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# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# TODO: Address all TODOs and remove all explanatory comments
"""TODO: Add a description here."""
import csv
import json
import os
from PIL import Image
import numpy as np
import datasets
# TODO: Add BibTeX citation
# Find for instance the citation on arxiv or on the dataset repo/website
_CITATION = """\
@InProceedings{huggingface:dataset,
title = {A great new dataset},
author={huggingface, Inc.
},
year={2020}
}
"""
# TODO: Add description of the dataset here
# You can copy an official description
_DESCRIPTION = """\
This new dataset is designed to solve this great NLP task and is crafted with a lot of care.
"""
# TODO: Add a link to an official homepage for the dataset here
_HOMEPAGE = ""
# TODO: Add the licence for the dataset here if you can find it
_LICENSE = ""
# TODO: Add link to the official dataset URLs here
# The HuggingFace Datasets library doesn't host the datasets but only points to the original files.
# This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)
# _URLS = {
# "first_domain": "https://huggingface.co/great-new-dataset-first_domain.zip",
# "second_domain": "https://huggingface.co/great-new-dataset-second_domain.zip",
# }
# TODO: Name of the dataset usually matches the script name with CamelCase instead of snake_case
class NewDataset(datasets.GeneratorBasedBuilder):
"""TODO: Short description of my dataset."""
VERSION = datasets.Version("1.1.0")
# This is an example of a dataset with multiple configurations.
# If you don't want/need to define several sub-sets in your dataset,
# just remove the BUILDER_CONFIG_CLASS and the BUILDER_CONFIGS attributes.
# If you need to make complex sub-parts in the datasets with configurable options
# You can create your own builder configuration class to store attribute, inheriting from datasets.BuilderConfig
# BUILDER_CONFIG_CLASS = MyBuilderConfig
# You will be able to load one or the other configurations in the following list with
# data = datasets.load_dataset('my_dataset', 'first_domain')
# data = datasets.load_dataset('my_dataset', 'second_domain')
# BUILDER_CONFIGS = [
# datasets.BuilderConfig(name="first_domain", version=VERSION, description="This part of my dataset covers a first domain"),
# datasets.BuilderConfig(name="second_domain", version=VERSION, description="This part of my dataset covers a second domain"),
# ]
# DEFAULT_CONFIG_NAME = "first_domain" # It's not mandatory to have a default configuration. Just use one if it make sense.
def _info(self):
features = datasets.Features({
"image_id": datasets.Value("string"),
"species": datasets.Value("string"),
"scientific_name": datasets.Value("string"),
"pics_array": datasets.Array3D(dtype="uint8", shape=(3, 768, 1024)), # Assuming images are RGB with shape 768x1024
"image_resolution": {
"width": datasets.Value("int32"),
"height": datasets.Value("int32"),
},
"annotations": datasets.Sequence({
"category_id": datasets.Value("int32"),
"bounding_box": {
"x_min": datasets.Value("float32"),
"y_min": datasets.Value("float32"),
"x_max": datasets.Value("float32"),
"y_max": 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):
# Only download data, no need to split
data_files = dl_manager.download_and_extract({
"csv": "https://huggingface.co/datasets/XintongHe/Populus_Stomatal_Images_Datasets/resolve/main/Labeled Stomatal Images.csv",
"zip": "https://huggingface.co/datasets/XintongHe/Populus_Stomatal_Images_Datasets/resolve/main/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()
# No longer need to randomize and split the dataset
return [datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={
"filepaths": all_image_filenames,
"species_info": species_info,
"data_dir": extracted_images_path,
},
)]
def save_metadata_as_json(image_id, annotations, species, scientific_name, json_path):
metadata = {
"image_id": image_id,
"species": species,
"scientific_name": scientific_name,
"annotations": annotations
}
with open(json_path, 'w') as json_file:
json.dump(metadata, json_file)
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
annotations.append({
"category_id": int(class_id),
"bounding_box": {
"x_min": x_min,
"y_min": y_min,
"x_max": x_max,
"y_max": y_max
}
})
return annotations
def _generate_examples(self, filepaths, species_info, data_dir, split):
"""Yields examples as (key, example) tuples."""
for file_name in filepaths:
image_id = os.path.splitext(file_name)[0] # Extract the base name without the file extension
image_path = os.path.join(data_dir, f"{image_id}.jpg")
label_path = os.path.join(data_dir, f"{image_id}.txt")
# 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]
width = species_row['Width'].values[0]
height = species_row['Height'].values[0]
else:
# Default values if not found
species = None
scientific_name = None
width = 1024 # or some default value
height = 768 # or some default value
with Image.open(image_path) as img:
pics_array = np.array(img) # Convert the PIL image to a numpy array
annotations = self._parse_yolo_labels(label_path, width, height)
yield image_id, {
"image_id": image_id,
"species": species,
"scientific_name": scientific_name,
"pics_array": pics_array,
"image_resolution": {"width": width, "height": height},
"annotations": annotations,
"image": img # Return the PIL image
}
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