從 CSV 檔案建立清單檔案。

從 CSV 檔案建立清單檔案

1. 建立 CSV 檔案，每一資料列中包含以下欄位 (每個影像一個資料列)。請勿將標題資料列新增至 CSV 檔案。

   欄位 1	欄位 2	欄位 n
   影像名稱或 Amazon S3 路徑影像。例如：s3://my-bucket/flowers/train/camellia1.jpg。您不能混合使用具有 Amazon S3 路徑的影像和不具有 Amazon S3 路徑的影像。	影像的第一個影像層級標籤。	一或多個以逗號分隔的其他影像層級標籤。只有在您想要建立支援多標籤影像分類的清單檔案時才新增。

   例如：camellia1.jpg,camellia,with_leaves 或 s3://my-bucket/flowers/train/camellia1.jpg,camellia,with_leaves

2. 儲存 CSV 檔案。

3. 執行下列 Python 指令碼。提供下列引數：

   • csv_file — 您在步驟 1 中建立的 CSV 檔案。

   • manifest_file — 您要建立的清單檔案的名稱。

   • (選用) --s3_path s3://path_to_folder/ — 要新增至影像檔案名稱的 Amazon S3 路徑 (欄位 1)。如果欄位 1 中的影像尚未包含 S3 路徑，請使用 --s3_path。

   # 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. 如果您打算使用測試資料集，請重複步驟 1 至 3，為測試資料集建立清單檔案。

5. 如有必要，請將影像複製到您在 CSV 檔案的資料欄 1 中指定的 Amazon S3 儲存貯體路徑 (或在 --s3_path 命令列中指定)。您可以使用下列 AWS S3 命令。

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

6. 將清單檔案上傳到您要用於存放清單檔案的 Amazon S3 儲存貯體。

   注意

   請確保 Amazon Rekognition 自訂標籤可存取清單檔案 JSON Lines 的 source-ref 欄位中參考的 Amazon S3 儲存貯體。如需詳細資訊，請參閱存取外部 Amazon S3 儲存貯體。如果您的 Ground Truth 任務將影像存放在 Amazon Rekognition 自訂標籤主控台儲存貯體中，則不需要新增權限。

7. 請遵循 使用 SageMaker AI Ground Truth 資訊清單檔案 （主控台） 建立資料集 中的指示，使用上傳的資訊清單檔案建立資料集。對於步驟 8，請在 .manifest 檔案位置，輸入清單檔案位置的 Amazon S3 URL。如果您使用 AWS SDK，請執行 使用 SageMaker AI Ground Truth 資訊清單檔案 (SDK) 建立資料集。