Analysieren eines Bildes mit einem trainierten Modell

Um benutzerdefinierte Beschriftungen zu erkennen (API)

1. Wenn Sie dies noch nicht getan haben:

   1. Stellen Sie sicher, dass Sie Berechtigungen für DetectCustomLabels und AmazonS3ReadOnlyAccess haben. Weitere Informationen finden Sie unter Einrichten von SDK-Berechtigungen.

   2. Installieren und konfigurieren Sie das AWS CLI und das AWS SDKs. Weitere Informationen finden Sie unter Schritt 4: Richten Sie die AWS CLI und SDKs ein AWS.

2. Trainieren und implementieren Sie Ihr Modell. Weitere Informationen finden Sie unter Erstellen eines Amazon Rekognition-Custom-Custom-Label-Erstellen eines neuen.

3. Stellen Sie sicher, dass der Benutzer, der DetectCustomLabels aufruft, Zugang zu dem Modell, das Sie in Schritt 2 verwendet haben, hat. Weitere Informationen finden Sie unter SicherungDetectCustomLabels.

4. Laden Sie ein Bild hoch, das Sie für ein S3-Bucket analysieren möchten.

   Weitere Anleitungen finden Sie unter Upload eines Objekts in Amazon S3 im Benutzerhandbuch für Amazon Simple Storage Service. Die Beispiele für Python, Java und Java 2 zeigen Ihnen auch, wie Sie eine lokale Bilddatei verwenden, um ein Bild mithilfe von Raw-Bytes zu übermitteln. Die Datei muss kleiner als 4 MB sein.

5. Verwenden Sie die folgenden Beispiele zum Aufrufen der DetectCustomLabels-Operation. Die Python- und Java-Beispiele zeigen das Bild und überlagern die Analyseergebnisse, ähnlich wie in der folgenden Abbildung. Die folgenden Bilder enthalten Begrenzungsfelder und Beschriftungen für eine Leiterplatte mit einem Potentiometer, einem Infrarot-Phototransistor und Komponenten. LED

   

   AWS CLI

   Dieser AWS CLI Befehl zeigt die JSON Ausgabe für den Vorgang an DetectCustomLabelsCLI. Ändern Sie die Werte der folgenden Eingabeparameter.

   • bucket durch den Namen des Amazon-S3-Buckets, den Sie in Schritt 4 verwendet haben.

   • image durch den Namen der Eingabebilddatei, die Sie in Schritt 4 hochgeladen haben.

   • projectVersionArnmit ARN dem Modell, das Sie verwenden möchten.

   aws rekognition detect-custom-labels --project-version-arn model_arn \
      --image '{"S3Object":{"Bucket":"bucket","Name":"image"}}' \
      --min-confidence 70 \
      --profile custom-labels-access

   Python

   Der folgende Beispielcode zeigt Begrenzungsrahmen und Labels auf Bildebene eines Bildes an.

   Um ein lokales Bild zu analysieren, führen Sie das Programm aus und geben Sie die folgenden Befehlszeilenargumente ein:

   • Das ARN Modell, mit dem Sie das Bild analysieren möchten.

   • Der Name und der Speicherort einer lokalen Bilddatei.

   Um ein in einem Amazon-S3-Bucket gespeichertes Bild zu analysieren, führen Sie das Programm aus und geben Sie die folgenden Befehlszeilenargumente ein:

   • Das ARN Modell, mit dem Sie das Bild analysieren möchten.

   • Der Name und Speicherort eines Bildes innerhalb des Amazon-S3-Buckets, den Sie in Schritt 4 verwendet haben.

   • --bucket bucket name — Der Amazon S3 S3-Bucket, den Sie in Schritt 4 verwendet haben.

   Beachten Sie, dass in diesem Beispiel davon ausgegangen wird, dass Ihre Version von Pillow >= 8.0.0 ist.

   # Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved.
   # SPDX-License-Identifier: Apache-2.0
   """
   Purpose
   Amazon Rekognition Custom Labels detection example used in the service documentation:
   https://docs.aws.amazon.com/rekognition/latest/customlabels-dg/detecting-custom-labels.html
   Shows how to detect custom labels by using an Amazon Rekognition Custom Labels model.
   The image can be stored on your local computer or in an Amazon S3 bucket.
   """
   
   import io
   import logging
   import argparse
   import boto3
   from PIL import Image, ImageDraw, ImageFont
   
   from botocore.exceptions import ClientError
   
   logger = logging.getLogger(__name__)
   
