API

Import livecellx as:

import livecellx as lcx
import livecellx
import livecellx.annotation
import livecellx.annotation.labelme2coco

Annotation

`annotation.labelme2coco.get_coco_from_labelme_folder`(...)	Generate coco object from labelme annotations.
`annotation.labelme2coco.convert`(labelme_folder)	Args:

`core.datasets.read_img_default`(url, **kwargs)	rtype: `ndarray`
`core.io_sc.process_scs_from_label_mask`(...)	process single cells from one label mask.
`core.io_sc.process_mask_wrapper`(args)
`core.io_sc.prep_scs_from_mask_dataset`(...[, ...])
`core.io_utils.save_png`(path, img[, mode])	save image to png file
`core.io_utils.save_tiff`(img, path[, mode])
`core.io_utils.save_general`(img, path[, mode])	save image to img file
`core.pl_utils.add_colorbar`(im, ax, fig)
`core.sc_seg_operator.create_sc_seg_napari_ui`(...)	Usage # viewer = napari.view_image(dic_dataset.to_dask(), name="dic_image", cache=True) # shape_layer = NapariVisualizer.viz_trajectories(traj_collection, viewer, contour_sample_num=20) # sct_operator = SctOperator(traj_collection, shape_layer, viewer) # sct_operator.setup_shape_layer(shape_layer)
`core.sct_operator.create_sct_napari_ui`(...)	Usage # viewer = napari.view_image(dic_dataset.to_dask(), name="dic_image", cache=True) # shape_layer = NapariVisualizer.viz_trajectories(traj_collection, viewer, contour_sample_num=20) # sct_operator = SctOperator(traj_collection, shape_layer, viewer) # sct_operator.setup_shape_layer(shape_layer)
`core.sct_operator.create_scts_operator_viewer`(sctc)	rtype: `SctOperator`
`core.sct_operator.create_scs_edit_viewer`(...)	Creates a viewer for editing SingleCellStatic objects.

`segment.ou_simulator.viz_check_combined_sc_result`(...)
`segment.ou_simulator.compute_distance_by_contour`(...)
`segment.ou_simulator.combine_two_scs_monte_carlo`(...)
`segment.ou_simulator.gen_synthetic_overlap_scs`(...)
`segment.ou_simulator.gen_gauss_sc_bg`(sc, shape)	generate background for sc by gaussian noise
`segment.ou_simulator.gen_sc_bg_crop`(sc, shape)	generate background for sc by cropping from the sc's image.
`segment.ou_simulator.move_two_scs`(sc1, sc2, ...)
`segment.utils.get_contours_from_pred_masks`(...)
`segment.utils.match_mask_labels_by_iou`(...)	compute the similarity between ground truth mask and segmentation mask by intersection over union
`segment.utils.filter_labels_match_map`(...)
`segment.utils.compute_match_label_map`(t1, ...)	Compute the label map (mapping between objects) between two time points
`segment.utils.process_scs_from_one_label_mask`(...)
`segment.utils.judge_connected_bfs`(mask, ...)	rtype: `Tuple`[`bool`, `int`]

`track.classify_utils.video_frames_and_masks_from_sample`(sample)	Given a sample of SingleCell objects, returns a list of video frames and a list of video frame masks.
`track.classify_utils.combine_video_frames_and_masks`(...)	returns a list of combined video frames and masks, each item contains a 3-channel image with first channel as frame and second channel as mask
`track.sort_tracker.associate_detections_with_trackers`(...)	Assigns detections to tracked object (both represented as bounding boxes)

`trajectory.contour_utils.get_cellTool_contour_points`(traj)	rtype: `List`[`Contour`]
`trajectory.contour_utils.viz_contours`(...)
`trajectory.feature_extractors.compute_haralick_features`(sc)	Returns a list of texture features for the given image.
`trajectory.feature_extractors.compute_skimage_regionprops`(sc)	rtype: `Series`