def test_imageprocessor_read():
"""Test the Imageprocessor."""
# Test with 4 channels
transform_sequence = [ToPILImage('RGBA')]
p = ImageProcessor(transform_sequence)
img_out = p.apply_transforms(test_input)
assert np.array(img_out).shape == (678, 1024, 4)
# Test with 3 channels
transform_sequence = [ToPILImage('RGB')]
p = ImageProcessor(transform_sequence)
img_out = p.apply_transforms(test_input)
assert np.array(img_out).shape == (678, 1024, 3)
# Test the values of the image
transform_sequence = [ToPILImage('RGBA'), PILtoarray()]
p = ImageProcessor(transform_sequence)
img_out = p.apply_transforms(test_input)
assert np.min(img_out) >= 0
assert np.max(img_out) <= 255
# Test the values of the image
transform_sequence = [ToPILImage('L'), PILtoarray()]
p = ImageProcessor(transform_sequence)
img_out = p.apply_transforms(test_input)
assert np.min(img_out) >= 0
assert np.max(img_out) <= 255
def test_imageprocessor_rotate():
"""Test the Imageprocessor's rotate function."""
# Test rotate (int)
transform_sequence = [ToPILImage('RGBA'), Resize((200, 200)), Rotate(5)]
p = ImageProcessor(transform_sequence)
img_out = p.apply_transforms(test_input)
assert np.array(img_out).shape == (200, 200, 4)
# Test rotate vs Pillow rotate
transform_sequence = [ToPILImage('RGBA'), Resize((200, 200))]
p = ImageProcessor(transform_sequence)
img_in = p.apply_transforms(test_input)
transform_sequence = [Rotate(5)]
p = ImageProcessor(transform_sequence)
img_out = p.apply_transforms(img_in)
assert img_in.rotate(5) == img_out
# Test rotate (negative int)
transform_sequence = [ToPILImage('RGBA'), Resize((200, 200)), Rotate(-5)]
p = ImageProcessor(transform_sequence)
img_out = p.apply_transforms(test_input)
assert np.array(img_out).shape == (200, 200, 4)
# Test rotate (float)
transform_sequence = [
ToPILImage('RGBA'),
Resize((200, 200)),
Rotate(499.99)
]
p = ImageProcessor(transform_sequence)
img_out = p.apply_transforms(test_input)
assert np.array(img_out).shape == (200, 200, 4)
def test_imageprocessor_normalize():
"""Test the Imageprocessor's normalize function."""
# Test normalize
transform_sequence = [ToPILImage('RGBA'), Normalize()]
p = ImageProcessor(transform_sequence)
img_out = p.apply_transforms(test_input)
assert np.max(img_out) <= 1 and np.min(img_out) >= 0
# Test normalize
transform_sequence = [ToPILImage('RGB'), Normalize()]
p = ImageProcessor(transform_sequence)
img_out = p.apply_transforms(test_input)
assert np.max(img_out) <= 1 and np.min(img_out) >= 0
# Test normalize
transform_sequence = [ToPILImage('L'), Normalize()]
p = ImageProcessor(transform_sequence)
img_out = p.apply_transforms(test_input)
assert np.max(img_out) <= 1 and np.min(img_out) >= 0
# Test for wrong use
transform_sequence = [
ToPILImage('L'), Normalize(),
Resize(size=(200, 200))
]
p = ImageProcessor(transform_sequence)
with nose.tools.assert_raises(Exception):
p.apply_transforms(test_input)
# Test for wrong use
transform_sequence = [ToPILImage('RGBA'), Normalize(), Normalize()]
p = ImageProcessor(transform_sequence)
with nose.tools.assert_raises(Exception):
p.apply_transforms(test_input)
def test_imageprocessor_combinations():
"""Test various combinations of the Imageprocessor's functionality."""
# Combination 1
transform_sequence = [
ToPILImage('RGB'),
Resize((2000, 2000)),
Rotate(5),
Grayscale(num_output_channels=4),
Resize((200, 200)),
Normalize()
]
p = ImageProcessor(transform_sequence)
img_out = p.apply_transforms(test_input)
assert np.array(img_out).shape == (200, 200, 4)
# Combination 2
transform_sequence = [ToPILImage('RGB'), Resize((200, 200)), Normalize()]
p = ImageProcessor(transform_sequence)
img_out = p.apply_transforms(test_input)
assert np.array(img_out).shape == (200, 200, 3)
# Combination 3
transform_sequence = [ToPILImage('RGB'), Resize((200, 200)), Standardize()]
p = ImageProcessor(transform_sequence)
img_out = p.apply_transforms(test_input)
assert np.array(img_out).shape == (200, 200, 3)
# Combination 5 - including pixel value tests
transform_sequence = [
ToPILImage('RGB'),
Resize((2000, 2000)),
Rotate(5),
Grayscale(num_output_channels=4),
Resize((200, 200)),
PILtoarray()
]
p = ImageProcessor(transform_sequence)
img_out = p.apply_transforms(test_input)
assert img_out.shape == (200, 200, 4)
assert np.min(img_out) >= 0
assert np.max(img_out) <= 255
# Combination 6 - utilize the pipeline multiple times
transform_sequence = [
ToPILImage('RGB'),
Resize((2000, 2000)),
Rotate(5),
Grayscale(num_output_channels=4),
Resize((200, 200)),
PILtoarray()
]
p = ImageProcessor(transform_sequence)
p.apply_transforms(test_input)
p.apply_transforms(test_input)
def test_imageprocessor_resize():
"""Test the Imageprocessor's resize function."""
