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# Menampilkan kotak pembatas
Operasi Amazon Rekognition Image dapat mengembalikan koordinat kotak pembatas untuk item yang terdeteksi dalam citra. Misalnya, operasi [DetectFaces](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_DetectFaces.html) mengembalikan kotak pembatas ([BoundingBox](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_BoundingBox.html)) untuk setiap wajah yang terdeteksi dalam citra. Anda dapat menggunakan koordinat kotak pembatas untuk menampilkan kotak di sekitar item yang terdeteksi. Misalnya, citra berikut menunjukkan kotak pembatas yang mengelilingi wajah.
`BoundingBox` memiliki properti berikut:
+ Tinggi – Tinggi kotak pembatas sebagai rasio tinggi citra secara keseluruhan.
+ Kiri – Koordinat kiri kotak pembatas sebagai rasio lebar citra secara keseluruhan.
+ Atas – Koordinat atas kotak pembatas sebagai rasio tinggi citra secara keseluruhan.
+ Lebar – Lebar kotak pembatas sebagai rasio lebar citra secara keseluruhan.
Setiap BoundingBox properti memiliki nilai antara 0 dan 1. Setiap nilai properti adalah rasio lebar citra keseluruhan (`Left` dan `Width`) atau tinggi (`Height` dan `Top`). Misalnya, jika gambar input berukuran 700 x 200 piksel, dan koordinat kiri atas kotak pembatas adalah 350 x 50 piksel, API mengembalikan `Left` nilai 0,5 (350/700) dan `Top` nilai 0,25 (). 50/200
Diagram berikut menunjukkan rentang citra yang dikover oleh tiap properti kotak pembatas.
Untuk menampilkan kotak pembatas dengan lokasi dan ukuran yang benar, Anda harus mengalikan BoundingBox nilai dengan lebar atau tinggi gambar (tergantung pada nilai yang Anda inginkan) untuk mendapatkan nilai piksel. Anda menggunakan nilai-nilai piksel untuk menampilkan kotak pembatas. Misalnya, dimensi piksel dari citra sebelumnya adalah lebar 608 x tinggi 588. Nilai kotak pembatas untuk wajah adalah:
```
BoundingBox.Left: 0.3922065
BoundingBox.Top: 0.15567766
BoundingBox.Width: 0.284666
BoundingBox.Height: 0.2930403
```
Lokasi kotak pembatas wajah dalam hitungan piksel adalah sebagai berikut:
`Left coordinate = BoundingBox.Left (0.3922065) * image width (608) = 238`
`Top coordinate = BoundingBox.Top (0.15567766) * image height (588) = 91`
`Face width = BoundingBox.Width (0.284666) * image width (608) = 173`
`Face height = BoundingBox.Height (0.2930403) * image height (588) = 172`
Anda menggunakan nilai-nilai ini untuk menampilkan kotak pembatas di sekitar wajah.
**catatan**
Sebuah citra dapat diorientasikan dalam berbagai cara. Aplikasi Anda mungkin perlu memutar citra untuk menampilkannya dengan orientasi koreksi. Koordinat kotak pembatas dipengaruhi oleh orientasi citra. Anda mungkin perlu menerjemahkan koordinat sebelum Anda dapat menampilkan kotak pembatas di lokasi yang tepat. Untuk informasi selengkapnya, lihat [Mendapatkan orientasi citra dan koordinat kotak pembatas](images-orientation.md).
Contoh berikut menunjukkan cara menampilkan kotak pembatas di sekitar wajah yang terdeteksi dengan memanggil [DetectFaces](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_DetectFaces.html). Contoh tersebut menunjukkan citra yang berorientasi pada 0 derajat. Contoh tersebut juga menunjukkan cara mengunduh citra dari bucket Amazon S3.
**Untuk menampilkan kotak pembatas**
1. Jika belum:
1. Buat atau perbarui pengguna dengan `AmazonRekognitionFullAccess` dan `AmazonS3ReadOnlyAccess` izin. Untuk informasi selengkapnya, lihat [Langkah 1: Siapkan akun AWS dan buat Pengguna](setting-up.md#setting-up-iam).
1. Instal dan konfigurasikan AWS CLI dan AWS SDK. Untuk informasi selengkapnya, lihat [Langkah 2: Siapkan AWS CLI and AWS SDK](setup-awscli-sdk.md).
