diff --git a/CONTRIBUTORS.txt b/CONTRIBUTORS.txt index bff86ccc..72880521 100644 --- a/CONTRIBUTORS.txt +++ b/CONTRIBUTORS.txt @@ -28,7 +28,7 @@ Code to generate skimage logo. - Zachary Pincus - Tracing of low cost paths, FreeImage I/O plugin + Tracing of low cost paths, FreeImage I/O plugin, iso-contours - Almar Klein Binary heap class for graph algorithms diff --git a/doc/examples/plot_contours.py b/doc/examples/plot_contours.py new file mode 100644 index 00000000..020d3dfc --- /dev/null +++ b/doc/examples/plot_contours.py @@ -0,0 +1,42 @@ +""" +=============== +Contour finding +=============== + +``skimage.measure.find_contours`` uses a marching squares method to find +constant valued contours in an image. Array values are linearly interpolated +to provide better precision of the output contours. Contours which intersect +the image edge are open; all others are closed. + +The `marching squares algorithm +`__ is a special case of +the marching cubes algorithm (Lorensen, William and Harvey E. Cline. Marching +Cubes: A High Resolution 3D Surface Construction Algorithm. Computer Graphics +(SIGGRAPH 87 Proceedings) 21(4) July 1987, p. 163-170). + +""" + +from skimage import data +from skimage import measure + +import numpy as np +import matplotlib.pyplot as plt + +# Construct some test data +x, y = np.ogrid[-np.pi:np.pi:100j, -np.pi:np.pi:100j] +r = np.sin(np.exp((np.sin(x)**3 + np.cos(y)**2))) + +# Find contours at a constant value of 0.8 +contours = measure.find_contours(r, 0.8) + +# Display the image and plot all contours found +plt.imshow(r, interpolation='nearest') + +for n, contour in enumerate(contours): + plt.plot(contour[:, 1], contour[:, 0], linewidth=2) + +plt.axis('image') +plt.xticks([]) +plt.yticks([]) +plt.show() + diff --git a/skimage/io/collection.py b/skimage/io/collection.py index 7de3c9cc..317298d1 100644 --- a/skimage/io/collection.py +++ b/skimage/io/collection.py @@ -8,7 +8,7 @@ from glob import glob import os.path import numpy as np -from io import imread +from ._io import imread class MultiImage(object): diff --git a/skimage/measure/__init__.py b/skimage/measure/__init__.py index c7182c20..a9eb1526 100755 --- a/skimage/measure/__init__.py +++ b/skimage/measure/__init__.py @@ -1 +1 @@ -from find_contours import find_contours \ No newline at end of file +from find_contours import find_contours diff --git a/skimage/measure/_find_contours.c b/skimage/measure/_find_contours.c deleted file mode 100644 index da4b865d..00000000 --- a/skimage/measure/_find_contours.c +++ /dev/null @@ -1,262 +0,0 @@ -#include -#include "numpy/arrayobject.h" - -static char _find_contours_doc[] = -"This module defines C helper functions for find_contours"; - - -static char iterate_and_store_doc[] = -"iterate_and_store(array, level, vertex_connect_high)\n\ -\n\ -Iterate across the given array in a marching-squares fashion, looking for\n\ -segments that cross 'level'. If such a segment is found, its coordinates are\n\ -added to a growing list of segments, which is returned by the function.\n\ -if vertex_connect_high is nonzero, high-values pixels are considered to be\n\ -face+vertex connected into objects; otherwise low-valued pixels are."; - - -// Nasty macros to inline the inner loop of interpolating the position of -// the contour and add an appropriate tuple to the output list. -// These macros define blocks of code that can only be used within the inner -// loop of 'iterate_and_store' because they use variables therefrom. - -#define GET_FRACTION(from_value, to_value) \ - ((level - from_value) / (to_value - from_value)) - -#define TOP { \ - output0 = coords[0]; \ - output1 = coords[1] + GET_FRACTION(*ul_ptr, *ur_ptr); \ -} - -#define BOTTOM { \ - output0 = coords[0] + 1; \ - output1 = coords[1] + GET_FRACTION(*ll_ptr, *lr_ptr); \ -} - -#define LEFT { \ - output0 = coords[0] + GET_FRACTION(*ul_ptr, *ll_ptr); \ - output1 = coords[1]; \ -} - -#define RIGHT { \ - output0 = coords[0] + GET_FRACTION(*ur_ptr, *lr_ptr); \ - output1 = coords[1] + 1; \ -} - -#define ADD_TUPLE { \ - PyObject* tuple = Py_BuildValue("(dd)", output0, output1); \ - if (!tuple) { \ - Py_DECREF(double_array); \ - Py_DECREF(arc_list); \ - return NULL; \ - } \ - char res = PyList_Append(arc_list, tuple); \ - Py_DECREF(tuple); \ - if (res < 0) { \ - Py_DECREF(double_array); \ - Py_DECREF(arc_list); \ - return NULL; \ - } \ -} - -#define ADD_SEGMENT(START, END) { \ - double output0, output1; \ - START \ - ADD_TUPLE \ - END \ - ADD_TUPLE \ -} - -static PyObject* -iterate_and_store(PyObject *self, PyObject *args) -{ - PyObject* array; - double level; - int vertex_connect_high; - if (!PyArg_ParseTuple(args, "Odi:iterate_and_store", &array, &level, - &vertex_connect_high)) { - return NULL; - } - - PyObject* double_array = PyArray_FromAny(array, - PyArray_DescrFromType(NPY_DOUBLE), 2, 2, NPY_CONTIGUOUS | NPY_ALIGNED, - NULL); - if (!double_array) { - return NULL; - } - - npy_intp *dims = PyArray_DIMS(double_array); - if (dims[0] < 2 || dims[1] < 2) { - Py_DECREF(double_array); - PyErr_SetString(PyExc_ValueError, "Input array must be at least 2x2."); - return NULL; - } - - // The plan is to iterate a 2x2 square across the input array. This means - // that the upper-left corner of the square needs to iterate across a - // sub-array that's one-less-large in each direction (so that the square - // never steps out of bounds). The square is represented by four pointers: - // ul, ur, ll, and lr (for 'upper left', etc.). We also maintain the current - // 2D coordinates for the position of the upper-left pointer. Note that we - // ensured that the array is of type 'double' and is C-contiguous (last - // index varies the fastest). - - // Current coords start at 0,0. - npy_intp coords[2] = {0,0}; - // Precompute the size of the array minus 2 in each direction, so we'll know - // when to update the coordinates and double-increment the square pointers - // so that the upper-left pointer never visits the last column. - npy_intp dims_m2[2]; - dims_m2[0] = dims[0] - 2; - dims_m2[1] = dims[1] - 2; - // Calculate the number of iterations we'll need - npy_intp num_square_steps = (dims[0] - 1) * (dims[1] - 1); - // and set up the square pointers. - double* ul_ptr = PyArray_DATA(double_array); - double* ur_ptr = ul_ptr + 1; - double* ll_ptr = ul_ptr + dims[1]; - double* lr_ptr = ll_ptr + 1; - - // make a list to hold the returned coordinates - PyObject* arc_list = PyList_New(0); - if (!arc_list) { - Py_DECREF(double_array); - return NULL; - } - while(num_square_steps--) { - // There are sixteen different possible square types, diagramed below. - // A + indicates that the vertex is above the contour value, and a - - // indicates that the vertex is below or equal to the contour value. - // The vertices of each square are: - // ul ur - // ll lr - // and can be treated as a binary value with the bits in that order. Thus - // each square case can be numbered: - // 0-- 1+- 2-+ 3++ 4-- 5+- 6-+ 7++ - // -- -- -- -- +- +- +- +- - // - // 8-- 9+- 10-+ 11++ 12-- 13+- 14-+ 15++ - // -+ -+ -+ -+ ++ ++ ++ ++ - // - // The position of the line segment that cuts through (or doesn't, in case - // 0 and 15) each square is clear, except in cases 6 and 9. In this case, - // where the segments are placed is determined by vertex_connect_high. - // If vertex_connect_high is false, then lines like \\ are drawn - // through square 6, and lines like // are drawn through square 9. - // Otherwise, the situation is reversed. - // Finally, recall that we draw the lines so that (moving from tail to - // head) the lower-valued pixels are on the left of the line. So, for - // example, case 1 entails a line slanting from the middle of the top of - // the square to the middle of the left side of the square. - - unsigned char square_case = 0; - if ((*ul_ptr) > level) square_case += 1; - if ((*ur_ptr) > level) square_case += 2; - if ((*ll_ptr) > level) square_case += 4; - if ((*lr_ptr) > level) square_case += 8; - - switch(square_case) - { - case 0: // no line - break; - case 1: // top to left - ADD_SEGMENT(TOP, LEFT); - break; - case 2: // right to top - ADD_SEGMENT(RIGHT, TOP); - break; - case 3: // right to left - ADD_SEGMENT(RIGHT, LEFT); - break; - case 4: // left to bottom - ADD_SEGMENT(LEFT, BOTTOM); - break; - case 5: // top to bottom - ADD_SEGMENT(TOP, BOTTOM); - break; - case 6: - if (vertex_connect_high) - { - // left to top - ADD_SEGMENT(LEFT, TOP); - // right to bottom - ADD_SEGMENT(RIGHT, BOTTOM); - } - else - { - // right to top - ADD_SEGMENT(RIGHT, TOP); - // left to bottom - ADD_SEGMENT(LEFT, BOTTOM); - } - break; - case 7: // right to bottom - ADD_SEGMENT(RIGHT, BOTTOM); - break; - case 