total 128452
-rwxrwxr-x. 1 templier templier  2449948 Aug 10 14:44 brainaligner
-rwxrwxr-x. 1 templier templier     1589 Aug 10 14:45 test1_256.marker
-rwxrwxr-x. 1 templier templier  7098416 Aug 10 14:45 test1_256.tif
-rwxrwxr-x. 1 templier templier  7098416 Aug 10 14:45 test2_256.tif
-rw-rw-r--. 1 templier templier  7077931 Aug 10 15:09 test1target.raw
-rw-rw-r--. 1 templier templier      336 Aug 10 15:10 t21_256.tif_target.csv
-rw-rw-r--. 1 templier templier      333 Aug 10 15:10 t21_256.tif_subject.csv
-rw-rw-r--. 1 templier templier       54 Aug 10 15:10 t21_256.tif_matching_quality.csv
-rw-rw-r--. 1 templier templier 33554475 Aug 10 15:10 test1_999_DF_sx.v3draw
-rw-rw-r--. 1 templier templier 33554475 Aug 10 15:10 test1_999_DF_sy.v3draw
-rw-rw-r--. 1 templier templier 33554475 Aug 10 15:10 test1_999_DF_sz.v3draw
-rw-rw-r--. 1 templier templier  7098416 Aug 10 15:10 t21_256.tif

The current input file has the surfix [tif]
#original slice=108 original rez=1.00000 -> #output slices=108 new rez=1.00000
The current input file has the surfix [tif]
#original slice=108 original rez=1.00000 -> #output slices=108 new rez=1.00000
After preprocessing:
 Size of subject_image = [256 256 108 1], size of target_image = [256 256 108 1].

-------------------------------------------------
***BrainAligner [version=0.942] parameters:
Target   file: [test1_256.tif]
Target    rez: [1.0000][1.0000][1.0000]
Ref chanl tgt: [0]
Landmark tgt:  [test1_256.marker]
Subject  file: [test2_256.tif]
Subject   rez: [1.0000][1.0000][1.0000]
Warp res file: [t21_256.tif]
Warp img size: [512]
Ref chanl sbj: [0]
Landmark sbj:  [(null)]
Align G/L type: [Local]
Hierarchical landmark search level: [1]
Landmark matching method: [1]
Displacement field producing method: [3]
Use nearest neighbor interpolation method: [0]
Displacement field file: [test1]
Convert to UINT8 for processing: [No]
Resample to pixel size 0.58um for processing: [No]
Volume change map file: [(null)]
Target copy channel (for attaching mask): [-1]
Subject paste channel (for attaching mask): [-1]
Verbose print: [0]
---------------------------------------------------

********************* target=uint8 subject=uint8
in setInitData(), the target image data normalizer=255.000
The current input file has the surfix [raw]
The data is not with a TIF surfix, -- now this program assumes it is RAW format defined by Hanchuan Peng.
size of [V3DLONG]=[8], [V3DLONG]=[8] [int]=[4], [short int]=[2], [double]=[8], [float]=[4]
in setInitData(), the subject image data normalizer=255.000
Done: data initilization.
Read 65 predefined control points from the file. Thus will not invoke the automatic control point detection.
target coord[0]=[87,    138,    43] around subject [87,    138,    43]:     target [43][138][87] -> subject[49][122][87]
target coord[1]=[2,    148,    31] around subject [2,    148,    31]:     ...... not added due to inconsistency of various matching scores.
target coord[2]=[6,    80,    33] around subject [6,    80,    33]:     ...... not added due to inconsistency of various matching scores.
target coord[3]=[68,    58,    56] around subject [68,    58,    56]:     target [56][58][68] -> subject[66][58][72]
target coord[4]=[121,    203,    67] around subject [121,    203,    67]:     target [67][203][121] -> subject[71][198][111]
target coord[5]=[226,    145,    67] around subject [226,    145,    67]:     ...... not added due to inconsistency of various matching scores.
target coord[6]=[87,    184,    20] around subject [87,    184,    20]:     ...... not added due to inconsistency of various matching scores.
target coord[7]=[151,    96,    72] around subject [151,    96,    72]:     target [72][96][151] -> subject[77][103][159]
target coord[8]=[23,    108,    25] around subject [23,    108,    25]:     ...... not added due to inconsistency of various matching scores.
target coord[9]=[49,    198,    40] around subject [49,    198,    40]:     target [40][198][49] -> subject[40][179][43]
target coord[10]=[121,    145,    26] around subject [121,    145,    26]:     ...... not added due to inconsistency of various matching scores.
target coord[11]=[31,    112,    40] around subject [31,    112,    40]:     ...... not added due to inconsistency of various matching scores.
target coord[12]=[29,    70,    63] around subject [29,    70,    63]:     target [63][70][29] -> subject[69][60][27]
target coord[13]=[86,    230,    58] around subject [86,    230,    58]:     target [58][230][86] -> subject[57][219][85]
target coord[14]=[6,    142,    77] around subject [6,    142,    77]:     target [77][142][6] -> subject[81][136][1]
target coord[15]=[166,    197,    34] around subject [166,    197,    34]:     target [34][197][166] -> subject[35][188][162]
target coord[16]=[78,    85,    34] around subject [78,    85,    34]:     target [34][85][78] -> subject[43][84][84]
target coord[17]=[198,    81,    69] around subject [198,    81,    69]:     target [69][81][198] -> subject[73][79][201]
target coord[18]=[13,    240,    55] around subject [13,    240,    55]:     ...... not added due to inconsistency of various matching scores.
target coord[19]=[89,    170,    44] around subject [89,    170,    44]:     target [44][170][89] -> subject[48][152][88]
target coord[20]=[69,    123,    46] around subject [69,    123,    46]:     target [46][123][69] -> subject[52][108][71]
target coord[21]=[76,    125,    36] around subject [76,    125,    36]:     ...... not added due to inconsistency of various matching scores.
target coord[22]=[31,    116,    39] around subject [31,    116,    39]:     ...... not added due to inconsistency of various matching scores.
target coord[23]=[170,    200,    36] around subject [170,    200,    36]:     ...... not added due to inconsistency of various matching scores.
target coord[24]=[77,    84,    36] around subject [77,    84,    36]:     ...... not added due to inconsistency of various matching scores.
target coord[25]=[74,    82,    74] around subject [74,    82,    74]:     target [74][82][74] -> subject[84][81][77]
target coord[26]=[147,    208,    74] around subject [147,    208,    74]:     target [74][208][147] -> subject[77][204][138]
target coord[27]=[60,    113,    74] around subject [60,    113,    74]:     target [74][113][60] -> subject[83][114][60]
target coord[28]=[66,    63,    53] around subject [66,    63,    53]:     target [53][63][66] -> subject[63][62][71]
target coord[29]=[197,    195,    59] around subject [197,    195,    59]:     target [59][195][197] -> subject[61][188][190]
target coord[30]=[137,    206,    59] around subject [137,    206,    59]:     target [59][206][137] -> subject[64][199][128]
target coord[31]=[172,    202,    59] around subject [172,    202,    59]:     target [59][202][172] -> subject[63][197][168]
target coord[32]=[104,    191,    77] around subject [104,    191,    77]:     ...... not added due to inconsistency of various matching scores.
target coord[33]=[83,    152,    81] around subject [83,    152,    81]:     ...... not added due to inconsistency of various matching scores.
target coord[34]=[76,    169,    81] around subject [76,    169,    81]:     target [81][169][76] -> subject[85][164][67]
target coord[35]=[46,    179,    76] around subject [46,    179,    76]:     ...... not added due to inconsistency of various matching scores.
target coord[36]=[89,    145,    49] around subject [89,    145,    49]:     target [49][145][89] -> subject[55][125][87]
target coord[37]=[73,    146,    49] around subject [73,    146,    49]:     ...... not added due to inconsistency of various matching scores.
target coord[38]=[30,    73,    55] around subject [30,    73,    55]:     ...... not added due to inconsistency of various matching scores.
target coord[39]=[12,    147,    38] around subject [12,    147,    38]:     target [38][147][12] -> subject[36][141][8]
target coord[40]=[48,    197,    39] around subject [48,    197,    39]:     target [39][197][48] -> subject[38][178][41]
target coord[41]=[48,    221,    39] around subject [48,    221,    39]:     target [39][221][48] -> subject[42][208][47]
target coord[42]=[12,    214,    39] around subject [12,    214,    39]:     ...... not added due to inconsistency of various matching scores.
target coord[43]=[34,    235,    39] around subject [34,    235,    39]:     ...... not added due to inconsistency of various matching scores.
target coord[44]=[47,    116,    54] around subject [47,    116,    54]:     target [54][116][47] -> subject[58][105][40]
target coord[45]=[42,    221,    54] around subject [42,    221,    54]:     target [54][221][42] -> subject[54][212][39]
target coord[46]=[11,    148,    54] around subject [11,    148,    54]:     target [54][148][11] -> subject[54][142][7]
target coord[47]=[77,    176,    54] around subject [77,    176,    54]:     ...... not added due to inconsistency of various matching scores.
target coord[48]=[27,    205,    54] around subject [27,    205,    54]:     target [54][205][27] -> subject[53][197][22]
target coord[49]=[84,    238,    54] around subject [84,    238,    54]:     ...... not added due to inconsistency of various matching scores.
target coord[50]=[92,    112,    45] around subject [92,    112,    45]:     target [45][112][92] -> subject[53][111][99]
target coord[51]=[137,    184,    45] around subject [137,    184,    45]:     target [45][184][137] -> subject[50][176][128]
target coord[52]=[121,    189,    45] around subject [121,    189,    45]:     ...... not added due to inconsistency of various matching scores.
target coord[53]=[178,    135,    71] around subject [178,    135,    71]:     ...... not added due to inconsistency of various matching scores.
target coord[54]=[178,    183,    69] around subject [178,    183,    69]:     ...... not added due to inconsistency of various matching scores.
target coord[55]=[81,    156,    26] around subject [81,    156,    26]:     ...... not added due to inconsistency of various matching scores.
target coord[56]=[30,    191,    26] around subject [30,    191,    26]:     ...... not added due to inconsistency of various matching scores.
target coord[57]=[33,    234,    26] around subject [33,    234,    26]:     target [26][234][33] -> subject[17][238][13]
target coord[58]=[13,    179,    26] around subject [13,    179,    26]:     target [26][179][13] -> subject[17][161][2]
target coord[59]=[29,    146,    22] around subject [29,    146,    22]:     ...... not added due to inconsistency of various matching scores.
target coord[60]=[18,    226,    18] around subject [18,    226,    18]:     ...... not added due to inconsistency of various matching scores.
target coord[61]=[9,    185,    12] around subject [9,    185,    12]:     target [12][185][9] -> subject[4][169][3]
target coord[62]=[13,    231,    12] around subject [13,    231,    12]:     target [12][231][13] -> subject[4][228][0]
target coord[63]=[23,    107,    29] around subject [23,    107,    29]:     ...... not added due to inconsistency of various matching scores.
target coord[64]=[71,    124,    29] around subject [71,    124,    29]:     target [29][124][71] -> subject[19][138][76]
(1). normalize the point cloud
(2). estimate the initial affine matrix from initial matching using RANSAC

       0,
    (   0). err_invp_min=5.313598
   
    (   3). err_invp_min=5.036110
       5,
    (   9). err_invp_min=4.519104
      10,  15,  20,  25,
    (  29). err_invp_min=4.481841
      30,  35,  40,
    (  43). err_invp_min=4.087120
      45,
      50,  55,
    (  56). err_invp_min=3.705978
      60,  65,  70,  75,  80,  85,
    (  88). err_invp_min=3.688620
      90,  95,
     100, 105, 110, 115, 120, 125, 130, 135, 140, 145,
     150, 155, 160, 165, 170, 175, 180, 185, 190, 195,
     200, 205, 210, 215, 220, 225, 230, 235, 240, 245,
     250, 255, 260, 265, 270, 275, 280, 285, 290, 295,
     300, 305, 310, 315, 320, 325, 330, 335, 340, 345,
     350, 355, 360, 365, 370, 375, 380,
    ( 381). err_invp_min=3.660280
     385, 390, 395,
     400, 405, 410, 415, 420, 425, 430, 435, 440, 445,
     450, 455, 460, 465, 470, 475, 480, 485, 490, 495,
     500, 505, 510, 515, 520, 525, 530, 535, 540, 545,
     550, 555, 560, 565, 570, 575, 580, 585, 590, 595,
     600, 605, 610, 615, 620, 625, 630, 635, 640, 645,
     650, 655, 660, 665, 670, 675, 680, 685, 690, 695,
     700, 705, 710, 715, 720, 725, 730, 735, 740, 745,
     750, 755, 760, 765, 770, 775, 780, 785, 790, 795,
     800, 805, 810, 815, 820, 825, 830, 835, 840, 845,
     850, 855, 860, 865, 870, 875, 880, 885, 890, 895,
     900, 905, 910, 915, 920, 925, 930, 935, 940, 945,
     950, 955, 960, 965, 970, 975, 980, 985, 990, 995,
(3). kill the wrong matches by comparing the distance between A and the corresponding B_invp
(4). de-normalization and get arr_2_invp!
    kill 4 pairs, 32 pairs left!
angle12=12.064 [tedge len=23.622 e1=24.434, e2=46.957]  - drop this plane
angle13=8.083 [tedge len=6.164 e1=41.400, e2=38.962]  - drop this plane
angle13=6.596 [tedge len=6.164 e1=36.235, e2=31.448]  - drop this plane
angle13=11.578 [tedge len=6.164 e1=30.594, e2=29.428]  - drop this plane
angle13=1.975 [tedge len=1.732 e1=48.990, e2=49.346]  - drop this plane
angle12=3.519 [tedge len=1.732 e1=24.000, e2=23.043]  - drop this plane
angle12=2.685 [tedge len=1.732 e1=28.931, e2=27.821]  - drop this plane
angle12=3.675 [tedge len=1.732 e1=27.019, e2=27.000]  - drop this plane
angle12=0.907 [tedge len=1.732 e1=41.449, e2=43.046]  - drop this plane
angle12=160.162 [tedge len=39.774 e1=16.062, e2=24.290]  - drop this plane
angle13=18.892 [tedge len=16.062 e1=49.417, e2=48.166]  - drop this plane
angle23=14.836 [tedge len=15.748 e1=46.141, e2=34.191]  - drop this plane
angle23=17.702 [tedge len=15.748 e1=48.929, e2=41.449]  - drop this plane
angle23=16.322 [tedge len=15.748 e1=49.880, e2=40.694]  - drop this plane
=============================
cc[1]=13.548     target=[87,138,43] subject=[87,122,49]
cc[2]=6.050     target=[68,58,56] subject=[72,58,66]
cc[3]=24.096     target=[121,203,67] subject=[111,198,71]
cc[4]=46.566     target=[151,96,72] subject=[159,103,77]
cc[5]=31.145     target=[49,198,40] subject=[43,179,40]
cc[6]=43.801     target=[29,70,63] subject=[27,60,69]
cc[7]=9.623     target=[86,230,58] subject=[85,219,57]
cc[8]=29.279     target=[6,142,77] subject=[1,136,81]
cc[9]=39.209     target=[166,197,34] subject=[162,188,35]
cc[10]=0.000     target=[78,85,34] subject=[84,84,43]
cc[11]=76.491     target=[198,81,69] subject=[201,79,73]
cc[12]=13.102     target=[89,170,44] subject=[88,152,48]
cc[13]=15.982     target=[69,123,46] subject=[71,108,52]
cc[14]=40.400     target=[74,82,74] subject=[77,81,84]
cc[15]=31.833     target=[147,208,74] subject=[138,204,77]
cc[16]=0.000     target=[60,113,74] subject=[60,114,83]
cc[17]=7.052     target=[66,63,53] subject=[71,62,63]
cc[18]=48.946     target=[197,195,59] subject=[190,188,61]
cc[19]=25.529     target=[137,206,59] subject=[128,199,64]
cc[20]=42.054     target=[172,202,59] subject=[168,197,63]
cc[21]=0.000     target=[76,169,81] subject=[67,164,85]
cc[22]=50.794     target=[12,147,38] subject=[8,141,36]
cc[23]=30.009     target=[48,197,39] subject=[41,178,38]
cc[24]=51.661     target=[48,221,39] subject=[47,208,42]
cc[25]=20.506     target=[47,116,54] subject=[40,105,58]
cc[26]=50.273     target=[42,221,54] subject=[39,212,54]
cc[27]=21.883     target=[11,148,54] subject=[7,142,54]
cc[28]=44.135     target=[27,205,54] subject=[22,197,53]
cc[29]=8.767     target=[92,112,45] subject=[99,111,53]
cc[30]=31.467     target=[137,184,45] subject=[128,176,50]
cc[31]=71.150     target=[13,179,26] subject=[2,161,17]
cc[32]=90.935     target=[9,185,12] subject=[3,169,4]
Overall mean=31.759      std=22.562
-------------------- Finished detecting the best matching points. Now compute displacement field. -------

Prepare save the matching quality record file [t21_256.tif_matching_quality.csv]
method_inconsistency= 0.5077,    model_violation=31.7590
Now save to matching point file of subject to [t21_256.tif_subject.csv]
Now save to matching point file of target to [t21_256.tif_target.csv]

---------------------------------
n=0     x=[87.000 -> 87.000] y=[138.000 -> 122.000] z=[43.000 -> 49.000]
n=1     x=[68.000 -> 72.000] y=[58.000 -> 58.000] z=[56.000 -> 66.000]
n=2     x=[121.000 -> 111.000] y=[203.000 -> 198.000] z=[67.000 -> 71.000]
n=3     x=[151.000 -> 159.000] y=[96.000 -> 103.000] z=[72.000 -> 77.000]
n=4     x=[49.000 -> 43.000] y=[198.000 -> 179.000] z=[40.000 -> 40.000]
n=5     x=[29.000 -> 27.000] y=[70.000 -> 60.000] z=[63.000 -> 69.000]
n=6     x=[86.000 -> 85.000] y=[230.000 -> 219.000] z=[58.000 -> 57.000]
n=7     x=[6.000 -> 1.000] y=[142.000 -> 136.000] z=[77.000 -> 81.000]
n=8     x=[166.000 -> 162.000] y=[197.000 -> 188.000] z=[34.000 -> 35.000]
n=9     x=[78.000 -> 84.000] y=[85.000 -> 84.000] z=[34.000 -> 43.000]
n=10     x=[198.000 -> 201.000] y=[81.000 -> 79.000] z=[69.000 -> 73.000]
n=11     x=[89.000 -> 88.000] y=[170.000 -> 152.000] z=[44.000 -> 48.000]
n=12     x=[69.000 -> 71.000] y=[123.000 -> 108.000] z=[46.000 -> 52.000]
n=13     x=[74.000 -> 77.000] y=[82.000 -> 81.000] z=[74.000 -> 84.000]
n=14     x=[147.000 -> 138.000] y=[208.000 -> 204.000] z=[74.000 -> 77.000]
n=15     x=[60.000 -> 60.000] y=[113.000 -> 114.000] z=[74.000 -> 83.000]
n=16     x=[66.000 -> 71.000] y=[63.000 -> 62.000] z=[53.000 -> 63.000]
n=17     x=[197.000 -> 190.000] y=[195.000 -> 188.000] z=[59.000 -> 61.000]
n=18     x=[137.000 -> 128.000] y=[206.000 -> 199.000] z=[59.000 -> 64.000]
n=19     x=[172.000 -> 168.000] y=[202.000 -> 197.000] z=[59.000 -> 63.000]
n=20     x=[76.000 -> 67.000] y=[169.000 -> 164.000] z=[81.000 -> 85.000]
n=21     x=[12.000 -> 8.000] y=[147.000 -> 141.000] z=[38.000 -> 36.000]
n=22     x=[48.000 -> 41.000] y=[197.000 -> 178.000] z=[39.000 -> 38.000]
n=23     x=[48.000 -> 47.000] y=[221.000 -> 208.000] z=[39.000 -> 42.000]
n=24     x=[47.000 -> 40.000] y=[116.000 -> 105.000] z=[54.000 -> 58.000]
n=25     x=[42.000 -> 39.000] y=[221.000 -> 212.000] z=[54.000 -> 54.000]
n=26     x=[11.000 -> 7.000] y=[148.000 -> 142.000] z=[54.000 -> 54.000]
n=27     x=[27.000 -> 22.000] y=[205.000 -> 197.000] z=[54.000 -> 53.000]
n=28     x=[92.000 -> 99.000] y=[112.000 -> 111.000] z=[45.000 -> 53.000]
n=29     x=[137.000 -> 128.000] y=[184.000 -> 176.000] z=[45.000 -> 50.000]
n=30     x=[13.000 -> 2.000] y=[179.000 -> 161.000] z=[26.000 -> 17.000]
n=31     x=[9.000 -> 3.000] y=[185.000 -> 169.000] z=[12.000 -> 4.000]

#################################
-------------------- Now compute the distances of pixels to the mapping points. -------

z=0 z=1 z=2 z=3 z=4 z=5 z=6 z=7 z=8 z=9 z=10 z=11 z=12 z=13 z=14 z=15 z=16 z=17 z=18 z=19 z=20 z=21 z=22 z=23 z=24 z=25 z=26 z=27 z=28 z=29 z=30 z=31 z=32
-------------------- Now interpolate the DF using linear method. -------

in ApplyDFToChannel(), the subject image data normalizer=255.000
Finish warping the 0th channel.
Done: warping.
Succeed to warp using the DF to the specified channel(s).
The current input file has the surfix [v3draw]
The data is not with a TIF surfix, -- now this program assumes it is RAW format defined by Hanchuan Peng.
size of [V3DLONG]=[8], [V3DLONG]=[8] [int]=[4], [short int]=[2], [double]=[8], [float]=[4]
Succeed to save to file test1_999_DF_sx.v3draw
The current input file has the surfix [v3draw]
The data is not with a TIF surfix, -- now this program assumes it is RAW format defined by Hanchuan Peng.
size of [V3DLONG]=[8], [V3DLONG]=[8] [int]=[4], [short int]=[2], [double]=[8], [float]=[4]
Succeed to save to file test1_999_DF_sy.v3draw
The current input file has the surfix [v3draw]
The data is not with a TIF surfix, -- now this program assumes it is RAW format defined by Hanchuan Peng.
size of [V3DLONG]=[8], [V3DLONG]=[8] [int]=[4], [short int]=[2], [double]=[8], [float]=[4]
Succeed to save to file test1_999_DF_sz.v3draw
Now save the output/warped image [pointer=0x811d250] datatype_warped=1 sz_warped=[256, 256, 108, 1]
The current input file has the surfix [tif]
The warped image has been saved to the file [t21_256.tif].