本文為英文版的機器翻譯版本，如內容有任何歧義或不一致之處，概以英文版為準。
將更多影像新增至資料集
您可以使用 Amazon Rekognition 自訂標籤主控台或呼叫UpdateDatasetEntries API，將更多影像新增至資料集。
添加更多圖像（控制台）
當您使用 Amazon Rekognition 自訂標籤主控台時，您可以從本機電腦上傳影像。映像檔會新增至 Amazon S3 儲存貯體位置 (主控台或外部)，用於建立資料集的映像存放在該位置。
若要將更多影像新增至資料集 (主控台)
開啟亞馬遜重新認知主控台，網址為 https://console.aws.amazon.com/rekognition/。
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在左窗格中選擇使用自訂標籤。顯示 Amazon Rekognition 自訂標籤登陸頁面。
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在左側導覽窗格中選擇 Projects (專案)。將顯示「專案」檢視。
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選擇您要使用的專案。
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在左側導覽窗格的專案名稱下，選擇 Databet (資料集)。
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選擇 Actions (動作)，然後選取要新增影像的資料集。
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選擇您要上傳至資料集的影像。您可以拖動圖像或選擇要從本地計算機上傳的圖像。您一次最多可以上傳 30 張圖片。
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選擇 [上傳圖片]。
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選擇 Save changes (儲存變更)。
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標記影像。如需詳細資訊，請參閱標記檔案。
新增更多影像 (SDK)
UpdateDatasetEntries更新或將 JSON 行添加到清單文件中。您將 JSON 行作為GroundTruth字段中的字節 64 編碼數據對象傳遞。如果您使用AWS SDK 來呼叫UpdateDatasetEntries，SDK 會為您編碼資料。每個 JSON 行都包含單一影像的資訊，例如指派的標籤或邊界方框資訊。例如：
{"source-ref":"s3://bucket/image","BB":{"annotations":[{"left":1849,"top":1039,"width":422,"height":283,"class_id":0},{"left":1849,"top":1340,"width":443,"height":415,"class_id":1},{"left":2637,"top":1380,"width":676,"height":338,"class_id":2},{"left":2634,"top":1051,"width":673,"height":338,"class_id":3}],"image_size":[{"width":4000,"height":2667,"depth":3}]},"BB-metadata":{"job-name":"labeling-job/BB","class-map":{"0":"comparator","1":"pot_resistor","2":"ir_phototransistor","3":"ir_led"},"human-annotated":"yes","objects":[{"confidence":1},{"confidence":1},{"confidence":1},{"confidence":1}],"creation-date":"2021-06-22T10:11:18.006Z","type":"groundtruth/object-detection"}}
如需詳細資訊，請參閱建立清單檔案。
使用source-ref欄位作為關鍵字，以識別您要更新的影像。如果資料集不包含相符的source-ref欄位值，則會將 JSON 行新增為新影像。
若要將更多影像新增至資料集 (SDK)
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若您尚未這樣做，請安裝AWS CLI並設定和AWS SDK。如需詳細資訊，請參閱步驟 4：設定 AWS CLI 和開 AWS 發套件。
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使用下列範例將 JSON 行新增至資料集。
- CLI
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以您要使GroundTruth用的 JSON 行取代的值。您需要轉義 JSON 行中的任何特殊字符。
aws rekognition update-dataset-entries\
--dataset-arn dataset_arn \
--changes '{"GroundTruth" : "{\"source-ref\":\"s3://your_bucket/your_image\",\"BB\":{\"annotations\":[{\"left\":1776,\"top\":1017,\"width\":458,\"height\":317,\"class_id\":0},{\"left\":1797,\"top\":1334,\"width\":418,\"height\":415,\"class_id\":1},{\"left\":2597,\"top\":1361,\"width\":655,\"height\":329,\"class_id\":2},{\"left\":2581,\"top\":1020,\"width\":689,\"height\":338,\"class_id\":3}],\"image_size\":[{\"width\":4000,\"height\":2667,\"depth\":3}]},\"BB-metadata\":{\"job-name\":\"labeling-job/BB\",\"class-map\":{\"0\":\"comparator\",\"1\":\"pot_resistor\",\"2\":\"ir_phototransistor\",\"3\":\"ir_led\"},\"human-annotated\":\"yes\",\"objects\":[{\"confidence\":1},{\"confidence\":1},{\"confidence\":1},{\"confidence\":1}],\"creation-date\":\"2021-06-22T10:10:48.492Z\",\"type\":\"groundtruth/object-detection\"}}" }' \
--cli-binary-format raw-in-base64-out \
--profile custom-labels-access
- Python
-
使用下列程式碼。提供以下命令行參數：
資料集
更新文件
# Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved.
