delta.data.predict_compile_from_seg_track#
- delta.data.predict_compile_from_seg_track(img_path, seg_path, files_list, *, target_size=(256, 32), crop_windows=False)[source]#
Compile an inputs array for tracking prediction with the tracking U-Net, directly from U-Net segmentation masks saved to disk.
- Parameters
- img_pathPath
Path to original single-chamber images folder. The filenames are expected in the printf format Position%02d_Chamber%02d_Frame%03d.png
- seg_pathPath
Path to segmentation output masks folder. The filenames must be the same as in the img_path folder.
- files_listlist[Path]
List of filenames to compile in the img_path and seg_path folders.
- target_sizetuple of 2 ints, optional
Input and output image size. The default is (256,32).
- crop_windowsbool, optional
TODO The default is False.
- Returns
- inputs_arr4D numpy array of floats
Input images and masks for the tracking U-Net training routine. Dimensions of the tensor are (cells_to_track, target_size[0], target_size[1], 4), with cells_to_track the number of segmented cells in all segmentation masks of the files_list.
- seg_name_list[Path]
Filenames to save the tracking outputs as. The printf format is Position%02d_Chamber%02d_Frame%03d_Cell%02d.png, with the ‘_Cell%02d’ string appended to signal which cell is being seeded/tracked (from top to bottom)
- boxeslist of CroppingBox
Cropping box to re-place output prediction masks in the original image coordinates.