SyMBac.misc
- SyMBac.misc.get_sample_images()[source]
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
- SyMBac.misc.resize_mask(mask, resize_shape, ret_label)[source]
Resize masks while maintaining their connectivity and values
- SyMBac.misc.unet_weight_map(y, wc=None, w0=10, sigma=5)[source]
Generate weight maps as specified in the U-Net paper for boolean mask.
- Parameters
- 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