本頁面由 Cloud Translation API 翻譯而成。

偵測多個物件

Vision API 可透過物件定位功能，偵測及擷取圖片中的多個物件。

物件定位功能可辨識圖片中的多個物件，並為圖片中的每個物件提供 LocalizedObjectAnnotation。每個 LocalizedObjectAnnotation 都會識別物件的相關資訊、物件位置，以及包含物件的圖片區域矩形界線。

物件定位功能可識別圖片中的重要和次要物件。

物件資訊只會以英文傳回。Cloud Translation 可將英文標籤翻譯成各種其他語言。

附有定界框的圖片 — *圖片來源：* Unsplash 上的 Bogdan Dada (*已新增註解*)。

舉例來說，API 會傳回前一張圖片中物件的下列資訊和邊界位置資料：

名稱	mid	分數	範圍
自行車輪	/m/01bqk0	0.89648587	(0.32076266, 0.78941387), (0.43812272, 0.78941387), (0.43812272, 0.97331065), (0.32076266, 0.97331065)
單車	/m/0199g	0.886761	(0.312, 0.6616471)、(0.638353, 0.6616471)、(0.638353, 0.9705882)、(0.312, 0.9705882)
自行車輪	/m/01bqk0	0.6345275	(0.5125398, 0.760708)、(0.6256646, 0.760708)、(0.6256646, 0.94601655)、(0.5125398, 0.94601655)
相框	/m/06z37_	0.6207608	(0.79177403, 0.16160682), (0.97047985, 0.16160682), (0.97047985, 0.31348917), (0.79177403, 0.31348917)
輪胎	/m/0h9mv	0.55886006	(0.32076266, 0.78941387)、(0.43812272, 0.78941387)、(0.43812272, 0.97331065)、(0.32076266, 0.97331065)
門	/m/02dgv	0.5160098	(0.77569866, 0.37104446)、(0.9412425, 0.37104446)、(0.9412425, 0.81507325)、(0.77569866, 0.81507325)

mid 包含對應於標籤 Google 知識圖譜項目的機器建立識別碼 (MID)。如要瞭解如何檢查 mid 值，請參閱 Google 知識圖譜搜尋 API 說明文件。

歡迎試用

如果您未曾使用過 Google Cloud，歡迎建立帳戶，親自體驗實際使用 Cloud Vision API 的成效。新客戶可以獲得價值 $300 美元的免費抵免額，可用於執行、測試及部署工作負載。

免費試用 Cloud Vision API

物件本地化要求

設定 Google Cloud 專案和驗證

如果您尚未建立 Google Cloud 專案，請立即建立。展開這個部分即可查看操作說明。

Sign in to your Google Cloud account. If you're new to Google Cloud, create an account to evaluate how our products perform in real-world scenarios. New customers also get $300 in free credits to run, test, and deploy workloads.

In the Google Cloud console, on the project selector page, select or create a Google Cloud project.

Roles required to select or create a project

Select a project: Selecting a project doesn't require a specific IAM role—you can select any project that you've been granted a role on.
Create a project: To create a project, you need the Project Creator (roles/resourcemanager.projectCreator), which contains the resourcemanager.projects.create permission. Learn how to grant roles.

Go to project selector

Verify that billing is enabled for your Google Cloud project.

Enable the Vision API.

Roles required to enable APIs

To enable APIs, you need the Service Usage Admin IAM role (roles/serviceusage.serviceUsageAdmin), which contains the serviceusage.services.enable permission. Learn how to grant roles.

Enable the API

Install the Google Cloud CLI.

如果您使用外部識別資訊提供者 (IdP)，請先使用聯合身分登入 gcloud CLI。

如要初始化 gcloud CLI，請執行下列指令：

gcloud init

In the Google Cloud console, on the project selector page, select or create a Google Cloud project.

Roles required to select or create a project

Select a project: Selecting a project doesn't require a specific IAM role—you can select any project that you've been granted a role on.
Create a project: To create a project, you need the Project Creator (roles/resourcemanager.projectCreator), which contains the resourcemanager.projects.create permission. Learn how to grant roles.

Go to project selector

Verify that billing is enabled for your Google Cloud project.

