How to get started:
Requirements
Note that pupil-apriltags currently only runs on Python 3.6 or
higher.
Also we are using a newer python build system, which can fail for older
versions of pip with potentially misleading errors. Please make sure you
are using pip > 19 or consider upgrading pip to the latest version to be
on the safe side:
python -m pip install --upgrade pip
Install from PyPI
This is the recommended and easiest way to install pupil-apriltags.
pip install pupil-apriltags
We offer pre-built binary wheels for common operating systems. In case
your system does not match, the installation might take some time, since
the native library (apriltags-source) will be compiled first.
Manual installation from source (for development)
You can of course clone the repository and build from there. For
development you should install the development requirements as well.
This project uses the new python build system configuration from PEP
517 and PEP
518.
# clone the repository
git clone --recursive https://github.com/pupil-labs/apriltags.git
cd apriltags
# install apriltags in editable mode with development requirements
pip install -e .[dev]
# run tests
tox
Usage
Some examples of usage can be seen in the
src/pupil_apriltags/bindings.py file.
The Detector class is a wrapper around the Apriltags functionality.
You can initialize it as following:
from pupil_apriltags import Detector
at_detector = Detector(
families="tag36h11",
nthreads=1,
quad_decimate=1.0,
quad_sigma=0.0,
refine_edges=1,
decode_sharpening=0.25,
debug=0
)
The options are:
O
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D
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Explanation |
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f
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‘
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3
6
h
1
1
‘ |
Tag families, separated with a space |
n
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1 |
Number of threads |
q
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c
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2 |
Detection of quads can be done on a lower-resolution image,
improving speed at a cost of pose accuracy and a slight
decrease in detection rate. Decoding the binary payload is
still done at full resolution. Set this to 1.0 to use the full
resolution. |
q
u
a
d
_
s
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m
a |
0 |
What Gaussian blur should be applied to the segmented image.
Parameter is the standard deviation in pixels. Very noisy
images benefit from non-zero values (e.g. 0.8) |
r
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1 |
When non-zero, the edges of the each quad are adjusted to
“snap to” strong gradients nearby. This is useful when
decimation is employed, as it can increase the quality of the
initial quad estimate substantially. Generally recommended to
be on (1). Very computationally inexpensive. Option is ignored
if quad_decimate = 1 |
d
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2
5
e
-
2 |
How much sharpening should be done to decoded images? This can
help decode small tags but may or may not help in odd lighting
conditions or low light conditions |
d
e
b
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g |
0 |
If 1, will save debug images. Runs very slow |
Detection of tags in images is done by running the detect method of
the detector:
tags = at_detector.detect(img, estimate_tag_pose=False, camera_params=None, tag_size=None)
If you also want to extract the tag pose, estimate_tag_pose should
be set to True and camera_params ([fx, fy, cx, cy]) and
tag_size (in meters) should be supplied. The detect method
returns a list of Detection objects each having the following
attributes (note that the ones with an asterisks are computed only if
estimate_tag_pose=True):
A
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b
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Explanation |
|---|
t
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The family of the tag. |
t
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The decoded ID of the tag. |
h
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How many error bits were corrected? Note: accepting large numbers
of corrected errors leads to greatly increased false positive
rates. NOTE: As of this implementation, the detector cannot detect
tags with a Hamming distance greater than 2. |
d
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c
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s
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_
m
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A measure of the quality of the binary decoding process: the
average difference between the intensity of a data bit versus the
decision threshold. Higher numbers roughly indicate better
decodes. This is a reasonable measure of detection accuracy only
for very small tags–not effective for larger tags (where we could
have sampled anywhere within a bit cell and still gotten a good
detection.) |
h
o
m
o
g
r
a
p
h
y |
The 3x3 homography matrix describing the projection from an
“ideal” tag (with corners at (-1,1), (1,1), (1,-1), and (-1, -1))
to pixels in the image. |
c
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t
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The center of the detection in image pixel coordinates. |
c
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The corners of the tag in image pixel coordinates. These always
wrap counter-clock wise around the tag. |
p
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s
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R
* |
Rotation matrix of the pose estimate. |
p
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s
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_
t
* |
Translation of the pose estimate. |
p
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s
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r
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* |
Object-space error of the estimation. |
