def read(self, img_rows=IMAGE_SIZE, img_cols=IMAGE_SIZE, img_channels=3, nb_classes=2):
images, labels = extract_data('C:\\video\\') # labels : boss 이면 0 , 아니면 1
# print(images.shape)
# print(labels.shape)
labels = np.reshape(labels, [-1])
# print(labels.shape)
# numpy.reshape
X_train, X_test, y_train, y_test = train_test_split(images, labels, test_size=0.2, random_state=random.randint(0, 100)) # random.randint(0, 100) 0과 100사이의 랜덤한 정수를 생성
X_valid, X_test, y_valid, y_test = train_test_split(X_train, y_train, test_size=0.33, random_state=random.randint(0, 100))
if K.image_dim_ordering() == 'th':
X_train = X_train.reshape(X_train.shape[0], 3, img_rows, img_cols)
X_valid = X_valid.reshape(X_valid.shape[0], 3, img_rows, img_cols)
X_test = X_test.reshape(X_test.shape[0], 3, img_rows, img_cols)
input_shape = (3, img_rows, img_cols)
else:
X_train = X_train.reshape(X_train.shape[0], img_rows, img_cols, 3)
X_valid = X_valid.reshape(X_valid.shape[0], img_rows, img_cols, 3)
X_test = X_test.reshape(X_test.shape[0], img_rows, img_cols, 3)
input_shape = (img_rows, img_cols, 3)
# the data, shuffled and split between train and test sets
print('X_train shape:', X_train.shape)
print(X_train.shape[0], 'train samples')
print(X_valid.shape[0], 'valid samples')
print(X_test.shape[0], 'test samples')
# convert class vectors to binary class matrices
Y_train = np_utils.to_categorical(y_train, nb_classes) # 원핫인코딩
Y_valid = np_utils.to_categorical(y_valid, nb_classes)
Y_test = np_utils.to_categorical(y_test, nb_classes)
print(X_train.dtype)
X_train = X_train.astype('float32')
X_valid = X_valid.astype('float32')
X_test = X_test.astype('float32')
# X_train /= 255 #데이터 정규화를 위해 , 255: 0부터 255사이의 이미지 픽셀 값
# X_valid /= 255
# X_test /= 255
# print('###',X_train) #정규화 된 data 확인
self.X_train = X_train # 정규화 된 data
self.X_valid = X_valid
self.X_test = X_test
self.Y_train = Y_train # 원핫 인코딩된 label
self.Y_valid = Y_valid
self.Y_test = Y_test
def read(self,
img_rows=IMAGE_SIZE,
img_cols=IMAGE_SIZE,
img_channels=3,
nb_classes=2):
images, labels = extract_data('./data/')
labels = np.reshape(labels, [-1])
# numpy.reshape
X_train, X_test, y_train, y_test = train_test_split(
images, labels, test_size=0.3, random_state=random.randint(0, 100))
X_valid, X_test, y_valid, y_test = train_test_split(
images, labels, test_size=0.5, random_state=random.randint(0, 100))
if K.image_dim_ordering() == 'th':
X_train = X_train.reshape(X_train.shape[0], 3, img_rows, img_cols)
X_valid = X_valid.reshape(X_valid.shape[0], 3, img_rows, img_cols)
X_test = X_test.reshape(X_test.shape[0], 3, img_rows, img_cols)
input_shape = (3, img_rows, img_cols)
else:
X_train = X_train.reshape(X_train.shape[0], img_rows, img_cols, 3)
X_valid = X_valid.reshape(X_valid.shape[0], img_rows, img_cols, 3)
X_test = X_test.reshape(X_test.shape[0], img_rows, img_cols, 3)
input_shape = (img_rows, img_cols, 3)
# the data, shuffled and split between train and test sets
print('X_train shape:', X_train.shape)
print(X_train.shape[0], 'train samples')
print(X_valid.shape[0], 'valid samples')
print(X_test.shape[0], 'test samples')
# convert class vectors to binary class matrices
Y_train = np_utils.to_categorical(y_train, nb_classes)
Y_valid = np_utils.to_categorical(y_valid, nb_classes)
Y_test = np_utils.to_categorical(y_test, nb_classes)
X_train = X_train.astype('float32')
X_valid = X_valid.astype('float32')
X_test = X_test.astype('float32')
X_train /= 255
X_valid /= 255
X_test /= 255
self.X_train = X_train
self.X_valid = X_valid
self.X_test = X_test
self.Y_train = Y_train
self.Y_valid = Y_valid
self.Y_test = Y_test
def read(self, img_rows=IMAGE_SIZE, img_cols=IMAGE_SIZE, img_channels=3, nb_classes=2):
images, labels = extract_data('d:/code/python/bossWin/data')
# numpy.reshape
labels = np.reshape(labels, [-1])
X_train, X_test, y_train, y_test = train_test_split(images, labels, test_size=0.3, random_state=random.randint(0, 100))
X_valid, X_test, y_valid, y_test = train_test_split(images, labels, test_size=0.5, random_state=random.randint(0, 100))
if K.image_dim_ordering() == 'th':
X_train = X_train.reshape(X_train.shape[0], 3, img_rows, img_cols)
X_valid = X_valid.reshape(X_valid.shape[0], 3, img_rows, img_cols)
X_test = X_test.reshape(X_test.shape[0], 3, img_rows, img_cols)
input_shape = (3, img_rows, img_cols)
else:
X_train = X_train.reshape(X_train.shape[0], img_rows, img_cols, 3)
X_valid = X_valid.reshape(X_valid.shape[0], img_rows, img_cols, 3)
X_test = X_test.reshape(X_test.shape[0], img_rows, img_cols, 3)
input_shape = (img_rows, img_cols, 3)
# the data, shuffled and split between train and test sets
print('X_train shape:', X_train.shape)
print(X_train.shape[0], 'train samples')
print(X_valid.shape[0], 'valid samples')
print(X_test.shape[0], 'test samples')
# convert class vectors to binary class matrices
Y_train = np_utils.to_categorical(y_train, nb_classes)
Y_valid = np_utils.to_categorical(y_valid, nb_classes)
Y_test = np_utils.to_categorical(y_test, nb_classes)
X_train = X_train.astype('float32')
X_valid = X_valid.astype('float32')
X_test = X_test.astype('float32')
X_train /= 255
X_valid /= 255
X_test /= 255
self.X_train = X_train
self.X_valid = X_valid
self.X_test = X_test
self.Y_train = Y_train
self.Y_valid = Y_valid
self.Y_test = Y_test
import matplotlib.pyplot as plt
from keras.utils import np_utils
from keras.models import Sequential
from keras.layers import Dense, Dropout, Activation
sys.path.insert(0, '/Users/shaomingliang/Downloads/BossSensor-master')
from boss_input import extract_data,resize_with_pad
#DatasetPath = []
#for i in os.listdir("yalefaces"):
# DatasetPath.append(os.path.join("yalefaces", i))
imageData = []
imageLabels = []
imageData1, imageLabels= extract_data('/Users/shaomingliang/Downloads/BossSensor-master/data')
imageData = []
labels = []
for index,img in enumerate(imageData1):
if img is None:
continue
frame = cv2.resize(img, (64, 64))
imageData.append(cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY))
labels.append(imageLabels[index])
imageDataFin = imageData
imageLabels = labels
#for i in DatasetPath:
# imgRead = io.imread(i,as_grey=True)
# imageData.append(imgRead)
#