I'm writing a script that requires me to figure out how many of an object are on the screen. The objects are kinda low resolution, think the ducks from Duck Hunt. I want to be able to track them in the screen and know their RGB value, even if acreen orientation changes.

However, I don't know much Python, let alone machine learning, so I'd like a library that has some capabilities built in.

I've tried pyautogui, but it doesn't always register the objects on screen, and I can't figure out how its confidence interval stuff works. I've heard of opencv, but it looks like it requires some machine learning knowledge.

Cheers.

What My Project Does

Labeling image data for training ML models is often a huge bottleneck - especially if you’ve collected your data via scraping or other raw sources.

I built Classto, a lightweight Python library that lets you manually classify images into custom categories through a clean browser UI. It’s fully local, fast to launch, and ideal for small to mid-sized datasets that need manual review or cleanup.

Target Audience

Classto is ideal for:

• ML practitioners who collect unlabeled image data (e.g. via scraping)

• Developers creating small or mid-sized datasets for classification tasks

• Researchers and students who want a no-fuss way to organize image data

It's not intended for large-scale automated pipelines, but rather for local, hands-on image labeling when you want full control.

Comparison

Unlike full-scale labeling platforms like Labelbox or CVAT, Classto:

• Runs entirely locally — no signup or cloud required

• Requires zero config — just pip install classto and launch

• Focuses on speed & simplicity, not bounding boxes or complex annotations

Features:

• One-click classification via web interface (built with Flask)

• Supports custom categories (e.g. "Dog", "Cat", "Unknown")

• Automatically moves files into subfolders by label

• Optionally logs each label to labels.csv

• Optionally adds suffixes to filenames to avoid overwriting

• Built-in delete button & dark mode

Quickstart

import classto as ct

app = ct.ImageLabeler(
    classes=["Cat", "Dog"],
    image_folder="images",
    suffix=True
)

app.launch()

Open your browser at http://127.0.0.1:5000 and start labeling.

Links:

• GitHub: https://github.com/SimonHRD/classto

• PyPI: https://pypi.org/project/classto/

Let me know what you think - feedback and contributions are very welcome 🙏
If you find Classto useful, I’d really appreciate a ⭐️ on the GitHub repo