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Template.py
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Template.py
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import tensorflow as tf
from tensorflow.keras import Model
import numpy as np
from hdf5storage import loadmat
from scipy.stats import zscore
from PIL import Image
import cv2
import csv
def data_block_loader_train_val_test_image_separate(use_all=False, eye_remove=True, do_norm=True, do_zscore=True):
# Load data
if use_all:
file_name1 = 'Data/01_All/Block_Sub001_all.mat'
full_data1 = loadmat(file_name1)
file_name2 = 'Data/02_All/Block_Sub001_all.mat'
full_data2 = loadmat(file_name2)
file_name3 = 'Data/03_All/Block_Sub001_all.mat'
full_data3 = loadmat(file_name3)
file_name4 = 'Data/04_All/Block_Sub001_all.mat'
full_data4 = loadmat(file_name4)
EEGs = np.concatenate([full_data1['data'], full_data2['data'],
full_data3['data'], full_data4['data']], axis=0)
full_data1_images=np.array([image.split('/')[1] for image in full_data1['images']])
full_data2_images=np.array([image.split('/')[1] for image in full_data2['images']])
full_data3_images=np.array([image.split('/')[1] for image in full_data3['images']])
full_data4_images=np.array([image.split('/')[1] for image in full_data4['images']])
images = np.concatenate([full_data1_images, full_data2_images,
full_data3_images, full_data4_images], axis=0)
labels = np.concatenate([full_data1['labels'], full_data2['labels'],
full_data3['labels'], full_data4['labels']], axis=0)
else:
if eye_remove:
file_name1 = 'data/Block_Sub001_Remove_Eye.mat'
full_data1 = loadmat(file_name1)
file_name2 = 'data/Block_Sub002_Remove_Eye.mat'
full_data2 = loadmat(file_name2)
file_name3 = 'data/Block_Sub003_Remove_Eye.mat'
full_data3 = loadmat(file_name3)
file_name4 = 'data/Block_Sub004_Remove_Eye.mat'
full_data4 = loadmat(file_name4)
EEGs = np.concatenate([full_data1['data'], full_data2['data'],
full_data3['data'], full_data4['data']], axis=0)
data1_images=np.array([image.split('/')[1] for image in full_data1['images']])
data2_images=np.array([image.split('/')[1] for image in full_data2['images']])
data3_images=np.array([image.split('/')[1] for image in full_data3['images']])
data4_images=np.array([image.split('/')[1] for image in full_data4['images']])
images = np.concatenate([data1_images, data2_images,
data3_images, data4_images], axis=0)
labels = np.concatenate([full_data1['labels'], full_data2['labels'],
full_data3['labels'], full_data4['labels']], axis=0)
else:
file_name1 = 'Data/02_Remove_Bad_Trial/Block_Sub001_Remove_Trial.mat'
full_data1 = loadmat(file_name1)
file_name2 = 'Data/02_Remove_Bad_Trial/Block_Sub002_Remove_Trial.mat'
full_data2 = loadmat(file_name2)
file_name3 = 'Data/02_Remove_Bad_Trial/Block_Sub003_Remove_Trial.mat'
full_data3 = loadmat(file_name3)
file_name4 = 'Data/02_Remove_Bad_Trial/Block_Sub004_Remove_Trial.mat'
full_data4 = loadmat(file_name4)
EEGs = np.concatenate([full_data1['data'], full_data2['data'],
full_data3['data'], full_data4['data']], axis=0)
images = np.concatenate([full_data1['images'], full_data2['images'],
full_data3['images'], full_data4['images']], axis=0)
labels = np.concatenate([full_data1['labels'], full_data2['labels'],
full_data3['labels'], full_data4['labels']], axis=0)
# Parameters
window_start = 0
window_end = window_start + 125
category_size = 40
# Shuffle
seed = 12345
np.random.seed(seed)
np.random.shuffle(EEGs)
np.random.seed(seed)
np.random.shuffle(images)
np.random.seed(seed)
np.random.shuffle(labels)
# Get Image features
images_for_vis_feature = np.empty((images.shape[0], 224, 224, 3))
for i in range(images.shape[0]):
image_path = 'finalEEGImages/' + images[i]
img = Image.open(image_path)
img_array = np.asarray(img)
img_array = Image.fromarray(img_array)
img_array = np.array(img_array.resize((224, 224), Image.LANCZOS))
if img_array.reshape(-1).shape[0] == 224 * 224:
img_array = cv2.cvtColor(img_array, cv2.COLOR_GRAY2RGB)
img_array = np.array(Image.fromarray(img_array))
images_for_vis_feature[i] = img_array
""" ResNet_Images = tf.keras.applications.resnet.preprocess_input(images_for_vis_feature)