Enable the Vision API.

Roles required to enable APIs

To enable APIs, you need the Service Usage Admin IAM role (roles/serviceusage.serviceUsageAdmin), which contains the serviceusage.services.enable permission. Learn how to grant roles.

Enable the API

Install the Google Cloud CLI.

如果您使用外部識別資訊提供者 (IdP)，請先使用聯合身分登入 gcloud CLI。

如要初始化 gcloud CLI，請執行下列指令：

gcloud init

偵測本機圖片中的物件

您可以使用 Vision API 對本機圖片檔執行特徵偵測。

如果是 REST 要求，請在要求主體中，以 base64 編碼字串的形式傳送圖片檔案內容。

如果是 gcloud 和用戶端程式庫要求，請在要求中指定本機圖片的路徑。

注意：這項功能會傳回 normalizedVertices [0,1] 的結果，而非實際的像素值 (vertices)。

REST

使用任何要求資料之前，請先替換以下項目：

BASE64_ENCODED_IMAGE：二進位圖片資料的 Base64 表示法 (ASCII 字串)。這個字串應類似下列字串：
- /9j/4QAYRXhpZgAA...9tAVx/zDQDlGxn//2Q==
如要瞭解詳情，請參閱 base64 編碼主題。
RESULTS_INT：(選填) 要傳回的結果整數值。如果省略 "maxResults" 欄位及其值，API 會傳回預設值，也就是 10 個結果。這個欄位不適用於下列特徵類型：TEXT_DETECTION、DOCUMENT_TEXT_DETECTION 或 CROP_HINTS。
PROJECT_ID：您的 Google Cloud 專案 ID。

HTTP 方法和網址：

POST https://vision.googleapis.com/v1/images:annotate

JSON 要求主體：

{
  "requests": [
    {
      "image": {
        "content": "BASE64_ENCODED_IMAGE"
      },
      "features": [
        {
          "maxResults": RESULTS_INT,
          "type": "OBJECT_LOCALIZATION"
        },
      ]
    }
  ]
}

如要傳送要求，請選擇以下其中一個選項：

curl

注意： 下列指令假設您已執行 gcloud init 或 gcloud auth login，透過使用者帳戶登入 gcloud CLI，或使用 Cloud Shell，自動登入 gcloud CLI。您可以執行 gcloud auth list 查看目前有效的帳戶。

將要求主體儲存在名為 request.json 的檔案中，然後執行下列指令：

curl -X POST \
     -H "Authorization: Bearer $(gcloud auth print-access-token)" \
     -H "x-goog-user-project: PROJECT_ID" \
     -H "Content-Type: application/json; charset=utf-8" \
     -d @request.json \
     "https://vision.googleapis.com/v1/images:annotate"

PowerShell

注意： 下列指令假設您已執行 gcloud init 或 gcloud auth login，透過使用者帳戶登入 gcloud CLI。您可以執行 gcloud auth list 查看目前有效的帳戶。

將要求主體儲存在名為 request.json 的檔案中，然後執行下列指令：

$cred = gcloud auth print-access-token
$headers = @{ "Authorization" = "Bearer $cred"; "x-goog-user-project" = "PROJECT_ID" }

Invoke-WebRequest `
    -Method POST `
    -Headers $headers `
    -ContentType: "application/json; charset=utf-8" `
    -InFile request.json `
    -Uri "https://vision.googleapis.com/v1/images:annotate" | Select-Object -Expand Content

