New scan workflow for snake scans, move next pos into scan baseclass
Update tests that were testing wrong method, update all to new select_nearby_focus_site
This commit is contained in:
Joe Knapper
parent
e59d5e82d8
commit
f230eef2dc
4 changed files with 260 additions and 166 deletions
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@ -13,6 +13,7 @@
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}
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},
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"histo_scan_workflow": "openflexure_microscope_server.things.scan_workflows:HistoScanWorkflow",
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"snake_workflow": "openflexure_microscope_server.things.scan_workflows:SnakeWorkflow",
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"bg_color_channels_luv": "openflexure_microscope_server.things.background_detect:ColourChannelDetectLUV",
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"bg_channel_deviations_luv": "openflexure_microscope_server.things.background_detect:ChannelDeviationLUV"
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},
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@ -252,41 +252,15 @@ class ScanPlanner:
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next_location = self._remaining_locations[0].xy_tuple
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# If focussed locations exist return closest location, favouring most recent
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closest_pos = self.closest_focus_site(next_location)
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# Each scanner defines its own method of choosing a representative nearby site
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closest_pos = self.select_nearby_focus_site(next_location)
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z = None if closest_pos is None else closest_pos[2]
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return next_location, z
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def closest_focus_site(self, xy_pos: XYPos) -> Optional[XYZPos]:
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"""Return the xyz position of the closest site where focus was achieved.
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The most recently taken image is returned in the case of a tie.
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:param xy_pos: The xy_position which the returned position should be closest
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to.
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Returns None if there if no focussed locations are present
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"""
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# save to variable rather than search for focussed sites each time.
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focused_locations = self.focused_locations
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if not focused_locations:
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return None
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# must be float64 (double precision) to deal with the huge numbers involved!
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current_pos = np.array(xy_pos, dtype="float64")
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path_pos = np.array(focused_locations, dtype="float64")[:, :2]
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# Use linalg.norm to calculate the direct distance bweween the points
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# Note linalg.norm always uses float64
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dists = np.linalg.norm((path_pos - current_pos), axis=1)
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# Get indices of all minima.
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# Note np.where always returns a tuple of arrays, hence the trailing [0]
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indices = np.where(dists == np.min(dists))[0]
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# The last index is most recent
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return focused_locations[indices[-1]]
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def select_nearby_focus_site(self, next_location: XYPos) -> Optional[XYZPos]:
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"""Return the focused site near xy_pos according to the tiebreak."""
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raise NotImplementedError("Did you call the ScanPlanner base class?")
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def mark_location_visited(
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self, xyz_pos: XYZPos, imaged: bool, focused: bool
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@ -316,6 +290,20 @@ class ScanPlanner:
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)
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)
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def _grid_to_future_locations(
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self,
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grid: list[list[XYPos]],
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) -> list[FutureScanLocation]:
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"""Flatten a 2D grid of coordinates into flat list of FutureScanLocation objects.
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:param grid: A 2D nested list of XY coordinates
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"""
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path = []
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for line in grid:
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path += line
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return [FutureScanLocation(location) for location in path]
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class SmartSpiral(ScanPlanner):
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"""A scan planner that spirals outward from the centre, prioritising short moves.
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@ -385,7 +373,7 @@ class SmartSpiral(ScanPlanner):
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def _initial_location_list(self) -> list[FutureScanLocation]:
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"""Set the initial list of locations for this scan planner.
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This is salled on initialisation.
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This is called on initialisation.
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For smart spiral this is just the first point
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"""
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@ -514,26 +502,6 @@ class SmartSpiral(ScanPlanner):
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self._remaining_locations.sort(key=sort_key)
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def get_next_location_and_z_estimate(self) -> tuple[XYPos, Optional[int]]:
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"""Return the next location to scan and its estimated z-position.
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This overrides the default behaviour of ScanPlanner to take the lowest value of
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nearest neighbours as this works best for smart stack.
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Note z-position may be None! This indicates that the current z position
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should be used.