ResNet = tf.keras.applications.resnet.ResNet101()
ResNet_model = Model(ResNet.input, ResNet.layers[-2].output) # Input = (224, 224, 3), Output = (2048,)
ResNet_Features = ResNet_model.predict(ResNet_Images)"""
# Preprocess images for ShuffleNet
ShuffleNet_Images = tf.keras.applications.shufflenet.preprocess_input(images_for_vis_feature)
# Load ShuffleNet model and get features
ShuffleNet = ShuffleNetV2()
ShuffleNet_model = Model(ShuffleNet.input, ShuffleNet.layers[-2].output) # Input = (224, 224, 3), Output = (1024,)
ShuffleNet_Features = ShuffleNet_model.predict(ShuffleNet_Images)
train_data = []
train_vis_features = []
train_label = []
train_image = []
validation_data = []
validation_vis_features = []
validation_label = []
validation_image = []
test_dat = []
test_vis_features = []
test_label = []
# test_image = []
counter = np.zeros(category_size)
counter_train = np.zeros(category_size)
counter_val = np.zeros(category_size)
counter_test = np.zeros(category_size)
images_file = 'data/Class_Images_Selected/Image_Stimuli_Full.csv'
with open(images_file) as f:
data = csv.reader(f, delimiter='/')
images_list = [row for row in data]
val_list = []
test_list = []
for i in range(40):
part_list = images_list[i * 50:i * 50 + 50]
np.random.seed(i)
np.random.shuffle(part_list)
for ii in range(5):
#img_path = part_list[ii][1] + part_list[ii][2]
img_path = part_list[ii][2]
val_list.append(img_path)
#img_path2 = 'finalEEGImages/' + part_list[ii + 5][2]
img_path2 = part_list[ii + 5][2]
test_list.append(img_path2)
for i in range(EEGs.shape[0]):
if images[i] in val_list:
if do_norm:
norm = EEGs[i] - np.average(EEGs[i], axis=0)
else:
norm = EEGs[i]
if do_zscore:
validation_data.append(zscore(norm, axis=1, ddof=1)[:, window_start:window_end])
else:
validation_data.append(norm[:, window_start:window_end])
validation_vis_features.append(ShuffleNet_Features[i])
validation_label.append(labels[i])
validation_image.append(images[i])
counter[labels[i]] += 1
counter_val[labels[i]] += 1
elif images[i] in test_list:
if do_norm:
norm = EEGs[i] - np.average(EEGs[i], axis=0)
else:
norm = EEGs[i]
if do_zscore:
test_dat.append(zscore(norm, axis=1, ddof=1)[:, window_start:window_end])
else:
test_dat.append(norm[:, window_start:window_end])
test_vis_features.append(ShuffleNet_Features[i])
test_label.append(labels[i])
# test_image.append(images[i])
counter[labels[i]] += 1
counter_test[labels[i]] += 1
else:
if do_norm:
norm = EEGs[i] - np.average(EEGs[i], axis=0)
else:
norm = EEGs[i]
if do_zscore:
train_data.append(zscore(norm, axis=1, ddof=1)[:, window_start:window_end])
else:
train_data.append(norm[:, window_start:window_end])
train_vis_features.append(ShuffleNet_Features[i])
train_label.append(labels[i])
train_image.append(images[i])
counter[labels[i]] += 1
counter_train[labels[i]] += 1
print('Train Data')
print(counter_train)
print('Validation Data')
print(counter_val)
print('Test Data')
print(counter_test)
train_data = np.array(train_data)
train_vis_features = np.array(train_vis_features)
train_label = np.array(train_label)
train_image = np.array(train_image)
validation_data = np.array(validation_data)
validation_vis_features = np.array(validation_vis_features)
validation_label = np.array(validation_label)
validation_image = np.array(validation_image)
test_dat = np.array(test_dat)
test_vis_features = np.array(test_vis_features)
test_label = np.array(test_label)
return train_data, train_vis_features, train_label, train_image, validation_data, validation_vis_features, validation_label, validation_image, test_dat, test_vis_features, test_label, counter
def data_block_loader_train_test_image_separate2(use_all=False, eye_remove=True, do_norm=True, do_zscore=True):
# Load data
if use_all:
file_name1 = 'Data/01_All/Block_Sub001_all.mat'
full_data1 = loadmat(file_name1)
file_name2 = 'Data/02_All/Block_Sub001_all.mat'
full_data2 = loadmat(file_name2)
file_name3 = 'Data/03_All/Block_Sub001_all.mat'
full_data3 = loadmat(file_name3)
file_name4 = 'Data/04_All/Block_Sub001_all.mat'
full_data4 = loadmat(file_name4)