# Resize to 200x200
transform_sequence = [ToPILImage('RGBA'), Resize(size=(200, 200))]
p = ImageProcessor(transform_sequence)
img_out = p.apply_transforms(test_input)
assert np.array(img_out).shape == (200, 200, 4)
# Resize to 2000x2000
transform_sequence = [ToPILImage('RGBA'), Resize(size=(2000, 2000))]
p = ImageProcessor(transform_sequence)
img_out = p.apply_transforms(test_input)
assert np.array(img_out).shape == (2000, 2000, 4)
def test_flask_error():
# Test invalid input format
transform_sequence = [ToPILImage('RGB')]
p = MAXImageProcessor(transform_sequence)
with nose.tools.assert_raises_regexp(Exception,
r".*Invalid input format*"):
p.apply_transforms("")
# Test invalid input dimensions
transform_sequence = [ToPILImage('RGB')]
p = MAXImageProcessor(transform_sequence)
with nose.tools.assert_raises_regexp(Exception,
r".*grayscale or a colour image*"):
p.apply_transforms(np.random.rand(10, 10, 10, 10))
def test_imageprocessor_grayscale():
"""Test the Imageprocessor's grayscale function."""
# Using the standard 1 output channel
transform_sequence = [
ToPILImage('RGBA'),
Resize(size=(200, 200)),
Grayscale()
]
p = ImageProcessor(transform_sequence)
img_out = p.apply_transforms(test_input)
assert np.array(img_out).shape == (200, 200)
# Using 3 output channels
transform_sequence = [
ToPILImage('RGBA'),
Resize(size=(200, 200)),
Grayscale(num_output_channels=3)
]
p = ImageProcessor(transform_sequence)
img_out = p.apply_transforms(test_input)
assert np.array(img_out).shape == (200, 200, 3)
# Using 4 output channels
transform_sequence = [
ToPILImage('RGBA'),
Resize(size=(200, 200)),
Grayscale(num_output_channels=4),
PILtoarray()
]
p = ImageProcessor(transform_sequence)
img_out = p.apply_transforms(test_input)
assert img_out.shape == (200, 200, 4)
# Test that the values in all 4 output channels are identical
assert img_out[..., 0].all() == img_out[..., 1].all() == img_out[
..., 2].all() == img_out[..., 3].all()
def test_imageprocessor_standardize():
"""Test the Imageprocessor's standardize function."""
# Test standardize (RGBA)
# The `A` channel cannot be standardized, and will therefore prompt an error message when attempted.
transform_sequence = [ToPILImage('RGBA'), Standardize()]
with nose.tools.assert_raises_regexp(
ValueError,
r".*must be converted to an image with 3 or fewer channels*"):
ImageProcessor(transform_sequence).apply_transforms(test_input)
# Test standardize (RGB)
transform_sequence = [ToPILImage('RGB'), Standardize()]
p = ImageProcessor(transform_sequence)
img_out = np.array(p.apply_transforms(test_input))
nose.tools.assert_almost_equal(np.std(img_out), 1)
nose.tools.assert_almost_equal(np.mean(img_out), 0)
# Test standardize (RGB) - check whether the mean centering works
transform_sequence = [ToPILImage('RGB'), Standardize(std=0)]
p = ImageProcessor(transform_sequence)
img_out = np.array(p.apply_transforms(test_input))
nose.tools.assert_almost_equal(np.mean(img_out), 0)
# Test standardize (RGB) - check whether the std division works
transform_sequence = [ToPILImage('RGB'), Standardize(mean=0)]
p = ImageProcessor(transform_sequence)
img_out = np.array(p.apply_transforms(test_input))
nose.tools.assert_almost_equal(np.std(img_out[..., 0]), 1)
nose.tools.assert_almost_equal(np.std(img_out[..., 1]), 1)
nose.tools.assert_almost_equal(np.std(img_out[..., 2]), 1)
# generate an image array
pil_img = ImageProcessor([ToPILImage('RGB'),
PILtoarray()]).apply_transforms(test_input)
# Test standardize (RGB) with 3 channel-wise values
transform_sequence = [
ToPILImage('RGB'),
Standardize(mean=[x for x in np.mean(pil_img, axis=(0, 1))],
std=[x for x in np.std(pil_img, axis=(0, 1))])
]
ImageProcessor(transform_sequence).apply_transforms(test_input)
# Test standardize (RGB) with 3 channel-wise values for the mean
transform_sequence = [
ToPILImage('RGB'),
