Improving Marker Detection
Explore how to enhance fiduciary marker detection using Python and OpenCV. Learn to customize detection parameters, preprocess images for better clarity, and apply additional methods like camera calibration and multiple dictionaries. This lesson equips you with practical skills to increase accuracy and efficiency when detecting ArUco markers and AprilTags in various lighting and environmental conditions.
We can improve the detection of ArUco markers and AprilTags when using Python and OpenCV. This involves adjusting various parameters, preprocessing the images, and incorporating additional techniques.
Here are some suggestions of what we can do to improve the detection process:
Adjust detection parameters
OpenCV provides a set of default ArUco marker detection parameters, but we can customize them to improve detection. To create custom parameters, use the following:
detector_params = cv2.aruco.DetectorParameters_create()
We can then modify specific parameters. For example:
detector_params.adaptiveThreshConstant = 7 # Default is 7
detector_params.adaptiveThreshWinSizeStep = 4 # Default is 10
detector_params.minMarkerPerimeterRate = 0.01 # Default is 0.03
detector_params.maxMarkerPerimeterRate = 1.0 # Default is 4.0
detector_params.polygonalApproxAccuracyRate = 0.01 # Default is 0.03
These parameters can be adjusted based on our specific application and environment. We can experiment with different values to find the optimal configuration ...