ArUCo Marker Measurement System

Installation

1. Create a python 3.11 virtual environment

Using Conda

conda create -n env python=3.11.5

Using Python VENV

python -m venv env

2. Install dependencies via the requirement.txt

pip install -r requirement.txt

Basic Usage

File Structure

The images should be structured in the following directory format. Each subdirectory must have two or more images.

/path/to/root/folder
    |--> /sub_directory_1
        |--> repeat1.jpg
        |--> repeat2.jpg
    |--> /sub_directory_2
        |--> repeat1.jpg
        |--> repeat2.jpg
    |--> /sub_directory_3
        |--> repeat1.jpg
        |--> repeat2.jpg
        |--> repeat3.jpg
    ...

Inference

Run the run.sh to run the inference script with the provided test data. The output directory of the test should be in the out folder.

OR

Open the ipynb notebook and run each cell, the inference process is identical.

Flags

• DICT_TYPE: ArUCo marker dictionary
• MARKER_SIZE: Marker size (can be in any unit)
• VALID_MARKER_IDS: Detection will only match the IDs provided below, leave the list empty if you want possible markers to be detected, highly advised to be used if USE_ADV_THRESH is enabled
• ROOT_DIR: Root directory of the images, see below for file structure
• OUT_DIR: Output directory for the results, log file, and generated images
• GEN_RESULTS: Boolean flag for generating result images
• GEN_OVERLAYS: Boolean flag for generating an overlaid result of all the result images of the subdirectory
• USE_ADV_THRESH: Boolean flag for enabling advanced thresholding. Sweeps for a multitude of thresholding settings, aids in detecting markers that are less visible.
• DETECTION_ONLY: Boolean flag for disabling comparison and calculations
• VERBOSE: Boolean flag for enabling command line output

Inference Script Process Breakdown

Initialisation Step

• Verifying that each subdirectory of the root has two or more images
• Create leaf directories in the output folder

Detection Step

• Go through each subdirectory and each image in the subdirectories
• Detect all markers present in the images
• Generate results images
• Saves the intermediate results (coordinates of detected markers) to JSON and CSV file in the output directory

Calculation and Comparison Step

• Recover edge length of each maker
• Calculate the distances of markers in the same group with the same ID
• Save result dictionary as JSON
• Convert dictionary to rows of data and save as CSV

Image Overlays Generation Step

• Go through each subdirectory in the output folder and overlay images with equal alpha