HughesLab

A collection of utilities useful for an (optical) imaging research lab.

All functions work with images and image-like data stored as numpy arrays.

Documentation is on Readthedocs.

Installation

Download from github and run:

pip install -r requirements.txt

Modules

Image Display and Plotting

Quick-display helpers for interactive use:

• qshow : display a 2D image in a matplotlib figure with sensible defaults
• cshow : display a complex image as side-by-side amplitude and phase plots
• qplot : display a 1D line plot with sensible defaults

Figure Generator

Tools to make publication-style multi-panel figure from images:

• multi_im : lay out a grid of images with automatic lettering and labels
• ax_zoom / img_zoom : add a zoomed inset to an existing axes or image
• scalebar : draw a scalebar on an image axes

Image Tools

Image processing and analysis utilities:

• Cropping: crop_image, crop_zero, crop_zero_box, extract_central
• Bit-depth conversion: to8bit, to16bit
• Scaling: log_scale_image, log_scale_min
• Channel operations: average_channels, max_channels, mean_channels
• Registration: get_shifts, find_shift
• Stack utilities: filter_stack, resize_stack, condense_stack
• Misc: radial_profile, rect_to_pol, hamming_window

Image Loading and Saving

Loading and saving images and image stacks:

• Loading: load_image, load_stack, load_folder_images
• Saving single images: save_image8, save_image16, save_image_colour, save_image8_scaled, save_image16_scaled
• Saving stacks: save_tif_stack, stack_to_folder, folder_to_tif_stack, tif_stack_to_folder
• Video: save_video

Image Analysis

Quantitative image analysis:

• profile_line : extract a line profile from an image
• fwhm : calculate full-width at half-maximum of a profile
• profile_gaussian_fit / gaussian_fit : fit a Gaussian to a profile
• GHT : generalised histogram transform for contrast enhancement

Requirements

• matplotlib
• numpy
• opencv-python
• Pillow