# 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 pandas as pd | |
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("1.1.1") | |
# 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.Image(), | |
# 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): | |
data_files = dl_manager.download_and_extract({ | |
"csv": "https://huggingface.co/datasets/XintongHe/Populus_Stomatal_Images_Datasets/resolve/main/data/Labeled Stomatal Images.csv", | |
"zip": "https://huggingface.co/datasets/XintongHe/Populus_Stomatal_Images_Datasets/resolve/main/data/Labeled Stomatal Images.zip", | |
"annotations_json": "https://huggingface.co/datasets/XintongHe/Populus_Stomatal_Images_Datasets/resolve/main/data/annotations.json" | |
}) | |
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, | |
"annotations_file": data_files["annotations_json"] | |
}, | |
)] | |
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): | |
# """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['Witdh'].values[0] | |
# height = species_row['Heigth'].values[0] | |
# else: | |
# # Default values if not found | |
# species = None | |
# scientific_name = None | |
# width = 1024 # Default value | |
# height = 768 # Default value | |
# pics_array = None | |
# with Image.open(image_path) as img: | |
# pics_array = np.array(img)# Convert the PIL image to a numpy array and then to a list | |
# # print(pics_array.shape) | |
# 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, | |
# "pics_array": pics_array, # Should be a list for JSON serializability | |
# "image_resolution": {"width": width, "height": height}, | |
# "annotations": annotations | |
# } | |
def _generate_examples(self, filepaths, species_info, data_dir, annotations_file): | |
"""Yields examples as (key, example) tuples.""" | |
# Load annotations from JSON file | |
with open(annotations_file, 'r') as file: | |
annotations_dict = json.load(file) | |
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") | |
# 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] # Corrected field name from 'Witdh' | |
height = species_row['Height'].values[0] # Corrected field name from 'Heigth' | |
else: | |
# Default values if not found | |
species = None | |
scientific_name = None | |
width = 1024 # Default value | |
height = 768 # Default value | |
pics_array = None | |
with Image.open(image_path) as img: | |
pics_array = np.array(img) # Convert the PIL image to a numpy array | |
# Retrieve annotations for the current image from the dictionary | |
annotations = annotations_dict.get(image_id, []) | |
# Yield the dataset example | |
yield image_id, { | |
"image_id": image_id, | |
"species": species, | |
"scientific_name": scientific_name, | |
"pics_array": pics_array.tolist(), # Convert numpy array to list for JSON serializability | |
"image_resolution": {"width": width, "height": height}, | |
"annotations": annotations | |
} | |