How to handle image data augmentation in TensorFlow?

In TensorFlow, image data augmentation is typically achieved using functions within the tf.image module. Below are some commonly used image data augmentation methods and their corresponding functions:

1. Random cropping: can be achieved using the tf.image.random_crop function.
2. Random flipping: This can be achieved using the functions tf.image.random_flip_left_right and tf.image.random_flip_up_down.
3. Color adjustment: You can achieve this through the functions tf.image.random_brightness, tf.image.random_contrast, tf.image.random_saturation, and tf.image.random_hue.
4. Random rotation: This can be achieved using the tf.image.rot90 function.
5. Random scaling: Achieve this by using the tf.image.random_crop and tf.image.resize functions.

In addition to the functions mentioned above, more complex image data augmentation operations can be performed using functions like tf.image.random_image_emboss, tf.image.random_image_flip_left_right, and tf.image.random_image_flip_up_down. It’s important to note that when using these functions, the image data should be converted into tensor format in TensorFlow.