bee_dataset.py

"""BeeDataset dataset."""
import json
import random
import tensorflow as tf
import tensorflow_datasets as tfds

_DESCRIPTION = """
# Bee Dataset
This dataset contains images and a set of labels that expose certain characterisitics of that images, such as *varroa-mite* infections, bees carrying *pollen-packets* or bee that are *cooling the hive* by flappingn their wings. Additionally, this dataset contains images of *wasps* to be able to distinguish bees and wasps.

The images of the bees are taken from above and rotated. The bee is vertical and either its head or the trunk is on top. All images were taken with a green background and the distance to the bees was always the same, thus all bees have the same size.

Each image can have multiple labels assigned to it. E.g. a bee can be cooling the hive and have a varrio-mite infection at the same time.

This dataset is designed as mutli-label dataset, where each label, e.g. *varroa_output*, contains 1 if the characterisitic was present in the image and a 0 if it wasn't. All images are provided by 300 pixel height and 150 pixel witdh. As default the dataset provides the images as 150x75 (h,w) pixel. You can select 300 pixel height by loading the datset with the name "bee_dataset/bee_dataset_300" and with 200 pixel height by "bee_dataset/bee_dataset_200".

License: GNU GENERAL PUBLIC LICENSE

Author: Fabian Hickert <Fabian.Hickert@raspbee.de>
"""

_CITATION = """
@misc{BeeAlarmed - A camera based bee-hive monitoring,
  title =   "Dataset for a camera based bee-hive monitoring",
  url={https://github.com/BeeAlarmed}, journal={BeeAlarmed},
  author =  "Fabian Hickert",
  year   =  "2021",
  NOTE   = "\\url{https://raspbee.de/} and \\url{https://github.com/BeeAlarmed/BeeAlarmed}"
}
"""


class BeeDatasetConfig(tfds.core.BuilderConfig):
  """BuilderConfig for the BeeDataset

    Args:
    image_width (int): Desired image width
    image_height (int): Desired image heigth
  """

  def __init__(self,
         image_height=300,
         image_width=150,
         **kwargs):
    super().__init__(**kwargs)
    self.width = image_width
    self.height = image_height
    self.depth = 3


class BeeDataset(tfds.core.GeneratorBasedBuilder):
  """DatasetBuilder for BeeDataset dataset."""

  VERSION = tfds.core.Version('1.0.0')

  URL = 'https://raspbee.de/BeeDataset_20201121.zip'

  BEE_CFG_300 = BeeDatasetConfig(
      name='bee_dataset_300',
      description='BeeDataset images with 300 pixel height and 150 pixel width',
      version='1.0.0',
      image_height=300,
      image_width=150
     )

  BEE_CFG_200 = BeeDatasetConfig(
      name='bee_dataset_200',
      description='BeeDataset images with 200 pixel height and 100 pixel width',
      version='1.0.0',
      image_height=200,
      image_width=100
     )

  BEE_CFG_150 = BeeDatasetConfig(
      name='bee_dataset_150',
      description='BeeDataset images with 200 pixel height and 100 pixel width',
      version='1.0.0',
      image_height=150,
      image_width=75
     )

  BUILDER_CONFIGS = [BEE_CFG_300, BEE_CFG_200, BEE_CFG_150]

  def _info(self) -> tfds.core.DatasetInfo:
    """Returns the dataset metadata."""
    t_shape = (self.builder_config.height,
            self.builder_config.width,
            self.builder_config.depth)
    features = tfds.features.FeaturesDict({
      'input': tfds.features.Image(shape=t_shape),
      'output': {
        'varroa_output': tf.float64,
        'pollen_output': tf.float64,
        'wasps_output': tf.float64,
        'cooling_output': tf.float64,
        }
      })

    return tfds.core.DatasetInfo(
        builder=self,
        description=_DESCRIPTION,
        features=features,
        supervised_keys=('input', 'output'),
        homepage='https://raspbee.de',
        citation=_CITATION,
        )

  def _split_generators(self, dl_manager):
    """Returns SplitGenerators."""
    path = dl_manager.download_and_extract(self.URL)
    return {
        'train': self._generate_examples(path),
        }

  def _generate_examples(self, path):
    with tf.io.gfile.GFile(path / 'data.json') as json_file:

      # Load labels and image path
      data = json.loads(json_file.read())
      indexes = list(data.keys())
      random.shuffle(indexes)
      for name in indexes:
        labels = []
        entry = data[name]

        for lbl in ['varroa', 'pollen', 'wasps', 'cooling']:
          labels.append(1.0 if entry[lbl] else 0.0)

        img = path / f'images_{self.builder_config.height}' / name
    
        yield name+str(self.builder_config.height), {
            'input': img,
            'output': {
              'varroa_output': labels[0],
              'pollen_output': labels[1],
              'wasps_output': labels[2],
              'cooling_output': labels[3]
              }
            }