def read(self,
img_rows=IMAGE_SIZE,
img_cols=IMAGE_SIZE,
img_channels=3,
nb_classes=2):
images, labels = extract_data(self.TRAIN_DATA)
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], img_channels, img_rows,
img_cols)
X_valid = X_valid.reshape(X_valid.shape[0], img_channels, img_rows,
img_cols)
X_test = X_test.reshape(X_test.shape[0], img_channels, img_rows,
img_cols)
input_shape = (img_channels, img_rows, img_cols)
else:
X_train = X_train.reshape(X_train.shape[0], img_rows, img_cols,
img_channels)
X_valid = X_valid.reshape(X_valid.shape[0], img_rows, img_cols,
img_channels)
X_test = X_test.reshape(X_test.shape[0], img_rows, img_cols,
img_channels)
input_shape = (img_rows, img_cols, img_channels)
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')
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):
image,label=extract_data()
label=label.reshape((label.shape[0],))
n=[i for i in range(image.shape[0])]
random.shuffle(n)
image=image[n]
label=label[n]
X_train=image[0:384]
Y_train=label[0:384]
X_valid=image[384:512]
Y_valid=label[384:512]
X_test=image[512:]
Y_test=label[512:]
img_rows,img_cols=64,64
nb_classes=2
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('./data/' + ip + '/')
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_HEIGHT,
img_cols=IMAGE_SIZE_WIDTH,
img_channels=3,
nb_classes=2):
images, labels = extract_data('./data1/')
# labels = np.reshape(labels, [-1])
# numpy.reshape
X_train, X_test, y_train, y_test = train_test_split(
images,
labels,
test_size=0.3,
stratify=labels,
random_state=random.randint(0, 100))
X_valid, _, y_valid, _ = train_test_split(images,
labels,
test_size=0.5,
random_state=random.randint(
0, 100))
# 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, input_dir):
images, labels, nb_classes = extract_data(input_dir)
# shuffle and split data between train and test sets
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)
)
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
return nb_classes
else: y = 0
h += 2 * CropPadding
if x > CropPadding: x = x - CropPadding
else: x = 0
w += 2 * CropPadding
return [x, y, w, h]
if __name__ == '__main__':
model = Model()
model.load()
# Get Cascade Classifier
cascade = cv2.CascadeClassifier(cascade_path)
images, labels = extract_data(test_path)
labels = np.reshape(labels, [-1])
right = 0
cross = 0
countme = 0
countnotme = 0
rightme = 0
rightnotme = 0
for idx, image in enumerate(images):
# To gray image
if GRAY_MODE == True:
#frame_gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
frame_gray = image
else: