Configuration

Note

This page is a high-level overview of the delta.config module of DeLTA, and you can find more technical explanations by clicking on any link.

DeLTA can work on different kinds of images, mothermachine and 2D (agar pads). Different steps and networks are used for that: this configuration is described by a delta.config.Config object.

The only places where this object needs to be explicitly manipulated is at the creation of a delta.pipeline.Pipeline, a delta.pipeline.Position or a delta.pipeline.ROI.

DeLTA provides two base configurations, one for each setting (mothermachine and 2D), available with the method delta.config.Config.default. They are good to use right away, but you would typically modify them if you want to use your own deep learning models instead of the ones included with DeLTA.

Note

Any change in the config option memory_growth_limit will not be taken into account unless the method delta.config.Config.apply_tensorflow_config is explicitly called. The default values are usually good, so you probably don’t need to care about this, unless you know that you do.

Deep learning model selection

The delta.config.Config knows where to find the deep learning models included with DeLTA, for example config.model("segmentation") returns the segmentation model as a keras object, should you ever need to access it directly.

Under the scenes, it downloads it and caches it on the disk if you don’t already have it (go to delta.assets to know how this works). This is what happens when a model-related attribute of config is None. But if you trained your own model, you will need to provide its location to the config, for example as following:

from delta.config import Config
config = Config.default("mothermachine")
# here, ``config.model("rois")`` would download and return the default model
config.model_file_rois = 'D:/data/delta_cache/unet_momachambers_seg.keras'
# here, ``config.model("rois")`` would now load your model
# but you can also of course feed this config to a ``Pipeline``

Saving and using a configuration file

If you want to modify a configuration object, you can always do it programmatically by accessing its attributes, as described on the documentation page of delta.config.Config, for example:

from delta.config import Config
config = Config.default("mothermachine")
config.target_size_seg = (1024, 1024)
config.min_cell_area = 0

But you might also want to save the config object as a configuration file, and modify it with a text editor, or save it for reusing, archiving or other purposes.

This is done with the method delta.config.Config.write which takes as argument the path to the json file where you want the configuration written:

config.write("my_custom_config.json")

To read later from this file, use the method delta.config.Config.read:

config = delta.config.Config.read("my_custom_config.json")