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import os
import shutil
from pathlib import Path
import random
# CODE FOR SPLITTING THE DATASET INTO TRAIN/TEST/VAL
def split_data(source_images_folder, source_labels_folder, destination_folder, train_ratio=0.8):
"""
Split the dataset into training, validation, and testing sets.
Parameters:
- source_images_folder: Path to the source folder containing images.
- source_labels_folder: Path to the source folder containing labels.
- destination_folder: Path to the destination folder for the split datasets.
- train_ratio: Ratio of training data. Default is 0.8.
"""
# Read image files from the source directory
image_files = os.listdir(source_images_folder)
# Shuffle and split image files into training, testing, and validation sets
random.seed(100)
random.shuffle(image_files)
split_1 = int(train_ratio * len(image_files))
split_2 = int((train_ratio + (1 - train_ratio) / 2) * len(image_files))
train_images = image_files[:split_1]
test_images = image_files[split_1:split_2]
val_images = image_files[split_2:]
# Create destination directories
destination_folder.mkdir(parents=True, exist_ok=True)
splits = [train_images, val_images, test_images]
for i in range(3):
if i == 0:
split_folder = 'train'
elif i == 1:
split_folder = 'val'
else:
split_folder = 'test'
for image in splits[i]:
image_path = os.path.join(source_images_folder, image)
destination_image_path = destination_folder / split_folder / "images" / image
destination_image_path.parent.mkdir(parents=True, exist_ok=True)
label_file = image.rsplit(".", 1)[0] + '.txt'
label_path = os.path.join(source_labels_folder, label_file)
destination_label_path = destination_folder / split_folder / "labels" / label_file
destination_label_path.parent.mkdir(parents=True, exist_ok=True)
# Copying files to respective splits
shutil.copy2(label_path, destination_label_path)
shutil.copy2(image_path, destination_image_path)
print("Image copied to ", destination_image_path)
# Print the number of images in each split
print(f"Number of train images: {len(train_images)}\n",
f"Number of validation images: {len(val_images)}\n",
f"Number of test images: {len(test_images)}\n")
# Define source and destination folders
source_images_folder = Path('../datasets/Nutrition5k/train/images')
source_labels_folder = Path('../datasets/Nutrition5k/train/labels')
destination_folder = Path('../datasets/new')
# Split the dataset
split_data(source_images_folder, source_labels_folder, destination_folder, train_ratio=0.8)
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