EEGs = np.concatenate([full_data1['data'], full_data2['data'],
full_data3['data'], full_data4['data']], axis=0)
full_data1['images']=np.array([image.split('/')[1] for image in full_data1['images']])
full_data2['images']=np.array([image.split('/')[1] for image in full_data2['images']])
full_data3['images']=np.array([image.split('/')[1] for image in full_data3['images']])
full_data4['images']=np.array([image.split('/')[1] for image in full_data4['images']])
images = np.concatenate([full_data1['images'], full_data2['images'],
full_data3['images'], full_data4['images']], axis=0)
labels = np.concatenate([full_data1['labels'], full_data2['labels'],
full_data3['labels'], full_data4['labels']], axis=0)
else:
if eye_remove:
file_name1 = 'data/Block_Sub001_Remove_Eye.mat'
full_data1 = loadmat(file_name1)
file_name2 = 'data/Block_Sub002_Remove_Eye.mat'
full_data2 = loadmat(file_name2)
file_name3 = 'data/Block_Sub003_Remove_Eye.mat'
full_data3 = loadmat(file_name3)
file_name4 = 'data/Block_Sub004_Remove_Eye.mat'
full_data4 = loadmat(file_name4)
EEGs = np.concatenate([full_data1['data'], full_data2['data'],
full_data3['data'], full_data4['data']], axis=0)
data1_images=np.array([image.split('/')[1] for image in full_data1['images']])
data2_images=np.array([image.split('/')[1] for image in full_data2['images']])
data3_images=np.array([image.split('/')[1] for image in full_data3['images']])
data4_images=np.array([image.split('/')[1] for image in full_data4['images']])
images = np.concatenate([data1_images, data2_images,
data3_images, data4_images], axis=0)
labels = np.concatenate([full_data1['labels'], full_data2['labels'],
full_data3['labels'], full_data4['labels']], axis=0)
else:
file_name1 = 'Data/02_Remove_Bad_Trial/Block_Sub001_Remove_Trial.mat'
full_data1 = loadmat(file_name1)
file_name2 = 'Data/02_Remove_Bad_Trial/Block_Sub002_Remove_Trial.mat'
full_data2 = loadmat(file_name2)
file_name3 = 'Data/02_Remove_Bad_Trial/Block_Sub003_Remove_Trial.mat'
full_data3 = loadmat(file_name3)
file_name4 = 'Data/02_Remove_Bad_Trial/Block_Sub004_Remove_Trial.mat'
full_data4 = loadmat(file_name4)
EEGs = np.concatenate([full_data1['data'], full_data2['data'],
full_data3['data'], full_data4['data']], axis=0)
images = np.concatenate([full_data1['images'], full_data2['images'],
full_data3['images'], full_data4['images']], axis=0)
labels = np.concatenate([full_data1['labels'], full_data2['labels'],
full_data3['labels'], full_data4['labels']], axis=0)
# Parameters
window_start = 0
window_end = window_start + 125
category_size = 40
# Shuffle
seed = 12345
np.random.seed(seed)
np.random.shuffle(EEGs)
np.random.seed(seed)
np.random.shuffle(images)
np.random.seed(seed)
np.random.shuffle(labels)
train_data = []
train_label = []
train_image = []
test_dat = []
test_label = []
test_image = []
counter = np.zeros(category_size)
counter_train = np.zeros(category_size)
counter_test = np.zeros(category_size)
images_file = 'data/Class_Images_Selected/Image_Stimuli_Full.csv'
with open(images_file) as f:
data = csv.reader(f, delimiter='/')
images_list = [row for row in data]
test_list = []
for i in range(40):
part_list = images_list[i * 50:i * 50 + 50]
np.random.seed(i)
np.random.shuffle(part_list)
for ii in range(5):
img_path2 = part_list[ii + 5][2]
test_list.append(img_path2)
for i in range(EEGs.shape[0]):
if images[i] in test_list:
if do_norm:
norm = EEGs[i] - np.average(EEGs[i], axis=0)
else:
norm = EEGs[i]
if do_zscore:
test_dat.append(zscore(norm, axis=1, ddof=1)[:, window_start:window_end])
else:
test_dat.append(norm[:, window_start:window_end])
test_label.append(labels[i])
test_image.append(images[i])
counter[labels[i]] += 1
counter_test[labels[i]] += 1
else:
if do_norm:
norm = EEGs[i] - np.average(EEGs[i], axis=0)
else:
norm = EEGs[i]
if do_zscore:
train_data.append(zscore(norm, axis=1, ddof=1)[:, window_start:window_end])
else:
train_data.append(norm[:, window_start:window_end])
train_label.append(labels[i])
train_image.append(images[i])
counter[labels[i]] += 1
counter_train[labels[i]] += 1
print('Train Data')
print(counter_train)
print('Test Data')
print(counter_test)
