDrawingType) return 'clip art';
       if (imageType.lineDrawingType && imageType.clipArtType < imageType.lineDrawingType) return 'a line drawing';
       return 'a photograph';
     }
     console.log();

     // Detect Category
     const categories = result.categories;
     console.log(`Categories: ${formatCategories(categories)}`);

     // Formats the image categories
     function formatCategories(categories) {
       categories.sort((a, b) => b.score - a.score);
       return categories.map(cat => `${cat.name} (${cat.score.toFixed(2)})`).join(', ');
     }
     console.log();

     // Detect Brands
     const brands = result.brands;

     // Print the brands found
     if (brands.length) {
       console.log(`${brands.length} brand${brands.length != 1 ? 's' : ''} found:`);
       for (const brand of brands) {
         console.log(`    ${brand.name} (${brand.confidence.toFixed(2)} confidence)`);
       }
     } else { console.log(`No brands found.`); }
     console.log();

     // Detect Colors
     const color = result.color;
     printColorScheme(color);

     // Print a detected color scheme
     function printColorScheme(colors) {
       console.log(`Image is in ${colors.isBwImg ? 'black and white' : 'color'}`);
       console.log(`Dominant colors: ${colors.dominantColors.join(', ')}`);
       console.log(`Dominant foreground color: ${colors.dominantColorForeground}`);
       console.log(`Dominant background color: ${colors.dominantColorBackground}`);
       console.log(`Suggested accent color: #${colors.accentColor}`);
     }
     console.log();

     // Detect landmarks
     const domain = result.landmarks;

     // Prints domain-specific, recognized objects
     if (domain.length) {
       console.log(`${domain.length} ${domain.length == 1 ? 'landmark' : 'landmarks'} found:`);
       for (const obj of domain) {
         console.log(`    ${obj.name}`.padEnd(20) + `(${obj.confidence.toFixed(2)} confidence)`.padEnd(20) + `${formatRectDomain(obj.faceRectangle)}`);
       }
     } else {
       console.log('No landmarks found.');
     }

     // Formats bounding box
     function formatRectDomain(rect) {
       if (!rect) return '';
       return `top=${rect.top}`.padEnd(10) + `left=${rect.left}`.padEnd(10) + `bottom=${rect.top + rect.height}`.padEnd(12) +
         `right=${rect.left + rect.width}`.padEnd(10) + `(${rect.width}x${rect.height})`;
     }

     console.log();

     // Detect Adult content
     // Function to confirm racy or not
     const isIt = flag => flag ? 'is' : "isn't";

     const adult = result.adult;
     console.log(`This probably ${isIt(adult.isAdultContent)} adult content (${adult.adultScore.toFixed(4)} score)`);
     console.log(`This probably ${isIt(adult.isRacyContent)} racy content (${adult.racyScore.toFixed(4)} score)`);
     console.log();

下列程式碼會呼叫影像分析 API，並將結果列印至主控台。

# Call API with URL and features
results_remote = computervision_client.analyze_image(remote_image_url , remote_image_features, remote_image_details)

# Print results with confidence score
print("Categories from remote image: ")
if (len(results_remote.categories) == 0):
    print("No categories detected.")
else:
    for category in results_remote.categories:
        print("'{}' with confidence {:.2f}%".format(category.name, category.score * 100))
print()

# Detect faces
# Print the results with gender, age, and bounding box
print("Faces in the remote image: ")
if (len(results_remote.faces) == 0):
    print("No faces detected.")
else:
    for face in results_remote.faces:
        print("'{}' of age {} at location {}, {}, {}, {}".format(face.gender, face.age, \
        face.face_rectangle.left, face.face_rectangle.top, \
        face.face_rectangle.left + face.face_rectangle.width, \
        face.face_rectangle.top + face.face_rectangle.height))

