148 lines
5.5 KiB
Python
148 lines
5.5 KiB
Python
import math
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import pytest
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import numpy as np
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from skimage import data
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from skimage.transform import pyramids
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from skimage._shared import testing
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from skimage._shared.testing import (assert_array_equal, assert_, assert_equal,
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assert_almost_equal)
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image = data.astronaut()
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image_gray = image[..., 0]
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def test_pyramid_reduce_rgb():
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rows, cols, dim = image.shape
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out = pyramids.pyramid_reduce(image, downscale=2, multichannel=True)
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assert_array_equal(out.shape, (rows / 2, cols / 2, dim))
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def test_pyramid_reduce_gray():
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rows, cols = image_gray.shape
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out1 = pyramids.pyramid_reduce(image_gray, downscale=2,
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multichannel=False)
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assert_array_equal(out1.shape, (rows / 2, cols / 2))
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assert_almost_equal(out1.ptp(), 1.0, decimal=2)
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out2 = pyramids.pyramid_reduce(image_gray, downscale=2,
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multichannel=False, preserve_range=True)
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assert_almost_equal(out2.ptp() / image_gray.ptp(), 1.0, decimal=2)
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def test_pyramid_reduce_nd():
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for ndim in [1, 2, 3, 4]:
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img = np.random.randn(*((8, ) * ndim))
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out = pyramids.pyramid_reduce(img, downscale=2,
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multichannel=False)
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expected_shape = np.asarray(img.shape) / 2
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assert_array_equal(out.shape, expected_shape)
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def test_pyramid_expand_rgb():
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rows, cols, dim = image.shape
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out = pyramids.pyramid_expand(image, upscale=2,
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multichannel=True)
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assert_array_equal(out.shape, (rows * 2, cols * 2, dim))
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def test_pyramid_expand_gray():
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rows, cols = image_gray.shape
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out = pyramids.pyramid_expand(image_gray, upscale=2,
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multichannel=False)
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assert_array_equal(out.shape, (rows * 2, cols * 2))
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def test_pyramid_expand_nd():
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for ndim in [1, 2, 3, 4]:
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img = np.random.randn(*((4, ) * ndim))
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out = pyramids.pyramid_expand(img, upscale=2,
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multichannel=False)
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expected_shape = np.asarray(img.shape) * 2
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assert_array_equal(out.shape, expected_shape)
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def test_build_gaussian_pyramid_rgb():
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rows, cols, dim = image.shape
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pyramid = pyramids.pyramid_gaussian(image, downscale=2,
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multichannel=True)
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for layer, out in enumerate(pyramid):
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layer_shape = (rows / 2 ** layer, cols / 2 ** layer, dim)
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assert_array_equal(out.shape, layer_shape)
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def test_build_gaussian_pyramid_gray():
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rows, cols = image_gray.shape
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pyramid = pyramids.pyramid_gaussian(image_gray, downscale=2,
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multichannel=False)
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for layer, out in enumerate(pyramid):
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layer_shape = (rows / 2 ** layer, cols / 2 ** layer)
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assert_array_equal(out.shape, layer_shape)
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def test_build_gaussian_pyramid_nd():
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for ndim in [1, 2, 3, 4]:
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img = np.random.randn(*((8, ) * ndim))
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original_shape = np.asarray(img.shape)
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pyramid = pyramids.pyramid_gaussian(img, downscale=2,
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multichannel=False)
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for layer, out in enumerate(pyramid):
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layer_shape = original_shape / 2 ** layer
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assert_array_equal(out.shape, layer_shape)
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def test_build_laplacian_pyramid_rgb():
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rows, cols, dim = image.shape
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pyramid = pyramids.pyramid_laplacian(image, downscale=2,
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multichannel=True)
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for layer, out in enumerate(pyramid):
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layer_shape = (rows / 2 ** layer, cols / 2 ** layer, dim)
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assert_array_equal(out.shape, layer_shape)
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def test_build_laplacian_pyramid_nd():
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for ndim in [1, 2, 3, 4]:
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img = np.random.randn(*(16, )*ndim)
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original_shape = np.asarray(img.shape)
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pyramid = pyramids.pyramid_laplacian(img, downscale=2,
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multichannel=False)
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for layer, out in enumerate(pyramid):
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print(out.shape)
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layer_shape = original_shape / 2 ** layer
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assert_array_equal(out.shape, layer_shape)
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def test_laplacian_pyramid_max_layers():
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for downscale in [2, 3, 5, 7]:
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img = np.random.randn(32, 8)
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pyramid = pyramids.pyramid_laplacian(img, downscale=downscale,
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multichannel=False)
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max_layer = int(np.ceil(math.log(np.max(img.shape), downscale)))
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for layer, out in enumerate(pyramid):
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if layer < max_layer:
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# should not reach all axes as size 1 prior to final level
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assert_(np.max(out.shape) > 1)
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# total number of images is max_layer + 1
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assert_equal(max_layer, layer)
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# final layer should be size 1 on all axes
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assert_array_equal((out.shape), (1, 1))
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def test_check_factor():
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with testing.raises(ValueError):
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pyramids._check_factor(0.99)
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with testing.raises(ValueError):
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pyramids._check_factor(- 2)
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@pytest.mark.parametrize('dtype, expected',
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zip(['float32', 'float64', 'uint8', 'int64'],
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['float32', 'float64', 'float64', 'float64']))
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def test_pyramid_gaussian_dtype_support(dtype, expected):
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img = np.random.randn(32, 8).astype(dtype)
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pyramid = pyramids.pyramid_gaussian(img)
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assert np.all([im.dtype == expected for im in pyramid])
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