1. Gunakan contoh berikut untuk memanggil operasi `DetectFaces`.
------
#### [ Java ]
Ubah nilai `bucket` untuk bucket Amazon S3 yang berisi file citra. Ubah nilai `photo` ke nama file dari file citra (format .jpg atau .png).
```
//Loads images, detects faces and draws bounding boxes.Determines exif orientation, if necessary.
package com.amazonaws.samples;
//Import the basic graphics classes.
import java.awt.*;
import java.awt.image.BufferedImage;
import java.util.List;
import javax.imageio.ImageIO;
import javax.swing.*;
import com.amazonaws.services.rekognition.AmazonRekognition;
import com.amazonaws.services.rekognition.AmazonRekognitionClientBuilder;
import com.amazonaws.services.rekognition.model.BoundingBox;
import com.amazonaws.services.rekognition.model.DetectFacesRequest;
import com.amazonaws.services.rekognition.model.DetectFacesResult;
import com.amazonaws.services.rekognition.model.FaceDetail;
import com.amazonaws.services.rekognition.model.Image;
import com.amazonaws.services.rekognition.model.S3Object;
import com.amazonaws.services.s3.AmazonS3;
import com.amazonaws.services.s3.AmazonS3ClientBuilder;
import com.amazonaws.services.s3.model.S3ObjectInputStream;
// Calls DetectFaces and displays a bounding box around each detected image.
public class DisplayFaces extends JPanel {
private static final long serialVersionUID = 1L;
BufferedImage image;
static int scale;
DetectFacesResult result;
public DisplayFaces(DetectFacesResult facesResult, BufferedImage bufImage) throws Exception {
super();
scale = 1; // increase to shrink image size.
result = facesResult;
image = bufImage;
}
// Draws the bounding box around the detected faces.
public void paintComponent(Graphics g) {
float left = 0;
float top = 0;
int height = image.getHeight(this);
int width = image.getWidth(this);
Graphics2D g2d = (Graphics2D) g; // Create a Java2D version of g.
// Draw the image.
g2d.drawImage(image, 0, 0, width / scale, height / scale, this);
g2d.setColor(new Color(0, 212, 0));
// Iterate through faces and display bounding boxes.
List faceDetails = result.getFaceDetails();
for (FaceDetail face : faceDetails) {
BoundingBox box = face.getBoundingBox();
left = width * box.getLeft();
top = height * box.getTop();
g2d.drawRect(Math.round(left / scale), Math.round(top / scale),
Math.round((width * box.getWidth()) / scale), Math.round((height * box.getHeight())) / scale);
}
}
public static void main(String arg[]) throws Exception {
String photo = "photo.png";
String bucket = "bucket";
int height = 0;
int width = 0;
// Get the image from an S3 Bucket
AmazonS3 s3client = AmazonS3ClientBuilder.defaultClient();
com.amazonaws.services.s3.model.S3Object s3object = s3client.getObject(bucket, photo);
S3ObjectInputStream inputStream = s3object.getObjectContent();
BufferedImage image = ImageIO.read(inputStream);
DetectFacesRequest request = new DetectFacesRequest()
.withImage(new Image().withS3Object(new S3Object().withName(photo).withBucket(bucket)));
width = image.getWidth();
height = image.getHeight();
// Call DetectFaces
AmazonRekognition amazonRekognition = AmazonRekognitionClientBuilder.defaultClient();
DetectFacesResult result = amazonRekognition.detectFaces(request);
//Show the bounding box info for each face.
List faceDetails = result.getFaceDetails();
for (FaceDetail face : faceDetails) {
BoundingBox box = face.getBoundingBox();
float left = width * box.getLeft();
float top = height * box.getTop();
System.out.println("Face:");
System.out.println("Left: " + String.valueOf((int) left));
System.out.println("Top: " + String.valueOf((int) top));
System.out.println("Face Width: " + String.valueOf((int) (width * box.getWidth())));
System.out.println("Face Height: " + String.valueOf((int) (height * box.getHeight())));
System.out.println();
}
// Create frame and panel.
JFrame frame = new JFrame("RotateImage");
frame.setDefaultCloseOperation(JFrame.EXIT_ON_CLOSE);
DisplayFaces panel = new DisplayFaces(result, image);
panel.setPreferredSize(new Dimension(image.getWidth() / scale, image.getHeight() / scale));
frame.setContentPane(panel);
frame.pack();
frame.setVisible(true);
}
}
```
------
#### [ Python ]
Ubah nilai `bucket` untuk bucket Amazon S3 yang berisi file citra. Ubah nilai `photo` ke nama file dari file citra (format .jpg atau .png). Ganti nilai `profile_name` di baris yang membuat sesi Rekognition dengan nama profil pengembang Anda.
