napari_dmc_brainmap.stitching package

Submodules

napari_dmc_brainmap.stitching.stitching module

DMC-BrainMap widget for stitching .tif files.

2024 - FJ, XC

class napari_dmc_brainmap.stitching.stitching.StitchingWidget(napari_viewer: Viewer)[source]

Bases: QWidget

QWidget for configuring and initiating the stitching process.

progress_signal

Signal emitted to update the progress bar with an integer value.

napari_dmc_brainmap.stitching.stitching.do_stitching(input_path: Path, filter_list: List[str], params_dict: Dict, stitch_tiles: bool, direct_sharpy_track: bool) → str[source]

Perform stitching operation using input path and parameters provided.

Parameters:

  • input_path (Path) – Path to directory containing data for stitching.

  • filter_list (List[str]) – List of channels to stitch.

  • params_dict (Dict) – Dictionary containing stitching parameters.

  • stitch_tiles (bool) – Whether to stitch individual tiles or use DMC-FluoImager data.

  • direct_sharpy_track (bool) – Whether to create data for SHARPy-track.

Yields:

int – Progress value for the stitching process.

Returns:

The animal ID for which stitching was performed.

Return type:

str

napari_dmc_brainmap.stitching.stitching.initialize_widget() → FunctionGui[source]

Initialize the magicgui widget for stitching configuration.

Returns: FunctionGui: Initialized magicgui widget.

napari_dmc_brainmap.stitching.stitching.load_tile_stack(in_chan: Path, stack: List[str], c_size: int = 2048) → ndarray[source]

Load a stack of tiles from the specified input channel.

Parameters:

  • in_chan (Path) – Input path containing tiles.

  • stack (List[str]) – List of tile file names.

  • c_size (int, optional) – Size of the tiles. Default is 2048.

Returns: np.ndarray: Loaded stack of images as a numpy array.

napari_dmc_brainmap.stitching.stitching.process_stitch_folder(input_path: Path, in_obj: Path, f: str, stitch_dir: Path, animal_id: str, obj: str, params_dict: Dict, resolution: Tuple[int, int], direct_sharpy_track: bool, overlap: int = 205) → None[source]

Process stitching for a folder of tiles.

Parameters:

  • input_path (Path) – Base path to animal data.

  • in_obj (Path) – Input path for object data.

  • f (str) – Channel to process.

  • stitch_dir (Path) – Directory to save stitched data.

  • animal_id (str) – Animal ID.

  • obj (str) – Object name.

  • params_dict (Dict) – Parameters for stitching.

  • resolution (Tuple[int, int]) – Resolution of the atlas used for registration.

  • direct_sharpy_track (bool) – Whether to create SHARPy-track data directly.

  • overlap (int, optional) – Overlap for stitching tiles. Defaults to 205.

napari_dmc_brainmap.stitching.stitching.process_stitch_stack(input_path: Path, in_obj: Path, f: str, stitch_dir: Path, animal_id: str, obj: str, params_dict: Dict, resolution: Tuple[int, int], direct_sharpy_track: bool, overlap: int = 205) → None[source]

Process stitching for a stack of tiles.

Parameters:

  • input_path (Path) – Base path to animal data.

  • in_obj (Path) – Input path for object data.

  • f (str) – Channel to process.

  • stitch_dir (Path) – Directory to save stitched data.

  • animal_id (str) – Animal ID.

  • obj (str) – Object name.

  • params_dict (Dict) – Parameters for stitching.

  • resolution (Tuple[int, int]) – Resolution of the atlas used for registration.

  • direct_sharpy_track (bool) – Whether to create SHARPy-track data directly.

  • overlap (int, optional) – Overlap for stitching tiles. Defaults to 205.

napari_dmc_brainmap.stitching.stitching_tools module

napari_dmc_brainmap.stitching.stitching_tools.downsample_image(input_tiff: str | ndarray, size_tuple: Tuple[int, int], contrast_tuple: Tuple[int, int]) → ndarray[source]

Downsample an image and adjust its brightness.

Parameters:

  • input_tiff (Union[str, np.ndarray]) – Input image (file path or image matrix).

  • size_tuple (Tuple[int, int]) – Target size for downsampling.

  • contrast_tuple (Tuple[int, int]) – Contrast adjustment parameters.

Returns:

Downsampled image as a NumPy array.

Return type:

np.ndarray

napari_dmc_brainmap.stitching.stitching_tools.fill_canvas(width: int, height: int, stitch_canvas: ndarray, loc_map: Dict[int, int], data_dict: Dict, overlap: int = 205, c_size: int = 2048, stack: bool = True) → ndarray[source]

Fill the stitching canvas with image tiles.

Parameters:

  • width (int) – Width of the grid.

  • height (int) – Height of the grid.

  • stitch_canvas (np.ndarray) – The canvas to be filled.

  • loc_map (Dict[int, int]) – Mapping of tile locations.

  • data_dict (Dict) – Dictionary containing image data.

  • overlap (int, optional) – Overlap between tiles. Defaults to 205.

  • c_size (int, optional) – Size of each tile. Defaults to 2048.

  • stack (bool, optional) – Whether to use a stack or individual sections. Defaults to True.

Returns:

Filled stitching canvas.

Return type:

np.ndarray

napari_dmc_brainmap.stitching.stitching_tools.get_canvas(width: int, height: int, overlap: int = 205, c_size: int = 2048) → Tuple[ndarray, Dict[int, int]][source]

Create a blank canvas and generate a tile location map for stitching.

Parameters:

  • width (int) – Width of the grid.

  • height (int) – Height of the grid.

  • overlap (int, optional) – Overlap between tiles. Defaults to 205.

  • c_size (int, optional) – Size of each tile. Defaults to 2048.

Returns:

Tuple containing the blank canvas and location map.

Return type:

Tuple[np.ndarray, Dict[int, int]]

napari_dmc_brainmap.stitching.stitching_tools.get_size_json(pos_list: List[Tuple[int, int]]) → Tuple[int, int][source]

Calculate the width and height of the grid from a list of positions.

Parameters:

pos_list (List[Tuple[int, int]]) – List of positions as tuples of (x, y).

Returns:

Width and height of the grid.

Return type:

Tuple[int, int]

napari_dmc_brainmap.stitching.stitching_tools.load_meta(section_dir: Path) → Dict[source]

Load metadata from a .tif file in the specified directory.

Parameters:

section_dir (Path) – Directory containing the .tif file.

Returns:

Metadata as a dictionary.

Return type:

Dict

napari_dmc_brainmap.stitching.stitching_tools.map_loc(width: int, height: int) → Dict[int, int][source]

Generate a mapping of tile locations for constructing a stitched image.

Parameters:

  • width (int) – Width of the grid.

  • height (int) – Height of the grid.

Returns:

Dictionary mapping original tile indices to new locations.

Return type:

Dict[int, int]

napari_dmc_brainmap.stitching.stitching_tools.padding_for_atlas(input_array: ndarray, resolution: Tuple[int, int] | None) → ndarray[source]

Apply padding to an image for atlas registration.

Parameters:

  • input_array (np.ndarray) – Input image as a NumPy array.

  • resolution (Optional[Tuple[int, int]]) – Desired resolution for padding.

Returns:

Padded image as a NumPy array.

Return type:

np.ndarray

napari_dmc_brainmap.stitching.stitching_tools.stitch_folder(section_dir: Path, overlap: int, stitched_path: Path, params: dict, chan: str, downsampled_path: Path | None = False, resolution: Tuple[int, int] | None = False) → None[source]

Stitch images from a folder into a single image. :param section_dir: Directory containing the images. :type section_dir: Path :param overlap: Overlap between tiles. :type overlap: int :param stitched_path: Path to save the stitched image. :type stitched_path: Path :param params: Dictionary of parameters. :type params: dict :param chan: Channel information. :type chan: str :param downsampled_path: Path to save the downsampled image (optional). :type downsampled_path: Optional[Path] :param resolution: Resolution for padding (optional). :type resolution: Optional[Tuple[int, int]]

napari_dmc_brainmap.stitching.stitching_tools.stitch_stack(pos_list: List[Tuple[int, int]], whole_stack: ndarray, overlap: int, stitched_path: str, params: dict, chan: str, downsampled_path: Path | None = False, resolution: Tuple[int, int] | None = False) → None[source]

Stitch a stack of images into a single image. :param pos_list: List of positions as tuples of (x, y). :type pos_list: List[Tuple[int, int]] :param whole_stack: Stack of images to be stitched. :type whole_stack: np.ndarray :param overlap: Overlap between tiles. :type overlap: int :param stitched_path: Path to save the stitched image. :type stitched_path: str :param params: Dictionary of parameters. :type params: dict :param chan: Channel information. :type chan: str :param downsampled_path: Path to save the downsampled image (optional). :type downsampled_path: Optional[Path] :param resolution: Resolution for padding (optional). :type resolution: Optional[Tuple[int, int]]

Module contents