离线批量文件注释

Vision API 可以检测 Cloud Storage 中存储的 PDF 和 TIFF 文件中的任何 Vision API 特征。

必须使用 files:asyncBatchAnnotate 函数请求对 PDF 和 TIFF 执行特征检测；该函数将执行离线（异步）请求并通过 operations 资源提供其状态。

PDF/TIFF 请求的输出会写入在指定的 Cloud Storage 存储桶中创建的 JSON 文件。

限制

Vision API 可接受最多 2,000 页的 PDF/TIFF 文件。如果文件包含更多页面，则会返回错误。

身份验证

files:asyncBatchAnnotate 请求不支持 API 密钥。如需了解如何使用服务账号进行身份验证，请参阅使用服务账号。

用于进行身份验证的账号必须能够访问您为输出指定的 Cloud Storage 存储桶（roles/editor、roles/storage.objectCreator 或权限更高的角色）。

您可以使用 API 密钥来查询该操作的状态；如需查看相关说明，请参阅使用 API 密钥。

特征检测请求

目前，PDF/TIFF 文档检测仅适用于存储在 Cloud Storage 存储分区中的文件。响应 JSON 文件同样会保存到 Cloud Storage 存储桶。

命令行

如需执行 PDF/TIFF 文档文本检测，请发出 POST 请求并提供相应的请求正文：

curl -X POST \
-H "Authorization: Bearer $(gcloud auth application-default print-access-token)" \
-H "Content-Type: application/json; charset=utf-8" \
https://vision.googleapis.com/v1/files:asyncBatchAnnotate -d "{
  'requests':[
    {
      'inputConfig': {
        'gcsSource': {
          'uri': 'gs://your-source-bucket-name/folder/multi-page-file.pdf'
        },
        'mimeType': 'application/pdf'
      },
      'features': [
        {
          'type': 'DOCUMENT_TEXT_DETECTION'
        }
      ],
      'outputConfig': {
        'gcsDestination': {
          'uri': 'gs://your-bucket-name/folder/'
        },
        'batchSize': 1
      }
    }
  ]
}"

其中：

inputConfig - 替换其他 Vision API 请求中所用的 image 字段。它包含两个子字段：
- gcsSource.uri - PDF 或 TIFF 文件的 Cloud Storage URI（可供发出请求的用户或服务账号访问）。
- mimeType - 接受的文件类型之一：application/pdf 或 image/tiff。
outputConfig - 指定输出详细信息。它包含两个子字段：
- gcsDestination.uri - 有效的 Cloud Storage URI。该存储分区必须可供发出请求的用户或服务账号写入。文件名为 output-x-to-y，其中 x 和 y 表示包含在该输出文件中的 PDF/TIFF 页码。如果该文件已存在，其内容将被覆盖。
- batchSize - 指定每个输出 JSON 文件中应包含多少页输出。

响应：

如果 asyncBatchAnnotate 请求成功，返回的响应中将包含单个名称字段：

{
  "name": "projects/usable-auth-library/operations/1efec2285bd442df"
}

此名称表示具有一个关联 ID（例如 1efec2285bd442df）的长时间运行的操作，您可以使用 v1.operations API 对其进行查询。

如需检索您的 Vision 注释响应，请向 v1.operations 端点发送 GET 请求，同时在网址中传递操作 ID：

curl -X GET -H "Authorization: Bearer $(gcloud auth application-default print-access-token)" \
-H "Content-Type: application/json" \
https://vision.googleapis.com/v1/operations/1efec2285bd442df

如果操作正在进行：

{
  "name": "operations/1efec2285bd442df",
  "metadata": {
    "@type": "type.googleapis.com/google.cloud.vision.v1.OperationMetadata",
    "state": "RUNNING",
    "createTime": "2019-05-15T21:10:08.401917049Z",
    "updateTime": "2019-05-15T21:10:33.700763554Z"
  }
}

操作完成后，state 会显示为 DONE，并且您的结果会写入您指定的 Cloud Storage 文件中：

{
  "name": "operations/1efec2285bd442df",
  "metadata": {
    "@type": "type.googleapis.com/google.cloud.vision.v1.OperationMetadata",
    "state": "DONE",
    "createTime": "2019-05-15T20:56:30.622473785Z",
    "updateTime": "2019-05-15T20:56:41.666379749Z"
  },
  "done": true,
  "response": {
    "@type": "type.googleapis.com/google.cloud.vision.v1.AsyncBatchAnnotateFilesResponse",
    "responses": [
      {
        "outputConfig": {
          "gcsDestination": {
            "uri": "gs://your-bucket-name/folder/"
          },
          "batchSize": 1
        }
      }
    ]
  }
}

输出文件中的 JSON 类似于图片文档文本检测请求的 JSON；不过，前者多了一个 context 字段，用于显示指定的 PDF 或 TIFF 的位置以及相应文件中的页数：

output-1-to-1.json

完整响应

{
  "inputConfig": {
    "gcsSource": {
      "uri": "gs://cloud-samples-data/vision/pdf_tiff/census2010.pdf"
    },
    "mimeType": "application/pdf"
  },
  "responses": [
    {
      "fullTextAnnotation": {
        "pages": [
          {
            "property": {
              "detectedLanguages": [
                {
                  "languageCode": "en",
                  "confidence": 0.94
                }
              ]
            },
            "width": 612,
            "height": 792,
            "blocks": [
              {
                "boundingBox": {
                  "normalizedVertices": [
                    {
                      "x": 0.12908497,
                      "y": 0.10479798
                    },
                    ...
                    {
                      "x": 0.12908497,
                      "y": 0.1199495
                    }
                  ]
                },
                "paragraphs": [
                  {
                  ...
                    },
                    "words": [
                      {
                        ...
                        },
                        "symbols": [
                          {
                          ...
                            "text": "C",
                            "confidence": 0.99
                          },
                          {
                            "property": {
                              "detectedLanguages": [
                                {
                                  "languageCode": "en"
                                }
                              ]
                            },
                            "text": "O",
                            "confidence": 0.99
                          },
             ...
             }
            ]
          }
        ],
        "text": "CONTENTS\n.\n1-1\nII-1\nIII-1\nList of Statistical Tables...
        \nHow to Use This Census Report ..\nTable Finding Guide .\nUser
        Notes .......\nStatistical Tables.........\nAppendixes
        \nA Geographic Terms and Concepts .........\nB Definitions of
        Subject Characteristics.\nData Collection and Processing Procedures...
        \nQuestionnaire. ........\nE Maps .................\nF Operational
        Overview and accuracy of the Data.......\nG Residence Rule and
        Residence Situations for the \n2010 Census of the United States...
        \nH Acknowledgments .....\nE\n*Appendix may be found in the separate
        volume, CPH-1-A, Summary Population and\nHousing Characteristics,
        Selected Appendixes, on the Internet at
        <www.census.gov\n/prod/cen2010/cph-1-a.pdf>.\nContents\n"
      },
      "context": {
        "uri": "gs://cloud-samples-data/vision/pdf_tiff/census2010.pdf",
        "pageNumber": 1
      }
    }
  ]
}

Go

试用此示例之前，请按照《Vision 快速入门：使用客户端库》中的 Go 设置说明进行操作。如需了解详情，请参阅 Vision Go API 参考文档。

如需向 Vision 进行身份验证，请设置应用默认凭据。如需了解详情，请参阅为本地开发环境设置身份验证。


// detectAsyncDocumentURI performs Optical Character Recognition (OCR) on a
// PDF file stored in GCS.
func detectAsyncDocumentURI(w io.Writer, gcsSourceURI, gcsDestinationURI string) error {
	ctx := context.Background()

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

	request := &visionpb.AsyncBatchAnnotateFilesRequest{
		Requests: []*visionpb.AsyncAnnotateFileRequest{
			{
				Features: []*visionpb.Feature{
					{
						Type: visionpb.Feature_DOCUMENT_TEXT_DETECTION,
					},
				},
				InputConfig: &visionpb.InputConfig{
					GcsSource: &visionpb.GcsSource{Uri: gcsSourceURI},
					// Supported MimeTypes are: "application/pdf" and "image/tiff".
					MimeType: "application/pdf",
				},
				OutputConfig: &visionpb.OutputConfig{
					GcsDestination: &visionpb.GcsDestination{Uri: gcsDestinationURI},
					// How many pages should be grouped into each json output file.
					BatchSize: 2,
				},
			},
		},
	}

	operation, err := client.AsyncBatchAnnotateFiles(ctx, request)
	if err != nil {
		return err
	}

	fmt.Fprintf(w, "Waiting for the operation to finish.")

	resp, err := operation.Wait(ctx)
	if err != nil {
		return err
	}

	fmt.Fprintf(w, "%v", resp)

	return nil
}

Java

试用此示例之前，请按照《Vision 快速入门：使用客户端库》中的 Java 设置说明进行操作。如需了解详情，请参阅 Vision Java API 参考文档。

如需向 Vision 进行身份验证，请设置应用默认凭据。如需了解详情，请参阅为本地开发环境设置身份验证。

/**
 * Performs document text OCR with PDF/TIFF as source files on Google Cloud Storage.
 *
 * @param gcsSourcePath The path to the remote file on Google Cloud Storage to detect document
 *     text on.
 * @param gcsDestinationPath The path to the remote file on Google Cloud Storage to store the
 *     results on.
 * @throws Exception on errors while closing the client.
 */
public static void detectDocumentsGcs(String gcsSourcePath, String gcsDestinationPath)
    throws Exception {