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"""
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if self.scan_complete:
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raise RuntimeError("Can't get next position, scan is complete")
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next_location = self._remaining_locations[0].xy_tuple
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# If focused locations exist, return the neighbour with the lowest z position
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closest_pos = self.select_nearby_focus_site(next_location)
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z = None if closest_pos is None else closest_pos[2]
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return next_location, z
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def select_nearby_focus_site(self, xy_pos: XYPos) -> Optional[XYZPos]:
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"""Return the xyz position of the nearby site with the lowest z position.
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@ -575,7 +543,11 @@ class SmartSpiral(ScanPlanner):
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# Choose the lowest (smallest z) of the neighbouring sites. Smart stack works best
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# if started too low, so the lowest z will perform best
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chosen_focused_site = min(focused_locations_array[indices], key=lambda x: x[-1])
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candidates = focused_locations_array[indices]
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min_z = np.min(candidates[:, -1])
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# Find all with the minimum z, and select the latest
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chosen_focused_site = candidates[candidates[:, -1] == min_z][-1]
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# Convert back into list so values are of type int instead of np.int32
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return tuple(chosen_focused_site.tolist())
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@ -664,91 +636,16 @@ class SnakeScan(ScanPlanner):
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style="snake",
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)
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# create_rectangular_scan_path provides a nested list, which is flattened here
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path = []
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for line in grid:
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path += line
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return self._grid_to_future_locations(grid)
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return [FutureScanLocation(location) for location in path]
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def mark_location_visited(
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self, xyz_pos: XYZPos, imaged: bool = True, focused: bool = True
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) -> None:
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"""Mark the location as visited.
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:param xyz_pos: the x_y_z position
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:param imaged: true if an image was taken, false if not (due to background detect)
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:param focused: true if autofocus completed successfully
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"""
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# Only call the base class to update the positions
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super().mark_location_visited(xyz_pos, imaged, focused)
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def get_next_location_and_z_estimate(self) -> tuple[XYPos, Optional[int]]:
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"""Return the next location to scan and its estimated z-position.
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This overrides the default behaviour of ScanPlanner to take the lowest value of
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nearest neighbours as this works best for smart stack.
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Note z-position may be None! This indicates that the current z position
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should be used.
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"""
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if self.scan_complete:
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raise RuntimeError("Can't get next position, scan is complete")
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next_site = self._remaining_locations[0]
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next_location = next_site.xy_tuple
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# If focused locations exist, return the neighbour with the lowest z position
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closest_pos = self.select_nearby_focus_site(next_location)
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z = None if closest_pos is None else closest_pos[2]
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return next_location, z
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def select_nearby_focus_site(self, xy_pos: XYPos) -> Optional[XYZPos]:
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"""Return the xyz position of the nearby site with the lowest z position.
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Lowest position is best, as starting too high causes smart stacking to
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autofocus and restart. Starting too low just requires extra movements in +z.
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Nearby is defined as within 1.1 times the larger of the x and y scan offsets.
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If no focused sites are within this range, use the height of the nearest
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focused site.
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Returns None if no focused locations are present
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"""
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# save to variable rather than search for focussed sites each time.
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# The noqa statement is because next_position is unused but is needed for equivalence
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# with other workflows that require the next pos to select a neighbour.
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def select_nearby_focus_site(self, next_position: XYPos) -> Optional[XYZPos]: # noqa: ARG002
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"""For a snake scan, use the most recent focused site to predict focus."""
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focused_locations = self.focused_locations
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if not focused_locations:
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return None
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# must be float64 (double precision) to deal with the huge numbers involved!
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current_pos = np.array(xy_pos, dtype="float64")
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path_pos = np.array(focused_locations, dtype="float64")[:, :2]
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# Use linalg.norm to calculate the direct distance between the points
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# Note linalg.norm always uses float64
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dists = np.linalg.norm((path_pos - current_pos), axis=1)
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# Get indices of all focused sites within distance_cutoff.
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# Note np.where always returns a tuple of arrays, hence the trailing [0]
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indices = np.where(dists <= self._distance_cutoff)[0]
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# Handle the case that no focused positions are within this range, and
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# instead use the nearest focused position. This will always return a
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# height, due to the check that self._focused_locations exists.