如果要求成功，伺服器會傳回 200 OK HTTP 狀態碼與 JSON 格式的回應。

回覆：

回應

{
  "responses": [
    {
      "localizedObjectAnnotations": [
        {
          "mid": "/m/01bqk0",
          "name": "Bicycle wheel",
          "score": 0.89648587,
          "boundingPoly": {
            "normalizedVertices": [
              {
                "x": 0.32076266,
                "y": 0.78941387
              },
              {
                "x": 0.43812272,
                "y": 0.78941387
              },
              {
                "x": 0.43812272,
                "y": 0.97331065
              },
              {
                "x": 0.32076266,
                "y": 0.97331065
              }
            ]
          }
        },
        {
          "mid": "/m/0199g",
          "name": "Bicycle",
          "score": 0.886761,
          "boundingPoly": {
            "normalizedVertices": [
              {
                "x": 0.312,
                "y": 0.6616471
              },
              {
                "x": 0.638353,
                "y": 0.6616471
              },
              {
                "x": 0.638353,
                "y": 0.9705882
              },
              {
                "x": 0.312,
                "y": 0.9705882
              }
            ]
          }
        },
        {
          "mid": "/m/01bqk0",
          "name": "Bicycle wheel",
          "score": 0.6345275,
          "boundingPoly": {
            "normalizedVertices": [
              {
                "x": 0.5125398,
                "y": 0.760708
              },
              {
                "x": 0.6256646,
                "y": 0.760708
              },
              {
                "x": 0.6256646,
                "y": 0.94601655
              },
              {
                "x": 0.5125398,
                "y": 0.94601655
              }
            ]
          }
        },
        {
          "mid": "/m/06z37_",
          "name": "Picture frame",
          "score": 0.6207608,
          "boundingPoly": {
            "normalizedVertices": [
              {
                "x": 0.79177403,
                "y": 0.16160682
              },
              {
                "x": 0.97047985,
                "y": 0.16160682
              },
              {
                "x": 0.97047985,
                "y": 0.31348917
              },
              {
                "x": 0.79177403,
                "y": 0.31348917
              }
            ]
          }
        },
        {
          "mid": "/m/0h9mv",
          "name": "Tire",
          "score": 0.55886006,
          "boundingPoly": {
            "normalizedVertices": [
              {
                "x": 0.32076266,
                "y": 0.78941387
              },
              {
                "x": 0.43812272,
                "y": 0.78941387
              },
              {
                "x": 0.43812272,
                "y": 0.97331065
              },
              {
                "x": 0.32076266,
                "y": 0.97331065
              }
            ]
          }
        },
        {
          "mid": "/m/02dgv",
          "name": "Door",
          "score": 0.5160098,
          "boundingPoly": {
            "normalizedVertices": [
              {
                "x": 0.77569866,
                "y": 0.37104446
              },
              {
                "x": 0.9412425,
                "y": 0.37104446
              },
              {
                "x": 0.9412425,
                "y": 0.81507325
              },
              {
                "x": 0.77569866,
                "y": 0.81507325
              }
            ]
          }
        }
      ]
    }
  ]
}

Go

在試用這個範例之前，請先按照Go「使用用戶端程式庫的 Vision 快速入門導覽課程」中的設定說明操作。詳情請參閱 Vision Go API 參考說明文件。

如要向 Vision 進行驗證，請設定應用程式預設憑證。詳情請參閱「為本機開發環境設定驗證」。


// localizeObjects gets objects and bounding boxes from the Vision API for an image at the given file path.
func localizeObjects(w io.Writer, file string) error {
	ctx := context.Background()

	client, err := vision.NewImageAnnotatorClient(ctx)
	if err != nil {
		return err
	}

	f, err := os.Open(file)
	if err != nil {
		return err
	}
	defer f.Close()

	image, err := vision.NewImageFromReader(f)
	if err != nil {
		return err
	}
	annotations, err := client.LocalizeObjects(ctx, image, nil)
	if err != nil {
		return err
	}

	if len(annotations) == 0 {
		fmt.Fprintln(w, "No objects found.")
		return nil
	}

	fmt.Fprintln(w, "Objects:")
	for _, annotation := range annotations {
		fmt.Fprintln(w, annotation.Name)
		fmt.Fprintln(w, annotation.Score)

		for _, v := range annotation.BoundingPoly.NormalizedVertices {
			fmt.Fprintf(w, "(%f,%f)\n", v.X, v.Y)
		}
	}

	return nil
}

Java

在試用這個範例之前，請先按照使用用戶端程式庫的 Vision API 快速入門導覽課程中的 Java 設定操作說明進行操作。詳情請參閱 Vision API Java 參考說明文件。

/**
 * Detects localized objects in the specified local image.
 *
 * @param filePath The path to the file to perform localized object detection on.
 * @throws Exception on errors while closing the client.
 * @throws IOException on Input/Output errors.
 */
public static void detectLocalizedObjects(String filePath) throws IOException {
  List<AnnotateImageRequest> requests = new ArrayList<>();