   
   def analyze_local_image(rek_client, model, photo, min_confidence):
       """
       Analyzes an image stored as a local file.
       :param rek_client: The Amazon Rekognition Boto3 client.
       :param s3_connection: The Amazon S3 Boto3 S3 connection object.
       :param model: The ARN of the Amazon Rekognition Custom Labels model that you want to use.
       :param photo: The name and file path of the photo that you want to analyze.
       :param min_confidence: The desired threshold/confidence for the call.
       """
   
       try:
           logger.info("Analyzing local file: %s", photo)
           image = Image.open(photo)
           image_type = Image.MIME[image.format]
   
           if (image_type == "image/jpeg" or image_type == "image/png") is False:
               logger.error("Invalid image type for %s", photo)
               raise ValueError(
                   f"Invalid file format. Supply a jpeg or png format file: {photo}"
               )
   
           # get images bytes for call to detect_anomalies
           image_bytes = io.BytesIO()
           image.save(image_bytes, format=image.format)
           image_bytes = image_bytes.getvalue()
   
           response = rek_client.detect_custom_labels(Image={'Bytes': image_bytes},
                                                      MinConfidence=min_confidence,
                                                      ProjectVersionArn=model)
   
           show_image(image, response)
           return len(response['CustomLabels'])
   
       except ClientError as client_err:
           logger.error(format(client_err))
           raise
       except FileNotFoundError as file_error:
           logger.error(format(file_error))
           raise
   
   
   def analyze_s3_image(rek_client, s3_connection, model, bucket, photo, min_confidence):
       """
       Analyzes an image stored in the specified S3 bucket.
       :param rek_client: The Amazon Rekognition Boto3 client.
       :param s3_connection: The Amazon S3 Boto3 S3 connection object.
       :param model: The ARN of the Amazon Rekognition Custom Labels model that you want to use.
       :param bucket: The name of the S3 bucket that contains the image that you want to analyze.
       :param photo: The name of the photo that you want to analyze.
       :param min_confidence: The desired threshold/confidence for the call.
       """
   
       try:
           # Get image from S3 bucket.
   
           logger.info("analyzing bucket: %s image: %s", bucket, photo)
           s3_object = s3_connection.Object(bucket, photo)
           s3_response = s3_object.get()
   
           stream = io.BytesIO(s3_response['Body'].read())
           image = Image.open(stream)
   
           image_type = Image.MIME[image.format]
   
           if (image_type == "image/jpeg" or image_type == "image/png") is False:
               logger.error("Invalid image type for %s", photo)
               raise ValueError(
                   f"Invalid file format. Supply a jpeg or png format file: {photo}")
   
           ImageDraw.Draw(image)
   
           # Call DetectCustomLabels.
           response = rek_client.detect_custom_labels(
               Image={'S3Object': {'Bucket': bucket, 'Name': photo}},
               MinConfidence=min_confidence,
               ProjectVersionArn=model)
   
           show_image(image, response)
           return len(response['CustomLabels'])
   
       except ClientError as err:
           logger.error(format(err))
           raise
   
   
   def show_image(image, response):
       """
       Displays the analyzed image and overlays analysis results
       :param image: The analyzed image
       :param response: the response from DetectCustomLabels
       """
       try:
           font_size = 40
           line_width = 5
   
           img_width, img_height = image.size
           draw = ImageDraw.Draw(image)
   
           # Calculate and display bounding boxes for each detected custom label.
           image_level_label_height = 0
   
           for custom_label in response['CustomLabels']:
               confidence = int(round(custom_label['Confidence'], 0))
               label_text = f"{custom_label['Name']}:{confidence}%"
               fnt = ImageFont.truetype('Tahoma.ttf', font_size)
               text_left, text_top, text_right, text_bottom = draw.textbbox((0, 0), label_text, fnt)
               text_width, text_height = text_right - text_left, text_bottom - text_top
   
               logger.info("Label: %s", custom_label['Name'])
               logger.info("Confidence:  %s", confidence)
   
               # Draw bounding boxes, if present
               if 'Geometry' in custom_label:
                   box = custom_label['Geometry']['BoundingBox']
                   left = img_width * box['Left']
                   top = img_height * box['Top']
                   width = img_width * box['Width']
                   height = img_height * box['Height']
   
                   logger.info("Bounding box")
                   logger.info("\tLeft: {0:.0f}".format(left))
                   logger.info("\tTop: {0:.0f}".format(top))
                   logger.info("\tLabel Width: {0:.0f}".format(width))
                   logger.info("\tLabel Height: {0:.0f}".format(height))
   
                   points = (
                       (left, top),
                       (left + width, top),
                       (left + width, top + height),
                       (left, top + height),
                       (left, top))
                   # Draw bounding box and label text
                   draw.line(points, fill="limegreen", width=line_width)
                   draw.rectangle([(left + line_width, top+line_width),
                                   (left + text_width + line_width, top + line_width + text_height)], fill="black")
                   draw.text((left + line_width, top + line_width),
                             label_text, fill="limegreen", font=fnt)
   
