crm_gen
crm_gen generates realistic synthetic phase contrast microscope images of
bacteria. It combines the cr_mech_coli simulation framework with a modular
image synthesis pipeline and exposes three subcommands.
crm_gen run [--config path/to/gen_config.toml]
crm_gen clone img.tif mask.tif [--config path/to/gen_config.toml]
crm_gen fit path/to/real/images/ [--config path/to/fit_config.toml]
run and clone use a generation config; fit uses a separate
fit config. Default configs are located in
cr_mech_coli/crm_gen/configs/.
crm_gen run
Runs a bacteria growth simulation and applies synthetic microscope effects to each rendered frame. Each simulation produces paired output (a synthetic phase contrast image and a pixel-accurate segmentation mask), making this the primary tool for generating labelled training data for deep learning segmentation models.
crm_gen run
crm_gen run --config my_gen.toml
crm_gen clone
Creates a synthetic version of a real microscope image by extracting cell positions from a segmentation mask and re-rendering them with synthetic optical effects.
crm_gen clone image.tif mask.tif
crm_gen clone image.tif mask.tif --output ./out --config my_gen.toml
Options:
--output/-o— output directory (default:./synthetic_output)--n-vertices— vertices per cell, overrides config (default: 8)--seed— random seed, overrides config
crm_gen fit
Optimizes the 7 synthetic imaging parameters to match a directory of real microscope images using differential evolution. The imaging parameters are the output of the fit; the fit config contains only optimization hyperparameters and search bounds.
crm_gen fit path/to/real/images/
crm_gen fit path/to/real/images/ --config my_fit.toml
The fit config (configs/default_fit_config.toml) contains:
[optimization]— hyperparameters (maxiter,popsize,workers, etc.)[optimization.bounds]— search bounds[min, max]for each imaging parameter[optimization.metric_weights]— loss weights (SSIM, PSNR, histogram distance)[optimization.region_weights]— foreground vs. background loss weighting