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Diffstat (limited to 'silx/image/marchingsquares/_skimage.py')
| -rw-r--r-- | silx/image/marchingsquares/_skimage.py | 139 |
1 files changed, 0 insertions, 139 deletions
diff --git a/silx/image/marchingsquares/_skimage.py b/silx/image/marchingsquares/_skimage.py deleted file mode 100644 index d49eeb0..0000000 --- a/silx/image/marchingsquares/_skimage.py +++ /dev/null @@ -1,139 +0,0 @@ -# coding: utf-8 -# /*########################################################################## -# Copyright (C) 2018 European Synchrotron Radiation Facility -# -# Permission is hereby granted, free of charge, to any person obtaining a copy -# of this software and associated documentation files (the "Software"), to deal -# in the Software without restriction, including without limitation the rights -# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -# copies of the Software, and to permit persons to whom the Software is -# furnished to do so, subject to the following conditions: -# -# The above copyright notice and this permission notice shall be included in -# all copies or substantial portions of the Software. -# -# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE -# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN -# THE SOFTWARE. -# -# ############################################################################*/ - -__authors__ = ["V. Valls"] -__license__ = "MIT" -__date__ = "05/04/2018" - - -import numpy -import skimage.measure - - -class MarchingSquaresSciKitImage(object): - """Reference implementation of a marching squares using sci-kit image. - - It uses `skimage.measure.find_contours` to find iso contours taking care of - an optional mask. As result the computation is not accurate but can be used - as reference for benchmark or for testing the API without compiling the - cython part of silx. - - :param numpy.ndarray image: 2d-image containing the values - :param numpy.ndarray mask: Optional 2d-image containing mask to cancel - mask on part of the image. A `0` means the pixel at this location is - valid, else the pixel from the image will not be used. - """ - - def __init__(self, image, mask=None): - self._image = image - self._mask = mask - - _deltas = [(0.0, 0.0), (0.99, 0.0), (0.0, 0.99), (0.99, 0.99)] - - def _flag_coord_over_mask(self, coord): - """Flag coord over the mask as NaN""" - for dx, dy in self._deltas: - if self._mask[int(coord[0] + dx), int(coord[1] + dy)] != 0: - return float("nan"), float("nan") - return coord - - def find_pixels(self, level): - """ - Compute the pixels from the image over the requested iso contours - at this `level`. - - This implementation have to use `skimage.measure.find_contours` then - it is not accurate nor efficient. - - :param float level: Level of the requested iso contours. - :returns: An array of y-x coordinates. - :rtype: numpy.ndarray - """ - polylines = skimage.measure.find_contours(self._image, level=level) - size = 0 - for polyline in polylines: - size += len(polyline) - result = numpy.empty((size, 2), dtype=numpy.int32) - size = 0 - delta = numpy.array([0.5, 0.5]) - for polyline in polylines: - if len(polyline) == 0: - continue - integer_polyline = numpy.floor(polyline + delta) - result[size:size + len(polyline)] = integer_polyline - size += len(polyline) - - if len(result) == 0: - return result - - if self._mask is not None: - # filter out pixels over the mask - x_dim = self._image.shape[1] - indexes = result[:, 0] * x_dim + result[:, 1] - indexes = indexes.ravel() - mask = self._mask.ravel() - indexes = numpy.unique(indexes) - indexes = indexes[mask[indexes] == 0] - pixels = numpy.concatenate((indexes // x_dim, indexes % x_dim)) - pixels.shape = 2, -1 - pixels = pixels.T - result = pixels - else: - # Note: Cound be done using a single line numpy.unique(result, axis=0) - # But here it supports Debian 8 - x_dim = self._image.shape[1] - indexes = result[:, 0] * x_dim + result[:, 1] - indexes = indexes.ravel() - indexes = numpy.unique(indexes) - pixels = numpy.concatenate((indexes // x_dim, indexes % x_dim)) - pixels.shape = 2, -1 - pixels = pixels.T - result = pixels - return result - - def find_contours(self, level): - """ - Compute the list of polygons of the iso contours at this `level`. - - If no mask is involved, the result is the same as - `skimage.measure.find_contours`. - - If the result have to be filtered with a mask, the result is not - accurate nor efficient. Polygons are not splited, but only points are - filtered out using NaN coordinates. This could create artefacts. - - :param float level: Level of the requested iso contours. - :returns: A list of array containg y-x coordinates of points - :rtype: List[numpy.ndarray] - """ - polylines = skimage.measure.find_contours(self._image, level=level) - if self._mask is None: - return polylines - result = [] - for polyline in polylines: - polyline = map(self._flag_coord_over_mask, polyline) - polyline = list(polyline) - polyline = numpy.array(polyline) - result.append(polyline) - return result |