from math import ceil
import pytest
import numpy as np
from numpy.testing import assert_allclose
from numpy.testing import assert_equal
from numpy.testing import assert_raises
import keras
# TODO: remove the 3 lines below once the Keras release
# is configured to use keras_preprocessing
import keras_preprocessing
keras_preprocessing.set_keras_submodules(
backend=keras.backend, utils=keras.utils)
from keras_preprocessing import sequence
def test_pad_sequences():
a = [[1], [1, 2], [1, 2, 3]]
# test padding
b = sequence.pad_sequences(a, maxlen=3, padding='pre')
assert_allclose(b, [[0, 0, 1], [0, 1, 2], [1, 2, 3]])
b = sequence.pad_sequences(a, maxlen=3, padding='post')
assert_allclose(b, [[1, 0, 0], [1, 2, 0], [1, 2, 3]])
# test truncating
b = sequence.pad_sequences(a, maxlen=2, truncating='pre')
assert_allclose(b, [[0, 1], [1, 2], [2, 3]])
b = sequence.pad_sequences(a, maxlen=2, truncating='post')
import pytest
from PIL import Image
import numpy as np
import os
import tempfile
import shutil
import keras
import pandas as pd
import random
# TODO: remove the 3 lines below once the Keras release
# is configured to use keras_preprocessing
import keras_preprocessing
keras_preprocessing.set_keras_submodules(
backend=keras.backend, utils=keras.utils)
# This enables this import
from keras_preprocessing import image
class TestImage(object):
def setup_class(cls):
cls.img_w = cls.img_h = 20
rgb_images = []
rgba_images = []
gray_images = []
for n in range(8):
bias = np.random.rand(cls.img_w, cls.img_h, 1) * 64
variance = np.random.rand(cls.img_w, cls.img_h, 1) * (255 - 64)
imarray = np.random.rand(cls.img_w, cls.img_h, 3) * variance + bias