               # draw image-level label text.
               else:
                   draw.rectangle([(10, image_level_label_height),
                                   (text_width + 10, image_level_label_height+text_height)], fill="black")
                   draw.text((10, image_level_label_height),
                             label_text, fill="limegreen", font=fnt)
   
                   image_level_label_height += text_height
   
           image.show()
   
       except Exception as err:
           logger.error(format(err))
           raise
   
   
   def add_arguments(parser):
       """
       Adds command line arguments to the parser.
       :param parser: The command line parser.
       """
   
       parser.add_argument(
           "model_arn", help="The ARN of the model that you want to use."
       )
   
       parser.add_argument(
           "image", help="The path and file name of the image that you want to analyze"
       )
       parser.add_argument(
           "--bucket",  help="The bucket that contains the image. If not supplied, image is assumed to be a local file.", required=False
       )
   
   
   def main():
   
       try:
           logging.basicConfig(level=logging.INFO,
                               format="%(levelname)s: %(message)s")
   
           # Get command line arguments.
           parser = argparse.ArgumentParser(usage=argparse.SUPPRESS)
           add_arguments(parser)
           args = parser.parse_args()
   
           label_count = 0
           min_confidence = 50
   
           session = boto3.Session(profile_name='custom-labels-access')
           rekognition_client = session.client("rekognition")
   
           if args.bucket is None:
               # Analyze local image.
               label_count = analyze_local_image(rekognition_client,
                                                 args.model_arn,
                                                 args.image,
                                                 min_confidence)
           else:
               # Analyze image in S3 bucket.
               s3_connection = session.resource('s3')
               label_count = analyze_s3_image(rekognition_client,
                                              s3_connection,
                                              args.model_arn,
                                              args.bucket,
                                              args.image,
                                              min_confidence)
   
           print(f"Custom labels detected: {label_count}")
   
       except ClientError as client_err:
           print("A service client error occurred: " +
                 format(client_err.response["Error"]["Message"]))
   
       except ValueError as value_err:
           print("A value error occurred: " + format(value_err))
   
       except FileNotFoundError as file_error:
           print("File not found error: " + format(file_error))
   
       except Exception as err:
           print("An error occurred: " + format(err))
   
   
   if __name__ == "__main__":
       main()
   

   Java

   Der folgende Beispielcode zeigt Begrenzungsrahmen und Labels auf Bildebene eines Bildes an.

   Um ein lokales Bild zu analysieren, führen Sie das Programm aus und geben Sie die folgenden Befehlszeilenargumente ein:

   • Das ARN Modell, mit dem Sie das Bild analysieren möchten.

   • Der Name und der Speicherort einer lokalen Bilddatei.

   Um ein in einem Amazon-S3-Bucket gespeichertes Bild zu analysieren, führen Sie das Programm aus und geben Sie die folgenden Befehlszeilenargumente ein:

   • Das ARN Modell, mit dem Sie das Bild analysieren möchten.

   • Der Name und Speicherort eines Bildes innerhalb des Amazon-S3-Buckets, den Sie in Schritt 4 verwendet haben.

   • Der Amazon-S3-Bucket mit dem Bild, das Sie in Schritt 4 verwendet haben.

   /*
      Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved.
      SPDX-License-Identifier: Apache-2.0
   */
   
   package com.amazonaws.samples;
   
   import java.awt.*;
   import java.awt.image.BufferedImage;
   import java.io.IOException;
   import java.util.List;
   import javax.imageio.ImageIO;
   import javax.swing.*;
   import java.io.FileNotFoundException;
   import java.awt.font.FontRenderContext;
   import java.util.logging.Level;
   import java.util.logging.Logger;
   import java.io.File;
   import java.io.FileInputStream;
   import java.io.InputStream;
   import java.nio.ByteBuffer;
   import java.io.ByteArrayInputStream;
   import java.io.ByteArrayOutputStream;
   
   import com.amazonaws.auth.AWSCredentialsProvider;
   import com.amazonaws.auth.profile.ProfileCredentialsProvider;
   import com.amazonaws.regions.Regions;
   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.CustomLabel;
   import com.amazonaws.services.rekognition.model.DetectCustomLabelsRequest;
   import com.amazonaws.services.rekognition.model.DetectCustomLabelsResult;
   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;
   
   import com.amazonaws.services.rekognition.model.AmazonRekognitionException;
   import com.amazonaws.services.s3.model.AmazonS3Exception;
   import com.amazonaws.util.IOUtils;
   
