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#######################################################################
#
# Copyright 2009-2010 by Ullrich Koethe
#
# This file is part of the VIGRA computer vision library.
# The VIGRA Website is
# http://hci.iwr.uni-heidelberg.de/vigra/
# Please direct questions, bug reports, and contributions to
# ullrich.koethe@iwr.uni-heidelberg.de or
# vigra@informatik.uni-hamburg.de
#
# Permission is hereby granted, free of charge, to any person
# obtaining a copy of this software and associated documentation
# files (the "Software"), to deal in the Software without
# restriction, including without limitation the rights to use,
# copy, modify, merge, publish, distribute, sublicense, and/or
# sell copies of the Software, and to permit persons to whom the
# Software is furnished to do so, subject to the following
# conditions:
#
# The above copyright notice and this permission notice shall be
# included in all copies or substantial portions of the
# Software.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND
# EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES
# OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND
# NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT
# HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY,
# WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
# FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR
# OTHER DEALINGS IN THE SOFTWARE.
#
#######################################################################
import sys
print("\nexecuting test file", __file__, file=sys.stderr)
exec(compile(open('set_paths.py', "rb").read(), 'set_paths.py', 'exec'))
import numpy as np
from vigra import *
from vigra import arraytypes as at
from vigra.filters import *
from vigra.analysis import *
img_rgb_f = at.RGBImage(np.random.rand(100,200,3)*255,dtype=np.float32)
img_scalar_f = at.ScalarImage(np.random.rand(100,200)*255,dtype=np.float32)
img_multi_f = at.Vector4Image(np.random.rand(100,200,4)*255,dtype=np.float32)
img_3_f = at.Vector3Image(np.random.rand(100,200,3)*255,dtype=np.float32)
img_rgb_i = at.RGBImage(np.random.rand(100,200,3)*255,dtype=np.int32)
img_scalar_i = at.ScalarImage(np.random.rand(100,200)*255,dtype=np.int32)
img_multi_i = at.Vector4Image(np.random.rand(100,200,4)*255,dtype=np.int32)
vol_rgb_f = at.RGBVolume(np.random.rand(100,200,60,3)*255,dtype=np.float32)
vol_scalar_f = at.ScalarVolume(np.random.rand(100,200,50)*255,dtype=np.float32)
vol_multi_f = at.Vector6Volume(np.random.rand(100,200,50,6)*255,dtype=np.float32)
vol_rgb_i = at.RGBVolume(np.random.rand(100,200,60,3)*255,dtype=np.int32)
vol_scalar_i = at.ScalarVolume(np.random.rand(100,200,50)*255,dtype=np.int32)
vol_multi_i = at.Vector6Volume(np.random.rand(100,200,50,6)*255,dtype=np.int32)
def checkShape(shape1, shape2):
if isinstance(shape1, np.ndarray):
shape1 = shape1.shape
if isinstance(shape2, np.ndarray):
shape2 = shape2.shape
assert shape1 == shape2
def checkImages(i1,i2):
checkShape(i1.shape, i2.shape)
assert(np.sum(i1==i2)!=0)
def checkAboutSame(i1,i2):
checkShape(i1.shape, i2.shape)
difference=np.sum(np.abs(i1-i2))/float(np.size(i1))
assert(difference<5)
def test_watersheds():
res = watersheds(img_scalar_f)
checkShape(res[0].shape, img_scalar_f.shape)
def test_structureTensor():
res = structureTensor(img_scalar_f,1.0,2.0, out=img_3_f)
res = structureTensor(img_scalar_f,1.0,2.0)
res = structureTensor(img_rgb_f,1.0,2.0)
checkShape(res.shape, img_scalar_f.shape[:2] + (3,))
def test_simpleSharpening():
res = simpleSharpening2D(img_scalar_f)
def test_gaussianSharpening():
res = gaussianSharpening2D(img_scalar_f)
def test_convolution():
krnl = gaussianKernel(0.5)
k2_ = Kernel2D()
k2_.initDisk(10)
#k3 = gaussianDerivativeKernel(sigma, order)
#guassianDerivative(img, sx,ox,sy,oy,sz,oz)
#guassianDerivative(img, (sx,sy, sz), (ox, oy,oz))
k2=Kernel2D()
k2.initExplicitly((-1,-1),(1,1),np.array([[0,1,2],[1,2,3],[2,3,4]],dtype=np.float64))
res = convolve(img_scalar_f, k2)
def test_multiconvolution():
vol = vol_scalar_f
k=Kernel1D()
k.initGaussian(0.5)
a=convolveOneDimension(vol, 0, k)
a=convolveOneDimension(a, 1, k)
a=convolveOneDimension(a, 2, k)
b=convolve(vol, k)
c=convolve(vol, (k, k, k))
checkAboutSame(a, b)
checkAboutSame(a, c)
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