# Copyright (C) 2003-2005 Peter J. Verveer # # Redistribution and use in source and binary forms, with or without # modification, are permitted provided that the following conditions # are met: # # 1. Redistributions of source code must retain the above copyright # notice, this list of conditions and the following disclaimer. # # 2. Redistributions in binary form must reproduce the above # copyright notice, this list of conditions and the following # disclaimer in the documentation and/or other materials provided # with the distribution. # # 3. The name of the author may not be used to endorse or promote # products derived from this software without specific prior # written permission. # # THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS # OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED # WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE # ARE DISCLAIMED. IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY # DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL # DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE # GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS # INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, # WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING # NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS # SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. import sys import unittest import math import numpy from numpy import fft from numpy.testing import * set_package_path() try: import scipy.ndimage as ndimage except: import ndimage restore_path() #import numarray.numinclude as numinclude eps = 1e-12 def diff(a, b): if not isinstance(a, numpy.ndarray): a = numpy.asarray(a) if not isinstance(b, numpy.ndarray): b = numpy.asarray(b) if (0 in a.shape) and (0 in b.shape): return 0.0 if (a.dtype in [numpy.complex64, numpy.complex128] or b.dtype in [numpy.complex64, numpy.complex128]): a = numpy.asarray(a, numpy.complex128) b = numpy.asarray(b, numpy.complex128) t = ((a.real - b.real)**2).sum() + ((a.imag - b.imag)**2).sum() else: a = numpy.asarray(a) a = a.astype(numpy.float64) b = numpy.asarray(b) b = b.astype(numpy.float64) t = ((a - b)**2).sum() return math.sqrt(t) class test_ndimage(NumpyTestCase): def setUp(self): # list of numarray data types self.types = [numpy.int8, numpy.uint8, numpy.int16, numpy.uint16, numpy.int32, numpy.uint32, numpy.int64, numpy.uint64, numpy.float32, numpy.float64] # list of boundary modes: self.modes = ['nearest', 'wrap', 'reflect', 'constant'] def test_correlate01(self): "correlation 1" array = numpy.array([1, 2]) weights = numpy.array([2]) true = [2, 4] output = ndimage.correlate(array, weights) self.failUnless(diff(output, true) < eps) output = ndimage.convolve(array, weights) self.failUnless(diff(output, true) < eps) output = ndimage.correlate1d(array, weights) self.failUnless(diff(output, true) < eps) output = ndimage.convolve1d(array, weights) self.failUnless(diff(output, true) < eps) def test_correlate02(self): "correlation 2" array = numpy.array([1, 2, 3]) kernel = numpy.array([1]) output = ndimage.correlate(array, kernel) self.failUnless(diff(array, output) < eps) output = ndimage.convolve(array, kernel) self.failUnless(diff(array, output) < eps) output = ndimage.correlate1d(array, kernel) self.failUnless(diff(array, output) < eps) output = ndimage.convolve1d(array, kernel) self.failUnless(diff(array, output) < eps) def test_correlate03(self): "correlation 3" array = numpy.array([1]) weights = numpy.array([1, 1]) true = [2] output = ndimage.correlate(array, weights) self.failUnless(diff(output, true) < eps) output = ndimage.convolve(array, weights) self.failUnless(diff(output, true) < eps) output = ndimage.correlate1d(array, weights) self.failUnless(diff(output, true) < eps) output = ndimage.convolve1d(array, weights) self.failUnless(diff(output, true) < eps) def test_correlate04(self): "correlation 4" array = numpy.array([1, 2]) tcor = [2, 3] tcov = [3, 4] weights = numpy.array([1, 1]) output = ndimage.correlate(array, weights) self.failUnless(diff(output, tcor) < eps) output = ndimage.convolve(array, weights) self.failUnless(diff(output, tcov) < eps) output = ndimage.correlate1d(array, weights) self.failUnless(diff(output, tcor) < eps) output = ndimage.convolve1d(array, weights) self.failUnless(diff(output, tcov) < eps) def test_correlate05(self): "correlation 5" array = numpy.array([1, 2, 3]) tcor = [2, 3, 5] tcov = [3, 5, 6] kernel = numpy.array([1, 1]) output = ndimage.correlate(array, kernel) self.failUnless(diff(tcor, output) < eps) output = ndimage.convolve(array, kernel) self.failUnless(diff(tcov, output) < eps) output = ndimage.correlate1d(array, kernel) self.failUnless(diff(tcor, output) < eps) output = ndimage.convolve1d(array, kernel) self.failUnless(diff(tcov, output) < eps) def test_correlate06(self): "correlation 6" array = numpy.array([1, 2, 3]) tcor = [9, 14, 17] tcov = [7, 10, 15] weights = numpy.array([1, 2, 3]) output = ndimage.correlate(array, weights) self.failUnless(diff(output, tcor) < eps) output = ndimage.convolve(array, weights) self.failUnless(diff(output, tcov) < eps) output = ndimage.correlate1d(array, weights) self.failUnless(diff(output, tcor) < eps) output = ndimage.convolve1d(array, weights) self.failUnless(diff(output, tcov) < eps) def test_correlate07(self): "correlation 7" array = numpy.array([1, 2, 3]) true = [5, 8, 11] weights = numpy.array([1, 2, 1]) output = ndimage.correlate(array, weights) self.failUnless(diff(output, true) < eps) output = ndimage.convolve(array, weights) self.failUnless(diff(output, true) < eps) output = ndimage.correlate1d(array, weights) self.failUnless(diff(output, true) < eps) output = ndimage.convolve1d(array, weights) self.failUnless(diff(output, true) < eps) def test_correlate08(self): "correlation 8" array = numpy.array([1, 2, 3]) tcor = [1, 2, 5] tcov = [3, 6, 7] weights = numpy.array([1, 2, -1]) output = ndimage.correlate(array, weights) self.failUnless(diff(output, tcor) < eps) output = ndimage.convolve(array, weights) self.failUnless(diff(output, tcov) < eps) output = ndimage.correlate1d(array, weights) self.failUnless(diff(output, tcor) < eps) output = ndimage.convolve1d(array, weights) self.failUnless(diff(output, tcov) < eps) def test_correlate09(self): "correlation 9" array = [] kernel = numpy.array([1, 1]) output = ndimage.correlate(array, kernel) self.failUnless(diff(array, output) < eps) output = ndimage.convolve(array, kernel) self.failUnless(diff(array, output) < eps) output = ndimage.correlate1d(array, kernel) self.failUnless(diff(array, output) < eps) output = ndimage.convolve1d(array, kernel) self.failUnless(diff(array, output) < eps) def test_correlate10(self): "correlation 10" array = [[]] kernel = numpy.array([[1, 1]]) output = ndimage.correlate(array, kernel) self.failUnless(diff(array, output) < eps) output = ndimage.convolve(array, kernel) self.failUnless(diff(array, output) < eps) def test_correlate11(self): "correlation 11" array = numpy.array([[1, 2, 3], [4, 5, 6]]) kernel = numpy.array([[1, 1], [1, 1]]) output = ndimage.correlate(array, kernel) self.failUnless(diff([[4, 6, 10], [10, 12, 16]], output) < eps) output = ndimage.convolve(array, kernel) self.failUnless(diff([[12, 16, 18], [18, 22, 24]], output) < eps) def test_correlate12(self): "correlation 12" array = numpy.array([[1, 2, 3], [4, 5, 6]]) kernel = numpy.array([[1, 0], [0, 1]]) output = ndimage.correlate(array, kernel) self.failUnless(diff([[2, 3, 5], [5, 6, 8]], output) < eps) output = ndimage.convolve(array, kernel) self.failUnless(diff([[6, 8, 9], [9, 11, 12]], output) < eps) def test_correlate13(self): "correlation 13" kernel = numpy.array([[1, 0], [0, 1]]) for type1 in self.types: array = numpy.array([[1, 2, 3], [4, 5, 6]], type1) for type2 in self.types: output = ndimage.correlate(array, kernel, output = type2) error = diff([[2, 3, 5], [5, 6, 8]], output) self.failUnless(error < eps and output.dtype.type == type2) output = ndimage.convolve(array, kernel, output = type2) error = diff([[6, 8, 9], [9, 11, 12]], output) self.failUnless(error < eps and output.dtype.type == type2) def test_correlate14(self): "correlation 14" kernel = numpy.array([[1, 0], [0, 1]]) for type1 in self.types: array = numpy.array([[1, 2, 3], [4, 5, 6]], type1) for type2 in self.types: output = numpy.zeros(array.shape, type2) ndimage.correlate(array, kernel, output = output) error = diff([[2, 3, 5], [5, 6, 8]], output) self.failUnless(error < eps and output.dtype.type == type2) ndimage.convolve(array, kernel, output = output) error = diff([[6, 8, 9], [9, 11, 12]], output) self.failUnless(error < eps and output.dtype.type == type2) def test_correlate15(self): "correlation 15" kernel = numpy.array([[1, 0], [0, 1]]) for type1 in self.types: array = numpy.array([[1, 2, 3], [4, 5, 6]], type1) output = ndimage.correlate(array, kernel, output = numpy.float32) error = diff([[2, 3, 5], [5, 6, 8]], output) self.failUnless(error < eps and output.dtype.type == numpy.float32) output = ndimage.convolve(array, kernel, output = numpy.float32) error = diff([[6, 8, 9], [9, 11, 12]], output) self.failUnless(error < eps and output.dtype.type == numpy.float32) def test_correlate16(self): "correlation 16" kernel = numpy.array([[0.5, 0 ], [0, 0.5]]) for type1 in self.types: array = numpy.array([[1, 2, 3], [4, 5, 6]], type1) output = ndimage.correlate(array, kernel, output = numpy.float32) error = diff([[1, 1.5, 2.5], [2.5, 3, 4]], output) self.failUnless(error < eps and output.dtype.type == numpy.float32) output = ndimage.convolve(array, kernel, output = numpy.float32) error = diff([[3, 4, 4.5], [4.5, 5.5, 6]], output) self.failUnless(error < eps and output.dtype.type == numpy.float32) def test_correlate17(self): "correlation 17" array = numpy.array([1, 2, 3]) tcor = [3, 5, 6] tcov = [2, 3, 5] kernel = numpy.array([1, 1]) output = ndimage.correlate(array, kernel, origin = -1) self.failUnless(diff(tcor, output) < eps) output = ndimage.convolve(array, kernel, origin = -1) self.failUnless(diff(tcov, output) < eps) output = ndimage.correlate1d(array, kernel, origin = -1) self.failUnless(diff(tcor, output) < eps) output = ndimage.convolve1d(array, kernel, origin = -1) self.failUnless(diff(tcov, output) < eps) def test_correlate18(self): "correlation 18" kernel = numpy.array([[1, 0], [0, 1]]) for type1 in self.types: array = numpy.array([[1, 2, 3], [4, 5, 6]], type1) output = ndimage.correlate(array, kernel, output = numpy.float32, mode = 'nearest', origin = -1) error = diff([[6, 8, 9], [9, 11, 12]], output) self.failUnless(error < eps and output.dtype.type == numpy.float32) output = ndimage.convolve(array, kernel, output = numpy.float32, mode = 'nearest', origin = -1) error = diff([[2, 3, 5], [5, 6, 8]], output) self.failUnless(error < eps and output.dtype.type == numpy.float32) def test_correlate19(self): "correlation 19" kernel = numpy.array([[1, 0], [0, 1]]) for type1 in self.types: array = numpy.array([[1, 2, 3], [4, 5, 6]], type1) output = ndimage.correlate(array, kernel, output = numpy.float32, mode = 'nearest', origin = [-1, 0]) error = diff([[5, 6, 8], [8, 9, 11]], output) self.failUnless(error < eps and output.dtype.type == numpy.float32) output = ndimage.convolve(array, kernel, output = numpy.float32, mode = 'nearest', origin = [-1, 0]) error = diff([[3, 5, 6], [6, 8, 9]], output) self.failUnless(error < eps and output.dtype.type == numpy.float32) def test_correlate20(self): "correlation 20" weights = numpy.array([1, 2, 1]) true = [[5, 10, 15], [7, 14, 21]] for type1 in self.types: array = numpy.array([[1, 2, 3], [2, 4, 6]], type1) for type2 in self.types: output = numpy.zeros((2, 3), type2) ndimage.correlate1d(array, weights, axis = 0, output = output) self.failUnless(diff(output, true) < eps) ndimage.convolve1d(array, weights, axis = 0, output = output) self.failUnless(diff(output, true) < eps) def test_correlate21(self): "correlation 21" array = numpy.array([[1, 2, 3], [2, 4, 6]]) true = [[5, 10, 15], [7, 14, 21]] weights = numpy.array([1, 2, 1]) output = ndimage.correlate1d(array, weights, axis = 0) self.failUnless(diff(output, true) < eps) output = ndimage.convolve1d(array, weights, axis = 0) self.failUnless(diff(output, true) < eps) def test_correlate22(self): "correlation 22" weights = numpy.array([1, 2, 1]) true = [[6, 12, 18], [6, 12, 18]] for type1 in self.types: array = numpy.array([[1, 2, 3], [2, 4, 6]], type1) for type2 in self.types: output = numpy.zeros((2, 3), type2) ndimage.correlate1d(array, weights, axis = 0, mode = 'wrap', output = output) self.failUnless(diff(output, true) < eps) ndimage.convolve1d(array, weights, axis = 0, mode = 'wrap', output = output) self.failUnless(diff(output, true) < eps) def test_correlate23(self): "correlation 23" weights = numpy.array([1, 2, 1]) true = [[5, 10, 15], [7, 14, 21]] for type1 in self.types: array = numpy.array([[1, 2, 3], [2, 4, 6]], type1) for type2 in self.types: output = numpy.zeros((2, 3), type2) ndimage.correlate1d(array, weights, axis = 0, mode = 'nearest', output = output) self.failUnless(diff(output, true) < eps) ndimage.convolve1d(array, weights, axis = 0, mode = 'nearest', output = output) self.failUnless(diff(output, true) < eps) def test_correlate24(self): "correlation 24" weights = numpy.array([1, 2, 1]) tcor = [[7, 14, 21], [8, 16, 24]] tcov = [[4, 8, 12], [5, 10, 15]] for type1 in self.types: array = numpy.array([[1, 2, 3], [2, 4, 6]], type1) for type2 in self.types: output = numpy.zeros((2, 3), type2) ndimage.correlate1d(array, weights, axis = 0, mode = 'nearest', output = output, origin = -1) self.failUnless(diff(output, tcor) < eps) ndimage.convolve1d(array, weights, axis = 0, mode = 'nearest', output = output, origin = -1) self.failUnless(diff(output, tcov) < eps) def test_correlate25(self): "correlation 25" weights = numpy.array([1, 2, 1]) tcor = [[4, 8, 12], [5, 10, 15]] tcov = [[7, 14, 21], [8, 16, 24]] for type1 in self.types: array = numpy.array([[1, 2, 3], [2, 4, 6]], type1) for type2 in self.types: output = numpy.zeros((2, 3), type2) ndimage.correlate1d(array, weights, axis = 0, mode = 'nearest', output = output, origin = 1) self.failUnless(diff(output, tcor) < eps) ndimage.convolve1d(array, weights, axis = 0, mode = 'nearest', output = output, origin = 1) self.failUnless(diff(output, tcov) < eps) def test_gauss01(self): "gaussian filter 1" input = numpy.array([[1, 2, 3], [2, 4, 6]], numpy.float32) output = ndimage.gaussian_filter(input, 0) self.failUnless(diff(output, input) < eps) def test_gauss02(self): "gaussian filter 2" input = numpy.array([[1, 2, 3], [2, 4, 6]], numpy.float32) output = ndimage.gaussian_filter(input, 1.0) self.failUnless(input.dtype == output.dtype and input.shape == output.shape) def test_gauss03(self): "gaussian filter 3" input = numpy.arange(100 * 100).astype(numpy.float32) input.shape = (100, 100) output = ndimage.gaussian_filter(input, [1.0, 1.0]) self.failUnless(input.dtype == output.dtype and input.shape == output.shape and output.sum(dtype='d') - input.sum(dtype='d') < eps and diff(input, output) > 1.0) def test_gauss04(self): "gaussian filter 4" input = numpy.arange(100 * 100).astype(numpy.float32) input.shape = (100, 100) otype = numpy.float64 output = ndimage.gaussian_filter(input, [1.0, 1.0], output = otype) self.failUnless(output.dtype.type == numpy.float64 and input.shape == output.shape and diff(input, output) > 1.0) def test_gauss05(self): "gaussian filter 5" input = numpy.arange(100 * 100).astype(numpy.float32) input.shape = (100, 100) otype = numpy.float64 output = ndimage.gaussian_filter(input, [1.0, 1.0], order = 1, output = otype) self.failUnless(output.dtype.type == numpy.float64 and input.shape == output.shape and diff(input, output) > 1.0) def test_gauss06(self): "gaussian filter 6" input = numpy.arange(100 * 100).astype(numpy.float32) input.shape = (100, 100) otype = numpy.float64 output1 = ndimage.gaussian_filter(input, [1.0, 1.0], output = otype) output2 = ndimage.gaussian_filter(input, 1.0, output = otype) self.failUnless(diff(output1, output2) < eps) def test_prewitt01(self): "prewitt filter 1" for type in self.types: array = numpy.array([[3, 2, 5, 1, 4], [5, 8, 3, 7, 1], [5, 6, 9, 3, 5]], type) t = ndimage.correlate1d(array, [-1.0, 0.0, 1.0], 0) t = ndimage.correlate1d(t, [1.0, 1.0, 1.0], 1) output = ndimage.prewitt(array, 0) self.failUnless(diff(t, output) < eps) def test_prewitt02(self): "prewitt filter 2" for type in self.types: array = numpy.array([[3, 2, 5, 1, 4], [5, 8, 3, 7, 1], [5, 6, 9, 3, 5]], type) t = ndimage.correlate1d(array, [-1.0, 0.0, 1.0], 0) t = ndimage.correlate1d(t, [1.0, 1.0, 1.0], 1) output = numpy.zeros(array.shape, type) ndimage.prewitt(array, 0, output) self.failUnless(diff(t, output) < eps) def test_prewitt03(self): "prewitt filter 3" for type in self.types: array = numpy.array([[3, 2, 5, 1, 4], [5, 8, 3, 7, 1], [5, 6, 9, 3, 5]], type) t = ndimage.correlate1d(array, [-1.0, 0.0, 1.0], 1) t = ndimage.correlate1d(t, [1.0, 1.0, 1.0], 0) output = ndimage.prewitt(array, 1) self.failUnless(diff(t, output) < eps) def test_prewitt04(self): "prewitt filter 4" for type in self.types: array = numpy.array([[3, 2, 5, 1, 4], [5, 8, 3, 7, 1], [5, 6, 9, 3, 5]], type) t = ndimage.prewitt(array, -1) output = ndimage.prewitt(array, 1) self.failUnless(diff(t, output) < eps) def test_sobel01(self): "sobel filter 1" for type in self.types: array = numpy.array([[3, 2, 5, 1, 4], [5, 8, 3, 7, 1], [5, 6, 9, 3, 5]], type) t = ndimage.correlate1d(array, [-1.0, 0.0, 1.0], 0) t = ndimage.correlate1d(t, [1.0, 2.0, 1.0], 1) output = ndimage.sobel(array, 0) self.failUnless(diff(t, output) < eps) def test_sobel02(self): "sobel filter 2" for type in self.types: array = numpy.array([[3, 2, 5, 1, 4], [5, 8, 3, 7, 1], [5, 6, 9, 3, 5]], type) t = ndimage.correlate1d(array, [-1.0, 0.0, 1.0], 0) t = ndimage.correlate1d(t, [1.0, 2.0, 1.0], 1) output = numpy.zeros(array.shape, type) ndimage.sobel(array, 0, output) self.failUnless(diff(t, output) < eps) def test_sobel03(self): "sobel filter 3" for type in self.types: array = numpy.array([[3, 2, 5, 1, 4], [5, 8, 3, 7, 1], [5, 6, 9, 3, 5]], type) t = ndimage.correlate1d(array, [-1.0, 0.0, 1.0], 1) t = ndimage.correlate1d(t, [1.0, 2.0, 1.0], 0) output = numpy.zeros(array.shape, type) output = ndimage.sobel(array, 1) self.failUnless(diff(t, output) < eps) def test_sobel04(self): "sobel filter 4" for type in self.types: array = numpy.array([[3, 2, 5, 1, 4], [5, 8, 3, 7, 1], [5, 6, 9, 3, 5]], type) t = ndimage.sobel(array, -1) output = ndimage.sobel(array, 1) self.failUnless(diff(t, output) < eps) def test_laplace01(self): "laplace filter 1" for type in [numpy.int32, numpy.float32, numpy.float64]: array = numpy.array([[3, 2, 5, 1, 4], [5, 8, 3, 7, 1], [5, 6, 9, 3, 5]], type) * 100 tmp1 = ndimage.correlate1d(array, [1, -2, 1], 0) tmp2 = ndimage.correlate1d(array, [1, -2, 1], 1) output = ndimage.laplace(array) self.failUnless(diff(tmp1 + tmp2, output) < eps) def test_laplace02(self): "laplace filter 2" for type in [numpy.int32, numpy.float32, numpy.float64]: array = numpy.array([[3, 2, 5, 1, 4], [5, 8, 3, 7, 1], [5, 6, 9, 3, 5]], type) * 100 tmp1 = ndimage.correlate1d(array, [1, -2, 1], 0) tmp2 = ndimage.correlate1d(array, [1, -2, 1], 1) output = numpy.zeros(array.shape, type) ndimage.laplace(array, output = output) self.failUnless(diff(tmp1 + tmp2, output) < eps) def test_gaussian_laplace01(self): "gaussian laplace filter 1" for type in [numpy.int32, numpy.float32, numpy.float64]: array = numpy.array([[3, 2, 5, 1, 4], [5, 8, 3, 7, 1], [5, 6, 9, 3, 5]], type) * 100 tmp1 = ndimage.gaussian_filter(array, 1.0, [2, 0]) tmp2 = ndimage.gaussian_filter(array, 1.0, [0, 2]) output = ndimage.gaussian_laplace(array, 1.0) self.failUnless(diff(tmp1 + tmp2, output) < eps) def test_gaussian_laplace02(self): "gaussian laplace filter 2" for type in [numpy.int32, numpy.float32, numpy.float64]: array = numpy.array([[3, 2, 5, 1, 4], [5, 8, 3, 7, 1], [5, 6, 9, 3, 5]], type) * 100 tmp1 = ndimage.gaussian_filter(array, 1.0, [2, 0]) tmp2 = ndimage.gaussian_filter(array, 1.0, [0, 2]) output = numpy.zeros(array.shape, type) ndimage.gaussian_laplace(array, 1.0, output) self.failUnless(diff(tmp1 + tmp2, output) < eps) def test_generic_laplace01(self): "generic laplace filter 1" def derivative2(input, axis, output, mode, cval, a, b): sigma = [a, b / 2.0] input = numpy.asarray(input) order = [0] * input.ndim order[axis] = 2 return ndimage.gaussian_filter(input, sigma, order, output, mode, cval) for type in self.types: array = numpy.array([[3, 2, 5, 1, 4], [5, 8, 3, 7, 1], [5, 6, 9, 3, 5]], type) output = numpy.zeros(array.shape, type) tmp = ndimage.generic_laplace(array, derivative2, extra_arguments = (1.0,), extra_keywords = {'b': 2.0}) ndimage.gaussian_laplace(array, 1.0, output) self.failUnless(diff(tmp, output) < eps) def test_gaussian_gradient_magnitude01(self): "gaussian gradient magnitude filter 1" for type in [numpy.int32, numpy.float32, numpy.float64]: array = numpy.array([[3, 2, 5, 1, 4], [5, 8, 3, 7, 1], [5, 6, 9, 3, 5]], type) * 100 tmp1 = ndimage.gaussian_filter(array, 1.0, [1, 0]) tmp2 = ndimage.gaussian_filter(array, 1.0, [0, 1]) output = ndimage.gaussian_gradient_magnitude(array, 1.0) true = tmp1 * tmp1 + tmp2 * tmp2 numpy.sqrt(true, true) self.failUnless(diff(true, output) < eps) def test_gaussian_gradient_magnitude02(self): "gaussian gradient magnitude filter 2" for type in [numpy.int32, numpy.float32, numpy.float64]: array = numpy.array([[3, 2, 5, 1, 4], [5, 8, 3, 7, 1], [5, 6, 9, 3, 5]], type) * 100 tmp1 = ndimage.gaussian_filter(array, 1.0, [1, 0]) tmp2 = ndimage.gaussian_filter(array, 1.0, [0, 1]) output = numpy.zeros(array.shape, type) ndimage.gaussian_gradient_magnitude(array, 1.0, output) true = tmp1 * tmp1 + tmp2 * tmp2 numpy.sqrt(true, true) self.failUnless(diff(true, output) < eps) def test_generic_gradient_magnitude01(self): "generic gradient magnitude 1" array = numpy.array([[3, 2, 5, 1, 4], [5, 8, 3, 7, 1], [5, 6, 9, 3, 5]], numpy.float64) def derivative(input, axis, output, mode, cval, a, b): sigma = [a, b / 2.0] input = numpy.asarray(input) order = [0] * input.ndim order[axis] = 1 return ndimage.gaussian_filter(input, sigma, order, output, mode, cval) tmp1 = ndimage.gaussian_gradient_magnitude(array, 1.0) tmp2 = ndimage.generic_gradient_magnitude(array, derivative, extra_arguments = (1.0,), extra_keywords = {'b': 2.0}) self.failUnless(diff(tmp1, tmp2) < eps) def test_uniform01(self): "uniform filter 1" array = numpy.array([2, 4, 6]) size = 2 output = ndimage.uniform_filter1d(array, size, origin = -1) self.failUnless(diff([3, 5, 6], output) < eps) def test_uniform02(self): "uniform filter 2" array = numpy.array([1, 2, 3]) filter_shape = [0] output = ndimage.uniform_filter(array, filter_shape) self.failUnless(diff(array, output) < eps) def test_uniform03(self): "uniform filter 3" array = numpy.array([1, 2, 3]) filter_shape = [1] output = ndimage.uniform_filter(array, filter_shape) self.failUnless(diff(array, output) < eps) def test_uniform04(self): "uniform filter 4" array = numpy.array([2, 4, 6]) filter_shape = [2] output = ndimage.uniform_filter(array, filter_shape) self.failUnless(diff([2, 3, 5], output) < eps) def test_uniform05(self): "uniform filter 5" array = [] filter_shape = [1] output = ndimage.uniform_filter(array, filter_shape) self.failUnless(diff([], output) < eps) def test_uniform06(self): "uniform filter 6" filter_shape = [2, 2] for type1 in self.types: array = numpy.array([[4, 8, 12], [16, 20, 24]], type1) for type2 in self.types: output = ndimage.uniform_filter(array, filter_shape, output = type2) error = diff([[4, 6, 10], [10, 12, 16]], output) self.failUnless(error < eps and output.dtype.type == type2) def test_minimum_filter01(self): "minimum filter 1" array = numpy.array([1, 2, 3, 4, 5]) filter_shape = numpy.array([2]) output = ndimage.minimum_filter(array, filter_shape) self.failUnless(diff([1, 1, 2, 3, 4], output) < eps) def test_minimum_filter02(self): "minimum filter 2" array = numpy.array([1, 2, 3, 4, 5]) filter_shape = numpy.array([3]) output = ndimage.minimum_filter(array, filter_shape) self.failUnless(diff([1, 1, 2, 3, 4], output) < eps) def test_minimum_filter03(self): "minimum filter 3" array = numpy.array([3, 2, 5, 1, 4]) filter_shape = numpy.array([2]) output = ndimage.minimum_filter(array, filter_shape) self.failUnless(diff([3, 2, 2, 1, 1], output) < eps) def test_minimum_filter04(self): "minimum filter 4" array = numpy.array([3, 2, 5, 1, 4]) filter_shape = numpy.array([3]) output = ndimage.minimum_filter(array, filter_shape) self.failUnless(diff([2, 2, 1, 1, 1], output) < eps) def test_minimum_filter05(self): "minimum filter 5" array = numpy.array([[3, 2, 5, 1, 4], [7, 6, 9, 3, 5], [5, 8, 3, 7, 1]]) filter_shape = numpy.array([2, 3]) output = ndimage.minimum_filter(array, filter_shape) self.failUnless(diff([[2, 2, 1, 1, 1], [2, 2, 1, 1, 1], [5, 3, 3, 1, 1]], output) < eps) def test_minimum_filter06(self): "minimum filter 6" array = numpy.array([[3, 2, 5, 1, 4], [7, 6, 9, 3, 5], [5, 8, 3, 7, 1]]) footprint = [[1, 1, 1], [1, 1, 1]] output = ndimage.minimum_filter(array, footprint = footprint) self.failUnless(diff([[2, 2, 1, 1, 1], [2, 2, 1, 1, 1], [5, 3, 3, 1, 1]], output) < eps) def test_minimum_filter07(self): "minimum filter 7" array = numpy.array([[3, 2, 5, 1, 4], [7, 6, 9, 3, 5], [5, 8, 3, 7, 1]]) footprint = [[1, 0, 1], [1, 1, 0]] output = ndimage.minimum_filter(array, footprint = footprint) self.failUnless(diff([[2, 2, 1, 1, 1], [2, 3, 1, 3, 1], [5, 5, 3, 3, 1]], output) < eps) def test_minimum_filter08(self): "minimum filter 8" array = numpy.array([[3, 2, 5, 1, 4], [7, 6, 9, 3, 5], [5, 8, 3, 7, 1]]) footprint = [[1, 0, 1], [1, 1, 0]] output = ndimage.minimum_filter(array, footprint = footprint, origin = -1) self.failUnless(diff([[3, 1, 3, 1, 1], [5, 3, 3, 1, 1], [3, 3, 1, 1, 1]], output) < eps) def test_minimum_filter09(self): "minimum filter 9" array = numpy.array([[3, 2, 5, 1, 4], [7, 6, 9, 3, 5], [5, 8, 3, 7, 1]]) footprint = [[1, 0, 1], [1, 1, 0]] output = ndimage.minimum_filter(array, footprint = footprint, origin = [-1, 0]) self.failUnless(diff([[2, 3, 1, 3, 1], [5, 5, 3, 3, 1], [5, 3, 3, 1, 1]], output) < eps) def test_maximum_filter01(self): "maximum filter 1" array = numpy.array([1, 2, 3, 4, 5]) filter_shape = numpy.array([2]) output = ndimage.maximum_filter(array, filter_shape) self.failUnless(diff([1, 2, 3, 4, 5], output) < eps) def test_maximum_filter02(self): "maximum filter 2" array = numpy.array([1, 2, 3, 4, 5]) filter_shape = numpy.array([3]) output = ndimage.maximum_filter(array, filter_shape) self.failUnless(diff([2, 3, 4, 5, 5], output) < eps) def test_maximum_filter03(self): "maximum filter 3" array = numpy.array([3, 2, 5, 1, 4]) filter_shape = numpy.array([2]) output = ndimage.maximum_filter(array, filter_shape) self.failUnless(diff([3, 3, 5, 5, 4], output) < eps) def test_maximum_filter04(self): "maximum filter 4" array = numpy.array([3, 2, 5, 1, 4]) filter_shape = numpy.array([3]) output = ndimage.maximum_filter(array, filter_shape) self.failUnless(diff([3, 5, 5, 5, 4], output) < eps) def test_maximum_filter05(self): "maximum filter 5" array = numpy.array([[3, 2, 5, 1, 4], [7, 6, 9, 3, 5], [5, 8, 3, 7, 1]]) filter_shape = numpy.array([2, 3]) output = ndimage.maximum_filter(array, filter_shape) self.failUnless(diff([[3, 5, 5, 5, 4], [7, 9, 9, 9, 5], [8, 9, 9, 9, 7]], output) < eps) def test_maximum_filter06(self): "maximum filter 6" array = numpy.array([[3, 2, 5, 1, 4], [7, 6, 9, 3, 5], [5, 8, 3, 7, 1]]) footprint = [[1, 1, 1], [1, 1, 1]] output = ndimage.maximum_filter(array, footprint = footprint) self.failUnless(diff([[3, 5, 5, 5, 4], [7, 9, 9, 9, 5], [8, 9, 9, 9, 7]], output) < eps) def test_maximum_filter07(self): "maximum filter 7" array = numpy.array([[3, 2, 5, 1, 4], [7, 6, 9, 3, 5], [5, 8, 3, 7, 1]]) footprint = [[1, 0, 1], [1, 1, 0]] output = ndimage.maximum_filter(array, footprint = footprint) self.failUnless(diff([[3, 5, 5, 5, 4], [7, 7, 9, 9, 5], [7, 9, 8, 9, 7]], output) < eps) def test_maximum_filter08(self): "maximum filter 8" array = numpy.array([[3, 2, 5, 1, 4], [7, 6, 9, 3, 5], [5, 8, 3, 7, 1]]) footprint = [[1, 0, 1], [1, 1, 0]] output = ndimage.maximum_filter(array, footprint = footprint, origin = -1) self.failUnless(diff([[7, 9, 9, 5, 5], [9, 8, 9, 7, 5], [8, 8, 7, 7, 7]], output) < eps) def test_maximum_filter09(self): "maximum filter 9" array = numpy.array([[3, 2, 5, 1, 4], [7, 6, 9, 3, 5], [5, 8, 3, 7, 1]]) footprint = [[1, 0, 1], [1, 1, 0]] output = ndimage.maximum_filter(array, footprint = footprint, origin = [-1, 0]) self.failUnless(diff([[7, 7, 9, 9, 5], [7, 9, 8, 9, 7], [8, 8, 8, 7, 7]], output) < eps) def test_rank01(self): "rank filter 1" array = numpy.array([1, 2, 3, 4, 5]) output = ndimage.rank_filter(array, 1, size = 2) self.failUnless(diff(array, output) < eps) output = ndimage.percentile_filter(array, 100, size = 2) self.failUnless(diff(array, output) < eps) output = ndimage.median_filter(array, 2) self.failUnless(diff(array, output) < eps) def test_rank02(self): "rank filter 2" array = numpy.array([1, 2, 3, 4, 5]) output = ndimage.rank_filter(array, 1, size = [3]) self.failUnless(diff(array, output) < eps) output = ndimage.percentile_filter(array, 50, size = 3) self.failUnless(diff(array, output) < eps) output = ndimage.median_filter(array, (3,)) self.failUnless(diff(array, output) < eps) def test_rank03(self): "rank filter 3" array = numpy.array([3, 2, 5, 1, 4]) output = ndimage.rank_filter(array, 1, size = [2]) self.failUnless(diff([3, 3, 5, 5, 4], output) < eps) output = ndimage.percentile_filter(array, 100, size = 2) self.failUnless(diff([3, 3, 5, 5, 4], output) < eps) def test_rank04(self): "rank filter 4" array = numpy.array([3, 2, 5, 1, 4]) true = [3, 3, 2, 4, 4] output = ndimage.rank_filter(array, 1, size = 3) self.failUnless(diff(true, output) < eps) output = ndimage.percentile_filter(array, 50, size = 3) self.failUnless(diff(true, output) < eps) output = ndimage.median_filter(array, size = 3) self.failUnless(diff(true, output) < eps) def test_rank05(self): "rank filter 5" array = numpy.array([3, 2, 5, 1, 4]) true = [3, 3, 2, 4, 4] output = ndimage.rank_filter(array, -2, size = 3) self.failUnless(diff(true, output) < eps) def test_rank06(self): "rank filter 6" array = numpy.array([[3, 2, 5, 1, 4], [5, 8, 3, 7, 1], [5, 6, 9, 3, 5]]) true = [[2, 2, 1, 1, 1], [3, 3, 2, 1, 1], [5, 5, 3, 3, 1]] output = ndimage.rank_filter(array, 1, size = [2, 3]) self.failUnless(diff(true, output) < eps) output = ndimage.percentile_filter(array, 17, size = (2, 3)) self.failUnless(diff(true, output) < eps) def test_rank07(self): "rank filter 7" array = numpy.array([[3, 2, 5, 1, 4], [5, 8, 3, 7, 1], [5, 6, 9, 3, 5]]) true = [[3, 5, 5, 5, 4], [5, 5, 7, 5, 4], [6, 8, 8, 7, 5]] output = ndimage.rank_filter(array, -2, size = [2, 3]) self.failUnless(diff(true, output) < eps) def test_rank08(self): "median filter 8" array = numpy.array([[3, 2, 5, 1, 4], [5, 8, 3, 7, 1], [5, 6, 9, 3, 5]]) true = [[3, 3, 2, 4, 4], [5, 5, 5, 4, 4], [5, 6, 7, 5, 5]] kernel = numpy.array([2, 3]) output = ndimage.percentile_filter(array, 50.0, size = (2, 3)) self.failUnless(diff(true, output) < eps) output = ndimage.rank_filter(array, 3, size = (2, 3)) self.failUnless(diff(true, output) < eps) output = ndimage.median_filter(array, size = (2, 3)) self.failUnless(diff(true, output) < eps) def test_rank09(self): "rank filter 9" true = [[3, 3, 2, 4, 4], [3, 5, 2, 5, 1], [5, 5, 8, 3, 5]] footprint = [[1, 0, 1], [0, 1, 0]] for type in self.types: array = numpy.array([[3, 2, 5, 1, 4], [5, 8, 3, 7, 1], [5, 6, 9, 3, 5]], type) output = ndimage.rank_filter(array, 1, footprint = footprint) self.failUnless(diff(true, output) < eps) output = ndimage.percentile_filter(array, 35, footprint = footprint) self.failUnless(diff(true, output) < eps) def test_rank10(self): "rank filter 10" array = numpy.array([[3, 2, 5, 1, 4], [7, 6, 9, 3, 5], [5, 8, 3, 7, 1]]) true = [[2, 2, 1, 1, 1], [2, 3, 1, 3, 1], [5, 5, 3, 3, 1]] footprint = [[1, 0, 1], [1, 1, 0]] output = ndimage.rank_filter(array, 0, footprint = footprint) self.failUnless(diff(true, output) < eps) output = ndimage.percentile_filter(array, 0.0, footprint = footprint) self.failUnless(diff(true, output) < eps) def test_rank11(self): "rank filter 11" array = numpy.array([[3, 2, 5, 1, 4], [7, 6, 9, 3, 5], [5, 8, 3, 7, 1]]) true = [[3, 5, 5, 5, 4], [7, 7, 9, 9, 5], [7, 9, 8, 9, 7]] footprint = [[1, 0, 1], [1, 1, 0]] output = ndimage.rank_filter(array, -1, footprint = footprint) self.failUnless(diff(true, output) < eps) output = ndimage.percentile_filter(array, 100.0, footprint = footprint) self.failUnless(diff(true, output) < eps) def test_rank12(self): "rank filter 12" true = [[3, 3, 2, 4, 4], [3, 5, 2, 5, 1], [5, 5, 8, 3, 5]] footprint = [[1, 0, 1], [0, 1, 0]] for type in self.types: array = numpy.array([[3, 2, 5, 1, 4], [5, 8, 3, 7, 1], [5, 6, 9, 3, 5]], type) output = ndimage.rank_filter(array, 1, footprint = footprint) self.failUnless(diff(true, output) < eps) output = ndimage.percentile_filter(array, 50.0, footprint = footprint) self.failUnless(diff(true, output) < eps) output = ndimage.median_filter(array, footprint = footprint) self.failUnless(diff(true, output) < eps) def test_rank13(self): "rank filter 13" true = [[5, 2, 5, 1, 1], [5, 8, 3, 5, 5], [6, 6, 5, 5, 5]] footprint = [[1, 0, 1], [0, 1, 0]] for type in self.types: array = numpy.array([[3, 2, 5, 1, 4], [5, 8, 3, 7, 1], [5, 6, 9, 3, 5]], type) output = ndimage.rank_filter(array, 1, footprint = footprint, origin = -1) self.failUnless(diff(true, output) < eps) def test_rank14(self): "rank filter 14" true = [[3, 5, 2, 5, 1], [5, 5, 8, 3, 5], [5, 6, 6, 5, 5]] footprint = [[1, 0, 1], [0, 1, 0]] for type in self.types: array = numpy.array([[3, 2, 5, 1, 4], [5, 8, 3, 7, 1], [5, 6, 9, 3, 5]], type) output = ndimage.rank_filter(array, 1, footprint = footprint, origin = [-1, 0]) self.failUnless(diff(true, output) < eps) def test_generic_filter1d01(self): "generic 1d filter 1" weights = numpy.array([1.1, 2.2, 3.3]) def _filter_func(input, output, fltr, total): fltr = fltr / total for ii in range(input.shape[0] - 2): output[ii] = input[ii] * fltr[0] output[ii] += input[ii + 1] * fltr[1] output[ii] += input[ii + 2] * fltr[2] for type in self.types: a = numpy.arange(12, dtype = type) a.shape = (3,4) r1 = ndimage.correlate1d(a, weights / weights.sum(), 0, origin = -1) r2 = ndimage.generic_filter1d(a, _filter_func, 3, axis = 0, origin = -1, extra_arguments = (weights,), extra_keywords = {'total': weights.sum()}) self.failUnless(diff(r1, r2) < eps) def test_generic_filter01(self): "generic filter 1" filter = numpy.array([[1.0, 2.0], [3.0, 4.0]]) footprint = numpy.array([[1, 0], [0, 1]]) cf = numpy.array([1., 4.]) def _filter_func(buffer, weights, total = 1.0): weights = cf / total return (buffer * weights).sum() for type in self.types: a = numpy.arange(12, dtype = type) a.shape = (3,4) r1 = ndimage.correlate(a, filter * footprint) / 5 r2 = ndimage.generic_filter(a, _filter_func, footprint = footprint, extra_arguments = (cf,), extra_keywords = {'total': cf.sum()}) self.failUnless(diff(r1, r2) < eps) def test_extend01(self): "line extension 1" array = numpy.array([1, 2, 3]) weights = numpy.array([1, 0]) true_values = [[1, 1, 2], [3, 1, 2], [1, 1, 2], [0, 1, 2]] for mode, true_value in zip(self.modes, true_values): output = ndimage.correlate1d(array, weights, 0, mode = mode, cval = 0) self.failUnless(diff(output, true_value) < eps) def test_extend02(self): "line extension 2" array = numpy.array([1, 2, 3]) weights = numpy.array([1, 0, 0, 0, 0, 0, 0, 0]) true_values = [[1, 1, 1], [3, 1, 2], [3, 3, 2], [0, 0, 0]] for mode, true_value in zip(self.modes, true_values): output = ndimage.correlate1d(array, weights, 0, mode = mode, cval = 0) self.failUnless(diff(output, true_value) < eps) def test_extend03(self): "line extension 3" array = numpy.array([1, 2, 3]) weights = numpy.array([0, 0, 1]) true_values = [[2, 3, 3], [2, 3, 1], [2, 3, 3], [2, 3, 0]] for mode, true_value in zip(self.modes, true_values): output = ndimage.correlate1d(array, weights, 0, mode = mode, cval = 0) self.failUnless(diff(output, true_value) < eps) def test_extend04(self): "line extension 4" array = numpy.array([1, 2, 3]) weights = numpy.array([0, 0, 0, 0, 0, 0, 0, 0, 1]) true_values = [[3, 3, 3], [2, 3, 1], [2, 1, 1], [0, 0, 0]] for mode, true_value in zip(self.modes, true_values): output = ndimage.correlate1d(array, weights, 0, mode = mode, cval = 0) self.failUnless(diff(output, true_value) < eps) def test_extend05(self): "line extension 5" array = numpy.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]]) weights = numpy.array([[1, 0], [0, 0]]) true_values = [[[1, 1, 2], [1, 1, 2], [4, 4, 5]], [[9, 7, 8], [3, 1, 2], [6, 4, 5]], [[1, 1, 2], [1, 1, 2], [4, 4, 5]], [[0, 0, 0], [0, 1, 2], [0, 4, 5]]] for mode, true_value in zip(self.modes, true_values): output = ndimage.correlate(array, weights, mode = mode, cval = 0) self.failUnless(diff(output, true_value) < eps) def test_extend06(self): "line extension 6" array = numpy.