   // Calls DetectCustomLabels and displays a bounding box around each detected image.
   public class DetectCustomLabels extends JPanel {
   
       private transient DetectCustomLabelsResult response;
       private transient Dimension dimension;
       private transient BufferedImage image;
   
       public static final Logger logger = Logger.getLogger(DetectCustomLabels.class.getName());
   
       // Finds custom labels in an image stored in an S3 bucket.
       public DetectCustomLabels(AmazonRekognition rekClient,
               AmazonS3 s3client,
               String projectVersionArn,
               String bucket,
               String key,
               Float minConfidence) throws AmazonRekognitionException, AmazonS3Exception, IOException {
   
           logger.log(Level.INFO, "Processing S3 bucket: {0} image {1}", new Object[] { bucket, key });
   
           // Get image from S3 bucket and create BufferedImage
           com.amazonaws.services.s3.model.S3Object s3object = s3client.getObject(bucket, key);
           S3ObjectInputStream inputStream = s3object.getObjectContent();
           image = ImageIO.read(inputStream);
   
           // Set image size
           setWindowDimensions();
   
           DetectCustomLabelsRequest request = new DetectCustomLabelsRequest()
                   .withProjectVersionArn(projectVersionArn)
                   .withImage(new Image().withS3Object(new S3Object().withName(key).withBucket(bucket)))
                   .withMinConfidence(minConfidence);
   
           // Call DetectCustomLabels
   
           response = rekClient.detectCustomLabels(request);
           logFoundLabels(response.getCustomLabels());
           drawLabels();
   
       }
   
       // Finds custom label in a local image file.
       public DetectCustomLabels(AmazonRekognition rekClient,
               String projectVersionArn,
               String photo,
               Float minConfidence)
               throws IOException, AmazonRekognitionException {
   
           logger.log(Level.INFO, "Processing local file: {0}", photo);
   
           // Get image bytes and buffered image
           ByteBuffer imageBytes;
           try (InputStream inputStream = new FileInputStream(new File(photo))) {
               imageBytes = ByteBuffer.wrap(IOUtils.toByteArray(inputStream));
           }
   
           // Get image for display
           InputStream imageBytesStream;
           imageBytesStream = new ByteArrayInputStream(imageBytes.array());
   
           ByteArrayOutputStream baos = new ByteArrayOutputStream();
           image = ImageIO.read(imageBytesStream);
           ImageIO.write(image, "jpg", baos);
   
           // Set image size
           setWindowDimensions();
   
           // Analyze image
           DetectCustomLabelsRequest request = new DetectCustomLabelsRequest()
                   .withProjectVersionArn(projectVersionArn)
                   .withImage(new Image()
                           .withBytes(imageBytes))
                   .withMinConfidence(minConfidence);
   
           response = rekClient.detectCustomLabels(request);
   
           logFoundLabels(response.getCustomLabels());
   
           drawLabels();
   
       }
   
       // Log the labels found by DetectCustomLabels
       private void logFoundLabels(List<CustomLabel> customLabels) {
           logger.info("Custom labels found");
           if (customLabels.isEmpty()) {
               logger.log(Level.INFO, "No Custom Labels found. Consider lowering min confidence.");
           } else {
               for (CustomLabel customLabel : customLabels) {
                   logger.log(Level.INFO, " Label: {0} Confidence: {1}",
                           new Object[] { customLabel.getName(), customLabel.getConfidence() });
               }
   
           }
       }
   
       // Sets window dimensions to 1/2 screen size, unless image is smaller
       public void setWindowDimensions() {
           dimension = java.awt.Toolkit.getDefaultToolkit().getScreenSize();
   
           dimension.width = (int) dimension.getWidth() / 2;
           if (image.getWidth() < dimension.width) {
               dimension.width = image.getWidth();
           }
           dimension.height = (int) dimension.getHeight() / 2;
   
           if (image.getHeight() < dimension.height) {
               dimension.height = image.getHeight();
           }
   
           setPreferredSize(dimension);
   
       }
   
       // Draws the image containing the bounding boxes and labels.
       @Override
       public void paintComponent(Graphics g) {
   
           Graphics2D g2d = (Graphics2D) g; // Create a Java2D version of g.
   