8: // bottom to right - ADD_SEGMENT(BOTTOM, RIGHT); - break; - case 9: - if (vertex_connect_high) - { - // top to right - ADD_SEGMENT(TOP, RIGHT); - // bottom to left - ADD_SEGMENT(BOTTOM, LEFT); - } - else - { - // top to left - ADD_SEGMENT(TOP, LEFT); - // bottom to right - ADD_SEGMENT(BOTTOM, RIGHT); - } - break; - case 10: // bottom to top - ADD_SEGMENT(BOTTOM, TOP); - break; - case 11: // bottom to left - ADD_SEGMENT(BOTTOM, LEFT); - break; - case 12: // left to right - ADD_SEGMENT(LEFT, RIGHT); - break; - case 13: // top to right - ADD_SEGMENT(TOP, RIGHT); - break; - case 14: // left to top - ADD_SEGMENT(LEFT, TOP); - break; - case 15: // no line - break; - } // switch square_case - - if (coords[1] < dims_m2[1]) { - coords[1]++; - } else { - coords[1] = 0; - coords[0]++; - // Double-increment pointers to advance them to the next row, since - // we're skipping the last column, as far as ul_ptr is concerned. - ul_ptr++; ur_ptr++; ll_ptr++; lr_ptr++; - } - ul_ptr++; ur_ptr++; ll_ptr++; lr_ptr++; - } // iteration - - // get rid of the double array reference that we own - Py_DECREF(double_array); - return arc_list; -} - - -static PyMethodDef _find_contours_methods[] = { - {"iterate_and_store", iterate_and_store, METH_VARARGS, iterate_and_store_doc}, - {NULL, NULL, 0, NULL} -}; - -PyMODINIT_FUNC -init_find_contours(void) -{ - Py_InitModule3("_find_contours", _find_contours_methods, _find_contours_doc); - import_array(); -} diff --git a/skimage/measure/_find_contours.pyx b/skimage/measure/_find_contours.pyx new file mode 100644 index 00000000..f3a26f74 --- /dev/null +++ b/skimage/measure/_find_contours.pyx @@ -0,0 +1,185 @@ +# -*- python -*- +# cython: cdivision=True + +import numpy as np +cimport numpy as np + +np.import_array() + +cdef double _get_fraction(double from_value, double to_value, double level): + if (to_value == from_value): + return 0 + return ((level - from_value) / (to_value - from_value)) + + +def iterate_and_store(np.ndarray[double, ndim=2, mode='c'] array, + double level, int vertex_connect_high): + """Iterate across the given array in a marching-squares fashion, + looking for segments that cross 'level'. If such a segment is + found, its coordinates are added to a growing list of segments, + which is returned by the function. if vertex_connect_high is + nonzero, high-values pixels are considered to be face+vertex + connected into objects; otherwise low-valued pixels are. + + """ + if array.shape[0] < 2 or array.shape[1] < 2: + raise ValueError("Input array must be at least 2x2.") + + cdef list arc_list = [] + cdef int n + + # The plan is to iterate a 2x2 square across the input array. This means + # that the upper-left corner of the square needs to iterate across a + # sub-array that's one-less-large in each direction (so that the square + # never steps out of bounds). The square is represented by four pointers: + # ul, ur, ll, and lr (for 'upper left', etc.). We also maintain the current + # 2D coordinates for the position of the upper-left pointer. Note that we + # ensured that the array is of type 'double' and is C-contiguous (last + # index varies the fastest). + + # Current coords start at 0,0. + cdef int[2] coords + coords[0] = 0 + coords[1] = 0 + + # Calculate the number of iterations we'll need + cdef int num_square_steps = (array.shape[0] - 1) * (array.shape[1] - 1) + + cdef unsigned char square_case = 0 + cdef tuple top, bottom, left, right + cdef double ul, ur, ll, lr + cdef int r0, r1, c0, c1 + + for n in range(num_square_steps): + # There are sixteen different possible square types, diagramed below. + # A + indicates that the vertex is above the contour value, and a - + # indicates that the vertex is below or equal to the contour value. + # The vertices of each square are: + # ul ur + # ll lr + # and can be treated as a binary value with the bits in that order. Thus + # each square case can be numbered: + # 0-- 1+- 2-+ 3++ 4-- 5+- 6-+ 7++ + # -- -- -- -- +- +- +- +- + # + # 8-- 9+- 10-+ 11++ 12-- 13+- 14-+ 15++ + # -+ -+ -+ -+ ++ ++ ++ ++ + # + # The position of the line segment that cuts through (or + # doesn't, in case 0 and 15) each square is clear, except in + # cases 6 and 9. In this case, where the segments are placed + # is determined by vertex_connect_high. If + # vertex_connect_high is