imgshape v3.0.0

🖼️ imgshape — Smart Dataset Intelligence Toolkit (v3.0.0 • Aurora)

imgshape is a modular Python toolkit for image analysis, dataset inspection, augmentation & preprocessing recommendations, visualization, and pipeline export — now evolved into a Streamlit-powered dataset assistant for modern ML/DL workflows.

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✨ What's New in v3.0.0 — Aurora Major Release

A complete redesign: from a static CLI toolkit → to an intelligent dataset analysis framework.

🧭 Highlights

• Full Streamlit App (app.py) with 6 powerful tabs:
  • 📐 Shape → instant image shape detection
  • 🔍 Analyze → entropy, color channels, dataset insights
  • 🧠 Recommend → preprocessing & augmentation planning
  • 🎨 Augment Visualizer → real-time augmentation previews
  • 📄 Reports → export Markdown / HTML dataset reports
  • 🔗 Pipeline Export → generate ready-to-run code snippets

🧩 Modular Architecture

• New RecommendationPipeline system for building, saving, and exporting end-to-end pipelines.
• Plugin framework (/src/imgshape/plugins) with support for:
  • AnalyzerPlugin
  • RecommenderPlugin
  • ExporterPlugin
• Unified lazy import system for ultra-fast startup.

💡 Smart Recommendations

• RecommendEngine provides preprocessing & augmentation strategies based on:
  • Entropy, resolution, and dataset diversity
  • User preferences (e.g. preserve_aspect, low_res)
  • Optional YAML profiles (/profiles/)

📊 Dataset Analyzer Improvements

• Counts only unique readable images (no overcount)
• Aggregates shapes, channels, entropy, and unreadable stats
• Sample summaries for representative examples

📁 Reports

• Markdown, HTML, and PDF (optional via weasyprint + reportlab)
• Embedded metadata, augmentations, and preprocessing recommendations

🧰 CLI Modernization

• imgshape --web → directly launches Streamlit UI
• Extended with new actions:
  • --pipeline-export, --pipeline-apply, --snapshot-save, --snapshot-diff
• Plugin controls: --plugin-list, --plugin-add, --plugin-remove

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⚙️ Installation

pip install imgshape

Requires Python 3.8+
Core dependencies: Pillow, numpy, matplotlib, scikit-image, streamlit

Optional extras:

Extra	Description
imgshape[torch]	PyTorch / torchvision support
imgshape[pdf]	PDF report generation via WeasyPrint
imgshape[viz]	Advanced plots with Seaborn & Plotly
imgshape[ui]	Streamlit UI + profile parsing
imgshape[full]	Full suite with all optional features

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💻 CLI Usage

# Shape detection
imgshape --path ./sample.jpg --shape

# Single image analysis
imgshape --path ./sample.jpg --analyze

# Preprocessing + augmentations
imgshape --path ./sample.jpg --recommend --augment

# Dataset compatibility check
imgshape --dir ./images --check mobilenet_v2

# Dataset visualization
imgshape --viz ./images

# Dataset report (md + html)
imgshape --path ./images --report --augment --report-format md,html --out report

# Torch integration (transform/DataLoader)
imgshape --path ./images --torchloader --augment --out transform_snippet.py

# Launch the Streamlit web UI
imgshape --web

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🖥️ Streamlit Interface (v3)

Run the visual interface directly:

streamlit run app.py

Tabs Overview

Tab	Function
📐 Shape	Detects image dimensions & color channels
🔍 Analyze	Dataset entropy, shapes, and channel distributions
🧠 Recommend	Suggests preprocessing & augmentations
🎨 Augment Visualizer	Interactive augmentation intensity slider
📄 Reports	Generates Markdown & HTML dataset summaries
🔗 Pipeline Export	Exports pipelines as code (PyTorch/YAML/JSON)

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🧠 Python API Example

from imgshape.shape import get_shape
from imgshape.analyze import analyze_type
from imgshape.recommender import recommend_preprocessing
from imgshape.pipeline import RecommendationPipeline

print(get_shape("sample.jpg"))
print(analyze_type("sample.jpg"))
print(recommend_preprocessing("sample.jpg"))

# Build a pipeline from a recommendation
rec = recommend_preprocessing("sample.jpg")
pipeline = RecommendationPipeline.from_recommender_output(rec)
print(pipeline.as_dict())

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🧩 Plugins

Extend imgshape with your own plugins:

# src/imgshape/plugins/custom_brightness.py
from imgshape.plugins import RecommenderPlugin

class CustomBrightnessPlugin(RecommenderPlugin):
    NAME = "CustomBrightness"

    def recommend(self, analysis):
        return [{"name": "adjust_brightness", "spec": {"factor": 1.2}}]

Then register it via CLI:

imgshape --plugin-add ./src/imgshape/plugins/custom_brightness.py

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📝 Reports (Markdown, HTML, PDF)

# Markdown & HTML reports
imgshape --report --path ./datasets/cats --report-format md,html

# Generate PDF (requires extras)
pip install imgshape[pdf]
imgshape --report --path ./datasets/dogs --report-format pdf

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🧪 Testing

Run all tests locally:

pytest -q

Or install dev tools:

pip install imgshape[dev]
black --check src tests
flake8 src tests

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🧱 Developer & Build Guide

# Clean build artifacts
rm -rf dist build *.egg-info

# Build
python -m build

# Check metadata
twine check dist/*

# Upload (TestPyPI)
twine upload --repository testpypi dist/*

# Install locally
pip install dist/imgshape-3.0.0-py3-none-any.whl

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🔗 Resources

• Documentation: https://stifler7.github.io/imgshape
• GitHub Repository: https://github.com/STiFLeR7/imgshape
• Issues: https://github.com/STiFLeR7/imgshape/issues
• License: MIT

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💫 Credits

Developed with ❤️ by Stifler
Researched / Developer
Empowering AI at the Edge.

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🧭 Roadmap (v3.1.x)

• ONNX / TensorRT export for edge inference
• Auto-EDA visualization (class imbalance, histograms)
• Enhanced Streamlit dashboard with live metrics
• HuggingFace Spaces demo & CI/CD workflow

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### 🧩 Summary of Key Updates
- Updated version → `v3.0.0 (Aurora)`  
- Removed Gradio references (Streamlit is now primary)  
- Added new **Pipeline**, **Plugins**, and **Recommender Engine** details  
- Expanded CLI + Streamlit examples  
- Ready for **PyPI rendering** and **GitHub preview**