perceptimg 0.1.0

Perceptual, policy-driven image optimization engine for modern workflows

perceptimg

Perceptual, policy-driven image optimization for modern workflows

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Overview

perceptimg is a Python library for perceptual, policy-driven image optimization. Unlike traditional tools that optimize for file size or a single quality knob, perceptimg analyzes image content, interprets declarative policies, tests multiple strategies, and selects the best candidate based on perceptual quality metrics.

Key Features

Feature	Description
Policy-Driven	Declarative constraints for size, quality, and use case
Content-Aware	Analyzes images for text, faces, and edge density
Multi-Format	JPEG, PNG, WebP, AVIF, JXL, HEIF, GIF, TIFF, APNG
Perceptual Metrics	SSIM, PSNR, and weighted perceptual scoring
Explainable Results	Full decision trail for every optimization
Batch Processing	Parallel processing with progress callbacks
Enterprise Ready	Checkpointing, retry, rate limiting, Prometheus metrics
Clean Architecture	Dependency inversion, modular engines, extensible

Supported Formats

Core Formats     JPEG, PNG, TIFF, GIF (always available via Pillow)
Modern Formats   WebP, AVIF, JPEG XL (JXL), HEIF/HEIC, APNG (codec-dependent)

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Installation

From PyPI (Recommended)

pip install perceptimg

From Source

git clone https://github.com/seifreed/perceptimg.git
cd perceptimg
python3 -m venv venv
source venv/bin/activate  # Windows: venv\Scripts\activate
pip install -e ".[dev]"

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Quick Start

from perceptimg import optimize, Policy

# Define policy constraints
policy = Policy(
    max_size_kb=150,
    min_ssim=0.97,
    preserve_text=True,
    target_use_case="web"
)

# Optimize image
result = optimize("input.png", policy)

# Access results
print(f"Format: {result.report.chosen_format}")
print(f"Size: {result.report.size_before_kb:.1f}KB → {result.report.size_after_kb:.1f}KB")
print(f"SSIM: {result.report.ssim:.4f}")
print(f"Reasons: {result.report.reasons}")

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Usage

Command Line Interface

# Basic optimization
perceptimg optimize input.png --policy policy.json --out output.webp

# With inline constraints
perceptimg optimize input.png --max-size-kb 100 --min-ssim 0.95 --formats webp,avif

# JSON output
perceptimg optimize input.png --policy policy.json --log-json

Available Options

Option	Description
--policy	Path to JSON policy file
--out	Output file path
--max-size-kb	Maximum output size in KB
--min-ssim	Minimum SSIM threshold (0.0-1.0)
--formats	Preferred formats (comma-separated)
--preserve-text	Prefer text clarity
--preserve-faces	Prefer face quality
--log-json	Structured JSON logging
--log-level	Log level (DEBUG, INFO, WARNING)

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Policy Configuration

Python API

from perceptimg import Policy

policy = Policy(
    max_size_kb=120,
    min_ssim=0.98,
    preserve_text=True,
    preserve_faces=True,
    allow_lossy=True,
    preferred_formats=("webp", "avif", "jpeg"),
    target_use_case="mobile",
)

JSON Configuration

{
  "max_size_kb": 150,
  "min_ssim": 0.97,
  "preserve_text": true,
  "preserve_faces": false,
  "allow_lossy": true,
  "preferred_formats": ["webp", "avif", "jpeg"],
  "target_use_case": "web"
}

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Batch Processing

Basic Batch

from perceptimg import Policy, optimize_batch

policy = Policy(max_size_kb=100, min_ssim=0.9)
images = ["img1.png", "img2.png", "img3.png"]

result = optimize_batch(images, policy)
print(f"Success: {len(result.successful)}/{result.total}")
print(f"Success rate: {result.success_rate:.1%}")

Async Processing

import asyncio
from perceptimg import Policy, optimize_batch_async

async def main():
    policy = Policy(max_size_kb=100)
    result = await optimize_batch_async(images, policy)
    print(f"Processed {result.total} images")

asyncio.run(main())

Memory-Efficient Streaming

from perceptimg import Policy, optimize_lazy

policy = Policy(max_size_kb=100)

for path, result in optimize_lazy(large_image_list, policy):
    if isinstance(result, Exception):
        print(f"Error {path}: {result}")
    else:
        print(f"OK {path}: {result.report.size_after_kb:.1f}KB")

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Enterprise Features

Checkpoint/Resume

from perceptimg import Policy, optimize_batch_with_checkpoint

result = optimize_batch_with_checkpoint(
    images,
    policy,
    checkpoint_path="checkpoint.json",
    checkpoint_interval=10,  # Save every 10 images
)
# Resume after interruption by calling again with same checkpoint_path

Retry with Exponential Backoff

from perceptimg import Policy, optimize_batch_with_retry
from perceptimg.core.retry import RetryConfig

retry_config = RetryConfig(max_retries=3, base_delay_ms=100)

result = optimize_batch_with_retry(
    images,
    policy,
    retry_config=retry_config,
    continue_on_error=True,
)

Rate Limiting

from perceptimg import Policy, optimize_batch_with_rate_limit
from perceptimg.core.rate_limiter import RateLimitConfig

rate_limit = RateLimitConfig(requests_per_second=5)

result = optimize_batch_with_rate_limit(
    images,
    policy,
    rate_limit=rate_limit,
)

Prometheus Metrics

from perceptimg import Policy, optimize_batch_with_metrics
from perceptimg.core.metrics_exporter import MetricsCollector

metrics = MetricsCollector()
result, stats = optimize_batch_with_metrics(images, policy, metrics=metrics)

print(f"Average SSIM: {stats['average_ssim']:.2f}")
print(f"Compression ratio: {stats['average_compression_ratio']:.1%}")

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Architecture

perceptimg/
├── adapters/           # Framework adapters (PIL)
│   └── pil_adapter.py
├── core/               # Domain logic
│   ├── interfaces.py   # Abstractions (ImageAdapter Protocol)
│   ├── analyzer.py     # Content analysis
│   ├── metrics.py      # SSIM, PSNR, perceptual scoring
│   ├── optimizer.py    # Orchestration
│   ├── policy.py       # Declarative constraints
│   ├── strategy.py     # Candidate generation
│   └── batch/          # Batch processing
├── engines/            # Format-specific encoders
│   ├── webp_engine.py
│   ├── avif_engine.py
│   └── ...
└── utils/              # IO, heuristics, logging

Clean Architecture Compliance

Layer	Responsibility
Core	Business logic, domain models, policies
Adapters	Framework-specific implementations
Engines	Format-specific encoding
Utils	Infrastructure services

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Extensibility

Custom Optimization Engine

from perceptimg.engines.base import OptimizationEngine, EngineResult
from perceptimg.core.optimizer import Optimizer, register_engine

class MyCustomEngine(OptimizationEngine):
    format = "custom"
    priority = 100
    
    @property
    def is_available(self) -> bool:
        return True
    
    def optimize(self, image, strategy) -> EngineResult:
        # Custom optimization logic
        return EngineResult(data=optimized_bytes, format="custom", quality=90)

# Register custom engine
optimizer = Optimizer()
register_engine(optimizer, MyCustomEngine())

Custom Analyzer

from perceptimg.core.analyzer import Analyzer

class CustomAnalyzer(Analyzer):
    def analyze(self, image):
        result = super().analyze(image)
        # Add custom heuristics
        result.custom_score = self._compute_custom(image)
        return result

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API Reference

OptimizationResult

Field	Type	Description
image_bytes	bytes	Optimized image data
image	PIL.Image	Pillow image object
report	OptimizationReport	Detailed results

OptimizationReport

Field	Type	Description
chosen_format	str	Selected format
quality	int	Quality setting
size_before_kb	float	Original size
size_after_kb	float	Optimized size
ssim	float	SSIM score
psnr	float	PSNR score
perceptual_score	float	Weighted quality score
reasons	list[str]	Decision trail

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Requirements

• Python 3.13+
• Pillow (PIL fork)
• NumPy
• scikit-image
• See pyproject.toml for full dependencies

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Quality

This project enforces:

Tool	Purpose
ruff	Linting
black	Formatting
mypy	Type checking
bandit	Security analysis
pytest	Testing (145 tests)

# Run all quality checks
ruff check perceptimg tests
black perceptimg tests --check
mypy perceptimg --ignore-missing-imports
bandit -r perceptimg -q --exclude perceptimg/tests
pytest tests/

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Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

1. Fork the repository
2. Create your feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

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Support the Project

If you find perceptimg useful, consider supporting its development:

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License

This project is licensed under the MIT License - see the LICENSE file for details.

Attribution Required:

• Author: Marc Rivero | @seifreed
• Repository: github.com/seifreed/perceptimg

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_{Made with dedication for the image optimization community}