Membuat file manifes dari CSV file

Untuk membuat file manifes dari CSV file

1. Buat CSV file dengan bidang berikut di setiap baris (satu baris per gambar). Jangan menambahkan baris header ke CSV file.

   Bidang 1	Bidang 2	Bidang n
   Nama gambar atau jalur Amazon S3 pada gambar. Misalnya, s3://my-bucket/flowers/train/camellia1.jpg. Anda tidak dapat memiliki campuran gambar dengan jalur Amazon S3 dan gambar tanpa.	Label tingkat gambar pertama untuk gambar.	Satu atau lebih label tingkat gambar tambahan dipisahkan dengan koma. Tambahkan hanya jika Anda ingin membuat file manifes yang mendukung klasifikasi gambar Multi-label.

   Misalnya camellia1.jpg,camellia,with_leaves atau s3://my-bucket/flowers/train/camellia1.jpg,camellia,with_leaves

2. Simpan file CSV.

3. Jalankan skrip Python berikut. Berikan argumen berikut:

   • csv_file— CSV File yang Anda buat di langkah 1.

   • manifest_file— Nama file manifes yang ingin Anda buat.

   • (Opsional) --s3_path s3://path_to_folder/ - Jalur Amazon S3 untuk ditambahkan ke nama file gambar (bidang 1). Gunakan --s3_path jika gambar di bidang 1 belum berisi jalur S3.

   # Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved.
   # SPDX-License-Identifier:  Apache-2.0
   
   from datetime import datetime, timezone
   import argparse
   import logging
   import csv
   import os
   import json
   
   """
   Purpose
   Amazon Rekognition Custom Labels model example used in the service documentation.
   Shows how to create an image-level (classification) manifest file from a CSV file.
   You can specify multiple image level labels per image.
   CSV file format is
   image,label,label,..
   If necessary, use the bucket argument to specify the S3 bucket folder for the images.
   https://docs.aws.amazon.com/rekognition/latest/customlabels-dg/md-gt-cl-transform.html
   """
   
   logger = logging.getLogger(__name__)
   
   
   def check_duplicates(csv_file, deduplicated_file, duplicates_file):
       """
       Checks for duplicate images in a CSV file. If duplicate images
       are found, deduplicated_file is the deduplicated CSV file - only the first
       occurence of a duplicate is recorded. Other duplicates are recorded in duplicates_file.
       :param csv_file: The source CSV file.
       :param deduplicated_file: The deduplicated CSV file to create. If no duplicates are found
       this file is removed.
       :param duplicates_file: The duplicate images CSV file to create. If no duplicates are found
       this file is removed.
       :return: True if duplicates are found, otherwise false.
       """
   
       logger.info("Deduplicating %s", csv_file)
   
       duplicates_found = False
   
       # Find duplicates.
       with open(csv_file, 'r', newline='', encoding="UTF-8") as f,\
               open(deduplicated_file, 'w', encoding="UTF-8") as dedup,\
               open(duplicates_file, 'w', encoding="UTF-8") as duplicates:
   
           reader = csv.reader(f, delimiter=',')
           dedup_writer = csv.writer(dedup)
           duplicates_writer = csv.writer(duplicates)
   
           entries = set()
           for row in reader:
               # Skip empty lines.
               if not ''.join(row).strip():
                   continue
   
               key = row[0]
               if key not in entries:
                   dedup_writer.writerow(row)
                   entries.add(key)
               else:
                   duplicates_writer.writerow(row)
                   duplicates_found = True
   
       if duplicates_found:
           logger.info("Duplicates found check %s", duplicates_file)
   
       else:
           os.remove(duplicates_file)
           os.remove(deduplicated_file)
   
       return duplicates_found
   
   
   def create_manifest_file(csv_file, manifest_file, s3_path):
       """
       Reads a CSV file and creates a Custom Labels classification manifest file.
       :param csv_file: The source CSV file.
       :param manifest_file: The name of the manifest file to create.
       :param s3_path: The S3 path to the folder that contains the images.
       """
       logger.info("Processing CSV file %s", csv_file)
   
       image_count = 0
       label_count = 0
   
       with open(csv_file, newline='', encoding="UTF-8") as csvfile,\
               open(manifest_file, "w", encoding="UTF-8") as output_file:
   
           image_classifications = csv.reader(
               csvfile, delimiter=',', quotechar='|')
   
