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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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import os |
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from PIL import Image |
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import numpy as np |
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import pandas as pd |
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import datasets |
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_CITATION = """\ |
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@InProceedings{huggingface:dataset, |
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title = {A great new dataset}, |
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author={huggingface, Inc. |
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}, |
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year={2020} |
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} |
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""" |
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_DESCRIPTION = """\ |
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This new dataset is designed to solve this great NLP task and is crafted with a lot of care. |
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""" |
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_HOMEPAGE = "" |
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_LICENSE = "" |
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class NewDataset(datasets.GeneratorBasedBuilder): |
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"""TODO: Short description of my dataset.""" |
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VERSION = datasets.Version("1.1.0") |
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def _info(self): |
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features = datasets.Features({ |
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"image_id": datasets.Value("string"), |
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"species": datasets.Value("string"), |
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"scientific_name": datasets.Value("string"), |
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"pics_array": datasets.Array3D(dtype="uint8", shape=(3, 768, 1024)), |
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"image_resolution": { |
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"width": datasets.Value("int32"), |
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"height": datasets.Value("int32"), |
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}, |
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"annotations": datasets.Sequence({ |
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"category_id": datasets.Value("int32"), |
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"bounding_box": { |
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"x_min": datasets.Value("float32"), |
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"y_min": datasets.Value("float32"), |
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"x_max": datasets.Value("float32"), |
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"y_max": datasets.Value("float32"), |
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}, |
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}), |
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}) |
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return datasets.DatasetInfo( |
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description=_DESCRIPTION, |
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features=features, |
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homepage=_HOMEPAGE, |
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license=_LICENSE, |
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citation=_CITATION, |
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) |
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def _split_generators(self, dl_manager): |
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data_files = dl_manager.download_and_extract({ |
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"csv": "https://huggingface.co/datasets/XintongHe/Populus_Stomatal_Images_Datasets/resolve/main/data/Labeled Stomatal Images.csv", |
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"zip_config": "https://huggingface.co/datasets/XintongHe/Populus_Stomatal_Images_Datasets/resolve/main/data/Labeled Stomatal Images_config.zip" |
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}) |
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csv_path = data_files["csv"] |
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extracted_config_path = data_files["zip_config"] |
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species_info = pd.read_csv(csv_path) |
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image_filenames_config = species_info['FileName'].apply(lambda x: x + '.jpg').tolist() |
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image_filenames = [f for f in image_filenames_config if os.path.exists(os.path.join(extracted_config_path, f))] |
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return [ |
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datasets.SplitGenerator( |
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name=datasets.Split.TRAIN, |
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gen_kwargs={ |
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"filepaths": image_filenames, |
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"species_info": species_info, |
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"data_dir": extracted_config_path |
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}, |
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) |
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] |
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def _parse_yolo_labels(self, label_path, width, height): |
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annotations = [] |
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with open(label_path, 'r') as file: |
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yolo_data = file.readlines() |
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for line in yolo_data: |
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class_id, x_center_rel, y_center_rel, width_rel, height_rel = map(float, line.split()) |
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x_min = (x_center_rel - width_rel / 2) * width |
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y_min = (y_center_rel - height_rel / 2) * height |
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x_max = (x_center_rel + width_rel / 2) * width |
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y_max = (y_center_rel + height_rel / 2) * height |
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annotations.append({ |
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"category_id": int(class_id), |
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"bounding_box": { |
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"x_min": x_min, |
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"y_min": y_min, |
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"x_max": x_max, |
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"y_max": y_max |
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} |
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}) |
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return annotations |
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def _generate_examples(self, filepaths, species_info, data_dir): |
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"""Yields examples as (key, example) tuples.""" |
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for file_name in filepaths: |
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image_id = os.path.splitext(file_name)[0] |
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image_path = os.path.join(data_dir, f"{image_id}.jpg") |
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label_path = os.path.join(data_dir, f"{image_id}.txt") |
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species_row = species_info.loc[species_info['FileName'] == image_id] |
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if not species_row.empty: |
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species = species_row['Species'].values[0] |
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scientific_name = species_row['ScientificName'].values[0] |
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width = species_row['Witdh'].values[0] |
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height = species_row['Heigth'].values[0] |
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else: |
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species = None |
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scientific_name = None |
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width = 1024 |
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height = 768 |
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pics_array = None |
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with Image.open(image_path) as img: |
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pics_array = np.array(img).tolist() |
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annotations = self._parse_yolo_labels(label_path, width, height) |
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yield image_id, { |
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"image_id": image_id, |
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"species": species, |
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"scientific_name": scientific_name, |
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"pics_array": pics_array, |
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"image_resolution": {"width": width, "height": height}, |
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"annotations": annotations |
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} |
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