ttAugment 0.4.1

Test Time Augmentations

TTAugment

Perform Augmentation during Inference and aggregate the results of all the applied augmentation to create a final output

Installation

pip install ttAugment

Supported Augmentation

Library supports all color, blur and contrast transformation provided by imgaug along with custom Geometric Transformation.

1. Mirror : Crop an image to crop_to_dimension and mirror pixels to match the size of window_dimension
2. CropScale : Crop an image to crop_to_dimension and rescale the image to match the size of window_dimension
3. NoAugment : Keep the input unchanged
4. Crop : Crop an image to crop_to_dimension
5. Rot : Rotate an Image
6. FlipHorizontal
7. FlipVertical

Usage

How to use when test image is much larger than what the model requires, Don't worry the library has it covered, it will generate fragments according to the specified dimension, so the inference can be performed while applying augmentation.

• window_size: Break the image into smaller images of said size
• output_dimension: It must be greater the input image in order for the fragments to be restored back on the image.

import numpy as np
from tt_augment.augment import generate_seg_augmenters

transformation_to_apply = [
  {"name": "Mirror", "crop_to_dimension": (256, 256)},
  {"name": "CropScale", "crop_to_dimension": (256, 256)},
]

for i in range(0, 10):
  image = np.random.rand(512, 512, 3) * 255
  image = np.expand_dims(image, 0)

  # Load augmentation object for the image, this includes to break the image in smaller fragments.
  tta = generate_seg_augmenters(
    image=image,
    window_size=(384, 384),
    output_dimension=(1, 512, 512, 3),
    transformation_to_apply=transformation_to_apply,
  )

  # Iterate over transformation_to_apply
  for iterator, transformation in enumerate(tta):
    # Iterate over individual fragments
    for augmented_fragment in transformation.transform_fragment():
      #     ---> transformed_fragment.shape = (1, 384, 384, 3) 
      # Inference steps for augmented fragment
      # 1. perform image normalization
      #     ---> normalised_image = image_normalization(augmented_fragment)
      # 2. perform model prediction
      #     ---> prediction = model.predict(normalised_image)
      # 3. convert prediction to numpy with shape [batch, h, w, channel]
      # 4. place the prediction fragment on its position in the original image
      #     ---> transformation.restore_fragment(prediction)

      transformation.restore_fragment(augmented_fragment)

  # Aggregate the result for the input image over all applied augmentations
  tta.merge()

  output = tta.tta_output()