imgshape 2.2.0

imgshape — robust image shape detection, dataset analysis, preprocessing & augmentation recommendations, visualization, compatibility reports, and PyTorch loader helpers

🖼️ imgshape — Smart Image Analysis & Preprocessing Toolkit (v2.2.0)

imgshape is a Python toolkit for image shape detection, dataset inspection, preprocessing & augmentation recommendations, visualization, report generation, and PyTorch DataLoader helpers — making it a smarter dataset assistant for ML/DL workflows.

⚡️ Why use imgshape?

• 📐 Detect image shapes (H × W × C) for single files or whole datasets.

• 🔍 Compute entropy, edge density, dominant color, and guess image type.

• 🧠 Get preprocessing recommendations (resize, normalization, suitable model family).

• 🔄 Augmentation recommender: suggest flips, crops, color jitter, etc., based on dataset stats.

• 📊 Visualizations: size histograms, dimension scatter plots, channel distribution.

• ✅ Model compatibility checks: verify dataset readiness for models like mobilenet_v2, resnet18, etc.

• 📝 Dataset reports: export Markdown/HTML/PDF with stats, plots, preprocessing, and augmentation plans.

• 🔗 Torch integration: generate ready-to-use torchvision.transforms or even a DataLoader.

• 🌐 Interactive GUI modes:

  • Streamlit app (app.py) → modern multi-tab UI
  • Gradio app (--web) → quick prototyping

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🚀 Installation

pip install imgshape

Requires Python 3.8+ Core deps: Pillow, numpy, matplotlib, scikit-image, streamlit Optional extras:

• imgshape[torch] → PyTorch / torchvision support
• imgshape[pdf] → PDF report generation (weasyprint)
• imgshape[viz] → prettier plots (seaborn)

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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 Streamlit app
streamlit run app.py

# Launch Gradio GUI
imgshape --web

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📦 Python API

from imgshape.shape import get_shape
from imgshape.analyze import analyze_type
from imgshape.recommender import recommend_preprocessing
from imgshape.augmentations import AugmentationRecommender

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

# Augmentation plan
ar = AugmentationRecommender(seed=42)
plan = ar.recommend_for_dataset({"entropy_mean": 6.2, "image_count": 100})
print(plan.recommended_order)

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📝 New in v2.2.0

• 🌐 Streamlit App (app.py) with 5 interactive tabs:

  • Shape → instant image shape detection
  • Analyze → entropy, channels, and dataset visualization
  • Recommend → preprocessing + heuristic augmentation plan
  • Report → export dataset reports in Markdown/HTML
  • TorchLoader → export torchvision.transforms pipelines or snippets

• 🔗 TorchLoader:

  • Safe wrapper for Compose/snippet/no-op callable depending on availability.
  • Backward compatibility with old (plan, preprocessing) test calls.

• 🧠 AugmentationRecommender:

  • Deterministic heuristic plans with .as_dict() export.
  • Handles entropy, resolution, and imbalance.

• ✅ Compatibility Fixes:

  • check_compatibility() outputs structured results.
  • Deprecated alias check_model_compatibility() preserved.

• 📝 Report Generators:

  • Markdown + HTML outputs improved.

• ⚡️ Test Suite:

  • Fixed pytest failures in compatibility, report, and torchloader.

• 🎨 UI Polishing:

  • Defensive wrappers for analyze_type, recommend_preprocessing, TorchLoader.
  • Footer links to Instagram, GitHub, HuggingFace, Kaggle, Medium.

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📎 Resources

• Source Code
• Issues
• License: MIT