|
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": datasets.Image(), |
|
"image_path": datasets.Value("string"), |
|
"label_path": datasets.Value("string"), |
|
"magnification": datasets.Value("int32"), |
|
"width": datasets.Value("int32"), |
|
"height": datasets.Value("int32"), |
|
"resolution": datasets.Value("int32"), |
|
"annotations": datasets.Sequence({ |
|
"category_id": datasets.Value("int32"), |
|
"bounding_box": { |
|
"x_center_rel": datasets.Value("float32"), |
|
"y_center_rel": datasets.Value("float32"), |
|
"width_rel": datasets.Value("float32"), |
|
"height_rel": datasets.Value("float32"), |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
}, |
|
}), |
|
}) |
|
return datasets.DatasetInfo( |
|
description=_DESCRIPTION, |
|
features=features, |
|
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/Stomatal_Images_Datasets/resolve/main/data/Labeled Stomatal Images.csv", |
|
"zip": "https://huggingface.co/datasets/XintongHe/Stomatal_Images_Datasets/resolve/main/data/Labeled Stomatal Images.zip" |
|
}) |
|
|
|
|
|
species_info = pd.read_csv(data_files["csv"]) |
|
extracted_images_path = os.path.join(data_files["zip"], "Labeled Stomatal Images") |
|
|
|
|
|
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()) |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
annotations.append({ |
|
"category_id": int(class_id), |
|
"bounding_box": { |
|
"x_center_rel": x_center_rel , |
|
"y_center_rel": y_center_rel, |
|
"width_rel": width_rel, |
|
"height_rel": height_rel |
|
|
|
|
|
|
|
|
|
} |
|
}) |
|
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] |
|
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) |
|
|
|
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] |
|
magnification = species_row['Magnification'].values[0] |
|
width = species_row['Witdh'].values[0] |
|
height = species_row['Heigth'].values[0] |
|
resolution = species_row['Resolution'].values[0] |
|
|
|
else: |
|
|
|
species = None |
|
scientific_name = None |
|
width = 1024 |
|
height = 768 |
|
|
|
annotations = self._parse_yolo_labels(label_path, width, height) |
|
|
|
|
|
yield image_id, { |
|
"image_id": image_id, |
|
"species": species, |
|
"scientific_name": scientific_name, |
|
"image": img, |
|
"image_path": image_path, |
|
"label_path": label_path, |
|
"magnification": magnification, |
|
"width": width, |
|
"height": height, |
|
"resolution": resolution, |
|
"annotations": annotations |
|
} |