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]]) weights = numpy.array([[0, 0, 0], [0, 0, 0], [0, 0, 1]]) true_values = [[[5, 6, 6], [8, 9, 9], [8, 9, 9]], [[5, 6, 4], [8, 9, 7], [2, 3, 1]], [[5, 6, 6], [8, 9, 9], [8, 9, 9]], [[5, 6, 0], [8, 9, 0], [0, 0, 0]]] for mode, true_value in zip(self.modes, true_values): output = ndimage.correlate(array, weights, mode = mode, cval = 0) self.failUnless(diff(output, true_value) < eps) def test_extend07(self): "line extension 7" array = numpy.array([1, 2, 3]) weights = numpy.array([0, 0, 0, 0, 0, 0, 0, 0, 1]) true_values = [[3, 3, 3], [2, 3, 1], [2, 1, 1], [0, 0, 0]] for mode, true_value in zip(self.modes, true_values): output = ndimage.correlate(array, weights, mode = mode, cval = 0) self.failUnless(diff(output, true_value) < eps) def test_extend08(self): "line extension 8" array = numpy.array([[1], [2], [3]]) weights = numpy.array([[0], [0], [0], [0], [0], [0], [0], [0], [1]]) true_values = [[[3], [3], [3]], [[2], [3], [1]], [[2], [1], [1]], [[0], [0], [0]]] for mode, true_value in zip(self.modes, true_values): output = ndimage.correlate(array, weights, mode = mode, cval = 0) self.failUnless(diff(output, true_value) < eps) def test_extend09(self): "line extension 9" array = numpy.array([1, 2, 3]) weights = numpy.array([0, 0, 0, 0, 0, 0, 0, 0, 1]) true_values = [[3, 3, 3], [2, 3, 1], [2, 1, 1], [0, 0, 0]] for mode, true_value in zip(self.modes, true_values): output = ndimage.correlate(array, weights, mode = mode, cval = 0) self.failUnless(diff(output, true_value) < eps) def test_extend10(self): "line extension 10" array = numpy.array([[1], [2], [3]]) weights = numpy.array([[0], [0], [0], [0], [0], [0], [0], [0], [1]]) true_values = [[[3], [3], [3]], [[2], [3], [1]], [[2], [1], [1]], [[0], [0], [0]]] for mode, true_value in zip(self.modes, true_values): output = ndimage.correlate(array, weights, mode = mode, cval = 0) self.failUnless(diff(output, true_value) < eps) def test_boundaries(self): "boundary modes" def shift(x): return (x[0] + 0.1,) data = numpy.array([1,2,3,4]) expected = {'constant': [1,2,3,-1,-1,-1], 'wrap': [1,2,3,4,1,2], 'reflect' : [1,2,3,4,4,3], 'mirror' : [1,2,3,4,3,2], 'nearest' : [1,2,3,4,4,4]} for mode in expected.keys(): assert_array_equal(expected[mode], ndimage.geometric_transform(data,shift, cval=-1,mode=mode, output_shape=(6,))) def test_fourier_gaussian_real01(self): "gaussian fourier filter for real transforms 1" for shape in [(32, 16), (31, 15)]: for type in [numpy.float32, numpy.float64]: a = numpy.zeros(shape, type) a[0, 0] = 1.0 a = fft.rfft(a, shape[0], 0) a = fft.fft(a, shape[1], 1) a = ndimage.fourier_gaussian(a, [5.0, 2.5], shape[0], 0) a = fft.ifft(a, shape[1], 1) a = fft.irfft(a, shape[0], 0) self.failUnless(diff(ndimage.sum(a), 1.0) < eps) def test_fourier_gaussian_complex01(self): "gaussian fourier filter for complex transforms 1" for shape in [(32, 16), (31, 15)]: for type in [numpy.complex64, numpy.complex128]: a = numpy.zeros(shape, type) a[0, 0] = 1.0 a = fft.fft(a, shape[0], 0) a = fft.fft(a, shape[1], 1) a = ndimage.fourier_gaussian(a, [5.0, 2.5], -1, 0) a = fft.ifft(a, shape[1], 1) a = fft.ifft(a, shape[0], 0) error = diff(ndimage.sum(a.real), 1.0) self.failUnless(error < eps) def test_fourier_uniform_real01(self): "uniform fourier filter for real transforms 1" for shape in [(32, 16), (31, 15)]: for type in [numpy.float32, numpy.float64]: a = numpy.zeros(shape, type) a[0, 0] = 1.0 a = fft.rfft(a, shape[0], 0) a = fft.fft(a, shape[1], 1) a = ndimage.fourier_uniform(a, [5.0, 2.5], shape[0], 0) a = fft.ifft(a, shape[1], 1) a = fft.irfft(a, shape[0], 0) self.failUnless(diff(ndimage.sum(a), 1.0) < eps) def test_fourier_uniform_complex01(self): "uniform fourier filter for complex transforms 1" for shape in [(32, 16), (31, 15)]: for type in [numpy.complex64, numpy.complex128]: a = numpy.zeros(shape, type) a[0, 0] = 1.0 a = fft.fft(a, shape[0], 0) a = fft.fft(a, shape[1], 1) a = ndimage.fourier_uniform(a, [5.0, 2.5], -1, 0) a = fft.ifft(a, shape[1], 1) a = fft.ifft(a, shape[0], 0) error = diff(ndimage.sum(a.real), 1.0) self.failUnless(error < eps) def test_fourier_shift_real01(self): "shift filter for real transforms 1" for shape in [(32, 16), (31, 15)]: for dtype in [numpy.float32, numpy.float64]: true = numpy.arange(shape[0] * shape[1], dtype = dtype) true.shape = shape a = fft.rfft(true, shape[0], 0) a = fft.fft(a, shape[1], 1) a = ndimage.fourier_shift(a, [1, 1], shape[0], 0) a = fft.ifft(a, shape[1], 1) a = fft.irfft(a, shape[0], 0) error1 = diff(a[1:, 1:], true[:-1, :-1]) error2 = diff(a.imag, numpy.zeros(shape)) self.failUnless(error1 < 1e-10 and error2 < 1e-10) def test_fourier_shift_complex01(self): "shift filter for complex transforms 1" for shape in [(32, 16), (31, 15)]: for type in [numpy.complex64, numpy.complex128]: true = numpy.arange(shape[0] * shape[1], dtype = type) true.shape = shape a = fft.fft(true, shape[0], 0) a = fft.fft(a, shape[1], 1) a = ndimage.fourier_shift(a, [1, 1], -1, 0) a = fft.ifft(a, shape[1], 1) a = fft.ifft(a, shape[0], 0) error1 = diff(a.real[1:, 1:], true[:-1, :-1]) error2 = diff(a.imag, numpy.zeros(shape)) self.failUnless(error1 < 1e-10 and error2 < 1e-10) def test_fourier_ellipsoid_real01(self): "ellipsoid fourier filter for real transforms 1" for shape in [(32, 16), (31, 15)]: for type in [numpy.float32, numpy.float64]: a = numpy.zeros(shape, type) a[0, 0] = 1.0 a = fft.rfft(a, shape[0], 0) a = fft.fft(a, shape[1], 1) a = ndimage.fourier_ellipsoid(a, [5.0, 2.5], shape[0], 0) a = fft.ifft(a, shape[1], 1) a = fft.irfft(a, shape[0], 0) self.failUnless(diff(ndimage.sum(a), 1.0) < eps) def test_fourier_ellipsoid_complex01(self): "ellipsoid fourier filter for complex transforms 1" for shape in [(32, 16), (31, 15)]: for type in [numpy.complex64, numpy.complex128]: a = numpy.zeros(shape, type) a[0, 0] = 1.0 a = fft.fft(a, shape[0], 0) a = fft.fft(a, shape[1], 1) a = ndimage.fourier_ellipsoid(a, [5.0, 2.5], -1, 0) a = fft.ifft(a, shape[1], 1) a = fft.ifft(a, shape[0], 0) error = diff(ndimage.sum(a.real), 1.0) self.failUnless(error < eps) def test_spline01(self): "spline filter 1" for type in self.types: data = numpy.ones([], type) for order in range(2, 6): out = ndimage.spline_filter(data, order = order) self.failUnless(diff(out, 1)< eps and out.dtype.type == numpy.float64) def test_spline02(self): "spline filter 2" for type in self.types: data = numpy.array([1]) for order in range(2, 6): out = ndimage.spline_filter(data, order = order) self.failUnless(diff(out, [1]) < eps and out.dtype.type == numpy.float64) def test_spline03(self): "spline filter 3" for type in self.types: data = numpy.ones([], type) for order in range(2, 6): out = ndimage.spline_filter(data, order, output = type) self.failUnless(diff(out, 1) < eps and out.dtype.type == type) def test_spline04(self): "spline filter 4" for type in self.types: data = numpy.ones([4], type) for order in range(2, 6): out = ndimage.spline_filter(data, order) self.failUnless(diff(out, [1, 1, 1, 1]) < eps) def test_spline05(self): "spline filter 5" for type in self.types: data = numpy.ones([4, 4], type) for order in range(2, 6): out = ndimage.spline_filter(data, order = order) self.failUnless(diff(out, [[1, 1, 1, 1], [1, 1, 1, 1], [1, 1, 1, 1], [1, 1, 1, 1]]) < eps) def test_geometric_transform01(self): "geometric transform 1" data = numpy.array([1]) def mapping(x): return x for order in range(0, 6): out = ndimage.geometric_transform(data, mapping, data.shape, order=order) self.failUnless(diff(out, [1]) < eps) def test_geometric_transform02(self): "geometric transform 2" data = numpy.ones([4]) def mapping(x): return x for order in range(0, 6): out = ndimage.geometric_transform(data, mapping, data.shape, order=order) self.failUnless(diff(out, [1, 1, 1, 1]) < eps) def test_geometric_transform03(self): "geometric transform 3" data = numpy.ones([4]) def mapping(x): return (x[0] - 1,) for order in range(0, 6): out = ndimage.geometric_transform(data, mapping, data.shape, order=order) self.failUnless(diff(out, [0, 1, 1, 1]) < eps) def test_geometric_transform04(self): "geometric transform 4" data = numpy.array([4, 1, 3, 2]) def mapping(x): return (x[0] - 1,) for order in range(0, 6): out = ndimage.geometric_transform(data, mapping, data.shape, order=order) self.failUnless(diff(out, [0, 4, 1, 3]) < eps) def test_geometric_transform05(self): "geometric transform 5" data = numpy.array([[1, 1, 1, 1], [1, 1, 1, 1], [1, 1, 1, 1]]) def mapping(x): return (x[0], x[1] - 1) for order in range(0, 6): out = ndimage.geometric_transform(data, mapping, data.shape, order=order) self.failUnless(diff(out, [[0, 1, 1, 1], [0, 1, 1, 1], [0, 1, 1, 1]]) < eps) def test_geometric_transform06(self): "geometric transform 6" data = numpy.array([[4, 1, 3, 2], [7, 6, 8, 5], [3, 5, 3, 6]]) def mapping(x): return (x[0], x[1] - 1) for order in range(0, 6): out = ndimage.geometric_transform(data, mapping, data.shape, order=order) self.failUnless(diff(out, [[0, 4, 1, 3], [0, 7, 6, 8], [0, 3, 5, 3]]) < eps) def test_geometric_transform07(self): "geometric transform 7" data = numpy.array([[4, 1, 3, 2], [7, 6, 8, 5], [3, 5, 3, 6]]) def mapping(x): return (x[0] - 1, x[1]) for order in range(0, 6): out = ndimage.geometric_transform(data, mapping, data.shape, order=order) self.failUnless(diff(out, [[0, 0, 0, 0], [4, 1, 3, 2], [7, 6, 8, 5]]) < eps) def test_geometric_transform08(self): "geometric transform 8" data = numpy.array([[4, 1, 3, 2], [7, 6, 8, 5], [3, 5, 3, 6]]) def mapping(x): return (x[0] - 1, x[1] - 1) for order in range(0, 6): out = ndimage.geometric_transform(data, mapping, data.shape, order=order) self.failUnless(diff(out, [[0, 0, 0, 0], [0, 4, 1, 3], [0, 7, 6, 8]]) < eps) def test_geometric_transform10(self): "geometric transform 10" data = numpy.array([[4, 1, 3, 2], [7, 6, 8, 5], [3, 5, 3, 6]]) def mapping(x): return (x[0] - 1, x[1] - 1) for order in range(0, 6): if (order > 1): filtered = ndimage.spline_filter(data, order=order) else: filtered = data out = ndimage.geometric_transform(filtered, mapping, data.shape, order=order, prefilter = False) self.failUnless(diff(out, [[0, 0, 0, 0], [0, 4, 1, 3], [0, 7, 6, 8]]) < eps) def test_geometric_transform13(self): "geometric transform 13" data = numpy.ones([2], numpy.float64) def mapping(x): return (x[0] / 2,) for order in range(0, 6): out = ndimage.geometric_transform(data, mapping, [4], order=order) self.failUnless(diff(out, [1, 1, 1, 1]) < eps) def test_geometric_transform14(self): "geometric transform 14" data = [1, 5, 2, 6, 3, 7, 4, 4] def mapping(x): return (2 * x[0],) for order in range(0, 6): out = ndimage.geometric_transform(data, mapping, [4], order=order) self.failUnless(diff(out, [1, 2, 3, 4]) < eps) def test_geometric_transform15(self): "geometric transform 15" data = [1, 2, 3, 4] def mapping(x): return (x[0] / 2,) for order in range(0, 6): out = ndimage.geometric_transform(data, mapping, [8], order=order) self.failUnless(diff(out[::2], [1, 2, 3, 4]) < eps) def test_geometric_transform16(self): "geometric transform 16" data = [[1, 2, 3, 4], [5, 6, 7, 8], [9.0, 10, 11, 12]] def mapping(x): return (x[0], x[1] * 2) for order in range(0, 6): out = ndimage.geometric_transform(data, mapping, (3, 2), order=order) self.failUnless(diff(out, [[1, 3], [5, 7], [9, 11]]) < eps) def test_geometric_transform17(self): "geometric transform 17" data = [[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]] def mapping(x): return (x[0] * 2, x[1]) for order in range(0, 6): out = ndimage.geometric_transform(data, mapping, (1, 4), order=order) self.failUnless(diff(out, [[1, 2, 3, 4]]) < eps) def test_geometric_transform18(self): "geometric transform 18" data = [[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]] def mapping(x): return (x[0] * 2, x[1] * 2) for order in range(0, 6): out = ndimage.geometric_transform(data, mapping, (1, 2), order=order) self.failUnless(diff(out, [[1, 3]]) < eps) def test_geometric_transform19(self): "geometric transform 19" data = [[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]] def mapping(x): return (x[0], x[1] / 2) for order in range(0, 6): out = ndimage.geometric_transform(data, mapping, (3, 8), order=order) self.failUnless(diff(out[..., ::2], data) < eps) def test_geometric_transform20(self): "geometric transform 20" data = [[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]] def mapping(x): return (x[0] / 2, x[1]) for order in range(0, 6): out = ndimage.geometric_transform(data, mapping, (6, 4), order=order) self.failUnless(diff(out[::2, ...], data) < eps) def test_geometric_transform21(self): "geometric transform 21" data = [[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]] def mapping(x): return (x[0] / 2, x[1] / 2) for order in range(0, 6): out = ndimage.geometric_transform(data, mapping, (6, 8), order=order) self.failUnless(diff(out[::2, ::2], data) < eps) def test_geometric_transform22(self): "geometric transform 22" data = numpy.array([[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]], numpy.float64) def mapping1(x): return (x[0] / 2, x[1] / 2) def mapping2(x): return (x[0] * 2, x[1] * 2) for order in range(0, 6): out = ndimage.geometric_transform(data, mapping1, (6, 8), order=order) out = ndimage.geometric_transform(out, mapping2, (3, 4), order=order) error = diff(out, data) self.failUnless(diff(out, data) < eps) def test_geometric_transform23(self): "geometric transform 23" data = [[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]] def mapping(x): return (1, x[0] * 2) for order in range(0, 6): out = ndimage.geometric_transform(data, mapping, (2,), order=order) out = out.astype(numpy.int32) self.failUnless(diff(out, [5, 7]) < eps) def test_geometric_transform24(self): "geometric transform 24" data = [[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]] def mapping(x, a, b): return (a, x[0] * b) for order in range(0, 6): out = ndimage.geometric_transform(data, mapping, (2,), order=order, extra_arguments = (1,), extra_keywords = {'b': 2}) self.failUnless(diff(out, [5, 7]) < eps) def test_map_coordinates01(self): "map coordinates 1" data = numpy.array([[4, 1, 3, 2], [7, 6, 8, 5], [3, 5, 3, 6]]) idx = numpy.indices(data.shape) idx -= 1 for order in range(0, 6): out = ndimage.map_coordinates(data, idx, order=order) self.failUnless(diff(out, [[0, 0, 0, 0], [0, 4, 1, 3], [0, 7, 6, 8]]) < eps) def test_map_coordinates02(self): "map coordinates 2" data = numpy.array([[4, 1, 3, 2], [7, 6, 8, 5], [3, 5, 3, 6]]) idx = numpy.indices(data.shape, numpy.float64) idx -= 0.5 for order in range(0, 6): out1 = ndimage.shift(data, 0.5, order=order) out2 = ndimage.map_coordinates(data, idx, order=order) self.failUnless(diff(out1, out2) < eps) def test_affine_transform01(self): "affine_transform 1" data = numpy.array([1]) for order in range(0, 6): out = ndimage.affine_transform(data, [[1]], order=order) self.failUnless(diff(out, [1]) < eps) def test_affine_transform02(self): "affine transform 2" data = numpy.ones([4]) for order in range(0, 6): out = ndimage.affine_transform(data, [[1]], order=order) self.failUnless(diff(out, [1, 1, 1, 1]) < eps) def test_affine_transform03(self): "affine transform 3" data = numpy.ones([4]) for order in range(0, 6): out = ndimage.affine_transform(data, [[1]], -1, order=order) self.failUnless(diff(out, [0, 1, 1, 1]) < eps) def test_affine_transform04(self): "affine transform 4" data = numpy.array([4, 1, 3, 2]) for order in range(0, 6): out = ndimage.affine_transform(data, [[1]], -1, order=order) self.failUnless(diff(out, [0, 4, 1, 3]) < eps) def test_affine_transform05(self): "affine transform 5" data = numpy.array([[1, 1, 1, 1], [1, 1, 1, 1], [1, 1, 1, 1]]) for order in range(0, 6): out = ndimage.affine_transform(data, [[1, 0], [0, 1]], [0, -1], order=order) self.failUnless(diff(out, [[0, 1, 1, 1], [0, 1, 1, 1], [0, 1, 1, 1]]) < eps) def test_affine_transform06(self): "affine transform 6" data = numpy.array([[4, 1, 3, 2], [7, 6, 8, 5], [3, 5, 3, 6]]) for order in range(0, 6): out = ndimage.affine_transform(data, [[1, 0], [0, 1]], [0, -1], order=order) self.failUnless(diff(out, [[0, 4, 1, 3], [0, 7, 6, 8], [0, 3, 5, 3]]) < eps) def test_affine_transform07(self): "affine transform 7" data = numpy.array([[4, 1, 3, 2], [7, 6, 8, 5], [3, 5, 3, 6]]) for order in range(0, 6): out = ndimage.affine_transform(data, [[1, 0], [0, 1]], [-1, 0], order=order) self.failUnless(diff(out, [[0, 0, 0, 0], [4, 1, 3, 2], [7, 6, 8, 5]]) < eps) def test_affine_transform08(self): "affine transform 8" data = numpy.array([[4, 1, 3, 2], [7, 6, 8, 5], [3, 5, 3, 6]]) for order in range(0, 6): out = ndimage.affine_transform(data, [[1, 0], [0, 1]], [-1, -1], order=order) self.failUnless(diff(out, [[0, 0, 0, 0], [0, 4, 1, 3], [0, 7, 6, 8]]) < eps) def test_affine_transform09(self): "affine transform 9" data = numpy.array([[4, 1, 3, 2], [7, 6, 8, 5], [3, 5, 3, 6]]) for order in range(0, 6): if (order > 1): filtered = ndimage.spline_filter(data, order=order) else: filtered = data out = ndimage.affine_transform(filtered,[[1, 0], [0, 1]], [-1, -1], order=order, prefilter = False) self.failUnless(diff(out, [[0, 0, 0, 0], [0, 4, 1, 3], [0, 7, 6, 8]]) < eps) def test_affine_transform10(self): "affine transform 10" data = numpy.ones([2], numpy.float64) for order in range(0, 6): out = ndimage.affine_transform(data, [[0.5]], output_shape = (4,), order=order) self.failUnless(diff(out, [1, 1, 1, 0]) < eps) def test_affine_transform11(self): "affine transform 11" data = [1, 5, 2, 6, 3, 7, 4, 4] for order in range(0, 6): out = ndimage.affine_transform(data, [[2]], 0, (4,), order=order) self.failUnless(diff(out, [1, 2, 3, 4]) < eps) def test_affine_transform12(self): "affine transform 12" data = [1, 2, 3, 4] for order in range(0, 6): out = ndimage.affine_transform(data, [[0.5]], 0, (8,), order=order) self.failUnless(diff(out[::2], [1, 2, 3, 4]) < eps) def test_affine_transform13(self): "affine transform 13" data = [[1, 2, 3, 4], [5, 6, 7, 8], [9.0, 10, 11, 12]] for order in range(0, 6): out = ndimage.affine_transform(data, [[1, 0], [0, 2]], 0, (3, 2), order=order) self.failUnless(diff(out, [[1, 3], [5, 7], [9, 11]]) < eps) def test_affine_transform14(self): "affine transform 14" data = [[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]] for order in range(0, 6): out = ndimage.affine_transform(data, [[2, 0], [0, 1]], 0, (1, 4), order=order) self.failUnless(diff(out, [[1, 2, 3, 4]]) < eps) def test_affine_transform15(self): "affine transform 15" data = [[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]] for order in range(0, 6): out = ndimage.affine_transform(data, [[2, 0], [0, 2]], 0, (1, 2), order=order) self.failUnless(diff(out, [[1, 3]]) < eps) def test_affine_transform16(self): "affine transform 16" data = [[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]] for order in range(0, 6): out = ndimage.affine_transform(data, [[1, 0.0], [0, 0.5]], 0, (3, 8), order=order) self.failUnless(diff(out[..., ::2], data) < eps) def test_affine_transform17(self): "affine transform 17" data = [[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]] for order in range(0, 6): out = ndimage.affine_transform(data, [[0.5, 0], [0, 1]], 0, (6, 4), order=order) self.failUnless(diff(out[::2, ...], data) < eps) def test_affine_transform18(self): "affine transform 18" data = [[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]] for order in range(0, 6): out = ndimage.affine_transform(data, [[0.5, 0], [0, 0.5]], 0, (6, 8), order=order) self.failUnless(diff(out[::2, ::2], data) < eps) def test_affine_transform19(self): "affine transform 19" data = numpy.array([[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]], numpy.float64) for order in range(0, 6): out = ndimage.affine_transform(data, [[0.5, 0], [0, 0.5]], 0, (6, 8), order=order) out = ndimage.affine_transform(out, [[2.0, 0], [0, 2.0]], 0, (3, 4), order=order) self.failUnless(diff(out, data) < eps) def test_affine_transform20(self): "affine transform 20" data = [[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]] for