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if len(indices) == 0:
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distance_cutoff = min(dists)
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indices = np.where(dists <= distance_cutoff)[0]
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# Turning into an array allows slicing based on a list
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focused_locations_array = np.array(focused_locations)
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# Choose the lowest (smallest z) of the neighbouring sites. Smart stack works best
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# if started too low, so the lowest z will perform best
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chosen_focused_site = min(focused_locations_array[indices], key=lambda x: x[-1])
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# Convert back into list so values are of type int instead of np.int32
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return tuple(chosen_focused_site.tolist())
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return focused_locations[-1]
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def distance_between(
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@ -6,6 +6,7 @@ as well as specific workflows.
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from __future__ import annotations
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import os
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from typing import (
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Generic,
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Mapping,
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@ -17,7 +18,11 @@ from pydantic import BaseModel
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import labthings_fastapi as lt
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from openflexure_microscope_server.scan_planners import ScanPlanner, SmartSpiral
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from openflexure_microscope_server.scan_planners import (
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ScanPlanner,
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SmartSpiral,
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SnakeScan,
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)
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from openflexure_microscope_server.stitching import (
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STITCHING_RESOLUTION,
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StitchingSettings,
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@ -33,6 +38,7 @@ from openflexure_microscope_server.things.background_detect import (
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)
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from openflexure_microscope_server.things.camera import BaseCamera
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from openflexure_microscope_server.things.camera_stage_mapping import CameraStageMapper
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from openflexure_microscope_server.things.stage import BaseStage
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from openflexure_microscope_server.ui import PropertyControl, property_control_for
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SettingModelType = TypeVar("SettingModelType", bound=BaseModel)
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@ -59,6 +65,9 @@ class ScanWorkflow(Generic[SettingModelType], lt.Thing):
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save_resolution: tuple[int, int] = lt.setting(default=(1640, 1232))
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"""A tuple of the image resolution to capture."""
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# CSM may not be set, and isn't required for a workflow. Allow for it to exist or be None
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_csm: Optional[CameraStageMapper] = None
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def check_before_start(self, scan_name: str) -> None:
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"""Check before the scan starts. Throw an error if the scan shouldn't start.
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@ -119,6 +128,42 @@ class ScanWorkflow(Generic[SettingModelType], lt.Thing):
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"Each scan workflow must implement a settings_ui method."
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)
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def _require_csm(self) -> CameraStageMapper:
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"""Give each model the option to require CSM. Return it if present."""
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if self._csm is None:
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raise RuntimeError(
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"CameraStageMapping not set, and is required for this workflow."
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)
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return self._csm
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def _calc_displacement_from_overlap(self, overlap: float) -> tuple[int, int]:
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"""Use camera stage mapping to calculate x and y displacement from given overlap.
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:param overlap: The desired overlap as a fraction of the image. i.e. 0.5 means
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that each image should overlap its nearest neighbour by 50%.
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:returns: (dx, dy) - the x and y displacements in steps
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"""
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csm = self._require_csm()
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csm_image_res = csm.image_resolution
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if csm_image_res is None:
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raise RuntimeError("CSM not set. Scan shouldn't have progresses this far.")
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# Calculate displacements in image coordinates
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dx_img = csm_image_res[1] * (1 - overlap)
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dy_img = csm_image_res[0] * (1 - overlap)
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x_move_stage = csm.convert_image_to_stage_coordinates(x=dx_img, y=0)
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y_move_stage = csm.convert_image_to_stage_coordinates(x=0, y=dy_img)
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# Assume no rotation or skew and take only the aligned axis of vector.
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# Coerce to positive integer, but correct if x and y are flipped
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if abs(x_move_stage["x"]) > abs(x_move_stage["y"]):
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return x_move_stage["x"], y_move_stage["y"]
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# If not use the other stage axes. Note "dx" will be the movement in camera y.
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return y_move_stage["x"], x_move_stage["y"]
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class HistoScanSettingsModel(BaseModel):
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"""The settings for a scan with the HistoScanWorkflow.
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@ -285,32 +330,6 @@ class HistoScanWorkflow(ScanWorkflow[HistoScanSettingsModel]):
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return scan_settings, stitching_settings
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def _calc_displacement_from_overlap(self, overlap: float) -> tuple[int, int]:
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"""Use camera stage mapping to calculate x and y displacement.
