plantsdataset.py

# -*- coding: utf-8 -*-
"""PlantsDataset

Automatically generated by Colaboratory.

Original file is located at
    https://colab.research.google.com/drive/1nkvgrtbJQaIBdnxYHl8WTpKVL_AzAzux
"""

import datasets
from datasets import load_dataset, DatasetInfo, Features, Value, ClassLabel, Split, SplitGenerator, GeneratorBasedBuilder, BuilderConfig, Array3D, Version
import os
from PIL import Image
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import geopandas as gpd
from datasets import (
    GeneratorBasedBuilder, Version, DownloadManager, SplitGenerator, Split,
    Features, Value, BuilderConfig, DatasetInfo
)
import matplotlib.pyplot as plt
import seaborn as sns
import csv
import json
from shapely.geometry import Point
import base64
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
import io
import os
from PIL import Image
import numpy as np
from datasets import DatasetInfo, Features, Value, ClassLabel, Split, SplitGenerator, GeneratorBasedBuilder, BuilderConfig
from datasets import NamedSplit, Split, SplitGenerator
import gdown
_DRIVE_ID = "1fXgVwhdU5YGj0SPIcHxSpxkhvRh54oEH"
_URL = f"https://drive.google.com/uc?export=download&id={_DRIVE_ID}"

class PlantsDataset(GeneratorBasedBuilder):
    VERSION = datasets.Version("1.0.0")
    BUILDER_CONFIGS = [
        BuilderConfig(name="default", version=VERSION, description="Default configuration for PlantsDataset"),
    ]

    def _info(self):
        features = Features({
            "image": Value("string"),  # Change to Array3D to store image arrays
            "label": ClassLabel(names=["aleo vera", "calotropis gigantea"]),
        })
        return DatasetInfo(
            description="Your dataset description",
            features=features,
            supervised_keys=("image", "label"),
            homepage="Your dataset homepage",
            citation="Citation for your dataset",
        )

    def _split_generators(self, dl_manager):
        downloaded_file = dl_manager.download_and_extract(_URL)

        return [
            SplitGenerator(
                name=Split.TRAIN,
                gen_kwargs={
                    "data_folder": os.path.join(downloaded_file, "train"),
                },
            ),
            SplitGenerator(
                name=Split.TEST,
                gen_kwargs={
                    "data_folder": os.path.join(downloaded_file, "test"),
                },
            ),
        ]

    def _generate_examples(self, data_folder):
        label_names = self.info.features['label'].names
        for label_name in label_names:
            subfolder_path = os.path.join(data_folder, label_name)
            label = label_names.index(label_name)
            for root, _, files in os.walk(subfolder_path):
                for file_name in files:
                    file_path = os.path.join(root, file_name)
                    if os.path.isfile(file_path) and file_name.lower().endswith(('.png', '.jpg', '.jpeg')):
                        # Image ID should be unique, use filename for simplicity
                        image_id = os.path.splitext(file_name)[0]
                        yield image_id, {
                            "image": file_path,  # Store file path as a string
                            "label": label,
                        }
                    else:
                        print(f"Skipped file {file_path}, since it is not an image."）
# Instantiate the dataset builder for PlantsDataset
plants_dataset = PlantsDataset()

# Download the data and prepare the dataset
plants_dataset.download_and_prepare()

# Access the dataset as a `DatasetDict`
dataset_dict = plants_dataset.as_dataset()

# Access the train and test splits
train_dataset = dataset_dict['train']
test_dataset = dataset_dict['test']

# Now you can use `train_dataset` and `test_dataset` as needed
# For example, you can iterate over the dataset and access the file paths and labels
for example in train_dataset:
    print(example['image'], example['label'])