# Vision

Computer Vision allows your robots to understand their environment. For the competition, this is used to locate markers. It will give you information about the type of the marker, the distance/angle to the marker, etc.

# Python

To look for markers call see():

markers = R.see()


markers is a Python list of marker objects. Which looks like a the following:

[arena Marker 0: 0.856m @0.754 degrees
  dist = 0.856
  bearing.y = 0.754
  bearing.x = 1.03e+02
  rotation.y = 5.1
  rotation.x = -11.4
  rotation.z = 9.47
  detection = TOO BIG TO PRINT

Full reference of the properties are further below but some useful properties are:

Property Description
marker.dist Distance to the marker in metres
marker.bearing.y The angle your robot needs to turn to get to the marker in degrees
marker.info.id Numeric code of the marker
marker.info.type One of arena or cube

# Codes

Every april tag has a code:

  • April tags 0-39 will be used for cubes. Although each shepherdess only has 6 sheep to find, there are 9 codes allocated to each team. This is to allow us to have spare sheep incase some get damaged during play

  • April tags 100+ will around the arena on the walls. See the rules for specifics on where around the rules they will be placed

Code Team
0 to 9 Leon
10 to 19 Zhora
20 to 29 Pris
30 to 39 Roy

# Blockly

Blocks for vision can be found in the Vision section.

Here's an example of a Blockly program that does some basic vision:

# Example

After reading the motors documentation you should be able to create a function which moves your robot by a number of meters as well as turn. We can then use this to write some code where a robot:

  • Looks for a marker

  • If it can see a marker:

    • Turn so that it is facing the marker
    • Drive the distance to the marker
  • If there is no marker in sight turn a bit and look again, maybe there is a marker out of view.

import robot

R = robot.Robot()

def move(distance):
    """The robot drives `distance` meters forwards"""

def turn(rotation):
    """The robot turns `rotation` degrees"""

while True:
    for marker in R.see():
        turn(marker.bearing.y)  # Face the marker
        move(marker.dist)       # Drive to the marker
        turn(20)  # The robot didn't see anything and so we turn and maybe see
                  # another marker

# The Marker object

Details about the markers can be accessed using the following syntax:

markers = R.see()  # returns list of markers which the robot can see

for marker in markers:
    print(marker.dist)       # The distance to the marker in meters
    print(marker.bearing.y)  # The rotation the robot would need to turn to
                             # face the marker
    print(marker.info.id)  # The number of the marker
    print("The robot didn't see any markers and skipped the for loop!")

A Marker object contains information about a detected marker. It has the following attributes:

Attribute What it does
dist The distance to the Marker from the camera in meters.
bearing How far the BrainBox would have to rotate to face that Marker in degrees
bearing.x The up/down bearing. 0 is vertically bellow the camera
bearing.y The left/right bearing. 0 is straight ahead from the camera
rotation How much the Marker would need to be rotated to face the BrainBox. (0,0,0) Is if the marker was facing the BrainBox in the upright position
rotation.x The roll of the marker
rotation.y The pitch of the marker
rotation.z The yaw of the marker
info An object with various information about the marker
info.id The ID number of the marker
info.type The type of marker, one of MARKER_TYPE
info.size The length of the black edge of the marker in meters
info.owner Who or what owns the marker, a MARKER_OWNER
info.type The type of marker, a MARKER_TYPE
info.owning_team Which team owns the marker, a TEAM, only set when info.owner is not MARKER_OWNER.ARENA
info.wool_type What type of wool the sheep has, a WOOL_TYPE, only set when info.type is MARKER_TYPE.SHEEP
info.bounding_box_colour A tuple describing the colour which is drawn around the marker in the preview image (Blue, Red, Green)
detection Technical information which has been inferred from the image.
detection.tag_family The family of AprilTag which is detected. RoboCon currently only uses tag36h11.
detection.tag_id The ID number of the detected marker. Aliased by marker.code.
detection.hamming The number of bits which were corrected. The detector cannon detect tags with a hamming distance greater than 2.
detection.decision_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. Only effective for tags which appear small.
detection.homography 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.
detection.center The image pixel coordinates of the center of the marker.
detection.corners The image pixel coordinates of corners of the detected marker
detection.pose_R The 3x3 Rotational matrix which describes the rotation of the marker relative to the origin.
detection.pose_T The 1x3 translation vector of the marker in meters.
detection.pose_err The uncertainty of the detection in meters. This number can vary massively between detections depending on if local minima were bypassed. See Apriltag: A robust and flexible visual fiducial system
dectection.dist The distance to the marker in meters.
detection.rotation How much the Marker would need to be rotated to face the BrainBox. (0,0,0) Is if the marker was facing the BrainBox in the upright position.
detection.bearing How far the BrainBox would have to rotate to face that Marker in degrees.


