# 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.
# TODO: Address all TODOs and remove all explanatory comments
"""TODO: Add a description here."""


import csv
import json
import os
from PIL import Image
import numpy as np
import datasets


# TODO: Add BibTeX citation
# Find for instance the citation on arxiv or on the dataset repo/website
_CITATION = """\
@InProceedings{huggingface:dataset,
title = {A great new dataset},
author={huggingface, Inc.
},
year={2020}
}
"""

# TODO: Add description of the dataset here
# You can copy an official description
_DESCRIPTION = """\
This new dataset is designed to solve this great NLP task and is crafted with a lot of care.
"""

# TODO: Add a link to an official homepage for the dataset here
_HOMEPAGE = ""

# TODO: Add the licence for the dataset here if you can find it
_LICENSE = ""

# TODO: Add link to the official dataset URLs here
# The HuggingFace Datasets library doesn't host the datasets but only points to the original files.
# This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)
# _URLS = {
#     "first_domain": "https://huggingface.co./great-new-dataset-first_domain.zip",
#     "second_domain": "https://huggingface.co./great-new-dataset-second_domain.zip",
# }


# TODO: Name of the dataset usually matches the script name with CamelCase instead of snake_case
class NewDataset(datasets.GeneratorBasedBuilder):
    """TODO: Short description of my dataset."""

    VERSION = datasets.Version("1.1.0")

    # This is an example of a dataset with multiple configurations.
    # If you don't want/need to define several sub-sets in your dataset,
    # just remove the BUILDER_CONFIG_CLASS and the BUILDER_CONFIGS attributes.

    # If you need to make complex sub-parts in the datasets with configurable options
    # You can create your own builder configuration class to store attribute, inheriting from datasets.BuilderConfig
    # BUILDER_CONFIG_CLASS = MyBuilderConfig

    # You will be able to load one or the other configurations in the following list with
    # data = datasets.load_dataset('my_dataset', 'first_domain')
    # data = datasets.load_dataset('my_dataset', 'second_domain')
    # BUILDER_CONFIGS = [
    #     datasets.BuilderConfig(name="first_domain", version=VERSION, description="This part of my dataset covers a first domain"),
    #     datasets.BuilderConfig(name="second_domain", version=VERSION, description="This part of my dataset covers a second domain"),
    # ]

    # DEFAULT_CONFIG_NAME = "first_domain"  # It's not mandatory to have a default configuration. Just use one if it make sense.

    def _info(self):
    features = datasets.Features({
        "image_id": datasets.Value("string"),
        "species": datasets.Value("string"),
        "scientific_name": datasets.Value("string"),
        "pics_array": datasets.Array3D(dtype="uint8", shape=(3, 768, 1024)), # Assuming images are RGB with shape 768x1024
        "image_resolution": {
            "width": datasets.Value("int32"),
            "height": datasets.Value("int32"),
        },
        "annotations": datasets.Sequence({
            "category_id": datasets.Value("int32"),
            "bounding_box": {
                "x_min": datasets.Value("float32"),
                "y_min": datasets.Value("float32"),
                "x_max": datasets.Value("float32"),
                "y_max": datasets.Value("float32"),
            },
        }),
    })
    return datasets.DatasetInfo(
        description=_DESCRIPTION,
        features=features,  # Here we define them because they are different between the two configurations
        homepage=_HOMEPAGE,
        license=_LICENSE,
        citation=_CITATION,
    )

    def _split_generators(self, dl_manager):
        # Only download data, no need to split
        data_files = dl_manager.download_and_extract({
            "csv": "https://huggingface.co./datasets/XintongHe/Populus_Stomatal_Images_Datasets/resolve/main/Labeled Stomatal Images.csv",
            "zip": "https://huggingface.co./datasets/XintongHe/Populus_Stomatal_Images_Datasets/resolve/main/Labeled Stomatal Images.zip"
        })
        
        species_info = pd.read_csv(data_files["csv"])
        extracted_images_path = os.path.join(data_files["zip"], "Labeled Stomatal Images")

        # Get all image filenames
        all_image_filenames = species_info['FileName'].apply(lambda x: x + '.jpg').tolist()
        
        # No longer need to randomize and split the dataset
        return [datasets.SplitGenerator(
            name=datasets.Split.TRAIN,
            gen_kwargs={
                "filepaths": all_image_filenames,
                "species_info": species_info,
                "data_dir": extracted_images_path,
            },
        )]

 
def save_metadata_as_json(image_id, annotations, species, scientific_name, json_path):
    metadata = {
        "image_id": image_id,
        "species": species,
        "scientific_name": scientific_name,
        "annotations": annotations
    }
    with open(json_path, 'w') as json_file:
        json.dump(metadata, json_file)

def _parse_yolo_labels(self, label_path, width, height):
    annotations = []
    with open(label_path, 'r') as file:
        yolo_data = file.readlines()

    for line in yolo_data:
        class_id, x_center_rel, y_center_rel, width_rel, height_rel = map(float, line.split())
        x_min = (x_center_rel - width_rel / 2) * width
        y_min = (y_center_rel - height_rel / 2) * height
        x_max = (x_center_rel + width_rel / 2) * width
        y_max = (y_center_rel + height_rel / 2) * height
        annotations.append({
            "category_id": int(class_id),
            "bounding_box": {
                "x_min": x_min,
                "y_min": y_min,
                "x_max": x_max,
                "y_max": y_max
            }
        })
    return annotations

def _generate_examples(self, filepaths, species_info, data_dir, split):
    """Yields examples as (key, example) tuples."""
    for file_name in filepaths:
        image_id = os.path.splitext(file_name)[0]  # Extract the base name without the file extension
        image_path = os.path.join(data_dir, f"{image_id}.jpg")
        label_path = os.path.join(data_dir, f"{image_id}.txt")
        
        # Find the corresponding row in the CSV for the current image
        species_row = species_info.loc[species_info['FileName'] == image_id]
        if not species_row.empty:
            species = species_row['Species'].values[0]
            scientific_name = species_row['ScientificName'].values[0]
            width = species_row['Width'].values[0]
            height = species_row['Height'].values[0]
        else:
            # Default values if not found
            species = None
            scientific_name = None
            width = 1024  # or some default value
            height = 768   # or some default value
        
        with Image.open(image_path) as img:
            pics_array = np.array(img)  # Convert the PIL image to a numpy array

        annotations = self._parse_yolo_labels(label_path, width, height)
        
        yield image_id, {
            "image_id": image_id,
            "species": species,
            "scientific_name": scientific_name,
            "pics_array": pics_array,
            "image_resolution": {"width": width, "height": height},
            "annotations": annotations,
            "image": img  # Return the PIL image
        }