napari-microplate 0.1.1

napari plugin — full microplate image pipeline (align plate → detect wells → classify wells)

napari-microplate

A napari plugin for end-to-end analysis of microbiology microplate images: align the plate, detect every well, and classify each well as Growth / NoGrowth / NoAgar.

The plugin runs a three-stage pipeline directly in the napari viewer and renders the result as overlay layers you can inspect interactively.

raw microplate image
   │
   ├──► Stage 1 — Align plate       (EfficientNet-B3 corner regression, grayscale)
   │       4-corner detection → perspective warp → canonical plate
   ├──► Stage 2 — Detect wells      (YOLOv8n, single-channel grayscale, ch=1)
   │       bounding box per well
   └──► Stage 3 — Classify wells    (Random Forest)
           Growth / NoGrowth / NoAgar

Installation

pip install napari-microplate

This installs the plugin and all its pure-Python dependencies, including PyTorch (CPU build). On first run the model weights (~700 MB total) are downloaded automatically from the HuggingFace Hub and cached under ~/.cache/napari_microplate/.

GPU acceleration (optional, recommended)

The CPU PyTorch build works but Stage 1 + Stage 2 are noticeably faster on GPU. To use a CUDA GPU, install a CUDA-enabled PyTorch before or after the plugin:

pip install torch torchvision --index-url https://download.pytorch.org/whl/cu126

The plugin auto-detects CUDA via torch.cuda.is_available().

Usage

1. Launch napari:

   napari
   

2. Open a raw microplate image (File → Open file(s) or drag-and-drop).

3. Start the plugin: Plugins → Microplate Pipeline.

4. In the dock widget:

   • pick the Image layer holding your microplate image,
   • (optional) tick Save PNG and choose a Save dir,
   • click Run Pipeline.

Two new layers are added to the viewer:

Layer	Type	Content
aligned_plate	Image (gray)	Plate after Stage 1 perspective warp
wells	Shapes (rectangles)	One box per well, colored by predicted class

Well counts (Growth / NoGrowth / NoAgar) are shown in the napari status bar.

Configuration

Environment variable	Purpose	Default
MICROPLATE_HF_REPO	HuggingFace repo id for the weights	tiendoan274/napari-microplate-weights
MICROPLATE_WEIGHTS_DIR	Local folder of pre-downloaded weights (skip download)	unset

To run fully offline, download the three weight files into a folder and set MICROPLATE_WEIGHTS_DIR to that folder:

$MICROPLATE_WEIGHTS_DIR/
  stage1_efficientnet_b3.pt
  stage2_yolov8n_well.pt
  stage3_random_forest.joblib

Programmatic use (without the GUI)

from napari_microplate._pipeline import MicroplatePipeline

pipe = MicroplatePipeline()          # loads weights lazily on first run
result = pipe.run("plate.png")       # path to a raw microplate image

print(result["boxes"])               # [(x1,y1,x2,y2,conf), ...]
print(result["classes"])             # [0, 1, 2, ...]  (0=Growth,1=NoGrowth,2=NoAgar)

License

Copyright (C) 2026 Tien Doan. Distributed under the GNU Affero General Public License v3.0 or later (AGPL-3.0+) — see LICENSE.

This package vendors a modified copy of Ultralytics 8.3.2 (AGPL-3.0) under napari_microplate/_vendor/ to support single-channel grayscale well detection. See NOTICE for details.

Citation

If you use this plugin in published research, please cite the accompanying paper (see paper/ in the source repository).