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from datasets import DatasetInfo, Features, Value, ClassLabel, Split, SplitGenerator, GeneratorBasedBuilder, BuilderConfig, Array3D |
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import os |
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from PIL import Image |
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import matplotlib.pyplot as plt |
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import numpy as np |
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_DRIVE_ID = "1fXgVwhdU5YGj0SPIcHxSpxkhvRh54oEH" |
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_URL = f"https://drive.google.com/uc?export=download&id={_DRIVE_ID}" |
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class PlantsDataset(GeneratorBasedBuilder): |
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VERSION = datasets.Version("1.0.0") |
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BUILDER_CONFIGS = [ |
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BuilderConfig(name="default", version=VERSION, description="Default configuration for PlantsDataset"), |
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] |
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def _info(self): |
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features = Features({ |
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"image": Array3D(dtype="uint8", shape=(None, None, 3)), |
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"label": ClassLabel(names=["aleo vera", "calotropis gigantea"]), |
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}) |
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return DatasetInfo( |
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description="Your dataset description", |
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features=features, |
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supervised_keys=("image", "label"), |
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homepage="Your dataset homepage", |
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citation="Citation for your dataset", |
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) |
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def _split_generators(self, dl_manager): |
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downloaded_file = dl_manager.download_and_extract(_URL) |
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return [ |
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SplitGenerator( |
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name=Split.TRAIN, |
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gen_kwargs={ |
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"data_folder": os.path.join(downloaded_file, "train"), |
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}, |
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), |
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SplitGenerator( |
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name=Split.TEST, |
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gen_kwargs={ |
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"data_folder": os.path.join(downloaded_file, "test"), |
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}, |
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), |
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] |
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def _generate_examples(self, data_folder): |
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label_names = self.info.features['label'].names |
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for label_name in label_names: |
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subfolder_path = os.path.join(data_folder, label_name) |
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label = label_names.index(label_name) |
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for root, _, files in os.walk(subfolder_path): |
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for file_name in files: |
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file_path = os.path.join(root, file_name) |
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if os.path.isfile(file_path) and file_name.lower().endswith(('.png', '.jpg', '.jpeg')): |
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try: |
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with Image.open(file_path) as image: |
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image_array = np.array(image) |
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yield file_path, { |
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"image": image_array, |
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"label": label, |
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} |
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except (IOError, OSError): |
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print(f"Skipped file {file_path}, due to an error opening the image.") |
