magscope.auto_bead_selection

magscope.auto_bead_selection#

`_DEFAULT_CORRELATION_CHUNK_ROWS`
`_CANDIDATE_PROGRESS_UPDATE_INTERVAL`
`_MIN_CANDIDATE_SCORE`

Raised when auto bead matching is canceled between correlation chunks.

`copy_latest_image`(→ numpy.ndarray)	Copy a recent image snapshot in the same orientation shown in the UI.
`crop_roi`(→ numpy.ndarray)	Return the ROI crop from a viewer-oriented `(height, width)` image.
`normalized_cross_correlation`(→ numpy.ndarray)	Compute 2D normalized cross-correlation over valid ROI-sized positions.
`_ones_kernel`(→ numpy.ndarray)
`_correlate2d_valid_chunked`(→ numpy.ndarray)	Compute `correlate2d(..., mode='valid')` in row stripes.
`_window_sum_integral`(→ numpy.ndarray)	Compute valid sliding-window sums via a summed-area table.
`_mark_blocked_roi`(→ None)	Mark all top-left positions whose ROI would overlap `roi`.
`normalized_cross_correlation_chunked`(→ numpy.ndarray)	Compute 2D normalized cross-correlation over valid ROI-sized positions.
`roi_overlaps`(→ bool)
`roi_is_within_image`(→ bool)
`filter_candidates_by_score_threshold`(...)
`default_candidate_score_threshold`(→ float)	Choose a default score threshold that favors the strongest score cluster.
`detect_matching_beads`(→ tuple[numpy.ndarray, ...)	Detect non-overlapping ROI candidates that match the seed ROI template.
`run_auto_bead_search_process`(→ None)	Serve auto-bead search requests from a temporary worker process.

magscope.auto_bead_selection._CANDIDATE_PROGRESS_UPDATE_INTERVAL = 512[source]#

exception magscope.auto_bead_selection.AutoBeadSearchCancelled[source]#

Bases: RuntimeError

Raised when auto bead matching is canceled between correlation chunks.

class magscope.auto_bead_selection.AutoBeadCandidate[source]#

magscope.auto_bead_selection.copy_latest_image(image_bytes: memoryview | bytes | bytearray, image_shape: tuple[int, int], dtype: numpy.dtype) → numpy.ndarray[source]#: Copy a recent image snapshot in the same orientation shown in the UI.

magscope.auto_bead_selection.crop_roi(image: numpy.ndarray, roi: tuple[int, int, int, int]) → numpy.ndarray[source]#: Return the ROI crop from a viewer-oriented (height, width) image.

magscope.auto_bead_selection.normalized_cross_correlation(image: numpy.ndarray, template: numpy.ndarray) → numpy.ndarray[source]#: Compute 2D normalized cross-correlation over valid ROI-sized positions.

magscope.auto_bead_selection._ones_kernel(shape: tuple[int, int]) → numpy.ndarray[source]#

magscope.auto_bead_selection._correlate2d_valid_chunked(image: numpy.ndarray, kernel: numpy.ndarray, *, chunk_rows: int, cancel_check: collections.abc.Callable[[], bool] | None = None, progress_callback: collections.abc.Callable[[int, int], None] | None = None) → numpy.ndarray[source]#: Compute correlate2d(..., mode='valid') in row stripes.

magscope.auto_bead_selection._window_sum_integral(image: numpy.ndarray, window_shape: tuple[int, int]) → numpy.ndarray[source]#: Compute valid sliding-window sums via a summed-area table.

magscope.auto_bead_selection._mark_blocked_roi(blocked_mask: numpy.ndarray, roi: tuple[int, int, int, int], roi_size: tuple[int, int]) → None[source]#: Mark all top-left positions whose ROI would overlap roi.

magscope.auto_bead_selection.normalized_cross_correlation_chunked(image: numpy.ndarray, template: numpy.ndarray, *, chunk_rows: int = _DEFAULT_CORRELATION_CHUNK_ROWS, cancel_check: collections.abc.Callable[[], bool] | None = None, progress_callback: collections.abc.Callable[[int, int], None] | None = None) → numpy.ndarray[source]#: Compute 2D normalized cross-correlation over valid ROI-sized positions.

magscope.auto_bead_selection.roi_overlaps(roi_a: tuple[int, int, int, int], roi_b: tuple[int, int, int, int]) → bool[source]#

magscope.auto_bead_selection.roi_is_within_image(roi: tuple[int, int, int, int], image_shape: tuple[int, int]) → bool[source]#

magscope.auto_bead_selection.filter_candidates_by_score_threshold(candidates: Iterable[AutoBeadCandidate], threshold: float) → list[AutoBeadCandidate][source]#

magscope.auto_bead_selection.default_candidate_score_threshold(candidates: Iterable[AutoBeadCandidate]) → float[source]#: Choose a default score threshold that favors the strongest score cluster.

magscope.auto_bead_selection.detect_matching_beads(image: numpy.ndarray, seed_roi: tuple[int, int, int, int], existing_rois: Iterable[tuple[int, int, int, int]], *, chunk_rows: int = _DEFAULT_CORRELATION_CHUNK_ROWS, cancel_check: collections.abc.Callable[[], bool] | None = None, progress_callback: collections.abc.Callable[[int, int], None] | None = None) → tuple[numpy.ndarray, list[AutoBeadCandidate]][source]#: Detect non-overlapping ROI candidates that match the seed ROI template.

magscope.auto_bead_selection.run_auto_bead_search_process(request_queue, result_queue, active_request_id: multiprocessing.sharedctypes.Synchronized, *, chunk_rows: int = _DEFAULT_CORRELATION_CHUNK_ROWS) → None[source]#: Serve auto-bead search requests from a temporary worker process.