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。
分析存放在 Amazon S3 儲存貯體中的映像
Amazon Rekognition Image 可以分析存放在 Amazon S3 儲存貯體中的映像，或做為映像位元組提供的映像。
在本主題中，您可以使用 DetectLabelsAPI 操作來偵測存放在 Amazon S3 儲存貯體的影像 (JPEG 或 PNG) 中的物件、概念和場景。您可以使用映像輸入參數，將映像傳遞至 Amazon Rekognition Image API 操作。在 Image 內，您指定 S3Object 物件屬性以參考存放在 S3 儲存貯體中的映像。存放在 Amazon S3 儲存貯體中的映像位元組，不需要 Base64 編碼。如需詳細資訊，請參閱 映像規格。
範例請求
在此範例中，JSON 要求 DetectLabels，而來源映像 (input.jpg) 是從名為 MyBucket 的 Amazon S3 儲存貯體載入。含有 S3 物件的 S3 儲存貯體區域必須符合您用於 Amazon Rekognition Image 操作的區域。
{
"Image": {
"S3Object": {
"Bucket": "MyBucket",
"Name": "input.jpg"
}
},
"MaxLabels": 10,
"MinConfidence": 75
}
下列範例使用各種 AWS SDK 和呼叫DetectLabels。 AWS CLI 如需有關 DetectLabels 操作回應的資訊，請參閱 DetectLabels 回應。
偵測映像中的標籤
如果您尚未執行：
建立或更新具有 AmazonRekognitionFullAccess 和 AmazonS3ReadOnlyAccess 許可的使用者。如需詳細資訊，請參閱 步驟 1：設定 AWS 帳戶並建立使用者。
安裝和設定 AWS CLI AWS 軟體開發套件。如需詳細資訊，請參閱 步驟 2：設定 AWS CLI 和開 AWS 發套件。請確定您已為呼叫 API 操作的使用者授予程式設計存取的適當權限，請參閱 授與程式設計存取權 以取得如何執行此操作的指示。
-
將包含一個或多個物件的映像 (例如樹、房子和船) 上傳至您的 S3 儲存貯體。映像的格式必須是 .jpg 或 .png 格式。
如需指示說明，請參閱《Amazon Simple Storage Service 使用者指南》中的上傳物件至 Amazon S3。
-
使用下列範例來呼叫 DetectLabels 操作。
- Java
此範例顯示一份在輸入映像中偵測到的標籤清單。將 bucket 與 photo 的數值取代為您在步驟 2 中所使用的 Amazon S3 儲存貯體名稱與映像名稱。
//Copyright 2018 Amazon.com, Inc. or its affiliates. All Rights Reserved.
//PDX-License-Identifier: MIT-0 (For details, see https://github.com/awsdocs/amazon-rekognition-developer-guide/blob/master/LICENSE-SAMPLECODE.)
package com.amazonaws.samples;
import com.amazonaws.services.rekognition.AmazonRekognition;
import com.amazonaws.services.rekognition.AmazonRekognitionClientBuilder;
import com.amazonaws.services.rekognition.model.AmazonRekognitionException;
import com.amazonaws.services.rekognition.model.DetectLabelsRequest;
import com.amazonaws.services.rekognition.model.DetectLabelsResult;
import com.amazonaws.services.rekognition.model.Image;
import com.amazonaws.services.rekognition.model.Label;
import com.amazonaws.services.rekognition.model.S3Object;
import java.util.List;
public class DetectLabels {
public static void main(String[] args) throws Exception {
String photo = "input.jpg";
String bucket = "bucket";
AmazonRekognition rekognitionClient = AmazonRekognitionClientBuilder.defaultClient();
DetectLabelsRequest request = new DetectLabelsRequest()
.withImage(new Image()
.withS3Object(new S3Object()
.withName(photo).withBucket(bucket)))
.withMaxLabels(10)
.withMinConfidence(75F);
try {
DetectLabelsResult result = rekognitionClient.detectLabels(request);
List <Label> labels = result.getLabels();
System.out.println("Detected labels for " + photo);
for (Label label: labels) {
System.out.println(label.getName() + ": " + label.getConfidence().toString());
}
} catch(AmazonRekognitionException e) {
e.printStackTrace();
}
}
}
- AWS CLI
-
此範例顯示 detect-labels CLI 操作的 JSON 輸出。將 bucket 與 photo 的數值取代為您在步驟 2 中所使用的 Amazon S3 儲存貯體名稱與映像名稱。將建立 Rekognition 工作階段的行中 profile_name 值取代為您開發人員設定檔的名稱。
aws rekognition detect-labels --image '{ "S3Object": { "Bucket": "bucket-name", "Name": "file-name" } }' \
--features GENERAL_LABELS IMAGE_PROPERTIES \
--settings '{"ImageProperties": {"MaxDominantColors":1}, {"GeneralLabels":{"LabelInclusionFilters":["Cat"]}}}' \