  ByteString imgBytes = ByteString.readFrom(new FileInputStream(filePath));

  Image img = Image.newBuilder().setContent(imgBytes).build();
  AnnotateImageRequest request =
      AnnotateImageRequest.newBuilder()
          .addFeatures(Feature.newBuilder().setType(Type.OBJECT_LOCALIZATION))
          .setImage(img)
          .build();
  requests.add(request);

  // Initialize client that will be used to send requests. This client only needs to be created
  // once, and can be reused for multiple requests. After completing all of your requests, call
  // the "close" method on the client to safely clean up any remaining background resources.
  try (ImageAnnotatorClient client = ImageAnnotatorClient.create()) {
    // Perform the request
    BatchAnnotateImagesResponse response = client.batchAnnotateImages(requests);
    List<AnnotateImageResponse> responses = response.getResponsesList();

    // Display the results
    for (AnnotateImageResponse res : responses) {
      for (LocalizedObjectAnnotation entity : res.getLocalizedObjectAnnotationsList()) {
        System.out.format("Object name: %s%n", entity.getName());
        System.out.format("Confidence: %s%n", entity.getScore());
        System.out.format("Normalized Vertices:%n");
        entity
            .getBoundingPoly()
            .getNormalizedVerticesList()
            .forEach(vertex -> System.out.format("- (%s, %s)%n", vertex.getX(), vertex.getY()));
      }
    }
  }
}

Node.js

在試用這個範例之前，請先按照Node.js「使用用戶端程式庫的 Vision 快速入門導覽課程」中的設定說明操作。詳情請參閱 Vision Node.js API 參考說明文件。

如要向 Vision 進行驗證，請設定應用程式預設憑證。詳情請參閱「為本機開發環境設定驗證」。

// Imports the Google Cloud client libraries
const vision = require('@google-cloud/vision');
const fs = require('fs');

// Creates a client
const client = new vision.ImageAnnotatorClient();

/**
 * TODO(developer): Uncomment the following line before running the sample.
 */
// const fileName = `/path/to/localImage.png`;
const request = {
  image: {content: fs.readFileSync(fileName)},
};

const [result] = await client.objectLocalization(request);
const objects = result.localizedObjectAnnotations;
objects.forEach(object => {
  console.log(`Name: ${object.name}`);
  console.log(`Confidence: ${object.score}`);
  const vertices = object.boundingPoly.normalizedVertices;
  vertices.forEach(v => console.log(`x: ${v.x}, y:${v.y}`));
});

Python

在試用這個範例之前，請先按照Python「使用用戶端程式庫的 Vision 快速入門導覽課程」中的設定說明操作。詳情請參閱 Vision Python API 參考說明文件。

如要向 Vision 進行驗證，請設定應用程式預設憑證。詳情請參閱「為本機開發環境設定驗證」。

def localize_objects(path):
    """Localize objects in the local image.

    Args:
    path: The path to the local file.
    """
    from google.cloud import vision

    client = vision.ImageAnnotatorClient()

    with open(path, "rb") as image_file:
        content = image_file.read()
    image = vision.Image(content=content)

    objects = client.object_localization(image=image).localized_object_annotations

    print(f"Number of objects found: {len(objects)}")
    for object_ in objects:
        print(f"\n{object_.name} (confidence: {object_.score})")
        print("Normalized bounding polygon vertices: ")
        for vertex in object_.bounding_poly.normalized_vertices:
            print(f" - ({vertex.x}, {vertex.y})")

其他語言

C#：請按照用戶端程式庫頁面上的C# 設定說明操作，然後前往 .NET 適用的 Vision 參考說明文件。

PHP：請按照用戶端程式庫頁面上的 PHP 設定說明操作，然後前往 PHP 適用的 Vision 參考文件。

Ruby：請按照用戶端程式庫頁面的 Ruby 設定說明操作，然後前往 Ruby 適用的 Vision 參考說明文件。

偵測遠端圖片中的物件

您可以透過 Vision API，對位於 Cloud Storage 或網路上的遠端圖片檔案執行特徵偵測。如要傳送遠端檔案要求，請在要求內文中指定檔案的網頁網址或 Cloud Storage URI。

