Quick start

Once DeLTA is installed, you can already use it without writing any code, with its default configuration, through its command-line interface.

You can read the instructions to use the command-line interface by calling delta with the --help flag:

(delta_env)$ python -m delta --help

    usage: delta [-h] -c CONFIG -i INPUT [-o OUTPUT] [--positions POSITIONS] [--frames FRAMES]

    Deep Learning for Time-Lapse Analysis

    options:
      -h, --help            show this help message and exit
      -c CONFIG, --config CONFIG
                            Configuration file. Can be either `2D`, `mothermachine`, or a
                            path to a previously saved custom config file.
      -i INPUT, --input INPUT
                            Input file or directory. Can include `{p}`, `{c}` and `{t}` as
                            placeholders for the position, channel, and frame number.
                            Example for micromanager: `/path/to/folder/Pos{p}/img_channel{c}
                            _position{p}_time{t}_z000.tif`
      -o OUTPUT, --output OUTPUT
                            Output directory (by default `delta_results` inside the input
                            directory)
      --positions POSITIONS
                            Positions to process, ex.: 0-2,4,7-10 (default: all)
      --frames FRAMES       Range of frames to process, ex.: -150 (up to frame 150), 15-
                            (from frame 15), 15-30 (frames 15 to 30), 40 (just frame 40)
                            (default: all)

The only two arguments required are CONFIG (use mothermachine if you are using one, 2D otherwise, or the path to a config file that you previously saved), and INPUT. If your images are saved as a single file, for example an nd2 file, just provide its name, but if they are saved as individual files inside a directory, then provide their naming pattern with the placeholders {p} for the position number, {c} for the channel number and {t} for the frame number.

Example uses

• You recorded mothermachine images as a .nd2 file: you can just launch DeLTA on them with

(delta_env)$ python -m delta -c mothermachine -i my_images.nd2

• You did an agar pad experiment where each frame is an individual .tif image, all saved in the same directory images/ with names posXXX_channelXXX_tXXX.tif. Then, you can launch DeLTA on them with

(delta_env)$ python -m delta -c 2D -i images/pos{p}_channel{c}_t{t}.tif