# Adult content
# Print results with adult/racy score
print("Analyzing remote image for adult or racy content ... ")
print("Is adult content: {} with confidence {:.2f}".format(results_remote.adult.is_adult_content, results_remote.adult.adult_score * 100))
print("Has racy content: {} with confidence {:.2f}".format(results_remote.adult.is_racy_content, results_remote.adult.racy_score * 100))
print()

# Detect colors
# Print results of color scheme
print("Getting color scheme of the remote image: ")
print("Is black and white: {}".format(results_remote.color.is_bw_img))
print("Accent color: {}".format(results_remote.color.accent_color))
print("Dominant background color: {}".format(results_remote.color.dominant_color_background))
print("Dominant foreground color: {}".format(results_remote.color.dominant_color_foreground))
print("Dominant colors: {}".format(results_remote.color.dominant_colors))
print()

# Detect image type
# Prints type results with degree of accuracy
print("Type of remote image:")
if results_remote.image_type.clip_art_type == 0:
    print("Image is not clip art.")
elif results_remote.image_type.line_drawing_type == 1:
    print("Image is ambiguously clip art.")
elif results_remote.image_type.line_drawing_type == 2:
    print("Image is normal clip art.")
else:
    print("Image is good clip art.")

if results_remote.image_type.line_drawing_type == 0:
    print("Image is not a line drawing.")
else:
    print("Image is a line drawing")

# Detect brands
print("Detecting brands in remote image: ")
if len(results_remote.brands) == 0:
    print("No brands detected.")
else:
    for brand in results_remote.brands:
        print("'{}' brand detected with confidence {:.1f}% at location {}, {}, {}, {}".format( \
        brand.name, brand.confidence * 100, brand.rectangle.x, brand.rectangle.x + brand.rectangle.w, \
        brand.rectangle.y, brand.rectangle.y + brand.rectangle.h))

# Detect objects
# Print detected objects results with bounding boxes
print("Detecting objects in remote image:")
if len(results_remote.objects) == 0:
    print("No objects detected.")
else:
    for object in detect_objects_results_remote.objects:
        print("object at location {}, {}, {}, {}".format( \
        object.rectangle.x, object.rectangle.x + object.rectangle.w, \
        object.rectangle.y, object.rectangle.y + object.rectangle.h))


# Describe image
# Get the captions (descriptions) from the response, with confidence level
print("Description of remote image: ")
if (len(results_remote.description.captions) == 0):
    print("No description detected.")
else:
    for caption in results_remote.description.captions:
        print("'{}' with confidence {:.2f}%".format(caption.text, caption.confidence * 100))
print()

# Return tags
# Print results with confidence score
print("Tags in the remote image: ")
if (len(results_remote.tags) == 0):
    print("No tags detected.")
else:
    for tag in results_remote.tags:
        print("'{}' with confidence {:.2f}%".format(tag.name, tag.confidence * 100))

# Detect celebrities
print("Celebrities in the remote image:")
if (len(results_remote.categories.detail.celebrities) == 0):
    print("No celebrities detected.")
else:
    for celeb in results_remote.categories.detail.celebrities:
        print(celeb["name"])

# Detect landmarks
print("Landmarks in the remote image:")
if len(results_remote.categories.detail.landmarks) == 0:
    print("No landmarks detected.")
else:
    for landmark in results_remote.categories.detail.landmarks:
        print(landmark["name"])

提示

使用 Azure AI 視覺時，您可能會遇到由服務強制執行的速率限制或其他暫時性問題所造成的暫時性失敗，例如網路中斷。如需如何處理這些失敗類型的相關資訊，請參閱《雲端設計模式》指南中的重試模式，以及相關的斷路器模式。

下一步

探索概念文章，以深入瞭解每項功能。
請參閱 API 參考，以深入瞭解 API 功能。

共用方式為

呼叫影像分析 3.2 API

提交資料至服務

決定如何處理資料

選取視覺功能

指定語言

取得服務的結果

錯誤碼

下一步

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