注意：這項功能會傳回 normalizedVertices [0,1] 的結果，而非實際的像素值 (vertices)。

REST

使用任何要求資料之前，請先替換以下項目：

CLOUD_STORAGE_IMAGE_URI：Cloud Storage 值區中有效圖片檔案的路徑。您必須至少擁有檔案的讀取權限。範例：
- ```
https://cloud.google.com/vision/docs/images/bicycle_example.png
```
RESULTS_INT：(選填) 要傳回的結果整數值。如果省略 "maxResults" 欄位及其值，API 會傳回預設值，也就是 10 個結果。這個欄位不適用於下列特徵類型：TEXT_DETECTION、DOCUMENT_TEXT_DETECTION 或 CROP_HINTS。
PROJECT_ID：您的 Google Cloud 專案 ID。

HTTP 方法和網址：

POST https://vision.googleapis.com/v1/images:annotate

JSON 要求主體：

{
  "requests": [
    {
      "image": {
        "source": {
          "imageUri": "CLOUD_STORAGE_IMAGE_URI"
        }
      },
      "features": [
        {
          "maxResults": RESULTS_INT,
          "type": "OBJECT_LOCALIZATION"
        },
      ]
    }
  ]
}

如要傳送要求，請選擇以下其中一個選項：

curl

將要求主體儲存在名為 request.json 的檔案中，然後執行下列指令：

curl -X POST \
     -H "Authorization: Bearer $(gcloud auth print-access-token)" \
     -H "x-goog-user-project: PROJECT_ID" \
     -H "Content-Type: application/json; charset=utf-8" \
     -d @request.json \
     "https://vision.googleapis.com/v1/images:annotate"

PowerShell

注意： 下列指令假設您已執行 gcloud init 或 gcloud auth login，透過使用者帳戶登入 gcloud CLI。您可以執行 gcloud auth list 查看目前有效的帳戶。

將要求主體儲存在名為 request.json 的檔案中，然後執行下列指令：

$cred = gcloud auth print-access-token
$headers = @{ "Authorization" = "Bearer $cred"; "x-goog-user-project" = "PROJECT_ID" }

Invoke-WebRequest `
    -Method POST `
    -Headers $headers `
    -ContentType: "application/json; charset=utf-8" `
    -InFile request.json `
    -Uri "https://vision.googleapis.com/v1/images:annotate" | Select-Object -Expand Content

如果要求成功，伺服器會傳回 200 OK HTTP 狀態碼與 JSON 格式的回應。

回覆：

回應

{
  "responses": [
    {
      "localizedObjectAnnotations": [
        {
          "mid": "/m/01bqk0",
          "name": "Bicycle wheel",
          "score": 0.89648587,
          "boundingPoly": {
            "normalizedVertices": [
              {
                "x": 0.32076266,
                "y": 0.78941387
              },
              {
                "x": 0.43812272,
                "y": 0.78941387
              },
              {
                "x": 0.43812272,
                "y": 0.97331065
              },
              {
                "x": 0.32076266,
                "y": 0.97331065
              }
            ]
          }
        },
        {
          "mid": "/m/0199g",
          "name": "Bicycle",
          "score": 0.886761,
          "boundingPoly": {
            "normalizedVertices": [
              {
                "x": 0.312,
                "y": 0.6616471
              },
              {
                "x": 0.638353,
                "y": 0.6616471
              },
              {
                "x": 0.638353,
                "y": 0.9705882
              },
              {
                "x": 0.312,
                "y": 0.9705882
              }
            ]
          }
        },
        {
          "mid": "/m/01bqk0",
          "name": "Bicycle wheel",
          "score": 0.6345275,
          "boundingPoly": {
            "normalizedVertices": [
              {
                "x": 0.5125398,
                "y": 0.760708
              },
              {
                "x": 0.6256646,
                "y": 0.760708
              },
              {
                "x": 0.6256646,
                "y": 0.94601655
              },
              {
                "x": 0.5125398,
                "y": 0.94601655
              }
            ]
          }
        },
        {
          "mid": "/m/06z37_",
          "name": "Picture frame",
          "score": 0.6207608,
          "boundingPoly": {
            "normalizedVertices": [
              {
                "x": 0.79177403,
                "y": 0.16160682
              },
              {
                "x": 0.97047985,
                "y": 0.16160682
              },
              {
                "x": 0.97047985,
                "y": 0.31348917
              },
              {
                "x": 0.79177403,
                "y": 0.31348917
              }
            ]
          }
        },
        {
          "mid": "/m/0h9mv",
          "name": "Tire",
          "score": 0.55886006,
          "boundingPoly": {
            "normalizedVertices": [
              {
                "x": 0.32076266,
                "y": 0.78941387
              },
              {
                "x": 0.43812272,
                "y": 0.78941387
              },
              {
                "x": 0.43812272,
                "y": 0.97331065
              },
              {
                "x": 0.32076266,
                "y": 0.97331065
              }
            ]
          }
        },
        {
          "mid": "/m/02dgv",
          "name": "Door",
          "score": 0.5160098,
          "boundingPoly": {
            "normalizedVertices": [
              {
                "x": 0.77569866,
                "y": 0.37104446
              },
              {
                "x": 0.9412425,
                "y": 0.37104446
              },
              {
                "x": 0.9412425,
                "y": 0.81507325
              },
              {
                "x": 0.77569866,
                "y": 0.81507325
              }
            ]
          }
        }
      ]
    }
  ]
}

