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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.filters import *
from vigra.sampling import *
from vigra.noise import *
import vigra.arraytypes as at
#in the hope, that functions are tested in C++, we basicly test return types
#image=readImage("/export/home/nhuesken/sas/experiments/testdata/bmpmultilabel.bmp")
image=at.RGBImage(np.random.rand(100,100,3)*255,dtype=np.float32)
scalar_image=at.ScalarImage(np.random.rand(100,100)*255,dtype=np.float32)
volume256=at.Volume(np.random.rand(100,100,100)*255,dtype=np.uint8)
volumeBin=at.Volume(np.random.rand(100,100,100))>0.5
def checkImages(i1,i2):
assert(i1.shape==i2.shape)
assert(np.sum(i1==i2)!=0)
def checkAboutSame(i1,i2):
assert(i1.shape==i2.shape)
difference=np.sum(np.abs(i1-i2))/float(np.size(i1))
assert(difference<5)
def test_simpleRotate():
i2=rotateImageSimple(image,orientation=RotationDirection.CLOCKWISE);
#simple basic test
assert(i2.shape[0] == image.shape[1])
assert(i2.shape[1] == image.shape[0])
#test, that they are compatible
i3=rotateImageSimple(i2,orientation=RotationDirection.COUNTER_CLOCKWISE);
checkImages(image,i3)
i2=rotateImageSimple(image,orientation=RotationDirection.UPSIDE_DOWN);
i3=rotateImageSimple(i2,orientation=RotationDirection.COUNTER_CLOCKWISE);
i2=rotateImageSimple(i3,orientation=RotationDirection.COUNTER_CLOCKWISE);
checkImages(image,i2)
def test_rotate():
i2=rotateImageSimple(image)
i3=i2
rotateImageDegree(image,degree=90,out=i3)
checkImages(i2,i3)
def test_resample():
#just testing the size
i2=resampleImage(image,factor=0.5)
assert(i2.shape[0]==image.shape[0]*0.5)
def test_resize():
i2=resizeImageNoInterpolation(image,shape=(image.shape[0]+10,image.shape[1]+10))
i2=resizeImageNoInterpolation(image,shape=(image.shape[0],image.shape[1]))
checkAboutSame(i2,image)
i2=resizeImageCatmullRomInterpolation(image,shape=(image.shape[0]+10,image.shape[1]+10))
i2=resizeImageCatmullRomInterpolation(image,shape=(image.shape[0],image.shape[1]))
checkAboutSame(i2,image)
i2=resizeImageCoscotInterpolation( image,shape=(image.shape[0]+10,image.shape[1]+10))
i2=resizeImageCoscotInterpolation( image,shape=(image.shape[0],image.shape[1]))
checkAboutSame(i2,image)
i2=resizeImageLinearInterpolation( image,shape=(image.shape[0]+10,image.shape[1]+10))
i2=resizeImageLinearInterpolation( image,shape=(image.shape[0],image.shape[1]))
checkAboutSame(i2,image)
i2=resizeImageSplineInterpolation(
image,shape=(image.shape[0]+10,image.shape[1]+10),order=4)
i2=resizeImageSplineInterpolation(
image,shape=(image.shape[0],image.shape[1]), order=4)
checkAboutSame(i2,image)
def test_2DMorphology():
i2=discErosion(image.astype(np.uint8),radius=2)
i3=(255-discDilation((256-image).astype(np.uint8),radius=2))
checkImages(i2,i3)
i2=discOpening(image.astype(np.uint8),radius=2)
i3=(255-discDilation((256-image).astype(np.uint8),radius=2))
checkImages(i2,i3)
def test_3DMorphologyBinary():
i3=0
i2=multiBinaryClosing(volumeBin,radius=2)
i3=multiBinaryOpening(volumeBin==False,radius=2)
i3=i3==False
checkImages(i2,i3)
def test_3DMorphologyGrayscale():
i2=multiGrayscaleErosion(volume256,sigma=2)
i3=(256-multiGrayscaleDilation(256-volume256,sigma=2))
checkImages(i2,i3)
i2=multiGrayscaleOpening(volume256,sigma=2)
i3=(256-multiGrayscaleClosing(256-volume256,sigma=2))
checkImages(i2,i3)
def test_Noise():
# ATM, we only test that these things run
image = RGBImage((100,100))
image[:50,:50] = (10, 10, 10)
image[50:,:50] = (20, 20, 20)
image[50:,50:] = (30, 30, 30)
image += np.random.normal(size=(100,100, 3))
noiseVarianceEstimation(image.bindAxis('c', 0))
noiseVarianceClustering(image.bindAxis('c', 0))
nonparametricNoiseNormalization(image)
quadraticNoiseNormalizationEstimated(image)
linearNoiseNormalizationEstimated(image)
quadraticNoiseNormalization(image,1.0,1.0,1.0)
linearNoiseNormalization(image,1.0,1.0)
def test_Kernel1D():
# we just test functions that were not directly imported
contents = np.array([-1,2,3,5,3,2,-1], dtype=np.float64)
k1 = Kernel1D()
k1.initExplicitly(-3,3, contents)
for k in range(-3, 4):
assert(k1[k]==contents[k+3])
k1[-2]=5
assert(k1[-2]==5)
def test_Kernel2D():
contents = np.array([[0,1,2],[3,4,5],[6,7,8]],dtype=np.float64)
k2=Kernel2D()
k2.initExplicitly((-1,-1),(1,1), contents)
for i in range(-1, 2):
for j in range(-1, 2):
assert(k2[i,j]==contents[i+1, j+1])
k2[0,-1]=-5
assert(k2[0,-1]==-5)
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