Difference between revisions of "Tutorial Fiducials"

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Image:Fiducial_squre_binary.png| Square Binary
Image:Fiducial_squre_binary.png| Square Binary
Image:Fiducial_square_image.png| Square Image
Image:Fiducial_square_image.png| Square Image
Image:Calib_target_chess_small.png| Calibration Target
File:Calibration_letter_chessboard_7x5.png| Calibration Target
Image:Fiducial square binary detected.jpg| Cubes rendered on top of detected fiducials
Image:Fiducial square binary detected.jpg| Cubes rendered on top of detected fiducials
</gallery>
</gallery>
</center>
</center>


In computer vision, a fiducial marker is a known object which can be easily identified.  BoofCV provides built in support several different fiducials, all of which can be easily printed on paper.  Applications are provided for creating your own postscript files.  BoofCV library provides a single high level interface for detection, identification and pose estimation of all fiducials. Alternatively you can use the low level API and access fiducial specific information. 


In computer vision, a fiducial marker is a known object which can be identified and have its pose estimated. BoofCV provides built in support several different fiducials, which can be easily printed.  Applications are provided for automatically creating printable postscript files and a high level interface for detection, identication and pose estimation. 
<center>[https://youtu.be/qJWDK_FrgHE Fiducial Video Overview]</center>


There are two types of fiducials supported in BoofCV, square and calibration targets.  Square fiducials encode a pattern inside a black square box.  These targets can be uniquely identified and provide a full pose estimate.  Calibration targets fiducials are repurposed targets used to calibrate cameras.  Calibration fiducials tend to provide very accurate pose estimation when close to the camera, but can have difficulty as they move away.  There are two significant disadvantage for calibration targets. 1) They don't provide a unique ID.  2) Most patterns are not fully orientation invariant.  You can see the lack of rotation invariance when it suddenly flips 180 degrees.
There are two types of fiducials supported in BoofCV, square and calibration targets.  Square fiducials encode a large number of unique patterns inside a black square box.  These targets can be uniquely identified and provide a full pose estimate.  Calibration targets fiducials are repurposed targets used to calibrate cameras.  Calibration fiducials tend to provide very accurate pose estimation when close to the camera, but can have difficulty as they move away.  There are two significant disadvantage for calibration targets. 1) They don't provide a unique ID.  2) Most patterns are not fully orientation invariant.  You can see the lack of rotation invariance when it suddenly flips 180 degrees.


<center>
<center>
'''Fiducial Summary Table'''
'''Fiducial Summary Table'''
{| class="wikitable"
{| class="wikitable"
! Type        !! Variant   !! Speed (FPS)  !! Unique    !! Pose    !! Accuracy
! Type        !! Variant     !! Speed (FPS)  !! Unique    !! Pose    !! Accuracy
|-
|-
|Square Binary|| Fast       ||style="text-align:center;"| 175          || 4096      || Full    || Good
|Square Binary|| Fast       ||style="text-align:center;"| 175          || Varies    || Full    || Good
|-
|-
|            || Robust     ||style="text-align:center;"| 67          ||          ||        ||
|            || Robust     ||style="text-align:center;"| 67          ||          ||        ||
|-
|-
|Square Image || Fast       ||style="text-align:center;"| 170          || &infin;  || Full    || Good
|Square Image || Fast       ||style="text-align:center;"| 170          || &infin;  || Full    || Good
|-
|-
|            || Robust     ||style="text-align:center;"| 66          ||          ||        ||
|            || Robust     ||style="text-align:center;"| 66          ||          ||        ||
|-
|-
|Calibration  || Chessboard ||style="text-align:center;"| 69           || 1        || Partial || Best Close
|Calibration  || Chessboard ||style="text-align:center;"| 97           || 1        || Partial || Best Close
|-
|            || Square Grid ||style="text-align:center;"| 100          || 1        || Partial || Best Close
|}
|}
''Speed'' to detect multiple fiducials in a 640x480 image on a Intel Core i7-2600 3.4 Ghz.  ''Unique'' max number of unique targets it can identify.  ''Pose'' indicates if a full 6-DOF estimate is found or subset.  Infinity symbol really means "lots".
''Speed'' to detect multiple fiducials in a 640x480 image on a Intel Core i7-2600 3.4 Ghz.  ''Unique'' max number of unique targets it can identify.  ''Pose'' indicates if a full 6-DOF estimate is found or subset.  Infinity symbol really means "lots".  Binary fiducials can be configured with different inner grids.  Total number of unique fiducials will very from 32 to 1.15e18 for a 3x3 or 8x8 grid, respectively.
</center>
</center>