Standardize(mean=[x for x in np.mean(pil_img, axis=(0, 1))])
]
ImageProcessor(transform_sequence).apply_transforms(test_input)
# Test standardize (RGB) with 3 channel-wise values for the std
transform_sequence = [
ToPILImage('RGB'),
Standardize(std=[x for x in np.std(pil_img, axis=(0, 1))])
]
ImageProcessor(transform_sequence).apply_transforms(test_input)
# Test standardize (RGB)
transform_sequence = [
ToPILImage('RGB'),
Standardize(mean=np.mean(pil_img))
]
ImageProcessor(transform_sequence).apply_transforms(test_input)
# Test standardize (RGB)
transform_sequence = [
ToPILImage('RGB'),
Standardize(mean=np.mean(pil_img), std=np.std(pil_img))
]
ImageProcessor(transform_sequence).apply_transforms(test_input)
# Test standardize (RGB)
transform_sequence = [ToPILImage('RGB'), Standardize(std=np.std(pil_img))]
ImageProcessor(transform_sequence).apply_transforms(test_input)
# Test standardize (RGB)
transform_sequence = [ToPILImage('RGB'), Standardize(mean=127, std=5)]
ImageProcessor(transform_sequence).apply_transforms(test_input)
# Test standardize error (RGB)
transform_sequence = [
ToPILImage('RGB'),
Standardize(mean=[127, 127], std=5)
]
with nose.tools.assert_raises_regexp(
ValueError, r".*must correspond to the number of channels.*"):
ImageProcessor(transform_sequence).apply_transforms(test_input)
# Test standardize error (RGB)
transform_sequence = [ToPILImage('RGB'), Standardize(std=[5, 5, 5, 5])]
with nose.tools.assert_raises_regexp(
ValueError, r".*must correspond to the number of channels.*"):
ImageProcessor(transform_sequence).apply_transforms(test_input)
# Test standardize (L)
transform_sequence = [ToPILImage('L'), Standardize()]
p = ImageProcessor(transform_sequence)
img_out = p.apply_transforms(test_input)
nose.tools.assert_almost_equal(np.std(img_out), 1)
# Test standardize (L)
transform_sequence = [ToPILImage('L'), Standardize(mean=np.mean(pil_img))]
ImageProcessor(transform_sequence).apply_transforms(test_input)
# Test standardize (L)
transform_sequence = [
ToPILImage('L'),
Standardize(mean=np.mean(pil_img), std=np.std(pil_img))
]
ImageProcessor(transform_sequence).apply_transforms(test_input)
# Test standardize (L)
transform_sequence = [ToPILImage('L'), Standardize(std=np.std(pil_img))]
ImageProcessor(transform_sequence).apply_transforms(test_input)
# Test standardize (L)
transform_sequence = [ToPILImage('L'), Standardize(mean=127, std=5)]
ImageProcessor(transform_sequence).apply_transforms(test_input)
# Test standardize (L) - check whether the mean centering works
transform_sequence = [ToPILImage('L'), Standardize(std=0)]
p = ImageProcessor(transform_sequence)
img_out = np.array(p.apply_transforms(test_input))
nose.tools.assert_almost_equal(np.mean(img_out), 0)
# Test standardize (L) - check whether the std division works
transform_sequence = [ToPILImage('L'), Standardize(mean=0)]
p = ImageProcessor(transform_sequence)
img_out = np.array(p.apply_transforms(test_input))
nose.tools.assert_almost_equal(np.std(img_out), 1)
# Test standardize error (L)
transform_sequence = [ToPILImage('L'), Standardize(mean=[127, 127], std=5)]
with nose.tools.assert_raises(ValueError):
ImageProcessor(transform_sequence).apply_transforms(test_input)
# Test standardize error (L)
transform_sequence = [ToPILImage('L'), Standardize(std=[5, 5, 5, 5])]
with nose.tools.assert_raises(ValueError):
ImageProcessor(transform_sequence).apply_transforms(test_input)
# Test for wrong use (L)
transform_sequence = [
ToPILImage('L'),
Standardize(), Resize(size=(200, 200))
]
p = ImageProcessor(transform_sequence)
with nose.tools.assert_raises(Exception):
p.apply_transforms(test_input)
# Test for wrong use (RGBA)
transform_sequence = [ToPILImage('RGBA'), Standardize(), Standardize()]
p = ImageProcessor(transform_sequence)
with nose.tools.assert_raises(Exception):
p.apply_transforms(test_input)