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ModelDataGenerators.py
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ModelDataGenerators.py
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# -*- coding: utf-8 -*-
"""
Created on Thurs Oct 13 2022
@author: Simon Bilik
This class is used to set data generators and to save the original data to the npz file
"""
import os
import logging
import traceback
import cv2 as cv
import numpy as np
import tensorflow as tf
class ModelDataGenerators():
## Set the constants and paths
def __init__(self, experimentPath, datasetPath, labelInfo, imageDim, batchSize, npzSave):
# Set the paths and constants
self.experimentPath = experimentPath
self.datasetPath = datasetPath
self.labelInfo = labelInfo
self.batchSize = batchSize
self.imageDim = imageDim
self.npzSave = npzSave
# Create datasets
self.getGenerators()
# Save the original data to NPZ
self.saveOrgData()
## Normalize and augment training dataset (image as label)
def changeInputsTrain(self, images, labels):
normalization_layer = tf.keras.layers.Rescaling(1./255)
x_norm = tf.image.resize(normalization_layer(images),[self.imageDim[0], self.imageDim[1]], method=tf.image.ResizeMethod.NEAREST_NEIGHBOR)
# Augment the image
x_norm = tf.image.random_flip_left_right(x_norm)
x_norm = tf.image.random_contrast(x_norm, lower=0.0, upper=1.0)
# Random image inversion and saturation
x_norm = (1-x_norm) if tf.random.uniform([]) < 0.5 else x_norm
x_norm = tf.image.stateless_random_saturation(x_norm, 0.5, 1.0)
# Random brightness and hue
x_norm = tf.image.stateless_random_brightness(x_norm, 0.2)
x_norm = tf.image.stateless_random_hue(x_norm, 0.2)
# Add salt and pepper noise
random_values = tf.random.uniform(shape=x_norm[0, ..., -1:].shape)
x_noise = tf.where(random_values < 0.1, 1., x_norm)
x_noise = tf.where(1 - random_values < 0.1, 0., x_noise)
return x_noise, x_norm
## Normalize test and validation datasets (image as label)
def changeInputsTest(self, images, labels):
normalization_layer = tf.keras.layers.Rescaling(1./255)
x_norm = tf.image.resize(normalization_layer(images),[self.imageDim[0], self.imageDim[1]], method=tf.image.ResizeMethod.NEAREST_NEIGHBOR)
return x_norm, x_norm
## Normalize dataset (text as label)
def changeInputs(self, images, labels):
normalization_layer = tf.keras.layers.Rescaling(1./255)
x = tf.image.resize(normalization_layer(images),[self.imageDim[0], self.imageDim[1]], method=tf.image.ResizeMethod.NEAREST_NEIGHBOR)
return x, labels
## Set the data generator
def setGenerator(self, mode):
# Returns generator with and without the labels, map the datasets (and augment only Train)
if mode == 'Train':
ds = tf.keras.utils.image_dataset_from_directory(
(self.datasetPath + mode),
image_size = self.tSize,
color_mode = self.cMode,
batch_size = self.batchSize,
label_mode = 'int',
shuffle = True)
dsL = tf.keras.utils.image_dataset_from_directory(
(self.datasetPath + mode),
image_size = self.tSize,
color_mode = self.cMode,
batch_size = self.batchSize,
label_mode = 'binary',
shuffle = True)
ds = ds.map(self.changeInputsTrain)
else:
ds = tf.keras.utils.image_dataset_from_directory(
(self.datasetPath + mode),
image_size = self.tSize,
color_mode = self.cMode,
batch_size = self.batchSize,
label_mode = 'int',
shuffle = False)
dsL = tf.keras.utils.image_dataset_from_directory(
(self.datasetPath + mode),
image_size = self.tSize,
color_mode = self.cMode,
batch_size = self.batchSize,
label_mode = 'binary',
shuffle = False)
ds = ds.map(self.changeInputsTest)
# Map the labeled dataset
dsL = dsL.map(self.changeInputs)
return ds, dsL
## Get the data generators
def getGenerators(self):
try:
# Set image dimensions
self.tSize = (self.imageDim[0], self.imageDim[1])
if self.imageDim[2] == 3:
self.cMode = 'rgb'
else:
self.cMode = 'grayscale'
# Get train DS
self.dsTrain, self.dsTrainL = self.setGenerator('train')
# Get validation DS
self.dsValid, self.dsValidL = self.setGenerator('valid')
# Get test DS
self.dsTest, self.dsTestL = self.setGenerator('test')
except:
logging.error('Data generators initialization for the ' + self.labelInfo + ' experiment failed...')
traceback.print_exc()
return
else:
logging.info('Data generators of the ' + self.labelInfo + ' experiment initialized...')
## Save the original data and labels to NPZ file
def saveOrgData(self):
self.processedData = {}
actStrs = ['Train', 'Test', 'Valid']
dataGens = [self.dsTrainL, self.dsTestL, self.dsValidL]
for actStr, dataGen in zip(actStrs, dataGens):
tempDict = {}
# Get the original data to be saved in npz
orig_data = np.concatenate([img for img, _ in dataGen], axis=0)
# Normalize the original data
for i in range(orig_data.shape[0]):
orig_data[i] = np.atleast_3d(cv.normalize(orig_data[i], None, 0, 1, cv.NORM_MINMAX, cv.CV_32F))
# Get labels and transform them to format to -1: NOK and 1:OK
labels = np.concatenate([labels for _, labels in dataGen], axis=0)
nokIdx = np.where(labels == 0)
labels[nokIdx] = -1
# Filter the original data
filterStrength = 0.5
orig_data = np.array([(filterStrength*img + (1 - filterStrength)*np.atleast_3d(cv.GaussianBlur(img, (25,25), 0))) for img in orig_data])
# Save the obtained data to NPZ
if self.npzSave:
outputPath = os.path.join(self.experimentPath, 'Org_' + actStr)
np.savez_compressed(outputPath, orgData = orig_data, labels = labels)
# Save the processed data to dictionary for a later acces
tempDict = {'Org': orig_data, 'Lab': labels}
self.processedData[actStr] = tempDict