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# Point camera at fiducial
# Point camera at fiducial


== Launching Fiducial Application ==
== Application ==
 
To help you get started and quickly test ideas there's a fiducial application included with BoofCV.  It will detect fiducials, then draw a 1/2 cube on top of them so you can see how accurate its 3D estimate is.  To run the application do the following:


<syntaxhighlight lang="bash">
<syntaxhighlight lang="bash">
cd boofcv/applications
cd boofcv/applications
gradle applciationsJar
gradle applicationsJar
java -cp applications.jar boofcv.app.WebcamTrackFiducial --Resolution=640:480 BINARY
java -jar applications.jar WebcamTrackFiducial --Resolution=640:480 BINARY
</syntaxhighlight>
</syntaxhighlight>


Line 50: Line 55:


<syntaxhighlight lang="bash">
<syntaxhighlight lang="bash">
java -cp applications.jar boofcv.app.WebcamTrackFiducial --Intrinsic=intrinsic.xml BINARY
java -jar applications.jar WebcamTrackFiducial --Intrinsic=intrinsic.xml BINARY
</syntaxhighlight>
</syntaxhighlight>


To get a list of commands and see how to track other types of fiducials just enter the command with no arguments.
To get a list of commands and see how to track other types of fiducials just enter the command with no arguments.
<syntaxhighlight lang="bash">
<syntaxhighlight lang="bash">
java -cp applications.jar boofcv.app.WebcamTrackFiducial
java -jar applications.jar WebcamTrackFiducial
</syntaxhighlight>
</syntaxhighlight>


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The following is a printable documents for all the types of fiducials supported in BoofCV.  Print these to get started quickly, but creating your own is also easy.
The following is a printable documents for all the types of fiducials supported in BoofCV.  Print these to get started quickly, but creating your own is also easy.


* Square Binary
{| class="wikitable"
*# [http://boofcv.org/notwiki/fiducials/square0643.eps File Binary #0643]
! Square Binary !!  Chessboard
*# [http://boofcv.org/notwiki/fiducials/square0284.eps File Binary #0284]
|-
* Square Image
|
*# [http://boofcv.org/notwiki/fiducials/dog.eps File Dog]
* [http://boofcv.org/notwiki/fiducials/square0643.eps File 4x4 Binary #0643]
*# [http://boofcv.org/notwiki/fiducials/text.eps File Text]
* [http://boofcv.org/notwiki/fiducials/square0284.eps File 4x4 Binary #0284]
* Calibration Chessboard
|
*# [http://boofcv.org/notwiki/calibration/letter_chess.ps Letter Sized Paper: Chessboard, 5 by 7, 30mm Squares]
* [http://boofcv.org/notwiki/calibration/letter_chess.ps Letter Sized Paper: Chessboard, 5 by 7, 30mm Squares]
*# [http://boofcv.org/notwiki/calibration/A4_chess.ps A4 Sized Paper: Chessboard, 5 by 8, 30mm Squares]
* [http://boofcv.org/notwiki/calibration/A4_chess.ps A4 Sized Paper: Chessboard, 5 by 8, 30mm Squares]
*# [http://boofcv.org/notwiki/calibration/A1_chess.ps A1 Sized Paper: Chessboard, 8 by 12, 60mm Squares]
* [http://boofcv.org/notwiki/calibration/A1_chess.ps A1 Sized Paper: Chessboard, 8 by 12, 60mm Squares]
* Calibration Square Grid
|-
*# [http://boofcv.org/notwiki/calibration/letter_square.ps Letter Sized Paper: Square Grid, 5 by 7, 30mm Squares]
! Square Image !! Square grid
*# [http://boofcv.org/notwiki/calibration/A4_square.ps A4 Sized Paper: Square Grid, 5 by 9, 30mm Squares]
|-
*# [http://boofcv.org/notwiki/calibration/A1_square.ps A1 Sized Paper: Square Grid, 7 by 11 60mm Squares]
|
* [http://boofcv.org/notwiki/fiducials/10cm_pentarose.eps Pentarose]
* [http://boofcv.org/notwiki/fiducials/10cm_yinyang.eps Yin Yang]
* [http://boofcv.org/notwiki/fiducials/all_a4.eps Sheet of All, A4]
* [http://boofcv.org/notwiki/fiducials/all_letter.eps Sheet of All, Letter]
* [https://github.com/lessthanoptimal/BoofCV-Data/tree/master/applet/fiducial/image/eps bunch more]
|
* [http://boofcv.org/notwiki/calibration/letter_square.ps Letter Sized Paper: Square Grid, 3 by 4, 30mm Squares]
* [http://boofcv.org/notwiki/calibration/A4_square.ps A4 Sized Paper: Square Grid, 3 by 5, 30mm Squares]
* [http://boofcv.org/notwiki/calibration/A1_square.ps A1 Sized Paper: Square Grid, 4 by 6 60mm Squares]
|}