```
import boto3
import io
from PIL import Image, ImageDraw
def show_faces(photo, bucket):
session = boto3.Session(profile_name='profile-name')
client = session.client('rekognition')
# Load image from S3 bucket
s3_connection = boto3.resource('s3')
s3_object = s3_connection.Object(bucket, photo)
s3_response = s3_object.get()
stream = io.BytesIO(s3_response['Body'].read())
image = Image.open(stream)
# Call DetectFaces
response = client.detect_faces(Image={'S3Object': {'Bucket': bucket, 'Name': photo}},
Attributes=['ALL'])
imgWidth, imgHeight = image.size
draw = ImageDraw.Draw(image)
# calculate and display bounding boxes for each detected face
print('Detected faces for ' + photo)
for faceDetail in response['FaceDetails']:
print('The detected face is between ' + str(faceDetail['AgeRange']['Low'])
+ ' and ' + str(faceDetail['AgeRange']['High']) + ' years old')
box = faceDetail['BoundingBox']
left = imgWidth * box['Left']
top = imgHeight * box['Top']
width = imgWidth * box['Width']
height = imgHeight * box['Height']
print('Left: ' + '{0:.0f}'.format(left))
print('Top: ' + '{0:.0f}'.format(top))
print('Face Width: ' + "{0:.0f}".format(width))
print('Face Height: ' + "{0:.0f}".format(height))
points = (
(left, top),
(left + width, top),
(left + width, top + height),
(left, top + height),
(left, top)
)
draw.line(points, fill='#00d400', width=2)
# Alternatively can draw rectangle. However you can't set line width.
# draw.rectangle([left,top, left + width, top + height], outline='#00d400')
image.show()
return len(response['FaceDetails'])
def main():
bucket = "bucket-name"
photo = "photo-name"
faces_count = show_faces(photo, bucket)
print("faces detected: " + str(faces_count))
if __name__ == "__main__":
main()
```
------
#### [ Java V2 ]
Kode ini diambil dari GitHub repositori contoh SDK AWS Dokumentasi. Lihat contoh lengkapnya [di sini](https://github.com/awsdocs/aws-doc-sdk-examples/blob/master/javav2/example_code/rekognition/src/main/java/com/example/rekognition/DisplayFacesFrame.java).
Perhatikan bahwa `s3` mengacu pada klien Amazon S3 dari AWS SDK Amazon dan `rekClient` mengacu pada klien AWS SDK Amazon Rekognition.
```
//snippet-start:[rekognition.java2.detect_labels.import]
import java.awt.*;
import java.awt.image.BufferedImage;
import java.io.ByteArrayInputStream;
import java.io.FileNotFoundException;
import java.io.IOException;
import java.io.InputStream;
import java.util.List;
import javax.imageio.ImageIO;
import javax.swing.*;
import software.amazon.awssdk.auth.credentials.ProfileCredentialsProvider;
import software.amazon.awssdk.core.ResponseBytes;
import software.amazon.awssdk.core.SdkBytes;
import software.amazon.awssdk.regions.Region;
import software.amazon.awssdk.services.rekognition.model.Attribute;
import software.amazon.awssdk.services.rekognition.model.BoundingBox;
import software.amazon.awssdk.services.rekognition.model.DetectFacesRequest;
import software.amazon.awssdk.services.rekognition.model.DetectFacesResponse;
import software.amazon.awssdk.services.rekognition.model.FaceDetail;
import software.amazon.awssdk.services.rekognition.model.Image;
import software.amazon.awssdk.services.rekognition.model.RekognitionException;
import software.amazon.awssdk.services.s3.S3Client;
import software.amazon.awssdk.services.rekognition.RekognitionClient;
import software.amazon.awssdk.services.s3.model.GetObjectRequest;
import software.amazon.awssdk.services.s3.model.GetObjectResponse;
import software.amazon.awssdk.services.s3.model.S3Exception;
//snippet-end:[rekognition.java2.detect_labels.import]
/**
* 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 DisplayFaces extends JPanel {
static DetectFacesResponse result;
static BufferedImage image;
static int scale;
public static void main(String[] args) throws Exception {
final String usage = "\n" +
"Usage: " +
" \n\n" +
"Where:\n" +