Go

在試用這個範例之前，請先按照Go「使用用戶端程式庫的 Vision 快速入門導覽課程」中的設定說明操作。詳情請參閱 Vision Go API 參考說明文件。

如要向 Vision 進行驗證，請設定應用程式預設憑證。詳情請參閱「為本機開發環境設定驗證」。


// localizeObjects gets objects and bounding boxes from the Vision API for an image at the given file path.
func localizeObjectsURI(w io.Writer, file string) error {
	ctx := context.Background()

	client, err := vision.NewImageAnnotatorClient(ctx)
	if err != nil {
		return err
	}

	image := vision.NewImageFromURI(file)
	annotations, err := client.LocalizeObjects(ctx, image, nil)
	if err != nil {
		return err
	}

	if len(annotations) == 0 {
		fmt.Fprintln(w, "No objects found.")
		return nil
	}

	fmt.Fprintln(w, "Objects:")
	for _, annotation := range annotations {
		fmt.Fprintln(w, annotation.Name)
		fmt.Fprintln(w, annotation.Score)

		for _, v := range annotation.BoundingPoly.NormalizedVertices {
			fmt.Fprintf(w, "(%f,%f)\n", v.X, v.Y)
		}
	}

	return nil
}

Java

/**
 * Detects localized objects in a remote image on Google Cloud Storage.
 *
 * @param gcsPath The path to the remote file on Google Cloud Storage to detect localized objects
 *     on.
 * @throws Exception on errors while closing the client.
 * @throws IOException on Input/Output errors.
 */
public static void detectLocalizedObjectsGcs(String gcsPath) throws IOException {
  List<AnnotateImageRequest> requests = new ArrayList<>();

  ImageSource imgSource = ImageSource.newBuilder().setGcsImageUri(gcsPath).build();
  Image img = Image.newBuilder().setSource(imgSource).build();

  AnnotateImageRequest request =
      AnnotateImageRequest.newBuilder()
          .addFeatures(Feature.newBuilder().setType(Type.OBJECT_LOCALIZATION))
          .setImage(img)
          .build();
  requests.add(request);

  // Initialize client that will be used to send requests. This client only needs to be created
  // once, and can be reused for multiple requests. After completing all of your requests, call
  // the "close" method on the client to safely clean up any remaining background resources.
  try (ImageAnnotatorClient client = ImageAnnotatorClient.create()) {
    // Perform the request
    BatchAnnotateImagesResponse response = client.batchAnnotateImages(requests);
    List<AnnotateImageResponse> responses = response.getResponsesList();
    client.close();
    // Display the results
    for (AnnotateImageResponse res : responses) {
      for (LocalizedObjectAnnotation entity : res.getLocalizedObjectAnnotationsList()) {
        System.out.format("Object name: %s%n", entity.getName());
        System.out.format("Confidence: %s%n", entity.getScore());
        System.out.format("Normalized Vertices:%n");
        entity
            .getBoundingPoly()
            .getNormalizedVerticesList()
            .forEach(vertex -> System.out.format("- (%s, %s)%n", vertex.getX(), vertex.getY()));
      }
    }
  }
}

