import datasets
import pandas as pd

_CITATION = """\
@InProceedings{huggingface:dataset,
title = {plantations_segmentation},
author = {TrainingDataPro},
year = {2023}
}
"""

_DESCRIPTION = """\
The dataset consist of screenshots from videos of basketball games with
the ball labeled with a bounging box. 
The dataset can be used to train a neural network in ball control recognition.
The dataset is useful for automating the camera operator's work during a match,
allowing the ball to be efficiently kept in frame.
"""
_NAME = 'plantations_segmentation'

_HOMEPAGE = f"https://huggingface.co/datasets/TrainingDataPro/{_NAME}"

_LICENSE = ""

_DATA = f"https://huggingface.co/datasets/TrainingDataPro/{_NAME}/resolve/main/data/"


class PlantationsSegmentation(datasets.GeneratorBasedBuilder):
    """Small sample of image-text pairs"""

    def _info(self):
        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=datasets.Features({
                'image_id': datasets.Value('int32'),
                'image': datasets.Image(),
                'class_segmentation': datasets.Image(),
                'object_segmentation': datasets.Image(),
                'shapes': datasets.Value('string')
            }),
            supervised_keys=None,
            homepage=_HOMEPAGE,
            citation=_CITATION,
        )

    def _split_generators(self, dl_manager):
        images = dl_manager.download(f"{_DATA}images.tar.gz")
        class_segmentation_masks = dl_manager.download(
            f"{_DATA}class_segmentation.tar.gz")
        object_segmentation_masks = dl_manager.download(
            f"{_DATA}object_segmentation.tar.gz")
        annotations = dl_manager.download(f"{_DATA}{_NAME}.csv")
        images = dl_manager.iter_archive(images)
        class_segmentation_masks = dl_manager.iter_archive(
            class_segmentation_masks)
        object_segmentation_masks = dl_manager.iter_archive(
            object_segmentation_masks)
        return [
            datasets.SplitGenerator(
                name=datasets.Split.TRAIN,
                gen_kwargs={
                    "images": images,
                    'class_segmentation_masks': class_segmentation_masks,
                    'object_segmentation_masks': object_segmentation_masks,
                    'annotations': annotations
                }),
        ]

    def _generate_examples(self, images, class_segmentation_masks,
                           object_segmentation_masks, annotations):
        annotations_df = pd.read_csv(annotations)

        for idx, ((image_path, image), (class_segmentation_path,
                                        class_segmentation),
                  (object_segmentation_path,
                   object_segmentation)) in enumerate(
                       zip(images, class_segmentation_masks,
                           object_segmentation_masks)):
            yield idx, {
                'image_id':
                    annotations_df.loc[
                        annotations_df['image_name'] == image_path]
                    ['image_id'].values[0],
                "image": {
                    "path": image_path,
                    "bytes": image.read()
                },
                "class_segmentation": {
                    "path": class_segmentation_path,
                    "bytes": class_segmentation.read()
                },
                "object_segmentation": {
                    "path": object_segmentation_path,
                    "bytes": object_segmentation.read()
                },
                'shapes':
                    annotations_df.loc[
                        annotations_df['image_name'] == image_path]
                    ['shapes'].values[0]
            }