= Creating your Own Fiducial =
= Creating your Own Fiducial =


For square fiducials, a convenient command-line application is provided which can create printable EPS documents which contain one or more fiducials on them.  For calibration targets, prefabricated patterns are provided which can also be printed.
For square fiducials, a convenient command-line application is provided which can create printable postscript (.ps) documents which contain one or more fiducials on them.  For calibration targets, prefabricated patterns are provided which can also be printed.


== Square Binary ==
== Square Binary ==
Line 85: Line 100:


<syntaxhighlight lang="bash">
<syntaxhighlight lang="bash">
java -cp applications.jar boofcv.app.CreateFiducialSquareBinaryEPS -OutputFile=fiducial.eps -PrintInfo -Units=cm 12 284
java -jar applications.jar CreateFiducialSquareBinary -OutputFile=fiducial.ps -PrintInfo -Units=cm 12 284
</syntaxhighlight>
</syntaxhighlight>


That will create a printable fiducial.eps file that encodes the number 284 in a square that's 12 centimeters.
That will create a printable fiducial.ps file that encodes the number 284 in a 4x4 square that's 12 centimeters.  The number of elements which compose its inner grid and the width of the outside border are both configurable.  By default all code assumes a 4x4 grid and 0.25 fractional border width.  This produces a reasonable compose between detection robustness and the number of features it can encode, which is 4096.


== Square Image ==
== Square Image ==
Line 95: Line 110:


<syntaxhighlight lang="bash">
<syntaxhighlight lang="bash">
java -cp applications.jar boofcv.app.CreateFiducialSquareImageEPS -OutputFile=fiducial.eps -PrintInfo -Units=cm 12 pentarose.png
java -jar applications.jar CreateFiducialSquareImage -OutputFile=fiducial.ps -PrintInfo -Units=cm 12 pentarose.png
</syntaxhighlight>
</syntaxhighlight>
This will create a pattern which is 10cm wide and encodes the image contained in 'dog.png'.  The output will be saved in "boofcv/applications/fiducial_image.eps" file.  See the top figure the resulting pattern.
This will create a pattern which is 12cm wide and encodes the image contained in 'pentarose.png'.  The output will be saved in "fiducial.ps" file.
 
Not feeling very creative or just want to see some example images? Several patterns are contained in [https://github.com/lessthanoptimal/BoofCV-Data/tree/master/example/fiducial/image/patterns "data/example/fiducial/image/patterns/"].


= How Do Square Fiducials Work? =
= How Do Square Fiducials Work? =
[[File:Square fiducial parts.png|thumb|300px|A) White outside region makes it easier to detect. B) Black square border which.  C) Encoded image or pattern.]]
[[File:Square fiducial parts.png|thumb|300px|The origin of the fiducial is at its center with x and y axises as shown.  The fiducial itself can be divided into three regions, a) white outside region which provides contrast against the black border, b) black square border, and c) encoded image or pattern.]]


