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DataP1.py
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############################################################################################
#
# Project: Asociacion De Investigacion En Inteligencia Artificial Para La Leucemia Peter Moss
# Repository: ALL-IDB Classifiers
# Project: Paper 1
#
# Author: Adam Milton-Barker
# Contributors:
#
# Title: Data Class
# Description: Data helper class for the Paper 1 Evaluation.
# License: MIT License
# Last Modified: 2019-07-23
#
############################################################################################
import cv2, os, pathlib, random
import numpy as np
import matplotlib.pyplot as plt
from pathlib import Path
from numpy.random import seed
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import OneHotEncoder
from sklearn.utils import shuffle
from scipy import ndimage
from skimage import transform as tm
from Classes.Helpers import Helpers
from Classes.Augmentation import Augmentation
class Data():
""" Data Class
Data helper class for the Paper 1 Evaluation.
"""
def __init__(self, model, optimizer, do_augmentation = False):
""" Initializes the Data class. """
self.Helpers = Helpers("Data", False)
self.model_type = model
self.optimizer = optimizer
if do_augmentation == False:
self.seed = self.Helpers.confs[self.model_type]["data"]["seed_" + self.optimizer]
self.dim = self.Helpers.confs[self.model_type]["data"]["dim"]
else:
self.Augmentation = Augmentation(self.model_type, self.optimizer)
self.seed = self.Helpers.confs[self.model_type]["data"]["seed_" + self.optimizer + "_augmentation"]
self.dim = self.Helpers.confs[self.model_type]["data"]["dim_augmentation"]
seed(self.seed)
random.seed(self.seed)
self.data = []
self.labels = []
self.Helpers.logger.info("Data class initialization complete.")
def data_and_labels_sort(self):
""" Sorts the training data and labels for your model. """
data_dir = pathlib.Path(
self.Helpers.confs[self.model_type]["data"]["train_dir"])
data = list(data_dir.glob(
'*' + self.Helpers.confs[self.model_type]["data"]["file_type"]))
count = 0
neg_count = 0
pos_count = 0
for rimage in data:
fpath = str(rimage)
fname = os.path.basename(rimage)
if "_0" in fname:
neg_count += 1
else:
pos_count += 1
count += 1
self.data.append((fpath, 0 if "_0" in fname else 1))
random.Random(self.seed).shuffle(self.data)
self.Helpers.logger.info("All data: " + str(count))
self.Helpers.logger.info("Positive data: " + str(pos_count))
self.Helpers.logger.info("Negative data: " + str(neg_count))
def data_and_labels_prepare(self):
""" Prepares the training data for your model. """
for i in range(len(self.data)):
fpath = str(self.data[i][0])
image = self.resize(
fpath, self.dim)
if image.shape[2] == 1:
image = np.dstack(
[image, image, image])
self.labels.append(self.data[i][1])
self.data[i] = image.astype(np.float32)/255.
self.convert_data()
self.encode_labels()
self.Helpers.logger.info("All data: " + str(self.data.shape))
self.Helpers.logger.info("All Labels: " + str(self.labels.shape))
def data_and_labels_augmentation_prepare(self):
""" Sorts the training data for your model. """
neg_count = 0
pos_count = 0
augmented_data = []
augmented_labels = []
for i in range(len(self.data)):
fpath = str(self.data[i][0])
fname = os.path.basename(fpath)
label = self.data[i][1]
if "_0" in fname:
neg_count += 9
else:
pos_count += 9
image = self.resize(fpath, self.dim)
if image.shape[2] == 1:
image = np.dstack(
[image, image, image])
augmented_data.append(image.astype(np.float32)/255.)
augmented_labels.append(label)
augmented_data.append(self.Augmentation.grayscale(image))
augmented_labels.append(label)
augmented_data.append(self.Augmentation.equalize_hist(image))
augmented_labels.append(label)
horizontal, vertical = self.Augmentation.reflection(image)
augmented_data.append(horizontal)
augmented_labels.append(label)
augmented_data.append(vertical)
augmented_labels.append(label)
augmented_data.append(self.Augmentation.gaussian(image))
augmented_labels.append(label)
augmented_data.append(self.Augmentation.translate(image))
augmented_labels.append(label)
augmented_data.append(self.Augmentation.shear(image))
augmented_labels.append(label)
augmented_data, augmented_labels =self.Augmentation.rotation(image, label, augmented_data, augmented_labels)
self.data = augmented_data
self.labels = augmented_labels
self.convert_data()
self.encode_labels()
self.Helpers.logger.info("Augmented data: " + str(self.data.shape))
self.Helpers.logger.info("All Labels: " + str(self.labels.shape))
def convert_data(self):
""" Converts the training data to a numpy array. """
self.data = np.array(self.data)
self.Helpers.logger.info("Data shape: " + str(self.data.shape))
def encode_labels(self):
""" One Hot Encodes the labels. """
encoder = OneHotEncoder(categories='auto')
self.labels = np.reshape(self.labels, (-1, 1))
self.labels = encoder.fit_transform(self.labels).toarray()
self.Helpers.logger.info("Labels shape: " + str(self.labels.shape))
def shuffle(self):
""" Shuffles the data and labels. """
self.data, self.labels = shuffle(self.data, self.labels, random_state=self.seed)
def get_split(self):
""" Splits the data and labels creating training and validation datasets. """
self.X_train, self.X_test, self.y_train, self.y_test = train_test_split(
self.data, self.labels, test_size=0.255, random_state=self.seed)
self.Helpers.logger.info("Training data: " + str(self.X_train.shape))
self.Helpers.logger.info("Training labels: " + str(self.y_train.shape))
self.Helpers.logger.info("Validation data: " + str(self.X_test.shape))
self.Helpers.logger.info("Validation labels: " + str(self.y_test.shape))
def resize(self, path, dim):
""" Resizes an image to the provided dimensions (dim). """
return cv2.resize(cv2.imread(path), (dim, dim))