add_test_folder

import os
from sklearn.model_selection import train_test_split
import shutil

# Set the path to your 'train' folder
train_folder = '/content/drive/MyDrive/tomato/train'

# Set the path to create 'test' folder
test_folder = '/content/drive/MyDrive/tomato/test'

# List all the classes in the 'train' folder
class_folders = [os.path.join(train_folder, c) for c in os.listdir(train_folder) if os.path.isdir(os.path.join(train_folder, c))]

# Create the 'test' folder if it doesn't exist
if not os.path.exists(test_folder):
    os.makedirs(test_folder)

# Loop through each class folder and split 100 images for testing
for class_folder in class_folders:
    class_name = os.path.basename(class_folder)
    
    # Get the list of images for the current class
    images = [os.path.join(class_folder, img) for img in os.listdir(class_folder)]
    
    # Ensure there are at least 100 images in the class folder
    if len(images) >= 100:
        # Split the images into training and testing sets
        train_images, test_images = train_test_split(images, test_size=100, random_state=42)
        
        # Create class folders in 'test' if they don't exist
        test_class_folder = os.path.join(test_folder, class_name)
        if not os.path.exists(test_class_folder):
            os.makedirs(test_class_folder)
        
        # Move images to the 'test' folder
        for test_image in test_images:
            shutil.move(test_image, os.path.join(test_class_folder, os.path.basename(test_image)))
    else:
        print(f"Skipping {class_name} as it has less than 100 images.")

print("Data split successfully!")