https://github.com/fuzailpalnak/ttaugment
Test Time Augmentation for Deep Learning Inference
https://github.com/fuzailpalnak/ttaugment
augmentation computer-vision deep-learning inference segmentation segmentation-models test-time-augmentation tta
Last synced: 2 months ago
JSON representation
Test Time Augmentation for Deep Learning Inference
- Host: GitHub
- URL: https://github.com/fuzailpalnak/ttaugment
- Owner: fuzailpalnak
- License: mit
- Created: 2020-08-15T12:08:55.000Z (almost 6 years ago)
- Default Branch: master
- Last Pushed: 2023-06-05T12:39:03.000Z (about 3 years ago)
- Last Synced: 2025-06-22T01:46:48.765Z (about 1 year ago)
- Topics: augmentation, computer-vision, deep-learning, inference, segmentation, segmentation-models, test-time-augmentation, tta
- Language: Python
- Homepage:
- Size: 64.5 KB
- Stars: 1
- Watchers: 0
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- Changelog: CHANGES.txt
- Funding: .github/FUNDING.yml
- License: LICENSE
Awesome Lists containing this project
README
# 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](https://imgaug.readthedocs.io/en/latest/source/api_augmenters_color.html),
[blur](https://imgaug.readthedocs.io/en/latest/source/api_augmenters_blur.html) and [contrast](https://imgaug.readthedocs.io/en/latest/source/api_augmenters_contrast.html)
transformation provided by [imgaug](https://imgaug.readthedocs.io/en/latest/) 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.
```python
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()
```
## Example Implementation
[Library used for Predicting Building footprint](https://github.com/fuzailpalnak/building-footprint-segmentation/blob/main/examples/PredictionWithAugmentations.ipynb)