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:param overlap: The desired overlap as a fraction of the image. i.e. 0.5 means
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that each image should overlap its nearest neighbour by 50%.
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:returns: (dx, dy) - the x and y displacements in steps
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"""
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csm_image_res = self._csm.image_resolution
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if csm_image_res is None:
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raise RuntimeError("CSM not set. Scan shouldn't have progressed this far.")
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# Calculate displacements in image coordinates
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dx_img = csm_image_res[1] * (1 - overlap)
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dy_img = csm_image_res[0] * (1 - overlap)
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x_move_stage = self._csm.convert_image_to_stage_coordinates(x=dx_img, y=0)
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y_move_stage = self._csm.convert_image_to_stage_coordinates(x=0, y=dy_img)
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# Assume no rotation or skew and take only the aligned axis of vector.
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# Coerce to positive integer, but correct if x and y are flipped
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if abs(x_move_stage["x"]) > abs(x_move_stage["y"]):
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return x_move_stage["x"], y_move_stage["y"]
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# If not use the other stage axes. Note "dx" will be the movement in camera y.
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return y_move_stage["x"], x_move_stage["y"]
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def create_smart_stack_params(
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self,
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images_dir: str,
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@ -478,3 +497,173 @@ class HistoScanWorkflow(ScanWorkflow[HistoScanSettingsModel]):
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property_control_for(self, "autofocus_dz", label="Autofocus Range (steps)"),
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property_control_for(self, "max_range", label="Maximum Distance (steps)"),
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]
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class SnakeSettingsModel(BaseModel):
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"""The settings for a scan with the SnakeWorkflow.
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This includes settings calculated when starting. This will be held by smart scan
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during a scan and serialised to disk.
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"""
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overlap: float
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dx: int
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dy: int
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x_count: int
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y_count: int
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images_dir: str
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autofocus_dz: int
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save_resolution: tuple[int, int]
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class SnakeWorkflow(ScanWorkflow[SnakeSettingsModel]):
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"""A workflow optimised for snaking around samples.
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This workflow generates a list of coordinates in a rectangle, and snakes
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around them from the top left (assuming positive dx and dy).
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"""
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display_name: str = lt.property(default="Snake Scan", readonly=True)
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ui_blurb: str = lt.property(
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default=(
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"This scan workflow is optimised for scanning over a rectangle. It "
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"snakes down and right from the starting point, over a defined grid."
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),
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readonly=True,
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)
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_settings_model = SnakeSettingsModel
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_planner_cls: type[ScanPlanner] = SnakeScan
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# Thing Slots
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_background_detector: ChannelDeviationLUV = lt.thing_slot()
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_cam: BaseCamera = lt.thing_slot()
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_csm: CameraStageMapper = lt.thing_slot()
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_autofocus: AutofocusThing = lt.thing_slot()
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_stage: BaseStage = lt.thing_slot()
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# Scan settings
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autofocus_dz: int = lt.setting(default=1000, ge=200, le=2000)
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"""The z distance to perform an autofocus in steps.
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Must be greater than or equal to 200, and less than or equal to 2000.
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"""
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overlap: float = lt.setting(default=0.45, ge=0.1, le=0.7)
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"""The fraction that adjacent images should overlap in x or y.
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This must be between 0.1 and 0.7.
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"""
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x_count: int = lt.setting(default=3)
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y_count: int = lt.setting(default=2)
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# The noqa statement is because scan_name is unused but is needed for equivalence
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# with other workflows that may want to validate the scan name.
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def check_before_start(self, scan_name: str) -> None: # noqa: ARG002
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"""Before starting a scan, check that camera-stage-mapping is set.
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Raise error if:
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- camera stage mapping is not set
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"""
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if self._csm.calibration_required:
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raise RuntimeError("Camera Stage Mapping is not calibrated.")
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@lt.property
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def ready(self) -> bool:
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"""Whether this scanworkflow is ready to start."""
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return not self._csm.calibration_required
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def all_settings(
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self, images_dir: str
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) -> tuple[SnakeSettingsModel, StitchingSettings]:
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"""Return the workflow and stitching settings.