Node.js

在試用這個範例之前，請先按照Node.js「使用用戶端程式庫的 Vision 快速入門導覽課程」中的設定說明操作。詳情請參閱 Vision Node.js API 參考說明文件。

如要向 Vision 進行驗證，請設定應用程式預設憑證。詳情請參閱「為本機開發環境設定驗證」。

// Imports the Google Cloud client libraries
const vision = require('@google-cloud/vision');

// Creates a client
const client = new vision.ImageAnnotatorClient();

/**
 * TODO(developer): Uncomment the following line before running the sample.
 */
// const gcsUri = `gs://bucket/bucketImage.png`;

const [result] = await client.objectLocalization(gcsUri);
const objects = result.localizedObjectAnnotations;
objects.forEach(object => {
  console.log(`Name: ${object.name}`);
  console.log(`Confidence: ${object.score}`);
  const veritices = object.boundingPoly.normalizedVertices;
  veritices.forEach(v => console.log(`x: ${v.x}, y:${v.y}`));
});

Python

在試用這個範例之前，請先按照Python「使用用戶端程式庫的 Vision 快速入門導覽課程」中的設定說明操作。詳情請參閱 Vision Python API 參考說明文件。

如要向 Vision 進行驗證，請設定應用程式預設憑證。詳情請參閱「為本機開發環境設定驗證」。

def localize_objects_uri(uri):
    """Localize objects in the image on Google Cloud Storage

    Args:
    uri: The path to the file in Google Cloud Storage (gs://...)
    """
    from google.cloud import vision

    client = vision.ImageAnnotatorClient()

    image = vision.Image()
    image.source.image_uri = uri

    objects = client.object_localization(image=image).localized_object_annotations

    print(f"Number of objects found: {len(objects)}")
    for object_ in objects:
        print(f"\n{object_.name} (confidence: {object_.score})")
        print("Normalized bounding polygon vertices: ")
        for vertex in object_.bounding_poly.normalized_vertices:
            print(f" - ({vertex.x}, {vertex.y})")

gcloud

如要在圖片中偵測標籤，請使用 gcloud ml vision detect-objects 指令，如下列範例所示：

gcloud ml vision detect-objects https://cloud.google.com/vision/docs/images/bicycle_example.png

其他語言

C#：請按照用戶端程式庫頁面上的C# 設定說明操作，然後前往 .NET 適用的 Vision 參考說明文件。

PHP：請按照用戶端程式庫頁面上的 PHP 設定說明操作，然後前往 PHP 適用的 Vision 參考文件。

Ruby：請按照用戶端程式庫頁面的 Ruby 設定說明操作，然後前往 Ruby 適用的 Vision 參考說明文件。

試試看

請使用下列工具試用物件偵測和定位功能。你可以使用已指定的圖片 (https://cloud.google.com/vision/docs/images/bicycle_example.png)，也可以改為指定自己的圖片。選取「Execute」，傳送要求。

不含定界框的圖片 — *圖片出處：* Bogdan Dada 發表於 Unsplash。

要求主體：

{
  "requests": [
    {
      "features": [
        {
          "maxResults": 10,
          "type": "OBJECT_LOCALIZATION"
        }
      ],
      "image": {
        "source": {
          "imageUri": "https://cloud.google.com/vision/docs/images/bicycle_example.png"
        }
      }
    }
  ]
}

偵測多個物件 透過集合功能整理內容 你可以依據偏好儲存及分類內容。

歡迎試用

物件本地化要求

設定 Google Cloud 專案和驗證

偵測本機圖片中的物件

REST

curl

PowerShell

回應

Go

Java

Node.js

Python

其他語言

偵測遠端圖片中的物件

REST

curl

PowerShell

回應

Go

Java

Node.js

Python

gcloud

其他語言

試試看

偵測多個物件