train_data = np.array(train_data)
train_label = np.array(train_label)
train_image = np.array(train_image)
test_dat = np.array(test_dat)
test_label = np.array(test_label)
test_image = np.array(test_image)
return train_data, train_label, train_image, test_dat, test_label, test_image, counter
def data_imagination40_loader_train_val_test_sub001(do_norm=True, do_zscore=True):
# Load data
file_name1 = 'data/Imagine_Sub001.mat'
full_data1 = loadmat(file_name1)
EEGs = full_data1['data']
full_data1['images']=np.array([image.split('/')[1] for image in full_data1['images']])
images = full_data1['images']
labels = full_data1['labels']
# Parameters
window_start = 0
window_end = window_start + 125
category_size = 40
# Shuffle
seed = 12345
np.random.seed(seed)
np.random.shuffle(EEGs)
np.random.seed(seed)
np.random.shuffle(images)
np.random.seed(seed)
np.random.shuffle(labels)
# Get Image features
images_for_vis_feature = np.empty((images.shape[0], 224, 224, 3))
for i in range(images.shape[0]):
image_path = 'finalEEGImages/' + images[i]
img = Image.open(image_path)
img_array = np.asarray(img)
img_array = Image.fromarray(img_array)
img_array = np.array(img_array.resize((224, 224), Image.LANCZOS))
if img_array.reshape(-1).shape[0] == 224 * 224:
img_array = cv2.cvtColor(img_array, cv2.COLOR_GRAY2RGB)
img_array = np.array(Image.fromarray(img_array))
images_for_vis_feature[i] = img_array
"""
ResNet_Images = tf.keras.applications.resnet.preprocess_input(images_for_vis_feature)
ResNet = tf.keras.applications.resnet.ResNet101()
ResNet_model = Model(ResNet.input, ResNet.layers[-2].output) # Input = (224, 224, 3), Output = (2048,)
ResNet_Features = ResNet_model.predict(ResNet_Images)"""
# Preprocess images for ShuffleNet
ShuffleNet_Images = tf.keras.applications.shufflenet.preprocess_input(images_for_vis_feature)
# Load ShuffleNet model and get features
ShuffleNet = ShuffleNetV2()
ShuffleNet_model = Model(ShuffleNet.input, ShuffleNet.layers[-2].output) # Input = (224, 224, 3), Output = (1024,)
ShuffleNet_Features = ShuffleNet_model.predict(ShuffleNet_Images)
train_data = []
train_vis_features = []
train_label = []
train_image = []
validation_data = []
validation_vis_features = []
validation_label = []
validation_image = []
test_dat = []
test_vis_features = []
test_label = []
# test_image = []
counter = np.zeros(category_size)
counter_train = np.zeros(category_size)
counter_val = np.zeros(category_size)
counter_test = np.zeros(category_size)
for i in range(EEGs.shape[0]):
if counter[labels[i]] < EEGs.shape[0] / category_size * 0.8:
if do_norm:
norm = EEGs[i] - np.average(EEGs[i], axis=0)
else:
norm = EEGs[i]
if do_zscore:
train_data.append(zscore(norm, axis=1, ddof=1)[:, window_start:window_end])
else:
train_data.append(norm[:, window_start:window_end])
train_vis_features.append(ShuffleNet_Features[i])
train_label.append(labels[i])
train_image.append(images[i])
counter[labels[i]] += 1
counter_train[labels[i]] += 1
elif counter[labels[i]] < EEGs.shape[0] / category_size * 0.9:
if do_norm:
norm = EEGs[i] - np.average(EEGs[i], axis=0)
else:
norm = EEGs[i]
if do_zscore:
validation_data.append(zscore(norm, axis=1, ddof=1)[:, window_start:window_end])
else:
validation_data.append(norm[:, window_start:window_end])
validation_vis_features.append(ShuffleNet_Features[i])
validation_label.append(labels[i])
validation_image.append(images[i])
counter[labels[i]] += 1
counter_val[labels[i]] += 1
else:
if do_norm:
norm = EEGs[i] - np.average(EEGs[i], axis=0)
else:
norm = EEGs[i]
if do_zscore:
test_dat.append(zscore(norm, axis=1, ddof=1)[:, window_start:window_end])
else:
test_dat.append(norm[:, window_start:window_end])
test_vis_features.append(ShuffleNet_Features[i])
test_label.append(labels[i])
# test_image.append(images[i])
counter[labels[i]] += 1
counter_test[labels[i]] += 1
print('Train Data')
print(counter_train)
print('Validation Data')
print(counter_val)
print('Test Data')
print(counter_test)
train_data = np.array(train_data)
train_vis_features = np.array(train_vis_features)
train_label = np.array(train_label)
train_image = np.array(train_image)
validation_data = np.array(validation_data)
