The project auto_imgaug provides auto image pre-processing methods by models without human knowledge.
It is the implementation of Google's paper AutoAugment: Learning Augmentation Policies from Data but leveraging all the operations from imgaug and optimization algorithms from advisor.
Install with pip.
pip install auto_imgaug
Install from souce.
git clone https://github.com/tobegit3hub/auto_imgaug
cd ./auto_imgaug/
python ./setup.py install
Run with Policy with default Operations.
import numpy as np
import imgaug as ia
from auto_imgaug.auto_imgaug import model
# Input images as tensor
input_images = np.array([ia.quokka(size=(64, 64)) for _ in range(32)], dtype=np.uint8)
# Use policy with 3 sub-policies(operations)
policy = model.AutoImgaugPolicy(3)
# Initialized policy with default operations
policy.init_with_default_operations()
# Run image pre-processing with policy
output_ndarray = policy.process(input_images)
Run with Advisor and machine learning algorithms.
study_configuration = {
"goal": "MINIMIZE",
"randomInitTrials": 1,
"maxTrials": 5,
"maxParallelTrials": 1,
"params": [{
"parameterName": "operation_name1",
"type": "CATEGORICAL",
"minValue": 0,
"maxValue": 0,
"feasiblePoints": "Fliplr, Crop, GaussianBlur, ContrastNormalization, AdditiveGaussianNoise, Multiply",
"scallingType": "LINEAR"
}, {
"parameterName": "magnitude1",
"type": "INTEGER",
"minValue": 1,
"maxValue": 11,
"scallingType": "LINEAR"
}, {
"parameterName": "probability1",
"type": "INTEGER",
"minValue": 1,
"maxValue": 10,
"scallingType": "LINEAR"
}, {
"parameterName": "operation_name2",
"type": "CATEGORICAL",
"minValue": 0,
"maxValue": 0,
"feasiblePoints": "Fliplr, Crop, GaussianBlur, ContrastNormalization, AdditiveGaussianNoise, Multiply",
"scallingType": "LINEAR"
}, {
"parameterName": "magnitude2",
"type": "INTEGER",
"minValue": 1,
"maxValue": 11,
"scallingType": "LINEAR"
}, {
"parameterName": "probability2",
"type": "INTEGER",
"minValue": 1,
"maxValue": 10,
"scallingType": "LINEAR"
}, {
"parameterName": "operation_name3",
"type": "CATEGORICAL",
"minValue": 0,
"maxValue": 0,
"feasiblePoints": "Fliplr, Crop, GaussianBlur, ContrastNormalization, AdditiveGaussianNoise, Multiply",
"scallingType": "LINEAR"
}, {
"parameterName": "magnitude3",
"type": "INTEGER",
"minValue": 1,
"maxValue": 11,
"scallingType": "LINEAR"
}, {
"parameterName": "probability3",
"type": "INTEGER",
"minValue": 1,
"maxValue": 10,
"scallingType": "LINEAR"
}]
}
More exmaples code in examples.
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