离线时对 Cloud Storage 中的一批图片文件添加注释。
代码示例
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。
import com.google.api.gax.longrunning.OperationFuture;
import com.google.api.gax.paging.Page;
import com.google.cloud.storage.Blob;
import com.google.cloud.storage.Bucket;
import com.google.cloud.storage.Storage;
import com.google.cloud.storage.Storage.BlobListOption;
import com.google.cloud.storage.StorageOptions;
import com.google.cloud.vision.v1p4beta1.AnnotateImageRequest;
import com.google.cloud.vision.v1p4beta1.AsyncBatchAnnotateImagesRequest;
import com.google.cloud.vision.v1p4beta1.AsyncBatchAnnotateImagesResponse;
import com.google.cloud.vision.v1p4beta1.BatchAnnotateImagesResponse;
import com.google.cloud.vision.v1p4beta1.BatchAnnotateImagesResponse.Builder;
import com.google.cloud.vision.v1p4beta1.Feature;
import com.google.cloud.vision.v1p4beta1.Feature.Type;
import com.google.cloud.vision.v1p4beta1.GcsDestination;
import com.google.cloud.vision.v1p4beta1.Image;
import com.google.cloud.vision.v1p4beta1.ImageAnnotatorClient;
import com.google.cloud.vision.v1p4beta1.ImageSource;
import com.google.cloud.vision.v1p4beta1.OperationMetadata;
import com.google.cloud.vision.v1p4beta1.OutputConfig;
import com.google.protobuf.util.JsonFormat;
import java.util.ArrayList;
import java.util.List;
import java.util.concurrent.TimeUnit;
import java.util.regex.Matcher;
import java.util.regex.Pattern;
public class AsyncBatchAnnotateImagesGcs {
// Performs asynchronous batch annotation of images on Google Cloud Storage
public static void asyncBatchAnnotateImagesGcs(String gcsSourcePath, String gcsDestinationPath)
throws Exception {
// String gcsSourcePath = "gs://YOUR_BUCKET_ID/path_to_your_data";
// String gcsDestinationPath = "gs://YOUR_BUCKET_ID/path_to_store_annotation";
try (ImageAnnotatorClient client = ImageAnnotatorClient.create()) {
List<AnnotateImageRequest> requests = new ArrayList<>();
ImageSource imgSource = ImageSource.newBuilder().setImageUri(gcsSourcePath).build();
Image image = Image.newBuilder().setSource(imgSource).build();
// Set the GCS destination path for where to save the results.
GcsDestination gcsDestination =
GcsDestination.newBuilder().setUri(gcsDestinationPath).build();
// Create the configuration for the output with the batch size.
// The batch size sets how many pages should be grouped into each json output file.
OutputConfig outputConfig =
OutputConfig.newBuilder().setGcsDestination(gcsDestination).setBatchSize(2).build();
// Select the Features required by the vision API
Feature features =
Feature.newBuilder()
.setType(Type.LABEL_DETECTION)
.setType(Type.TEXT_DETECTION)
.setType(Type.IMAGE_PROPERTIES)
.build();
// Build the request
AnnotateImageRequest annotateImageRequest =
AnnotateImageRequest.newBuilder().setImage(image).addFeatures(features).build();
requests.add(annotateImageRequest);
AsyncBatchAnnotateImagesRequest request =
AsyncBatchAnnotateImagesRequest.newBuilder()
.addAllRequests(requests)
.setOutputConfig(outputConfig)
.build();
OperationFuture<AsyncBatchAnnotateImagesResponse, OperationMetadata> response =
client.asyncBatchAnnotateImagesAsync(request);
System.out.println("Waiting for the operation to finish.");
// we're not processing the response, since we'll be reading the output from GCS.
response.get(180, TimeUnit.SECONDS);
// Once the request has completed and the output has been
// written to GCS, we can list all the 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);
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 output file from GCS.
// Since we specified batch size = 2, the first response contains
// the first two image requests
if (firstOutputFile == null) {
firstOutputFile = blob;
}
}
// Get the contents of the file and convert the JSON contents to an
// BatchAnnotateImagesResponse
// 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 = BatchAnnotateImagesResponse.newBuilder();
JsonFormat.parser().merge(jsonContents, builder);
// Build the AnnotateFileResponse object
BatchAnnotateImagesResponse batchAnnotateImagesResponse = builder.build();
// Here we print the response for the first image
// The response contains more information:
// annotation/pages/blocks/paragraphs/words/symbols/colors
// including confidence score and bounding boxes
System.out.format("\nResponse: %s\n", batchAnnotateImagesResponse.getResponses(0));
} else {
System.out.println("No MATCH");
}
} catch (Exception e) {
System.out.println("Error during asyncBatchAnnotateImagesGcs: \n" + e.toString());
}
}
}
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_batch_annotate_images_uri(input_image_uri, output_uri):
"""Batch annotation of images on Google Cloud Storage asynchronously.
Args:
input_image_uri: The path to the image in Google Cloud Storage (gs://...)
output_uri: The path to the output path in Google Cloud Storage (gs://...)
"""
import re
from google.cloud import storage
from google.cloud import vision_v1p4beta1 as vision
client = vision.ImageAnnotatorClient()
# Construct the request for the image(s) to be annotated:
image_source = vision.ImageSource(image_uri=input_image_uri)
image = vision.Image(source=image_source)
features = [
vision.Feature(type_=vision.Feature.Type.LABEL_DETECTION),
vision.Feature(type_=vision.Feature.Type.TEXT_DETECTION),
vision.Feature(type_=vision.Feature.Type.IMAGE_PROPERTIES),
]
requests = [
vision.AnnotateImageRequest(image=image, features=features),
]
gcs_destination = vision.GcsDestination(uri=output_uri)
output_config = vision.OutputConfig(gcs_destination=gcs_destination, batch_size=2)
operation = client.async_batch_annotate_images(
requests=requests, output_config=output_config
)
print("Waiting for the operation to finish.")
operation.result(timeout=10000)
# Once the request has completed and the output has been
# written to Google Cloud Storage, we can list all the output files.
storage_client = storage.Client()
match = re.match(r"gs://([^/]+)/(.+)", output_uri)
bucket_name = match.group(1)
prefix = match.group(2)
bucket = storage_client.get_bucket(bucket_name)
# Lists objects with the given prefix.
blob_list = list(bucket.list_blobs(prefix=prefix))
print("Output files:")
for blob in blob_list:
print(blob.name)
# Processes the first output file from Google Cloud Storage.
# Since we specified batch_size=2, the first response contains
# annotations for the first two annotate image requests.
output = blob_list[0]
json_string = output.download_as_bytes().decode("utf-8")
response = vision.BatchAnnotateImagesResponse.from_json(json_string)
# Prints the actual response for the first annotate image request.
print(
"The annotation response for the first request: {}".format(
response.responses[0]
)
)
后续步骤
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