How can I use custom image augmentation techniques in my project?

How can I use custom image augmentation techniques in my project?

Custom image augmentation techniques can enhance your dataset by applying transformations like rotation, scaling, and flipping. What specific types of augmentations are you interested in?

I'm particularly interested in color adjustments and adding noise.

Great! You can use libraries like TensorFlow or PyTorch to apply color adjustments such as brightness, contrast, and saturation. For adding noise, Gaussian noise is a common choice. Would you like code examples for these?

Yes, that would be helpful!

Sure! Here’s a simple example using TensorFlow:

import tensorflow as tf

image = tf.io.read_file('path_to_image.jpg')
image = tf.image.decode_jpeg(image, channels=3)

# Color adjustments
image = tf.image.adjust_brightness(image, delta=0.1)
image = tf.image.adjust_contrast(image, contrast_factor=1.5)

# Adding Gaussian noise
noise = tf.random.normal(shape=tf.shape(image), mean=0.0, stddev=0.1, dtype=tf.float32)
augmented_image = image + noise

This will adjust the brightness and contrast, then add noise. Let me know if you need more details!

Thanks! How do I save the augmented images?

You can save the augmented images using TensorFlow's tf.io.write_file function. Here’s how you can do it:

tf.io.write_file('path_to_save_augmented_image.jpg', tf.image.encode_jpeg(augmented_image))

This will save your augmented image in JPEG format. Let me know if you have any other questions!