--profile profile-name \
--region us-east-1
如果您使用的是 Windows，則可能需要逸出引號，如以下範例所示。
aws rekognition detect-labels --image "{\"S3Object\":{\"Bucket\":\"bucket-name\",\"Name\":\"file-name\"}}" --features GENERAL_LABELS IMAGE_PROPERTIES --settings "{\"GeneralLabels\":{\"LabelInclusionFilters\":[\"Car\"]}}" --profile profile-name --region us-east-1
- Java V2
-
此代碼取自 AWS 文檔 SDK 示例 GitHub 存儲庫。請參閱此處的完整範例。
//snippet-start:[rekognition.java2.detect_labels.import]
import software.amazon.awssdk.auth.credentials.ProfileCredentialsProvider;
import software.amazon.awssdk.regions.Region;
import software.amazon.awssdk.services.rekognition.RekognitionClient;
import software.amazon.awssdk.services.rekognition.model.Image;
import software.amazon.awssdk.services.rekognition.model.DetectLabelsRequest;
import software.amazon.awssdk.services.rekognition.model.DetectLabelsResponse;
import software.amazon.awssdk.services.rekognition.model.Label;
import software.amazon.awssdk.services.rekognition.model.RekognitionException;
import software.amazon.awssdk.services.rekognition.model.S3Object;
import java.util.List;
/**
* Before running this Java V2 code example, set up your development environment, including your credentials.
*
* For more information, see the following documentation topic:
*
* https://docs.aws.amazon.com/sdk-for-java/latest/developer-guide/get-started.html
*/
public class DetectLabels {
public static void main(String[] args) {
final String usage = "\n" +
"Usage: " +
" <bucket> <image>\n\n" +
"Where:\n" +
" bucket - The name of the Amazon S3 bucket that contains the image (for example, ,ImageBucket)." +
" image - The name of the image located in the Amazon S3 bucket (for example, Lake.png). \n\n";
if (args.length != 2) {
System.out.println(usage);
System.exit(1);
}
String bucket = args[0];
String image = args[1];
Region region = Region.US_WEST_2;
RekognitionClient rekClient = RekognitionClient.builder()
.region(region)
.credentialsProvider(ProfileCredentialsProvider.create("profile-name"))
.build();
getLabelsfromImage(rekClient, bucket, image);
rekClient.close();
}
// snippet-start:[rekognition.java2.detect_labels_s3.main]
public static void getLabelsfromImage(RekognitionClient rekClient, String bucket, String image) {
try {
S3Object s3Object = S3Object.builder()
.bucket(bucket)
.name(image)
.build() ;
Image myImage = Image.builder()
.s3Object(s3Object)
.build();
DetectLabelsRequest detectLabelsRequest = DetectLabelsRequest.builder()
.image(myImage)
.maxLabels(10)
.build();
DetectLabelsResponse labelsResponse = rekClient.detectLabels(detectLabelsRequest);
List<Label> labels = labelsResponse.labels();
System.out.println("Detected labels for the given photo");
for (Label label: labels) {
System.out.println(label.name() + ": " + label.confidence().toString());
}
} catch (RekognitionException e) {
System.out.println(e.getMessage());
System.exit(1);
}
}
// snippet-end:[rekognition.java2.detect_labels.main]
}
- Python
此範例顯示在輸入映像中偵測到的標籤。將 bucket 與 photo 的數值取代為您在步驟 2 中所使用的 Amazon S3 儲存貯體名稱與映像名稱。將建立 Rekognition 工作階段的行中 profile_name 值取代為您開發人員設定檔的名稱。
#Copyright 2018 Amazon.com, Inc. or its affiliates. All Rights Reserved.
