diff --git a/skimage/measure/__init__.py b/skimage/measure/__init__.py index a9fbae24..423aa9a7 100755 --- a/skimage/measure/__init__.py +++ b/skimage/measure/__init__.py @@ -3,7 +3,7 @@ from ._regionprops import regionprops, perimeter from ._structural_similarity import structural_similarity from ._polygon import approximate_polygon, subdivide_polygon from .fit import LineModel, CircleModel, EllipseModel, ransac -from ._sum_blocks import sum_blocks +from .block import block_reduce __all__ = ['find_contours', @@ -16,4 +16,4 @@ __all__ = ['find_contours', 'CircleModel', 'EllipseModel', 'ransac', - 'sum_blocks'] + 'block_reduce'] diff --git a/skimage/measure/_sum_blocks.py b/skimage/measure/_sum_blocks.py deleted file mode 100644 index c65c121e..00000000 --- a/skimage/measure/_sum_blocks.py +++ /dev/null @@ -1,38 +0,0 @@ -def sum_blocks(array, factors): - """Sums the elements in blocks of integer factors and pads the original - array with zeroes if the dimensions are not perfectly divisible by factors. - - This function is different from resize and rescale in transform._warps in - the sense that they use interpolation to upsample or downsample on a 2D - array, while this function performs only dawnsampling but on any - n-dimensional array and returns the sum of elements in a block of size - factors in the original array. - - Parameters - ---------- - array : ndarray - Input n-dimensional array. - factors: tuple - Tuple containing integer values representing block length along each - axis. - - Returns - ------- - array : ndarray - Downsampled array with same number of dimensions as that of input - array. - - Example - ------- - >>> a = np.arange(15).reshape(3, 5) - >>> a - array([[ 0, 1, 2, 3, 4], - [ 5, 6, 7, 8, 9], - [10, 11, 12, 13, 14]]) - >>> sum_blocks(a, (2,3)) - array([[21, 24], - [33, 27]]) - - """ - from ..transform._warps import _downsample - return _downsample(array, factors) diff --git a/skimage/measure/block.py b/skimage/measure/block.py new file mode 100644 index 00000000..fad5668c --- /dev/null +++ b/skimage/measure/block.py @@ -0,0 +1,77 @@ +import numpy as np +from skimage.util import view_as_blocks, pad + + +def block_reduce(image, block_size, func=np.sum, cval=0): + """Down-sample image by applying function to local blocks. + + Parameters + ---------- + image : ndarray + N-dimensional input image. + block_size : array_like + Array containing down-sampling integer factor along each axis. + func : callable + Function object which is used to calculate the return value for each + local block. This function must implement an ``axis`` parameter such as + ``numpy.sum`` or ``numpy.min``. + cval : float + Constant padding value if image is not perfectly divisible by the + block size. + + Returns + ------- + image : ndarray + Down-sampled image with same number of dimensions as input image. + + Examples + -------- + >>> from skimage.measure import block_reduce + >>> image = np.arange(3*3*4).reshape(3, 3, 4) + >>> image + array([[[ 0, 1, 2, 3], + [ 4, 5, 6, 7], + [ 8, 9, 10, 11]], + + [[12, 13, 14, 15], + [16, 17, 18, 19], + [20, 21, 22, 23]], + + [[24, 25, 26, 27], + [28, 29, 30, 31], + [32, 33, 34, 35]]]) + >>> block_reduce(image, block_size=(3, 3, 1), func=np.mean) + array([[[ 16., 17., 18., 19.]]]) + >>> block_reduce(image, block_size=(1, 3, 4), func=np.max) + array([[[11]], + + [[23]], + + [[35]]]) + >>> block_reduce(image, block_size=(3, 1, 4), func=np.max) + array([[[27], + [31], + [35]]]) + """ + + if len(block_size) != image.ndim: + raise ValueError("`block_size` must have the same length " + "as `image.shape`.") + + pad_width = [] + for i in range(len(block_size)): + if image.shape[i] % block_size[i] != 0: + after_width = block_size[i] - (image.shape[i] % block_size[i]) + else: + after_width = 0 + pad_width.append((0, after_width)) + + image = pad(image, pad_width=pad_width, mode='constant', + constant_values=cval) + + out = view_as_blocks(image, block_size) + + for i in range(len(out.shape) // 2): + out = func(out, axis=-1) + + return