# SPDX-License-Identifier: Apache-2.0
"""
Purpose
Shows how to add entries to an Amazon Rekognition Custom Labels dataset.
"""
import argparse
import logging
import time
import json
import boto3
from botocore.exceptions import ClientError
logger = logging.getLogger(__name__)
def update_dataset_entries(rek_client, dataset_arn, updates_file):
"""
Adds dataset entries to an Amazon Rekognition Custom Labels dataset.
:param rek_client: The Amazon Rekognition Custom Labels Boto3 client.
:param dataset_arn: The ARN of the dataset that yuo want to update.
:param updates_file: The manifest file of JSON Lines that contains the updates.
"""
try:
status=""
status_message=""
# Update dataset entries.
logger.info("Updating dataset %s", dataset_arn)
with open(updates_file) as f:
manifest_file = f.read()
changes=json.loads('{ "GroundTruth" : ' +
json.dumps(manifest_file) +
'}')
rek_client.update_dataset_entries(
Changes=changes, DatasetArn=dataset_arn
)
finished=False
while finished is False:
dataset=rek_client.describe_dataset(DatasetArn=dataset_arn)
status=dataset['DatasetDescription']['Status']
status_message=dataset['DatasetDescription']['StatusMessage']
if status == "UPDATE_IN_PROGRESS":
logger.info("Updating dataset: %s ", dataset_arn)
time.sleep(5)
continue
if status == "UPDATE_COMPLETE":
logger.info("Dataset updated: %s : %s : %s",
status, status_message, dataset_arn)
finished=True
continue
if status == "UPDATE_FAILED":
error_message = f"Dataset update failed: {status} : {status_message} : {dataset_arn}"
logger.exception(error_message)
raise Exception (error_message)
error_message = f"Failed. Unexpected state for dataset update: {status} : {status_message} : {dataset_arn}"
logger.exception(error_message)
raise Exception(error_message)
logger.info("Added entries to dataset")
return status, status_message
except ClientError as err:
logger.exception("Couldn't update dataset: %s", err.response['Error']['Message'])
raise
def add_arguments(parser):
"""
Adds command line arguments to the parser.
:param parser: The command line parser.
"""
parser.add_argument(
"dataset_arn", help="The ARN of the dataset that you want to update."
)
parser.add_argument(
"updates_file", help="The manifest file of JSON Lines that contains the updates."
)
def main():
logging.basicConfig(level=logging.INFO, format="%(levelname)s: %(message)s")
try:
#get command line arguments
parser = argparse.ArgumentParser(usage=argparse.SUPPRESS)
add_arguments(parser)
args = parser.parse_args()
print(f"Updating dataset {args.dataset_arn} with entries from {args.updates_file}.")
# Update the dataset.
session = boto3.Session(profile_name='custom-labels-access')
rekognition_client = session.client("rekognition")
status, status_message=update_dataset_entries(rekognition_client,
args.dataset_arn,
args.updates_file)
print(f"Finished updates dataset: {status} : {status_message}")
except ClientError as err:
logger.exception("Problem updating dataset: %s", err)
print(f"Problem updating dataset: {err}")
except Exception as err:
logger.exception("Problem updating dataset: %s", err)
print(f"Problem updating dataset: {err}")
if __name__ == "__main__":
main()
- Java V2
-
資料集
更新文件
/*
Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved.