false, then lines like \\ are drawn + # through square 6, and lines like # are drawn through square + # 9. Otherwise, the situation is reversed. + # Finally, recall that we draw the lines so that (moving from tail to + # head) the lower-valued pixels are on the left of the line. So, for + # example, case 1 entails a line slanting from the middle of the top of + # the square to the middle of the left side of the square. + + r0, c0 = coords[0], coords[1] + r1, c1 = r0 + 1, c0 + 1 + + ul = array[r0, c0] + ur = array[r0, c0 + 1] + ll = array[r0 + 1, c0] + lr = array[r0 + 1, c0 + 1] + + square_case = 0 + if (ul > level): square_case += 1 + if (ur > level): square_case += 2 + if (ll > level): square_case += 4 + if (lr > level): square_case += 8 + + top = coords[0], coords[1] + _get_fraction(ul, ur, level) + bottom = coords[0] + 1, coords[1] + _get_fraction(ll, lr, level) + left = coords[0] + _get_fraction(ul, ll, level), coords[1] + right = coords[0] + _get_fraction(ur, lr, level), coords[1] + 1 + + if (square_case == 0): + # no line + pass + elif (square_case == 1): + # top to left + arc_list.append(top) + arc_list.append(left) + elif (square_case == 2): + # right to top + arc_list.append(right) + arc_list.append(top) + elif (square_case == 3): + # right to left + arc_list.append(right) + arc_list.append(left) + elif (square_case == 4): + # left to bottom + arc_list.append(left) + arc_list.append(bottom) + elif (square_case == 5): + # top to bottom + arc_list.append(top) + arc_list.append(bottom) + elif (square_case == 6): + if vertex_connect_high: + arc_list.append(left) + arc_list.append(top) + + arc_list.append(right) + arc_list.append(bottom) + else: + arc_list.append(right) + arc_list.append(top) + arc_list.append(left) + arc_list.append(bottom) + elif (square_case == 7): + # right to bottom + arc_list.append(right) + arc_list.append(bottom) + elif (square_case == 8): + # bottom to right + arc_list.append(bottom) + arc_list.append(right) + elif (square_case == 9): + if vertex_connect_high: + arc_list.append(top) + arc_list.append(right) + + arc_list.append(bottom) + arc_list.append(left) + else: + arc_list.append(top) + arc_list.append(left) + + arc_list.append(bottom) + arc_list.append(right) + elif (square_case == 10): + # bottom to top + arc_list.append(bottom) + arc_list.append(top) + elif (square_case == 11): + # bottom to left + arc_list.append(bottom) + arc_list.append(left) + elif (square_case == 12): + # lef to right + arc_list.append(left) + arc_list.append(right) + elif (square_case == 13): + # top to right + arc_list.append(top) + arc_list.append(right) + elif (square_case == 14): + # left to top + arc_list.append(left) + arc_list.append(top) + elif (square_case == 15): + # no line + pass + + if coords[1] < array.shape[1] - 2: + coords[1] += 1 + else: + coords[0] += 1 + coords[1] = 0 + + return arc_list diff --git a/skimage/measure/find_contours.py b/skimage/measure/find_contours.py index 05d97fd8..582c1b52 100755 --- a/skimage/measure/find_contours.py +++ b/skimage/measure/find_contours.py @@ -5,44 +5,46 @@ from collections import deque _param_options = ('high', 'low') -def find_contours(array, level, fully_connected='low', positive_orientation='low'): - '''Find iso-valued contours in a 2D array for a given level value. - +def find_contours(array, level, + fully_connected='low', positive_orientation='low'): + """Find iso-valued contours in a 2D array for a given level value. + Uses the "marching squares" method to compute a the iso-valued contours of the input 2D array for a particular level value. Array values are linearly interpolated to provide better precision for the output contours. - + Parameters - ---------- - array : convertible to a 2D ndarray object - Input data in which to find isocontours. - level : float + ---------- + array : 2D ndarray of double + Input data in which to find contours. + level : float Value along which to find contours in the array. - fully_connected : either 'low' or 'high' - Indicates whether array elements below the given level value are to - be considered fully- connected (and hence elements above the value - will only be face connected), or vice-versa. (See below for details.) + fully_connected : str, {'low', 'high'} + Indicates whether array elements below the given level value are to be + considered