order in range(0, 6): out = ndimage.affine_transform(data, [[0], [2]], 0, (2,), order=order) self.failUnless(diff(out, [1, 3]) < eps) def test_affine_transform21(self): "affine transform 21" data = [[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]] for order in range(0, 6): out = ndimage.affine_transform(data, [[2], [0]], 0, (2,), order=order) self.failUnless(diff(out, [1, 9]) < eps) def test_shift01(self): "shift 1" data = numpy.array([1]) for order in range(0, 6): out = ndimage.shift(data, [1], order=order) self.failUnless(diff(out, [0]) < eps) def test_shift02(self): "shift 2" data = numpy.ones([4]) for order in range(0, 6): out = ndimage.shift(data, [1], order=order) self.failUnless(diff(out, [0, 1, 1, 1]) < eps) def test_shift03(self): "shift 3" data = numpy.ones([4]) for order in range(0, 6): out = ndimage.shift(data, -1, order=order) self.failUnless(diff(out, [1, 1, 1, 0]) < eps) def test_shift04(self): "shift 4" data = numpy.array([4, 1, 3, 2]) for order in range(0, 6): out = ndimage.shift(data, 1, order=order) self.failUnless(diff(out, [0, 4, 1, 3]) < eps) def test_shift05(self): "shift 5" data = numpy.array([[1, 1, 1, 1], [1, 1, 1, 1], [1, 1, 1, 1]]) for order in range(0, 6): out = ndimage.shift(data, [0, 1], order=order) self.failUnless(diff(out, [[0, 1, 1, 1], [0, 1, 1, 1], [0, 1, 1, 1]]) < eps) def test_shift06(self): "shift 6" data = numpy.array([[4, 1, 3, 2], [7, 6, 8, 5], [3, 5, 3, 6]]) for order in range(0, 6): out = ndimage.shift(data, [0, 1], order=order) self.failUnless(diff(out, [[0, 4, 1, 3], [0, 7, 6, 8], [0, 3, 5, 3]]) < eps) def test_shift07(self): "shift 7" data = numpy.array([[4, 1, 3, 2], [7, 6, 8, 5], [3, 5, 3, 6]]) for order in range(0, 6): out = ndimage.shift(data, [1, 0], order=order) self.failUnless(diff(out, [[0, 0, 0, 0], [4, 1, 3, 2], [7, 6, 8, 5]]) < eps) def test_shift08(self): "shift 8" data = numpy.array([[4, 1, 3, 2], [7, 6, 8, 5], [3, 5, 3, 6]]) for order in range(0, 6): out = ndimage.shift(data, [1, 1], order=order) self.failUnless(diff(out, [[0, 0, 0, 0], [0, 4, 1, 3], [0, 7, 6, 8]]) < eps) def test_shift09(self): "shift 9" data = numpy.array([[4, 1, 3, 2], [7, 6, 8, 5], [3, 5, 3, 6]]) for order in range(0, 6): if (order > 1): filtered = ndimage.spline_filter(data, order=order) else: filtered = data out = ndimage.shift(filtered, [1, 1], order=order, prefilter = False) self.failUnless(diff(out, [[0, 0, 0, 0], [0, 4, 1, 3], [0, 7, 6, 8]]) < eps) def test_zoom01(self): "zoom 1" data = numpy.ones([2], numpy.float64) for order in range(0, 6): out = ndimage.zoom(data, 2.0, order=order) self.failUnless(diff(out, [1, 1, 1, 0]) < eps) def test_zoom02(self): "zoom 2" data = [1, 5, 2, 6, 3, 7, 4, 4] for order in range(0, 6): out = ndimage.zoom(data, 0.5, order=order) self.failUnless(diff(out, [1, 2, 3, 4]) < eps) def test_zoom03(self): "zoom 3" data = [1, 2, 3, 4] for order in range(0, 6): out = ndimage.zoom(data, 2, order=order) self.failUnless(diff(out[::2], [1, 2, 3, 4]) < eps) def test_zoom04(self): "zoom 4" data = [[1, 2, 3, 4], [5, 6, 7, 8], [9.0, 10, 11, 12]] for order in range(0, 6): out = ndimage.zoom(data, [1, 0.5], order=order) self.failUnless(diff(out, [[1, 3], [5, 7], [9, 11]]) < eps) def test_zoom05(self): "zoom 5" data = [[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]] for order in range(0, 6): out = ndimage.zoom(data, [0.5, 1], order=order) self.failUnless(diff(out, [[1, 2, 3, 4]]) < eps) def test_zoom06(self): "zoom 6" data = [[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]] for order in range(0, 6): out = ndimage.zoom(data, [0.5, 0.5], order=order) self.failUnless(diff(out, [[1, 3]]) < eps) def test_zoom07(self): "zoom 7" data = [[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]] for order in range(0, 6): out = ndimage.zoom(data, [1, 2], order=order) self.failUnless(diff(out[..., ::2], data) < eps) def test_zoom08(self): "zoom 8" data = [[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]] for order in range(0, 6): out = ndimage.zoom(data, [2, 1], order=order) self.failUnless(diff(out[::2, ...], data) < eps) def test_zoom09(self): "zoom 9" data = [[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]] for order in range(0, 6): out = ndimage.zoom(data, [2, 2], order=order) self.failUnless(diff(out[::2, ::2], data) < eps) def test_zoom10(self): "zoom 10" data = numpy.array([[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]], numpy.float64) for order in range(0, 6): out = ndimage.zoom(data, [2, 2], order=order) out = ndimage.zoom(out, [0.5, 0.5], order=order) self.failUnless(diff(out, data) < eps) def test_zoom_affine01(self): "zoom by affine transformation 1" data = [[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]] for order in range(0, 6): out = ndimage.affine_transform(data, [0.5, 0.5], 0, (6, 8), order=order) self.failUnless(diff(out[::2, ::2], data) < eps) def test_rotate01(self): "rotate 1" data = numpy.array([[0, 0, 0, 0], [0, 1, 1, 0], [0, 0, 0, 0]], dtype = numpy.float64) for order in range(0, 6): out = ndimage.rotate(data, 0) self.failUnless(diff(out, data) < eps) def test_rotate02(self): "rotate 2" data = numpy.array([[0, 0, 0, 0], [0, 1, 0, 0], [0, 0, 0, 0]], dtype = numpy.float64) true = numpy.array([[0, 0, 0], [0, 0, 0], [0, 1, 0], [0, 0, 0]], dtype = numpy.float64) for order in range(0, 6): out = ndimage.rotate(data, 90) self.failUnless(diff(out, true) < eps) def test_rotate03(self): "rotate 3" data = numpy.array([[0, 0, 0, 0, 0], [0, 1, 1, 0, 0], [0, 0, 0, 0, 0]], dtype = numpy.float64) true = numpy.array([[0, 0, 0], [0, 0, 0], [0, 1, 0], [0, 1, 0], [0, 0, 0]], dtype = numpy.float64) for order in range(0, 6): out = ndimage.rotate(data, 90) self.failUnless(diff(out, true) < eps) def test_rotate04(self): "rotate 4" data = numpy.array([[0, 0, 0, 0, 0], [0, 1, 1, 0, 0], [0, 0, 0, 0, 0]], dtype = numpy.float64) true = numpy.array([[0, 0, 0, 0, 0], [0, 0, 1, 0, 0], [0, 0, 1, 0, 0]], dtype = numpy.float64) for order in range(0, 6): out = ndimage.rotate(data, 90, reshape = False) self.failUnless(diff(out, true) < eps) def test_rotate05(self): "rotate 5" data = numpy.empty((4,3,3)) for i in range(3): data[:,:,i] = numpy.array([[0,0,0], [0,1,0], [0,1,0], [0,0,0]], dtype = numpy.float64) true = numpy.array([[0,0,0,0], [0,1,1,0], [0,0,0,0]], dtype = numpy.float64) for order in range(0, 6): out = ndimage.rotate(data, 90) for i in range(3): self.failUnless(diff(out[:,:,i], true) < eps) def test_rotate06(self): "rotate 6" data = numpy.empty((3,4,3)) for i in range(3): data[:,:,i] = numpy.array([[0,0,0,0], [0,1,1,0], [0,0,0,0]], dtype = numpy.float64) true = numpy.array([[0,0,0], [0,1,0], [0,1,0], [0,0,0]], dtype = numpy.float64) for order in range(0, 6): out = ndimage.rotate(data, 90) for i in range(3): self.failUnless(diff(out[:,:,i], true) < eps) def test_rotate07(self): "rotate 7" data = numpy.array([[[0, 0, 0, 0, 0], [0, 1, 1, 0, 0], [0, 0, 0, 0, 0]]] * 2, dtype = numpy.float64) data = data.transpose() true = numpy.array([[[0, 0, 0], [0, 1, 0], [0, 1, 0], [0, 0, 0], [0, 0, 0]]] * 2, dtype = numpy.float64) true = true.transpose([2,1,0]) for order in range(0, 6): out = ndimage.rotate(data, 90, axes = (0, 1)) self.failUnless(diff(out, true) < eps) def test_rotate08(self): "rotate 8" data = numpy.array([[[0, 0, 0, 0, 0], [0, 1, 1, 0, 0], [0, 0, 0, 0, 0]]] * 2, dtype = numpy.float64) data = data.transpose() true = numpy.array([[[0, 0, 1, 0, 0], [0, 0, 1, 0, 0], [0, 0, 0, 0, 0]]] * 2, dtype = numpy.float64) true = true.transpose() for order in range(0, 6): out = ndimage.rotate(data, 90, axes = (0, 1), reshape = False) self.failUnless(diff(out, true) < eps) def test_watershed_ift01(self): "watershed_ift 1" data = numpy.array([[0, 0, 0, 0, 0, 0, 0], [0, 1, 1, 1, 1, 1, 0], [0, 1, 0, 0, 0, 1, 0], [0, 1, 0, 0, 0, 1, 0], [0, 1, 0, 0, 0, 1, 0], [0, 1, 1, 1, 1, 1, 0], [0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0]], numpy.uint8) markers = numpy.array([[ -1, 0, 0, 0, 0, 0, 0], [ 0, 0, 0, 0, 0, 0, 0], [ 0, 0, 0, 0, 0, 0, 0], [ 0, 0, 0, 1, 0, 0, 0], [ 0, 0, 0, 0, 0, 0, 0], [ 0, 0, 0, 0, 0, 0, 0], [ 0, 0, 0, 0, 0, 0, 0], [ 0, 0, 0, 0, 0, 0, 0]], numpy.int8) out = ndimage.watershed_ift(data, markers, structure = [[1,1,1], [1,1,1], [1,1,1]]) error = diff([[-1, -1, -1, -1, -1, -1, -1], [-1, 1, 1, 1, 1, 1, -1], [-1, 1, 1, 1, 1, 1, -1], [-1, 1, 1, 1, 1, 1, -1], [-1, 1, 1, 1, 1, 1, -1], [-1, 1, 1, 1, 1, 1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]], out) self.failUnless(error < eps) def test_watershed_ift02(self): "watershed_ift 2" data = numpy.array([[0, 0, 0, 0, 0, 0, 0], [0, 1, 1, 1, 1, 1, 0], [0, 1, 0, 0, 0, 1, 0], [0, 1, 0, 0, 0, 1, 0], [0, 1, 0, 0, 0, 1, 0], [0, 1, 1, 1, 1, 1, 0], [0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0]], numpy.uint8) markers = numpy.array([[ -1, 0, 0, 0, 0, 0, 0], [ 0, 0, 0, 0, 0, 0, 0], [ 0, 0, 0, 0, 0, 0, 0], [ 0, 0, 0, 1, 0, 0, 0], [ 0, 0, 0, 0, 0, 0, 0], [ 0, 0, 0, 0, 0, 0, 0], [ 0, 0, 0, 0, 0, 0, 0], [ 0, 0, 0, 0, 0, 0, 0]], numpy.int8) out = ndimage.watershed_ift(data, markers) error = diff([[-1, -1, -1, -1, -1, -1, -1], [-1, -1, 1, 1, 1, -1, -1], [-1, 1, 1, 1, 1, 1, -1], [-1, 1, 1, 1, 1, 1, -1], [-1, 1, 1, 1, 1, 1, -1], [-1, -1, 1, 1, 1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]], out) self.failUnless(error < eps) def test_watershed_ift03(self): "watershed_ift 3" data = numpy.array([[0, 0, 0, 0, 0, 0, 0], [0, 1, 1, 1, 1, 1, 0], [0, 1, 0, 1, 0, 1, 0], [0, 1, 0, 1, 0, 1, 0], [0, 1, 0, 1, 0, 1, 0], [0, 1, 1, 1, 1, 1, 0], [0, 0, 0, 0, 0, 0, 0]], numpy.uint8) markers = numpy.array([[ 0, 0, 0, 0, 0, 0, 0], [ 0, 0, 0, 0, 0, 0, 0], [ 0, 0, 0, 0, 0, 0, 0], [ 0, 0, 2, 0, 3, 0, 0], [ 0, 0, 0, 0, 0, 0, 0], [ 0, 0, 0, 0, 0, 0, 0], [ 0, 0, 0, 0, 0, 0, -1]], numpy.int8) out = ndimage.watershed_ift(data, markers) error = diff([[-1, -1, -1, -1, -1, -1, -1], [-1, -1, 2, -1, 3, -1, -1], [-1, 2, 2, 3, 3, 3, -1], [-1, 2, 2, 3, 3, 3, -1], [-1, 2, 2, 3, 3, 3, -1], [-1, -1, 2, -1, 3, -1, -1], [-1, -1, -1, -1, -1, -1, -1]], out) self.failUnless(error < eps) def test_watershed_ift04(self): "watershed_ift 4" data = numpy.array([[0, 0, 0, 0, 0, 0, 0], [0, 1, 1, 1, 1, 1, 0], [0, 1, 0, 1, 0, 1, 0], [0, 1, 0, 1, 0, 1, 0], [0, 1, 0, 1, 0, 1, 0], [0, 1, 1, 1, 1, 1, 0], [0, 0, 0, 0, 0, 0, 0]], numpy.uint8) markers = numpy.array([[ 0, 0, 0, 0, 0, 0, 0], [ 0, 0, 0, 0, 0, 0, 0], [ 0, 0, 0, 0, 0, 0, 0], [ 0, 0, 2, 0, 3, 0, 0], [ 0, 0, 0, 0, 0, 0, 0], [ 0, 0, 0, 0, 0, 0, 0], [ 0, 0, 0, 0, 0, 0, -1]], numpy.int8) out = ndimage.watershed_ift(data, markers, structure = [[1,1,1], [1,1,1], [1,1,1]]) error = diff([[-1, -1, -1, -1, -1, -1, -1], [-1, 2, 2, 3, 3, 3, -1], [-1, 2, 2, 3, 3, 3, -1], [-1, 2, 2, 3, 3, 3, -1], [-1, 2, 2, 3, 3, 3, -1], [-1, 2, 2, 3, 3, 3, -1], [-1, -1, -1, -1, -1, -1, -1]], out) self.failUnless(error < eps) def test_watershed_ift05(self): "watershed_ift 5" data = numpy.array([[0, 0, 0, 0, 0, 0, 0], [0, 1, 1, 1, 1, 1, 0], [0, 1, 0, 1, 0, 1, 0], [0, 1, 0, 1, 0, 1, 0], [0, 1, 0, 1, 0, 1, 0], [0, 1, 1, 1, 1, 1, 0], [0, 0, 0, 0, 0, 0, 0]], numpy.uint8) markers = numpy.array([[ 0, 0, 0, 0, 0, 0, 0], [ 0, 0, 0, 0, 0, 0, 0], [ 0, 0, 0, 0, 0, 0, 0], [ 0, 0, 3, 0, 2, 0, 0], [ 0, 0, 0, 0, 0, 0, 0], [ 0, 0, 0, 0, 0, 0, 0], [ 0, 0, 0, 0, 0, 0, -1]], numpy.int8) out = ndimage.watershed_ift(data, markers, structure = [[1,1,1], [1,1,1], [1,1,1]]) error = diff([[-1, -1, -1, -1, -1, -1, -1], [-1, 3, 3, 2, 2, 2, -1], [-1, 3, 3, 2, 2, 2, -1], [-1, 3, 3, 2, 2, 2, -1], [-1, 3, 3, 2, 2, 2, -1], [-1, 3, 3, 2, 2, 2, -1], [-1, -1, -1, -1, -1, -1, -1]], out) self.failUnless(error < eps) def test_watershed_ift06(self): "watershed_ift 6" data = numpy.array([[0, 1, 0, 0, 0, 1, 0], [0, 1, 0, 0, 0, 1, 0], [0, 1, 0, 0, 0, 1, 0], [0, 1, 1, 1, 1, 1, 0], [0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0]], numpy.uint8) markers = numpy.array([[ -1, 0, 0, 0, 0, 0, 0], [ 0, 0, 0, 1, 0, 0, 0], [ 0, 0, 0, 0, 0, 0, 0], [ 0, 0, 0, 0, 0, 0, 0], [ 0, 0, 0, 0, 0, 0, 0], [ 0, 0, 0, 0, 0, 0, 0]], numpy.int8) out = ndimage.watershed_ift(data, markers, structure = [[1,1,1], [1,1,1], [1,1,1]]) error = diff([[-1, 1, 1, 1, 1, 1, -1], [-1, 1, 1, 1, 1, 1, -1], [-1, 1, 1, 1, 1, 1, -1], [-1, 1, 1, 1, 1, 1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]], out) self.failUnless(error < eps) def test_watershed_ift07(self): "watershed_ift 7" shape = (7, 6) data = numpy.zeros(shape, dtype = numpy.uint8) data = data.transpose() data[...] = numpy.array([[0, 1, 0, 0, 0, 1, 0], [0, 1, 0, 0, 0, 1, 0], [0, 1, 0, 0, 0, 1, 0], [0, 1, 1, 1, 1, 1, 0], [0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0]], numpy.uint8) markers = numpy.array([[-1, 0, 0, 0, 0, 0, 0], [ 0, 0, 0, 1, 0, 0, 0], [ 0, 0, 0, 0, 0, 0, 0], [ 0, 0, 0, 0, 0, 0, 0], [ 0, 0, 0, 0, 0, 0, 0], [ 0, 0, 0, 0, 0, 0, 0]], numpy.int8) out = numpy.zeros(shape, dtype = numpy.int16) out = out.transpose() ndimage.watershed_ift(data, markers, structure = [[1,1,1], [1,1,1], [1,1,1]], output = out) error = diff([[-1, 1, 1, 1, 1, 1, -1], [-1, 1, 1, 1, 1, 1, -1], [-1, 1, 1, 1, 1, 1, -1], [-1, 1, 1, 1, 1, 1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]], out) self.failUnless(error < eps) def test_label01(self): "label 1" data = numpy.ones([]) out, n = ndimage.label(data) self.failUnless(diff(out, 1) < eps and n == 1) def test_label02(self): "label 2" data = numpy.zeros([]) out, n = ndimage.label(data) self.failUnless(diff(out, 0) < eps and n == 0) def test_label03(self): "label 3" data = numpy.ones([1]) out, n = ndimage.label(data) self.failUnless(diff(out, [1]) < eps and n == 1) def test_label04(self): "label 4" data = numpy.zeros([1]) out, n = ndimage.label(data) self.failUnless(diff(out, [0]) < eps and n == 0) def test_label05(self): "label 5" data = numpy.ones([5]) out, n = ndimage.label(data) self.failUnless(diff(out, [1, 1, 1, 1, 1]) < eps and n == 1) def test_label06(self): "label 6" data = numpy.array([1, 0, 1, 1, 0, 1]) out, n = ndimage.label(data) self.failUnless(diff(out, [1, 0, 2, 2, 0, 3]) < eps and n == 3) def test_label07(self): "label 7" data = numpy.array([[0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0]]) out, n = ndimage.label(data) self.failUnless(diff(out, [[0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0]]) < eps and n == 0) def test_label08(self): "label 8" data = numpy.array([[1, 0, 0, 0, 0, 0], [0, 0, 1, 1, 0, 0], [0, 0, 1, 1, 1, 0], [1, 1, 0, 0, 0, 0], [1, 1, 0, 0, 0, 0], [0, 0, 0, 1, 1, 0]]) out, n = ndimage.label(data) self.failUnless(diff(out, [[1, 0, 0, 0, 0, 0], [0, 0, 2, 2, 0, 0], [0, 0, 2, 2, 2, 0], [3, 3, 0, 0, 0, 0], [3, 3, 0, 0, 0, 0], [0, 0, 0, 4, 4, 0]]) < eps and n == 4) def test_label09(self): "label 9" data = numpy.array([[1, 0, 0, 0, 0, 0], [0, 0, 1, 1, 0, 0], [0, 0, 1, 1, 1, 0], [1, 1, 0, 0, 0, 0], [1, 1, 0, 0, 0, 0], [0, 0, 0, 1, 1, 0]]) struct = ndimage.generate_binary_structure(2, 2) out, n = ndimage.label(data, struct) self.failUnless(diff(out, [[1, 0, 0, 0, 0, 0], [0, 0, 2, 2, 0, 0], [0, 0, 2, 2, 2, 0], [2, 2, 0, 0, 0, 0], [2, 2, 0, 0, 0, 0], [0, 0, 0, 3, 3, 0]]) < eps and n == 3) def test_label10(self): "label 10" data = numpy.array([[0, 0, 0, 0, 0, 0], [0, 1, 1, 0, 1, 0], [0, 1, 1, 1, 1, 0], [0, 0, 0, 0, 0, 0]]) struct = ndimage.generate_binary_structure(2, 2) out, n = ndimage.label(data, struct) self.failUnless(diff(out, [[0, 0, 0, 0, 0, 0], [0, 1, 1, 0, 1, 0], [0, 1, 1, 1, 1, 0], [0, 0, 0, 0, 0, 0]]) < eps and n == 1) def test_label11(self): "label 11" for type in self.types: data = numpy.array([[1, 0, 0, 0, 0, 0], [0, 0, 1, 1, 0, 0], [0, 0, 1, 1, 1, 0], [1, 1, 0, 0, 0, 0], [1, 1, 0, 0, 0, 0], [0, 0, 0, 1, 1, 0]], type) out, n = ndimage.label(data) error = diff(out, [[1, 0, 0, 0, 0, 0], [0, 0, 2, 2, 0, 0], [0, 0, 2, 2, 2, 0], [3, 3, 0, 0, 0, 0], [3, 3, 0, 0, 0, 0], [0, 0, 0, 4, 4, 0]]) self.failUnless(error < eps and n == 4) def test_label12(self): "label 12" for type in self.types: data = numpy.array([[0, 0, 0, 0, 1, 1], [0, 0, 0, 0, 0, 1], [0, 0, 1, 0, 1, 1], [0, 0, 1, 1, 1, 1], [0, 0, 0, 1, 1, 0]], type) out, n = ndimage.label(data) error = diff(out, [[0, 0, 0, 0, 1, 1], [0, 0, 0, 0, 0, 1], [0, 0, 1, 0, 1, 1], [0, 0, 1, 1, 1, 1], [0, 0, 0, 1, 1, 0]]) self.failUnless(error < eps and n == 1) def test_label13(self): "label 13" for type in self.types: data = numpy.array([[1, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1], [1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1], [1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]], type) out, n = ndimage.label(data) error = diff(out, [[1, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1], [1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1], [1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]) self.failUnless(error < eps and n == 1) def test_find_objects01(self): "find_objects 1" data = numpy.ones([], dtype=int) out = ndimage.find_objects(data) self.failUnless(out == [()]) def test_find_objects02(self): "find_objects 2" data = numpy.zeros([], dtype=int) out = ndimage.find_objects(data) self.failUnless(out == []) def test_find_objects03(self): "find_objects 3" data = numpy.ones([1], dtype=int) out = ndimage.find_objects(data) self.failUnless(out == [(slice(0, 1, None),)]) def test_find_objects04(self): "find_objects 4" data = numpy.zeros([1], dtype=int) out = ndimage.find_objects(data) self.failUnless(out == []) def test_find_objects05(self): "find_objects 5" data = numpy.ones([5], dtype=int) out = ndimage.find_objects(data) self.failUnless(out == [(slice(0, 5, None),)]) def test_find_objects06(self): "find_objects 6" data = numpy.array([1, 0, 2, 2, 0, 3]) out = ndimage.find_objects(data) self.failUnless(out == [(slice(0, 1, None),), (slice(2, 4, None),), (slice(5, 6, None),)]) def test_find_objects07(self): "find_objects 7" data = numpy.array([[0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0]]) out = ndimage.find_objects(data) self.failUnless(out == []), def test_find_objects08(self): "find_objects 8" data = numpy.array([[1, 0, 0, 0, 0, 0], [0, 0, 2, 2, 0, 0], [0, 0, 2, 2, 2, 0], [3, 3, 0, 0, 0, 0], [3, 3, 0, 0, 0, 0], [0, 0, 0, 4, 4, 0]]) out = ndimage.find_objects(data) self.failUnless(out == [(slice(0, 1, None), slice(0, 1, None)), (slice(1, 3, None), slice(2, 5, None)), (slice(3, 5, None), slice(0, 2, None)), (slice(5, 6, None), slice(3, 5, None))]) def test_find_objects09(self): "find_objects 9" data = numpy.array([[1, 0, 0, 0, 0, 0], [0, 0, 2, 2, 0, 0], [0, 0, 2, 2, 2, 0], [0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0], [0, 0, 0, 4, 4, 0]]) out = ndimage.find_objects(data) self.failUnless(out == [(slice(0, 1, None), slice(0, 1, None)), (slice(1, 3, None), slice(2, 5, None)), None, (slice(5, 6, None), slice(3, 5, None))]) def test_sum01(self): "sum 1" for type in self.types: input = numpy.array([], type) output = ndimage.sum(input) self.failUnless(output == 0.0) def test_sum02(self): "sum 2" for type in self.types: input = numpy.zeros([0, 4], type) output = ndimage.sum(input) self.failUnless(output == 0.0) def test_sum03(self): "sum 3" for type in self.types: input = numpy.ones([], type) output = ndimage.sum(input) self.failUnless(output == 1.0) def test_sum04(self): "sum 4" for type in self.types: input = numpy.array([1, 2], type) output = ndimage.sum(input) self.failUnless(output == 3.0) def test_sum05(self): "sum 5" for type in self.types: input = numpy.array([[1, 2], [3, 4]], type) output = ndimage.sum(input) self.failUnless(output == 10.0) def test_sum06(self): "sum 6" labels = numpy.array([], bool) for type in self.types: input = numpy.array([], type) output = ndimage.sum(input, labels = labels) self.failUnless(output == 0.0) def test_sum07(self): "sum 7" labels = numpy.ones([0, 4], bool) for type in self.types: input = numpy.zeros([0, 4], type) output = ndimage.sum(input, labels = labels) self.failUnless(output == 0.0) def test_sum08(self): "sum 8" labels = numpy.array([1, 0], bool) for type in self.types: input = numpy.array([1, 2], type) output = ndimage.sum(input, labels = labels) self.failUnless(output == 1.0) def test_sum09(self): "sum 9" labels = numpy.array([1, 0], bool) for type in self.types: input = numpy.array([[1, 2], [3, 4]], type) output = ndimage.sum(input, labels = labels) self.failUnless(output == 4.0) def test_sum10(self): "sum 10" labels = numpy.array([1, 0], bool) input = numpy.array([[1, 2], [3, 4]], bool) output = ndimage.sum(input, labels = labels) self.failUnless(output == 2.0) def test_sum11(self): "sum 11" labels = numpy.array([1, 2], numpy.int8) for type in self.types: input = numpy.array([[1, 2], [3, 4]], type) output = ndimage.sum(input, labels = labels, index = 2) self.failUnless(output == 6.0) def test_sum12(self): "sum 12" labels = numpy.array([[1, 2], [2, 4]], numpy.int8) for type in self.types: input = numpy.array([[1, 2], [3, 4]], type) output = ndimage.sum(input, labels = labels, index = [4, 8, 2]) self.failUnless(output == [4.0, 0.0, 5.0]) def test_sum13(self): "sum 13" input = numpy.array([1,2,3,4]) labels = numpy.array([0,0,0,0]) index = numpy.array([0],numpy.uint64) self.failUnlessRaises(ValueError,ndimage.sum,input,labels,index) def test_mean01(self): "mean 1" labels = numpy.array([1, 0], bool) for type in self.types: input = numpy.array([[1, 2], [3, 4]], type) output = ndimage.mean(input, labels = labels) self.failUnless(output == 2.0) def test_mean02(self): "mean 2" labels = numpy.array([1, 0], bool) input = numpy.array([[1, 2], [3, 4]], bool) output = ndimage.mean(input, labels = labels) self.failUnless(output == 1.0) def test_mean03(self): "mean 3" labels = numpy.array([1, 2]) for type in self.types: input = numpy.array([[1, 2], [3, 4]], type) output = ndimage.mean(input, labels = labels, index = 2) self.failUnless(output == 3.0) def test_mean04(self): "mean 4" labels = numpy.array([[1, 2], [2, 4]], numpy.int8) for type in self.types: input = numpy.array([[1, 2], [3, 4]], type) output = ndimage.mean(input, labels = labels, index = [4, 8, 2]) self.failUnless(output == [4.0, 0.0, 2.5]) def test_minimum01(self): "minimum 1" labels = numpy.array([1, 0], bool) for type in self.types: input = numpy.array([[1, 2], [3, 4]], type) output = ndimage.minimum(input, labels = labels) self.failUnless(output == 1.0) def test_minimum02(self): "minimum 2" labels = numpy.array([1, 0], bool) input = numpy.array([[2, 2], [2, 4]], bool) output = ndimage.minimum(input, labels = labels) self.failUnless(output == 1.0) def test_minimum03(self): "minimum 3" labels = numpy.array([1, 2]) for type in self.types: input = numpy.array([[1, 2], [3, 4]], type) output = ndimage.minimum(input, labels = labels, index = 2) self.failUnless(output == 2.0) def test_minimum04(self): "minimum 4" labels = numpy.array([[1, 2], [2, 3]]) for type in self.types: input = numpy.array([[1, 2], [3, 4]], type) output = ndimage.minimum(input, labels = labels, index = [2, 3, 8]) self.failUnless(output == [2.0, 4.0, 0.0]) def test_maximum01(self): "maximum 1" labels = numpy.array([1, 0], bool) for type in self.types: input = numpy.array([[1, 2], [3, 4]], type) output = ndimage.maximum(input, labels = labels) self.failUnless(output == 3.0) def test_maximum02(self): "maximum 2" labels = numpy.array([1, 0], bool) input = numpy.array([[2, 2], [2, 4]], bool) output = ndimage.maximum(input, labels = labels) self.failUnless(output == 1.0) def test_maximum03(self): "maximum 3" labels = numpy.array([1, 2]) for type in self.types: input = numpy.array([[1, 2], [3, 4]], type) output = ndimage.maximum(input, labels = labels, index = 2) self.failUnless(output == 4.0) def test_maximum04(self): "maximum 4" labels = numpy.array([[1, 2], [2, 3]]) for type in self.types: input = numpy.array([[1, 2], [3, 4]], type) output = ndimage.maximum(input, labels = labels, index = [2, 3, 8]) self.failUnless(output == [3.0, 4.0, 0.0]) def test_variance01(self): "variance 1" for type in self.types: input = numpy.array([], type) output = ndimage.variance(input) self.failUnless(float(output) == 0.0) def test_variance02(self): "variance 2" for type in self.types: input = numpy.array([1], type) output = ndimage.variance(input) self.failUnless(float(output) == 0.0) def test_variance03(self): "variance 3" for type in self.types: input = numpy.array([1, 3], type) output = ndimage.variance(input) self.failUnless(output == 2.0) def test_variance04(self): "variance 4" input = numpy.array([1, 0], bool) output = ndimage.variance(input) self.failUnless(output == 0.5) def test_variance05(self): "variance 5" labels = [2, 2, 3] for type in self.types: input = numpy.array([1, 3, 8], type) output = ndimage.variance(input, labels, 2) self.failUnless(output == 2.0) def test_variance06(self): "variance 6" labels = [2, 2, 3, 3, 4] for type in self.types: input = numpy.array([1, 3, 8, 10, 8], type) output = ndimage.variance(input, labels, [2, 3, 4]) self.failUnless(output == [2.0, 2.0, 0.0]) def test_standard_deviation01(self): "standard deviation 1" for type in self.types: input = numpy.array([], type) output = ndimage.standard_deviation(input) self.failUnless(float(output) == 0.0) def test_standard_deviation02(self): "standard deviation 2" for type in self.types: input = numpy.array([1], type) output = ndimage.standard_deviation(input) self.failUnless(float(output) == 0.0) def test_standard_deviation03(self): "standard deviation 3" for type in self.types: input = numpy.array([1, 3], type) output = ndimage.standard_deviation(input) self.failUnless(output == math.sqrt(2.0)) def test_standard_deviation04(self): "standard deviation 4" input = numpy.array([1, 0], bool) output = ndimage.standard_deviation(input) self.failUnless(output == math.sqrt(0.5)) def test_standard_deviation05(self): "standard deviation 5" labels = [2, 2, 3] for type in self.types: input = numpy.array([1, 3, 8], type) output = ndimage.standard_deviation(input, labels, 2) self.failUnless(output == math.sqrt(2.0)) def test_standard_deviation06(self): "standard deviation 6" labels = [2, 2, 3, 3, 4] for type in self.types: input = numpy.array([1, 3, 8, 10, 8], type) output = ndimage.standard_deviation(input, labels, [2, 3, 4]) self.failUnless(output == [math.sqrt(2.0), math.sqrt(2.0), 0.0]) def test_minimum_position01(self): "minimum position 1" labels = numpy.array([1, 0], bool) for type in self.types: input = numpy.array([[1, 2], [3, 4]], type) output = ndimage.minimum_position(input, labels = labels) self.failUnless(output == (0, 0)) def test_minimum_position02(self): "minimum position 2" for type in self.types: input = numpy.array([[5, 4, 2, 5], [3, 7, 0, 2], [1, 5, 1, 1]], type) output = ndimage.minimum_position(input) self.failUnless(output == (1, 2)) def test_minimum_position03(self): "minimum position 3" input = numpy.array([[5, 4, 2, 5], [3, 7, 0, 2], [1, 5, 1, 1]], bool) output = ndimage.minimum_position(input) self.failUnless(output == (1, 2)) def test_minimum_position04(self): "minimum position 4" input = numpy.array([[5, 4, 2, 5], [3, 7, 1, 2], [1, 5, 1, 1]], bool) output = ndimage.minimum_position(input) self.failUnless(output == (0, 0)) def test_minimum_position05(self): "minimum position 5" labels = [1, 2, 0, 4] for type in self.types: input = numpy.array([[5, 4, 2, 5], [3, 7, 0, 2], [1, 5, 2, 3]], type) output = ndimage.minimum_position(input, labels) self.failUnless(output == (2, 0)) def test_minimum_position06(self): "minimum position 6" labels = [1, 2, 3, 4] for type in self.types: input = numpy.array([[5, 4, 2, 5], [3, 7, 0, 2], [1, 5, 1, 1]], type) output = ndimage.minimum_position(input, labels, 2) self.failUnless(output == (0, 1)) def test_minimum_position07(self): "minimum position 7" labels = [1, 2, 3, 4] for type in self.types: input = numpy.array([[5, 4, 2, 5], [3, 7, 0, 2], [1, 5, 1, 1]], type) output = ndimage.minimum_position(input, labels, [2, 3]) self.failUnless(output == [(0, 1), (1, 2)]) def test_maximum_position01(self): "maximum position 1" labels = numpy.array([1, 0], bool) for type in self.types: input = numpy.array([[1, 2], [3, 4]], type) output = ndimage.maximum_position(input, labels = labels) self.failUnless(output == (1, 0)) def test_maximum_position02(self): "maximum position 2" for type in self.types: input = numpy.array([[5, 4, 2, 5], [3, 7, 8, 2], [1, 5, 1, 1]], type) output = ndimage.maximum_position(input) self.failUnless(output == (1, 2)) def test_maximum_position03(self): "maximum position 3" input = numpy.array([[5, 4, 2, 5], [3, 7, 8, 2], [1, 5, 1, 1]], bool) output = ndimage.maximum_position(input) self.failUnless(output == (0, 0)) def test_maximum_position04(self): "maximum position 4" labels = [1, 2, 0, 4] for type in self.types: input = numpy.array([[5, 4, 2, 5], [3, 7, 8, 2], [1, 5, 1, 1]], type) output = ndimage.maximum_position(input, labels) self.failUnless(output == (1, 1)) def test_maximum_position05(self): "maximum position 5" labels = [1, 2, 0, 4] for type in self.types: input = numpy.array([[5, 4, 2, 5], [3, 7, 8, 2], [1, 5, 1, 1]], type) output = ndimage.maximum_position(input, labels, 1) self.failUnless(output == (0, 0)) def test_maximum_position06(self): "maximum position 6" labels = [1, 2, 0, 4] for type in self.types: input = numpy.array([[5, 4, 2, 5], [3, 7, 8, 2], [1, 5, 1, 1]], type) output = ndimage.maximum_position(input, labels, [1, 2]) self.failUnless(output == [(0, 0), (1, 1)]) def test_extrema01(self): "extrema 1" labels = numpy.array([1, 0], bool) for type in self.types: input = numpy.array([[1, 2], [3, 4]], type) output1 = ndimage.extrema(input, labels = labels) output2 = ndimage.minimum(input, labels = labels) output3 = ndimage.maximum(input, labels = labels) output4 = ndimage.minimum_position(input, labels = labels) output5 = ndimage.maximum_position(input, labels = labels) self.failUnless(output1 == (output2, output3, output4, output5)) def test_extrema02(self): "extrema 2" labels = numpy.array([1, 2]) for type in self.types: input = numpy.array([[1, 2], [3, 4]], type) output1 = ndimage.extrema(input, labels = labels, index = 2) output2 = ndimage.minimum(input, labels = labels, index = 2) output3 = ndimage.maximum(input, labels = labels, index = 2) output4 = ndimage.minimum_position(input, labels = labels, index = 2) output5 = ndimage.maximum_position(input, labels = labels, index = 2) self.failUnless(output1 == (output2, output3, output4, output5)) def test_extrema03(self): "extrema 3" labels = numpy.array([[1, 2], [2, 3]]) for type in self.types: input = numpy.array([[1, 2], [3, 4]], type) output1 = ndimage.extrema(input, labels = labels, index = [2, 3, 8]) output2 = ndimage.minimum(input, labels = labels, index = [2, 3, 8]) output3 = ndimage.maximum(input, labels = labels, index = [2, 3, 8]) output4 = ndimage.minimum_position(input, labels = labels, index = [2, 3, 8]) output5 = ndimage.maximum_position(input, labels = labels, index = [2, 3, 8]) self.failUnless(output1 == (output2, output3, output4, output5)) def test_extrema04(self): "extrema 4" labels = [1, 2, 0, 4] for type in self.types: input = numpy.array([[5, 4, 2, 5], [3, 7, 8, 2], [1, 5, 1, 1]], type) output1 = ndimage.extrema(input, labels, [1, 2]) output2 = ndimage.minimum(input, labels, [1, 2]) output3 = ndimage.maximum(input, labels, [1, 2]) output4 = ndimage.minimum_position(input, labels, [1, 2]) output5 = ndimage.maximum_position(input, labels, [1, 2]) self.failUnless(output1 == (output2, output3, output4, output5)) def test_center_of_mass01(self): "center of mass 1" true = [0.0, 0.0] for type in self.types: input = numpy.array([[1, 0], [0, 0]], type) output = ndimage.center_of_mass(input) e = diff(true, output) self.failUnless(e < eps) def test_center_of_mass02(self): "center of mass 2" true = [1, 0] for type in self.types: input = numpy.array([[0, 0], [1, 0]], type) output = ndimage.center_of_mass(input) e = diff(true, output) self.failUnless(e < eps) def test_center_of_mass03(self): "center of mass 3" true = [0, 1] for type in self.types: input = numpy.array([[0, 1], [0, 0]], type) output = ndimage.center_of_mass(input) e = diff(true, output) self.failUnless(e < eps) def test_center_of_mass04(self): "center of mass 4" true = [1, 1] for type in self.types: input = numpy.array([[0, 0], [0, 1]], type) output = ndimage.center_of_mass(input) e = diff(true, output) self.failUnless(e < eps) def test_center_of_mass05(self): "center of mass 5" true = [0.5, 0.5] for type in self.types: input = numpy.array([[1, 1], [1, 1]], type) output = ndimage.center_of_mass(input) e = diff(true, output) self.failUnless(e < eps) def test_center_of_mass06(self): "center of mass 6" true = [0.5, 0.5] input = numpy.array([[1, 2], [3, 1]], bool) output = ndimage.center_of_mass(input) e = diff(true, output) self.failUnless(e < eps) def test_center_of_mass07(self): "center of mass 7" labels = [1, 0] true = [0.5, 0.0] input = numpy.array([[1, 2], [3, 1]], bool) output = ndimage.center_of_mass(input, labels) e = diff(true, output) self.failUnless(e < eps) def test_center_of_mass08(self): "center of mass 8" labels = [1, 2] true = [0.5, 1.0] input = numpy.array([[5, 2], [3, 1]], bool) output = ndimage.center_of_mass(input, labels, 2) e = diff(true, output) self.failUnless(e < eps) def test_center_of_mass09(self): "center of mass 9" labels = [1, 2] true = [(0.5, 0.0), (0.5, 1.0)] input = numpy.array([[1, 2], [1, 1]], bool) output = ndimage.center_of_mass(input, labels, [1, 2]) e = diff(true, output) self.failUnless(e < eps) def test_histogram01(self): "histogram 1" true = numpy.ones(10) input = numpy.arange(10) output = ndimage.histogram(input, 0, 10, 10) e = diff(true, output) self.failUnless(e < eps) def test_histogram02(self): "histogram 2" labels = [1, 1, 1, 1, 2, 2, 2, 2] true = [0, 2, 0, 1, 0] input = numpy.array([1, 1, 3, 4, 3, 3, 3, 3]) output = ndimage.histogram(input, 0, 4, 5, labels, 1) e = diff(true, output) self.failUnless(e < eps) def test_histogram03(self): "histogram 3" labels = [1, 0, 1, 1, 2, 2, 2, 2] true1 = [0, 1, 0, 1, 0] true2 = [0, 0, 0, 3, 0] input = numpy.array([1, 1, 3, 4, 3, 5, 3, 3]) output = ndimage.histogram(input, 0, 4, 5, labels, (1,2)) e1 = diff(true1, output[0]) e2 = diff(true2, output[1]) self.failUnless(e1 < eps and e2 < eps) def test_distance_transform_bf01(self): "brute force distance transform 1" for type in self.types: data = numpy.array([[0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 1, 1, 0, 0, 0], [0, 0, 1, 1, 1, 1, 1, 0, 0], [0, 0, 1, 1, 1, 1, 1, 0, 0], [0, 0, 1, 1, 1, 1, 1, 0, 0], [0, 0, 0, 1, 1, 1, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0]], type) out, ft = ndimage.distance_transform_bf(data, 'euclidean', return_indices = True) error1 = diff([[0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 1, 1, 0, 0, 0], [0, 0, 1, 2, 4, 2, 1, 0, 0], [0, 0, 1, 4, 8, 4, 1, 0, 0], [0, 0, 1, 2, 4, 2, 1, 0, 0], [0, 0, 0, 1, 1, 1, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0]], out * out) error2 = diff([[[0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1], [2, 2, 2, 2, 1, 2, 2, 2, 2], [3, 3, 3, 2, 1, 2, 3, 3, 3], [4, 4, 4, 4, 6, 4, 4, 4, 4], [5, 5, 6, 6, 7, 6, 6, 5, 5], [6, 6, 6, 7, 7, 7, 6, 6, 6], [7, 7, 7, 7, 7, 7, 7, 7, 7], [8, 8, 8, 8, 8, 8, 8, 8, 8]], [[0, 1, 2, 3, 4, 5, 6, 7, 8], [0, 1, 2, 3, 4, 5, 6, 7, 8], [0, 1, 2, 2, 4, 6, 6, 7, 8], [0, 1, 1, 2, 4, 6, 7, 7, 8], [0, 1, 1, 1, 6, 7, 7, 7, 8], [0, 1, 2, 2, 4, 6, 6, 7, 8], [0, 1, 2, 3, 4, 5, 6, 7, 8], [0, 1, 2, 3, 4, 5, 6, 7, 8], [0, 1, 2, 3, 4, 5, 6, 7, 8]]], ft) self.failUnless(error1 < eps and error2 < eps) def test_distance_transform_bf02(self): "brute force distance transform 2" for type in self.types: data = numpy.array([[0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 1, 1, 0, 0, 0], [0, 0, 1, 1, 1, 1, 1, 0, 0], [0, 0, 1, 1, 1, 1, 1, 0, 0], [0, 0, 1, 1, 1, 1, 1, 0, 0], [0, 0, 0, 1, 1, 1, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0]], type) out, ft = ndimage.distance_transform_bf(data, 'cityblock', return_indices = True) error1 = diff([[0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 1, 1, 0, 0, 0], [0, 0, 1, 2, 2, 2, 1, 0, 0], [0, 0, 1, 2, 3, 2, 1, 0, 0], [0, 0, 1, 2, 2, 2, 1, 0, 0], [0, 0, 0, 1, 1, 1, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0]], out) error2 = diff([[[0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1], [2, 2, 2, 2, 1, 2, 2, 2, 2], [3, 3, 3, 3, 1, 3, 3, 3, 3], [4, 4, 4, 4, 7, 4, 4, 4, 4], [5, 5, 6, 7, 7, 7, 6, 5, 5], [6, 6, 6, 7, 7, 7, 6, 6, 6], [7, 7, 7, 7, 7, 7, 7, 7, 7], [8, 8, 8, 8, 8, 8, 8, 8, 8]], [[0, 1, 2, 3, 4, 5, 6, 7, 8], [0, 1, 2, 3, 4, 5, 6, 7, 8], [0, 1, 2, 2, 4, 6, 6, 7, 8], [0, 1, 1, 1, 4, 7, 7, 7, 8], [0, 1, 1, 1, 4, 7, 7, 7, 8], [0, 1, 2, 3, 4, 5, 6, 7, 8], [0, 1, 2, 3, 4, 5, 6, 7, 8], [0, 1, 2, 3, 4, 5, 6, 7, 8], [0, 1, 2, 3, 4, 5, 6, 7, 8]]], ft) self.failUnless(error1 < eps and error2 < eps) def test_distance_transform_bf03(self): "brute force distance transform 3" for type in self.types: data = numpy.array([[0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 1, 1, 0, 0, 0], [0, 0, 1, 1, 1, 1, 1, 0, 0], [0, 0, 1, 1, 1, 1, 1, 0, 0], [0, 0, 1, 1, 1, 1, 1, 0, 0], [0, 0, 0, 1, 1, 1, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0]], type) out, ft = ndimage.distance_transform_bf(data, 'chessboard', return_indices = True) error1 = diff([[0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 1, 1, 0, 0, 0], [0, 0, 1, 1, 2, 1, 1, 0, 0], [0, 0, 1, 2, 2, 2, 1, 0, 0], [0, 0, 1, 1, 2, 1, 1, 0, 0], [0, 0, 0, 1, 1, 1, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0]], out) error2 = diff([[[0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1], [2, 2, 2, 2, 1, 2, 2, 2, 2], [3, 3, 4, 2, 2, 2, 4, 3, 3], [4, 4, 5, 6, 6, 6, 5, 4, 4], [5, 5, 6, 6, 7, 6, 6, 5, 5], [6, 6, 6, 7, 7, 7, 6, 6, 6], [7, 7, 7, 7, 7, 7, 7, 7, 7], [8, 8, 8, 8, 8, 8, 8, 8, 8]], [[0, 1, 2, 3, 4, 5, 6, 7, 8], [0, 1, 2, 3, 4, 5, 6, 7, 8], [0, 1, 2, 2, 5, 6, 6, 7, 8], [0, 1, 1, 2, 6, 6, 7, 7, 8], [0, 1, 1, 2, 6, 7, 7, 7, 8], [0, 1, 2, 2, 6, 6, 7, 7, 8], [0, 1, 2, 4, 5, 6, 6, 7, 8], [0, 1, 2, 3, 4, 5, 6, 7, 8], [0, 1, 2, 3, 4, 5, 6, 7, 8]]], ft) self.failUnless(error1 < eps and error2 < eps) def test_distance_transform_bf04(self): "brute force distance transform 4" for type in self.types: data = numpy.array([[0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 1, 1, 0, 0, 0], [0, 0, 1, 1, 1, 1, 1, 0, 0], [0, 0, 1, 1, 1, 1, 1, 0, 0], [0, 0, 1, 1, 1, 1, 1, 0, 0], [0, 0, 0, 1, 1, 1, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0]], type) tdt, tft = ndimage.distance_transform_bf(data, return_indices = 1) dts = [] fts = [] dt = numpy.zeros(data.shape, dtype = numpy.float64) ndimage.distance_transform_bf(data, distances = dt) dts.append(dt) ft = ndimage.distance_transform_bf(data, return_distances = False, return_indices = 1) fts.append(ft) ft = numpy.indices(data.shape, dtype = numpy.int32) ndimage.distance_transform_bf(data, return_distances = False, return_indices = True, indices = ft) fts.append(ft) dt, ft = ndimage.distance_transform_bf(data, return_indices = 1) dts.append(dt) fts.append(ft) dt = numpy.zeros(data.shape, dtype = numpy.float64) ft = ndimage.distance_transform_bf(data, distances = dt, return_indices = True) dts.append(dt) fts.append(ft) ft = numpy.indices(data.shape, dtype = numpy.int32) dt = ndimage.distance_transform_bf(data, return_indices = True, indices = ft) dts.append(dt) fts.append(ft) dt = numpy.zeros(data.shape, dtype = numpy.float64) ft = numpy.indices(data.shape, dtype = numpy.int32) ndimage.distance_transform_bf(data, distances = dt, return_indices = True, indices = ft) dts.append(dt) fts.append(ft) for dt in dts: self.failUnless(diff(tdt, dt) < eps) for ft in fts: self.failUnless(diff(tft, ft) < eps) def test_distance_transform_bf05(self): "brute force distance transform 5" for type in self.types: data = numpy.array([[0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 1, 1, 0, 0, 0], [0, 0, 1, 1, 1, 1, 1, 0, 0], [0, 0, 1, 1, 1, 1, 1, 0, 0], [0, 0, 1, 1, 1, 1, 1, 0, 0], [0, 0, 0, 1, 1, 1, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0]], type) out, ft = ndimage.distance_transform_bf(data, 'euclidean', return_indices = True, sampling = [2, 2]) error1 = diff([[0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 4, 4, 4, 0, 0, 0], [0, 0, 4, 8, 16, 8, 4, 0, 0], [0, 0, 4, 16, 32, 16, 4, 0, 0], [0, 0, 4, 8, 16, 8, 4, 0, 0], [0, 0, 0, 4, 4, 4, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0]], out * out) error2 = diff([[[0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1], [2, 2, 2, 2, 1, 2, 2, 2, 2], [3, 3, 3, 2, 1, 2, 3, 3, 3], [4, 4, 4, 4, 6, 4, 4, 4, 4], [5, 5, 6, 6, 7, 6, 6, 5, 5], [6, 6, 6, 7, 7, 7, 6, 6, 6], [7, 7, 7, 7, 7, 7, 7, 7, 7], [8, 8, 8, 8, 8, 8, 8, 8, 8]], [[0, 1, 2, 3, 4, 5, 6, 7, 8], [0, 1, 2, 3, 4, 5, 6, 7, 8], [0, 1, 2, 2, 4, 6, 6, 7, 8], [0, 1, 1, 2, 4, 6, 7, 7, 8], [0, 1, 1, 1, 6, 7, 7, 7, 8], [0, 1, 2, 2, 4, 6, 6, 7, 8], [0, 1, 2, 3, 4, 5, 6, 7, 8], [0, 1, 2, 3, 4, 5, 6, 7, 8], [0, 1, 2, 3, 4, 5, 6, 7, 8]]], ft) self.failUnless(error1 < eps and error2 < eps) def test_distance_transform_bf06(self): "brute force distance transform 6" for type in self.types: data = numpy.array([[0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 1, 1, 0, 0, 0], [0, 0, 1, 1, 1, 1, 1, 0, 0], [0, 0, 1, 1, 1, 1, 1, 0, 0], [0, 0, 1, 1, 1, 1, 1, 0, 0], [0, 0, 0, 1, 1, 1, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0]], type) out, ft = ndimage.distance_transform_bf(data, 'euclidean', return_indices = True, sampling = [2, 1]) error1 = diff([[0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 4, 1, 0, 0, 0], [0, 0, 1, 4, 8, 4, 1, 0, 0], [0, 0, 1, 4, 9, 4, 1, 0, 0], [0, 0, 1, 4, 8, 4, 1, 0, 0], [0, 0, 0, 1, 4, 1, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0]], out * out) error2 = diff([[[0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1], [2, 2, 2, 2, 2, 2, 2, 2, 2], [3, 3, 3, 3, 2, 3, 3, 3, 3], [4, 4, 4, 4, 4, 4, 4, 4, 4], [5, 5, 5, 5, 6, 5, 5, 5, 5], [6, 6, 6, 6, 7, 6, 6, 6, 6], [7, 7, 7, 7, 7, 7, 7, 7, 7], [8, 8, 8, 8, 8, 8, 8, 8, 8]], [[0, 1, 2, 3, 4, 5, 6, 7, 8], [0, 1, 2, 3, 4, 5, 6, 7, 8], [0, 1, 2, 2, 6, 6, 6, 7, 8], [0, 1, 1, 1, 6, 7, 7, 7, 8], [0, 1, 1, 1, 7, 7, 7, 7, 8], [0, 1, 1, 1, 6, 7, 7, 7, 8], [0, 1, 2, 2, 4, 6, 6, 7, 8], [0, 1, 2, 3, 4, 5, 6, 7, 8], [0, 1, 2, 3, 4, 5, 6, 7, 8]]], ft) self.failUnless(error1 < eps and error2 < eps) self.failUnless(error1 < eps and error2 < eps) def test_distance_transform_cdt01(self): "chamfer type distance transform 1" for type in self.types: data = numpy.array([[0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 1, 1, 0, 0, 0], [0, 0, 1, 1, 1, 1, 1, 0, 0], [0, 0, 1, 1, 1, 1, 1, 0, 0], [0, 0, 1, 1, 1, 1, 1, 0, 0], [0, 0, 0, 1, 1, 1, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0]], type) out, ft = ndimage.distance_transform_cdt(data, 'cityblock', return_indices = True) bf = ndimage.distance_transform_bf(data, 'cityblock') error1 = diff(bf, out) error2 = diff([[[0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1], [2, 2, 2, 1, 1, 1, 2, 2, 2], [3, 3, 2, 1, 1, 1, 2, 3, 3], [4, 4, 4, 4, 1, 4, 4, 4, 4], [5, 5, 5, 5, 7, 7, 6, 5, 5], [6, 6, 6, 6, 7, 7, 6, 6, 6], [7, 7, 7, 7, 7, 7, 7, 7, 7], [8, 8, 8, 8, 8, 8, 8, 8, 8]], [[0, 1, 2, 3, 4, 5, 6, 7, 8], [0, 1, 2, 3, 4, 5, 6, 7, 8], [0, 1, 2, 3, 4, 5, 6, 7, 8], [0, 1, 2, 3, 4, 5, 6, 7, 8], [0, 1, 1, 1, 4, 7, 7, 7, 8], [0, 1, 1, 1, 4, 5, 6, 7, 8], [0, 1, 2, 2, 4, 5, 6, 7, 8], [0, 1, 2, 3, 4, 5, 6, 7, 8], [0, 1, 2, 3, 4, 5, 6, 7, 8],]], ft) self.failUnless(error1 < eps and error2 < eps) def test_distance_transform_cdt02(self): "chamfer type distance transform 2" for type in self.types: data = numpy.array([[0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 1, 1, 0, 0, 0], [0, 0, 1, 1, 1, 1, 1, 0, 0], [0, 0, 1, 1, 1, 1, 1, 0, 0], [0, 0, 1, 1, 1, 1, 1, 0, 0], [0, 0, 0, 1, 1, 1, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0]], type) out, ft = ndimage.distance_transform_cdt(data, 'chessboard', return_indices = True) bf = ndimage.distance_transform_bf(data, 'chessboard') error1 = diff(bf, out) error2 = diff([[[0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1], [2, 2, 2, 1, 1, 1, 2, 2, 2], [3, 3, 2, 2, 1, 2, 2, 3, 3], [4, 4, 3, 2, 2, 2, 3, 4, 4], [5, 5, 4, 6, 7, 6, 4, 5, 5], [6, 6, 6, 6, 7, 7, 6, 6, 6], [7, 7, 7, 7, 7, 7, 7, 7, 7], [8, 8, 8, 8, 8, 8, 8, 8, 8]], [[0, 1, 2, 3, 4, 5, 6, 7, 8], [0, 1, 2, 3, 4, 5, 6, 7, 8], [0, 1, 2, 2, 3, 4, 6, 7, 8], [0, 1, 1, 2, 2, 6, 6, 7, 8], [0, 1, 1, 1, 2, 6, 7, 7, 8], [0, 1, 1, 2, 6, 6, 7, 7, 8], [0, 1, 2, 2, 5, 6, 6, 7, 8], [0, 1, 2, 3, 4, 5, 6, 7, 8], [0, 1, 2, 3, 4, 5, 6, 7, 8],]], ft) self.failUnless(error1 < eps and error2 < eps) def test_distance_transform_cdt03(self): "chamfer type distance transform 3" for type in self.types: data = numpy.array([[0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 1, 1, 0, 0, 0], [0, 0, 1, 1, 1, 1, 1, 0, 0], [0, 0, 1, 1, 1, 1, 1, 0, 0], [0, 0, 1, 1, 1, 1, 1, 0, 0], [0, 0, 0, 1, 1, 1, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0]], type) tdt, tft = ndimage.distance_transform_cdt(data, return_indices = True) dts = [] fts = [] dt = numpy.zeros(data.shape, dtype = numpy.int32) ndimage.distance_transform_cdt(data, distances = dt) dts.append(dt) ft = ndimage.distance_transform_cdt(data, return_distances = False, return_indices = True) fts.append(ft) ft = numpy.indices(data.shape, dtype = numpy.int32) ndimage.distance_transform_cdt(data, return_distances = False, return_indices = True, indices = ft) fts.append(ft) dt, ft = ndimage.distance_transform_cdt(data, return_indices = True) dts.append(dt) fts.append(ft) dt = numpy.zeros(data.shape, dtype = numpy.int32) ft = ndimage.distance_transform_cdt(data, distances = dt, return_indices = True) dts.append(dt) fts.append(ft) ft = numpy.indices(data.shape, dtype = numpy.int32) dt = ndimage.distance_transform_cdt(data, return_indices = True, indices = ft) dts.append(dt) fts.append(ft) dt = numpy.zeros(data.shape, dtype = numpy.int32) ft = numpy.indices(data.shape, dtype = numpy.int32) ndimage.distance_transform_cdt(data, distances = dt, return_indices = True, indices = ft) dts.append(dt) fts.append(ft) for dt in dts: self.failUnless(diff(tdt, dt) < eps) for ft in fts: self.failUnless(diff(tft, ft) < eps) def test_distance_transform_edt01(self): "euclidean distance transform 1" for type in self.types: data = numpy.array([[0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 1, 1, 0, 0, 0], [0, 0, 1, 1, 1, 1, 1, 0, 0], [0, 0, 1, 1, 1, 1, 1, 0, 0], [0, 0, 1, 1, 1, 1, 1, 0, 0], [0, 0, 0, 1, 1, 1, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0]], type) out, ft = ndimage.distance_transform_edt(data, return_indices = True) bf = ndimage.distance_transform_bf(data, 'euclidean') error1 = diff(bf, out) dt = ft - numpy.indices(ft.shape[1:], dtype = ft.dtype) dt = dt.astype(numpy.float64) numpy.multiply(dt, dt, dt) dt = numpy.add.reduce(dt, axis = 0) numpy.sqrt(dt, dt) error2 = diff(bf, dt) self.failUnless(error1 < eps and error2 < eps) def test_distance_transform_edt02(self): "euclidean distance transform 2" for type in self.types: data = numpy.array([[0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 1, 1, 0, 0, 0], [0, 0, 1, 1, 1, 1, 1, 0, 0], [0, 0, 1, 1, 1, 1, 1, 0, 0], [0, 0, 1, 1, 1, 1, 1, 0, 0], [0, 0, 0, 1, 1, 1, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0]], type) tdt, tft = ndimage.distance_transform_edt(data, return_indices = True) dts = [] fts = [] dt = numpy.zeros(data.shape, dtype = numpy.float64) ndimage.distance_transform_edt(data, distances = dt) dts.append(dt) ft = ndimage.distance_transform_edt(data, return_distances = 0, return_indices = True) fts.append(ft) ft = numpy.indices(data.shape, dtype = numpy.int32) ndimage.distance_transform_edt(data, return_distances = False,return_indices = True, indices = ft) fts.append(ft) dt, ft = ndimage.distance_transform_edt(data, return_indices = True) dts.append(dt) fts.append(ft) dt = numpy.zeros(data.shape, dtype = numpy.float64) ft = ndimage.distance_transform_edt(data, distances = dt, return_indices = True) dts.append(dt) fts.append(ft) ft = numpy.indices(data.shape, dtype = numpy.int32) dt = ndimage.distance_transform_edt(data, return_indices = True, indices = ft) dts.append(dt) fts.append(ft) dt = numpy.zeros(data.shape, dtype = numpy.float64) ft = numpy.indices(data.shape, dtype = numpy.int32) ndimage.distance_transform_edt(data, distances = dt, return_indices = True, indices = ft) dts.append(dt) fts.append(ft) for dt in dts: self.failUnless(diff(tdt, dt) < eps) for ft in fts: self.failUnless(diff(tft, ft) < eps) def test_distance_transform_edt03(self): "euclidean distance transform 3" for type in self.types: data = numpy.array([[0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 1, 1, 0, 0, 0], [0, 0, 1, 1, 1, 1, 1, 0, 0], [0, 0, 1, 1, 1, 1, 1, 0, 0], [0, 0, 1, 1, 1, 1, 1, 0, 0], [0, 0, 0, 1, 1, 1, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0]], type) ref = ndimage.distance_transform_bf(data, 'euclidean', sampling = [2, 2]) out = ndimage.distance_transform_edt(data, sampling = [2, 2]) self.failUnless(diff(ref, out) < eps) def test_distance_transform_edt4(self): "euclidean distance transform 4" for type in self.types: data = numpy.array([[0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 1, 1, 0, 0, 0], [0, 0, 1, 1, 1, 1, 1, 0, 0], [0, 0, 1, 1, 1, 1, 1, 0, 0], [0, 0, 1, 1, 1, 1, 1, 0, 0], [0, 0, 0, 1, 1, 1, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0]], type) ref = ndimage.distance_transform_bf(data, 'euclidean', sampling = [2, 1]) out = ndimage.distance_transform_edt(data, sampling = [2, 1]) self.failUnless(diff(ref, out) < eps) def test_generate_structure01(self): "generation of a binary structure 1" struct = ndimage.generate_binary_structure(0, 1) self.failUnless(diff(struct, 1) < eps) def test_generate_structure02(self): "generation of a binary structure 2" struct = ndimage.generate_binary_structure(1, 1) self.failUnless(diff(struct, [1, 1, 1]) < eps) def test_generate_structure03(self): "generation of a binary structure 3" struct = ndimage.generate_binary_structure(2, 1) self.failUnless(diff(struct, [[0, 1, 0], [1, 1, 1], [0, 1, 0]]) < eps) def test_generate_structure04(self): "generation of a binary structure 4" struct = ndimage.generate_binary_structure(2, 2) self.failUnless(diff(struct, [[1, 1, 1], [1, 1, 1], [1, 1, 1]]) < eps) def test_iterate_structure01(self): "iterating a structure 1" struct = [[0, 1, 0], [1, 1, 1], [0, 1, 0]] out = ndimage.iterate_structure(struct, 2) self.failUnless(diff(out, [[0, 0, 1, 0, 0], [0, 1, 1, 1, 0], [1, 1, 1, 1, 1], [0, 1, 1, 1, 0], [0, 0, 1, 0, 0]]) < eps) def test_iterate_structure02(self): "iterating a structure 2" struct = [[0, 1], [1, 1], [0, 1]] out = ndimage.iterate_structure(struct, 2) self.failUnless(diff(out, [[0, 0, 1], [0, 1, 1], [1, 1, 1], [0, 1, 1], [0, 0, 1]]) < eps) def test_iterate_structure03(self): "iterating a structure 3" struct = [[0, 1, 0], [1, 1, 1], [0, 1, 0]] out = ndimage.iterate_structure(struct, 2, 1) error = diff(out[0], [[0, 0, 1, 0, 0], [0, 1, 1, 1, 0], [1, 1, 1, 1, 1], [0, 1, 1, 1, 0], [0, 0, 1, 0, 0]]) self.failUnless(error < eps and out[1] == [2, 2]) def test_binary_erosion01(self): "binary erosion 1" for type in self.types: data = numpy.ones([], type) out = ndimage.binary_erosion(data) self.failUnless(diff(out, 1) < eps) def test_binary_erosion02(self): "binary erosion 2" for type in self.types: data = numpy.ones([], type) out = ndimage.binary_erosion(data, border_value = 1) self.failUnless(diff(out, 1) < eps) def test_binary_erosion03(self): "binary erosion 3" for type in self.types: data = numpy.ones([1], type) out = ndimage.binary_erosion(data) self.failUnless(diff(out, [0]) < eps) def test_binary_erosion04(self): "binary erosion 4" for type in self.types: data = numpy.ones([1], type) out = ndimage.binary_erosion(data, border_value = 1) self.failUnless(diff(out, [1]) < eps) def test_binary_erosion05(self): "binary erosion 5" for type in self.types: data = numpy.ones([3], type) out = ndimage.binary_erosion(data) self.failUnless(diff(out, [0, 1, 0]) < eps) def test_binary_erosion06(self): "binary erosion 6" for type in self.types: data = numpy.ones([3], type) out = ndimage.binary_erosion(data, border_value = 1) self.failUnless(diff(out, [1, 1, 1]) < eps) def test_binary_erosion07(self): "binary erosion 7" for type in self.types: data = numpy.ones([5], type) out = ndimage.binary_erosion(data) self.failUnless(diff(out, [0, 1, 1, 1, 0]) < eps) def test_binary_erosion08(self): "binary erosion 8" for type in self.types: data = numpy.ones([5], type) out = ndimage.binary_erosion(data, border_value = 1) self.failUnless(diff(out, [1, 1, 1, 1, 1]) < eps) def test_binary_erosion09(self): "binary erosion 9" for type in self.types: data = numpy.ones([5], type) data[2] = 0 out = ndimage.binary_erosion(data) self.failUnless(diff(out, [0, 0, 0, 0, 0]) < eps) def test_binary_erosion10(self): "binary erosion 10" for type in self.types: data = numpy.ones([5], type) data[2] = 0 out = ndimage.binary_erosion(data, border_value = 1) self.failUnless(diff(out, [1, 0, 0, 0, 1]) < eps) def test_binary_erosion11(self): "binary erosion 11" for type in self.types: data = numpy.ones([5], type) data[2] = 0 struct = [1, 0, 1] out = ndimage.binary_erosion(data, struct, border_value = 1) self.failUnless(diff(out, [1, 0, 1, 0, 1]) < eps) def test_binary_erosion12(self): "binary erosion 12" for type in self.types: data = numpy.ones([5], type) data[2] = 0 struct = [1, 0, 1] out = ndimage.binary_erosion(data, struct, border_value = 1, origin = -1) self.failUnless(diff(out, [0, 1, 0, 1, 1]) < eps) def test_binary_erosion13(self): "binary erosion 13" for type in self.types: data = numpy.ones([5], type) data[2] = 0 struct = [1, 0, 1] out = ndimage.binary_erosion(data, struct, border_value = 1, origin = 1) self.failUnless(diff(out, [1, 1, 0, 1, 0]) < eps) def test_binary_erosion14(self): "binary erosion 14" for type in self.types: data = numpy.ones([5], type) data[2] = 0 struct = [1, 1] out = ndimage.binary_erosion(data, struct, border_value = 1) self.failUnless(diff(out, [1, 1, 0, 0, 1]) < eps) def test_binary_erosion15(self): "binary erosion 15" for type in self.types: data = numpy.ones([5], type) data[2] = 0 struct = [1, 1] out = ndimage.binary_erosion(data, struct, border_value = 1, origin = -1) self.failUnless(diff(out, [1, 0, 0, 1, 1]) < eps) def test_binary_erosion16(self): "binary erosion 16" for type in self.types: data = numpy.ones([1, 1], type) out = ndimage.binary_erosion(data, border_value = 1) self.failUnless(diff(out, [[1]]) < eps) def test_binary_erosion17(self): "binary erosion 17" for type in self.types: data = numpy.ones([1, 1], type) out = ndimage.binary_erosion(data) self.failUnless(diff(out, [[0]]) < eps) def test_binary_erosion18(self): "binary erosion 18" for type in self.types: data = numpy.ones([1, 3], type) out = ndimage.binary_erosion(data) self.failUnless(diff(out, [[0, 0, 0]]) < eps) def test_binary_erosion19(self): "binary erosion 19" for type in self.types: data = numpy.ones([1, 3], type) out = ndimage.binary_erosion(data, border_value = 1) self.failUnless(diff(out, [[1, 1, 1]]) < eps) def test_binary_erosion20(self): "binary erosion 20" for type in self.types: data = numpy.ones([3, 3], type) out = ndimage.binary_erosion(data) self.failUnless(diff(out, [[0, 0, 0], [0, 1, 0], [0, 0, 0]]) < eps) def test_binary_erosion21(self): "binary erosion 21" for type in self.types: data = numpy.ones([3, 3], type) out = ndimage.binary_erosion(data, border_value = 1) self.failUnless(diff(out, [[1, 1, 1], [1, 1, 1], [1, 1, 1]]) < eps) def test_binary_erosion22(self): "binary erosion 22" true = [[0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 1, 0, 0], [0, 0, 0, 1, 1, 0, 0, 0], [0, 0, 1, 0, 0, 1, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0]] for type in self.types: data = numpy.array([[0, 0, 0, 0, 0, 0, 0, 0], [0, 1, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 1, 1, 1], [0, 0, 1, 1, 1, 1, 1, 1], [0, 0, 1, 1, 1, 1, 0, 0], [0, 1, 1, 1, 1, 1, 1, 0], [0, 1, 1, 0, 0, 1, 1, 0], [0, 0, 0, 0, 0, 0, 0, 0]], type) out = ndimage.binary_erosion(data, border_value = 1) self.failUnless(diff(out, true) < eps) def test_binary_erosion23(self): "binary erosion 23" struct = ndimage.generate_binary_structure(2, 2) true = [[0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 1, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0]] for type in self.types: data = numpy.array([[0, 0, 0, 0, 0, 0, 0, 0], [0, 1, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 1, 1, 1], [0, 0, 1, 1, 1, 1, 1, 1], [0, 0, 1, 1, 1, 1, 0, 0], [0, 1, 1, 1, 1, 1, 1, 0], [0, 1, 1, 0, 0, 1, 1, 0], [0, 0, 0, 0, 0, 0, 0, 0]], type) out = ndimage.binary_erosion(data, struct, border_value = 1) self.failUnless(diff(out, true) < eps) def test_binary_erosion24(self): "binary erosion 24" struct = [[0, 1], [1, 1]] true = [[0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 1, 1, 1], [0, 0, 0, 1, 1, 1, 0, 0], [0, 0, 1, 1, 1, 1, 0, 0], [0, 0, 1, 0, 0, 0, 1, 0], [0, 0, 0, 0, 0, 0, 0, 0]] for type in self.types: data = numpy.array([[0, 0, 0, 0, 0, 0, 0, 0], [0, 1, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 1, 1, 1], [0, 0, 1, 1, 1, 1, 1, 1], [0, 0, 1, 1, 1, 1, 0, 0], [0, 1, 1, 1, 1, 1, 