Small moves and medium moves are now in separate functions
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Chish36
Julian Stirling
parent
61c3877202
commit
23d53949b4
1 changed files with 187 additions and 72 deletions
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@ -104,6 +104,157 @@ def move_and_measure(step_size: dict[str, float], axis: str, delta: dict[str, in
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return delta, offset, focus_data, wrong_axis
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def medium_moves(
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stream_resolution: list[int],
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direction: int,
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axis: str,
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delta: dict[str,int],
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focus_data: list[float],
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stage_coords: list,
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cor_lat_steps: list,
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csm: CSMDep,
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cam: CamDep,
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stage:StageDep,
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autofocus: AutofocusDep,
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logger: InvocationLogger
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) -> tuple:
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"""Carries out the medium sized steps section of the range of motion test to get 5 points to make z position predictions with
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:params stream_resolution: The resolution of the stream from the camera.
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:param direction: The direction the stage moves.
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:params axis: The axis which is being measured. This must be 'x' or 'y'.
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:params delta: A dictionary of 'x' and 'y' offsets.
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:params focus_data: A list of focus data returned by the autofocus procedure.
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:params stage_coords: A list of all previous positions the stage has been.
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:params cor_lat_steps: A list of all correlation values.
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:return: Stage_coords and cor_lat_steps are lists of data tracked throughout the test. Delta and parasitic_motion are updated and tracked after each move.
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"""
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parasitic_motion = False
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medium_step = 50
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wrong_axis_max_medium = dict_generate(
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medium_step, stream_resolution, direction, factor=0.1
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)
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for loop in range(5):
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image1 = cv2.resize(
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np.array(Image.open(cam.grab_jpeg().open())), dsize=(0, 0), fx=1, fy=1
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)
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delta, offset, focus_data, wrong_axis = move_and_measure(
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step_size=dict_generate(medium_step, stream_resolution, direction),
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axis=axis,
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delta=delta,
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image1=image1,
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autofocus_proc=True,
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focus_data=focus_data,
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csm=csm,
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autofocus=autofocus,
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cam=cam,
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)
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logger.info(f"Offset measured as {delta[axis]}")
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stage_coords.append(stage.position)
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cor_lat_steps.append(offset)
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if np.abs(delta[wrong_axis]) > np.abs(wrong_axis_max_medium[wrong_axis]):
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logger.info(
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f"Parasitic motion detected in {wrong_axis}-axis whilst measuring {axis}-axis."
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)
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parasitic_motion = True
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break
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return stage_coords, cor_lat_steps, delta, parasitic_motion
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def small_moves(
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small_step: int,
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stream_resolution: list[int],
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direction: int,
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axis: str,
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delta: dict[str, int],
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focus_data: list[float],
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stage_coords: list,
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cor_lat_steps: list,
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minimum_offset_small: dict[str, float],
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csm: CSMDep,
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cam: CamDep,
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stage: StageDep,
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autofocus: AutofocusDep,
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logger: InvocationLogger,
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) -> tuple:
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"""Carries out the medium sized steps section of the range of motion test to get 5 points to make z position predictions with
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:params small_step: The integer value used to generate the small step sizes.
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:params stream_resolution: The resolution of the stream from the camera.
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:param direction: The direction the stage moves.
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:params axis: The axis which is being measured. This must be 'x' or 'y'.
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:params delta: A dictionary of 'x' and 'y' offsets.
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:params focus_data: A list of focus data returned by the autofocus procedure.
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:params stage_coords: A list of all previous positions the stage has been.
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:params cor_lat_steps: A list of all correlation values.
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:params minimum_offset_small: A dictionary containing the minimum values for a successful correlation.
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:return: Stage_coords and cor_lat_steps are lists of data tracked throughout the test. Delta and parasitic_motion are updated and tracked after each move.