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:param images_dir: The directory that images are to be written to.
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:return: A tuple containing the settings model for this workflow and the
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settings model for stitching.
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"""
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stitching_settings = StitchingSettings(
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overlap=self.overlap,
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correlation_resize=STITCHING_RESOLUTION[0] / self.save_resolution[0],
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)
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dx, dy = self._calc_displacement_from_overlap(self.overlap)
|
||||
self.logger.info(
|
||||
f"Based on an overlap of {self.overlap}, the stage will make steps of "
|
||||
f"{dx}, {dy}"
|
||||
)
|
||||
|
||||
scan_settings = SnakeSettingsModel(
|
||||
overlap=self.overlap,
|
||||
dx=dx,
|
||||
dy=dy,
|
||||
x_count=self.x_count,
|
||||
y_count=self.y_count,
|
||||
images_dir=images_dir,
|
||||
autofocus_dz=self.autofocus_dz,
|
||||
save_resolution=self.save_resolution,
|
||||
)
|
||||
|
||||
return scan_settings, stitching_settings
|
||||
|
||||
def pre_scan_routine(self, settings: SnakeSettingsModel) -> None:
|
||||
"""Autofocus before starting the scan.
|
||||
|
||||
:param settings: The settings for this scan as a SnakeSettingsModel
|
||||
"""
|
||||
self._autofocus.looping_autofocus(dz=settings.autofocus_dz, start="centre")
|
||||
|
||||
def new_scan_planner(
|
||||
self, settings: SnakeSettingsModel, position: Mapping[str, int]
|
||||
) -> ScanPlanner:
|
||||
"""Return a new scan planner object.
|
||||
|
||||
:param settings: The settings for this scan as a SnakeSettingsModel
|
||||
:param position: The starting position as a mapping of axes names to int.
|
||||
"""
|
||||
# The initial plan for the scan should be a single x,y position. All future
|
||||
# moves will be planned around this point. In future, route planner could
|
||||
# have multiple starting positions, each of which will be visited before the
|
||||
# scan can end.
|
||||
planner_settings = {
|
||||
"dx": settings.dx,
|
||||
"dy": settings.dy,
|
||||
"x_count": settings.x_count,
|
||||
"y_count": settings.y_count,
|
||||
}
|
||||
return self._planner_cls(
|
||||
initial_position=(position["x"], position["y"]),
|
||||
planner_settings=planner_settings,
|
||||
)
|
||||
|
||||
def acquisition_routine(
|
||||
self, settings: SnakeSettingsModel, xyz_pos: tuple[int, int, int]
|
||||
) -> tuple[bool, Optional[int]]:
|
||||
"""Perform acquisition routine. This is run at each scan location.
|
||||
|
||||
:param settings: The settings for this scan as a SnakeSettingsModel
|
||||
:param xyz_pos: The current position as a tuple or 3 ints.
|
||||
:return: A tuple of whether an image was taken, and the z-position for focus.
|
||||
If failed to find focus, returns for the focus z-position.
|
||||
"""
|
||||
self._autofocus.fast_autofocus(dz=settings.autofocus_dz)
|
||||
focus_height = self._stage.get_xyz_position()[2]
|
||||
filename = f"img_{xyz_pos[0]}_{xyz_pos[1]}_{focus_height}.jpeg"
|
||||
self._cam.capture_and_save(
|
||||
jpeg_path=os.path.join(settings.images_dir, filename),
|
||||
save_resolution=settings.save_resolution,
|
||||
)
|
||||
|
||||
imaged = True
|
||||
return imaged, focus_height
|
||||
|
||||
@lt.property
|
||||
def settings_ui(self) -> list[PropertyControl]:
|
||||
"""A list of PropertyControl objects to create the settings in the scan tab."""
