How to Detect ArUco Markers Using OpenCV and Python


In this tutorial, I will show you how to detect an ArUco Marker in a real-time video stream (i.e. my webcam) using OpenCV (Python). I will follow this tutorial

By the end of this tutorial, you will be able to generate output like this:



Create the Code

Open your favorite code editor, and write the following code. I will name my program This program detects an ArUco marker in a real-time video stream (we’ll use the built-in webcam).

#!/usr/bin/env python
Welcome to the ArUco Marker Detector!
This program:
  - Detects ArUco markers using OpenCV and Python
from __future__ import print_function # Python 2/3 compatibility
import cv2 # Import the OpenCV library
import numpy as np # Import Numpy library

# Project: ArUco Marker Detector
# Date created: 12/18/2021
# Python version: 3.8
# Reference:

desired_aruco_dictionary = "DICT_ARUCO_ORIGINAL"

# The different ArUco dictionaries built into the OpenCV library. 
  "DICT_4X4_50": cv2.aruco.DICT_4X4_50,
  "DICT_4X4_100": cv2.aruco.DICT_4X4_100,
  "DICT_4X4_250": cv2.aruco.DICT_4X4_250,
  "DICT_4X4_1000": cv2.aruco.DICT_4X4_1000,
  "DICT_5X5_50": cv2.aruco.DICT_5X5_50,
  "DICT_5X5_100": cv2.aruco.DICT_5X5_100,
  "DICT_5X5_250": cv2.aruco.DICT_5X5_250,
  "DICT_5X5_1000": cv2.aruco.DICT_5X5_1000,
  "DICT_6X6_50": cv2.aruco.DICT_6X6_50,
  "DICT_6X6_100": cv2.aruco.DICT_6X6_100,
  "DICT_6X6_250": cv2.aruco.DICT_6X6_250,
  "DICT_6X6_1000": cv2.aruco.DICT_6X6_1000,
  "DICT_7X7_50": cv2.aruco.DICT_7X7_50,
  "DICT_7X7_100": cv2.aruco.DICT_7X7_100,
  "DICT_7X7_250": cv2.aruco.DICT_7X7_250,
  "DICT_7X7_1000": cv2.aruco.DICT_7X7_1000,
def main():
  Main method of the program.
  # Check that we have a valid ArUco marker
  if ARUCO_DICT.get(desired_aruco_dictionary, None) is None:
    print("[INFO] ArUCo tag of '{}' is not supported".format(
  # Load the ArUco dictionary
  print("[INFO] detecting '{}' markers...".format(
  this_aruco_dictionary = cv2.aruco.Dictionary_get(ARUCO_DICT[desired_aruco_dictionary])
  this_aruco_parameters = cv2.aruco.DetectorParameters_create()
  # Start the video stream
  cap = cv2.VideoCapture(0)
    # Capture frame-by-frame
    # This method returns True/False as well
    # as the video frame.
    ret, frame =  
    # Detect ArUco markers in the video frame
    (corners, ids, rejected) = cv2.aruco.detectMarkers(
      frame, this_aruco_dictionary, parameters=this_aruco_parameters)
    # Check that at least one ArUco marker was detected
    if len(corners) > 0:
      # Flatten the ArUco IDs list
      ids = ids.flatten()
      # Loop over the detected ArUco corners
      for (marker_corner, marker_id) in zip(corners, ids):
        # Extract the marker corners
        corners = marker_corner.reshape((4, 2))
        (top_left, top_right, bottom_right, bottom_left) = corners
        # Convert the (x,y) coordinate pairs to integers
        top_right = (int(top_right[0]), int(top_right[1]))
        bottom_right = (int(bottom_right[0]), int(bottom_right[1]))
        bottom_left = (int(bottom_left[0]), int(bottom_left[1]))
        top_left = (int(top_left[0]), int(top_left[1]))
        # Draw the bounding box of the ArUco detection
        cv2.line(frame, top_left, top_right, (0, 255, 0), 2)
        cv2.line(frame, top_right, bottom_right, (0, 255, 0), 2)
        cv2.line(frame, bottom_right, bottom_left, (0, 255, 0), 2)
        cv2.line(frame, bottom_left, top_left, (0, 255, 0), 2)
        # Calculate and draw the center of the ArUco marker
        center_x = int((top_left[0] + bottom_right[0]) / 2.0)
        center_y = int((top_left[1] + bottom_right[1]) / 2.0), (center_x, center_y), 4, (0, 0, 255), -1)
        # Draw the ArUco marker ID on the video frame
        # The ID is always located at the top_left of the ArUco marker
        cv2.putText(frame, str(marker_id), 
          (top_left[0], top_left[1] - 15),
          0.5, (0, 255, 0), 2)
    # Display the resulting frame
    # If "q" is pressed on the keyboard, 
    # exit this loop
    if cv2.waitKey(1) & 0xFF == ord('q'):
  # Close down the video stream
if __name__ == '__main__':

Save the file, and close it.

You need to have opencv-contrib-python installed and not opencv-python. Open a terminal window, and type:

pip uninstall opencv-python
pip install opencv-contrib-python

To run the program in Linux for example, type the following command:


If you want to restore OpenCV to the previous version after you’re finished creating the ArUco markers, type:

pip uninstall opencv-contrib-python
pip install opencv-python

To set the changes, I recommend rebooting your computer.

That’s it. Keep building!