  // 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()) {
    List<AsyncAnnotateFileRequest> requests = new ArrayList<>();

    // Set the GCS source path for the remote file.
    GcsSource gcsSource = GcsSource.newBuilder().setUri(gcsSourcePath).build();

    // Create the configuration with the specified MIME (Multipurpose Internet Mail Extensions)
    // types
    InputConfig inputConfig =
        InputConfig.newBuilder()
            .setMimeType(
                "application/pdf") // Supported MimeTypes: "application/pdf", "image/tiff"
            .setGcsSource(gcsSource)
            .build();

    // Set the GCS destination path for where to save the results.
    GcsDestination gcsDestination =
        GcsDestination.newBuilder().setUri(gcsDestinationPath).build();

    // Create the configuration for the System.output with the batch size.
    // The batch size sets how many pages should be grouped into each json System.output file.
    OutputConfig outputConfig =
        OutputConfig.newBuilder().setBatchSize(2).setGcsDestination(gcsDestination).build();

    // Select the Feature required by the vision API
    Feature feature = Feature.newBuilder().setType(Feature.Type.DOCUMENT_TEXT_DETECTION).build();

    // Build the OCR request
    AsyncAnnotateFileRequest request =
        AsyncAnnotateFileRequest.newBuilder()
            .addFeatures(feature)
            .setInputConfig(inputConfig)
            .setOutputConfig(outputConfig)
            .build();

    requests.add(request);

    // Perform the OCR request
    OperationFuture<AsyncBatchAnnotateFilesResponse, OperationMetadata> response =
        client.asyncBatchAnnotateFilesAsync(requests);

    System.out.println("Waiting for the operation to finish.");

    // Wait for the request to finish. (The result is not used, since the API saves the result to
    // the specified location on GCS.)
    List<AsyncAnnotateFileResponse> result =
        response.get(180, TimeUnit.SECONDS).getResponsesList();

    // Once the request has completed and the System.output has been
    // written to GCS, we can list all the System.output files.
    Storage storage = StorageOptions.getDefaultInstance().getService();

    // Get the destination location from the gcsDestinationPath
    Pattern pattern = Pattern.compile("gs://([^/]+)/(.+)");
    Matcher matcher = pattern.matcher(gcsDestinationPath);

    if (matcher.find()) {
      String bucketName = matcher.group(1);
      String prefix = matcher.group(2);

      // Get the list of objects with the given prefix from the GCS bucket
      Bucket bucket = storage.get(bucketName);
      com.google.api.gax.paging.Page<Blob> pageList = bucket.list(BlobListOption.prefix(prefix));

      Blob firstOutputFile = null;

      // List objects with the given prefix.
      System.out.println("Output files:");
      for (Blob blob : pageList.iterateAll()) {
        System.out.println(blob.getName());

        // Process the first System.output file from GCS.
        // Since we specified batch size = 2, the first response contains
        // the first two pages of the input file.
        if (firstOutputFile == null) {
          firstOutputFile = blob;
        }
      }

      // Get the contents of the file and convert the JSON contents to an AnnotateFileResponse
      // object. If the Blob is small read all its content in one request
      // (Note: the file is a .json file)
      // Storage guide: https://cloud.google.com/storage/docs/downloading-objects
      String jsonContents = new String(firstOutputFile.getContent());
      Builder builder = AnnotateFileResponse.newBuilder();
      JsonFormat.parser().merge(jsonContents, builder);

      // Build the AnnotateFileResponse object
      AnnotateFileResponse annotateFileResponse = builder.build();

      // Parse through the object to get the actual response for the first page of the input file.
      AnnotateImageResponse annotateImageResponse = annotateFileResponse.getResponses(0);

      // Here we print the full text from the first page.
      // The response contains more information:
      // annotation/pages/blocks/paragraphs/words/symbols
      // including confidence score and bounding boxes
      System.out.format("%nText: %s%n", annotateImageResponse.getFullTextAnnotation().getText());
    } else {
      System.out.println("No MATCH");
    }
  }
}

Node.js

试用此示例之前，请按照《Vision 快速入门：使用客户端库》中的 Node.js 设置说明进行操作。如需了解详情，请参阅 Vision Node.js API 参考文档。

如需向 Vision 进行身份验证，请设置应用默认凭据。如需了解详情，请参阅为本地开发环境设置身份验证。


// Imports the Google Cloud client libraries
const vision = require('@google-cloud/vision').v1;

