Data Augmentation

.py

import tensorflow as tf
from tensorflow.keras.preprocessing.image import ImageDataGenerator
import os
from tensorflow.keras.preprocessing import image
import matplotlib.pyplot as plt

# Set the path to your dataset directory
dataset_directory = r"Path to your folder"

# Create an ImageDataGenerator with augmentation parameters
datagen = ImageDataGenerator(
    rotation_range=40,
    width_shift_range=0.2,
    height_shift_range=0.2,
    shear_range=0.2,
    zoom_range=0.2,
    horizontal_flip=True,
    fill_mode='nearest'
)

# Specify the directory containing your images
image_directory = os.path.join(dataset_directory, 'images')

# Create the output directory if it doesn't exist
output_directory = os.path.join(dataset_directory, 'augmented_images')
if not os.path.exists(output_directory):
    os.makedirs(output_directory)

# List all files in the input directory
image_files = [f for f in os.listdir(image_directory) if f.endswith(('.jpg', '.png'))]

# Loop through each image in the input directory
for image_file in image_files:
    # Load the image
    img_path = os.path.join(image_directory, image_file)
    img = image.load_img(img_path, target_size=(150, 150))
    x = image.img_to_array(img)
    x = tf.expand_dims(x, axis=0)

    # Generate augmented images and save to the output directory
    i = 0
    for batch in datagen.flow(x, batch_size=1, save_to_dir=output_directory, save_prefix='aug', save_format='jpeg'):
        i += 1
        if i > 4:  # Generate 4 augmented images per original image
            break