           // Draw the image.
           g2d.drawImage(image, 0, 0, dimension.width, dimension.height, this);
   
       }
   
       public void drawLabels() {
           // Draws bounding boxes (if present) and label text.
   
           int boundingBoxBorderWidth = 5;
           int imageHeight = image.getHeight(this);
           int imageWidth = image.getWidth(this);
   
           // Set up drawing
           Graphics2D g2d = image.createGraphics();
           g2d.setColor(Color.GREEN);
           g2d.setFont(new Font("Tahoma", Font.PLAIN, 50));
           Font font = g2d.getFont();
           FontRenderContext frc = g2d.getFontRenderContext();
           g2d.setStroke(new BasicStroke(boundingBoxBorderWidth));
   
           List<CustomLabel> customLabels = response.getCustomLabels();
   
           int imageLevelLabelHeight = 0;
           for (CustomLabel customLabel : customLabels) {
   
               String label = customLabel.getName();
   
               int textWidth = (int) (font.getStringBounds(label, frc).getWidth());
               int textHeight = (int) (font.getStringBounds(label, frc).getHeight());
   
               // Draw bounding box, if present
               if (customLabel.getGeometry() != null) {
   
                   BoundingBox box = customLabel.getGeometry().getBoundingBox();
                   float left = imageWidth * box.getLeft();
                   float top = imageHeight * box.getTop();
   
                   // Draw black rectangle
                   g2d.setColor(Color.BLACK);
                   g2d.fillRect(Math.round(left + (boundingBoxBorderWidth)), Math.round(top + (boundingBoxBorderWidth)),
                           textWidth + boundingBoxBorderWidth, textHeight + boundingBoxBorderWidth);
   
                   // Write label onto black rectangle
                   g2d.setColor(Color.GREEN);
                   g2d.drawString(label, left + boundingBoxBorderWidth, (top + textHeight));
   
                   // Draw bounding box around label location
                   g2d.drawRect(Math.round(left), Math.round(top), Math.round((imageWidth * box.getWidth())),
                           Math.round((imageHeight * box.getHeight())));
               }
               // Draw image level labels.
               else {
                   // Draw black rectangle
                   g2d.setColor(Color.BLACK);
                   g2d.fillRect(10, 10 + imageLevelLabelHeight, textWidth, textHeight);
                   g2d.setColor(Color.GREEN);
                   g2d.drawString(label, 10, textHeight + imageLevelLabelHeight);
   
                   imageLevelLabelHeight += textHeight;
               }
   
           }
           g2d.dispose();
   
       }
   
       public static void main(String args[]) throws Exception {
   
           String photo = null;
           String bucket = null;
           String projectVersionArn = null;
           float minConfidence = 50;
   
           final String USAGE = "\n" + "Usage: " + "<model_arn> <image> <bucket>\n\n" + "Where:\n"
                   + "   model_arn - The ARN of the model that you want to use. \n\n"
                   + "   image - The location of the image on your local file system or within an S3 bucket.\n\n"
                   + "   bucket - The S3 bucket that contains the image. Don't specify if image is local.\n\n";
   
           // Collect the arguments. If 3 arguments are present, the image is assumed to be
           // in an S3 bucket.
   
           if (args.length < 2 || args.length > 3) {
               System.out.println(USAGE);
               System.exit(1);
           }
   
           projectVersionArn = args[0];
           photo = args[1];
   
           if (args.length == 3) {
               bucket = args[2];
           }
   
           DetectCustomLabels panel = null;
   
           try {
   
               AWSCredentialsProvider provider =new ProfileCredentialsProvider("custom-labels-access");
    
               AmazonRekognition rekClient = AmazonRekognitionClientBuilder.standard()
                       .withCredentials(provider)
                       .withRegion(Regions.US_WEST_2)
                       .build();
             
   
               AmazonS3 s3client = AmazonS3ClientBuilder.standard()
               .withCredentials(provider)
               .withRegion(Regions.US_WEST_2)
               .build();
   
               // Create frame and panel.
               JFrame frame = new JFrame("Custom Labels");
               frame.setDefaultCloseOperation(JFrame.EXIT_ON_CLOSE);
   
               if (args.length == 2) {
                   // Analyze local image
                   panel = new DetectCustomLabels(rekClient, projectVersionArn, photo, minConfidence);
               } else {
                   // Analyze image in S3 bucket
                   panel = new DetectCustomLabels(rekClient, s3client, projectVersionArn, bucket, photo, minConfidence);
               }
   
               frame.setContentPane(panel);
               frame.pack();
               frame.setVisible(true);
   
