aliby.tile.tiler

Tiler: Tiles and tracks traps.

The tasks of the Tiler are selecting regions of interest, or tiles, of an image - with one tile per trap, tracking and correcting for the drift of the microscope stage over time, and handling errors and bridging between the image data and ALIBY’s image-processing steps.

Tiler subclasses deal with either network connections or local files.

To find traps, we use a two-step process: we analyse the bright-field image to produce the template of a trap, and we fit this template to the image to find the traps’ centres.

We use texture-based segmentation (entropy) to split the image into foreground – cells and traps – and background, which we then identify with an Otsu filter. Two methods are used to produce a template trap from these regions: pick the trap with the smallest minor axis length and average over all validated traps.

A peak-identifying algorithm recovers the x and y-axis location of traps in the original image, and we choose the templating approach that identifies the most traps

One key method is Tiler.run.

The image-processing is performed by traps/segment_traps.

The experiment is stored as an array with a standard indexing order of (Time, Channels, Z-stack, X, Y).

Functions

find_channel_index(image_channels, channel)	Access
find_channel_name(image_channels, channel)	Find the name of the channel according to a given channel regex.

Classes

Tiler(image, metadata, parameters[, trap_locs])	Remote Timelapse Tiler.
TilerParameters(**kwargs)	Defines parameters as attributes
Trap(centre, parent, size, max_size)	Stores a trap's location and size.
TrapLocations(initial_location[, tile_size, ...])	Stores each trap as an instance of Trap.