In [3]:

    

import os
os.chdir('/Users/albert/ndreg')


    

In [4]:

    

import numpy as np
import SimpleITK as sitk
import matplotlib.pyplot as plt


    

In [5]:

    

import SimpleITK as sitk
import matplotlib.pyplot as plt
from ndreg import *


    

In [6]:

    

myImg = imgDownload("checkerBig")


    

In [7]:

    

type(myImg)


    

    
Out[7]:





SimpleITK.SimpleITK.Image

In [8]:

    

plt.imshow(sitk.GetArrayFromImage(myImg), cmap=plt.cm.gray)
plt.show()


    

    




/usr/local/lib/python2.7/site-packages/matplotlib/font_manager.py:273: UserWarning: Matplotlib is building the font cache using fc-list. This may take a moment.
  warnings.warn('Matplotlib is building the font cache using fc-list. This may take a moment.')






    

In [9]:

    

imgShow(myImg)


    

In [10]:

    

%matplotlib inline


    

In [11]:

    

inImg = imgDownload("checkerBig")
refImg = imgDownload("checkerSmall")


    

In [12]:

    

imgShow(inImg)


    

In [13]:

    

imgShow(refImg)


    

In [14]:

    

plt.imshow(sitk.GetArrayFromImage(refImg - inImg))


    

    
Out[14]:





<matplotlib.image.AxesImage at 0x122b8b490>






    

In [15]:

    

affine = imgAffine(inImg, refImg)
print(affine)


    

    




[1.3698699228263693, -0.014549856768631108, -0.011269730636309839, 1.3716329255377866, -0.284440040054589, -0.2720956369497831]

In [16]:

    

defInImg = imgApplyAffine(inImg, affine, size=refImg.GetSize())
imgShow(defInImg)


    

In [17]:

    

plt.imshow(sitk.GetArrayFromImage(refImg - defInImg))


    

    
Out[17]:





<matplotlib.image.AxesImage at 0x123144150>






    

In [18]:

    

(field, invField) = imgMetamorphosis(inImg, refImg, iterations=100, verbose=True)


    

    