#PDX-License-Identifier: MIT-0 (For details, see https://github.com/awsdocs/amazon-rekognition-developer-guide/blob/master/LICENSE-SAMPLECODE.)
import boto3
def detect_labels(photo, bucket):
session = boto3.Session(profile_name='profile-name')
client = session.client('rekognition')
response = client.detect_labels(Image={'S3Object':{'Bucket':bucket,'Name':photo}},
MaxLabels=10,
# Uncomment to use image properties and filtration settings
#Features=["GENERAL_LABELS", "IMAGE_PROPERTIES"],
#Settings={"GeneralLabels": {"LabelInclusionFilters":["Cat"]},
# "ImageProperties": {"MaxDominantColors":10}}
)
print('Detected labels for ' + photo)
print()
for label in response['Labels']:
print("Label: " + label['Name'])
print("Confidence: " + str(label['Confidence']))
print("Instances:")
for instance in label['Instances']:
print(" Bounding box")
print(" Top: " + str(instance['BoundingBox']['Top']))
print(" Left: " + str(instance['BoundingBox']['Left']))
print(" Width: " + str(instance['BoundingBox']['Width']))
print(" Height: " + str(instance['BoundingBox']['Height']))
print(" Confidence: " + str(instance['Confidence']))
print()
print("Parents:")
for parent in label['Parents']:
print(" " + parent['Name'])
print("Aliases:")
for alias in label['Aliases']:
print(" " + alias['Name'])
print("Categories:")
for category in label['Categories']:
print(" " + category['Name'])
print("----------")
print()
if "ImageProperties" in str(response):
print("Background:")
print(response["ImageProperties"]["Background"])
print()
print("Foreground:")
print(response["ImageProperties"]["Foreground"])
print()
print("Quality:")
print(response["ImageProperties"]["Quality"])
print()
return len(response['Labels'])
def main():
photo = 'photo-name'
bucket = 'bucket-name'
label_count = detect_labels(photo, bucket)
print("Labels detected: " + str(label_count))
if __name__ == "__main__":
main()
- Node.Js
-
此範例顯示與映像中偵測到的名人有關的資訊。
將 photo 的值變更為某個映像檔案的路徑和檔案名稱，而該映像檔案含有一個或多個名人臉孔。將 bucket 的值變更為包含映像檔案的 S3 儲存貯體名稱。將 REGION 的值變更為與您帳戶相關聯的地區名稱。將建立 Rekognition 工作階段的行中 profile_name 值取代為您開發人員設定檔的名稱。
// Import required AWS SDK clients and commands for Node.js
import { DetectLabelsCommand } from "@aws-sdk/client-rekognition";
import { RekognitionClient } from "@aws-sdk/client-rekognition";
import {fromIni} from '@aws-sdk/credential-providers';
// Set the AWS Region.
const REGION = "region-name"; //e.g. "us-east-1"
// Create SNS service object.
const rekogClient = new RekognitionClient({
region: REGION,
credentials: fromIni({
profile: 'profile-name',
}),
});
const bucket = 'bucket-name'
const photo = 'photo-name'
// Set params
const params = {For example, to grant
Image: {
S3Object: {
Bucket: bucket,
Name: photo
},
},
}
const detect_labels = async () => {
try {
const response = await rekogClient.send(new DetectLabelsCommand(params));
console.log(response.Labels)
response.Labels.forEach(label =>{
console.log(`Confidence: ${label.Confidence}`)
console.log(`Name: ${label.Name}`)
console.log('Instances:')
label.Instances.forEach(instance => {
console.log(instance)
})
console.log('Parents:')
label.Parents.forEach(name => {
console.log(name)
})
console.log("-------")
})
return response; // For unit tests.
