image_manual.py

# coding: utf8
from __future__ import unicode_literals

import prodigy
from prodigy.components.loaders import Images
from prodigy.util import split_string


# Recipe decorator with argument annotations: (description, argument type,
# shortcut, type / converter function called on value before it's passed to
# the function). Descriptions are also shown when typing --help.
@prodigy.recipe('image.manual',
    dataset=("The dataset to use", "positional", None, str),
    source=("Path to a directory of images", "positional", None, str),
    label=("One or more comma-separated labels", "option", "l", split_string),
    exclude=("Names of datasets to exclude", "option", "e", split_string),
    darken=("Darken image to make boxes stand out more", "flag", "D", bool)
)
def image_manual(dataset, source, label=None, exclude=None, darken=False):
    """
    Manually annotate images by drawing rectangular bounding boxes or polygon
    shapes on the image.
    """
    # Load a stream of images from a directory and return a generator that
    # yields a dictionary for each example in the data. All images are
    # converted to base64-encoded data URIs.
    stream = Images(source)

    return {
        'view_id': 'image_manual', # Annotation interface to use
        'dataset': dataset,        # Name of dataset to save annotations
        'stream': stream,          # Incoming stream of examples
        'exclude': exclude,        # List of dataset names to exclude
        'config': {                # Additional config settings, mostly for app UI
            'label': ', '.join(label) if label is not None else 'all',
            'labels': label,       # Selectable label options,
            'darken_image': 0.3 if darken else 0
        }
    }