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generator.py
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generator.py
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from config import IMG_DIR, IMAGE_SIZE, BATCH_SIZE
from keras.preprocessing.image import ImageDataGenerator
import random
import numpy as np
VARIABILITY = 8
# Generator
def add_noise(img):
'''Add random noise to an image'''
deviation = VARIABILITY*random.random()
noise = np.random.normal(0, deviation, img.shape)
img += noise
np.clip(img, 0., 255.)
return img
def generator(train_df, validate_df):
#generate batches of image data with configuration below, also add noise
train_datagen = ImageDataGenerator(
brightness_range=[0.2, 1.6],
rescale=1. / 255,
rotation_range=0,
width_shift_range=0.1,
height_shift_range=0.1,
shear_range=0.2,
zoom_range=0.2,
horizontal_flip=True,
fill_mode="nearest",
preprocessing_function=add_noise,
)
train_generator = train_datagen.flow_from_dataframe(
train_df,
IMG_DIR,
x_col='filename',
y_col='category',
target_size=IMAGE_SIZE,
color_mode = 'rgb',
class_mode='categorical',
batch_size=BATCH_SIZE
)
validation_datagen = ImageDataGenerator(rescale=1./255)
validation_generator = validation_datagen.flow_from_dataframe(
validate_df,
IMG_DIR,
x_col='filename',
y_col='category',
target_size=IMAGE_SIZE,
color_mode = 'rgb',
class_mode='categorical',
shuffle=False,
batch_size=BATCH_SIZE
)
return [train_generator, validation_generator]