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augment.py
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augment.py
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import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt
class DataAugmentor:
"""
A class used for data augmentation (partially taken from : https://www.wouterbulten.nl/blog/tech/data-augmentation-using-tensorflow-data-dataset/)
Attributes
----------
batch : tf.Tensor, optional
The batch to augment
batchSize: int
The batch size
seed: int, optional
Random seed
Methods
-------
flip
Flip Augmentation
color
Color Augmentation
gaussian
Gaussian Noise
brightness
Custom Brightness Augmentation
zoom
Crop Augmentation
kerasAug
Inbuilt Keras Augmentations
augment
Wrapper Augmentation Function
"""
def __init__(self, batch=None, batchSize=50, seed=0):
if batch is not None:
self.dataset = batch
self.seed = seed
tf.random.set_seed(self.seed)
np.random.seed(self.seed)
self.batchSize = batchSize
def flip(self, x: tf.Tensor) -> tf.Tensor:
"""Flip augmentation
Args:
x: Image to flip
Returns:
Augmented image
"""
x = tf.image.random_flip_left_right(x, seed=self.seed)
return x
def color(self, x: tf.Tensor) -> tf.Tensor:
"""Color augmentation
Args:
x: Image
Returns:
Augmented image
# """
x = tf.image.random_hue(x, 0.05, seed=self.seed)
x = tf.image.random_saturation(x, 0.6, 1.2, seed=self.seed)
x = tf.image.random_brightness(x, 0.05, seed=self.seed)
x = tf.image.random_contrast(x, 0.7, 1.0, seed=self.seed)
return x
def gaussian(self, x: tf.Tensor) -> tf.Tensor:
mean = tf.keras.backend.mean(x)
std = tf.keras.backend.std(x)
max_ = tf.keras.backend.max(x)
min_ = tf.keras.backend.min(x)
ptp = max_ - min_
noise = tf.random.normal(shape=tf.shape(x), mean=0, stddev=0.3*self.var, dtype=tf.float32, seed=self.seed)
# noise_img = tf.clip_by_value(((x - mean)/std + noise)*std + mean,
# clip_value_min = min_, clip_value_max=max_)
noise_img = x+noise
return noise_img
def brightness(self, x: tf.Tensor) -> tf.Tensor:
max_ = tf.keras.backend.max(x)
min_ = tf.keras.backend.min(x)
brightness_val = 0.1*np.random.random_sample() - 0.05
noise = tf.constant(brightness_val, shape=x.shape)
noise_img = x+noise
noise_img = tf.clip_by_value(x,
clip_value_min = min_, clip_value_max=max_)
return noise_img
def zoom(self, x: tf.Tensor) -> tf.Tensor:
"""Zoom augmentation
Args:
x: Image
Returns:
Augmented image
"""
# Generate 20 crop settings, ranging from a 1% to 20% crop.
scales = list(np.arange(0.85, 1.0, 0.01))
boxes = np.zeros((len(scales), 4))
for i, scale in enumerate(scales):
x1 = y1 = 0.5 - (0.5 * scale)
x2 = y2 = 0.5 + (0.5 * scale)
boxes[i] = [x1, y1, x2, y2]
def random_crop(img):
# Create different crops for an image
crops = tf.image.crop_and_resize([img], boxes=boxes, box_indices=np.zeros(len(scales)), crop_size=(x.shape[0], x.shape[1]))
# Return a random crop
return crops[tf.random.uniform(shape=[], minval=0, maxval=len(scales), dtype=tf.int32, seed=self.seed)]
choice = tf.random.uniform(shape=[], minval=0., maxval=1., dtype=tf.float32, seed=self.seed)
# Only apply cropping 50% of the time
return tf.cond(choice < 0.5, lambda: x, lambda: random_crop(x))
def kerasAug(self, x: tf.Tensor) -> tf.Tensor:
datagen = tf.keras.preprocessing.image.ImageDataGenerator(
rotation_range=2,
width_shift_range=0,
height_shift_range=0,
horizontal_flip=False,
shear_range = 0,
fill_mode='nearest',
dtype = tf.float32)
return datagen.flow(x, batch_size=self.batchSize, shuffle=False, seed=self.seed).next()
def augment(self, batch=None):
if batch is not None:
self.dataset = batch
self.dataset = tf.data.Dataset.from_tensor_slices(self.dataset.numpy())
# Add augmentations
augmentations = [self.flip, self.color, self.zoom]
# Add the augmentations to the dataset
for f in augmentations:
# Apply the augmentation, run 4 jobs in parallel.
self.dataset = self.dataset.map(f)
self.dataset = next(iter(self.dataset.batch(self.batchSize)))
return(self.dataset)