228 lines
9.1 KiB
Python
228 lines
9.1 KiB
Python
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"""watershed.py - watershed algorithm
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This module implements a watershed algorithm that apportions pixels into
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marked basins. The algorithm uses a priority queue to hold the pixels
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with the metric for the priority queue being pixel value, then the time
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of entry into the queue - this settles ties in favor of the closest marker.
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Some ideas taken from
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Soille, "Automated Basin Delineation from Digital Elevation Models Using
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Mathematical Morphology", Signal Processing 20 (1990) 171-182.
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The most important insight in the paper is that entry time onto the queue
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solves two problems: a pixel should be assigned to the neighbor with the
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largest gradient or, if there is no gradient, pixels on a plateau should
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be split between markers on opposite sides.
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Originally part of CellProfiler, code licensed under both GPL and BSD licenses.
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Website: http://www.cellprofiler.org
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Copyright (c) 2003-2009 Massachusetts Institute of Technology
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Copyright (c) 2009-2011 Broad Institute
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All rights reserved.
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Original author: Lee Kamentsky
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"""
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import numpy as np
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from scipy import ndimage as ndi
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from . import _watershed_cy
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from ..morphology.extrema import local_minima
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from ..morphology._util import (_validate_connectivity,
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_offsets_to_raveled_neighbors)
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from ..util import crop, regular_seeds
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def _validate_inputs(image, markers, mask, connectivity):
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"""Ensure that all inputs to watershed have matching shapes and types.
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Parameters
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----------
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image : array
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The input image.
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markers : int or array of int
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The marker image.
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mask : array, or None
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A boolean mask, True where we want to compute the watershed.
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connectivity : int in {1, ..., image.ndim}
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The connectivity of the neighborhood of a pixel.
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Returns
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-------
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image, markers, mask : arrays
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The validated and formatted arrays. Image will have dtype float64,
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markers int32, and mask int8. If ``None`` was given for the mask,
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it is a volume of all 1s.
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Raises
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------
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ValueError
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If the shapes of the given arrays don't match.
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"""
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n_pixels = image.size
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if mask is None:
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# Use a complete `True` mask if none is provided
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mask = np.ones(image.shape, bool)
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else:
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mask = np.asanyarray(mask, dtype=bool)
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n_pixels = np.sum(mask)
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if mask.shape != image.shape:
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message = ("`mask` (shape {}) must have same shape as "
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"`image` (shape {})".format(mask.shape, image.shape))
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raise ValueError(message)
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if markers is None:
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markers_bool = local_minima(image, connectivity=connectivity) * mask
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markers = ndi.label(markers_bool)[0]
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elif not isinstance(markers, (np.ndarray, list, tuple)):
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# not array-like, assume int
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# given int, assume that number of markers *within mask*.
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markers = regular_seeds(image.shape,
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int(markers / (n_pixels / image.size)))
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markers *= mask
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else:
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markers = np.asanyarray(markers) * mask
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if markers.shape != image.shape:
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message = ("`markers` (shape {}) must have same shape as "
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"`image` (shape {})".format(markers.shape, image.shape))
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raise ValueError(message)
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return (image.astype(np.float64),
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markers.astype(np.int32),
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mask.astype(np.int8))
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def watershed(image, markers=None, connectivity=1, offset=None, mask=None,
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compactness=0, watershed_line=False):
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"""Find watershed basins in `image` flooded from given `markers`.
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Parameters
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----------
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image : ndarray (2-D, 3-D, ...) of integers
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Data array where the lowest value points are labeled first.
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markers : int, or ndarray of int, same shape as `image`, optional
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The desired number of markers, or an array marking the basins with the
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values to be assigned in the label matrix. Zero means not a marker. If
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``None`` (no markers given), the local minima of the image are used as
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markers.
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connectivity : ndarray, optional
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An array with the same number of dimensions as `image` whose
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non-zero elements indicate neighbors for connection.
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Following the scipy convention, default is a one-connected array of
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the dimension of the image.
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offset : array_like of shape image.ndim, optional
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offset of the connectivity (one offset per dimension)
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mask : ndarray of bools or 0s and 1s, optional
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Array of same shape as `image`. Only points at which mask == True
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will be labeled.
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compactness : float, optional
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Use compact watershed [3]_ with given compactness parameter.