" sourceImage - The name of the image in an Amazon S3 bucket (for example, people.png). \n\n" +
" bucketName - The name of the Amazon S3 bucket (for example, amzn-s3-demo-bucket). \n\n";
if (args.length != 2) {
System.out.println(usage);
System.exit(1);
}
String sourceImage = args[0];
String bucketName = args[1];
Region region = Region.US_EAST_1;
S3Client s3 = S3Client.builder()
.region(region)
.credentialsProvider(ProfileCredentialsProvider.create("profile-name"))
.build();
RekognitionClient rekClient = RekognitionClient.builder()
.region(region)
.credentialsProvider(ProfileCredentialsProvider.create("profile-name"))
.build();
displayAllFaces(s3, rekClient, sourceImage, bucketName);
s3.close();
rekClient.close();
}
// snippet-start:[rekognition.java2.display_faces.main]
public static void displayAllFaces(S3Client s3,
RekognitionClient rekClient,
String sourceImage,
String bucketName) {
int height;
int width;
byte[] data = getObjectBytes (s3, bucketName, sourceImage);
InputStream is = new ByteArrayInputStream(data);
try {
SdkBytes sourceBytes = SdkBytes.fromInputStream(is);
image = ImageIO.read(sourceBytes.asInputStream());
width = image.getWidth();
height = image.getHeight();
// Create an Image object for the source image
software.amazon.awssdk.services.rekognition.model.Image souImage = Image.builder()
.bytes(sourceBytes)
.build();
DetectFacesRequest facesRequest = DetectFacesRequest.builder()
.attributes(Attribute.ALL)
.image(souImage)
.build();
result = rekClient.detectFaces(facesRequest);
// Show the bounding box info for each face.
List faceDetails = result.faceDetails();
for (FaceDetail face : faceDetails) {
BoundingBox box = face.boundingBox();
float left = width * box.left();
float top = height * box.top();
System.out.println("Face:");
System.out.println("Left: " + (int) left);
System.out.println("Top: " + (int) top);
System.out.println("Face Width: " + (int) (width * box.width()));
System.out.println("Face Height: " + (int) (height * box.height()));
System.out.println();
}
// Create the frame and panel.
JFrame frame = new JFrame("RotateImage");
frame.setDefaultCloseOperation(JFrame.EXIT_ON_CLOSE);
DisplayFaces panel = new DisplayFaces(image);
panel.setPreferredSize(new Dimension(image.getWidth() / scale, image.getHeight() / scale));
frame.setContentPane(panel);
frame.pack();
frame.setVisible(true);
} catch (RekognitionException | FileNotFoundException e) {
System.out.println(e.getMessage());
System.exit(1);
} catch (IOException e) {
e.printStackTrace();
}
}
public static byte[] getObjectBytes (S3Client s3, String bucketName, String keyName) {
try {
GetObjectRequest objectRequest = GetObjectRequest
.builder()
.key(keyName)
.bucket(bucketName)
.build();
ResponseBytes objectBytes = s3.getObjectAsBytes(objectRequest);
return objectBytes.asByteArray();
} catch (S3Exception e) {
System.err.println(e.awsErrorDetails().errorMessage());
System.exit(1);
}
return null;
}
public DisplayFaces(BufferedImage bufImage) {
super();
scale = 1; // increase to shrink image size.
image = bufImage;
}
// Draws the bounding box around the detected faces.
public void paintComponent(Graphics g) {
float left;
float top;
int height = image.getHeight(this);
int width = image.getWidth(this);
Graphics2D g2d = (Graphics2D) g; // Create a Java2D version of g.
// Draw the image
g2d.drawImage(image, 0, 0, width / scale, height / scale, this);
g2d.setColor(new Color(0, 212, 0));
// Iterate through the faces and display bounding boxes.
List faceDetails = result.faceDetails();
for (FaceDetail face : faceDetails) {
BoundingBox box = face.boundingBox();
left = width * box.left();
top = height * box.top();
g2d.drawRect(Math.round(left / scale), Math.round(top / scale),
Math.round((width * box.width()) / scale), Math.round((height * box.height())) / scale);
}
}
// snippet-end:[rekognition.java2.display_faces.main]
}
```
------