SCTrack.generate_tracking_stack module

class SCTrack.generate_tracking_stack.JsonParser(file)[source]

Bases: object

get_coords_by_frame_index(frame_index=None)[source]
get_coords_by_frame_name(frame_name)[source]
get_frame_name_by_index(index)[source]
parse_json()[source]
class SCTrack.generate_tracking_stack.Mask(mask=None, center=None, coord=None)[source]

Bases: object

property center: Tuple[int | float]
property mask
class SCTrack.generate_tracking_stack.RefinedParser(path)[source]

Bases: object

get_stack()[source]
parse_id()[source]
parse_position()[source]
class SCTrack.generate_tracking_stack.Stack[source]

Bases: object

SCTrack.generate_tracking_stack.coord2counter(coord)[source]
SCTrack.generate_tracking_stack.coordinate2mask(coords: np.ndarray | list | tuple, value: int = 255, image_size: Tuple[int, int] = None) → List[Mask][source]
SCTrack.generate_tracking_stack.csv2mask(jsonfile, excelfile, mask_filename)[source]
SCTrack.generate_tracking_stack.extractRoiFromImg(images: str | np.ndarray, mask: np.ndarray) → np.ndarray[source]

Extract the area in the original image according to the mask. Note that it can only be a single-channel image. If it is rgb, please convert it to a grayscale image first. :param images: original images :param mask: mask file :return: single cell image data