imgshape 2.1.4

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

🖼️ imgshape — Smart Image Analysis & Preprocessing Toolkit (v2.1.3)

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.
• 🌐 GUI mode: run a Gradio app for point-and-click analysis.

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🚀 Installation

pip install imgshape

Requires Python 3.8+ Core deps: Pillow, numpy, matplotlib, scikit-image, gradio 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 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 v2.1.3

• 🔄 Augmentation recommender (--augment, augmentations.py)
• 📝 Dataset report generator (--report, Markdown/HTML/PDF export)
• 🔗 TorchLoader integration (--torchloader, to_dataloader, to_torch_transform)
• 📊 Improved visualizations (works even for 1-image datasets)
• 🌐 Modernized GUI with analysis + recommendations tabs

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📎 Resources

• Source Code
• Issues
• License: MIT