validation_vis_features = np.array(validation_vis_features)
validation_label = np.array(validation_label)
validation_image = np.array(validation_image)
test_dat = np.array(test_dat)
test_vis_features = np.array(test_vis_features)
test_label = np.array(test_label)
return train_data, train_vis_features, train_label, train_image, validation_data, validation_vis_features, validation_label, validation_image, test_dat, test_vis_features, test_label, counter
def data_imagination40_loader_train_val_test_sub002(do_norm=True, do_zscore=True):
# Load data
file_name1 = 'data/Imagine_Sub002.mat'
full_data1 = loadmat(file_name1)
EEGs = full_data1['data']
full_data1['images']=np.array([image.split('/')[1] for image in full_data1['images']])
images = full_data1['images']
labels = full_data1['labels']
# Parameters
window_start = 0
window_end = window_start + 125
category_size = 40
# Shuffle
seed = 54321
np.random.seed(seed)
np.random.shuffle(EEGs)
np.random.seed(seed)
np.random.shuffle(images)
np.random.seed(seed)
np.random.shuffle(labels)
# Get Image features
images_for_vis_feature = np.empty((images.shape[0], 224, 224, 3))
for i in range(images.shape[0]):
image_path = 'finalEEGImages/' + images[i]
img = Image.open(image_path)
img_array = np.asarray(img)
img_array = Image.fromarray(img_array)
img_array = np.array(img_array.resize((224, 224), Image.LANCZOS))
if img_array.reshape(-1).shape[0] == 224 * 224:
img_array = cv2.cvtColor(img_array, cv2.COLOR_GRAY2RGB)
img_array = np.array(Image.fromarray(img_array))
images_for_vis_feature[i] = img_array
"""
ResNet_Images = tf.keras.applications.resnet.preprocess_input(images_for_vis_feature)
ResNet = tf.keras.applications.resnet.ResNet101()
ResNet_model = Model(ResNet.input, ResNet.layers[-2].output) # Input = (224, 224, 3), Output = (2048,)
ResNet_Features = ResNet_model.predict(ResNet_Images)"""
# Preprocess images for ShuffleNet
ShuffleNet_Images = tf.keras.applications.shufflenet.preprocess_input(images_for_vis_feature)
# Load ShuffleNet model and get features
ShuffleNet = ShuffleNetV2()
ShuffleNet_model = Model(ShuffleNet.input, ShuffleNet.layers[-2].output) # Input = (224, 224, 3), Output = (1024,)
ShuffleNet_Features = ShuffleNet_model.predict(ShuffleNet_Images)
train_data = []
train_vis_features = []
train_label = []
train_image = []
validation_data = []
validation_vis_features = []
validation_label = []
validation_image = []
test_dat = []
test_vis_features = []
test_label = []
# test_image = []
counter = np.zeros(category_size)
counter_train = np.zeros(category_size)
counter_val = np.zeros(category_size)
counter_test = np.zeros(category_size)
for i in range(EEGs.shape[0]):
if counter[labels[i]] < EEGs.shape[0] / category_size * 0.8:
if do_norm:
norm = EEGs[i] - np.average(EEGs[i], axis=0)
else:
norm = EEGs[i]
if do_zscore:
train_data.append(zscore(norm, axis=1, ddof=1)[:, window_start:window_end])
else:
train_data.append(norm[:, window_start:window_end])
train_vis_features.append(ShuffleNet_Features[i])
train_label.append(labels[i])
train_image.append(images[i])
counter[labels[i]] += 1
counter_train[labels[i]] += 1
elif counter[labels[i]] < EEGs.shape[0] / category_size * 0.9:
if do_norm:
norm = EEGs[i] - np.average(EEGs[i], axis=0)
else:
norm = EEGs[i]
if do_zscore:
validation_data.append(zscore(norm, axis=1, ddof=1)[:, window_start:window_end])
else:
validation_data.append(norm[:, window_start:window_end])
validation_vis_features.append(ShuffleNet_Features[i])
validation_label.append(labels[i])
validation_image.append(images[i])
counter[labels[i]] += 1
counter_val[labels[i]] += 1
else:
if do_norm:
norm = EEGs[i] - np.average(EEGs[i], axis=0)
else:
norm = EEGs[i]
if do_zscore:
test_dat.append(zscore(norm, axis=1, ddof=1)[:, window_start:window_end])
else:
test_dat.append(norm[:, window_start:window_end])
test_vis_features.append(ShuffleNet_Features[i])
test_label.append(labels[i])
# test_image.append(images[i])
counter[labels[i]] += 1
counter_test[labels[i]] += 1
print('Train Data')
print(counter_train)
print('Validation Data')
print(counter_val)
print('Test Data')