           } catch (AmazonRekognitionException rekError) {
               String errorMessage = "Rekognition client error: " + rekError.getMessage();
               logger.log(Level.SEVERE, errorMessage);
               System.out.println(errorMessage);
               System.exit(1);
           } catch (FileNotFoundException fileError) {
               String errorMessage = "File not found: " + photo;
               logger.log(Level.SEVERE, errorMessage);
               System.out.println(errorMessage);
               System.exit(1);
           } catch (IOException fileError) {
               String errorMessage = "Input output exception: " + fileError.getMessage();
               logger.log(Level.SEVERE, errorMessage);
               System.out.println(errorMessage);
               System.exit(1);
           } catch (AmazonS3Exception s3Error) {
               String errorMessage = "S3 error: " + s3Error.getErrorMessage();
               logger.log(Level.SEVERE, errorMessage);
               System.out.println(errorMessage);
               System.exit(1);
           }
   
       }
   }
   

   Java V2

   Der folgende Beispielcode zeigt Begrenzungsrahmen und Labels auf Bildebene eines Bildes an.

   Um ein lokales Bild zu analysieren, führen Sie das Programm aus und geben Sie die folgenden Befehlszeilenargumente ein:

   • projectVersionArn— Das ARN Modell, mit dem Sie das Bild analysieren möchten.

   • photo — Der Name und der Speicherort einer lokalen Bilddatei.

   Um ein in einem S3-Bucket gespeichertes Bild zu analysieren, führen Sie das Programm aus und geben Sie die folgenden Befehlszeilenargumente ein:

   • Das ARN Modell, mit dem Sie das Bild analysieren möchten.

   • Der Name und Speicherort eines Bildes innerhalb des Amazon-S3-Buckets, den Sie in Schritt 4 verwendet haben.

   • Der Amazon-S3-Bucket mit dem Bild, das Sie in Schritt 4 verwendet haben.

   /*
      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.ResponseBytes;
   import software.amazon.awssdk.core.SdkBytes;
   import software.amazon.awssdk.core.sync.ResponseTransformer;
   import software.amazon.awssdk.regions.Region;
   import software.amazon.awssdk.services.rekognition.RekognitionClient;
   import software.amazon.awssdk.services.rekognition.model.S3Object;
   import software.amazon.awssdk.services.rekognition.model.Image;
   import software.amazon.awssdk.services.rekognition.model.DetectCustomLabelsRequest;
   import software.amazon.awssdk.services.rekognition.model.DetectCustomLabelsResponse;
   import software.amazon.awssdk.services.rekognition.model.CustomLabel;
   import software.amazon.awssdk.services.rekognition.model.RekognitionException;
   import software.amazon.awssdk.services.rekognition.model.BoundingBox;
   
   import software.amazon.awssdk.services.s3.S3Client;
   import software.amazon.awssdk.services.s3.model.GetObjectRequest;
   import software.amazon.awssdk.services.s3.model.GetObjectResponse;
   import software.amazon.awssdk.services.s3.model.NoSuchBucketException;
   import software.amazon.awssdk.services.s3.model.NoSuchKeyException;
   
   import java.io.ByteArrayInputStream;
   import java.io.File;
   import java.io.FileInputStream;
   import java.io.FileNotFoundException;
   import java.io.IOException;
   import java.io.InputStream;
   import java.util.List;
   
   import java.awt.*;
   import java.awt.font.FontRenderContext;
   import java.awt.image.BufferedImage;
   import javax.imageio.ImageIO;
   import javax.swing.*;
   
   import java.util.logging.Level;
   import java.util.logging.Logger;
   
   // Calls DetectCustomLabels on an image. Displays bounding boxes or
   // image level labels found in the image.
   public class ShowCustomLabels extends JPanel {
   
       private transient BufferedImage image;
       private transient DetectCustomLabelsResponse response;
       private transient Dimension dimension;
       public static final Logger logger = Logger.getLogger(ShowCustomLabels.class.getName());
   
       // Finds custom labels in an image stored in an S3 bucket.
       public ShowCustomLabels(RekognitionClient rekClient,
               S3Client s3client,
               String projectVersionArn,
               String bucket,
               String key,
               Float minConfidence) throws RekognitionException, NoSuchBucketException, NoSuchKeyException, IOException {
   
           logger.log(Level.INFO, "Processing S3 bucket: {0} image {1}", new Object[] { bucket, key });
           // Get image from S3 bucket and create BufferedImage
           GetObjectRequest requestObject = GetObjectRequest.builder().bucket(bucket).key(key).build();
           ResponseBytes<GetObjectResponse> result = s3client.getObject(requestObject, ResponseTransformer.toBytes());
           ByteArrayInputStream bis = new ByteArrayInputStream(result.asByteArray());
           image = ImageIO.read(bis);
   
