| name | run2_object-counting |
| description | Count the number of object occurrences in an image using OpenCV template matching in Python. |
Object Counting
Count the number of objects in an image using computer vision, specifically Normalized Cross-Correlation (Template Matching).
Usage
The count_objects.py script provided in the environment performs template matching and outputs the number of matches found.
python3 /root/.agents/skills/object_counter/scripts/count_objects.py \
--tool count \
--input_image <image_file> \
--object_image <object_template_file> \
--threshold <threshold_value> \
--dedup_min_dist <distance>
Arguments Explained:
--input_image: The target image where objects should be counted.--object_image: The template image of the object you are searching for.--threshold: The matching confidence threshold (e.g., 0.9 for high fidelity).--dedup_min_dist: Minimum pixel distance to deduplicate overlapping matches (e.g., 3).
Example
python3 /root/.agents/skills/object_counter/scripts/count_objects.py \
--tool count \
--input_image frame_001.png \
--object_image coin.png \
--threshold 0.9 \
--dedup_min_dist 3
Parsing Output
The script prints the result in the following format to standard output:
There are X objects (\<template_path>`) in file: `<image_path>``
You can parse this programmatically using regular expressions in a Python wrapper script:
import re, subprocess
result = subprocess.run(cmd, stdout=subprocess.PIPE, text=True)
match = re.search(r'There are (\d+) objects', result.stdout)
if match:
count = int(match.group(1))