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

/**
 * TODO(developer): Uncomment the following lines before running the sample.
 */
// Bucket where the file resides
// const bucketName = 'my-bucket';
// Path to PDF file within bucket
// const fileName = 'path/to/document.pdf';
// The folder to store the results
// const outputPrefix = 'results'

const gcsSourceUri = `gs://${bucketName}/${fileName}`;
const gcsDestinationUri = `gs://${bucketName}/${outputPrefix}/`;

const inputConfig = {
  // Supported mime_types are: 'application/pdf' and 'image/tiff'
  mimeType: 'application/pdf',
  gcsSource: {
    uri: gcsSourceUri,
  },
};
const outputConfig = {
  gcsDestination: {
    uri: gcsDestinationUri,
  },
};
const features = [{type: 'DOCUMENT_TEXT_DETECTION'}];
const request = {
  requests: [
    {
      inputConfig: inputConfig,
      features: features,
      outputConfig: outputConfig,
    },
  ],
};

const [operation] = await client.asyncBatchAnnotateFiles(request);
const [filesResponse] = await operation.promise();
const destinationUri =
  filesResponse.responses[0].outputConfig.gcsDestination.uri;
console.log('Json saved to: ' + destinationUri);

Python

试用此示例之前，请按照《Vision 快速入门：使用客户端库》中的 Python 设置说明进行操作。如需了解详情，请参阅 Vision Python API 参考文档。

如需向 Vision 进行身份验证，请设置应用默认凭据。如需了解详情，请参阅为本地开发环境设置身份验证。

def async_detect_document(gcs_source_uri, gcs_destination_uri):
    """OCR with PDF/TIFF as source files on GCS"""
    import json
    import re
    from google.cloud import vision
    from google.cloud import storage

    # Supported mime_types are: 'application/pdf' and 'image/tiff'
    mime_type = "application/pdf"

    # How many pages should be grouped into each json output file.
    batch_size = 2

    client = vision.ImageAnnotatorClient()

    feature = vision.Feature(type_=vision.Feature.Type.DOCUMENT_TEXT_DETECTION)

    gcs_source = vision.GcsSource(uri=gcs_source_uri)
    input_config = vision.InputConfig(gcs_source=gcs_source, mime_type=mime_type)

    gcs_destination = vision.GcsDestination(uri=gcs_destination_uri)
    output_config = vision.OutputConfig(
        gcs_destination=gcs_destination, batch_size=batch_size
    )

    async_request = vision.AsyncAnnotateFileRequest(
        features=[feature], input_config=input_config, output_config=output_config
    )

    operation = client.async_batch_annotate_files(requests=[async_request])

    print("Waiting for the operation to finish.")
    operation.result(timeout=420)

    # Once the request has completed and the output has been
    # written to GCS, we can list all the output files.
    storage_client = storage.Client()

    match = re.match(r"gs://([^/]+)/(.+)", gcs_destination_uri)
    bucket_name = match.group(1)
    prefix = match.group(2)

    bucket = storage_client.get_bucket(bucket_name)

    # List objects with the given prefix, filtering out folders.
    blob_list = [
        blob
        for blob in list(bucket.list_blobs(prefix=prefix))
        if not blob.name.endswith("/")
    ]
    print("Output files:")
    for blob in blob_list:
        print(blob.name)

    # Process the first output file from GCS.
    # Since we specified batch_size=2, the first response contains
    # the first two pages of the input file.
    output = blob_list[0]

    json_string = output.download_as_bytes().decode("utf-8")
    response = json.loads(json_string)

    # The actual response for the first page of the input file.
    first_page_response = response["responses"][0]
    annotation = first_page_response["fullTextAnnotation"]

    # Here we print the full text from the first page.
    # The response contains more information:
    # annotation/pages/blocks/paragraphs/words/symbols
    # including confidence scores and bounding boxes
    print("Full text:\n")
    print(annotation["text"])

gcloud

您使用的 gcloud 命令取决于文件类型。

如需执行 PDF 文本检测，请使用 gcloud ml vision detect-text-pdf 命令，如以下示例所示：
```
gcloud ml vision detect-text-pdf gs://my_bucket/input_file  gs://my_bucket/out_put_prefix
```
如需执行 TIFF 文本检测，请使用 gcloud ml vision detect-text-tiff 命令，如以下示例所示：
```
gcloud ml vision detect-text-tiff gs://my_bucket/input_file  gs://my_bucket/out_put_prefix
```

其他语言

C#：请按照客户端库页面上的 C# 设置说明操作，然后访问 .NET 版 Vision 参考文档。

PHP：请按照客户端库页面上的 PHP 设置说明操作，然后访问 PHP 版 Vision 参考文档。

Ruby 版：请按照客户端库页面上的 Ruby 设置说明操作，然后访问 Ruby 版 Vision 参考文档。