           // Set image size
           setWindowDimensions();
   
           // Construct request parameter for DetectCustomLabels
           S3Object s3Object = S3Object.builder().bucket(bucket).name(key).build();
   
           Image s3Image = Image.builder().s3Object(s3Object).build();
   
           DetectCustomLabelsRequest request = DetectCustomLabelsRequest.builder().image(s3Image)
                   .projectVersionArn(projectVersionArn).minConfidence(minConfidence).build();
   
           response = rekClient.detectCustomLabels(request);
           logFoundLabels(response.customLabels());
           drawLabels();
   
       }
   
       // Finds custom label in a local image file.
       public ShowCustomLabels(RekognitionClient rekClient,
               String projectVersionArn,
               String photo,
               Float minConfidence)
               throws IOException, RekognitionException {
   
           logger.log(Level.INFO, "Processing local file: {0}", photo);
           // Get image bytes and buffered image
           InputStream sourceStream = new FileInputStream(new File(photo));
           SdkBytes imageBytes = SdkBytes.fromInputStream(sourceStream);
           ByteArrayInputStream inputStream = new ByteArrayInputStream(imageBytes.asByteArray());
           image = ImageIO.read(inputStream);
   
           setWindowDimensions();
   
           // Construct request parameter for DetectCustomLabels
           Image localImageBytes = Image.builder().bytes(imageBytes).build();
   
           DetectCustomLabelsRequest request = DetectCustomLabelsRequest.builder().image(localImageBytes)
                   .projectVersionArn(projectVersionArn).minConfidence(minConfidence).build();
   
           response = rekClient.detectCustomLabels(request);
   
           logFoundLabels(response.customLabels());
           drawLabels();
   
       }
   
       // Sets window dimensions to 1/2 screen size, unless image is smaller
       public void setWindowDimensions() {
           dimension = java.awt.Toolkit.getDefaultToolkit().getScreenSize();
   
           dimension.width = (int) dimension.getWidth() / 2;
           if (image.getWidth() < dimension.width) {
               dimension.width = image.getWidth();
           }
           dimension.height = (int) dimension.getHeight() / 2;
   
           if (image.getHeight() < dimension.height) {
               dimension.height = image.getHeight();
           }
   
           setPreferredSize(dimension);
   
       }
   
       // Draws bounding boxes (if present) and label text.
       public void drawLabels() {
   
           int boundingBoxBorderWidth = 5;
           int imageHeight = image.getHeight(this);
           int imageWidth = image.getWidth(this);
   
           // Set up drawing
           Graphics2D g2d = image.createGraphics();
           g2d.setColor(Color.GREEN);
           g2d.setFont(new Font("Tahoma", Font.PLAIN, 50));
           Font font = g2d.getFont();
           FontRenderContext frc = g2d.getFontRenderContext();
           g2d.setStroke(new BasicStroke(boundingBoxBorderWidth));
   
           List<CustomLabel> customLabels = response.customLabels();
   
           int imageLevelLabelHeight = 0;
           for (CustomLabel customLabel : customLabels) {
   
               String label = customLabel.name();
   
               int textWidth = (int) (font.getStringBounds(label, frc).getWidth());
               int textHeight = (int) (font.getStringBounds(label, frc).getHeight());
   
               // Draw bounding box, if present
               if (customLabel.geometry() != null) {
   
                   BoundingBox box = customLabel.geometry().boundingBox();
                   float left = imageWidth * box.left();
                   float top = imageHeight * box.top();
   
                   // Draw black rectangle
                   g2d.setColor(Color.BLACK);
                   g2d.fillRect(Math.round(left + (boundingBoxBorderWidth)), Math.round(top + (boundingBoxBorderWidth)),
                           textWidth + boundingBoxBorderWidth, textHeight + boundingBoxBorderWidth);
   
                   // Write label onto black rectangle
                   g2d.setColor(Color.GREEN);
                   g2d.drawString(label, left + boundingBoxBorderWidth, (top + textHeight));
   
