mirror of
https://github.com/wassname/scikit-image.git
synced 2026-07-03 20:57:09 +08:00
Fixed minor documentation issues
This commit is contained in:
Guillem Palou Visa
+1
-1
@@ -86,4 +86,4 @@ def _changed(filename):
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with open(filename_cache, 'wb') as cf:
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cf.write(md5_new.encode('utf-8'))
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return md5_cached != md5_new
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return md5_cached != md5_new.encode('utf-8')
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@@ -115,7 +115,6 @@ def _slic_cython(double[:, :, :, ::1] image_zyx,
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dist_center += (image_zyx[z, y, x, c - 3]
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- segments[k, c]) ** 2
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if distance[z, y, x] > dist_center:
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# segments start at 1
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nearest_segments[z, y, x] = k
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distance[z, y, x] = dist_center
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change = 1
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@@ -132,7 +131,6 @@ def _slic_cython(double[:, :, :, ::1] image_zyx,
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for z in range(depth):
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for y in range(height):
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for x in range(width):
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#compensate the label offset 1
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k = nearest_segments[z, y, x]
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n_segment_elems[k] += 1
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segments[k, 0] += z
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@@ -151,8 +149,8 @@ def _slic_cython(double[:, :, :, ::1] image_zyx,
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def _enforce_label_connectivity_cython(Py_ssize_t[:, :, ::1] segments,
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Py_ssize_t n_segments,
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int min_size,
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int max_size):
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Py_ssize_t min_size,
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Py_ssize_t max_size):
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""" Helper function to remove small disconnected regions from the labels
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Parameters
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@@ -160,11 +158,11 @@ def _enforce_label_connectivity_cython(Py_ssize_t[:, :, ::1] segments,
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segments : 3D array of int, shape (Z, Y, X)
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The label field/superpixels found by SLIC.
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n_segments: int
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number of specified segments
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Number of specified segments
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min_size: int
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minimum size of the segment
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Minimum size of the segment
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max_size: int
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maximum size of the segment. This is done for performance reasons,
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Maximum size of the segment. This is done for performance reasons,
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to pre-allocate a sufficiently large array for the breadth first search
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Returns
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-------
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@@ -172,25 +170,25 @@ def _enforce_label_connectivity_cython(Py_ssize_t[:, :, ::1] segments,
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A label field with connected labels starting at label=1
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"""
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#get image dimensions
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# get image dimensions
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cdef Py_ssize_t depth, height, width
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depth = segments.shape[0]
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height = segments.shape[1]
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width = segments.shape[2]
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#neighborhood arrays
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# neighborhood arrays
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cdef Py_ssize_t[::1] ddx = np.array((1, -1, 0, 0, 0, 0))
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cdef Py_ssize_t[::1] ddy = np.array((0, 0, 1, -1, 0, 0))
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cdef Py_ssize_t[::1] ddz = np.array((0, 0, 0, 0, 1, -1))
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#new object with connected segments initialized to -1
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# new object with connected segments initialized to -1
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cdef Py_ssize_t[:, :, ::1] connected_segments \
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= -1 * np.ones_like(segments, dtype=np.intp)
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cdef Py_ssize_t current_new_label = 0
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cdef Py_ssize_t label = 0
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#variables for the breadth first search
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# variables for the breadth first search
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cdef Py_ssize_t current_segment_size = 1
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cdef Py_ssize_t bfs_visited = 0
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cdef Py_ssize_t adjacent
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@@ -199,13 +197,13 @@ def _enforce_label_connectivity_cython(Py_ssize_t[:, :, ::1] segments,
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cdef Py_ssize_t[:, ::1] coord_list = np.zeros((max_size, 3), dtype=np.intp)
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#loop through all image
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# loop through all image
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for z in range(depth):
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for y in range(height):
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for x in range(width):
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if connected_segments[z, y, x] >= 0:
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continue
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#find the component size
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# find the component size
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adjacent = 0
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label = segments[z, y, x]
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connected_segments[z, y, x] = current_new_label
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@@ -238,7 +236,7 @@ def _enforce_label_connectivity_cython(Py_ssize_t[:, :, ::1] segments,
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adjacent = connected_segments[zz, yy, xx]
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bfs_visited += 1
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#change to an adjacent one, like in the original paper
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# change to an adjacent one, like in the original paper
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if current_segment_size < min_size:
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for i in range(current_segment_size):
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connected_segments[coord_list[i, 0],
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@@ -12,7 +12,7 @@ from skimage.color import rgb2lab
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def slic(image, n_segments=100, compactness=10., max_iter=10, sigma=None,
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spacing=None, multichannel=True, convert2lab=True, ratio=None,
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enforce_connectivity=True, min_size_factor=0.5, max_size_factor=3):
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enforce_connectivity=False, min_size_factor=0.5, max_size_factor=3):
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"""Segments image using k-means clustering in Color-(x,y,z) space.
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Parameters
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@@ -51,10 +51,10 @@ def slic(image, n_segments=100, compactness=10., max_iter=10, sigma=None,
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enforce_connectivity: bool, optional
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Whether the generated segments are connected or not
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min_size_factor: float, optional
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proportion of the minimum segment size to be removed with respect
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to the supposed segment size (depth*width*height/n_segments)
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Proportion of the minimum segment size to be removed with respect
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to the supposed segment size ```depth*width*height/n_segments```
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max_size_factor: float, optional
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proportion of the maximum connected segment size. A value of 3 works
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Proportion of the maximum connected segment size. A value of 3 works
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in most of the cases.
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Returns
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-------
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@@ -173,10 +173,12 @@ def slic(image, n_segments=100, compactness=10., max_iter=10, sigma=None,
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if enforce_connectivity:
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segment_size = depth * height * width / n_segments
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min_size = int(min_size_factor * segment_size)
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max_size = int(max_size_factor * segment_size)
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labels = _enforce_label_connectivity_cython(labels,
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n_segments,
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min_size_factor * segment_size,
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max_size_factor * segment_size)
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min_size,
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max_size)
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if is_2d:
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labels = labels[0]
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