new_dataset_script.py

# 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

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):
    # Download and extract the dataset using Hugging Face's datasets library
    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"
    })
    
    # Load the CSV file containing species and scientific names
    species_info = pd.read_csv(data_files["csv"])
    
    # The directory 'Labeled Stomatal Images' is where the images and labels are stored after extraction
    extracted_images_path = os.path.join(data_files["zip"], "Labeled Stomatal Images")

    # Get the list of image filenames from the CSV
    all_image_filenames = species_info['FileName'].apply(lambda x: x + '.jpg').tolist()

    # Shuffle the list for random split
    random.seed(42)  # Set a random seed for reproducibility
    random.shuffle(all_image_filenames)
    
    # Split the files into train/validation/test
    num_files = len(all_image_filenames)
    train_split_end = int(num_files * 0.7)
    val_split_end = train_split_end + int(num_files * 0.15)
    
    train_files = all_image_filenames[:train_split_end]
    val_files = all_image_filenames[train_split_end:val_split_end]
    test_files = all_image_filenames[val_split_end:]

    return [
        SplitGenerator(
            name=Split.TRAIN,
            gen_kwargs={
                "filepaths": train_files,
                "species_info": species_info,
                "data_dir": extracted_images_path,
                "split": "train",
            },
        ),
        SplitGenerator(
            name=Split.VALIDATION,
            gen_kwargs={
                "filepaths": val_files,
                "species_info": species_info,
                "data_dir": extracted_images_path,
                "split": "validation",
            },
        ),
        SplitGenerator(
            name=Split.TEST,
            gen_kwargs={
                "filepaths": test_files,
                "species_info": species_info,
                "data_dir": extracted_images_path,
                "split": "test",
            },
        ),
    ]

 

# ... other necessary imports and class definitions
    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")
            
            with Image.open(image_path) as img:
                pics_array = np.array(img)
                width, height = img.size
            
            species_row = species_info.loc[species_info['FileName'] == file_name]
            species = species_row['Species'].values[0] if not species_row.empty else None
            scientific_name = species_row['ScientificName'].values[0] if not species_row.empty else None
    
            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
            }