SyMBac.misc

SyMBac.misc.get_sample_images()

Return a dict of sample mother machine images.

Returns:

A dict with sample images, current keys are: “E. coli 100x”, “E. coli 100x stationary”, “E. coli DeLTA”

Return type:

dict

SyMBac.misc.resize_mask(mask, resize_shape, ret_label)

Resize masks while maintaining their connectivity and values

SyMBac.misc.unet_weight_map(y, wc=None, w0=10, sigma=5)

Generate weight maps as specified in the U-Net paper for boolean mask.

Parameters:

• mask (Numpy array) – 2D array of shape (image_height, image_width) representing binary mask of objects.

• wc (dict) – Dictionary of weight classes.

• w0 (int) – Border weight parameter.

• sigma (int) – Border width parameter.

Returns:

Training weights. A 2D array of shape (image_height, image_width).

Return type:

Numpy array

References

Taken from the original U-net paper [1]

[1]

Ronneberger, O., Fischer, P., Brox, T. (2015). U-Net: Convolutional Networks for Biomedical Image Segmentation. In: Navab, N., Hornegger, J., Wells, W., Frangi, A. (eds) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2015. MICCAI 2015. Lecture Notes in Computer Science(), vol 9351. Springer, Cham. https://doi.org/10.1007/978-3-319-24574-4_28