Datasets:
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import datasets
from datasets import load_dataset, DatasetInfo, Features, Value, ClassLabel, Split, SplitGenerator, GeneratorBasedBuilder, BuilderConfig, Array3D, Version
import os
from PIL import Image
import matplotlib.pyplot as plt
import numpy as np
_DRIVE_ID = "1fXgVwhdU5YGj0SPIcHxSpxkhvRh54oEH"
_URL = f"https://drive.google.com/uc?export=download&id={_DRIVE_ID}"
class PlantsDataset(GeneratorBasedBuilder):
VERSION = datasets.Version("1.0.0")
BUILDER_CONFIGS = [
BuilderConfig(name="default", version=VERSION, description="Default configuration for PlantsDataset"),
]
def _info(self):
features = Features({
"image": Array3D(dtype="uint8", shape=(None, None, 3)), # Change to Array3D to store image arrays
"label": ClassLabel(names=["aleo vera", "calotropis gigantea"]),
})
return DatasetInfo(
description="Your dataset description",
features=features,
supervised_keys=("image", "label"),
homepage="Your dataset homepage",
citation="Citation for your dataset",
)
def _split_generators(self, dl_manager):
downloaded_file = dl_manager.download_and_extract(_URL)
return [
SplitGenerator(
name=Split.TRAIN,
gen_kwargs={
"data_folder": os.path.join(downloaded_file, "train"),
},
),
SplitGenerator(
name=Split.TEST,
gen_kwargs={
"data_folder": os.path.join(downloaded_file, "test"),
},
),
]
def _generate_examples(self, data_folder):
label_names = self.info.features['label'].names
for label_name in label_names:
subfolder_path = os.path.join(data_folder, label_name)
label = label_names.index(label_name)
for root, _, files in os.walk(subfolder_path):
for file_name in files:
file_path = os.path.join(root, file_name)
if os.path.isfile(file_path) and file_name.lower().endswith(('.png', '.jpg', '.jpeg')):
try:
with Image.open(file_path) as image:
image_array = np.array(image)
yield file_path, {
"image": image_array, # Keep as numpy array
"label": label,
}
except (IOError, OSError):
print(f"Skipped file {file_path}, due to an error opening the image.") |