# coding=utf-8
# Copyright 2020 The TensorFlow Datasets Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

# Lint as: python3
"""Beans leaf dataset with images of diseased and health leaves."""

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import os

import tensorflow_datasets.public_api as tfds

_CITATION = """\
@ONLINE {beansdata,
    author="Makerere AI Lab",
    title="Bean disease dataset",
    month="January",
    year="2020",
    url="https://github.com/AI-Lab-Makerere/ibean/"
}
"""

_DESCRIPTION = """\
Beans is a dataset of images of beans taken in the field using smartphone
cameras. It consists of 3 classes: 2 disease classes and the healthy class.
Diseases depicted include Angular Leaf Spot and Bean Rust. Data was annotated
by experts from the National Crops Resources Research Institute (NaCRRI) in
Uganda and collected by the Makerere AI research lab.
"""

_TRAIN_URL = "https://storage.googleapis.com/ibeans/train.zip"
_VALIDATION_URL = "https://storage.googleapis.com/ibeans/validation.zip"
_TEST_URL = "https://storage.googleapis.com/ibeans/test.zip"

_IMAGE_SIZE = 500
_IMAGE_SHAPE = (_IMAGE_SIZE, _IMAGE_SIZE, 3)

_LABELS = ["angular_leaf_spot", "bean_rust", "healthy"]


class Beans(tfds.core.GeneratorBasedBuilder):
  """Beans plant leaf images dataset."""

  VERSION = tfds.core.Version("0.1.0")

  def _info(self):
    return tfds.core.DatasetInfo(
        builder=self,
        description=_DESCRIPTION,
        features=tfds.features.FeaturesDict({
            "image": tfds.features.Image(shape=_IMAGE_SHAPE),
            "label": tfds. features.ClassLabel(names=_LABELS)
        }),
        supervised_keys=("image", "label"),
        homepage="https://github.com/AI-Lab-Makerere/ibean/",
        citation=_CITATION,
    )

  def _split_generators(self, dl_manager):
    train_path, val_path, test_path = dl_manager.download(
        [_TRAIN_URL, _VALIDATION_URL, _TEST_URL])

    return [
        tfds.core.SplitGenerator(
            name=tfds.Split.TRAIN,
            gen_kwargs={
                "archive": dl_manager.iter_archive(train_path)},
        ),
        tfds.core.SplitGenerator(
            name=tfds.Split.VALIDATION,
            gen_kwargs={
                "archive": dl_manager.iter_archive(val_path)},
        ),
        tfds.core.SplitGenerator(
            name=tfds.Split.TEST,
            gen_kwargs={
                "archive": dl_manager.iter_archive(test_path)},
        ),
    ]

  def _generate_examples(self, archive):
    """Yields examples."""
    for fname, fobj in archive:
      if not fname.endswith(".jpg"):
        continue
      label = fname.split(os.path.sep)[-2]
      record = {
          "image": fobj,
          "label": label,
      }
      yield fname, record