1, 0], [0, 1, 1, 0, 0, 1, 1, 0], [0, 0, 0, 0, 0, 0, 0, 0]], type) out = ndimage.binary_erosion(data, struct, border_value = 1) self.failUnless(diff(out, true) < eps) def test_binary_erosion25(self): "binary erosion 25" struct = [[0, 1, 0], [1, 0, 1], [0, 1, 0]] true = [[0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 1, 0, 0], [0, 0, 0, 1, 0, 0, 0, 0], [0, 0, 1, 0, 0, 1, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0]] for type in self.types: data = numpy.array([[0, 0, 0, 0, 0, 0, 0, 0], [0, 1, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 1, 1, 1], [0, 0, 1, 1, 1, 0, 1, 1], [0, 0, 1, 0, 1, 1, 0, 0], [0, 1, 0, 1, 1, 1, 1, 0], [0, 1, 1, 0, 0, 1, 1, 0], [0, 0, 0, 0, 0, 0, 0, 0]], type) out = ndimage.binary_erosion(data, struct, border_value = 1) self.failUnless(diff(out, true) < eps) def test_binary_erosion26(self): "binary erosion 26" struct = [[0, 1, 0], [1, 0, 1], [0, 1, 0]] true = [[0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 1], [0, 0, 0, 0, 1, 0, 0, 1], [0, 0, 1, 0, 0, 0, 0, 0], [0, 1, 0, 0, 1, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 1]] for type in self.types: data = numpy.array([[0, 0, 0, 0, 0, 0, 0, 0], [0, 1, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 1, 1, 1], [0, 0, 1, 1, 1, 0, 1, 1], [0, 0, 1, 0, 1, 1, 0, 0], [0, 1, 0, 1, 1, 1, 1, 0], [0, 1, 1, 0, 0, 1, 1, 0], [0, 0, 0, 0, 0, 0, 0, 0]], type) out = ndimage.binary_erosion(data, struct, border_value = 1, origin = (-1, -1)) self.failUnless(diff(out, true) < eps) def test_binary_erosion27(self): "binary erosion 27" struct = [[0, 1, 0], [1, 1, 1], [0, 1, 0]] true = [[0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0]] data = numpy.array([[0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 0, 0, 0], [0, 0, 1, 1, 1, 0, 0], [0, 1, 1, 1, 1, 1, 0], [0, 0, 1, 1, 1, 0, 0], [0, 0, 0, 1, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0]], bool) out = ndimage.binary_erosion(data, struct, border_value = 1, iterations = 2) self.failUnless(diff(out, true) < eps) def test_binary_erosion28(self): "binary erosion 28" struct = [[0, 1, 0], [1, 1, 1], [0, 1, 0]] true = [[0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0]] data = numpy.array([[0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 0, 0, 0], [0, 0, 1, 1, 1, 0, 0], [0, 1, 1, 1, 1, 1, 0], [0, 0, 1, 1, 1, 0, 0], [0, 0, 0, 1, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0]], bool) out = numpy.zeros(data.shape, bool) ndimage.binary_erosion(data, struct, border_value = 1, iterations = 2, output = out) self.failUnless(diff(out, true) < eps) def test_binary_erosion29(self): "binary erosion 29" struct = [[0, 1, 0], [1, 1, 1], [0, 1, 0]] true = [[0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0]] data = numpy.array([[0, 0, 0, 1, 0, 0, 0], [0, 0, 1, 1, 1, 0, 0], [0, 1, 1, 1, 1, 1, 0], [1, 1, 1, 1, 1, 1, 1], [0, 1, 1, 1, 1, 1, 0], [0, 0, 1, 1, 1, 0, 0], [0, 0, 0, 1, 0, 0, 0]], bool) out = ndimage.binary_erosion(data, struct, border_value = 1, iterations = 3) self.failUnless(diff(out, true) < eps) def test_binary_erosion30(self): "binary erosion 30" struct = [[0, 1, 0], [1, 1, 1], [0, 1, 0]] true = [[0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0]] data = numpy.array([[0, 0, 0, 1, 0, 0, 0], [0, 0, 1, 1, 1, 0, 0], [0, 1, 1, 1, 1, 1, 0], [1, 1, 1, 1, 1, 1, 1], [0, 1, 1, 1, 1, 1, 0], [0, 0, 1, 1, 1, 0, 0], [0, 0, 0, 1, 0, 0, 0]], bool) out = numpy.zeros(data.shape, bool) ndimage.binary_erosion(data, struct, border_value = 1, iterations = 3, output = out) self.failUnless(diff(out, true) < eps) def test_binary_erosion31(self): "binary erosion 31" struct = [[0, 1, 0], [1, 1, 1], [0, 1, 0]] true = [[0, 0, 1, 0, 0, 0, 0], [0, 1, 1, 1, 0, 0, 0], [1, 1, 1, 1, 1, 0, 1], [0, 1, 1, 1, 0, 0, 0], [0, 0, 1, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 0, 0, 0, 1]] data = numpy.array([[0, 0, 0, 1, 0, 0, 0], [0, 0, 1, 1, 1, 0, 0], [0, 1, 1, 1, 1, 1, 0], [1, 1, 1, 1, 1, 1, 1], [0, 1, 1, 1, 1, 1, 0], [0, 0, 1, 1, 1, 0, 0], [0, 0, 0, 1, 0, 0, 0]], bool) out = numpy.zeros(data.shape, bool) ndimage.binary_erosion(data, struct, border_value = 1, iterations = 1, output = out, origin = (-1, -1)) self.failUnless(diff(out, true) < eps) def test_binary_erosion32(self): "binary erosion 32" struct = [[0, 1, 0], [1, 1, 1], [0, 1, 0]] true = [[0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0]] data = numpy.array([[0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 0, 0, 0], [0, 0, 1, 1, 1, 0, 0], [0, 1, 1, 1, 1, 1, 0], [0, 0, 1, 1, 1, 0, 0], [0, 0, 0, 1, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0]], bool) out = ndimage.binary_erosion(data, struct, border_value = 1, iterations = 2) self.failUnless(diff(out, true) < eps) def test_binary_erosion33(self): "binary erosion 33" struct = [[0, 1, 0], [1, 1, 1], [0, 1, 0]] true = [[0, 0, 0, 0, 0, 1, 1], [0, 0, 0, 0, 0, 0, 1], [0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0]] mask = [[1, 1, 1, 1, 1, 0, 0], [1, 1, 1, 1, 1, 1, 0], [1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1]] data = numpy.array([[0, 0, 0, 0, 0, 1, 1], [0, 0, 0, 1, 0, 0, 1], [0, 0, 1, 1, 1, 0, 0], [0, 0, 1, 1, 1, 0, 0], [0, 0, 1, 1, 1, 0, 0], [0, 0, 0, 1, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0]], bool) out = ndimage.binary_erosion(data, struct, border_value = 1, mask = mask, iterations = -1) self.failUnless(diff(out, true) < eps) def test_binary_erosion34(self): "binary erosion 34" struct = [[0, 1, 0], [1, 1, 1], [0, 1, 0]] true = [[0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 0, 0, 0], [0, 0, 0, 1, 0, 0, 0], [0, 1, 1, 1, 1, 1, 0], [0, 0, 0, 1, 0, 0, 0], [0, 0, 0, 1, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0]] mask = [[0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 1, 1, 0, 0], [0, 0, 1, 0, 1, 0, 0], [0, 0, 1, 1, 1, 0, 0], [0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0]] data = numpy.array([[0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 0, 0, 0], [0, 0, 1, 1, 1, 0, 0], [0, 1, 1, 1, 1, 1, 0], [0, 0, 1, 1, 1, 0, 0], [0, 0, 0, 1, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0]], bool) out = ndimage.binary_erosion(data, struct, border_value = 1, mask = mask) self.failUnless(diff(out, true) < eps) def test_binary_erosion35(self): "binary erosion 35" struct = [[0, 1, 0], [1, 1, 1], [0, 1, 0]] mask = [[0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 1, 1, 0, 0], [0, 0, 1, 0, 1, 0, 0], [0, 0, 1, 1, 1, 0, 0], [0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0]] data = numpy.array([[0, 0, 0, 1, 0, 0, 0], [0, 0, 1, 1, 1, 0, 0], [0, 1, 1, 1, 1, 1, 0], [1, 1, 1, 1, 1, 1, 1], [0, 1, 1, 1, 1, 1, 0], [0, 0, 1, 1, 1, 0, 0], [0, 0, 0, 1, 0, 0, 0]], bool) tmp = [[0, 0, 1, 0, 0, 0, 0], [0, 1, 1, 1, 0, 0, 0], [1, 1, 1, 1, 1, 0, 1], [0, 1, 1, 1, 0, 0, 0], [0, 0, 1, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 0, 0, 0, 1]] true = numpy.logical_and(tmp, mask) tmp = numpy.logical_and(data, numpy.logical_not(mask)) true = numpy.logical_or(true, tmp) out = numpy.zeros(data.shape, bool) ndimage.binary_erosion(data, struct, border_value = 1, iterations = 1, output = out, origin = (-1, -1), mask = mask) self.failUnless(diff(out, true) < eps) def test_binary_erosion36(self): "binary erosion 36" struct = [[0, 1, 0], [1, 0, 1], [0, 1, 0]] mask = [[0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 1, 1, 0, 0, 0], [0, 0, 1, 0, 1, 0, 0, 0], [0, 0, 1, 1, 1, 0, 0, 0], [0, 0, 1, 1, 1, 0, 0, 0], [0, 0, 1, 1, 1, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0]] tmp = [[0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 1], [0, 0, 0, 0, 1, 0, 0, 1], [0, 0, 1, 0, 0, 0, 0, 0], [0, 1, 0, 0, 1, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 1]] data = numpy.array([[0, 0, 0, 0, 0, 0, 0, 0], [0, 1, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 1, 1, 1], [0, 0, 1, 1, 1, 0, 1, 1], [0, 0, 1, 0, 1, 1, 0, 0], [0, 1, 0, 1, 1, 1, 1, 0], [0, 1, 1, 0, 0, 1, 1, 0], [0, 0, 0, 0, 0, 0, 0, 0]]) true = numpy.logical_and(tmp, mask) tmp = numpy.logical_and(data, numpy.logical_not(mask)) true = numpy.logical_or(true, tmp) out = ndimage.binary_erosion(data, struct, mask = mask, border_value = 1, origin = (-1, -1)) self.failUnless(diff(out, true) < eps) def test_binary_dilation01(self): "binary dilation 1" for type in self.types: data = numpy.ones([], type) out = ndimage.binary_dilation(data) self.failUnless(diff(out, 1) < eps) def test_binary_dilation02(self): "binary dilation 2" for type in self.types: data = numpy.zeros([], type) out = ndimage.binary_dilation(data) self.failUnless(diff(out, 0) < eps) def test_binary_dilation03(self): "binary dilation 3" for type in self.types: data = numpy.ones([1], type) out = ndimage.binary_dilation(data) self.failUnless(diff(out, [1]) < eps) def test_binary_dilation04(self): "binary dilation 4" for type in self.types: data = numpy.zeros([1], type) out = ndimage.binary_dilation(data) self.failUnless(diff(out, [0]) < eps) def test_binary_dilation05(self): "binary dilation 5" for type in self.types: data = numpy.ones([3], type) out = ndimage.binary_dilation(data) self.failUnless(diff(out, [1, 1, 1]) < eps) def test_binary_dilation06(self): "binary dilation 6" for type in self.types: data = numpy.zeros([3], type) out = ndimage.binary_dilation(data) self.failUnless(diff(out, [0, 0, 0]) < eps) def test_binary_dilation07(self): "binary dilation 7" struct = ndimage.generate_binary_structure(1, 1) for type in self.types: data = numpy.zeros([3], type) data[1] = 1 out = ndimage.binary_dilation(data) self.failUnless(diff(out, [1, 1, 1]) < eps) def test_binary_dilation08(self): "binary dilation 8" for type in self.types: data = numpy.zeros([5], type) data[1] = 1 data[3] = 1 out = ndimage.binary_dilation(data) self.failUnless(diff(out, [1, 1, 1, 1, 1]) < eps) def test_binary_dilation09(self): "binary dilation 9" for type in self.types: data = numpy.zeros([5], type) data[1] = 1 out = ndimage.binary_dilation(data) self.failUnless(diff(out, [1, 1, 1, 0, 0]) < eps) def test_binary_dilation10(self): "binary dilation 10" for type in self.types: data = numpy.zeros([5], type) data[1] = 1 out = ndimage.binary_dilation(data, origin = -1) self.failUnless(diff(out, [0, 1, 1, 1, 0]) < eps) def test_binary_dilation11(self): "binary dilation 11" for type in self.types: data = numpy.zeros([5], type) data[1] = 1 out = ndimage.binary_dilation(data, origin = 1) self.failUnless(diff(out, [1, 1, 0, 0, 0]) < eps) def test_binary_dilation12(self): "binary dilation 12" for type in self.types: data = numpy.zeros([5], type) data[1] = 1 struct = [1, 0, 1] out = ndimage.binary_dilation(data, struct) self.failUnless(diff(out, [1, 0, 1, 0, 0]) < eps) def test_binary_dilation13(self): "binary dilation 13" for type in self.types: data = numpy.zeros([5], type) data[1] = 1 struct = [1, 0, 1] out = ndimage.binary_dilation(data, struct, border_value = 1) self.failUnless(diff(out, [1, 0, 1, 0, 1]) < eps) def test_binary_dilation14(self): "binary dilation 14" for type in self.types: data = numpy.zeros([5], type) data[1] = 1 struct = [1, 0, 1] out = ndimage.binary_dilation(data, struct, origin = -1) self.failUnless(diff(out, [0, 1, 0, 1, 0]) < eps) def test_binary_dilation15(self): "binary dilation 15" for type in self.types: data = numpy.zeros([5], type) data[1] = 1 struct = [1, 0, 1] out = ndimage.binary_dilation(data, struct, origin = -1, border_value = 1) self.failUnless(diff(out, [1, 1, 0, 1, 0]) < eps) def test_binary_dilation16(self): "binary dilation 16" for type in self.types: data = numpy.ones([1, 1], type) out = ndimage.binary_dilation(data) self.failUnless(diff(out, [[1]]) < eps) def test_binary_dilation17(self): "binary dilation 17" for type in self.types: data = numpy.zeros([1, 1], type) out = ndimage.binary_dilation(data) self.failUnless(diff(out, [[0]]) < eps) def test_binary_dilation18(self): "binary dilation 18" for type in self.types: data = numpy.ones([1, 3], type) out = ndimage.binary_dilation(data) self.failUnless(diff(out, [[1, 1, 1]]) < eps) def test_binary_dilation19(self): "binary dilation 19" for type in self.types: data = numpy.ones([3, 3], type) out = ndimage.binary_dilation(data) self.failUnless(diff(out, [[1, 1, 1], [1, 1, 1], [1, 1, 1]]) < eps) def test_binary_dilation20(self): "binary dilation 20" for type in self.types: data = numpy.zeros([3, 3], type) data[1, 1] = 1 out = ndimage.binary_dilation(data) self.failUnless(diff(out, [[0, 1, 0], [1, 1, 1], [0, 1, 0]]) < eps) def test_binary_dilation21(self): "binary dilation 21" struct = ndimage.generate_binary_structure(2, 2) for type in self.types: data = numpy.zeros([3, 3], type) data[1, 1] = 1 out = ndimage.binary_dilation(data, struct) self.failUnless(diff(out, [[1, 1, 1], [1, 1, 1], [1, 1, 1]]) < eps) def test_binary_dilation22(self): "binary dilation 22" true = [[0, 1, 0, 0, 0, 0, 0, 0], [1, 1, 1, 0, 0, 0, 0, 0], [0, 1, 0, 0, 0, 1, 0, 0], [0, 0, 0, 1, 1, 1, 1, 0], [0, 0, 1, 1, 1, 1, 0, 0], [0, 1, 1, 1, 1, 1, 1, 0], [0, 0, 1, 0, 0, 1, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0]] for type in self.types: data = numpy.array([[0, 0, 0, 0, 0, 0, 0, 0], [0, 1, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 1, 0, 0], [0, 0, 0, 1, 1, 0, 0, 0], [0, 0, 1, 0, 0, 1, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0]], type) out = ndimage.binary_dilation(data) self.failUnless(diff(out, true) < eps) def test_binary_dilation23(self): "binary dilation 23" true = [[1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 0, 0, 0, 0, 1], [1, 1, 0, 0, 0, 1, 0, 1], [1, 0, 0, 1, 1, 1, 1, 1], [1, 0, 1, 1, 1, 1, 0, 1], [1, 1, 1, 1, 1, 1, 1, 1], [1, 0, 1, 0, 0, 1, 0, 1], [1, 1, 1, 1, 1, 1, 1, 1]] for type in self.types: data = numpy.array([[0, 0, 0, 0, 0, 0, 0, 0], [0, 1, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 1, 0, 0], [0, 0, 0, 1, 1, 0, 0, 0], [0, 0, 1, 0, 0, 1, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0]], type) out = ndimage.binary_dilation(data, border_value = 1) self.failUnless(diff(out, true) < eps) def test_binary_dilation24(self): "binary dilation 24" true = [[1, 1, 0, 0, 0, 0, 0, 0], [1, 0, 0, 0, 1, 0, 0, 0], [0, 0, 1, 1, 1, 1, 0, 0], [0, 1, 1, 1, 1, 0, 0, 0], [1, 1, 1, 1, 1, 1, 0, 0], [0, 1, 0, 0, 1, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0]] for type in self.types: data = numpy.array([[0, 0, 0, 0, 0, 0, 0, 0], [0, 1, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 1, 0, 0], [0, 0, 0, 1, 1, 0, 0, 0], [0, 0, 1, 0, 0, 1, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0]], type) out = ndimage.binary_dilation(data, origin = (1, 1)) self.failUnless(diff(out, true) < eps) def test_binary_dilation25(self): "binary dilation 25" true = [[1, 1, 0, 0, 0, 0, 1, 1], [1, 0, 0, 0, 1, 0, 1, 1], [0, 0, 1, 1, 1, 1, 1, 1], [0, 1, 1, 1, 1, 0, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1], [0, 1, 0, 0, 1, 0, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1]] for type in self.types: data = numpy.array([[0, 0, 0, 0, 0, 0, 0, 0], [0, 1, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 1, 0, 0], [0, 0, 0, 1, 1, 0, 0, 0], [0, 0, 1, 0, 0, 1, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0]], type) out = ndimage.binary_dilation(data, origin = (1, 1), border_value = 1) self.failUnless(diff(out, true) < eps) def test_binary_dilation26(self): "binary dilation 26" struct = ndimage.generate_binary_structure(2, 2) true = [[1, 1, 1, 0, 0, 0, 0, 0], [1, 1, 1, 0, 0, 0, 0, 0], [1, 1, 1, 0, 1, 1, 1, 0], [0, 0, 1, 1, 1, 1, 1, 0], [0, 1, 1, 1, 1, 1, 1, 0], [0, 1, 1, 1, 1, 1, 1, 0], [0, 1, 1, 1, 1, 1, 1, 0], [0, 0, 0, 0, 0, 0, 0, 0]] for type in self.types: data = numpy.array([[0, 0, 0, 0, 0, 0, 0, 0], [0, 1, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 1, 0, 0], [0, 0, 0, 1, 1, 0, 0, 0], [0, 0, 1, 0, 0, 1, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0]], type) out = ndimage.binary_dilation(data, struct) self.failUnless(diff(out, true) < eps) def test_binary_dilation27(self): "binary dilation 27" struct = [[0, 1], [1, 1]] true = [[0, 1, 0, 0, 0, 0, 0, 0], [1, 1, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 1, 0, 0], [0, 0, 0, 1, 1, 1, 0, 0], [0, 0, 1, 1, 1, 1, 0, 0], [0, 1, 1, 0, 1, 1, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0]] for type in self.types: data = numpy.array([[0, 0, 0, 0, 0, 0, 0, 0], [0, 1, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 1, 0, 0], [0, 0, 0, 1, 1, 0, 0, 0], [0, 0, 1, 0, 0, 1, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0]], type) out = ndimage.binary_dilation(data, struct) self.failUnless(diff(out, true) < eps) def test_binary_dilation28(self): "binary dilation 28" true = [[1, 1, 1, 1], [1, 0, 0, 1], [1, 0, 0, 1], [1, 1, 1, 1]] for type in self.types: data = numpy.array([[0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0]], type) out = ndimage.binary_dilation(data, border_value = 1) self.failUnless(diff(out, true) < eps) def test_binary_dilation29(self): "binary dilation 29" struct = [[0, 1], [1, 1]] true = [[0, 0, 0, 0, 0], [0, 0, 0, 1, 0], [0, 0, 1, 1, 0], [0, 1, 1, 1, 0], [0, 0, 0, 0, 0]] data = numpy.array([[0, 0, 0, 0, 0], [0, 0, 0, 0, 0], [0, 0, 0, 0, 0], [0, 0, 0, 1, 0], [0, 0, 0, 0, 0]], bool) out = ndimage.binary_dilation(data, struct, iterations = 2) self.failUnless(diff(out, true) < eps) def test_binary_dilation30(self): "binary dilation 30" struct = [[0, 1], [1, 1]] true = [[0, 0, 0, 0, 0], [0, 0, 0, 1, 0], [0, 0, 1, 1, 0], [0, 1, 1, 1, 0], [0, 0, 0, 0, 0]] data = numpy.array([[0, 0, 0, 0, 0], [0, 0, 0, 0, 0], [0, 0, 0, 0, 0], [0, 0, 0, 1, 0], [0, 0, 0, 0, 0]], bool) out = numpy.zeros(data.shape, bool) ndimage.binary_dilation(data, struct, iterations = 2, output = out) self.failUnless(diff(out, true) < eps) def test_binary_dilation31(self): "binary dilation 31" struct = [[0, 1], [1, 1]] true = [[0, 0, 0, 1, 0], [0, 0, 1, 1, 0], [0, 1, 1, 1, 0], [1, 1, 1, 1, 0], [0, 0, 0, 0, 0]] data = numpy.array([[0, 0, 0, 0, 0], [0, 0, 0, 0, 0], [0, 0, 0, 0, 0], [0, 0, 0, 1, 0], [0, 0, 0, 0, 0]], bool) out = ndimage.binary_dilation(data, struct, iterations = 3) self.failUnless(diff(out, true) < eps) def test_binary_dilation32(self): "binary dilation 32" struct = [[0, 1], [1, 1]] true = [[0, 0, 0, 1, 0], [0, 0, 1, 1, 0], [0, 1, 1, 1, 0], [1, 1, 1, 1, 0], [0, 0, 0, 0, 0]] data = numpy.array([[0, 0, 0, 0, 0], [0, 0, 0, 0, 0], [0, 0, 0, 0, 0], [0, 0, 0, 1, 0], [0, 0, 0, 0, 0]], bool) out = numpy.zeros(data.shape, bool) ndimage.binary_dilation(data, struct, iterations = 3, output = out) self.failUnless(diff(out, true) < eps) def test_binary_dilation33(self): "binary dilation 33" struct = [[0, 1, 0], [1, 1, 1], [0, 1, 0]] true = numpy.array([[0, 1, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 1, 1, 0, 0], [0, 0, 1, 1, 1, 0, 0, 0], [0, 1, 1, 0, 1, 1, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0]], bool) mask = numpy.array([[0, 1, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 1, 0], [0, 0, 0, 0, 1, 1, 0, 0], [0, 0, 1, 1, 1, 0, 0, 0], [0, 1, 1, 0, 1, 1, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0]], bool) data = numpy.array([[0, 1, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0], [0, 1, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0]], bool) out = ndimage.binary_dilation(data, struct, iterations = -1, mask = mask, border_value = 0) self.failUnless(diff(out, true) < eps) def test_binary_dilation34(self): "binary dilation 34" struct = [[0, 1, 0], [1, 1, 1], [0, 1, 0]] true = [[0, 1, 0, 0, 0, 0, 0, 0], [0, 1, 1, 0, 0, 0, 0, 0], [0, 0, 1, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0]] mask = numpy.array([[0, 1, 0, 0, 0, 0, 0, 0], [0, 1, 1, 0, 0, 0, 0, 0], [0, 0, 1, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 1, 0, 0], [0, 0, 0, 1, 1, 0, 0, 0], [0, 0, 1, 0, 0, 1, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0]], bool) data = numpy.zeros(mask.shape, bool) out = ndimage.binary_dilation(data, struct, iterations = -1, mask = mask, border_value = 1) self.failUnless(diff(out, true) < eps) def test_binary_dilation35(self): "binary dilation 35" tmp = [[1, 1, 0, 0, 0, 0, 1, 1], [1, 0, 0, 0, 1, 0, 1, 1], [0, 0, 1, 1, 1, 1, 1, 1], [0, 1, 1, 1, 1, 0, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1], [0, 1, 0, 0, 1, 0, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1]] data = numpy.array([[0, 0, 0, 0, 0, 0, 0, 0], [0, 1, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 1, 0, 0], [0, 0, 0, 1, 1, 0, 0, 0], [0, 0, 1, 0, 0, 1, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0]]) mask = [[0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 1, 1, 1, 0, 0], [0, 0, 1, 1, 1, 1, 0, 0], [0, 0, 1, 1, 1, 1, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0]] true = numpy.logical_and(tmp, mask) tmp = numpy.logical_and(data, numpy.logical_not(mask)) true = numpy.logical_or(true, tmp) for type in self.types: data = numpy.array([[0, 0, 0, 0, 0, 0, 0, 0], [0, 1, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 1, 0, 0], [0, 0, 0, 1, 1, 0, 0, 0], [0, 0, 1, 0, 0, 1, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0]], type) out = ndimage.binary_dilation(data, mask = mask, origin = (1, 1), border_value = 1) self.failUnless(diff(out, true) < eps) def test_binary_propagation01(self): "binary propagation 1" struct = [[0, 1, 0], [1, 1, 1], [0, 1, 0]] true = numpy.array([[0, 1, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 1, 1, 0, 0], [0, 0, 1, 1, 1, 0, 0, 0], [0, 1, 1, 0, 1, 1, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0]], bool) mask = numpy.array([[0, 1, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 1, 0], [0, 0, 0, 0, 1, 1, 0, 0], [0, 0, 1, 1, 1, 0, 0, 0], [0, 1, 1, 0, 1, 1, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0]], bool) data = numpy.array([[0, 1, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0], [0, 1, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0]], bool) out = ndimage.binary_propagation(data, struct, mask = mask, border_value = 0) self.failUnless(diff(out, true) < eps) def test_binary_propagation02(self): "binary propagation 2" struct = [[0, 1, 0], [1, 1, 1], [0, 1, 0]] true = [[0, 1, 0, 0, 0, 0, 0, 0], [0, 1, 1, 0, 0, 0, 0, 0], [0, 0, 1, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0]] mask = numpy.array([[0, 1, 0, 0, 0, 0, 0, 0], [0, 1, 1, 0, 0, 0, 0, 0], [0, 0, 1, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 1, 0, 0], [0, 0, 0, 1, 1, 0, 0, 0], [0, 0, 1, 0, 0, 1, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0]], bool) data = numpy.zeros(mask.shape, bool) out = ndimage.binary_propagation(data, struct, mask = mask, border_value = 1) self.failUnless(diff(out, true) < eps) def test_binary_opening01(self): "binary opening 1" true = [[0, 1, 0, 0, 0, 0, 0, 0], [1, 1, 1, 0, 0, 0, 0, 0], [0, 1, 0, 0, 0, 1, 0, 0], [0, 0, 0, 0, 1, 1, 1, 0], [0, 0, 1, 0, 0, 1, 0, 0], [0, 1, 1, 1, 1, 1, 1, 0], [0, 0, 1, 0, 0, 1, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0]] for type in self.types: data = numpy.array([[0, 1, 0, 0, 0, 0, 0, 0], [1, 1, 1, 0, 0, 0, 0, 0], [0, 1, 0, 0, 0, 1, 0, 0], [0, 0, 0, 1, 1, 1, 1, 0], [0, 0, 1, 1, 0, 1, 0, 0], [0, 1, 1, 1, 1, 1, 1, 0], [0, 0, 1, 0, 0, 1, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0]], type) out = ndimage.binary_opening(data) self.failUnless(diff(out, true) < eps) def test_binary_opening02(self): "binary opening 2" struct = ndimage.generate_binary_structure(2, 2) true = [[1, 1, 1, 0, 0, 0, 0, 0], [1, 1, 1, 0, 0, 0, 0, 0], [1, 1, 1, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0], [0, 1, 1, 1, 0, 0, 0, 0], [0, 1, 1, 1, 0, 0, 0, 0], [0, 1, 1, 1, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0]] for type in self.types: data = numpy.array([[1, 1, 1, 0, 0, 0, 0, 0], [1, 1, 1, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 0], [0, 0, 1, 1, 1, 1, 1, 0], [0, 1, 1, 1, 0, 1, 1, 0], [0, 1, 1, 1, 1, 1, 1, 0], [0, 1, 1, 1, 1, 1, 1, 0], [0, 0, 0, 0, 0, 0, 0, 0]], type) out = ndimage.binary_opening(data, struct) self.failUnless(diff(out, true) < eps) def test_binary_closing01(self): "binary closing 1" true = [[0, 0, 0, 0, 0, 0, 0, 0], [0, 1, 1, 0, 0, 0, 0, 0], [0, 1, 1, 1, 0, 1, 0, 0], [0, 0, 1, 1, 1, 1, 1, 0], [0, 0, 1, 1, 1, 1, 0, 0], [0, 1, 1, 1, 1, 1, 1, 0], [0, 0, 1, 0, 0, 1, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0]] for type in self.types: data = numpy.array([[0, 1, 0, 0, 0, 0, 0, 0], [1, 1, 1, 0, 0, 0, 0, 0], [0, 1, 0, 0, 0, 1, 0, 0], [0, 0, 0, 1, 1, 1, 1, 0], [0, 0, 1, 1, 0, 1, 0, 0], [0, 1, 1, 1, 1, 1, 1, 0], [0, 0, 1, 0, 0, 1, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0]], type) out = ndimage.binary_closing(data) self.failUnless(diff(out, true) < eps) def test_binary_closing02(self): "binary closing 2" struct = ndimage.generate_binary_structure(2, 2) true = [[0, 0, 0, 0, 0, 0, 0, 0], [0, 1, 1, 0, 0, 0, 0, 0], [0, 1, 1, 1, 1, 1, 1, 0], [0, 1, 1, 1, 1, 1, 1, 0], [0, 1, 1, 1, 1, 1, 1, 0], [0, 1, 1, 1, 1, 1, 1, 0], [0, 1, 1, 1, 1, 1, 1, 0], [0, 0, 0, 0, 0, 0, 0, 0]] for type in self.types: data = numpy.array([[1, 1, 1, 0, 0, 0, 0, 0], [1, 1, 1, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 0], [0, 0, 1, 1, 1, 1, 1, 0], [0, 1, 1, 1, 0, 1, 1, 0], [0, 1, 1, 1, 1, 1, 1, 0], [0, 1, 1, 1, 1, 1, 1, 0], [0, 0, 0, 0, 0, 0, 0, 0]], type) out = ndimage.binary_closing(data, struct) self.failUnless(diff(out, true) < eps) def test_binary_fill_holes01(self): "binary fill holes 1" true = numpy.array([[0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 1, 1, 1, 0, 0], [0, 0, 1, 1, 1, 1, 0, 0], [0, 0, 1, 1, 1, 1, 0, 0], [0, 0, 1, 1, 1, 1, 0, 0], [0, 0, 1, 1, 1, 1, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0]], bool) data = numpy.array([[0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 1, 1, 1, 0, 0], [0, 0, 1, 0, 0, 1, 0, 0], [0, 0, 1, 0, 0, 1, 0, 0], [0, 0, 1, 0, 0, 1, 0, 0], [0, 0, 1, 1, 1, 1, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0]], bool) out = ndimage.binary_fill_holes(data) self.failUnless(diff(out, true) < eps) def test_binary_fill_holes02(self): "binary fill holes 2" true = numpy.array([[0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 1, 0, 0, 0], [0, 0, 1, 1, 1, 1, 0, 0], [0, 0, 1, 1, 1, 1, 0, 0], [0, 0, 1, 1, 1, 1, 0, 0], [0, 0, 0, 1, 1, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0]], bool) data = numpy.array([[0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 1, 0, 0, 0], [0, 0, 1, 0, 0, 1, 0, 0], [0, 0, 1, 0, 0, 1, 0, 0], [0, 0, 1, 0, 0, 1, 0, 0], [0, 0, 0, 1, 1, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0]], bool) out = ndimage.binary_fill_holes(data) self.failUnless(diff(out, true) < eps) def test_binary_fill_holes03(self): "binary fill holes 3" true = numpy.array([[0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 0, 0, 0, 0, 0], [0, 1, 1, 1, 0, 1, 1, 1], [0, 1, 1, 1, 0, 1, 1, 1], [0, 1, 1, 1, 0, 1, 1, 1], [0, 0, 1, 0, 0, 1, 1, 1], [0, 0, 0, 0, 0, 0, 0, 0]], bool) data = numpy.array([[0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 0, 0, 0, 0, 0], [0, 1, 0, 1, 0, 1, 1, 1], [0, 1, 0, 1, 0, 1, 0, 1], [0, 1, 0, 1, 0, 1, 0, 1], [0, 0, 1, 0, 0, 1, 1, 1], [0, 0, 0, 0, 0, 0, 0, 0]], bool) out = ndimage.binary_fill_holes(data) self.failUnless(diff(out, true) < eps) def test_grey_erosion01(self): "grey erosion 1" array = numpy.array([[3, 2, 5, 1, 4], [7, 6, 9, 3, 5], [5, 8, 3, 7, 1]]) footprint = [[1, 0, 1], [1, 1, 0]] output = ndimage.grey_erosion(array, footprint = footprint) self.failUnless(diff([[2, 2, 1, 1, 1], [2, 3, 1, 3, 1], [5, 5, 3, 3, 1]], output) < eps) def test_grey_erosion02(self): "grey erosion 2" array = numpy.array([[3, 2, 5, 1, 4], [7, 6, 9, 3, 5], [5, 8, 3, 7, 1]]) footprint = [[1, 0, 1], [1, 1, 0]] structure = [[0, 0, 0], [0, 0, 0]] output = ndimage.grey_erosion(array, footprint = footprint, structure = structure) self.failUnless(diff([[2, 2, 1, 1, 1], [2, 3, 1, 3, 1], [5, 5, 3, 3, 1]], output) < eps) def test_grey_erosion03(self): "grey erosion 3" array = numpy.array([[3, 2, 5, 1, 4], [7, 6, 9, 3, 5], [5, 8, 3, 7, 1]]) footprint = [[1, 0, 1], [1, 1, 0]] structure = [[1, 1, 1], [1, 1, 1]] output = ndimage.grey_erosion(array, footprint = footprint, structure = structure) self.failUnless(diff([[1, 1, 0, 0, 0], [1, 2, 0, 2, 0], [4, 4, 2, 2, 0]], output) < eps) def test_grey_dilation01(self): "grey dilation 1" array = numpy.array([[3, 2, 5, 1, 4], [7, 6, 9, 3, 5], [5, 8, 3, 7, 1]]) footprint = [[0, 1, 1], [1, 0, 1]] output = ndimage.grey_dilation(array, footprint = footprint) self.failUnless(diff([[7, 7, 9, 9, 5], [7, 9, 8, 9, 7], [8, 8, 8, 7, 7]], output) < eps) def test_grey_dilation02(self): "grey dilation 2" array = numpy.array([[3, 2, 5, 1, 4], [7, 6, 9, 3, 5], [5, 8, 3, 7, 1]]) footprint = [[0, 1, 1], [1, 0, 1]] structure = [[0, 0, 0], [0, 0, 0]] output = ndimage.grey_dilation(array, footprint = footprint, structure = structure) self.failUnless(diff([[7, 7, 9, 9, 5], [7, 9, 8, 9, 7], [8, 8, 8, 7, 7]], output) < eps) def test_grey_dilation03(self): "grey dilation 3" array = numpy.array([[3, 2, 5, 1, 4], [7, 6, 9, 3, 5], [5, 8, 3, 7, 1]]) footprint = [[0, 1, 1], [1, 0, 1]] structure = [[1, 1, 1], [1, 1, 1]] output = ndimage.grey_dilation(array, footprint = footprint, structure = structure) self.failUnless(diff([[8, 8, 10, 10, 6], [8, 10, 9, 10, 8], [9, 9, 9, 8, 8]], output) < eps) def test_grey_opening01(self): "grey opening 1" array = numpy.array([[3, 2, 5, 1, 4], [7, 6, 9, 3, 5], [5, 8, 3, 7, 1]]) footprint = [[1, 0, 1], [1, 1, 0]] tmp = ndimage.grey_erosion(array, footprint = footprint) true = ndimage.grey_dilation(tmp, footprint = footprint) output = ndimage.grey_opening(array, footprint = footprint) self.failUnless(diff(true, output) < eps) def test_grey_opening02(self): "grey opening 2" array = numpy.array([[3, 2, 5, 1, 4], [7, 6, 9, 3, 5], [5, 8, 3, 7, 1]]) footprint = [[1, 0, 1], [1, 1, 0]] structure = [[0, 0, 0], [0, 0, 0]] tmp = ndimage.grey_erosion(array, footprint = footprint, structure = structure) true = ndimage.grey_dilation(tmp, footprint = footprint, structure = structure) output = ndimage.grey_opening(array, footprint = footprint, structure = structure) self.failUnless(diff(true, output) < eps) def test_grey_closing01(self): "grey closing 1" array = numpy.array([[3, 2, 5, 1, 4], [7, 6, 9, 3, 5], [5, 8, 3, 7, 1]]) footprint = [[1, 0, 1], [1, 1, 0]] tmp = ndimage.grey_dilation(array, footprint = footprint) true = ndimage.grey_erosion(tmp, footprint = footprint) output = ndimage.grey_closing(array, footprint = footprint) self.failUnless(diff(true, output) < eps) def test_grey_closing02(self): "grey closing 2" array = numpy.array([[3, 2, 5, 1, 4], [7, 6, 9, 3, 5], [5, 8, 3, 7, 1]]) footprint = [[1, 0, 1], [1, 1, 0]] structure = [[0, 0, 0], [0, 0, 0]] tmp = ndimage.grey_dilation(array, footprint = footprint, structure = structure) true = ndimage.grey_erosion(tmp, footprint = footprint, structure = structure) output = ndimage.grey_closing(array, footprint = footprint, structure = structure) self.failUnless(diff(true, output) < eps) def test_morphological_gradient01(self): "morphological gradient 1" array = numpy.array([[3, 2, 5, 1, 4], [7, 6, 9, 3, 5], [5, 8, 3, 7, 1]]) footprint = [[1, 0, 1], [1, 1, 0]] structure = [[0, 0, 0], [0, 0, 0]] tmp1 = ndimage.grey_dilation(array, footprint = footprint, structure = structure) tmp2 = ndimage.grey_erosion(array, footprint = footprint, structure = structure) true = tmp1 - tmp2 output = numpy.zeros(array.shape, array.dtype) ndimage.morphological_gradient(array, footprint=footprint, structure=structure, output = output) self.failUnless(diff(true, output) < eps) def test_morphological_gradient02(self): "morphological gradient 2" array = numpy.array([[3, 2, 5, 1, 4], [7, 6, 9, 3, 5], [5, 8, 3, 7, 1]]) footprint = [[1, 0, 1], [1, 1, 0]] structure = [[0, 0, 0], [0, 0, 0]] tmp1 = ndimage.grey_dilation(array, footprint = footprint, structure = structure) tmp2 = ndimage.grey_erosion(array, footprint = footprint, structure = structure) true = tmp1 - tmp2 output =ndimage.morphological_gradient(array, footprint=footprint, structure=structure) self.failUnless(diff(true, output) < eps) def test_morphological_laplace01(self): "morphological laplace 1" array = numpy.array([[3, 2, 5, 1, 4], [7, 6, 9, 3, 5], [5, 8, 3, 7, 1]]) footprint = [[1, 0, 1], [1, 1, 0]] structure = [[0, 0, 0], [0, 0, 0]] tmp1 = ndimage.grey_dilation(array, footprint = footprint, structure = structure) tmp2 = ndimage.grey_erosion(array, footprint = footprint, structure = structure) true = tmp1 + tmp2 - 2 * array output = numpy.zeros(array.shape, array.dtype) ndimage.morphological_laplace(array, footprint=footprint, structure=structure, output = output) self.failUnless(diff(true, output) < eps) def test_morphological_laplace02(self): "morphological laplace 2" array = numpy.array([[3, 2, 5, 1, 4], [7, 6, 9, 3, 5], [5, 8, 3, 7, 1]]) footprint = [[1, 0, 1], [1, 1, 0]] structure = [[0, 0, 0], [0, 0, 0]] tmp1 = ndimage.grey_dilation(array, footprint = footprint, structure = structure) tmp2 = ndimage.grey_erosion(array, footprint = footprint, structure = structure) true = tmp1 + tmp2 - 2 * array output = ndimage.morphological_laplace(array, footprint=footprint, structure=structure) self.failUnless(diff(true, output) < eps) def test_white_tophat01(self): "white tophat 1" array = numpy.array([[3, 2, 5, 1, 4], [7, 6, 9, 3, 5], [5, 8, 3, 7, 1]]) footprint = [[1, 0, 1], [1, 1, 0]] structure = [[0, 0, 0], [0, 0, 0]] tmp = ndimage.grey_opening(array, footprint = footprint, structure = structure) true = array - tmp output = numpy.zeros(array.shape, array.dtype) ndimage.white_tophat(array, footprint=footprint, structure=structure, output = output) self.failUnless(diff(true, output) < eps) def test_white_tophat02(self): "white tophat 2" array = numpy.array([[3, 2, 5, 1, 4], [7, 6, 9, 3, 5], [5, 8, 3, 7, 1]]) footprint = [[1, 0, 1], [1, 1, 0]] structure = [[0, 0, 0], [0, 0, 0]] tmp = ndimage.grey_opening(array, footprint = footprint, structure = structure) true = array - tmp output = ndimage.white_tophat(array, footprint=footprint, structure=structure) self.failUnless(diff(true, output) < eps) def test_black_tophat01(self): "black tophat 1" array = numpy.array([[3, 2, 5, 1, 4], [7, 6, 9, 3, 5], [5, 8, 3, 7, 1]]) footprint = [[1, 0, 1], [1, 1, 0]] structure = [[0, 0, 0], [0, 0, 0]] tmp = ndimage.grey_closing(array, footprint = footprint, structure = structure) true = tmp - array output = numpy.zeros(array.shape, array.dtype) ndimage.black_tophat(array, footprint=footprint, structure=structure, output = output) self.failUnless(diff(true, output) < eps) def test_black_tophat02(self): "black tophat 2" array = numpy.array([[3, 2, 5, 1, 4], [7, 6, 9, 3, 5], [5, 8, 3, 7, 1]]) footprint = [[1, 0, 1], [1, 1, 0]] structure = [[0, 0, 0], [0, 0, 0]] tmp = ndimage.grey_closing(array, footprint = footprint, structure = structure) true = tmp - array output = ndimage.black_tophat(array, footprint=footprint, structure=structure) self.failUnless(diff(true, output) < eps) def test_hit_or_miss01(self): "binary hit-or-miss transform 1" struct = [[0, 1, 0], [1, 1, 1], [0, 1, 0]] true = [[0, 0, 0, 0, 0], [0, 1, 0, 0, 0], [0, 0, 0, 0, 0], [0, 0, 0, 0, 0], [0, 0, 0, 0, 0], [0, 0, 0, 0, 0], [0, 0, 0, 0, 0], [0, 0, 0, 0, 0]] for type in self.types: data = numpy.array([[0, 1, 0, 0, 0], [1, 1, 1, 0, 0], [0, 1, 0, 1, 1], [0, 0, 1, 1, 1], [0, 1, 1, 1, 0], [0, 1, 1, 1, 1], [0, 1, 1, 1, 1], [0, 0, 0, 0, 0]], type) out = numpy.zeros(data.shape, bool) ndimage.binary_hit_or_miss(data, struct, output = out) self.failUnless(diff(true, out) < eps) def test_hit_or_miss02(self): "binary hit-or-miss transform 2" struct = [[0, 1, 0], [1, 1, 1], [0, 1, 0]] true = [[0, 0, 0, 0, 0, 0, 0, 0], [0, 1, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0]] for type in self.types: data = numpy.array([[0, 1, 0, 0, 1, 1, 1, 0], [1, 1, 1, 0, 0, 1, 0, 0], [0, 1, 0, 1, 1, 1, 1, 0], [0, 0, 0, 0, 0, 0, 0, 0]], type) out = ndimage.binary_hit_or_miss(data, struct) self.failUnless(diff(true, out) < eps) def test_hit_or_miss03(self): "binary hit-or-miss transform 3" struct1 = [[0, 0, 0], [1, 1, 1], [0, 0, 0]] struct2 = [[1, 1, 1], [0, 0, 0], [1, 1, 1]] true = [[0, 0, 0, 0, 0, 1, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0]] for type in self.types: data = numpy.array([[0, 1, 0, 0, 1, 1, 1, 0], [1, 1, 1, 0, 0, 0, 0, 0], [0, 1, 0, 1, 1, 1, 1, 0], [0, 0, 1, 1, 1, 1, 1, 0], [0, 1, 1, 1, 0, 1, 1, 0], [0, 0, 0, 0, 1, 1, 1, 0], [0, 1, 1, 1, 1, 1, 1, 0], [0, 0, 0, 0, 0, 0, 0, 0]], type) out = ndimage.binary_hit_or_miss(data, struct1, struct2) self.failUnless(diff(true, out) < eps) #class NDImageTestResult(unittest.TestResult): # separator1 = '=' * 70 + '\n' # separator2 = '-' * 70 + '\n' # # def __init__(self, stream, verbose): # unittest.TestResult.__init__(self) # self.stream = stream # self.verbose = verbose # # def getDescription(self, test): # return test.shortDescription() or str(test) # # def startTest(self, test): # unittest.TestResult.startTest(self, test) # if self.verbose: # self.stream.write(self.getDescription(test)) # self.stream.write(" ... ") # # def addSuccess(self, test): # unittest.TestResult.addSuccess(self, test) # if self.verbose: # self.stream.write("ok\n") # # def addError(self, test, err): # unittest.TestResult.addError(self, test, err) # if self.verbose: # self.stream.write("ERROR\n") # # def addFailure(self, test, err): # unittest.TestResult.addFailure(self, test, err) # if self.verbose: # self.stream.write("FAIL\n") # # def printErrors(self): # self.printErrorList('ERROR', self.errors) # self.printErrorList('FAIL', self.failures) # # def printErrorList(self, flavour, errors): # for test, err in errors: # self.stream.write(self.separator1) # description = self.getDescription(test) # self.stream.write("%s: %s\n" % (flavour, description)) # self.stream.write(self.separator2) # self.stream.write(err) # #def test(): # if '-v' in sys.argv[1:]: # verbose = 1 # else: # verbose = 0 # suite = unittest.TestSuite() # suite.addTest(unittest.makeSuite(NDImageTest)) # result = NDImageTestResult(sys.stdout, verbose) # suite(result) # result.printErrors() # return len(result.failures), result.testsRun if __name__ == "__main__": #unittest.main() NumpyTest().run()