Image Generation

Image Generation Guide

Generate images programmatically using Python. This guide covers matplotlib for diagrams/charts and PIL for image manipulation.

Environment Setup

pip install matplotlib pillow numpy --break-system-packages

Chinese Font Configuration (Critical)

Chinese text renders as boxes without proper fonts. Always configure before plotting:

import matplotlib.pyplot as plt

# Check available CJK fonts
import matplotlib.font_manager as fm
cjk_fonts = [f.name for f in fm.fontManager.ttflist 
             if 'CJK' in f.name or 'Noto' in f.name or 'WenQuanYi' in f.name]
print(cjk_fonts)

# Configure (use first available)
plt.rcParams['font.sans-serif'] = ['Noto Sans CJK SC', 'WenQuanYi Zen Hei', 'DejaVu Sans']
plt.rcParams['axes.unicode_minus'] = False  # Fix minus sign display

If no CJK fonts available, install: apt-get install -y fonts-noto-cjk fonts-wqy-zenhei

Quick Start

Basic Chart

import matplotlib.pyplot as plt
import numpy as np

fig, ax = plt.subplots(figsize=(10, 6), dpi=150)
x = np.linspace(0, 10, 100)
ax.plot(x, np.sin(x), label='sin(x)')
ax.plot(x, np.cos(x), label='cos(x)')
ax.set_xlabel('x')
ax.set_ylabel('y')
ax.legend()
ax.set_title('Trigonometric Functions')
plt.savefig('chart.png', bbox_inches='tight', facecolor='white')
plt.close()

Basic Diagram (No Axes)

fig, ax = plt.subplots(figsize=(12, 8), dpi=150)
ax.set_xlim(0, 12)
ax.set_ylim(0, 8)
ax.set_aspect('equal')
ax.axis('off')  # Hide axes for diagrams

# Draw shapes
from matplotlib.patches import FancyBboxPatch, Circle, Arrow
box = FancyBboxPatch((1, 3), 3, 2, boxstyle="round,pad=0.1", 
                      facecolor='#3498DB', edgecolor='white', linewidth=2)
ax.add_patch(box)
ax.text(2.5, 4, 'Node A', ha='center', va='center', color='white', fontsize=12)

plt.savefig('diagram.png', bbox_inches='tight', facecolor='white')
plt.close()

Core Patterns

1. Mind Map / Tree Diagram

Use radial or hierarchical layout with FancyBboxPatch for nodes:

def draw_node(ax, x, y, text, color, width=2.5, height=0.8, fontsize=10):
    """Draw a rounded rectangle node with centered text."""
    from matplotlib.patches import FancyBboxPatch
    box = FancyBboxPatch((x - width/2, y - height/2), width, height,
                         boxstyle="round,pad=0.05,rounding_size=0.2",
                         facecolor=color, edgecolor='white', linewidth=2)
    ax.add_patch(box)
    ax.text(x, y, text, ha='center', va='center', fontsize=fontsize, 
            color='white', weight='bold')

def draw_connection(ax, start, end, color='#555'):
    """Draw a line between two points."""
    ax.plot([start[0], end[0]], [start[1], end[1]], 
            color=color, linewidth=2, zorder=0)

See scripts/mindmap_template.py for complete example.

2. Flowchart

Use different shapes for different node types:

Use FancyArrowPatch for arrows with arrowstyle='->'.

3. Data Visualization

# Bar chart
ax.bar(categories, values, color=['#E74C3C', '#3498DB', '#2ECC71'])

# Pie chart
ax.pie(sizes, labels=labels, autopct='%1.1f%%', colors=colors)

# Heatmap
im = ax.imshow(data, cmap='viridis')
plt.colorbar(im)

# Scatter with size/color encoding
ax.scatter(x, y, s=sizes, c=colors, alpha=0.6)

4. Annotated Images (PIL + matplotlib)

from PIL import Image
import matplotlib.pyplot as plt

# Load and display image
img = Image.open('photo.jpg')
fig, ax = plt.subplots()
ax.imshow(img)

# Add annotations
ax.annotate('Important!', xy=(100, 150), xytext=(200, 100),
            arrowprops=dict(arrowstyle='->', color='red'),
            fontsize=12, color='red')
ax.add_patch(plt.Rectangle((50, 50), 100, 80, fill=False, edgecolor='red', linewidth=2))

ax.axis('off')
plt.savefig('annotated.png', bbox_inches='tight')

Color Palettes

Professional color schemes:

# Modern flat colors
COLORS = {
    'red': '#E74C3C',
    'blue': '#3498DB', 
    'green': '#2ECC71',
    'orange': '#F39C12',
    'purple': '#9B59B6',
    'teal': '#1ABC9C',
    'dark': '#2C3E50',
    'gray': '#95A5A6'
}

# Categorical palette (up to 10 items)
CATEGORICAL = ['#E74C3C', '#3498DB', '#2ECC71', '#F39C12', '#9B59B6',
               '#1ABC9C', '#E67E22', '#16A085', '#8E44AD', '#2980B9']

# Sequential palette (for gradients)
# Use matplotlib colormaps: 'viridis', 'plasma', 'Blues', 'Reds'

Common Issues & Solutions

Issue: Chinese characters show as boxes

Solution: Configure CJK fonts before any plotting (see Environment Setup)

Issue: Image has extra whitespace

Solution: Use bbox_inches='tight' in savefig()

Issue: Low resolution output

Solution: Increase dpi (150-300 for print quality)

Issue: Transparent background needed

Solution: savefig('out.png', transparent=True)

Issue: Elements overlap

Solution: Adjust figsize, use plt.tight_layout(), or manually position

Issue: Subscript/superscript characters missing

Solution: Avoid Unicode subscripts (₀₁₂). Use LaTeX: $H_2O$, $x^2$

Output Guidelines

1. Always use dpi=150 minimum (300 for print)
2. Use facecolor='white' for consistent background
3. Call plt.close() after saving to free memory
4. Save to /mnt/user-data/outputs/ for user access
5. Prefer PNG for diagrams, JPG for photos

File Structure

scripts/
├── mindmap_template.py    # Mind map with radial layout
├── flowchart_template.py  # Flowchart with decision nodes
└── chart_templates.py     # Common chart types

references/
├── MATPLOTLIB_SHAPES.md   # All available shapes and patches
└── COLOR_REFERENCE.md     # Extended color palettes

Next Steps

• For complex mind maps: See scripts/mindmap_template.py
• For flowcharts: See scripts/flowchart_template.py
• For shape reference: See references/MATPLOTLIB_SHAPES.md
• For extended colors: See references/COLOR_REFERENCE.md

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