napari-em-assistant 0.2.3

Task-based EM image processing assistant for napari

napari-em-assistant

Task-based electron microscopy image processing tools for napari.

napari-em-assistant is organized around small, explicit processing tasks that can be used interactively on an active napari layer or applied to folders of EM images. The first tasks are ImageJ/Fiji-style local contrast enhancement with CLAHE and frictionless 2D/3D image cropping.

Current Task

Enhance Local Contrast CLAHE

The CLAHE task provides a napari widget for 2D grayscale EM images and 3D grayscale stacks. It follows the ImageJ/Fiji dialog shape and default values:

• block size: 127
• histogram bins: 256
• maximum slope: 3.00
• mask: *None*
• fast: checked

The widget can:

• show a top acceleration bar for CPU, CUDA via CuPy, or CPU fallback
• preview CLAHE on the active image layer
• apply CLAHE to the active 2D image layer or 3D grayscale stack
• create a new layer named <source_layer_name>_CLAHE
• batch process TIFF folders to an output folder
• show batch progress with load/process checks, status, and output path

Backends

• opencv_cpu: fast CPU approximation using cv2.createCLAHE.
• imagej_reference: Python implementation ported from Fiji's mpicbg.ij.clahe.PlugIn 2D grayscale CLAHE path. 3D stacks are processed slice-by-slice. The fast checkbox only affects this backend.
• gpu_cupy: experimental CuPy/CUDA batch backend. If CuPy or a CUDA device is not available, it falls back to opencv_cpu. It may be slower than opencv_cpu for many current workloads because the implementation pays GPU transfer overhead and is not yet a fully optimized CUDA CLAHE kernel.

The acceleration bar shows the active mode for the selected backend. Detailed CuPy/OpenCV CUDA availability is still available in the tooltip for diagnostics.

OpenCV CLAHE parameters are not identical to ImageJ/Fiji CLAHE parameters. The OpenCV backend is faster, but it rescales the ImageJ-style maximum slope before passing it as OpenCV clipLimit for a more ImageJ-like user experience; maximum slope values up to 1 apply no enhancement in the OpenCV backend. Use imagej_reference when Fiji-style output is the priority. Current OpenCV and CuPy paths should be treated as practical approximations.

Install

From this repository:

pip install -e .

Optional CuPy support depends on the workstation CUDA stack. A convenience extra is provided:

pip install -e ".[gpu]"

If that wheel does not match the local CUDA runtime, install the correct CuPy package manually and keep the base plugin install CPU-only.

Use In napari

After installation, open napari and choose:

Plugins > Enhance Local Contrast CLAHE

For interactive use, select a 2D grayscale image layer or 3D grayscale stack and use Preview or Apply to Active Layer.

Crop Image

The crop task provides a compact widget for 2D images and 3D grayscale stacks. It is organized around four imaging workflows: crop from a drawn ROI, tile by total tile count, tile by pixel size, and advanced coordinate cropping. A progress bar reports saved or created crop pieces as completed / total. The widget asks for confirmation before likely mistakes, including very tiny crops from a large source image/stack and operations that would save more than 1000 crop TIFF files.

For detailed step-by-step crop workflows, see src/napari_em_assistant/tasks/crop_image/README.md.

For large images, choose a save folder and use either tiling workflow:

• Tile by Total Count: enter the total number of output tiles wanted. The plugin chooses a near-even Y/X grid automatically and keeps full Z depth for 3D stacks. For example, 6 creates six image tiles.
• Tile by Pixel Size: save tiles with the requested output dimensions. For example, a 6000 x 6000 x 500 stack with z size = 0, y size = 2000, and x size = 2000 writes nine 500 x 2000 x 2000 stack tiles. z size = 0 keeps the full Z depth in every tile.

For batch use, choose an input folder containing 2D images or 3D grayscale stacks saved as .tif or .tiff files, choose an output folder, select a backend, and run the batch. Outputs are saved as:

<stem>_clahe.tif

Validation

The current implementation is tested for:

• widget and backend imports
• Fiji-derived imagej_reference fast and exact paths
• OpenCV CLAHE on synthetic uint8 and uint16 images
• shape preservation
• dtype preservation where possible
• 3D stack support
• GPU stub behavior
• gpu_cupy CPU fallback behavior
• batch result and progress reporting

Run tests with:

python3 -m pytest tests/tasks/test_enhance_local_contrast_clahe.py