} catch (err) {
console.log("Error", err);
}
};
detect_labels();
- .NET
此範例顯示一份在輸入映像中偵測到的標籤清單。將 bucket 與 photo 的數值取代為您在步驟 2 中所使用的 Amazon S3 儲存貯體名稱與映像名稱。
//Copyright 2018 Amazon.com, Inc. or its affiliates. All Rights Reserved.
//PDX-License-Identifier: MIT-0 (For details, see https://github.com/awsdocs/amazon-rekognition-developer-guide/blob/master/LICENSE-SAMPLECODE.)
using System;
using Amazon.Rekognition;
using Amazon.Rekognition.Model;
public class DetectLabels
{
public static void Example()
{
String photo = "input.jpg";
String bucket = "bucket";
AmazonRekognitionClient rekognitionClient = new AmazonRekognitionClient();
DetectLabelsRequest detectlabelsRequest = new DetectLabelsRequest()
{
Image = new Image()
{
S3Object = new S3Object()
{
Name = photo,
Bucket = bucket
},
},
MaxLabels = 10,
MinConfidence = 75F
};
try
{
DetectLabelsResponse detectLabelsResponse = rekognitionClient.DetectLabels(detectlabelsRequest);
Console.WriteLine("Detected labels for " + photo);
foreach (Label label in detectLabelsResponse.Labels)
Console.WriteLine("{0}: {1}", label.Name, label.Confidence);
}
catch (Exception e)
{
Console.WriteLine(e.Message);
}
}
}
- Ruby
此範例顯示一份在輸入映像中偵測到的標籤清單。將 bucket 與 photo 的數值取代為您在步驟 2 中所使用的 Amazon S3 儲存貯體名稱與映像名稱。
# Add to your Gemfile
# gem 'aws-sdk-rekognition'
require 'aws-sdk-rekognition'
credentials = Aws::Credentials.new(
ENV['AWS_ACCESS_KEY_ID'],
ENV['AWS_SECRET_ACCESS_KEY']
)
bucket = 'bucket' # the bucket name without s3://
photo = 'photo' # the name of file
client = Aws::Rekognition::Client.new credentials: credentials
attrs = {
image: {
s3_object: {
bucket: bucket,
name: photo
},
},
max_labels: 10
}
response = client.detect_labels attrs
puts "Detected labels for: #{photo}"
response.labels.each do |label|
puts "Label: #{label.name}"
puts "Confidence: #{label.confidence}"
puts "Instances:"
label['instances'].each do |instance|
box = instance['bounding_box']
puts " Bounding box:"
puts " Top: #{box.top}"
puts " Left: #{box.left}"
puts " Width: #{box.width}"
puts " Height: #{box.height}"
puts " Confidence: #{instance.confidence}"
end
puts "Parents:"
label.parents.each do |parent|
puts " #{parent.name}"
end
puts "------------"
puts ""
end
回應範例
DetectLabels 的回應是映像中偵測到的一系列標籤，以及偵測所依據的可信度層級。
當您對映像執行 DetectLabels 作業時，Amazon Rekognition 會傳回類似下列範例回應的輸出。
回應顯示操作偵測到多個標籤，包括人員、車輛和汽車。每個標籤都有一個相關的可信度等級。例如，偵測演算法對於映像中包含人員的可信度為 98.991432%。
回應也包含 Parents 陣列中標籤的上階標籤。例如 Automobile (汽車) 標籤有兩個名為 Vehicle (車輛) 和 Transportation (運輸) 的 父標籤。
常見物件標籤的回應包含輸入映像上標籤位置的週框方塊資訊。例如，人員標籤具有一個實例陣列，其中包含兩個週框方塊。這些是在映像中偵測到的兩個人員位置。
欄位 LabelModelVersion 包含 DetectLabels 所使用之偵測模型的版本編號。
如需使用此 DetectLabels 操作的詳細資訊，請參閱 偵測物件和概念。
{
{
"Labels": [
{
"Name": "Vehicle",
"Confidence": 99.15271759033203,
"Instances": [],
"Parents": [
{
"Name": "Transportation"
}
]
},
{
"Name": "Transportation",
"Confidence": 99.15271759033203,
"Instances": [],
"Parents": []
},
{
"Name": "Automobile",
"Confidence": 99.15271759033203,
"Instances": [],
"Parents": [
{
"Name": "Vehicle"
},
{
"Name": "Transportation"
}
]
},
{
"Name": "Car",
"Confidence": 99.15271759033203,
