canvas-design

name	canvas-design
description	Create programmatic digital art using Python libraries like Pillow (PIL), NumPy, and Matplotlib. Generate images, fractals, patterns, and abstract compositions.
license	MIT

Canvas Design Skill

This skill enables the creation of generative art, patterns, and abstract compositions using Python.

Core Capabilities

1. Generative Art: Use algorithms to create unique visuals (fractals, noise, cellular automata).
2. Pattern Generation: Create seamless tiles, geometric designs, and textures.
3. Image Manipulation: Apply filters, blend modes, and transformations.
4. Drawing Primitives: Render shapes, lines, and text using vector commands.

Dependencies

• Pillow (pip install Pillow) - Core image manipulation.
• numpy (pip install numpy) - Mathematical operations for generative art.
• matplotlib (pip install matplotlib) - Plotting complex forms.

Workflow Examples

1. Simple Geometric Composition (Pillow)

from PIL import Image, ImageDraw
import random

width, height = 800, 600
img = Image.new('RGB', (width, height), 'white')
draw = ImageDraw.Draw(img)

# Draw random circles
for _ in range(50):
    x = random.randint(0, width)
    y = random.randint(0, height)
    r = random.randint(10, 50)
    color = (random.randint(0, 255), random.randint(0, 255), random.randint(0, 255))
    draw.ellipse([x-r, y-r, x+r, y+r], fill=color, outline='black')

img.save('geometric_art.png')

2. Mandelbrot Fractal (NumPy + Matplotlib)

import numpy as np
import matplotlib.pyplot as plt

def mandelbrot(h, w, max_iter=20):
    y, x = np.ogrid[-1.4:1.4:h*1j, -2:0.8:w*1j]
    c = x + y*1j
    z = c
    divtime = max_iter + np.zeros(z.shape, dtype=int)

    for i in range(max_iter):
        z = z**2 + c
        diverge = z*np.conj(z) > 2**2            
        div_now = diverge & (divtime == max_iter)  
        divtime[div_now] = i                  
        z[diverge] = 2                        

    return divtime

plt.figure(figsize=(10, 10))
plt.imshow(mandelbrot(800, 800, 50), cmap='magma')
plt.axis('off')
plt.savefig('mandelbrot.png', bbox_inches='tight', pad_inches=0)

Best Practices

• Resolution: Default to reasonable sizes like 800x600 or 1024x1024 for quick generation.
• Vector vs Raster: Use matplotlib for complex curves/plots, Pillow for pixel manipulation.
• Performance: Avoid extremely high iterations or resolution in Python loops.