Source code for dclab.features.contour

#!/usr/bin/python
# -*- coding: utf-8 -*-
"""Computation of event contour from event mask"""
from __future__ import division, print_function, unicode_literals

import numpy as np

# equivalent to
# from skimage.measure import find_contours
from ..external.skimage.measure import find_contours


class NoValidContourFoundError(BaseException):
    pass


class LazyContourList(object):
    def __init__(self, masks):
        """A list-like object that computes contours upon indexing"""
        self.masks = masks
        self.contours = [None] * len(masks)
        #: used for hashing in ancillary features
        self.identifier = str(masks[0][:].tostring())

    def __getitem__(self, idx):
        """Compute contour if it does not already exists"""
        if self.contours[idx] is None:
            try:
                self.contours[idx] = get_contour(self.masks[idx])
            except BaseException as e:
                e.args = ("Event {}, {}".format(idx, e.args[0]),)
                raise
        return self.contours[idx]

    def __len__(self):
        return len(self.masks)


[docs]def get_contour(mask): """Compute the image contour from a mask The contour is computed in a very inefficient way using scikit-image and a conversion of float coordinates to pixel coordinates. Parameters ---------- mask: binary ndarray of shape (M,N) or (K,M,N) The mask outlining the pixel positions of the event. If a 3d array is given, then `K` indexes the individual contours. Returns ------- cont: ndarray or list of K ndarrays of shape (J,2) A 2D array that holds the contour of an event (in pixels) e.g. obtained using `mm.contour` where `mm` is an instance of `RTDCBase`. The first and second columns of `cont` correspond to the x- and y-coordinates of the contour. """ if isinstance(mask, np.ndarray) and len(mask.shape) == 2: mask = [mask] ret_list = False else: ret_list = True contours = [] for mi in mask: c0 = find_contours(mi.transpose(), level=.9999, positive_orientation="low", fully_connected="high")[0] # round all coordinates to pixel values c1 = np.asarray(np.round(c0), int) # remove duplicates c2 = remove_duplicates(c1) if len(c2) == 0: raise NoValidContourFoundError("No contour found!") contours.append(c2) if ret_list: return contours else: return contours[0]
def get_contour_lazily(mask): """Like :func:`get_contour`, but computes contours on demand Parameters ---------- mask: binary ndarray of shape (M,N) or (K,M,N) The mask outlining the pixel positions of the event. If a 3d array is given, then `K` indexes the individual contours. Returns ------- cont: ndarray or LazyContourList of K ndarrays of shape (J,2) A 2D array that holds the contour of an event (in pixels) e.g. obtained using `mm.contour` where `mm` is an instance of `RTDCBase`. The first and second columns of `cont` correspond to the x- and y-coordinates of the contour. """ if isinstance(mask, np.ndarray) and len(mask.shape) == 2: # same behavior as `get_contour` cont = get_contour(mask=mask) else: cont = LazyContourList(masks=mask) return cont def remove_duplicates(cont): out = [] for ii in range(len(cont)): if np.all(cont[ii] == cont[ii - 1]): pass else: out.append(cont[ii]) return np.array(out)