SPDX-License-Identifier: Apache-2.0
*/
package com.example.rekognition;
import software.amazon.awssdk.auth.credentials.ProfileCredentialsProvider;
import software.amazon.awssdk.core.SdkBytes;
import software.amazon.awssdk.regions.Region;
import software.amazon.awssdk.services.rekognition.RekognitionClient;
import software.amazon.awssdk.services.rekognition.model.DatasetChanges;
import software.amazon.awssdk.services.rekognition.model.DatasetDescription;
import software.amazon.awssdk.services.rekognition.model.DatasetStatus;
import software.amazon.awssdk.services.rekognition.model.DescribeDatasetRequest;
import software.amazon.awssdk.services.rekognition.model.DescribeDatasetResponse;
import software.amazon.awssdk.services.rekognition.model.RekognitionException;
import software.amazon.awssdk.services.rekognition.model.UpdateDatasetEntriesRequest;
import software.amazon.awssdk.services.rekognition.model.UpdateDatasetEntriesResponse;
import java.io.FileInputStream;
import java.io.InputStream;
import java.util.logging.Level;
import java.util.logging.Logger;
public class UpdateDatasetEntries {
public static final Logger logger = Logger.getLogger(UpdateDatasetEntries.class.getName());
public static String updateMyDataset(RekognitionClient rekClient, String datasetArn,
String updateFile
) throws Exception, RekognitionException {
try {
logger.log(Level.INFO, "Updating dataset {0}",
new Object[] { datasetArn});
InputStream sourceStream = new FileInputStream(updateFile);
SdkBytes sourceBytes = SdkBytes.fromInputStream(sourceStream);
DatasetChanges datasetChanges = DatasetChanges.builder()
.groundTruth(sourceBytes).build();
UpdateDatasetEntriesRequest updateDatasetEntriesRequest = UpdateDatasetEntriesRequest.builder()
.changes(datasetChanges)
.datasetArn(datasetArn)
.build();
UpdateDatasetEntriesResponse response = rekClient.updateDatasetEntries(updateDatasetEntriesRequest);
boolean updated = false;
//Wait until update completes
do {
DescribeDatasetRequest describeDatasetRequest = DescribeDatasetRequest.builder()
.datasetArn(datasetArn).build();
DescribeDatasetResponse describeDatasetResponse = rekClient.describeDataset(describeDatasetRequest);
DatasetDescription datasetDescription = describeDatasetResponse.datasetDescription();
DatasetStatus status = datasetDescription.status();
logger.log(Level.INFO, " dataset ARN: {0} ", datasetArn);
switch (status) {
case UPDATE_COMPLETE:
logger.log(Level.INFO, "Dataset updated");
updated = true;
break;
case UPDATE_IN_PROGRESS:
Thread.sleep(5000);
break;
case UPDATE_FAILED:
String error = "Dataset update failed: " + datasetDescription.statusAsString() + " "
+ datasetDescription.statusMessage() + " " + datasetArn;
logger.log(Level.SEVERE, error);
throw new Exception(error);
default:
String unexpectedError = "Unexpected update state: " + datasetDescription.statusAsString() + " "
+ datasetDescription.statusMessage() + " " + datasetArn;
logger.log(Level.SEVERE, unexpectedError);
throw new Exception(unexpectedError);
}
} while (updated == false);
return datasetArn;
} catch (RekognitionException e) {
logger.log(Level.SEVERE, "Could not update dataset: {0}", e.getMessage());
throw e;
}
}
public static void main(String args[]) {
String updatesFile = null;
String datasetArn = null;
final String USAGE = "\n" + "Usage: " + "<project_arn> <dataset_arn> <updates_file>\n\n" + "Where:\n"
+ " dataset_arn - the ARN of the dataset that you want to update.\n\n"
+ " update_file - The file that includes in JSON Line updates.\n\n";
if (args.length != 2) {
System.out.println(USAGE);
System.exit(1);
}
datasetArn = args[0];
updatesFile = args[1];
try {
// Get the Rekognition client.
RekognitionClient rekClient = RekognitionClient.builder()
.credentialsProvider(ProfileCredentialsProvider.create("custom-labels-access"))
.region(Region.US_WEST_2)
.build();
// Update the dataset
datasetArn = updateMyDataset(rekClient, datasetArn, updatesFile);
System.out.println(String.format("Dataset updated: %s", datasetArn));
rekClient.close();
} catch (RekognitionException rekError) {
logger.log(Level.SEVERE, "Rekognition client error: {0}", rekError.getMessage());
System.exit(1);
} catch (Exception rekError) {
logger.log(Level.SEVERE, "Error: {0}", rekError.getMessage());
System.exit(1);
}
}
}