                   // Draw bounding box around label location
                   g2d.drawRect(Math.round(left), Math.round(top), Math.round((imageWidth * box.width())),
                           Math.round((imageHeight * box.height())));
               }
               // Draw image level labels.
               else {
                   // Draw black rectangle
                   g2d.setColor(Color.BLACK);
                   g2d.fillRect(10, 10 + imageLevelLabelHeight, textWidth, textHeight);
                   g2d.setColor(Color.GREEN);
                   g2d.drawString(label, 10, textHeight + imageLevelLabelHeight);
   
                   imageLevelLabelHeight += textHeight;
               }
   
           }
           g2d.dispose();
   
       }
   
       // Log the labels found by DetectCustomLabels
       private void logFoundLabels(List<CustomLabel> customLabels) {
           logger.info("Custom labels found:");
           if (customLabels.isEmpty()) {
               logger.log(Level.INFO, "No Custom Labels found. Consider lowering min confidence.");
   
           }
           else {
           for (CustomLabel customLabel : customLabels) {
               logger.log(Level.INFO, " Label: {0} Confidence: {1}",
                       new Object[] { customLabel.name(), customLabel.confidence() } );
               }
           }
       }
   
       // Draws the image containing the bounding boxes and labels.
       @Override
       public void paintComponent(Graphics g) {
   
           Graphics2D g2d = (Graphics2D) g; // Create a Java2D version of g.
   
           // Draw the image.
           g2d.drawImage(image, 0, 0, dimension.width, dimension.height, this);
   
       }
   
       public static void main(String args[]) throws Exception {
   
           String photo = null;
           String bucket = null;
           String projectVersionArn = null;
   
           final String USAGE = "\n" + "Usage: " + "<model_arn> <image> <bucket>\n\n" + "Where:\n"
                   + "   model_arn - The ARN of the model that you want to use. \n\n"
                   + "   image - The location of the image on your local file system or within an S3 bucket.\n\n"
                   + "   bucket - The S3 bucket that contains the image. Don't specify if image is local.\n\n";
   
           // Collect the arguments. If 3 arguments are present, the image is assumed to be
           // in an S3 bucket.
   
           if (args.length < 2 || args.length > 3) {
               System.out.println(USAGE);
               System.exit(1);
           }
   
           projectVersionArn = args[0];
           photo = args[1];
   
           if (args.length == 3) {
               bucket = args[2];
           }
   
           float minConfidence = 50;
   
           ShowCustomLabels panel = null;
   
           try {
               // Get the Rekognition client
   
   
               // Get the Rekognition client.
               RekognitionClient rekClient = RekognitionClient.builder()
               .credentialsProvider(ProfileCredentialsProvider.create("custom-labels-access"))
               .region(Region.US_WEST_2)
               .build();
   
               S3Client s3Client = S3Client.builder()
               .credentialsProvider(ProfileCredentialsProvider.create("custom-labels-access"))
               .region(Region.US_WEST_2)
               .build();
   
   
               // Create frame and panel.
               JFrame frame = new JFrame("Custom Labels");
               frame.setDefaultCloseOperation(JFrame.EXIT_ON_CLOSE);
   
               if (args.length == 2) {
                   // Analyze local image
                   panel = new ShowCustomLabels(rekClient, projectVersionArn, photo, minConfidence);
               } else {
                   // Analyze image in S3 bucket
                   panel = new ShowCustomLabels(rekClient, s3Client, projectVersionArn, bucket, photo, minConfidence);
               }
   
               frame.setContentPane(panel);
               frame.pack();
               frame.setVisible(true);
   
           } catch (RekognitionException rekError) {
   
               String errorMessage = "Rekognition client error: " + rekError.getMessage();
               logger.log(Level.SEVERE, errorMessage);
               System.out.println(errorMessage);
               System.exit(1);
           } catch (FileNotFoundException fileError) {
               String errorMessage = "File not found: " + photo;
               logger.log(Level.SEVERE, errorMessage);
               System.out.println(errorMessage);
               System.exit(1);
           } catch (IOException fileError) {
               String errorMessage = "Input output exception: " + fileError.getMessage();
               logger.log(Level.SEVERE, errorMessage);
               System.out.println(errorMessage);
               System.exit(1);
           } catch (NoSuchKeyException bucketError) {
               String errorMessage = String.format("Image not found: %s in bucket %s.", photo, bucket);
               logger.log(Level.SEVERE, errorMessage);
               System.out.println(errorMessage);
               System.exit(1);
           } catch (NoSuchBucketException bucketError) {
               String errorMessage = "Bucket not found: " + bucket;
               logger.log(Level.SEVERE, errorMessage);
               System.out.println(errorMessage);
               System.exit(1);
           }
   
       }
   }