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_path": datasets.Value("string"), "image": 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() 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 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") 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] 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 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, #"pics_array": pics_array.tolist(), # Convert numpy array to list for JSON serializability "image_path": image_path, "image": img, "image_resolution": {"width": width, "height": height}, "annotations": annotations }