	E, E_velocity, E_rate, E_image (E_image %), LearningRate
0.	1036.05, 0.0357762, 0, 1036.01 (65.6301%), 8.593750e-06
1.	913.975, 0.0218348, 0, 913.953 (57.8979%), 9.453125e-06
2.	687.782, 0.0368255, 0, 687.746 (43.5679%), 1.039844e-05
3.	552.372, 0.0302024, 0, 552.342 (34.9902%), 2.859570e-06
4.	243.257, 0.0278379, 0, 243.229 (15.4083%), 3.145527e-06
5.	191.272, 0.0256142, 0, 191.246 (12.1152%), 8.650200e-07
6.	180.405, 0.0249982, 0, 180.38 (11.4268%), 9.515220e-07
7.	134.176, 0.0242637, 0, 134.152 (8.49835%), 5.233371e-07
8.	122.74, 0.0237509, 0, 122.717 (7.77396%), 5.756708e-07
9.	116.753, 0.0233563, 0, 116.73 (7.39471%), 6.332379e-07
10.	113.098, 0.023124, 0, 113.074 (7.16314%), 6.965617e-07
11.	111.144, 0.0228728, 0, 111.121 (7.03939%), 7.662179e-07
12.	105.353, 0.0227959, 0, 105.33 (6.67253%), 4.214198e-07
13.	102.511, 0.0226658, 0, 102.488 (6.49252%), 4.635618e-07
14.	99.4064, 0.0225444, 0, 99.3839 (6.29585%), 5.099180e-07
15.	96.242, 0.0224119, 0, 96.2195 (6.0954%), 5.609098e-07
16.	92.4903, 0.0222729, 0, 92.4681 (5.85774%), 6.170008e-07
17.	88.2906, 0.0221264, 0, 88.2685 (5.59171%), 6.787008e-07
18.	83.666, 0.0219675, 0, 83.6441 (5.29875%), 7.465709e-07
19.	79.4912, 0.021803, 0, 79.4694 (5.0343%), 8.212280e-07
	E, E_velocity, E_rate, E_image (E_image %), LearningRate
20.	78.11, 0.0216276, 0, 78.0884 (4.94681%), 9.033508e-07
21.	73.0635, 0.0215308, 0, 73.0419 (4.62712%), 4.968430e-07
22.	70.2745, 0.0214365, 0, 70.253 (4.45045%), 5.465272e-07
23.	68.4457, 0.0213274, 0, 68.4244 (4.3346%), 6.011800e-07
24.	63.1585, 0.0212718, 0, 63.1372 (3.99967%), 3.306490e-07
25.	61.329, 0.0212076, 0, 61.3078 (3.88378%), 3.637139e-07
26.	59.5987, 0.0211403, 0, 59.5776 (3.77417%), 4.000853e-07
27.	57.8339, 0.0210693, 0, 57.8128 (3.66237%), 4.400938e-07
28.	56.0158, 0.0209944, 0, 55.9948 (3.54721%), 4.841032e-07
29.	54.0822, 0.0209142, 0, 54.0613 (3.42472%), 5.325135e-07
30.	52.0699, 0.0208296, 0, 52.049 (3.29725%), 5.857648e-07
31.	49.9771, 0.020738, 0, 49.9563 (3.16468%), 6.443413e-07
32.	47.8826, 0.0206434, 0, 47.862 (3.032%), 7.087755e-07
33.	45.8674, 0.0205409, 0, 45.8468 (2.90434%), 7.796530e-07
34.	44.61, 0.0204435, 0, 44.5895 (2.82469%), 8.576183e-07
35.	43.4903, 0.020381, 0, 43.4699 (2.75377%), 4.716901e-07
36.	42.7078, 0.0203353, 0, 42.6875 (2.7042%), 5.188591e-07
37.	40.4615, 0.0202961, 0, 40.4412 (2.5619%), 2.853725e-07
38.	39.6718, 0.0202653, 0, 39.6515 (2.51188%), 3.139097e-07
39.	38.8972, 0.0202284, 0, 38.8769 (2.46281%), 3.453007e-07
	E, E_velocity, E_rate, E_image (E_image %), LearningRate
40.	38.0756, 0.020191, 0, 38.0555 (2.41077%), 3.798308e-07
41.	37.1948, 0.0201498, 0, 37.1747 (2.35497%), 4.178139e-07
42.	36.2457, 0.0201068, 0, 36.2256 (2.29485%), 4.595953e-07
43.	35.2247, 0.0200599, 0, 35.2046 (2.23017%), 5.055548e-07
44.	34.1313, 0.0200107, 0, 34.1113 (2.16091%), 5.561103e-07
45.	32.9399, 0.0199581, 0, 32.92 (2.08544%), 6.117213e-07
46.	31.6918, 0.019905, 0, 31.6719 (2.00637%), 6.728934e-07
47.	30.3651, 0.0198455, 0, 30.3453 (1.92234%), 7.401828e-07
48.	29.4515, 0.0197931, 0, 29.4317 (1.86447%), 8.142010e-07
49.	28.5963, 0.0197551, 0, 28.5765 (1.81029%), 4.478106e-07
50.	28.0885, 0.0197292, 0, 28.0687 (1.77812%), 4.925916e-07
51.	27.9972, 0.0196884, 0, 27.9775 (1.77234%), 5.418508e-07
52.	26.0422, 0.019675, 0, 26.0225 (1.6485%), 2.980179e-07
53.	25.3704, 0.019653, 0, 25.3508 (1.60594%), 3.278197e-07
54.	24.7518, 0.0196329, 0, 24.7322 (1.56676%), 3.606017e-07
55.	24.1347, 0.0196102, 0, 24.1151 (1.52766%), 3.966619e-07
56.	23.4398, 0.0195874, 0, 23.4202 (1.48364%), 4.363281e-07
57.	22.7436, 0.019562, 0, 22.7241 (1.43954%), 4.799609e-07
58.	21.9806, 0.019537, 0, 21.9611 (1.39121%), 5.279569e-07
59.	21.258, 0.0195084, 0, 21.2385 (1.34544%), 5.807526e-07
	E, E_velocity, E_rate, E_image (E_image %), LearningRate
60.	20.7053, 0.0194833, 0, 20.6858 (1.31042%), 6.388279e-07
61.	19.9884, 0.0194653, 0, 19.9689 (1.26501%), 3.513553e-07
62.	19.4306, 0.0194516, 0, 19.4112 (1.22968%), 3.864909e-07
63.	18.8919, 0.0194324, 0, 18.8725 (1.19555%), 4.251400e-07
64.	18.3652, 0.0194177, 0, 18.3458 (1.16218%), 4.676540e-07
65.	17.8901, 0.0193949, 0, 17.8707 (1.13209%), 5.144194e-07
66.	17.8091, 0.019383, 0, 17.7897 (1.12696%), 5.658613e-07
67.	16.7333, 0.0193664, 0, 16.7139 (1.05881%), 3.112237e-07
68.	16.2706, 0.0193581, 0, 16.2512 (1.0295%), 3.423461e-07
69.	15.8206, 0.0193443, 0, 15.8012 (1.00099%), 3.765807e-07
70.	15.3894, 0.0193342, 0, 15.3701 (0.973676%), 4.142388e-07
71.	14.9289, 0.0193195, 0, 14.9096 (0.944503%), 4.556626e-07
72.	14.4692, 0.0193085, 0, 14.4499 (0.915384%), 5.012289e-07
73.	14.0592, 0.0192915, 0, 14.0399 (0.889413%), 5.513518e-07
74.	14.0494, 0.0192827, 0, 14.0301 (0.888792%), 6.064870e-07
75.	13.234, 0.0192709, 0, 13.2148 (0.83714%), 3.335678e-07
76.	12.83, 0.0192647, 0, 12.8107 (0.811543%), 3.669246e-07
77.	12.4327, 0.0192553, 0, 12.4135 (0.786379%), 4.036171e-07
78.	12.1197, 0.0192474, 0, 12.1005 (0.766551%), 4.439788e-07
79.	11.8326, 0.0192384, 0, 11.8133 (0.748362%), 4.883767e-07
	E, E_velocity, E_rate, E_image (E_image %), LearningRate
80.	11.3635, 0.0192336, 0, 11.3443 (0.718648%), 2.686072e-07
81.	11.1145, 0.0192287, 0, 11.0953 (0.702875%), 2.954679e-07
82.	10.8692, 0.0192232, 0, 10.85 (0.687336%), 3.250147e-07
83.	10.6094, 0.0192178, 0, 10.5902 (0.670876%), 3.575161e-07
84.	10.3369, 0.019212, 0, 10.3177 (0.653614%), 3.932678e-07
85.	10.0517, 0.0192061, 0, 10.0325 (0.635547%), 4.325945e-07
86.	9.75438, 0.0191997, 0, 9.73518 (0.616712%), 4.758540e-07
87.	9.44409, 0.0191934, 0, 9.4249 (0.597056%), 5.234394e-07
88.	9.12185, 0.0191862, 0, 9.10266 (0.576643%), 5.757833e-07
89.	8.80835, 0.0191803, 0, 8.78917 (0.556784%), 6.333617e-07
90.	8.57757, 0.019171, 0, 8.5584 (0.542165%), 6.966978e-07
91.	8.35656, 0.0191698, 0, 8.33739 (0.528164%), 3.831838e-07
92.	8.15186, 0.0191635, 0, 8.1327 (0.515197%), 4.215022e-07
93.	8.03154, 0.0191618, 0, 8.01238 (0.507575%), 4.636524e-07
94.	7.70055, 0.0191579, 0, 7.68139 (0.486607%), 2.550088e-07
95.	7.57342, 0.0191558, 0, 7.55426 (0.478554%), 2.805097e-07
96.	7.44071, 0.0191532, 0, 7.42156 (0.470147%), 3.085607e-07
97.	7.30234, 0.0191504, 0, 7.28319 (0.461381%), 3.394167e-07
98.	7.15484, 0.0191475, 0, 7.13569 (0.452038%), 3.733584e-07
99.	6.99961, 0.0191444, 0, 6.98046 (0.442204%), 4.106943e-07
E = 6.99961 (0.442204%)
Length = 0.236798
Time = 1016.72s (16.9454m)

In [19]:

    

defInImg = imgApplyField(inImg, field, size=refImg.GetSize())
imgShow(defInImg)


    

In [20]:

    

plt.imshow(sitk.GetArrayFromImage(refImg - defInImg))


    

    
Out[20]:





<matplotlib.image.AxesImage at 0x12370b910>