print(counter_test)
train_data = np.array(train_data)
train_vis_features = np.array(train_vis_features)
train_label = np.array(train_label)
train_image = np.array(train_image)
validation_data = np.array(validation_data)
validation_vis_features = np.array(validation_vis_features)
validation_label = np.array(validation_label)
validation_image = np.array(validation_image)
test_dat = np.array(test_dat)
test_vis_features = np.array(test_vis_features)
test_label = np.array(test_label)
return train_data, train_vis_features, train_label, train_image, validation_data, validation_vis_features, validation_label, validation_image, test_dat, test_vis_features, test_label, counter
def data_imagination40_loader_train_val_test_sub003(do_norm=True, do_zscore=True):
# Load data
file_name1 = 'data/Imagine_Sub003.mat'
full_data1 = loadmat(file_name1)
EEGs = full_data1['data']
full_data1['images']=np.array([image.split('/')[1] for image in full_data1['images']])
images = full_data1['images']
labels = full_data1['labels']
# Parameters
window_start = 0
window_end = window_start + 125
category_size = 40
# Shuffle
seed = 13579
np.random.seed(seed)
np.random.shuffle(EEGs)
np.random.seed(seed)
np.random.shuffle(images)
np.random.seed(seed)
np.random.shuffle(labels)
# Get Image features
images_for_vis_feature = np.empty((images.shape[0], 224, 224, 3))
for i in range(images.shape[0]):
image_path = 'finalEEGImages/' + images[i]
img = Image.open(image_path)
img_array = np.asarray(img)
img_array = Image.fromarray(img_array)
img_array = np.array(img_array.resize((224, 224), Image.LANCZOS))
if img_array.reshape(-1).shape[0] == 224 * 224:
img_array = cv2.cvtColor(img_array, cv2.COLOR_GRAY2RGB)
img_array = np.array(Image.fromarray(img_array))
images_for_vis_feature[i] = img_array
"""
ResNet_Images = tf.keras.applications.resnet.preprocess_input(images_for_vis_feature)
ResNet = tf.keras.applications.resnet.ResNet101()
ResNet_model = Model(ResNet.input, ResNet.layers[-2].output) # Input = (224, 224, 3), Output = (2048,)
ResNet_Features = ResNet_model.predict(ResNet_Images)"""
# Preprocess images for ShuffleNet
ShuffleNet_Images = tf.keras.applications.shufflenet.preprocess_input(images_for_vis_feature)
# Load ShuffleNet model and get features
ShuffleNet = ShuffleNetV2()
ShuffleNet_model = Model(ShuffleNet.input, ShuffleNet.layers[-2].output) # Input = (224, 224, 3), Output = (1024,)
ShuffleNet_Features = ShuffleNet_model.predict(ShuffleNet_Images)
train_data = []
train_vis_features = []
train_label = []
train_image = []
validation_data = []
validation_vis_features = []
validation_label = []
validation_image = []
test_dat = []
test_vis_features = []
test_label = []
# test_image = []
counter = np.zeros(category_size)
counter_train = np.zeros(category_size)
counter_val = np.zeros(category_size)
counter_test = np.zeros(category_size)
for i in range(EEGs.shape[0]):
if counter[labels[i]] < EEGs.shape[0] / category_size * 0.8:
if do_norm:
norm = EEGs[i] - np.average(EEGs[i], axis=0)
else:
norm = EEGs[i]
if do_zscore:
train_data.append(zscore(norm, axis=1, ddof=1)[:, window_start:window_end])
else:
train_data.append(norm[:, window_start:window_end])
train_vis_features.append(ShuffleNet_Features[i])
train_label.append(labels[i])
train_image.append(images[i])
counter[labels[i]] += 1
counter_train[labels[i]] += 1
elif counter[labels[i]] < EEGs.shape[0] / category_size * 0.9:
if do_norm:
norm = EEGs[i] - np.average(EEGs[i], axis=0)
else:
norm = EEGs[i]
if do_zscore:
validation_data.append(zscore(norm, axis=1, ddof=1)[:, window_start:window_end])
else:
validation_data.append(norm[:, window_start:window_end])
validation_vis_features.append(ShuffleNet_Features[i])
validation_label.append(labels[i])
validation_image.append(images[i])
counter[labels[i]] += 1
counter_val[labels[i]] += 1
else:
if do_norm:
norm = EEGs[i] - np.average(EEGs[i], axis=0)
else:
norm = EEGs[i]
if do_zscore:
test_dat.append(zscore(norm, axis=1, ddof=1)[:, window_start:window_end])
else:
test_dat.append(norm[:, window_start:window_end])
test_vis_features.append(ShuffleNet_Features[i])
