PaddleClas/ppcls/data/preprocess/ops/functional.py

# encoding: utf-8

import numpy as np
from PIL import Image, ImageOps, ImageEnhance



def int_parameter(level, maxval):
    """Helper function to scale `val` between 0 and maxval .
    Args:
      level: Level of the operation that will be between [0, `PARAMETER_MAX`].
      maxval: Maximum value that the operation can have. This will be scaled to
        level/PARAMETER_MAX.
    Returns:
      An int that results from scaling `maxval` according to `level`.
    """
    return int(level * maxval / 10)


def float_parameter(level, maxval):
    """Helper function to scale `val` between 0 and maxval.
    Args:
      level: Level of the operation that will be between [0, `PARAMETER_MAX`].
      maxval: Maximum value that the operation can have. This will be scaled to
        level/PARAMETER_MAX.
    Returns:
      A float that results from scaling `maxval` according to `level`.
    """
    return float(level) * maxval / 10.


def sample_level(n):
    return np.random.uniform(low=0.1, high=n)


def autocontrast(pil_img, *args):
    return ImageOps.autocontrast(pil_img)


def equalize(pil_img, *args):
    return ImageOps.equalize(pil_img)


def posterize(pil_img, level, *args):
    level = int_parameter(sample_level(level), 4)
    return ImageOps.posterize(pil_img, 4 - level)


def rotate(pil_img, level, *args):
    degrees = int_parameter(sample_level(level), 30)
    if np.random.uniform() > 0.5:
        degrees = -degrees
    return pil_img.rotate(degrees, resample=Image.BILINEAR)


def solarize(pil_img, level, *args):
    level = int_parameter(sample_level(level), 256)
    return ImageOps.solarize(pil_img, 256 - level)


def shear_x(pil_img, level):
    level = float_parameter(sample_level(level), 0.3)
    if np.random.uniform() > 0.5:
        level = -level
    return pil_img.transform(pil_img.size,
                             Image.AFFINE, (1, level, 0, 0, 1, 0),
                             resample=Image.BILINEAR)


def shear_y(pil_img, level):
    level = float_parameter(sample_level(level), 0.3)
    if np.random.uniform() > 0.5:
        level = -level
    return pil_img.transform(pil_img.size,
                             Image.AFFINE, (1, 0, 0, level, 1, 0),
                             resample=Image.BILINEAR)


def translate_x(pil_img, level):
    level = int_parameter(sample_level(level), pil_img.size[0] / 3)
    if np.random.random() > 0.5:
        level = -level
    return pil_img.transform(pil_img.size,
                             Image.AFFINE, (1, 0, level, 0, 1, 0),
                             resample=Image.BILINEAR)


def translate_y(pil_img, level):
    level = int_parameter(sample_level(level), pil_img.size[1] / 3)
    if np.random.random() > 0.5:
        level = -level
    return pil_img.transform(pil_img.size,
                             Image.AFFINE, (1, 0, 0, 0, 1, level),
                             resample=Image.BILINEAR)


# operation that overlaps with ImageNet-C's test set
def color(pil_img, level, *args):
    level = float_parameter(sample_level(level), 1.8) + 0.1
    return ImageEnhance.Color(pil_img).enhance(level)


# operation that overlaps with ImageNet-C's test set
def contrast(pil_img, level, *args):
    level = float_parameter(sample_level(level), 1.8) + 0.1
    return ImageEnhance.Contrast(pil_img).enhance(level)


# operation that overlaps with ImageNet-C's test set
def brightness(pil_img, level, *args):
    level = float_parameter(sample_level(level), 1.8) + 0.1
    return ImageEnhance.Brightness(pil_img).enhance(level)


# operation that overlaps with ImageNet-C's test set
def sharpness(pil_img, level, *args):
    level = float_parameter(sample_level(level), 1.8) + 0.1
    return ImageEnhance.Sharpness(pil_img).enhance(level)


augmentations = [
    autocontrast, equalize, posterize, rotate, solarize, shear_x, shear_y,
    translate_x, translate_y
]