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https://github.com/andrewekhalel/edafa
Test Time Augmentation (TTA) wrapper for computer vision tasks: segmentation, classification, super-resolution, ... etc.
https://github.com/andrewekhalel/edafa
augmentation classification computer-vision keras pytorch segmentation super-resolution tensorflow wrapper
Last synced: 5 days ago
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Test Time Augmentation (TTA) wrapper for computer vision tasks: segmentation, classification, super-resolution, ... etc.
- Host: GitHub
- URL: https://github.com/andrewekhalel/edafa
- Owner: andrewekhalel
- License: mit
- Created: 2018-10-15T20:25:55.000Z (about 6 years ago)
- Default Branch: master
- Last Pushed: 2021-11-18T21:53:50.000Z (almost 3 years ago)
- Last Synced: 2024-10-15T20:21:25.968Z (21 days ago)
- Topics: augmentation, classification, computer-vision, keras, pytorch, segmentation, super-resolution, tensorflow, wrapper
- Language: Python
- Homepage:
- Size: 5.21 MB
- Stars: 124
- Watchers: 5
- Forks: 21
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# Edafa
 [](https://github.com/andrewekhalel/edafa/issues)
Edafa is a simple wrapper that implements Test Time Augmentations (TTA) on images for computer vision problems like: segmentation, classification, super-resolution, Pansharpening, etc. TTAs guarantees better results in most of the tasks.
### Test Time Augmentation (TTA)
Applying different transformations to test images and then average for more robust results.
### Installation
```shell
pip install edafa
```
### Getting started
The easiest way to get up and running is to follow [example notebooks](https://github.com/andrewekhalel/edafa/tree/master/examples) for segmentation and classification showing TTA effect on performance.
### How to use Edafa
The whole process can be done in 4 steps:
1. Import Predictor class based on your task category: Segmentation (`SegPredictor`) or Classification (`ClassPredictor`)
```python
from edafa import SegPredictor
```
2. Inherit Predictor class and implement the main function
* `predict_patches(self,patches)` : where your model takes image patches (numpy.ndarray) and return prediction (numpy.ndarray)
```python
class myPredictor(SegPredictor):
def __init__(self,model,*args,**kwargs):
super().__init__(*args,**kwargs)
self.model = model
def predict_patches(self,patches):
return self.model.predict(patches)
```
3. Create an instance of you class
```python
p = myPredictor(model,patch_size,model_output_channels,conf_file_path)
```
4. Call `predict_images()` to run the prediction process
```python
p.predict_images(images,overlap=0)
```
### Configuration file
Configuration file is a json file containing two pieces of information
1. Augmentations to apply (**augs**). Supported augmentations:
* **NO** : No augmentation
* **ROT90** : Rotate 90 degrees
* **ROT180** : Rotate 180 degrees
* **ROT270** : Rotate 270 degrees
* **FLIP_UD** : Flip upside-down
* **FLIP_LR** : Flip left-right
* **BRIGHT** : Change image brightness randomly
* **CONTRAST** : Change image contrast randomly
* **GAUSSIAN** : Add random gaussian noise
* **GAMMA** : Perform gamma correction with random gamma
2. Combination of the results (**mean**). Supported mean types:
* **ARITH** : Arithmetic mean
* **GEO** : Geometric mean
3. Number of bits image (default is 8-bits) (**bits**).
Example of a conf file in `json` format
```json
{
"augs":["NO",
"FLIP_UD",
"FLIP_LR"],
"mean":"ARITH",
"bits":8
}
```
Example of a conf file in `yaml` format
```yaml
augs: [NO,FLIP_UD,FLIP_LR]
mean: ARITH
bits: 8
```
You can either pass file path (json or yaml) or the actual json text to `conf` parameter.
## Contribution
All contributions are welcomed. Please make sure that all tests passed before pull request. To run tests
```shell
nosetests
```