test_label.append(labels[i])
# test_image.append(images[i])
counter[labels[i]] += 1
counter_test[labels[i]] += 1
print('Train Data')
print(counter_train)
print('Validation Data')
print(counter_val)
print('Test Data')
print(counter_test)
train_data = np.array(train_data)
train_vis_features = np.array(train_vis_features)
train_label = np.array(train_label)
train_image = np.array(train_image)
validation_data = np.array(validation_data)
validation_vis_features = np.array(validation_vis_features)
validation_label = np.array(validation_label)
validation_image = np.array(validation_image)
test_dat = np.array(test_dat)
test_vis_features = np.array(test_vis_features)
test_label = np.array(test_label)
return train_data, train_vis_features, train_label, train_image, validation_data, validation_vis_features, validation_label, validation_image, test_dat, test_vis_features, test_label, counter
def data_imagination40_loader_train_val_test_sub004(do_norm=True, do_zscore=True):
# Load data
file_name1 = 'data/Imagine_Sub004.mat'
full_data1 = loadmat(file_name1)
EEGs = full_data1['data']
full_data1['images']=np.array([image.split('/')[1] for image in full_data1['images']])
images = full_data1['images']
labels = full_data1['labels']
# Parameters
window_start = 0
window_end = window_start + 125
category_size = 40
# Shuffle
seed = 97531
np.random.seed(seed)
np.random.shuffle(EEGs)
np.random.seed(seed)
np.random.shuffle(images)
np.random.seed(seed)
np.random.shuffle(labels)
# Get Image features
images_for_vis_feature = np.empty((images.shape[0], 224, 224, 3))
for i in range(images.shape[0]):
image_path = 'finalEEGImages/' + images[i]
img = Image.open(image_path)
img_array = np.asarray(img)
img_array = Image.fromarray(img_array)
img_array = np.array(img_array.resize((224, 224), Image.LANCZOS))
if img_array.reshape(-1).shape[0] == 224 * 224:
img_array = cv2.cvtColor(img_array, cv2.COLOR_GRAY2RGB)
img_array = np.array(Image.fromarray(img_array))
images_for_vis_feature[i] = img_array
"""
ResNet_Images = tf.keras.applications.resnet.preprocess_input(images_for_vis_feature)
ResNet = tf.keras.applications.resnet.ResNet101()
ResNet_model = Model(ResNet.input, ResNet.layers[-2].output) # Input = (224, 224, 3), Output = (2048,)
ResNet_Features = ResNet_model.predict(ResNet_Images)"""
# Preprocess images for ShuffleNet
ShuffleNet_Images = tf.keras.applications.shufflenet.preprocess_input(images_for_vis_feature)
# Load ShuffleNet model and get features
ShuffleNet = ShuffleNetV2()
ShuffleNet_model = Model(ShuffleNet.input, ShuffleNet.layers[-2].output) # Input = (224, 224, 3), Output = (1024,)
ShuffleNet_Features = ShuffleNet_model.predict(ShuffleNet_Images)
train_data = []
train_vis_features = []
train_label = []
train_image = []
validation_data = []
validation_vis_features = []
validation_label = []
validation_image = []
test_dat = []
test_vis_features = []
test_label = []
# test_image = []
counter = np.zeros(category_size)
counter_train = np.zeros(category_size)
counter_val = np.zeros(category_size)
counter_test = np.zeros(category_size)
for i in range(EEGs.shape[0]):
if counter[labels[i]] < EEGs.shape[0] / category_size * 0.8:
if do_norm:
norm = EEGs[i] - np.average(EEGs[i], axis=0)
else:
norm = EEGs[i]
if do_zscore:
train_data.append(zscore(norm, axis=1, ddof=1)[:, window_start:window_end])
else:
train_data.append(norm[:, window_start:window_end])
train_vis_features.append(ShuffleNet_Features[i])
train_label.append(labels[i])
train_image.append(images[i])
counter[labels[i]] += 1
counter_train[labels[i]] += 1
elif counter[labels[i]] < EEGs.shape[0] / category_size * 0.9:
if do_norm:
norm = EEGs[i] - np.average(EEGs[i], axis=0)
else:
norm = EEGs[i]
if do_zscore:
validation_data.append(zscore(norm, axis=1, ddof=1)[:, window_start:window_end])
else:
validation_data.append(norm[:, window_start:window_end])
validation_vis_features.append(ShuffleNet_Features[i])
validation_label.append(labels[i])
validation_image.append(images[i])
counter[labels[i]] += 1
counter_val[labels[i]] += 1
else:
if do_norm:
norm = EEGs[i] - np.average(EEGs[i], axis=0)
else:
norm = EEGs[i]
