DEV Community: Ivan

QGIS DevTools plugin for easier plugin development

Ivan — Wed, 16 Jul 2025 21:45:19 +0000

Just came across this new debugging plugin for QGIS called DevTools that was released by NextGIS.

What it does

The plugin basically lets you connect VS Code to QGIS for debugging. Instead of adding logging statements everywhere or dealing with buggy setups, you can now set breakpoints, inspect variables, and step through your code directly from your IDE.

Main features

Launches a debugpy server from QGIS
Can be configured to start automatically when QGIS launches
Allows choosing a custom port for the debug server
Lets you connect from VS Code to debug your own plugins
Simple setup process

Why it's helpful

Before this, debugging QGIS plugins could be painful. Many developers relied on adding logging messages everywhere or used older plugins like debug_vs_plugin, which was often buggy and had issues on Windows and macOS. This new plugin provides a much more streamlined approach to remote debugging.

The plugin is available on the official QGIS plugin repository and the source code is on GitHub.

The documentation walks you through the setup process step by step.

This seems like a valuable tool for anyone developing QGIS plugins, and its foundation on the modern debugpy library is a promising sign.

One current limitation, however, is that debugging code in other threads (e.g., QgsTask) still requires some extra work. Hopefully, future versions will streamline this process.

While it did crash QGIS on me once during testing, the core functionality is reliable, making it a clear upgrade from the alternatives.

Thanks to the folks at NextGIS for making this - looks like a really helpful tool.

[Boost]

Ivan — Sat, 15 Feb 2025 00:28:01 +0000

How to Run a Local Model for Text Recognition in Images

Ivan ・ Feb 14

#llama #windows #python #llm

How to Run a Local Model for Text Recognition in Images

Ivan — Fri, 14 Feb 2025 19:08:46 +0000

Want to extract text from images without relying on cloud services?

You can run a powerful optical character recognition (OCR) model right on your own computer. This local approach gives you full control over the process and keeps your data private. In this article, we'll walk you through setting up and using a popular open-source OCR engine. You'll learn how to install the necessary libraries, load pre-trained models, and process images to recognize text in various languages. Whether you're working on a personal project or developing an application, this guide will help you get started with local text recognition quickly and easily.

This guide uses Windows 11, the Ollama model runner, the Llama 3.2 Vision model, and Python. Let's get started!

1. Install Ollama

First, head to https://ollama.com/download. Download the installer (it's about 768 MB) and run it to install Ollama.

2. Pull the Llama 3.2 Vision Model

Open your command prompt or terminal. We'll download the Llama 3.2 Vision model using Ollama. You have two size options.

The 11B and 90B parameters refer to the size of the Llama 3.2 Vision models, indicating the number of trainable parameters in each model:

11B model: This is the smaller version with 11 billion parameters.
90B model: This is the larger version with 90 billion parameters.

Both models are designed for multimodal tasks, capable of processing both text and images. They excel in various applications such as:

Document-level understanding
Chart and graph analysis
Image captioning
Visual grounding
Visual question answering

The choice between the 11B and 90B models depends on the specific use case, available computational resources, and the desired level of performance for complex visual reasoning tasks.

For the smaller model (11B, needs at least 8GB of VRAM (video memory)):

ollama pull llama3.2-vision:11b

For the larger model (90B, needs a whopping 64GB of VRAM):

ollama pull llama3.2-vision:90b

For home use, running the 90B model locally is extremely challenging due to its massive hardware requirements.

3. Run the Model

Once the model is downloaded, run it locally with:

ollama run llama3.2-vision

4. Install ollama-ocr

To easily process images, we'll use the ollama-ocr Python library. Install it using pip:

pip install ollama-ocr

5. Python Code for OCR

Here's the Python code to recognize text in an image:

from ollama_ocr import OCRProcessor

ocr = OCRProcessor(model_name='llama3.2-vision:11b')

result = ocr.process_image(
    image_path="./your_image.jpg",
    format_type="text"
)
print(result)

6. Run the Code

Replace "./your_image.jpg" with the actual path to your image file. Save the code as a .py file (e.g., ocr_script.py). Run the script from your command prompt:

python ocr_script.py

The script will send the image to your locally running Llama 3.2 Vision model, and the recognized text will be printed in your terminal.

To complement our guide on using Llama 3.2 Vision locally, we conducted performance tests on a home desktop computer. Here are the results:

Performance Test Results

We ran the Llama 3.2 Vision 11B model on a home desktop with the following specifications:

Processor: 13th Gen Intel(R) Core(TM) i7-13700K
Graphics Card: Gigabyte RTX 3060 Gaming OC 12G
RAM: 64.0 GB DDR4
Operating System: Windows 11 Pro 24H2

Image for Testing

For testing, we chose this amusing image.

Test Output

Using our Python script, we tasked the model with recognizing text in an image using the standard system prompt. After running the script multiple times on a single test image, we observed processing times ranging from 16.78 to 47.23 seconds. It's worth noting that these results were achieved with the graphics card running at default settings, without any additional tuning or optimizations.

The image is a black-and-white meme featuring two panels with stick figures and speech bubbles.

**Panel 1:**
In the first panel, a stick figure on the left side of the image has its arms outstretched towards another stick figure in the center. The central figure holds a large circle labeled "WEEKEND" in bold white letters. The stick figure on the right side of the image is partially cut off by the edge of the frame.

**Panel 2:**
In the second panel, the same two stick figures are depicted again. However, this time, the central figure now holds a smaller circle labeled "MONDAY" instead of "WEEKEND." The stick figure on the left side of the image has its arms outstretched towards the central figure once more.

**Text and Labels:**
The text in both panels is presented in white letters with bold outlines. In the first panel, the labels read:

* "ME" (on the stick figure's chest)
* "WEEKEND" (inside the large circle)

In the second panel, the labels are:

* "MONDAY" (inside the smaller circle)
* "ME" (on the stick figure's chest)

**Overall:**
The meme humorously portrays the anticipation and excitement of approaching the weekend, as well as the disappointment that follows when it arrives. The use of simple yet expressive stick figures and speech bubbles effectively conveys this sentiment in a relatable and entertaining manner.

Conclusion

That's it! You're now running a local image text recognition system using Ollama and Python. Remember to experiment with different images and adjust your approach as needed for best results.

You can find the scripts referenced in this article in the repository at https://github.com/karavanjo/dev-content/tree/main/llama-local-run.

Demonstration of the model running in the video at the link.

My city's climate - historical weather data viewer

Ivan — Thu, 13 Jan 2022 16:09:10 +0000

Overview of My Submission

"My city's climate" is a free online viewer for historical weather data of Poland. The web application is a simple way to get information about temperature and precipitations in a long time.

The application is a web map. The map displays weather stations as a points. If you click on the point will be opened color calendar. The calendar will be painted in different colors to indicate the respective weather characteristics.

The application was published on GitHub Pages. You can open it by link https://karavanjo.github.io/mcc-frontend/.

Data

The application uses public historical weather dataset IMGW-PIB.
You can explore the dataset on a page of Institute of Meteorology and Water Management by link.

Technical notes

The app built with React and D3. It's uses MongoDB Atlas as back-end. The app uses Time Series Collections for weather observations data storing.

To import initial data was created a console application using python and PyMongo.

Submission Category:

Prime Time

Link to Code

Front-end application

karavanjo / mcc-frontend

Free online viewer for historical weather data

"My city's climate" - historical weather data viewer

"My city's climate" is a free online viewer for historical weather data.

The application requires a climate database created by mcc-import application.

Run

Fill .env file with your Realm data.
npm install
npm run dev
Open your browser (http://localhost:3000/)

Screenshots

Weather stations map

Calendar: average air temperature

Calendar: snow depth

Demo

https://karavanjo.github.io/mcc-frontend/

View on GitHub

Import tool

karavanjo / mcc-import

Import tool for "My city's climate"

The command line application imports historical weather data in MongoDB Atlas database.

Technical notes

The application requires MongoDB > 5.0 with support time series.

You should specify connection credentials. The application only supports X.509 Authentication with .pem certificate.

How to use

Copy your weather data files to data/observations and data/stations.
Apply your credentials to config.yaml.
Install poetry.
poetry install --no-dev
poetry run import

About data

It is assumed that input weather data format corresponds to public data IMGW-PIB format.

You can explore the dataset on a page of Institute of Meteorology and Water Management by link.

View on GitHub

DEV Community: Ivan

QGIS DevTools plugin for easier plugin development

What it does

Main features

Why it's helpful

[Boost]

How to Run a Local Model for Text Recognition in Images

Ivan ・ Feb 14

How to Run a Local Model for Text Recognition in Images

1. Install Ollama

2. Pull the Llama 3.2 Vision Model

3. Run the Model

4. Install ollama-ocr

5. Python Code for OCR

6. Run the Code

Performance Test Results

Image for Testing

Test Output

Conclusion

My city's climate - historical weather data viewer

Overview of My Submission

Data

Technical notes

Submission Category:

Link to Code

Front-end application

karavanjo / mcc-frontend

Free online viewer for historical weather data

"My city's climate" - historical weather data viewer

Run

Screenshots

Demo

Import tool

karavanjo / mcc-import

Import tool for "My city's climate"

Import tool for "My city's climate"

Technical notes

How to use

About data

Additional Resources / Info

"My city's climate" in a web browser