"Instances": [
{
"BoundingBox": {
"Width": 0.10616336017847061,
"Height": 0.18528179824352264,
"Left": 0.0037978808395564556,
"Top": 0.5039216876029968
},
"Confidence": 99.15271759033203
},
{
"BoundingBox": {
"Width": 0.2429988533258438,
"Height": 0.21577216684818268,
"Left": 0.7309805154800415,
"Top": 0.5251884460449219
},
"Confidence": 99.1286392211914
},
{
"BoundingBox": {
"Width": 0.14233611524105072,
"Height": 0.15528248250484467,
"Left": 0.6494812965393066,
"Top": 0.5333095788955688
},
"Confidence": 98.48368072509766
},
{
"BoundingBox": {
"Width": 0.11086395382881165,
"Height": 0.10271988064050674,
"Left": 0.10355594009160995,
"Top": 0.5354844927787781
},
"Confidence": 96.45606231689453
},
{
"BoundingBox": {
"Width": 0.06254628300666809,
"Height": 0.053911514580249786,
"Left": 0.46083059906959534,
"Top": 0.5573825240135193
},
"Confidence": 93.65448760986328
},
{
"BoundingBox": {
"Width": 0.10105438530445099,
"Height": 0.12226245552301407,
"Left": 0.5743985772132874,
"Top": 0.534368634223938
},
"Confidence": 93.06217193603516
},
{
"BoundingBox": {
"Width": 0.056389667093753815,
"Height": 0.17163699865341187,
"Left": 0.9427769780158997,
"Top": 0.5235804319381714
},
"Confidence": 92.6864013671875
},
{
"BoundingBox": {
"Width": 0.06003860384225845,
"Height": 0.06737709045410156,
"Left": 0.22409997880458832,
"Top": 0.5441341400146484
},
"Confidence": 90.4227066040039
},
{
"BoundingBox": {
"Width": 0.02848697081208229,
"Height": 0.19150497019290924,
"Left": 0.0,
"Top": 0.5107086896896362
},
"Confidence": 86.65286254882812
},
{
"BoundingBox": {
"Width": 0.04067881405353546,
"Height": 0.03428703173995018,
"Left": 0.316415935754776,
"Top": 0.5566273927688599
},
"Confidence": 85.36471557617188
},
{
"BoundingBox": {
"Width": 0.043411049991846085,
"Height": 0.0893595889210701,
"Left": 0.18293385207653046,
"Top": 0.5394920110702515
},
"Confidence": 82.21705627441406
},
{
"BoundingBox": {
"Width": 0.031183116137981415,
"Height": 0.03989990055561066,
"Left": 0.2853088080883026,
"Top": 0.5579366683959961
},
"Confidence": 81.0157470703125
},
{
"BoundingBox": {
"Width": 0.031113790348172188,
"Height": 0.056484755128622055,
"Left": 0.2580395042896271,
"Top": 0.5504819750785828
},
"Confidence": 56.13441467285156
},
{
"BoundingBox": {
"Width": 0.08586374670267105,
"Height": 0.08550430089235306,
"Left": 0.5128012895584106,
"Top": 0.5438792705535889
},
"Confidence": 52.37760925292969
}
],
"Parents": [
{
"Name": "Vehicle"
},
{
"Name": "Transportation"
}
]
},
{
"Name": "Human",
"Confidence": 98.9914321899414,
"Instances": [],
"Parents": []
},
{
"Name": "Person",
"Confidence": 98.9914321899414,
"Instances": [
{
"BoundingBox": {
"Width": 0.19360728561878204,
"Height": 0.2742200493812561,
"Left": 0.43734854459762573,
"Top": 0.35072067379951477
},
"Confidence": 98.9914321899414
},
{
"BoundingBox": {
"Width": 0.03801717236638069,
"Height": 0.06597328186035156,
"Left": 0.9155802130699158,
"Top": 0.5010883808135986
},
"Confidence": 85.02790832519531
}
],
"Parents": []
}
],
"LabelModelVersion": "2.0"
}
}