if do_zscore:
test_dat.append(zscore(norm, axis=1, ddof=1)[:, window_start:window_end])
else:
test_dat.append(norm[:, window_start:window_end])
test_vis_features.append(ShuffleNet_Features[i])
test_label.append(labels[i])
# test_image.append(images[i])
counter[labels[i]] += 1
counter_test[labels[i]] += 1
print('Train Data')
print(counter_train)
print('Validation Data')
print(counter_val)
print('Test Data')
print(counter_test)
train_data = np.array(train_data)
train_vis_features = np.array(train_vis_features)
train_label = np.array(train_label)
train_image = np.array(train_image)
validation_data = np.array(validation_data)
validation_vis_features = np.array(validation_vis_features)
validation_label = np.array(validation_label)
validation_image = np.array(validation_image)
test_dat = np.array(test_dat)
test_vis_features = np.array(test_vis_features)
test_label = np.array(test_label)
return train_data, train_vis_features, train_label, train_image, validation_data, validation_vis_features, validation_label, validation_image, test_dat, test_vis_features, test_label, counter
def data_imagination40_loader_train_val_test_sub_all(do_norm=True, do_zscore=True):
train_data = []
train_vis_features = []
train_label = []
train_image = []
validation_data = []
validation_vis_features = []
validation_label = []
validation_image = []
test_dat = []
test_vis_features = []
test_label = []
# test_image = []
# Load data Sub001
file_name1 = 'data/Imagine_Sub001.mat'
full_data1 = loadmat(file_name1)
EEGs = full_data1['data']
full_data1['images']=np.array([image.split('/')[1] for image in full_data1['images']])
images = full_data1['images']
labels = full_data1['labels']
# Parameters
window_start = 0
window_end = window_start + 125
category_size = 40
# Shuffle
seed = 12345
np.random.seed(seed)
np.random.shuffle(EEGs)
np.random.seed(seed)
np.random.shuffle(images)
np.random.seed(seed)
np.random.shuffle(labels)
# Get Image features
images_for_vis_feature = np.empty((images.shape[0], 224, 224, 3))
for i in range(images.shape[0]):
image_path = 'finalEEGImages/' + images[i]
img = Image.open(image_path)
img_array = np.asarray(img)
img_array = Image.fromarray(img_array)
img_array = np.array(img_array.resize((224, 224), Image.LANCZOS))
if img_array.reshape(-1).shape[0] == 224 * 224:
img_array = cv2.cvtColor(img_array, cv2.COLOR_GRAY2RGB)
img_array = np.array(Image.fromarray(img_array))
images_for_vis_feature[i] = img_array
"""
ResNet_Images = tf.keras.applications.resnet.preprocess_input(images_for_vis_feature)
ResNet = tf.keras.applications.resnet.ResNet101()
ResNet_model = Model(ResNet.input, ResNet.layers[-2].output) # Input = (224, 224, 3), Output = (2048,)
ResNet_Features = ResNet_model.predict(ResNet_Images)"""
# Preprocess images for ShuffleNet
ShuffleNet_Images = tf.keras.applications.shufflenet.preprocess_input(images_for_vis_feature)
# Load ShuffleNet model and get features
ShuffleNet = ShuffleNetV2()
ShuffleNet_model = Model(ShuffleNet.input, ShuffleNet.layers[-2].output) # Input = (224, 224, 3), Output = (1024,)
ShuffleNet_Features = ShuffleNet_model.predict(ShuffleNet_Images)
counter = np.zeros(category_size)
counter_train = np.zeros(category_size)
counter_val = np.zeros(category_size)
counter_test = np.zeros(category_size)
for i in range(EEGs.shape[0]):
if counter[labels[i]] < EEGs.shape[0] / category_size * 0.8:
if do_norm:
norm = EEGs[i] - np.average(EEGs[i], axis=0)
else:
norm = EEGs[i]
if do_zscore:
train_data.append(zscore(norm, axis=1, ddof=1)[:, window_start:window_end])
else:
train_data.append(norm[:, window_start:window_end])
train_vis_features.append(ShuffleNet_Features[i])
train_label.append(labels[i])
train_image.append(images[i])
counter[labels[i]] += 1
counter_train[labels[i]] += 1
elif counter[labels[i]] < EEGs.shape[0] / category_size * 0.9:
if do_norm:
norm = EEGs[i] - np.average(EEGs[i], axis=0)
else:
norm = EEGs[i]
if do_zscore:
validation_data.append(zscore(norm, axis=1, ddof=1)[:, window_start:window_end])
else:
validation_data.append(norm[:, window_start:window_end])
validation_vis_features.append(ShuffleNet_Features[i])