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Add Python3 wrapper thanks to Dima Kogan (AprilRobotics#42)
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,60 @@ | ||
| AprilTag detector | ||
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| SYNOPSIS | ||
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| import cv2 | ||
| import numpy as np | ||
| from apriltag import apriltag | ||
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| imagepath = '/tmp/tst.jpg' | ||
| image = cv2.imread(imagepath, cv2.IMREAD_GRAYSCALE) | ||
| detector = apriltag("tag36h11") | ||
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| detections = detector.detect(image) | ||
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| print("Saw tags {} at\n{}". \ | ||
| format([d['id'] for d in detections], | ||
| np.array([d['center'] for d in detections]))) | ||
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| ----> Saw tags [3, 5, 7, 8, 10, 10, 14] at | ||
| [[582.42911184 172.90587335] | ||
| [703.32149701 271.50587376] | ||
| [288.1462089 227.01502779] | ||
| [463.63679264 227.91185418] | ||
| [ 93.88534443 241.61109765] | ||
| [121.94062798 237.97010936] | ||
| [356.46940849 260.20169159]] | ||
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| DESCRIPTION | ||
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| The AprilTags visual fiducial system project page is here: | ||
| https://april.eecs.umich.edu/software/apriltag | ||
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| This is a Python class to provide AprilTags functionality in Python programs. To | ||
| run the detector you | ||
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| 1. Construct an object of type apriltag.apriltag() | ||
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| 2. Invoke the detect() method on this object | ||
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| The detect() method takes a single argument: an image array. The return value is | ||
| a tuple containing the detections. Each detection is a dict with keys: | ||
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| - id: integer identifying each detected tag | ||
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| - center: pixel coordinates of the center of each detection | ||
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| - lb-rb-rt-lt: pixel coordinates of the 4 corners of each detection. The order | ||
| is left-bottom, right-bottom, right-top, left-top | ||
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| - hamming: 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. | ||
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| - margin: 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.) |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,80 @@ | ||
| AprilTag detector | ||
|
|
||
| SYNOPSIS | ||
|
|
||
| import cv2 | ||
| import numpy as np | ||
| from apriltag import apriltag | ||
|
|
||
| imagepath = '/tmp/tst.jpg' | ||
| image = cv2.imread(imagepath, cv2.IMREAD_GRAYSCALE) | ||
| detector = apriltag("tag36h11") | ||
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||
| detections = detector.detect(image) | ||
|
|
||
| print("Saw tags {} at\n{}". \ | ||
| format([d['id'] for d in detections], | ||
| np.array([d['center'] for d in detections]))) | ||
|
|
||
| ----> Saw tags [3, 5, 7, 8, 10, 10, 14] at | ||
| [[582.42911184 172.90587335] | ||
| [703.32149701 271.50587376] | ||
| [288.1462089 227.01502779] | ||
| [463.63679264 227.91185418] | ||
| [ 93.88534443 241.61109765] | ||
| [121.94062798 237.97010936] | ||
| [356.46940849 260.20169159]] | ||
|
|
||
| DESCRIPTION | ||
|
|
||
| The AprilTags visual fiducial system project page is here: | ||
| https://april.eecs.umich.edu/software/apriltag | ||
|
|
||
| This is a Python class to provide AprilTags functionality in Python programs. To | ||
| run the detector you | ||
|
|
||
| 1. Construct an object of type apriltag.apriltag() | ||
|
|
||
| 2. Invoke the detect() method on this object | ||
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| The constructor takes a number of arguments: | ||
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| - family: a string for the tag type we're detecting. This argument is required. | ||
| If an invalid string is given, the known list of tag families will be | ||
| reported. At the time of this writing the known families are: | ||
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| - "tag36h11" | ||
| - "tag25h9" | ||
| - "tag16h5" | ||
| - "tagCircle21h7" | ||
| - "tagCircle49h12" | ||
| - "tagStandard41h12" | ||
| - "tagStandard52h13" | ||
| - "tagCustom48h12" | ||
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| All the other arguments are optional: | ||
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| - Nthreads: how many threads the detector should use. Default is 1 | ||
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| - maxhamming: max number of corrected bits. Larger values guzzle RAM. Default is | ||
| 1 | ||
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| - decimate: 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. Default is 1.0 | ||
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| - blur: What Gaussian blur should be applied to the segmented image (used for | ||
| quad detection?) Parameter is the standard deviation in pixels. Very noisy | ||
| images benefit from non-zero values (e.g. 0.8). Default is 0.0 | ||
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| - refine_edges: 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. Very computationally inexpensive. Option is ignored if | ||
| decimate == 1. Default is True | ||
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| - debug: When non-zero, write a variety of debugging images to the current | ||
| working directory at various stages through the detection process. (Somewhat | ||
| slow). Default is False | ||
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| The detect() method takes a single argument: an image array |
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