All square fiducials share a common code base.  A target contains a black square of constant width and inside there is an image or pattern. The pattern is used to uniquely identify the fiducial and determine its orientation.  A full 6-DOF pose is estimated from these fiducials.  These targets are inspired by ARToolkit, but the code is not a port and was developed from scratched.   
All square fiducials share a common code base.  A target contains a black square of constant width and inside there is an image or pattern. The pattern is used to uniquely identify the fiducial and determine its orientation.  A full 6-DOF pose is estimated from these fiducials.  These targets are inspired by ARToolkit, but the code is not a port and was developed from scratched.   


When detecting a square fiducial, the first step is to threshold the image.  FactoryFiducial provides "robust" and "fast" techniques and the only difference between them is if they use an adaptive technique or a fixed threshold, respectively. Locally adaptive thresholding is invariant to local changes in lighting. The next step is to find the contour of blobs in the imageClearly invalid contours are pruned and a polygon fit to the contour.  This contour is used to provide the initial estimate of the squares edgesAn expectation-maximization algorithm is used to fit lines to the contour and the corners are found by the intersection of the linesOnce the corners are found a homography is computed and then decomposed to return the pose.   
'''Processing Steps'''
# Create binary image by thresholding
# Contours of dark objects
# Contour to crude quadrilateral
# Sub-pixel refinement of quadrilateral
# Undistort quadrilateral image and match to known patterns
# Extract 3D pose of fiducial
 
 
''Step 1:'' [[Example_Thresholding|Thresholding]] is performed using either a user configurable fixed threshold or locally adaptive threshold, fast and robust respectively. While slower robust can handle variations in lighting much better.
 
''Step 2:'' Contours are found from the binary blobs using [http://boofcv.org/javadoc/boofcv/alg/filter/binary/LinearContourLabelChang2004.html Chang 2004].
 
''Step 3:'' The contour is converted into a polygon using a [[Example_Fit_Polygon|split and merge]] techniqueOnly four sided polygons are accepted.
 
''Step 4:'' The crude polygons are refined to subpixel accuracy.  This is done by iteratively refining the estimate of each side's lineThat is done by computing the difference of line integrals tangential to each line, then using their intensity values to compute a weighted line.
 
''Step 5:'' The four corners are now known to a high level of precision, these are then used to compute a homography which is then used to generate an undistorted square image of the inner patternThis pattern is then used by the specific algorithm to identify a known target or compute a number.  Noisy images are rejected here.
 
''Step 6:'' The pose is computed through a combination of [[Example_PnP|P3P and P4P]] techniques.  The one with the smallest residual error is selected.  Then non-linear refinement is performed. Care is taken to reduce the effects of head on small fiducials, which have a degenerate geometry.  In that situation significant changes in orientation result in only a small change on the imageThat is handled by placing a greater emphasis on reprojection error caused by orientation.


Once the pose is known, perspective distortion can be removed and a synthetic image createdOrientation ambiguity is resolved using the fiducials pattern inside the square.  For the binary pattern 4 corners are usedFor image based fiducials, 4 different possible orientations are considered and the best match used.
NOTE: Steps 2 to 4 are carried out by the [[Example_Detect_Black_Polygons|black polygon detector]].
 
== Square Binary ==
 
The square binary fiducial encodes a 12-bit number, 4096 possible values, using a binary pattern. The number is encoded by breaking up the inner portion into 16 squares in a 4x4 grid. Three of the corners are always white and one black. This is how it resolves an orientation ambiguity.
 
== Square Image ==
 
When an image is loaded into this type of fiducial it is first converted into a square image then down sampled into a low resolution image and encoded efficiently When processing a video feed and a fiducial is detected the pattern is undistorted as usual. Then the hamming distance between the just observed pattern and all the known patterns is foundThe pattern with the best score within tolerance is accepted.


