From 64d945da71a1be1451a370f3365938ff44ea3935 Mon Sep 17 00:00:00 2001 From: Guillem Palou Visa Date: Tue, 24 Dec 2013 16:18:57 +0100 Subject: [PATCH] fixing tests --- skimage/segmentation/_slic.pyx | 201 ++++++++++++----------- skimage/segmentation/slic_superpixels.py | 12 +- skimage/segmentation/tests/test_slic.py | 53 ++++-- 3 files changed, 145 insertions(+), 121 deletions(-) diff --git a/skimage/segmentation/_slic.pyx b/skimage/segmentation/_slic.pyx index 6f97abff..f88fa56f 100644 --- a/skimage/segmentation/_slic.pyx +++ b/skimage/segmentation/_slic.pyx @@ -9,107 +9,10 @@ cimport numpy as cnp from skimage.util import regular_grid -def _enforce_label_connectivity_cython(Py_ssize_t[:, :, ::1] nearest_segments, - Py_ssize_t n_segments, - int min_size, - int max_size): - """ Helper function to remove small disconnected regions from the labels - - Parameters - ---------- - nearest_segments : 3D array of int, shape (Z, Y, X) - The label field/superpixels found by SLIC. - n_segments: int - number of specified segments - min_size: int - minimum size of the segment - max_size: int - maximum size of the segment. This is done for performance reasons, - to pre-allocate a sufficiently large array for the breadth first search - Returns - ------- - connected_nearest_segments : 3D array of int, shape (Z, Y, X) - A label field with connected labels starting at label=1 - """ - - #get image dimensions - cdef Py_ssize_t depth, height, width - depth = nearest_segments.shape[0] - height = nearest_segments.shape[1] - width = nearest_segments.shape[2] - - #neighborhood arrays - cdef Py_ssize_t[:] ddx = np.array((1,-1,0,0,0,0)) - cdef Py_ssize_t[:] ddy = np.array((0,0,1,-1,0,0)) - cdef Py_ssize_t[:] ddz = np.array((0,0,0,0,1,-1)) - - #new object with connected segments - cdef Py_ssize_t[:, :, ::1] new_nearest_segments \ - = np.zeros((depth, height, width), dtype=np.intp) - - cdef Py_ssize_t current_new_label = 0 - cdef Py_ssize_t label = 0 - - #variables for the breadth first search - cdef Py_ssize_t count = 1 - cdef Py_ssize_t p = 0 - cdef Py_ssize_t adjacent - - cdef Py_ssize_t zz,yy,xx - - cdef Py_ssize_t[:, :] coord_list \ - = np.zeros((max_size,3), dtype=np.intp) - - #loop through all image - for z in range(depth): - for y in range(height): - for x in range(width): - if (new_nearest_segments[z,y,x] > 0): - continue - #find the component size - adjacent = 0 - label = nearest_segments[z,y,x] - current_new_label = current_new_label+1 - new_nearest_segments[z,y,x] = current_new_label - - count = 1 - p = 0 - coord_list[p,0] = z - coord_list[p,1] = y - coord_list[p,2] = x - - #perform a breadth first search to find the size of the connected component - while (p != count): - for i in range(6): - zz = coord_list[p,0] + ddz[i] - yy = coord_list[p,1] + ddy[i] - xx = coord_list[p,2] + ddx[i] - if (xx >= 0 and xx < width and yy >= 0 and yy < height and zz >= 0 and zz < depth): - if (nearest_segments[zz,yy,xx] == label and new_nearest_segments[zz,yy,xx] == 0): - new_nearest_segments[zz,yy,xx] = current_new_label - coord_list[count,0] = zz - coord_list[count,1] = yy - coord_list[count,2] = xx - count = count + 1 - elif (new_nearest_segments[zz,yy,xx] > 0 and new_nearest_segments[zz,yy,xx] != current_new_label): - adjacent = new_nearest_segments[zz,yy,xx] - p = p + 1 - - - #change to an adjacent one, like in the original paper - if (count < min_size): - #print("Changing segment {0} label {1} ".format(current_new_label, label)) - for i in range(count): - new_nearest_segments[coord_list[i,0],coord_list[i,1],coord_list[i,2]] = adjacent - - return np.asarray(new_nearest_segments) - def _slic_cython(double[:, :, :, ::1] image_zyx, double[:, ::1] segments, Py_ssize_t max_iter, - double[::1] spacing, - int enforce_connectivity, - Py_ssize_t min_size = True): + double[::1] spacing): """Helper function for SLIC segmentation. Parameters @@ -124,8 +27,6 @@ def _slic_cython(double[:, :, :, ::1] image_zyx, The voxel spacing along each image dimension. This parameter controls the weights of the distances along z, y, and x during k-means clustering. - enforce_connectivity: int indicating whether the returned label - field must have connected labels Returns ------- @@ -244,3 +145,103 @@ def _slic_cython(double[:, :, :, ::1] image_zyx, segments[k, c] /= n_segment_elems[k] return np.asarray(nearest_segments) + + +def _enforce_label_connectivity_cython(Py_ssize_t[:, :, ::1] segments, + Py_ssize_t n_segments, + int min_size, + int max_size): + """ Helper function to remove small disconnected regions from the labels + + Parameters + ---------- + segments : 3D array of int, shape (Z, Y, X) + The label field/superpixels found by SLIC. + n_segments: int + number of specified segments + min_size: int + minimum size of the segment + max_size: int + maximum size of the segment. This is done for performance reasons, + to pre-allocate a sufficiently large array for the breadth first search + Returns + ------- + connected_segments : 3D array of int, shape (Z, Y, X) + A label field with connected labels starting at label=1 + """ + + #get image dimensions + cdef Py_ssize_t depth, height, width + depth = segments.shape[0] + height = segments.shape[1] + width = segments.shape[2] + + #neighborhood arrays + cdef Py_ssize_t[::1] ddx = np.array((1, -1, 0, 0, 0, 0)) + cdef Py_ssize_t[::1] ddy = np.array((0, 0, 1, -1, 0, 0)) + cdef Py_ssize_t[::1] ddz = np.array((0, 0, 0, 0, 1, -1)) + + #new object with connected segments + cdef Py_ssize_t[:, :, ::1] connected_segments \ + = np.zeros_like(segments, dtype=np.intp) + + cdef Py_ssize_t current_new_label = 0 + cdef Py_ssize_t label = 0 + + #variables for the breadth first search + cdef Py_ssize_t current_segment_size = 1 + cdef Py_ssize_t bfs_visited = 0 + cdef Py_ssize_t adjacent + + cdef Py_ssize_t zz, yy, xx + + cdef Py_ssize_t[:, ::1] coord_list = np.zeros((max_size, 3), dtype=np.intp) + + #loop through all image + for z in range(depth): + for y in range(height): + for x in range(width): + if connected_segments[z, y, x] > 0: + continue + #find the component size + adjacent = 0 + label = segments[z, y, x] + current_new_label += 1 + connected_segments[z, y, x] = current_new_label + current_segment_size = 1 + bfs_visited = 0 + coord_list[bfs_visited, 0] = z + coord_list[bfs_visited, 1] = y + coord_list[bfs_visited, 2] = x + + #perform a breadth first search to find + # the size of the connected component + while bfs_visited != current_segment_size: + for i in range(6): + zz = coord_list[bfs_visited, 0] + ddz[i] + yy = coord_list[bfs_visited, 1] + ddy[i] + xx = coord_list[bfs_visited, 2] + ddx[i] + if (xx >= 0 and xx < width and + yy >= 0 and yy < height and + zz >= 0 and zz < depth): + if (segments[zz, yy, xx] == label and + connected_segments[zz, yy, xx] == 0): + connected_segments[zz, yy, xx] = \ + current_new_label + coord_list[current_segment_size, 0] = zz + coord_list[current_segment_size, 1] = yy + coord_list[current_segment_size, 2] = xx + current_segment_size += 1 + elif (connected_segments[zz, yy, xx] > 0 and + connected_segments[zz, yy, xx] != current_new_label): + adjacent = connected_segments[zz, yy, xx] + bfs_visited += 1 + + #change to an adjacent one, like in the original paper + if current_segment_size < min_size: + for i in range(current_segment_size): + connected_segments[coord_list[i, 0], + coord_list[i, 1], + coord_list[i, 2]] = adjacent + + return np.asarray(connected_segments) diff --git a/skimage/segmentation/slic_superpixels.py b/skimage/segmentation/slic_superpixels.py index 60d4ac13..98801c62 100644 --- a/skimage/segmentation/slic_superpixels.py +++ b/skimage/segmentation/slic_superpixels.py @@ -50,10 +50,10 @@ def slic(image, n_segments=100, compactness=10., max_iter=10, sigma=None, Synonym for `compactness`. This keyword is deprecated. enforce_connectivity: bool, optional Whether the generated segments are connected or not - min_size_factor: float + min_size_factor: float, optional proportion of the minimum segment size to be removed with respect to the supposed segment size (depth*width*height/n_segments) - max_size_factor: float + max_size_factor: float, optional proportion of the maximum connected segment size. A value of 3 works in most of the cases. Returns @@ -169,14 +169,14 @@ def slic(image, n_segments=100, compactness=10., max_iter=10, sigma=None, ratio = float(max((step_z, step_y, step_x))) / compactness image = np.ascontiguousarray(image * ratio) - labels = _slic_cython(image, segments, max_iter, spacing, enforce_connectivity) + labels = _slic_cython(image, segments, max_iter, spacing) if (enforce_connectivity): - segment_size = depth*height*width/n_segments + segment_size = depth * height * width / n_segments labels = _enforce_label_connectivity_cython(labels, n_segments, - min_size_factor*segment_size, - max_size_factor*segment_size) + min_size_factor * segment_size, + max_size_factor * segment_size) if is_2d: labels = labels[0] diff --git a/skimage/segmentation/tests/test_slic.py b/skimage/segmentation/tests/test_slic.py index 15d1fe6d..be0c2d79 100644 --- a/skimage/segmentation/tests/test_slic.py +++ b/skimage/segmentation/tests/test_slic.py @@ -21,10 +21,10 @@ def test_color_2d(): # we expect 4 segments assert_equal(len(np.unique(seg)), 4) assert_equal(seg.shape, img.shape[:-1]) - assert_equal(seg[:10, :10], 0) - assert_equal(seg[10:, :10], 2) - assert_equal(seg[:10, 10:], 1) - assert_equal(seg[10:, 10:], 3) + assert_equal(seg[:10, :10], 1) + assert_equal(seg[10:, :10], 3) + assert_equal(seg[:10, 10:], 2) + assert_equal(seg[10:, 10:], 4) def test_gray_2d(): @@ -41,10 +41,10 @@ def test_gray_2d(): assert_equal(len(np.unique(seg)), 4) assert_equal(seg.shape, img.shape) - assert_equal(seg[:10, :10], 0) - assert_equal(seg[10:, :10], 2) - assert_equal(seg[:10, 10:], 1) - assert_equal(seg[10:, 10:], 3) + assert_equal(seg[:10, :10], 1) + assert_equal(seg[10:, :10], 3) + assert_equal(seg[:10, 10:], 2) + assert_equal(seg[10:, 10:], 4) def test_color_3d(): @@ -65,7 +65,7 @@ def test_color_3d(): assert_equal(len(np.unique(seg)), 8) for s, c in zip(slices, range(8)): - assert_equal(seg[s], c) + assert_equal(seg[s], c + 1) def test_gray_3d(): @@ -76,7 +76,7 @@ def test_gray_3d(): midpoint = dim_size // 2 slices.append((slice(None, midpoint), slice(midpoint, None))) slices = list(it.product(*slices)) - shades = np.arange(0, 1.000001, 1.0/7) + shades = np.arange(0, 1.000001, 1.0 / 7) for s, sh in zip(slices, shades): img[s] = sh img += 0.001 * rnd.normal(size=img.shape) @@ -87,7 +87,7 @@ def test_gray_3d(): assert_equal(len(np.unique(seg)), 8) for s, c in zip(slices, range(8)): - assert_equal(seg[s], c) + assert_equal(seg[s], c + 1) def test_list_sigma(): @@ -96,7 +96,7 @@ def test_list_sigma(): [0, 0, 0, 1, 1, 1]], np.float) img += 0.1 * rnd.normal(size=img.shape) result_sigma = np.array([[0, 0, 0, 1, 1, 1], - [0, 0, 0, 1, 1, 1]], np.int) + [0, 0, 0, 1, 1, 1]], np.int) + 1 seg_sigma = slic(img, n_segments=2, sigma=[1, 50, 1], multichannel=False) assert_equal(seg_sigma, result_sigma) @@ -106,9 +106,9 @@ def test_spacing(): img = np.array([[1, 1, 1, 0, 0], [1, 1, 0, 0, 0]], np.float) result_non_spaced = np.array([[0, 0, 0, 1, 1], - [0, 0, 1, 1, 1]], np.int) + [0, 0, 1, 1, 1]], np.int) + 1 result_spaced = np.array([[0, 0, 0, 0, 0], - [1, 1, 1, 1, 1]], np.int) + [1, 1, 1, 1, 1]], np.int) + 1 img += 0.1 * rnd.normal(size=img.shape) seg_non_spaced = slic(img, n_segments=2, sigma=0, multichannel=False, compactness=1.0) @@ -120,11 +120,34 @@ def test_spacing(): def test_invalid_lab_conversion(): img = np.array([[1, 1, 1, 0, 0], - [1, 1, 0, 0, 0]], np.float) + [1, 1, 0, 0, 0]], np.float) + 1 assert_raises(ValueError, slic, img, multichannel=True, convert2lab=True) +def test_enforce_connectivity(): + img = np.array([[0, 0, 0, 1, 1, 1], + [1, 0, 0, 1, 1, 0], + [0, 0, 0, 1, 1, 0]], np.float) + + segments_connected = slic(img, 2, compactness=0.0001, + enforce_connectivity=True, + convert2lab=False) + segments_disconnected = slic(img, 2, compactness=0.0001, + enforce_connectivity=False, + convert2lab=False) + + result_connected = np.array([[1, 1, 1, 2, 2, 2], + [1, 1, 1, 2, 2, 2], + [1, 1, 1, 2, 2, 2]], np.float) + + result_disconnected = np.array([[1, 1, 1, 2, 2, 2], + [2, 1, 1, 2, 2, 1], + [1, 1, 1, 2, 2, 1]], np.float) + + assert_equal(segments_connected, result_connected) + assert_equal(segments_disconnected, result_disconnected) if __name__ == '__main__': from numpy import testing + testing.run_module_suite()