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



_CITATION = """\
@article{nature},
  title={Labeled temperate hardwood tree stomatal image datasets from seven taxa of Populus and 17 hardwood species},
  author={Jiaxin Wang, Heidi J. Renninger and Qin Ma},
  journal={Sci Data 11, 1 (2024)},
  year={2024}
"""

_DESCRIPTION = """\
This new dataset is designed to solve image classification and segmentation tasks and is crafted with a lot of care.
"""

_HOMEPAGE = "https://zenodo.org/records/8271253"


_LICENSE = "https://creativecommons.org/licenses/by/4.0/"


class NewDataset(datasets.GeneratorBasedBuilder):
    """TODO: Short description of my dataset."""

    VERSION = datasets.Version("1.1.0")

    
    def _info(self):
        features = datasets.Features({
            "image_id": datasets.Value("string"),
            "species": datasets.Value("string"),
            "scientific_name": datasets.Value("string"),
            "image_path": datasets.Value("string"),
            "image": datasets.Image(),
            # 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):
        
        data_files = dl_manager.download_and_extract({
            "csv": "https://huggingface.co/datasets/XintongHe/Populus_Stomatal_Images_Datasets/resolve/main/data/Labeled Stomatal Images.csv",
            "zip": "https://huggingface.co/datasets/XintongHe/Populus_Stomatal_Images_Datasets/resolve/main/data/Labeled Stomatal Images.zip",
            "annotations_json": "https://huggingface.co/datasets/XintongHe/Populus_Stomatal_Images_Datasets/resolve/main/data/annotations.json"
        })

        
        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()
        
        return [datasets.SplitGenerator(
            name=datasets.Split.TRAIN,
            gen_kwargs={
                "filepaths": all_image_filenames,
                "species_info": species_info,
                "data_dir": extracted_images_path
            },
        )]
    

    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):
        """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")
            img = Image.open(image_path)
            # 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['Witdh'].values[0]
                height = species_row['Heigth'].values[0]
            else:
                # Default values if not found
                species = None
                scientific_name = None
                width = 1024 
                height = 768   
            
            annotations = self._parse_yolo_labels(label_path, width, height)
            
            # Yield the dataset example
            yield image_id, {
                "image_id": image_id,
                "species": species,
                "scientific_name": scientific_name,
                #"pics_array": pics_array.tolist(),  # Convert numpy array to list for JSON serializability
                "image_path": image_path,
                "image": img,
                "image_resolution": {"width": width, "height": height},
                "annotations": annotations
            }