= Programming =
= Programming =
Line 113: Line 157:


<syntaxhighlight lang="java">
<syntaxhighlight lang="java">
FiducialDetector<ImageFloat32> detector = FactoryFiducial.pickAFiducial(...);
FiducialDetector<GrayF32> detector = FactoryFiducial.pickAFiducial(...);
... additional fiducial specific configuration goes here ...
... additional fiducial specific configuration goes here ...
detector.setIntrinsic(param);
detector.setIntrinsic(param);
Line 121: Line 165:
System.out.println("Target ID = "+detector.getId(i));
System.out.println("Target ID = "+detector.getId(i));
System.out.println("Target width = "+detector.getWidth(i));
System.out.println("Target width = "+detector.getWidth(i));
detector.getFiducialToWorld(i,targetToSensor);
detector.getFiducialToCamera(i,targetToSensor);
System.out.println("Location:");
System.out.println("Location:");
}
}
Line 127: Line 171:


See the examples below for a more understanding of how to use these different types of fiducials.
See the examples below for a more understanding of how to use these different types of fiducials.
Applets:
* [[Applet_Fiducials| Applet Fiducials]]


Examples:
Examples:

Latest revision as of 10:50, 20 January 2018

In computer vision, a fiducial marker is a known object which can be easily identified. BoofCV provides built in support several different fiducials, all of which can be easily printed on paper. Applications are provided for creating your own postscript files. BoofCV library provides a single high level interface for detection, identification and pose estimation of all fiducials. Alternatively you can use the low level API and access fiducial specific information.

Fiducial Video Overview

There are two types of fiducials supported in BoofCV, square and calibration targets. Square fiducials encode a large number of unique patterns inside a black square box. These targets can be uniquely identified and provide a full pose estimate. Calibration targets fiducials are repurposed targets used to calibrate cameras. Calibration fiducials tend to provide very accurate pose estimation when close to the camera, but can have difficulty as they move away. There are two significant disadvantage for calibration targets. 1) They don't provide a unique ID. 2) Most patterns are not fully orientation invariant. You can see the lack of rotation invariance when it suddenly flips 180 degrees.

Fiducial Summary Table

Type Variant Speed (FPS) Unique Pose Accuracy
Square Binary Fast 175 Varies Full Good
Robust 67
Square Image Fast 170 Full Good
Robust 66
Calibration Chessboard 97 1 Partial Best Close
Square Grid 100 1 Partial Best Close

Speed to detect multiple fiducials in a 640x480 image on a Intel Core i7-2600 3.4 Ghz. Unique max number of unique targets it can identify. Pose indicates if a full 6-DOF estimate is found or subset. Infinity symbol really means "lots". Binary fiducials can be configured with different inner grids. Total number of unique fiducials will very from 32 to 1.15e18 for a 3x3 or 8x8 grid, respectively.

Quick Start

  1. Calibrate your camera and save results (Tutorial)
    • Technically optional, but highly recommended
  2. Print binary fiducial, e.g. Binary #0643
  3. Launch fiducial webcam application
  4. Point camera at fiducial

Application

To help you get started and quickly test ideas there's a fiducial application included with BoofCV. It will detect fiducials, then draw a 1/2 cube on top of them so you can see how accurate its 3D estimate is. To run the application do the following:

cd boofcv/applications
gradle applicationsJar
java -jar applications.jar WebcamTrackFiducial --Resolution=640:480 BINARY

If you calibrated your camera you can do the following and get better results:

java -jar applications.jar WebcamTrackFiducial --Intrinsic=intrinsic.xml BINARY

To get a list of commands and see how to track other types of fiducials just enter the command with no arguments.

java -jar applications.jar WebcamTrackFiducial

Printable Fiducials

The following is a printable documents for all the types of fiducials supported in BoofCV. Print these to get started quickly, but creating your own is also easy.

Square Binary Chessboard
Square Image Square grid

Creating your Own Fiducial

For square fiducials, a convenient command-line application is provided which can create printable postscript (.ps) documents which contain one or more fiducials on them. For calibration targets, prefabricated patterns are provided which can also be printed.

Square Binary

Fiducials can be made using the applications.jar you created earlier. Usual for complete instructions just enter in the classes name with no arguments.

java -jar applications.jar CreateFiducialSquareBinary -OutputFile=fiducial.ps -PrintInfo -Units=cm 12 284

That will create a printable fiducial.ps file that encodes the number 284 in a 4x4 square that's 12 centimeters. The number of elements which compose its inner grid and the width of the outside border are both configurable. By default all code assumes a 4x4 grid and 0.25 fractional border width. This produces a reasonable compose between detection robustness and the number of features it can encode, which is 4096.

