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xtmtd/entomokit

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20 stars 품질 64 트렌드 18MIT

A Python Toolkit for Insect Image Dataset Construction and Classification

개요

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

원문 README의 일부입니다. 원문 보기.

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.

  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.

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

GitHub에서 전체 README 보기

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