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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"),
# "x_min": datasets.Value("float32"),
# "y_min": datasets.Value("float32"),
# "x_max": datasets.Value("float32"),
# "y_max": datasets.Value("float32"),
# "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, # Here we define them because they are different between the two configurations
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")
# Get all image filenames
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())
# x_min = (x_center_rel - width_rel / 2) * width
# y_min = (y_center_rel - height_rel / 2) * height
# x_max = (x_center_rel + width_rel / 2) * width
# y_max = (y_center_rel + height_rel / 2) * height
# x_min = x_center_rel
# y_min = y_center_rel
# x_max = width_rel
# y_max = height_rel
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
# "x_min": x_min,
# "y_min": y_min,
# "x_max": x_max,
# "y_max": y_max
}
})
return annotations
# 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),
# "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)
# Find the corresponding row in the CSV for the current image
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:
# Default values if not found
species = None
scientific_name = None
width = 1024
height = 768
annotations = self._parse_yolo_labels(label_path, width, height)
# Yield the dataset example
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
} |