Square Image

A fiducial can be easily created from any image using "applications.jar".

java -jar applications.jar CreateFiducialSquareImage -OutputFile=fiducial.ps -PrintInfo -Units=cm 12 pentarose.png

This will create a pattern which is 12cm wide and encodes the image contained in 'pentarose.png'. The output will be saved in "fiducial.ps" file.

Not feeling very creative or just want to see some example images? Several patterns are contained in "data/example/fiducial/image/patterns/".

How Do Square Fiducials Work?

The origin of the fiducial is at its center with x and y axises as shown. The fiducial itself can be divided into three regions, a) white outside region which provides contrast against the black border, b) black square border, and c) encoded image or pattern.

All square fiducials share a common code base. A target contains a black square of constant width and inside there is an image or pattern. The pattern is used to uniquely identify the fiducial and determine its orientation. A full 6-DOF pose is estimated from these fiducials. These targets are inspired by ARToolkit, but the code is not a port and was developed from scratched.

Processing Steps

  1. Create binary image by thresholding
  2. Contours of dark objects
  3. Contour to crude quadrilateral
  4. Sub-pixel refinement of quadrilateral
  5. Undistort quadrilateral image and match to known patterns
  6. Extract 3D pose of fiducial


Step 1: Thresholding is performed using either a user configurable fixed threshold or locally adaptive threshold, fast and robust respectively. While slower robust can handle variations in lighting much better.

Step 2: Contours are found from the binary blobs using Chang 2004.

Step 3: The contour is converted into a polygon using a split and merge technique. Only four sided polygons are accepted.

Step 4: The crude polygons are refined to subpixel accuracy. This is done by iteratively refining the estimate of each side's line. That is done by computing the difference of line integrals tangential to each line, then using their intensity values to compute a weighted line.

Step 5: The four corners are now known to a high level of precision, these are then used to compute a homography which is then used to generate an undistorted square image of the inner pattern. This pattern is then used by the specific algorithm to identify a known target or compute a number. Noisy images are rejected here.

Step 6: The pose is computed through a combination of P3P and P4P techniques. The one with the smallest residual error is selected. Then non-linear refinement is performed. Care is taken to reduce the effects of head on small fiducials, which have a degenerate geometry. In that situation significant changes in orientation result in only a small change on the image. That is handled by placing a greater emphasis on reprojection error caused by orientation.

NOTE: Steps 2 to 4 are carried out by the black polygon detector.

Square Binary

The square binary fiducial encodes a 12-bit number, 4096 possible values, using a binary pattern. The number is encoded by breaking up the inner portion into 16 squares in a 4x4 grid. Three of the corners are always white and one black. This is how it resolves an orientation ambiguity.

Square Image

When an image is loaded into this type of fiducial it is first converted into a square image then down sampled into a low resolution image and encoded efficiently . When processing a video feed and a fiducial is detected the pattern is undistorted as usual. Then the hamming distance between the just observed pattern and all the known patterns is found. The pattern with the best score within tolerance is accepted.

Programming

All of these different types of fiducials can be used through a high level interface, *FiducialDetector*. FactoryFiducial is the easiest way to create instances of different fiducial types and it hides much of the complexity. Some detectors require additional information after construction. For example, square image fiducials require images be provided for each target it can detect. A sketch of how to process a single image is shown below.

FiducialDetector<GrayF32> detector = FactoryFiducial.pickAFiducial(...);
... additional fiducial specific configuration goes here ...
detector.setIntrinsic(param);
detector.detect(image);
Se3_F64 targetToSensor = new Se3_F64();
for (int i = 0; i < detector.totalFound(); i++){
	System.out.println("Target ID = "+detector.getId(i));
	System.out.println("Target width = "+detector.getWidth(i));
	detector.getFiducialToCamera(i,targetToSensor);
	System.out.println("Location:");
}

See the examples below for a more understanding of how to use these different types of fiducials.

Examples: