🧠 Converting images to ASCII: text instead of pixels

Want to turn any image into ASCII art? It's not magic, just simple brightness processing.

It's tedious and stupid to do it manually
img = [
    [255, 0, 0],
    [0, 255, 0]
]
# Now we need to pick a symbol for each pixel...
# What a hassle.

Problem:
Manually selecting symbols by brightness is a pain. We need to automate the conversion of grayscale to symbols.

✔️ The right way (using gradation)

from PIL import Image

def image_to_ascii(path, width=100):
    img = Image.open(path)
    aspect = img.height / img.width
    height = int(width * aspect * 0.55)
    img = img.resize((width, height)).convert('L')

    ascii_chars = '@%#*+=-:. '
    pixels = img.getdata()

    ascii_art = '\n'.join(
        ascii_chars[pixel * (len(ascii_chars) - 1) // 255]
        for pixel in pixels
    )
    lines = [ascii_art[i:i+width] for i in range(0, len(ascii_art), width)]
    return '\n'.join(lines)

print(image_to_ascii('cat.jpg'))

How it works:
convert('L') converts the image to grayscale

Each pixel (0-255) is assigned a symbol from the set

The darker the pixel, the "denser" the symbol (e.g., '@'), the lighter - the "weaker" (space)

Let's write a converter with customizable palette:

class AsciiConverter:
    PALETTES = {
        'default&#39: '@%#*+=-:. ',
        'blocks&#39: '█rayed ',
        'detailed&#39: '$@B%8&WM#*oahkbdpqwmZO0QLCJUYXzcvunxrjft/\\|()1{}[]?-_+~<>i!lI;:,"^`\'. '
    }

    def __init__(self, palette_name='default&#39):
        if palette_name not in self.PALETTES:
            raise ValueError(f'Нет такой палитры, идиот. Выбери из: {list(self.PALETTES.keys())}')
        self.chars = self.PALETTES[palette_name]

    def convert(self, image_path, width=80):
        # ... code to convert using self.chars ...
        return ascii_result

Try specifying a non-existent palette - you'll get a clear error.

Key parameters:

🔵Width - determines the size of the final ASCII art
🔵Character palette - affects the detail and style
🔵Aspect ratio - important for correct display
🔵Inversion - you can invert the brightness for a dark background

Important:
ASCII art isn't just a fun thing. It's used to visualize data in the console, create creative logs, and even "hide" information in plain sight.

👩‍💻 @CodeProgrammer

#KMeans clustering animation in the style of 3blue1brown

👉 @CODEPROGRAMMER

This channels is for Programmers, Coders, Software Engineers.

0️⃣ Python
1️⃣ Data Science
2️⃣ Machine Learning
3️⃣ Data Visualization
4️⃣ Artificial Intelligence
5️⃣ Data Analysis
6️⃣ Statistics
7️⃣ Deep Learning
8️⃣ programming Languages

✅ https://t.iss.one/addlist/8_rRW2scgfRhOTc0

✅ https://t.iss.one/Codeprogrammer

9 key concepts of artificial intelligence, explained in 7 minutes

- Tokenization
- #TextDecoding
- #PromptEngineering
- Multi Step #AI Agents
- #RAGs
- #RLHF
- #VAE
- #DiffusionModels
- #LoRA

👉 @Python53

https://t.iss.one/RAICompass

مبادرة جميلة يرجى الانضمام اليها - للسوريين (مبادرة هامة) 🇸🇾

🐱 5 of the Best GitHub Repos
🔃 for Data Scientists

👨🏻‍💻 When I was just starting out and trying to get into the "data" field, I had no one to guide me, nor did I know what exactly I should study. To be honest, I was confused for months and felt lost.

▶️ But doing projects was like water on fire and helped me a lot to build my skills.

〰 Repo Awesome Data Analysis

🏷 A complete treasure trove of everything you need to start: SQL, Python, AI, data analysis, and more... In short, if you want to start from zero and strengthen your foundation, start here first.

                   ➖ ➖ ➖

〰 Repo Data Scientist Handbook

🏷 A concise handbook that tells you what you need to learn and what you can ignore for now.

                   ➖ ➖ ➖

〰 Repo Cookiecutter Data Science

🏷 A standard project template used by professionals. With this template, you can structure your data analysis and AI projects like a pro.

                   ➖ ➖ ➖

〰 Repo Data Science Cookie Cutter

🏷 This is also a very clean project template that teaches you how to build a data project that won’t fall apart tomorrow and can be easily updated. Meaning your projects will be useful in the real world from the start.

                   ➖ ➖ ➖

〰 Repo ML From Scratch

🏷 Here, the main AI algorithms are implemented from scratch in simple language. It’s great for understanding how models really work and for explaining them well in your interviews.

🌐 #Data_Science #DataScience

These 9 lectures from Stanford are a pure goldmine for anyone wanting to learn and understand LLMs in depth

Lecture 1 - Transformer: https://lnkd.in/dGnQW39t

Lecture 2 - Transformer-Based Models & Tricks: https://lnkd.in/dT_VEpVH

Lecture 3 - Tranformers & Large Language Models: https://lnkd.in/dwjjpjaP

Lecture 4 - LLM Training: https://lnkd.in/dSi_xCEN

Lecture 5 - LLM tuning: https://lnkd.in/dUK5djpB

Lecture 6 - LLM Reasoning: https://lnkd.in/dAGQTNAM

Lecture 7 - Agentic LLMs: https://lnkd.in/dWD4j7vm

Lecture 8 - LLM Evaluation: https://lnkd.in/ddxE5zvb

Lecture 9 - Recap & Current Trends: https://lnkd.in/dGsTd8jN

Start understanding #LLMs in depth from the experts. Go through each step-by-step video.

https://t.iss.one/DataAnalyticsX 🔗

👍 A fresh deep learning course from MIT is now available publicly

A full-fledged educational course has been published on the university's website: 24 lectures, practical tasks, homework assignments, and a collection of materials for self-study.

The program includes modern neural network architectures, generative models, transformers, inference, and other key topics.

A great opportunity to study deep learning based on the structure of a top university, free of charge and without simplifications — let's learn here.
https://ocw.mit.edu/courses/6-7960-deep-learning-fall-2024/resources/lecture-videos/

tags: #python #deeplearning

➡ @codeprogrammer

🔖 An excellent resource for learning about neural networks

We're sharing a cool resource for learning about neural networks, offering clear, step-by-step instruction with dynamic visualizations and easy-to-understand explanations.

In addition, you'll find many other useful materials on machine learning on the site.

Find and use it — https://mlu-explain.github.io/neural-networks/

tags: #AI #ML #PYTHON

➡ @CODEPROGRAMMER

Data scientists are in high demand right now: there's just too much data to analyze.

In this course, Tatev and Vae teach #Python for #DataScience.

You'll be doing projects and exploring EDA, A/B testing, BI, and more.

https://t.iss.one/Python53 🌟