Axes are all defined relative to the camera not your robot. Since we have no way to know how you've mounted your camera. You may need to account for this.


You can import MARKER_OWNER, MARKER_TYPE, WOOL_TYPE and TEAM from robot, for example...

import robot

R = robot.Robot()

markers = R.see()

for marker in markers:
    if marker.info.owner == robot.MARKER_OWNER.ARENA:
        print(f"Marker {marker.info.id} is owned by the arena")
    elif marker.info.owning_team == R.zone:
        print(f"I own {marker.info.id}")
        print(f"Marker {marker.info.id} is owned by {marker.info.owning_team}")

# The Camera object

An interface to the camera is provided incase you want to do additional computer vision.

# Changing the resolution

The default the camera takes pictures at a resolution of 640x480px. You can change this by setting the res parameter.

import robot

R = robot.Robot()

print(f"The current res is set to {R.camera.res}")
R.camera.res = (1920, 1440)
print(f"The current res is set to {R.camera.res}")

You must use one of the following resolutions:

  • (640, 480) (default)
  • (1296, 736)
  • (1296, 976)
  • (1920, 1088)
  • (1920, 1440)


Using a higher resolution will increase the amount of time it takes to process the image, but you may be able to see more. Using a smaller resolution will be faster, but markers further away may stop being visible.

# Get data straight from the camera

If you wish to do your own computer vision you can capture frames directly from the camera using robot.camera.capture().

import robot

R = robot.Robot()

image = R.camera.capture()

image.grey_frame # A 2d numpy array of the image data uint8
image.colour_frame # A 3d numpy array of the image data
image.colour_type # The encoding method used to store the colour_frame defaults to 8 bit RGB.
image.time # A `datetime` object representing approximately the capture time.

# Using USB cameras

To use a USB camera you will need to initialize the Robot with something which inherits from robot.vision.Camera. Then just call R.see() as you would normally.

import robot
from robot.vision import RoboConUSBCamera

R = robot.Robot(camera=RoboConUSBCamera)


You will then need to calibrate your camera as the distance that it reports will not be accurate. You can do this by changing the value in the R.camera.params dictionary up or down.

To get the current value print it:

R.camera.params[(640, 480)] = (123, 123)

We recommend that you tune this value by placing a marker exactly 2m away, printing R.see() (remember to take an average), and tuning the focal length up or down until you get a value that is close to 2m. If you are feeling fancy you could even write a function to automatically tune the value.

Calibration data for a Logitech C270 is available:

LOGITECH_C270_FOCAL_LENGTHS = {  # fx, fy tuples
    (640, 480): (607.6669874845361, 607.6669874845361),
    (1296, 736): (1243.0561163806915, 1243.0561163806915),
    (1296, 976): (1232.4906991188611, 1232.4906991188611),
    (1920, 1088): (3142.634753484673, 3142.634753484673),
    (1920, 1440): (1816.5165227051677, 1816.5165227051677)

This data can be imported:

from robot.vision import LOGITECH_C270_FOCAL_LENGTHS