CreateML Multi-Label Classification Annotation Format

This script converts a multi-label CSV file with annotations into a JSON file compatible with Apple's CreateML. The CSV file must contain image filenames and their corresponding labels. The output JSON file will list the image filenames with their associated labels.

🤔Why use this converter

Currently, no online format converter can handle multi-label classification data from csv to json in a format compatible with CreateML. This repository fills that gap by providing an easy-to-use solution.

Requirements

• Python 3.0+
• Standard Python libraries: csv, json, and argparse (all three are included in Python)

📁Files

You can specify custom file paths for the input CSV and output JSON files when running the script. Use the following command structure:

python formatconvert.py <path_to_csv_file> <path_to_json_file>

Default file names:

• _classes.csv: The input CSV file containing image filenames and labels.
• annotations.json: The output JSON file where the processed data will be saved.

CSV File Format

The CSV file should be structured as follows:

• The first row must contain headers representing labels.

• Each subsequent row represents an image and its annotations.

• The first column of each row should contain the image filename.

• Each subsequent cell in a row should contain either 1 (if the label applies to the image) or 0 (if the label does not apply)

🏃‍♂️‍➡️Usage

1. Clone this Repo: If you haven't already cloned the repository, use the following command to download it to your local machine:

   git clone https://github.com/RadoKyselak/CreateML-multilabel-classification-annotation-format
   

2. Navigate to the Directory: Move into the directory where the script is located:

   cd CreateML-multilabel-classification-annotation-format
   

3. Ensure Python is Installed: Make sure you have Python 3.x installed. Check the version with:

   python --version
   

   or

   python3 --version
   

4. Run the Script: You can now execute the script using Python. If you want to use the default file paths ( CSV file: ./_classes.csv, JSON file: ./annotations.json), simply run:

   python formatconvert.py
   

   To use custom file paths, specify them as arguments:

   python formatconvert.py ./path/to/your/input.csv ./path/to/your/output.json
   

5. Review the Output: After running the script, the output JSON file will be saved in the specified location. You should see a message in the terminal confirming that the JSON data has been saved.

📜 Script Overview

• Imports:

  • csv: To read data from the CSV file.
  • json: To write data to the JSON file.
  • argparse: To handle command-line arguments for easy use.

• Arguments:

  • csv_file: Path to the input CSV file (default: ./_classes.csv).
  • json_file: Path to the output JSON file (default: ./annotations.json).

• Process:

  1. Read CSV File:

     • The script opens the CSV file and reads its content.
     • The first row is treated as headers representing the labels for each column.
     • Each of the rows below represents an image and its annotations.
     • If a cell contains '1', the corresponding label is added to the list of annotations for that image. A '0' indicates the label is not part of the image's annotations.

  2. Write JSON File:

     • The collected data is converted into JSON format and saved to the specified JSON file.

• Error Handling:

  • If the CSV file is not found, a FileNotFoundError message is printed.

Example

CSV Input:

JSON Output:

The script will convert the above CSV input into the following JSON format:

[
    {
        "image": "Screen-Shot-2024-png.rf.ms5jw5pllak4k9qa9ajjr556.jpg",
        "annotations": [
            "animal",
            "grass",
            "elephant"
        ]
    },
    {
        "image": "Screen-Shot-2024-png.rf.tz8je3xo05ak2p0a2qwji182.jpg",
        "annotations": [
            "animal",
            "fox"
        ]
    }
]

👾 Troubleshooting

• File Not Found Error: Ensure that the file paths are correct and that the specified files exist.

• General Errors: Check that the CSV file is properly formatted and free of unexpected characters or structures.

  The correct format:

  • The first row contains headers.

  • The first column of each row contains the image filename.

  • All subsequent columns contain only 1 or 0.

License

This project is licensed under the Apache License 2.0 - see the LICENSE file for details.