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Higher values result in more regularly-shaped watershed basins.
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watershed_line : bool, optional
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If watershed_line is True, a one-pixel wide line separates the regions
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obtained by the watershed algorithm. The line has the label 0.
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Returns
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-------
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out : ndarray
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A labeled matrix of the same type and shape as markers
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See also
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--------
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skimage.segmentation.random_walker: random walker segmentation
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A segmentation algorithm based on anisotropic diffusion, usually
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slower than the watershed but with good results on noisy data and
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boundaries with holes.
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Notes
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-----
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This function implements a watershed algorithm [1]_ [2]_ that apportions
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pixels into marked basins. The algorithm uses a priority queue to hold
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|
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the pixels with the metric for the priority queue being pixel value, then
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|
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the time of entry into the queue - this settles ties in favor of the
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closest marker.
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Some ideas taken from
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|
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Soille, "Automated Basin Delineation from Digital Elevation Models Using
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Mathematical Morphology", Signal Processing 20 (1990) 171-182
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|
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The most important insight in the paper is that entry time onto the queue
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|
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solves two problems: a pixel should be assigned to the neighbor with the
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largest gradient or, if there is no gradient, pixels on a plateau should
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be split between markers on opposite sides.
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This implementation converts all arguments to specific, lowest common
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denominator types, then passes these to a C algorithm.
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Markers can be determined manually, or automatically using for example
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the local minima of the gradient of the image, or the local maxima of the
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distance function to the background for separating overlapping objects
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(see example).
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References
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----------
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.. [1] https://en.wikipedia.org/wiki/Watershed_%28image_processing%29
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.. [2] http://cmm.ensmp.fr/~beucher/wtshed.html
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.. [3] Peer Neubert & Peter Protzel (2014). Compact Watershed and
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Preemptive SLIC: On Improving Trade-offs of Superpixel Segmentation
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Algorithms. ICPR 2014, pp 996-1001. :DOI:`10.1109/ICPR.2014.181`
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https://www.tu-chemnitz.de/etit/proaut/publications/cws_pSLIC_ICPR.pdf
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Examples
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--------
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The watershed algorithm is useful to separate overlapping objects.
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We first generate an initial image with two overlapping circles:
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>>> x, y = np.indices((80, 80))
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>>> x1, y1, x2, y2 = 28, 28, 44, 52
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>>> r1, r2 = 16, 20
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>>> mask_circle1 = (x - x1)**2 + (y - y1)**2 < r1**2
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>>> mask_circle2 = (x - x2)**2 + (y - y2)**2 < r2**2
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>>> image = np.logical_or(mask_circle1, mask_circle2)
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Next, we want to separate the two circles. We generate markers at the
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maxima of the distance to the background:
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>>> from scipy import ndimage as ndi
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>>> distance = ndi.distance_transform_edt(image)
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>>> from skimage.feature import peak_local_max
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>>> local_maxi = peak_local_max(distance, labels=image,
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... footprint=np.ones((3, 3)),
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... indices=False)
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>>> markers = ndi.label(local_maxi)[0]
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Finally, we run the watershed on the image and markers:
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>>> labels = watershed(-distance, markers, mask=image)
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The algorithm works also for 3-D images, and can be used for example to
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separate overlapping spheres.
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"""
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image, markers, mask = _validate_inputs(image, markers, mask, connectivity)
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connectivity, offset = _validate_connectivity(image.ndim, connectivity,
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offset)
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# pad the image, markers, and mask so that we can use the mask to
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# keep from running off the edges
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pad_width = [(p, p) for p in offset]
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image = np.pad(image, pad_width, mode='constant')
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mask = np.pad(mask, pad_width, mode='constant').ravel()
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output = np.pad(markers, pad_width, mode='constant')
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flat_neighborhood = _offsets_to_raveled_neighbors(
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image.shape, connectivity, center=offset)
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marker_locations = np.flatnonzero(output)
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image_strides = np.array(image.strides, dtype=np.intp) // image.itemsize
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_watershed_cy.watershed_raveled(image.ravel(),
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marker_locations, flat_neighborhood,
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mask, image_strides, compactness,
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output.ravel(),
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watershed_line)
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output = crop(output, pad_width, copy=True)
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return output
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