fully-connected (and hence elements above the value will + only be face connected), or vice-versa. (See notes below for details.) positive_orientation : either 'low' or 'high' - Indicates whether the output contours will produce - positively-oriented polygons around islands of low- or high-valued - elements. If 'low' then contours will wind counter- clockwise around - elements below the iso-value. Alternately, this means that low-valued - elements are always on the left of the contour. (See below for - details.) - + Indicates whether the output contours will produce positively-oriented + polygons around islands of low- or high-valued elements. If 'low' then + contours will wind counter- clockwise around elements below the + iso-value. Alternately, this means that low-valued elements are always + on the left of the contour. (See below for details.) + Returns - ------- - A list of contours, where each contour is an ndarray of shape (n, 2) - consisting of n (x,y) coordinates along the contour. - + ------- + contours : list of (n,2)-ndarrays + Each contour is an ndarray of shape ``(n, 2)``, + consisting of n ``(x, y)`` coordinates along the contour. + + Notes + ----- The marching squares algorithm is a special case of the marching cubes - algorithm (Lorensen, William and Harvey E. Cline. Marching Cubes: A High - Resolution 3D Surface Construction Algorithm. Computer Graphics (SIGGRAPH - 87 Proceedings) 21(4) July 1987, p. 163-170). A simple explanation is - available here: http://www.essi.fr/~lingrand/MarchingCubes/algo.html - + algorithm [1]_. A simple explanation is available here:: + + http://www.essi.fr/~lingrand/MarchingCubes/algo.html + There is a single ambiguous case in the marching squares algorithm: when - a given 2x2-element square has two high-valued and two low-valued + a given ``2 x 2``-element square has two high-valued and two low-valued elements, each pair diagonally adjacent. (Where high- and low-valued is with respect to the contour value sought.) In this case, either the high-valued elements can be 'connected together' via a thin isthmus that @@ -50,45 +52,54 @@ def find_contours(array, level, fully_connected='low', positive_orientation='low connected together across a diagonal, they are considered 'fully connected' (also known as 'face+vertex-connected' or '8-connected'). Only high-valued or low-valued elements can be fully-connected, the other set - will be considred as 'face-connected' or '4-connected'. By default, - low-valued elements are considered fully-connected; this can be altered + will be considered as 'face-connected' or '4-connected'. By default, + low-valued elements are considered fully-connected; this can be altered with the 'fully_connected' parameter. - + Output contours are not guaranteed to be closed: contours which intersect the array edge will be left open. All other contours will be closed. (The closed-ness of a contours can be tested by checking whether the beginning point is the same as the end point.) - + Contours are oriented. By default, array values lower than the contour value are to the left of the contour and values greater than the contour value are to the right. This means that contours will wind counter-clockwise (i.e. in 'positive orientation') around islands of low-valued pixels. This behavior can be altered with the 'positive_orientation' parameter. - + The order of the contours in the output list is determined by the position - of the smallest x,y (in lexicographical order) coordinate in the contour. - This is a side-effect of how the input array is traversed, but can be - relied upon. - - IMPORTANT NOTE ON COORDINATES AND VALUES: - Array coordinates/values are assumed to refer to the _center_ of the - array element. Take a simple example: [0, 1]. The interpolated position of - 0.5 in this array is midway between the 0-element (at x=0) and the - 1-element (at x=1), and thus would fall at x=0.5. - + of the smallest ``x,y`` (in lexicographical order) coordinate in the + contour. This is a side-effect of how the input array is traversed, but + can be relied upon. + + .. warning:: + + Array coordinates/values are assumed to refer to the *center* of the + array element. Take a simple example input: ``[0, 1]``. The interpolated + position of 0.5 in this array is midway between the 0-element (at + ``x=0``) and the 1-element (at ``x=1``), and thus would fall at + ``x=0.5``. + This means that to find reasonable contours, it is best to find contours midway between the expected "light" and "dark" values. In particular, - given a binarized array, DO NOT choose to find contours at the low or high + given a binarized array, *do not* choose to find contours at the low or high value of the array. This will often yield degenerate contours, especially around structures that are a single array element wide. Instead choose - a middle value, as above.''' - - array = np.asarray(array) + a middle value, as above. + + References + ---------- + .. [1] Lorensen, William and Harvey E. Cline. Marching Cubes: A High + Resolution 3D Surface Construction Algorithm. Computer Graphics + (SIGGRAPH 87 Proceedings) 21(4) July 1987, p. 163-170). + + """ + array = np.asarray(array, dtype=np.double) if array.ndim != 2: raise RuntimeError('Only 2D arrays are supported.') level = float(level) - if (fully_connected not in _param_options or + if (fully_connected not in _param_options or positive_orientation not in _param_options): raise ValueError('Parameters "fully_connected" and' ' "positive_orientation" must be either "high" or "low".') @@ -98,7 +109,7 @@ def find_contours(array, level, fully_connected='low', positive_orientation='low if positive_orientation == 'high': contours = [c[::-1] for c in contours] return contours - + def _take_2(seq): iterator = iter(seq) while(True): @@ -117,24 +128,24 @@ def _assemble_contours(points_iterator): # exactly the contour level, and the rest are above or below. # This degnerate vertex will be picked up later by neighboring squares. if from_point == to_point: continue - + tail_data = starts.get(to_point) head_data = ends.get(from_point) - + if tail_data is not None and head_data is not None: tail, tail_num = tail_data head, head_num = head_data - # We need to connect these two contours. + # We need to connect these two contours. if tail is head: # We need to closed a contour. - # Add the end point, and remove the contour from the + # Add the end point, and remove the contour from the # 'starts' and 'ends' dicts. head.append(to_point) del starts[to_point] del ends[from_point] else: # tail is not head # We need to join two distinct contours. - # We want to keep the first contour segment created, so that + # We want to keep the first contour segment created, so that # the final contours are ordered left->right, top->bottom. if tail_num > head_num: # tail was created second. Append tail to head. @@ -166,7 +177,7 @@ def _assemble_contours(points_iterator): ends[to_point] = (new_contour, new_num) elif tail_data is not None and head_data is None: tail, tail_num = tail_data - # We've found a single contour to which the new segment should be + # We've found a single contour to which the new segment should be # prepended. tail.appendleft(from_point) del starts[to_point] @@ -179,6 +190,5 @@ def _assemble_contours(points_iterator): del ends[from_point] ends[to_point] = (head, head_num) # end iteration over from_ and to_ points - + return [np.array(contour) for (num, contour) in sorted(contours.items())] - \ No newline at end of file diff --git a/skimage/measure/setup.py b/skimage/measure/setup.py index dc4d1768..9fc5ca2d 100644 --- a/skimage/measure/setup.py +++ b/skimage/measure/setup.py @@ -1,11 +1,17 @@ #!/usr/bin/env python +from skimage._build import cython + +import os +base_path = os.path.abspath(os.path.dirname(__file__)) + def configuration(parent_package='', top_path=None): from numpy.distutils.misc_util import Configuration, get_numpy_include_dirs config = Configuration('measure', parent_package, top_path) config.add_data_dir('tests') + cython(['_find_contours.pyx'], working_path=base_path) config.add_extension('_find_contours', sources=['_find_contours.c'], include_dirs=[get_numpy_include_dirs()]) diff --git a/skimage/measure/tests/test_find_contours.py b/skimage/measure/tests/test_find_contours.py index 408a679a..8d705878 100644 --- a/skimage/measure/tests/test_find_contours.py +++ b/skimage/measure/tests/test_find_contours.py @@ -1,7 +1,7 @@ import numpy as np from numpy.testing import * -from skimage.find_contours import find_contours +from skimage.measure import find_contours a = np.ones((8,8), dtype=np.float32) a[1:-1, 1] = 0