ArUco Marker Detection and Distance Estimation

Overview

This repository contains two Python scripts for real-time ArUco marker detection and distance estimation using OpenCV. These scripts utilize a webcam to identify ArUco markers, display their IDs, and estimate their position in 3D space relative to the camera.

Files

1. markerDetection.py: Detects ArUco markers in real-time and displays their IDs and corner coordinates.
2. distance_estimation.py: Detects ArUco markers, estimates their 3D position (distance and orientation) using camera calibration data, and displays this information in real-time.

Calibration Data

The file MultiMatrix.npz contains the camera calibration data:

• camMatrix: Camera matrix.
• distCoef: Distortion coefficients.
• rVector: Rotation vectors.
• tVector: Translation vectors.

Installation

Requirements

• Python 3.7+
• OpenCV (cv2)
• NumPy

Steps

1. Clone this repository:

   git clone https://github.com/PraharshG/Aruco-markers.git
   cd aruco-detection

2. Install the required Python packages:

   pip install numpy opencv-python

3. Ensure the MultiMatrix.npz file and the scripts are in the same directory.

Usage

1. ArUco Marker Detection

Run markerDetection.py to detect and identify ArUco markers in the webcam feed.

python markerDetection.py

• Press q to exit the video feed.
• The script will:
  • Detect ArUco markers.
  • Draw bounding boxes around detected markers.
  • Display the marker ID and corner coordinates in the console.

2. Distance Estimation

Run distance_estimation.py to estimate the distance and position of detected ArUco markers.

python distance_estimation.py

• Press q to exit the video feed.
• The script will:
  • Detect ArUco markers.
  • Estimate their 3D position relative to the camera.
  • Display the marker ID, distance, and x/y coordinates on the video feed.

Example Outputs

Marker Detection

ID: [23]   [[100, 100], [200, 100], [200, 200], [100, 200]]

• A bounding box will be drawn around the marker with its ID displayed.

Distance Estimation

ID: [42] Dist: 50.3
x: 10.5 y: -5.2

• Displays the marker's distance and x/y coordinates on the video feed.

Customization

Marker Dictionary

You can change the marker dictionary by modifying this line:

marker_dict = aruco.Dictionary_get(aruco.DICT_4X4_100)

Refer to OpenCV's ArUco documentation for more options.

Marker Size

Update the marker_size variable in distance_estimation.py to match the physical size of your markers (in centimeters):

marker_size = 10 # centimeters

Notes

• Ensure the camera is calibrated using OpenCV to generate the MultiMatrix.npz file. Use OpenCV's calibration tutorials if needed.
• Lighting conditions and marker quality may affect detection accuracy.