wehub-resource-sync
86 lines
3.9 KiB
Python
86 lines
3.9 KiB
Python
from __future__ import print_function, division
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import imgaug as ia
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# TODO ForceSign
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from imgaug.parameters import (
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Binomial, Choice, DiscreteUniform, Poisson, Normal, Laplace, ChiSquare,
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Weibull, Uniform, Beta, Deterministic, Clip, Discretize, Multiply, Add,
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Divide, Power, Absolute, RandomSign, Positive, Negative,
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SimplexNoise, FrequencyNoise, Sigmoid
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)
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import numpy as np
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def main():
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params = [
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("Binomial(0.1)", Binomial(0.1)),
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("Choice", Choice([0, 1, 2])),
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("Choice with p", Choice([0, 1, 2], p=[0.1, 0.2, 0.7])),
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("DiscreteUniform(0, 10)", DiscreteUniform(0, 10)),
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("Poisson(0)", Poisson(0)),
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("Poisson(5)", Poisson(5)),
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("Discretize(Poisson(5))", Discretize(Poisson(5))),
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("Normal(0, 1)", Normal(0, 1)),
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("Normal(1, 1)", Normal(1, 1)),
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("Normal(1, 2)", Normal(0, 2)),
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("Normal(Choice([-1, 1]), 2)", Normal(Choice([-1, 1]), 2)),
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("Discretize(Normal(0, 1.0))", Discretize(Normal(0, 1.0))),
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("Positive(Normal(0, 1.0))", Positive(Normal(0, 1.0))),
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("Positive(Normal(0, 1.0), mode='reroll')", Positive(Normal(0, 1.0), mode="reroll")),
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("Negative(Normal(0, 1.0))", Negative(Normal(0, 1.0))),
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("Negative(Normal(0, 1.0), mode='reroll')", Negative(Normal(0, 1.0), mode="reroll")),
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("Laplace(0, 1.0)", Laplace(0, 1.0)),
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("Laplace(1.0, 3.0)", Laplace(1.0, 3.0)),
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("Laplace([-1.0, 1.0], 1.0)", Laplace([-1.0, 1.0], 1.0)),
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("ChiSquare(1)", ChiSquare(1)),
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("ChiSquare([1, 6])", ChiSquare([1, 6])),
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("Weibull(0.5)", Weibull(0.5)),
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("Weibull((1.0, 3.0))", Weibull((1.0, 3.0))),
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("Uniform(0, 10)", Uniform(0, 10)),
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("Beta(0.5, 0.5)", Beta(0.5, 0.5)),
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("Deterministic(1)", Deterministic(1)),
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("Clip(Normal(0, 1), 0, None)", Clip(Normal(0, 1), minval=0, maxval=None)),
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("Multiply(Uniform(0, 10), 2)", Multiply(Uniform(0, 10), 2)),
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("Add(Uniform(0, 10), 5)", Add(Uniform(0, 10), 5)),
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("Absolute(Normal(0, 1))", Absolute(Normal(0, 1))),
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("RandomSign(Poisson(1))", RandomSign(Poisson(1))),
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("RandomSign(Poisson(1), 0.9)", RandomSign(Poisson(1), 0.9))
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]
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params_arithmetic = [
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("Normal(0, 1.0)", Normal(0.0, 1.0)),
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("Normal(0, 1.0) + 5", Normal(0.0, 1.0) + 5),
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("5 + Normal(0, 1.0)", 5 + Normal(0.0, 1.0)),
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("5 + Normal(0, 1.0)", Add(5, Normal(0.0, 1.0), elementwise=True)),
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("Normal(0, 1.0) * 10", Normal(0.0, 1.0) * 10),
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("10 * Normal(0, 1.0)", 10 * Normal(0.0, 1.0)),
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("10 * Normal(0, 1.0)", Multiply(10, Normal(0.0, 1.0), elementwise=True)),
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("Normal(0, 1.0) / 10", Normal(0.0, 1.0) / 10),
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("10 / Normal(0, 1.0)", 10 / Normal(0.0, 1.0)),
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("10 / Normal(0, 1.0)", Divide(10, Normal(0.0, 1.0), elementwise=True)),
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("Normal(0, 1.0) ** 2", Normal(0.0, 1.0) ** 2),
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("2 ** Normal(0, 1.0)", 2 ** Normal(0.0, 1.0)),
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("2 ** Normal(0, 1.0)", Power(2, Normal(0.0, 1.0), elementwise=True))
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]
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params_noise = [
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("SimplexNoise", SimplexNoise()),
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("Sigmoid(SimplexNoise)", Sigmoid(SimplexNoise())),
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("SimplexNoise(linear)", SimplexNoise(upscale_method="linear")),
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("SimplexNoise(nearest)", SimplexNoise(upscale_method="nearest")),
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("FrequencyNoise((-4, 4))", FrequencyNoise(exponent=(-4, 4))),
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("FrequencyNoise(-2)", FrequencyNoise(exponent=-2)),
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("FrequencyNoise(2)", FrequencyNoise(exponent=2))
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]
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images_params = [param.draw_distribution_graph() for (title, param) in params]
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images_arithmetic = [param.draw_distribution_graph() for (title, param) in params_arithmetic]
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images_noise = [param.draw_distribution_graph(size=(1000, 10, 10)) for (title, param) in params_noise]
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ia.imshow(np.vstack(images_params))
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ia.imshow(np.vstack(images_arithmetic))
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ia.imshow(np.vstack(images_noise))
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if __name__ == "__main__":
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main()
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