out diff --git a/skimage/measure/tests/test_block.py b/skimage/measure/tests/test_block.py new file mode 100644 index 00000000..a8bc62a9 --- /dev/null +++ b/skimage/measure/tests/test_block.py @@ -0,0 +1,81 @@ +import numpy as np +from numpy.testing import assert_array_equal +from skimage.measure import block_reduce + + +def test_block_reduce_sum(): + image1 = np.arange(4 * 6).reshape(4, 6) + out1 = block_reduce(image1, (2, 3)) + expected1 = np.array([[ 24, 42], + [ 96, 114]]) + assert_array_equal(expected1, out1) + + image2 = np.arange(5 * 8).reshape(5, 8) + out2 = block_reduce(image2, (3, 3)) + expected2 = np.array([[ 81, 108, 87], + [174, 192, 138]]) + assert_array_equal(expected2, out2) + + +def test_block_reduce_mean(): + image1 = np.arange(4 * 6).reshape(4, 6) + out1 = block_reduce(image1, (2, 3), func=np.mean) + expected1 = np.array([[ 4., 7.], + [ 16., 19.]]) + assert_array_equal(expected1, out1) + + image2 = np.arange(5 * 8).reshape(5, 8) + out2 = block_reduce(image2, (4, 5), func=np.mean) + expected2 = np.array([[14. , 10.8], + [ 8.5, 5.7]]) + assert_array_equal(expected2, out2) + + +def test_block_reduce_median(): + image1 = np.arange(4 * 6).reshape(4, 6) + out1 = block_reduce(image1, (2, 3), func=np.median) + expected1 = np.array([[ 4., 7.], + [ 16., 19.]]) + assert_array_equal(expected1, out1) + + image2 = np.arange(5 * 8).reshape(5, 8) + out2 = block_reduce(image2, (4, 5), func=np.median) + expected2 = np.array([[ 14., 17.], + [ 0., 0.]]) + assert_array_equal(expected2, out2) + + image3 = np.array([[1, 5, 5, 5], [5, 5, 5, 1000]]) + out3 = block_reduce(image3, (2, 4), func=np.median) + assert_array_equal(5, out3) + + +def test_block_reduce_min(): + image1 = np.arange(4 * 6).reshape(4, 6) + out1 = block_reduce(image1, (2, 3), func=np.min) + expected1 = np.array([[ 0, 3], + [12, 15]]) + assert_array_equal(expected1, out1) + + image2 = np.arange(5 * 8).reshape(5, 8) + out2 = block_reduce(image2, (4, 5), func=np.min) + expected2 = np.array([[0, 0], + [0, 0]]) + assert_array_equal(expected2, out2) + + +def test_block_reduce_max(): + image1 = np.arange(4 * 6).reshape(4, 6) + out1 = block_reduce(image1, (2, 3), func=np.max) + expected1 = np.array([[ 8, 11], + [20, 23]]) + assert_array_equal(expected1, out1) + + image2 = np.arange(5 * 8).reshape(5, 8) + out2 = block_reduce(image2, (4, 5), func=np.max) + expected2 = np.array([[28, 31], + [36, 39]]) + assert_array_equal(expected2, out2) + + +if __name__ == "__main__": + np.testing.run_module_suite() diff --git a/skimage/measure/tests/test_structural_similarity.py b/skimage/measure/tests/test_structural_similarity.py index ec5ce7ef..e08f2c31 100644 --- a/skimage/measure/tests/test_structural_similarity.py +++ b/skimage/measure/tests/test_structural_similarity.py @@ -68,5 +68,6 @@ def test_invalid_input(): assert_raises(ValueError, ssim, X, X, win_size=8) + if __name__ == "__main__": np.testing.run_module_suite() diff --git a/skimage/measure/tests/test_sum_blocks.py b/skimage/measure/tests/test_sum_blocks.py deleted file mode 100644 index b4910495..00000000 --- a/skimage/measure/tests/test_sum_blocks.py +++ /dev/null @@ -1,16 +0,0 @@ -import numpy as np -from numpy.testing import assert_array_equal -from skimage.measure._sum_blocks import sum_blocks - -def test_downsample_sum_blocks(): - """Verifying downsampling of an array with expected result in sum mode""" - image1 = np.arange(4*6).reshape(4, 6) - out1 = sum_blocks(image1, (2, 3)) - expected1 = np.array([[ 24, 42], - [ 96, 114]]) - assert_array_equal(expected1, out1) - image2 = np.arange(5*8).reshape(5, 8) - out2 = sum_blocks(image2, (3, 3)) - expected2 = np.array([[ 81, 108, 87], - [174, 192, 138]]) - assert_array_equal(expected2, out2) diff --git a/skimage/transform/__init__.py b/skimage/transform/__init__.py index 2cbc7007..8fa2cfdb 100644 --- a/skimage/transform/__init__.py +++ b/skimage/transform/__init__.py @@ -9,7 +9,7 @@ from ._geometric import (warp, warp_coords, estimate_transform, SimilarityTransform, AffineTransform, ProjectiveTransform, PolynomialTransform, PiecewiseAffineTransform) -from ._warps import swirl, resize, rotate, rescale, downscale_local_means +from ._warps import swirl, resize, rotate, rescale, downscale_local_mean from .pyramids import (pyramid_reduce, pyramid_expand, pyramid_gaussian, pyramid_laplacian) @@ -41,7 +41,7 @@ __all__ = ['hough_circle', 'resize', 'rotate', 'rescale', - 'downscale_local_means', + 'downscale_local_mean', 'pyramid_reduce', 'pyramid_expand', 'pyramid_gaussian', diff --git a/skimage/transform/_warps.py b/skimage/transform/_warps.py index c2727c74..d8da73d5 100644 --- a/skimage/transform/_warps.py +++ b/skimage/transform/_warps.py @@ -1,18 +1,18 @@ import numpy as np from scipy import ndimage -from ._geometric import warp, SimilarityTransform, AffineTransform -from skimage.util.shape import view_as_blocks, _pad_asymmetric_zeros +from skimage.transform._geometric import (warp, SimilarityTransform, + AffineTransform) +from skimage.measure import block_reduce def resize(image, output_shape, order=1, mode='constant', cval=0.): """Resize image to match a certain size. - Resize performs interpolation to upsample or downsample 2D arrays. For - downsampling any n-dimensional array by performing arithmetic sum or - arithmetic mean, see measure._sum_blocks.sum_blocks and - transform._warps.downscale_local_means respectively. - + Performs interpolation to up-size or down-size images. For down-sampling + N-dimensional images by applying the arithmetic sum or mean, see + `skimage.measure.local_sum` and `skimage.transform.downscale_local_mean`, + respectively. Parameters ---------- @@ -95,10 +95,10 @@ def resize(image, output_shape, order=1, mode='constant', cval=0.): def rescale(image, scale, order=1, mode='constant', cval=0.): """Scale image by a certain factor. - Rescale performs interpolation to upsample or downsample 2D arrays. For - downsampling any n-dimensional array by performing arithmetic sum or - arithmetic mean, see measure._sum_blocks.sum_blocks and - transform._warps.downscale_local_means respectively. + Performs interpolation to upscale or down-scale images. For down-sampling + N-dimensional images with integer factors by applying the arithmetic sum or + mean, see `skimage.measure.local_sum` and + `skimage.transform.downscale_local_mean`, respectively. Parameters ---------- @@ -226,6 +226,47 @@ def rotate(image, angle, resize=False, order=1, mode='constant', cval=0.): mode=mode, cval=cval) +def downscale_local_mean(image, factors, cval=0): + """Down-sample N-dimensional image by local averaging. + + The image is padded with `cval` if it is not perfectly divisible by the + integer factors. + + In contrast to the 2-D interpolation in `skimage.transform.resize` and + `skimage.transform.rescale` this function may be applied to N-dimensional + images and calculates the local mean of elements in each block of size + `factors` in the input image. + + Parameters + ---------- + image : ndarray + N-dimensional input image. + factors : array_like + Array containing down-sampling integer factor along each axis. + cval : float, optional + Constant padding value if image is not perfectly divisible by the + integer factors. + + Returns + ------- + image : ndarray + Down-sampled image with same number of dimensions as input image. + + Example + ------- + >>> a = np.arange(15).reshape(3, 5) + >>> a + array([[ 0, 1, 2, 3, 4], + [ 5, 6, 7, 8, 9], + [10, 11, 12, 13, 14]]) + >>> downscale_local_mean(a, (2, 3)) + array([[3.5, 4.], + [5.5, 4.5]]) + + """ + return block_reduce(image, factors, np.mean, cval) + + def _swirl_mapping(xy, center, rotation, strength, radius): x, y = xy.T x0, y0 = center @@ -296,90 +337,3 @@ def swirl(image, center=None, strength=1, radius=100, rotation=0, return warp(image, _swirl_mapping, map_args=warp_args, output_shape=output_shape, order=order, mode=mode, cval=cval) - - -def _downsample(array, factors, sum=True): - """Performs downsampling with integer factors. - - Parameters - ---------- - array : ndarray - Input n-dimensional array. - factors: tuple - Tuple containing downsampling factor along each axis. - sum : bool - If True, downsampled element is the sum of its corresponding - constituent elements in the input array. Default is True. - - Returns - ------- - array : ndarray - Downsampled array with same number of dimensions as that of input - array. - - """ - - pad_size = [] - if len(factors) != array.ndim: - raise ValueError("'factors' must have the same length " - "as 'array.shape'") - else: - for i in range(len(factors)): - if array.shape[i] % factors[i] != 0: - pad_size.append(factors[i] - (array.shape[i] % factors[i])) - else: - pad_size.append(0) - - for i in range(len(pad_size)): - array = _pad_asymmetric_zeros(array, pad_size[i], i) - - out = view_as_blocks(array, factors) - block_shape = out.shape - - if sum: - for i in range(len(block_shape) // 2): - out = out.sum(-1) - else: - for i in range(len(block_shape) // 2): - out = out.mean(-1) - return out - - -def downscale_local_means(array, factors): - """Downsamples the array in blocks of input integer factors after padding - the original array with zeroes if the dimensions are not perfectly - divisible by factors and replaces it with mean i.e. average value. - - This function is different from resize and rescale in the sense that they - use interpolation to upsample or downsample on a 2D array, while this - function performs only dawnsampling but on any n-dimensional array and - returns the arithmetic mean of elements in a block of size factors in the - original array. - - Parameters - ---------- - array : ndarray - Input n-dimensional array. - factors: tuple - Tuple containing integer values representing block length along each - axis. - - Returns - ------- - array : ndarray - Downsampled array with same number of dimensions as that of input - array. - - Example - ------- - >>> a = np.arange(15).reshape(3, 5) - >>> a - array([[ 0, 1, 2, 3, 4], - [ 5, 6, 7, 8, 9], - [10, 11, 12, 13, 14]]) - >>> downscale_local_means(a, (2,3)) - array([[3.5, 4.], - [5.5, 4.5]]) - - """ - return _downsample(array, factors, False) diff --git a/skimage/transform/tests/test_warps.py b/skimage/transform/tests/test_warps.py index 88ede530..af2b95da 100644 --- a/skimage/transform/tests/test_warps.py +++ b/skimage/transform/tests/test_warps.py @@ -6,7 +6,7 @@ from skimage.transform import (warp, warp_coords, rotate, resize, rescale, AffineTransform, ProjectiveTransform, SimilarityTransform, - downscale_local_means) + downscale_local_mean) from skimage import transform as tf, data, img_as_float from skimage.color import rgb2gray @@ -195,15 +195,15 @@ def test_warp_coords_example(): map_coordinates(image[:, :, 0], coords[:2]) -def test_downscale_local_means(): - """Verifying downsampling of an array with expected result in mean mode""" - image1 = np.arange(4*6).reshape(4, 6) - out1 = downscale_local_means(image1, (2, 3)) +def test_downscale_local_mean(): + image1 = np.arange(4 * 6).reshape(4, 6) + out1 = downscale_local_mean(image1, (2, 3)) expected1 = np.array([[ 4., 7.], [ 16., 19.]]) assert_array_equal(expected1, out1) - image2 = np.arange(5*8).reshape(5, 8) - out2 = downscale_local_means(image2, (4, 5)) + + image2 = np.arange(5 * 8).reshape(5, 8) + out2 = downscale_local_mean(image2, (4, 5)) expected2 = np.array([[ 14. , 10.8], [ 8.5, 5.7]]) assert_array_equal(expected2, out2) diff --git a/skimage/util/shape.py b/skimage/util/shape.py index 2763d3d4..0126d2e3 100644 --- a/skimage/util/shape.py +++ b/skimage/util/shape.py @@ -230,15 +230,3 @@ def view_as_windows(arr_in, window_shape): arr_out = as_strided(arr_in, shape=new_shape, strides=new_strides) return arr_out - - -def _pad_asymmetric_zeros(arr, pad_amt, axis=-1): - """Pads `arr` with zeros by `pad_amt` along specified `axis`""" - if axis == -1: - axis = arr.ndim - 1 - - zeroshape = tuple([x if i != axis else pad_amt - for (i, x) in enumerate(arr.shape)]) - - return np.concatenate((arr, np.zeros(zeroshape, dtype=arr.dtype)), - axis=axis)