|
||||
return [
|
||||
property_control_for(self, "overlap", label="Image Overlap (0.1-0.7)"),
|
||||
property_control_for(self, "x_count", label="Number of columns"),
|
||||
property_control_for(self, "y_count", label="Number of rows"),
|
||||
property_control_for(self, "autofocus_dz", label="Autofocus Range (steps)"),
|
||||
]
|
||||
|
|
|
|||
|
|
@ -143,6 +143,8 @@ def test_smart_spiral_first_few_pos():
|
|||
# if we mark this position as visited, imaged, and focused
|
||||
planner.mark_location_visited(xyz_pos1, imaged=True, focused=True)
|
||||
|
||||
# current visited path is [[100, 50, 10]]
|
||||
|
||||
# scan is not complete
|
||||
assert not planner.scan_complete
|
||||
|
||||
|
|
@ -172,6 +174,8 @@ def test_smart_spiral_first_few_pos():
|
|||
# if we mark this position as visited, imaged, and NOT focused
|
||||
planner.mark_location_visited(xyz_pos2, imaged=True, focused=False)
|
||||
|
||||
# current visited path is [[100, 50, 10], [50, 50, 10]]
|
||||
|
||||
# Check this position remove from planned
|
||||
assert xy_pos2 not in planner.remaining_locations
|
||||
# Check original position not re-added
|
||||
|
|
@ -195,19 +199,22 @@ def test_smart_spiral_first_few_pos():
|
|||
assert z_pos3 is z_focus
|
||||
# Check that the closest focus site to pos3 is pos 1 as
|
||||
# pos 2 is not focussed
|
||||
assert planner.closest_focus_site(xy_pos3) == xyz_pos1
|
||||
assert planner.select_nearby_focus_site(xy_pos3) == xyz_pos1
|
||||
|
||||
new_z_focus = 20
|
||||
xyz_pos3 = (xy_pos3[0], xy_pos3[1], new_z_focus)
|
||||
# Finally check that if this is focused...
|
||||
planner.mark_location_visited(xyz_pos3, imaged=True, focused=True)
|
||||
# current visited path is [[100, 50, 10], [50, 50, 10], [50, 0, 20]]
|
||||
|
||||
# ... then the new 4th point ...
|
||||
xy_pos4, z_pos4 = planner.get_next_location_and_z_estimate()
|
||||
# ...(100, 0)...
|
||||
assert xy_pos4 == (100, 0)
|
||||
# ... and it should get its focus from the lowest neighbouring point
|
||||
# lowest neighbour to [100, 0] is [100, 50, 10]
|
||||
assert z_pos4 is z_focus
|
||||
assert planner.closest_focus_site(xy_pos4) == xyz_pos3
|
||||
assert planner.select_nearby_focus_site(xy_pos4) == xyz_pos1
|
||||
|
||||
|
||||
def test_smart_spiral_stops_on_max_dist():
|
||||
|
|
@ -265,20 +272,20 @@ def test_closest_focus_with_large_numbers():
|
|||
scan_planners.VisitedScanLocation((1000000, 0, 0), imaged=True, focused=True),
|
||||
scan_planners.VisitedScanLocation((0, 1000000, 0), imaged=True, focused=True),
|
||||
]
|
||||
assert planner.closest_focus_site((0, 0)) == (0, 1000000, 0)
|
||||
assert planner.select_nearby_focus_site((0, 0)) == (0, 1000000, 0)
|
||||
# Try similar
|
||||
planner._path_history = [
|
||||
scan_planners.VisitedScanLocation((1234567, 0, 0), imaged=True, focused=True),
|
||||
scan_planners.VisitedScanLocation((-1234567, 0, 0), imaged=True, focused=True),
|
||||
]
|
||||
assert planner.closest_focus_site((0, 0)) == (-1234567, 0, 0)
|
||||
assert planner.select_nearby_focus_site((0, 0)) == (-1234567, 0, 0)
|
||||
|
||||
# Make the first point 1 step closer
|
||||
planner._path_history = [
|
||||
scan_planners.VisitedScanLocation((1234566, 0, 0), imaged=True, focused=True),
|
||||
scan_planners.VisitedScanLocation((-1234567, 0, 0), imaged=True, focused=True),
|
||||
]
|
||||
assert planner.closest_focus_site((0, 0)) == (1234566, 0, 0)
|
||||
assert planner.select_nearby_focus_site((0, 0)) == (1234566, 0, 0)
|
||||
|
||||
|
||||
def test_example_smart_spiral():
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue