[Artoolkit] Marker Training

Link: Documentation

About the Traditional Template Square Marker

Limitations (重要)

• Traditional Template Square Marker

Replacing the marker's pattern of the 4.25 inch square marker (used above) with a pattern of significantly increased complexity reduced the tracking range from 34 to 15 inches.

　　

• Traditional Template Square Marker

The total number of possible barcodes available depends on the number of rows and columns in the barcode and the type of error detection and correction (EDC) algorithm enabled. Using better EDC will result in a smaller set of barcodes being available, but lower likelihood of markers being misrecognized during tracking.

The barcode type is set via the function arSetMatrixCodeType

Multimarker Tracking

This refers specifically to the use of multiple square markers fixed to a single object. 一个固定物体上的多个识别点。

Some of the benefits of multimarkers include:

• • Increased robustness to occlusion: even when one marker is obscured, another may be visible. 提高鲁棒性。
  • Improved pose-estimation accuracy: in a multimarker set, all marker corners are used to calculate the pose, meaning that the markers effectively cover a larger optical angle, resulting in reduced numerical error. 提高计算精确性。
  • Possibility for robust pose estimation (using M-estimation). With multimarker tracking, statistical techniques can be applied to improve rejection of mis-read marker poses. 减少mis-read。

Note that these are the same advantages of using NFT (texture) tracking.

The advantages of multimarker tracking over NFT is that it is less CPU intensive, faster, and can operate reliably at greater distances from the camera.

The obvious disadvantage is that it requires the surface to be covered in square markers.

In ARToolKit for Android, any app using the ARBaseLib library or its underlying native implementation, ARWrapper, supports multimarker tracking without any further work by the developer. The code is provided in the ARMarkerMulti C++ class. Thus, the following examples include support for multimarker tracking, as well as other marker types:

• • ARSimple
  • ARSimpleInteraction
  • ARSimpleNative
  • ARSimpleNativeCars

A multimarker configuration file is structured as follows:

• • Lines beginning with a # character are treated as comments and ignored.
  • Blank lines are ignored. Blank lines do not play any part in the configuration structure.
  • The first non-blank/comment line in the file must be a single integer specifying the number of markers to be read from the multimarker configuration file. (This will usually be the actual number of markers in the set.)
  • Each five subsequent non-blank/comment lines specify a single marker in the set,
    • The first line of each marker specification is either an integer greater than or equal to 0 (if using barcode markers) or the path to a pictorial (template) marker pattern file, expressed relative to this multimarker configuration.
    • The next line specifies the size of this marker. Usually this will be the width of the outer border in millimeters. (Multiply inches by 25.4 to get millimeters).
    • The last three lines of the marker specification are the first three rows of a standard 4×4 homogenous coordinate transform matrix (in row-major order). This transform is from the combined multimarker set's coordinate system origin to the origin of this marker. More information on this transform is set out below.

Natural Feature Tracking with Fiducial Markers

The result can be seemingly marker-less tracking (since the fiducial markers need not be obvious to the human viewer). Although ARToolKit offers full marker-less tracking, there are situations where using one or more About the Traditional Template Square Marker has advantages:

• • Using the NFT 1.0 tracker plus fiducial markers is less computationally expensive than NFT 1.0 + 2.0 full marker-less tracking. Thus NFT 1.0 with fiducial markers is more practical for mobile devices.
  • The NFT 2.0 tracker has a practical limit on the number of distinct markers that can be distinguished at any one time. Thus, if a large number of images need to be tracked (e.g. a 100-page book), fiducial markers enable efficient identification of numerous images intended to be tracked.
  • Fiducial marker tracking adds significant robustness to tracking, particularly in poor lighting conditions, or when the camera is far away from the tracked image.

To use only the standard 1.0 version of ARToolKit's NFT tracking with a fiducial marker,

• • the tracked surface must have the marker either in the image or around the outside of it,
  • there must be at least one in each image,
  • the marker(s) must be square, and
  • the markers must all have a black border and lie on a white background, or vice-versa.

The marker(s) are not required to be of a particular size and the marker can embed colored patterns that blend with the image background.

/* 虽然简单，但用户体验不好 */

If implementing an app using standard ARToolKit NFT in which one or more fiducial markers is required,

an image and markers input set configuration file (.iset) is required to generate recognition and tracking data set files.

The generated files are a marker file (.mrk) and one (or more) pattern files (.pat-xx).

---

The output of this training is a set of data that can be used for realtime tracking in application using the ARToolKit SDK.

The following constraints apply to surfaces which can be used with ARToolKit NFT.

• • The surfaces to be tracked must be supplied as a rectangular image.
  • The images must be supplied in jpeg format.
  • The surface must be textured and have a reasonable amount of fine detail and sharp edges (a low degree of self-similarity and high spatial-frequency). Images with large areas of single flat color, that are blurred or have soft detail will not track well, if at all. In such images, it's difficult to locate distinct feature points.
  • Larger or higher resolution images (more pixels) will allow the extraction of feature points at higher levels of detail, and thus will track better when the camera is closer to the image, or when a higher resolution camera is used.

• 特征点提取：

unsw@unsw-UX303UB$ ./genTexData ~/Desktop/pinball.jpg
--
Generator started at -- :: +
Select extraction level for tracking features, (few) <--> (many), [default=]:
MAX_THRESH  = 0.900000
MIN_THRESH  = 0.550000
SD_THRESH   = 8.000000
Select extraction level for initializing features, (few) <--> (many), [default=]:
SURF_FEATURE =
Reading JPEG file...
   Done.
JPEG image '/home/unsw/Desktop/pinball.jpg' is 1637x2048.
JPEG image '/home/unsw/Desktop/pinball.jpg' does not contain embedded resolution data, and no resolution specified on command-line.
Enter resolution to use (in decimal DPI): 
Enter the minimum image resolution (DPI, in range [3.762, 220.000]):
Enter the maximum image resolution (DPI, in range [50.000, 220.000]): 
Image DPI (): 50.000000
Image DPI (): 62.996056
Image DPI (): 79.370056
Image DPI (): 100.000008
Image DPI (): 120.000000
Generating ImageSet...
   (Source image xsize=, ysize=, channels=, dpi=220.0).
  Done.
Saving to /home/unsw/Desktop/pinball.iset...
  Done.
Generating FeatureList...
Start for 120.000000 dpi image.
         ImageSize =  [pixel]
Extracted features =   [pixel]
 Filtered features =   [pixel]
/.
  Done.
Max feature =
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  : ( ,) : 0.379421 min=0.402622 max=0.801940, sd=50.645126
  : ( ,) : 0.380300 min=0.416025 max=0.776800, sd=48.386406
  : ( ,) : 0.445152 min=0.492460 max=0.809735, sd=61.714653
  : (, ) : 0.524282 min=0.524524 max=0.876935, sd=36.742512
  : (, ) : 0.545269 min=0.566303 max=0.859710, sd=32.648140
  : (,) : 0.556919 min=0.602261 max=0.858849, sd=19.694742
  : ( ,) : 0.558706 min=0.550294 max=0.766749, sd=36.739986
  : (, ) : 0.566552 min=0.560816 max=0.818152, sd=28.520765
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 : ( ,) : 0.714487 min=0.657192 max=0.824285, sd=43.777817
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 : (,) : 0.755940 min=0.583711 max=0.943352, sd=38.911907
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 : (,) : 0.763535 min=0.690581 max=0.915867, sd=39.687431
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 : (,) : 0.793819 min=0.754895 max=0.927860, sd=25.337523
 : (,) : 0.794728 min=0.557320 max=0.941102, sd=47.142551
 : (,) : 0.794926 min=0.551109 max=0.947475, sd=66.032494
 : (,) : 0.798013 min=0.770284 max=0.940773, sd=26.754530
 : (,) : 0.798614 min=0.732842 max=0.942958, sd=42.600384
 : (,) : 0.800596 min=0.829372 max=0.951824, sd=37.470352
 : (,) : 0.803065 min=0.660888 max=0.955436, sd=11.616018
 : (,) : 0.803943 min=0.554751 max=0.941155, sd=28.632961
 : (,) : 0.805024 min=0.709963 max=0.943415, sd=51.277973
 : (,) : 0.805752 min=0.803591 max=0.947742, sd=20.102901
 : (,) : 0.808967 min=0.626351 max=0.918251, sd=32.227089
 : (,) : 0.809588 min=0.677511 max=0.961718, sd=55.540771
 : (,) : 0.811643 min=0.661945 max=0.962027, sd=49.692898
 : (,) : 0.812565 min=0.626655 max=0.962040, sd=51.609238
 : (,) : 0.812870 min=0.672399 max=0.955859, sd=56.590508
 : (,) : 0.815031 min=0.715614 max=0.958174, sd=39.535885
 : (,) : 0.815342 min=0.618177 max=0.923080, sd=29.515026
 : (,) : 0.815806 min=0.810054 max=0.962029, sd=27.898920
 : (,) : 0.817358 min=0.553375 max=0.960826, sd=39.964111
 : (,) : 0.818157 min=0.723989 max=0.931619, sd=20.796185
 : (,) : 0.821157 min=0.815103 max=0.945559, sd=30.023014
 : (,) : 0.821407 min=0.575552 max=0.958943, sd=41.523037
 : (,) : 0.821567 min=0.740057 max=0.931550, sd=11.429000
 : (,) : 0.821830 min=0.778294 max=0.956450, sd=28.585604
 : (,) : 0.823122 min=0.813342 max=0.953675, sd=35.274818
 : (,) : 0.823791 min=0.830091 max=0.956633, sd=42.739384
 : (,) : 0.826842 min=0.750164 max=0.960751, sd=8.835805
 : (,) : 0.827690 min=0.568595 max=0.969311, sd=30.543201
 : (,) : 0.828143 min=0.727661 max=0.933358, sd=13.682913
 : (,) : 0.830045 min=0.574181 max=0.950994, sd=48.069145
 : (,) : 0.831641 min=0.618019 max=0.939475, sd=50.981564
: (,) : 0.833958 min=0.827165 max=0.963935, sd=25.540611
: (,) : 0.835869 min=0.789372 max=0.966805, sd=26.814104
: (,) : 0.836005 min=0.812730 max=0.955963, sd=12.860432
: (,) : 0.836044 min=0.604176 max=0.956743, sd=19.173864
: ( ,) : 0.836234 min=0.787960 max=0.949651, sd=15.605202
: (,) : 0.836281 min=0.699476 max=0.967093, sd=24.709869
: (,) : 0.837824 min=0.593167 max=0.970227, sd=26.464079
: (,) : 0.840960 min=0.553339 max=0.948325, sd=36.042049
: (,) : 0.841294 min=0.758295 max=0.973126, sd=34.514381
: (,) : 0.841896 min=0.590983 max=0.962583, sd=50.893532
: (,) : 0.843767 min=0.806483 max=0.963405, sd=25.815924
: (,) : 0.845366 min=0.722404 max=0.954271, sd=37.854237
: (,) : 0.845988 min=0.744495 max=0.955452, sd=35.659744
: (,) : 0.848209 min=0.794219 max=0.964603, sd=12.703241
: (,) : 0.849532 min=0.735853 max=0.953528, sd=25.841204
: (,) : 0.850685 min=0.720676 max=0.973533, sd=42.023205
: (,) : 0.854030 min=0.763552 max=0.957243, sd=50.512386
: ( ,) : 0.854542 min=0.694400 max=0.950297, sd=31.094616
: (,) : 0.855381 min=0.586259 max=0.964453, sd=26.304533
: ( ,) : 0.855954 min=0.769122 max=0.972207, sd=20.885775
: ( ,) : 0.856343 min=0.773897 max=0.969731, sd=20.200569
: ( ,) : 0.857231 min=0.660279 max=0.956343, sd=32.089931
: (,) : 0.858417 min=0.864698 max=0.961342, sd=28.840944
: (,) : 0.859689 min=0.874637 max=0.968092, sd=35.191395
: (,) : 0.859825 min=0.732891 max=0.969872, sd=21.927061
: ( ,) : 0.860233 min=0.817094 max=0.950137, sd=42.408623
: (,) : 0.861762 min=0.729305 max=0.938121, sd=32.645248
: (,) : 0.861990 min=0.760457 max=0.954904, sd=21.279266
: (,) : 0.863062 min=0.803295 max=0.972987, sd=31.389893
: (,) : 0.864507 min=0.835527 max=0.959412, sd=20.633755
: (,) : 0.865973 min=0.786729 max=0.961551, sd=14.772161
: (,) : 0.866957 min=0.723921 max=0.965726, sd=11.908808
: (,) : 0.868613 min=0.738751 max=0.978283, sd=37.001854
: (,) : 0.870105 min=0.705080 max=0.976399, sd=54.416012
: (,) : 0.870555 min=0.862716 max=0.963986, sd=52.231186
: (,) : 0.870636 min=0.690222 max=0.977169, sd=58.460876
: (,) : 0.871547 min=0.770074 max=0.969915, sd=34.714687
: (,) : 0.874115 min=0.807111 max=0.948065, sd=18.809776
: (,) : 0.874244 min=0.752791 max=0.974438, sd=49.741753
: (,) : 0.875194 min=0.829383 max=0.959214, sd=30.424299
: (,) : 0.875575 min=0.882799 max=0.965087, sd=29.212736
: (,) : 0.876628 min=0.789074 max=0.967728, sd=62.113373
: ( ,) : 0.876748 min=0.857133 max=0.972427, sd=23.289173
: (,) : 0.877818 min=0.833317 max=0.967093, sd=28.822382
: (,) : 0.878718 min=0.553808 max=0.966976, sd=46.079399
: (,) : 0.878864 min=0.688680 max=0.978990, sd=39.045341
: (,) : 0.882400 min=0.883747 max=0.967839, sd=11.069722
: (,) : 0.883863 min=0.666569 max=0.977000, sd=19.543068
: (,) : 0.884313 min=0.883567 max=0.971846, sd=25.047188
: (,) : 0.886579 min=0.776824 max=0.980528, sd=24.436762
: (,) : 0.887615 min=0.869838 max=0.963468, sd=15.290141
: (,) : 0.888368 min=0.835151 max=0.970944, sd=21.854877
: ( ,) : 0.888613 min=0.852755 max=0.977012, sd=20.664570
: (,) : 0.888915 min=0.701890 max=0.984152, sd=29.184855
: (,) : 0.889197 min=0.869495 max=0.974459, sd=11.696819
: (,) : 0.889327 min=0.768717 max=0.955065, sd=9.827321
: (,) : 0.890800 min=0.844556 max=0.972270, sd=45.839676
: ( ,) : 0.891264 min=0.842872 max=0.966874, sd=14.982453
: (,) : 0.891386 min=0.867655 max=0.976589, sd=21.863575
: ( ,) : 0.892803 min=0.558333 max=0.976258, sd=27.073399
: ( ,) : 0.894027 min=0.705994 max=0.960748, sd=47.561893
: (,) : 0.895706 min=0.875279 max=0.975085, sd=21.784636
: (,) : 0.896295 min=0.792583 max=0.952621, sd=30.285173
: (,) : 0.896768 min=0.902650 max=0.975427, sd=24.457178
: (,) : 0.897500 min=0.860278 max=0.969825, sd=25.603603
: (,) : 0.899012 min=0.742119 max=0.980758, sd=29.279078
---------------------------------------------------------------
Start for 100.000008 dpi image.
         ImageSize =  [pixel]
Extracted features =   [pixel]
 Filtered features =   [pixel]
 / .
  Done.
Max feature =
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  : (, ) : 0.429173 min=0.450797 max=0.820948, sd=28.798695
  : ( ,) : 0.495620 min=0.514524 max=0.820361, sd=55.195919
  : (,) : 0.540511 min=0.554955 max=0.876519, sd=33.614693
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  : (,) : 0.579497 min=0.577781 max=0.858645, sd=16.590513
  : (, ) : 0.580266 min=0.609462 max=0.853004, sd=32.476265
  : (,) : 0.603162 min=0.574921 max=0.914763, sd=40.661861
  : (, ) : 0.609202 min=0.567525 max=0.890933, sd=38.770107
 : (,) : 0.616884 min=0.606193 max=0.875591, sd=16.212690
 : (,) : 0.628818 min=0.559989 max=0.900930, sd=27.543072
 : (,) : 0.635330 min=0.626998 max=0.896662, sd=16.847752
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 : ( ,) : 0.658277 min=0.567431 max=0.797774, sd=41.324944
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 : (,) : 0.687030 min=0.691926 max=0.904222, sd=20.165924
 : (,) : 0.689154 min=0.554634 max=0.882771, sd=31.895771
 : (,) : 0.692668 min=0.553819 max=0.885160, sd=29.862997
 : (,) : 0.698154 min=0.686290 max=0.910936, sd=27.024332
 : (,) : 0.706138 min=0.584959 max=0.939056, sd=30.012547
 : (, ) : 0.712576 min=0.613834 max=0.908894, sd=18.724792
 : (,) : 0.714373 min=0.562389 max=0.940864, sd=49.591454
 : (,) : 0.715808 min=0.675634 max=0.894476, sd=40.163506
 : (,) : 0.716119 min=0.553789 max=0.909884, sd=40.418575
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: (,) : 0.898896 min=0.861754 max=0.979334, sd=22.827488
---------------------------------------------------------------
Start for 79.370056 dpi image.
         ImageSize =  [pixel]
Extracted features =   [pixel]
 Filtered features =    [pixel]
 / .
  Done.
Max feature =
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---------------------------------------------------------------
Start for 62.996056 dpi image.
         ImageSize =  [pixel]
Extracted features =   [pixel]
 Filtered features =    [pixel]
 / .
  Done.
Max feature =
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 : (,) : 0.796063 min=0.708768 max=0.926375, sd=21.610285
 : (,) : 0.798618 min=0.595742 max=0.955567, sd=55.855915
 : ( ,) : 0.801412 min=0.592979 max=0.937581, sd=36.052185
 : (,) : 0.804453 min=0.748329 max=0.949197, sd=32.405952
 : (,) : 0.808169 min=0.688386 max=0.911626, sd=28.636442
 : (,) : 0.809441 min=0.732770 max=0.930859, sd=25.023130
 : (,) : 0.810944 min=0.826716 max=0.954733, sd=36.062248
 : (,) : 0.821105 min=0.781620 max=0.950627, sd=36.201202
 : (,) : 0.825273 min=0.758560 max=0.943409, sd=13.854691
 : (,) : 0.826479 min=0.774573 max=0.955824, sd=32.626545
 : (,) : 0.826928 min=0.759029 max=0.939734, sd=48.020237
 : (,) : 0.827072 min=0.842281 max=0.957651, sd=37.085400
 : (,) : 0.829749 min=0.709671 max=0.956223, sd=54.552681
 : (,) : 0.831481 min=0.772146 max=0.956499, sd=40.525841
 : (,) : 0.833032 min=0.636060 max=0.960499, sd=53.648716
 : (,) : 0.835733 min=0.706491 max=0.948475, sd=57.130863
 : (,) : 0.846487 min=0.736950 max=0.949449, sd=35.986832
 : (, ) : 0.848287 min=0.819719 max=0.960537, sd=20.586964
 : (,) : 0.853023 min=0.674944 max=0.976931, sd=43.385296
 : (,) : 0.855907 min=0.814666 max=0.962084, sd=64.798088
 : (, ) : 0.857782 min=0.658946 max=0.974178, sd=9.765615
 : (,) : 0.859072 min=0.721597 max=0.964489, sd=64.371178
 : (,) : 0.863501 min=0.847128 max=0.957214, sd=30.939293
 : (,) : 0.867711 min=0.800812 max=0.967650, sd=39.102413
 : (,) : 0.871376 min=0.724515 max=0.966266, sd=48.850288
 : ( ,) : 0.876220 min=0.845073 max=0.971295, sd=42.473747
 : (,) : 0.892422 min=0.717912 max=0.979466, sd=28.400930
 : (, ) : 0.892945 min=0.786420 max=0.979330, sd=20.448406
 : (,) : 0.896210 min=0.851089 max=0.980158, sd=41.161392
 : (,) : 0.898482 min=0.736852 max=0.979373, sd=40.656662
 : ( ,) : 0.898493 min=0.820945 max=0.958475, sd=29.872866
---------------------------------------------------------------
Start for 50.000000 dpi image.
         ImageSize =  [pixel]
Extracted features =   [pixel]
 Filtered features =    [pixel]
 / .
  Done.
Max feature =
  : (,) : 0.409095 min=0.423076 max=0.786830, sd=38.354855
  : (,) : 0.565809 min=0.609225 max=0.883308, sd=27.912880
  : (,) : 0.576045 min=0.579438 max=0.813340, sd=21.002932
  : (,) : 0.579516 min=0.587601 max=0.884169, sd=38.661457
  : (,) : 0.644444 min=0.625607 max=0.872402, sd=40.615192
  : (,) : 0.656507 min=0.591580 max=0.855815, sd=43.474426
  : (, ) : 0.657504 min=0.555764 max=0.898058, sd=30.923130
  : (, ) : 0.662833 min=0.576045 max=0.902070, sd=26.458992
  : (,) : 0.662949 min=0.665047 max=0.875006, sd=49.367023
 : ( ,) : 0.670349 min=0.561400 max=0.865629, sd=37.549335
 : (,) : 0.675475 min=0.555582 max=0.905002, sd=48.826569
 : (,) : 0.677357 min=0.646990 max=0.927771, sd=27.972940
 : ( ,) : 0.686276 min=0.550450 max=0.910061, sd=53.431999
 : (,) : 0.689275 min=0.618263 max=0.907108, sd=31.436350
 : (,) : 0.709105 min=0.581470 max=0.902980, sd=40.365704
 : (,) : 0.713734 min=0.673753 max=0.908628, sd=50.451111
 : (, ) : 0.714805 min=0.704401 max=0.883490, sd=31.044941
 : ( ,) : 0.716260 min=0.604710 max=0.933287, sd=26.231071
 : ( ,) : 0.717083 min=0.556839 max=0.948899, sd=29.302229
 : (,) : 0.718400 min=0.567854 max=0.910843, sd=47.042545
 : (,) : 0.720351 min=0.730358 max=0.906248, sd=25.519068
 : (,) : 0.720452 min=0.635529 max=0.935623, sd=45.270584
 : (,) : 0.729541 min=0.752331 max=0.915800, sd=54.769844
 : (,) : 0.730325 min=0.650699 max=0.904523, sd=28.654446
 : (,) : 0.734137 min=0.726759 max=0.906466, sd=32.581650
 : ( ,) : 0.742655 min=0.622288 max=0.815669, sd=40.283253
 : (,) : 0.745526 min=0.664041 max=0.940655, sd=36.527069
 : (,) : 0.752473 min=0.553108 max=0.913527, sd=40.662380
 : (,) : 0.754339 min=0.768570 max=0.938685, sd=34.522438
 : (, ) : 0.781405 min=0.728405 max=0.901987, sd=37.145031
 : ( ,) : 0.797876 min=0.558383 max=0.915193, sd=32.620537
 : ( ,) : 0.799267 min=0.722797 max=0.931799, sd=40.063858
 : (,) : 0.799847 min=0.774071 max=0.929137, sd=28.837950
 : (,) : 0.803153 min=0.607517 max=0.961875, sd=44.224556
 : (,) : 0.805617 min=0.606686 max=0.948058, sd=57.030193
 : (, ) : 0.806086 min=0.553510 max=0.963031, sd=8.954105
 : (,) : 0.813065 min=0.590033 max=0.949263, sd=49.212040
 : ( ,) : 0.814844 min=0.726460 max=0.922312, sd=27.738249
 : (,) : 0.854165 min=0.788547 max=0.963051, sd=63.804325
 : ( , ) : 0.863481 min=0.822804 max=0.965674, sd=48.539753
 : (,379) : 0.864024 min=0.814080 max=0.966658, sd=43.544121
 : (,) : 0.868105 min=0.831030 max=0.970583, sd=46.397461
 : (, ) : 0.877267 min=0.830728 max=0.967012, sd=36.181847
 : (,) : 0.886550 min=0.733276 max=0.976608, sd=42.743977
 : ( , ) : 0.897586 min=0.874908 max=0.975650, sd=26.763504
---------------------------------------------------------------
  Done.
Saving FeatureSet...
  Done.
Generating FeatureSet3...
(, ) 120.000000[dpi]
Freak features - =========  ===========
(, ) 100.000008[dpi]
Freak features - =========  ===========
(, ) 79.370056[dpi]
Freak features - =========  ===========
(, ) 62.996056[dpi]
Freak features - =========  ===========
(, ) 50.000000[dpi]
Freak features - =========  ===========
  Done.
Saving FeatureSet3...
  Done.
Generator finished at -- :: +
--

• 查看特征点

After launching dispFeatureSet, the various image resolutions will be displayed on screen with the tracking features overlaid. The features used in continuous tracking are outlined by red boxes, and the features used in identifying the pages andinitializing tracking are marked by green crosses.

Debugging Marker Recognition Problems

If you also wish to display pose-estimates errors or wish to check recognition of template markers, you will need to define a multi-marker configuration file first.

By default, check_id reads its multimarker configuration from up to two multimarker configuration files specified on the command line. You can test (for example) using the pre-supplied file Data/cubeMarkerConfig.dat (which is set to track the cube marker whose image is supplied in PDF form in doc/patterns/Cubes/cube00-05-a4.pdf or /doc/patterns/Cubes/cube00-05-latter.pdf) using the following launch syntax. On Linux / OS X, type:

   #the number of patterns to be recognized

   #marker 

   40.0
    1.0000  0.0000  0.0000    0.0000
    0.0000  1.0000  0.0000    0.0000
    0.0000  0.0000  1.0000    0.0000

  #marker 

  40.0
   1.0000  0.0000  0.0000    0.0000
   0.0000  0.0000  1.0000   30.0000
   0.0000 -1.0000  0.0000  -30.0000

  #marker 

  40.0
   0.0000  0.0000  1.0000   30.0000
   0.0000  1.0000  0.0000    0.0000
  -1.0000  0.0000  0.0000  -30.0000

  #marker 

  40.0
   1.0000  0.0000  0.0000    0.0000
   0.0000 -1.0000  0.0000    0.0000
   0.0000  0.0000 -1.0000  -60.0000

  #marker 

  40.0
   1.0000  0.0000  0.0000    0.0000
   0.0000  0.0000 -1.0000  -30.0000
   0.0000  1.0000  0.0000  -30.0000

  #marker 

  40.0
   0.0000  0.0000 -1.0000  -30.0000
   0.0000  1.0000  0.0000    0.0000
   1.0000  0.0000  0.0000  -30.0000

cubeMarkerConfig.dat

日后再说。