
xtmtd/entomokit
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A Python Toolkit for Insect Image Dataset Construction and Classification
Обзор
A Python-based toolkit for building insect image datasets. EntomoKit provides a unified entomokit CLI for frame extraction, segmentation, morphology measurement, synthesis, cleaning, augmentation, dataset splitting, AutoMM classification and environment diagnostics, plus an entomokit-workflow skill for AI assistants. The pipeline is composable; only the steps your data needs are required. 1. extract-frames — turn videos into still frames. 2. segment — cut insects out of images and optionally write annotations. 3. measure — optional: morphology metrics from segmentation masks. 4. synthesize — optional: composite RGBA cutouts onto new backgrounds. 5. clean — resize, pad and deduplicate images. 6. augment — optional: expand a small training set. 7. split-csv — build train/val/test CSVs. 8. classify train → classify predict / classify evaluate / classify embed / classify cam / classify export-onnx.
README
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Insect Dataset Toolkit (EntomoKit)
中文文档 | English
A Python-based toolkit for building insect image datasets. EntomoKit provides a
unified entomokit CLI for frame extraction, segmentation, morphology
measurement, synthesis, cleaning, augmentation, dataset splitting, AutoMM
classification and environment diagnostics, plus an entomokit-workflow skill for
AI assistants.
Workflow Overview
The pipeline is composable; only the steps your data needs are required.
extract-frames— turn videos into still frames.segment— cut insects out of images and optionally write annotations.measure— optional: morphology metrics from segmentation masks.synthesize— optional: composite RGBA cutouts onto new backgrounds.clean— resize, pad and deduplicate images.augment— optional: expand a small training set.split-csv— build train/val/test CSVs.classify train→classify predict/classify evaluate/classify embed/classify cam/classify export-onnx.
You can start at any step: for example go straight from clean to classify train
when the images are already labelled, or use segment alone for mask extraction.
AI-assisted runs follow the skill’s own guided policy, not this list.
Requirements and Installation
- Python 3.9+
- Linux, macOS or Windows
git clone https://github.com/xtmtd/entomokit.git
cd entomokit
Install into an isolated environment (recommended):
uv venv .venv
source .venv/bin/activate
uv pip install -e .
Equivalent alternatives: python -m venv .venv && source .venv/bin/activate, or
conda create -n entomokit python=3.11 -y && conda activate entomokit. Installing
directly into a global environment is possible but risks dependency conflicts.
Command extras:
| Extra | Adds |
|---|---|
.[video] |
extract-frames (OpenCV video decoding) |
.[segmentation] |
SAM3 segmentation, SAM3 repair and synthesis annotation output |
.[measurement] |
measure (morphology metrics, skeleton computation) |
.[synthesis] |
synthesize (polygon simplification, COCO annotations) |
.[cleaning] |
clean perceptual-hash dedup |
.[augment] |
augment (albumentations) |
.[classify] |
classify train/predict/evaluate/embed/cam/export-onnx (AutoMM, timm, GradCAM, UMAP, ONNX) |
.[dev] |
pytest and coverage |
measure and synthesize also import scikit-image, which neither the
measurement nor the synthesis extra declares; install .[segmentation] (which
declares it) or add scikit-image explicitly alongside those extras.
Install several at once, for example the full development set:
uv pip install --only-binary :all: stringzilla
pip install -e ".[dev,classify,segmentation,synthesis,measurement,video,cleaning,augment]"
The stringzilla line above is required before installing .[augment] on platforms
where only its binary wheel is available. AutoMM publishes its own install notes at
Рекомендуемые инструменты
Попробуйте другой запрос или уберите фильтр.
Установка
npx skillfish add xtmtd/entomokit