Datasets:
Tasks:
Feature Extraction
Modalities:
Image
Formats:
imagefolder
Size:
10K - 100K
Tags:
climate
License:
| import os | |
| import random | |
| # Change the static percentage parameter to scale down | |
| def remove_random_images(base_path, percentage=5): | |
| """Removes 5% percentage of images randomly from each class subdirectory.""" | |
| subsets = ['test', 'train', 'val'] | |
| for subset in subsets: | |
| subset_path = os.path.join(base_path, subset) | |
| if not os.path.exists(subset_path): | |
| continue | |
| for class_name in os.listdir(subset_path): | |
| class_path = os.path.join(subset_path, class_name) | |
| if os.path.isdir(class_path): | |
| images = [f for f in os.listdir(class_path) if f.endswith('.jpg')] | |
| num_to_remove = int(len(images) * (percentage / 100)) | |
| if num_to_remove > 0: | |
| images_to_remove = random.sample(images, num_to_remove) | |
| for img in images_to_remove: | |
| os.remove(os.path.join(class_path, img)) | |
| print(f"Removed {num_to_remove} images from {subset}/{class_name}") | |
| # Define base path | |
| base_directory = "classify" | |
| # Run the function | |
| remove_random_images(base_directory) |