# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
#
# 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.
"""Script for reading 'Object Detection for Chess Pieces' dataset."""


import os
import datasets

from PIL import Image
from glob import glob

_CITATION = ""

_DESCRIPTION = """\
The "Object Detection for Chess Pieces" dataset is a toy dataset created (as suggested by the name!) to introduce object detection in a beginner friendly way.
"""

_HOMEPAGE = "https://github.com/faizankshaikh/chessDetection"

_LICENSE = "CC-BY-SA:2.0"

_REPO = "data"# "https://huggingface.co/datasets/jalFaizy/detect_chess_pieces/raw/main/data"
_URLS = {"train": f"{_REPO}/train.zip", "valid": f"{_REPO}/valid.zip"}

_CATEGORIES = ["blackKing", "whiteKing", "blackQueen", "whiteQueen"]


class DetectChessPieces(datasets.GeneratorBasedBuilder):
    """Object Detection for Chess Pieces dataset"""

    VERSION = datasets.Version("1.0.0")

    def _info(self):
        return datasets.DatasetInfo(
            features=datasets.Features(
                {
                    "image": datasets.Image(),
                    "objects": datasets.Sequence({
                        "label": datasets.ClassLabel(names=_CATEGORIES),
                        "bbox": datasets.Sequence(datasets.Value("int32"), length=4)
                    }),
                }
            ),
            supervised_keys=None,
            description=_DESCRIPTION,
            homepage=_HOMEPAGE,
            license=_LICENSE,
            citation=_CITATION,
        )

    def _split_generators(self, dl_manager):
        data_dir = dl_manager.download_and_extract(_URLS)
        print(data_dir["train"])
        return [
            datasets.SplitGenerator(
                name=datasets.Split.TRAIN,
                gen_kwargs={"split": "train", "data_dir": data_dir["train"]},
            ),
            datasets.SplitGenerator(
                name=datasets.Split.VALIDATION,
                gen_kwargs={"split": "valid", "data_dir": data_dir["valid"]},
            ),
        ]

    def _generate_examples(self, split, data_dir):
        image_dir = os.path.join(data_dir, "images")
        label_dir = os.path.join(data_dir, "labels")
        
        image_paths = sorted(glob(image_dir + "/*/*.png"))
        label_paths = sorted(glob(label_dir + "/*/*.txt"))
    
        for idx, (image_path, label_path) in enumerate(zip(image_paths, label_paths)):
            im = Image.open(image_path)
            width, height = im.size

            with open(label_path, "r") as f:
                lines = f.readlines()

            objects = []
            for line in lines:
                line = line.strip().split()
                try:
                    bbox_class = int(line[0])
                    bbox_xcenter = int(float(line[1]) * width)
                    bbox_ycenter = int(float(line[2]) * height)
                    bbox_width = int(float(line[3]) * width)
                    bbox_height = int(float(line[4]) * height)
                except:
                    print(f"Check file {f.name} for errors")

                objects.append({
                    "label": bbox_class,
                    "bbox": [bbox_xcenter, bbox_ycenter, bbox_width, bbox_height]
                })

            yield idx, {"image": image_path, "objects": objects}