Datasets:
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from datasets import DatasetInfo, Features, Value, ClassLabel, Split, SplitGenerator, GeneratorBasedBuilder, BuilderConfig
import os
from PIL import Image
import matplotlib.pyplot as plt
import numpy as np
# Rest of your code...
# Google Drive ID for your ZIP file
_DRIVE_ID = "1fXgVwhdU5YGj0SPIcHxSpxkhvRh54oEH"
_URL = f"https://drive.google.com/uc?export=download&id={_DRIVE_ID}"
class PlantsDataset(GeneratorBasedBuilder):
class MyConfig(BuilderConfig):
def __init__(self, **kwargs):
super().__init__(**kwargs)
BUILDER_CONFIGS = [
MyConfig(name="plants_default", description="Default configuration for PlantsDataset"),
]
BUILDER_CONFIGS = [
MyConfig(name="default", description="Default configuration"),
]
def _info(self):
features = Features({
"image": Value("string"),
"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, subfolder in enumerate(label_names):
subfolder_path = os.path.join(data_folder, subfolder)
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):
# Open the image using Pillow and convert it to an array
with Image.open(file_path) as image:
image_array = np.array(image)
# Yield the example with the image data and label
yield file_path, {
"image": image_array.tolist(), # Convert array to list
"label": label,
}
def _display_image(self, image_path, label):
with Image.open(image_path) as img:
plt.imshow(img)
plt.title(f"Label: {self.info.features['label'].int2str(label)}")
plt.axis('off') # Hide the axis
plt.show()
# Create an instance of the PlantsDataset class
plants_dataset = PlantsDataset()
# Build and upload the dataset
plants_dataset.download_and_prepare() |