           # Process each row (image) in CSV file.
           for row in image_classifications:
               source_ref = str(s3_path)+row[0]
   
               image_count += 1
   
               # Create JSON for image source ref.
               json_line = {}
               json_line['source-ref'] = source_ref
   
               # Process each image level label.
               for index in range(1, len(row)):
                   image_level_label = row[index]
   
                   # Skip empty columns.
                   if image_level_label == '':
                       continue
                   label_count += 1
   
                  # Create the JSON line metadata.
                   json_line[image_level_label] = 1
                   metadata = {}
                   metadata['confidence'] = 1
                   metadata['job-name'] = 'labeling-job/' + image_level_label
                   metadata['class-name'] = image_level_label
                   metadata['human-annotated'] = "yes"
                   metadata['creation-date'] = \
                       datetime.now(timezone.utc).strftime('%Y-%m-%dT%H:%M:%S.%f')
                   metadata['type'] = "groundtruth/image-classification"
   
                   json_line[f'{image_level_label}-metadata'] = metadata
   
                   # Write the image JSON Line.
               output_file.write(json.dumps(json_line))
               output_file.write('\n')
   
       output_file.close()
       logger.info("Finished creating manifest file %s\nImages: %s\nLabels: %s",
                   manifest_file, image_count, label_count)
   
       return image_count, label_count
   
   
   def add_arguments(parser):
       """
       Adds command line arguments to the parser.
       :param parser: The command line parser.
       """
   
       parser.add_argument(
           "csv_file", help="The CSV file that you want to process."
       )
   
       parser.add_argument(
           "--s3_path", help="The S3 bucket and folder path for the images."
           " If not supplied, column 1 is assumed to include the S3 path.", required=False
       )
   
   
   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()
   
           s3_path = args.s3_path
           if s3_path is None:
               s3_path = ''
   
           # Create file names.
           csv_file = args.csv_file
           file_name = os.path.splitext(csv_file)[0]
           manifest_file = f'{file_name}.manifest'
           duplicates_file = f'{file_name}-duplicates.csv'
           deduplicated_file = f'{file_name}-deduplicated.csv'
   
           # Create manifest file, if there are no duplicate images.
           if check_duplicates(csv_file, deduplicated_file, duplicates_file):
               print(f"Duplicates found. Use {duplicates_file} to view duplicates "
                     f"and then update {deduplicated_file}. ")
               print(f"{deduplicated_file} contains the first occurence of a duplicate. "
                     "Update as necessary with the correct label information.")
               print(f"Re-run the script with {deduplicated_file}")
           else:
               print("No duplicates found. Creating manifest file.")
   
               image_count, label_count = create_manifest_file(csv_file,
                                                               manifest_file,
                                                               s3_path)
   
               print(f"Finished creating manifest file: {manifest_file} \n"
                     f"Images: {image_count}\nLabels: {label_count}")
   
       except FileNotFoundError as err:
           logger.exception("File not found: %s", err)
           print(f"File not found: {err}. Check your input CSV file.")
   
   
   if __name__ == "__main__":
       main()
   
   
   

4. Jika Anda berencana menggunakan kumpulan data pengujian, ulangi langkah 1-3 untuk membuat file manifes untuk kumpulan data pengujian Anda.

5. Jika perlu, salin gambar ke jalur bucket Amazon S3 yang Anda tentukan di kolom 1 CSV file (atau ditentukan dalam baris --s3_path perintah). Anda dapat menggunakan perintah AWS S3 berikut.

   aws s3 cp --recursive your-local-folder s3://your-target-S3-location

6. Unggah file manifes Anda ke bucket Amazon S3 yang ingin Anda gunakan untuk menyimpan file manifes.

   catatan

   Pastikan Label Kustom Amazon Rekognition memiliki akses ke bucket Amazon S3 yang direferensikan di bidang baris file manifessource-ref. JSON Untuk informasi selengkapnya, lihat Mengakses Bucket Amazon S3 eksternal. Jika lowongan Ground Truth menyimpan gambar di Bucket Konsol Label Kustom Amazon Rekognition, Anda tidak perlu menambahkan izin.

7. Ikuti petunjuk di Membuat kumpulan data dengan file manifes SageMaker Ground Truth (Konsol) untuk membuat kumpulan data dengan file manifes yang diunggah. Untuk langkah 8, di lokasi file.manifest, masukkan Amazon URL S3 untuk lokasi file manifes. Jika Anda menggunakan AWS SDK, lakukanMembuat kumpulan data dengan file manifes SageMaker Ground Truth () SDK.