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"""TODO: Add a description here.""" |
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import csv |
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import json |
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from PIL import Image as IMG |
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import os |
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import datasets |
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from datasets import GeneratorBasedBuilder, DatasetInfo, Features, ClassLabel,Image, SplitGenerator, Sequence |
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_DESCRIPTION = """\ |
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The "DeepFruit" dataset is a comprehensive collection designed for the advancement of research in fruit detection, recognition, and classification. |
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The type of fruit is determined by various external appearance features. The dataset, from Mendeley, comprises 21,122 images of 20 diverse fruit types across 8 different combinations. This dataset includes separate images and CSV files for training and testing, each containing varying quantities of each fruit. The objective of this study is to convert fruit images into the PIL (Python Imaging Library) format. |
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""" |
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_URLS = { |
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"train": 'https://huggingface.co/datasets/sc890/DEEPFRUlT_DATASET/resolve/main/Fruits_Dataset_Train.zip', |
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"test": 'https://huggingface.co/datasets/sc890/DEEPFRUlT_DATASET/resolve/main/Fruits_Dataset_Test.zip', |
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} |
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class DeepFruitDataset(datasets.GeneratorBasedBuilder): |
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BUILDER_CONFIGS = [ |
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BuilderConfig(name="deepfruit_dataset", version=Version("1.0.0")) |
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] |
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_URLS = _URLS |
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def _info(self): |
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return datasets.DatasetInfo( |
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description=_DESCRIPTION, |
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features= datasets.Features({ |
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"image_id": datasets.Value("string"), |
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"folder_number":datasets.Value("int32"), |
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"image": datasets.Image(), |
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"image_path": datasets.Value("string"), |
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"label": datasets.Value("string"), |
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}), |
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homepage="https://data.mendeley.com/datasets/5prc54r4rt/1", |
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license="Mendeley License: CC BY 4.0", |
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) |
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def _split_generators(self, dl_manager): |
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urls_to_download = self._URLS |
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downloaded_files = dl_manager.download_and_extract(urls_to_download) |
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print(downloaded_files) |
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return [ |
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datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloaded_files["train"], "folder": "Fruits_Dataset_Train", "csv_name": "Labels_Train.csv"}), |
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datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": downloaded_files["test"], "folder": "Fruits_Dataset_Test","csv_name": "Labels_Test.csv"}), |
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] |
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def _generate_examples(self, filepath, folder, csv_name=None): |
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path = os.path.join(filepath, folder, csv_name) |
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label_dict = {} |
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count = 0 |
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with open(path, 'r') as file: |
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for line in file: |
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if count == 0: |
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count += 1 |
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continue |
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image_name = line.split(",")[0] |
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label = line.replace(image_name, "") |
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label = label.replace(",", "") |
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label = label.replace("\n", "") |
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label_dict[image_name] = label |
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for number_dir in range(1, 9): |
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image_dir = os.path.join(filepath,folder, str(number_dir)) |
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for image_file in os.listdir(image_dir): |
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if not image_file.endswith('.jpg'): |
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continue |
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last_dot_index = image_file.rfind('.') |
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if last_dot_index != -1: |
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image_id = image_file[:last_dot_index] |
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else: |
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image_id = image_file |
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image_id = str(number_dir) + image_id |
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image_path = os.path.join(image_dir, image_file) |
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relative_image_path = os.path.relpath(image_path, start=filepath) |
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img = IMG.open(image_path) |
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yield image_id, { |
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"image_id": image_id, |
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"folder_number": number_dir, |
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"image": img, |
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"image_path": relative_image_path, |
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"label": label_dict[image_file] |
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} |
