# 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.
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# Lint as: python3
"""Bounding boxes feature."""

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

import collections

import tensorflow.compat.v2 as tf

from tensorflow_datasets.core.features import feature


BBox = collections.namedtuple('BBox', 'ymin, xmin, ymax, xmax')


class BBoxFeature(feature.Tensor):
  """`FeatureConnector` for a normalized bounding box.

  Note: If you have multiple bounding boxes, you may want to wrap the feature
  inside a `tfds.feature.Sequence`.

  Input:
    * `tfds.features.BBox` tuple.

  Output:
    bbox: tf.Tensor of type `tf.float32` and shape `[4,]` which contains the
      normalized coordinates of the bounding box `[ymin, xmin, ymax, xmax]`

  Example:
    * In the DatasetInfo object:

    ```
    features=features.FeatureDict({
        'bbox': features.BBox(shape=(None, 64, 64, 3)),
    })
    ```

    * During generation:

    ```
    yield {
        'input': tfds.feature.BBox(ymin=0.3, xmin=0.8, ymax=0.5, xmax=1.0),
    }
    ```
  """

  def __init__(self):
    super(BBoxFeature, self).__init__(shape=(4,), dtype=tf.float32)

  def encode_example(self, bbox):
    """See base class for details."""
    # Validate the coordinates
    for coordinate in bbox:
      if not isinstance(coordinate, float):
        raise ValueError(
            'BBox coordinates should be float. Got {}.'.format(bbox))
      if not 0.0 <= coordinate <= 1.0:
        raise ValueError(
            'BBox coordinates should be between 0 and 1. Got {}.'.format(bbox))
      if bbox.xmax < bbox.xmin or bbox.ymax < bbox.ymin:
        raise ValueError(
            'BBox coordinates should have min <= max. Got {}.'.format(bbox))

    return super(BBoxFeature, self).encode_example(
        [bbox.ymin, bbox.xmin, bbox.ymax, bbox.xmax]
    )