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"""
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failure_count = 0
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wrong_axis_max_small = dict_generate(small_step, stream_resolution, direction, factor=0.1)
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parasitic_motion = False
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for loop in range(3):
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image1 = cv2.resize(
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np.array(Image.open(cam.grab_jpeg().open())), dsize=(0, 0), fx=1, fy=1
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)
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delta, offset, focus_data, wrong_axis = move_and_measure(
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step_size=dict_generate(small_step, stream_resolution, direction),
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axis=axis,
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delta=delta,
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image1=image1,
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autofocus_proc=False,
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focus_data=focus_data,
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csm=csm,
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autofocus=autofocus,
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cam=cam,
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)
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logger.info(f"Offset measured as {delta[axis]}")
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while (
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np.abs(delta[axis]) < np.abs(minimum_offset_small[axis])
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and failure_count < 3
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):
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logger.info(
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f"Correlation failed. Refocusing to check. Attempt {failure_count + 1}/3"
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)
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focus_data = autofocus.looping_autofocus(dz=1000)
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image2 = cv2.resize(
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np.array(Image.open(cam.grab_jpeg().open())), dsize=(0, 0), fx=1, fy=1
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)
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failure_count += 1
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offset = [
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x * 1
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for x in fft_image_tracking.displacement_between_images(
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image_0=image1,
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image_1=image2,
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sigma=10,
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fractional_threshold=0.1,
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pad=True,
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)
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] # Units is pixels
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delta["x"] = int(offset[1])
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delta["y"] = int(offset[0])
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logger.info(
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f"Displacement found was {delta[axis]}. Minimum offset is {minimum_offset_small[axis]}"
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)
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stage_coords.append(stage.position)
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cor_lat_steps.append(offset)
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if np.abs(delta[wrong_axis]) > np.abs(wrong_axis_max_small[wrong_axis]):
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logger.info(
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f"Parasitic motion detected in {wrong_axis}-axis whilst measuring {axis}-axis."
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)
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parasitic_motion = True
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break
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if np.abs(delta[axis]) < np.abs(minimum_offset_small[axis]): # this means the edge has been found
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logger.info("Edge has been found.")
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break
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return stage_coords, cor_lat_steps, delta, parasitic_motion
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def motion_detection(axis: str, direction: int, csm: CSMDep, stage: StageDep, cam: CamDep, logger: InvocationLogger) -> dict:
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"""Moves the stage until motion is detected. This happens at the end of each axis and direction and where the stage is moved in the opposite direction until
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motion is detected.
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@ -182,13 +333,8 @@ class RangeofMotionThing(Thing):
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# Generate required dictionaries for step sizes and minimum offsets
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stream_resolution = cam.stream_resolution
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small_step = 20
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medium_step = 50
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big_step = 200
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minimum_offset_small = dict_generate(small_step, stream_resolution, direction, factor=0.65)
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wrong_axis_max_small = dict_generate(small_step, stream_resolution, direction, factor=0.1)
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wrong_axis_max_z = dict_generate(medium_step, stream_resolution, direction, factor=0.1)
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small_step = 20
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step_sizes_big = dict_generate(big_step, stream_resolution, direction)
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delta = {
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@ -202,32 +348,27 @@ class RangeofMotionThing(Thing):
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logger.info("Moving the stage in 5 medium sized steps.")
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# Medium sized steps
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for loop in range(5):
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image1 = cv2.resize(np.array(Image.open(cam.grab_jpeg().open())), dsize=(0,0), fx= 1, fy= 1)
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delta, offset, focus_data, wrong_axis = move_and_measure(
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step_size=dict_generate(medium_step, stream_resolution, direction),
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axis=axis,
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delta=delta,
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image1=image1,
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autofocus_proc=True,
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focus_data=focus_data,
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csm=csm,
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autofocus=autofocus,
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cam=cam,
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)
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logger.info(f"Offset measured as {delta[axis]}")
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stage_coords.append(stage.position)
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cor_lat_steps.append(offset)
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# Check for parasitic motion
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if np.abs(delta[wrong_axis]) > np.abs(wrong_axis_max_z[wrong_axis]):
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logger.info(f"Parasitic motion detected in {wrong_axis}-axis whilst measuring {axis}-axis.")
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parasitic_motion = True
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break
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stage_coords, cor_lat_steps, delta, parasitic_motion = medium_moves(
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stream_resolution=stream_resolution,
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direction=direction,
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axis=axis,
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delta=delta,
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focus_data=focus_data,
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stage_coords=stage_coords,
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cor_lat_steps=cor_lat_steps,
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csm=csm,
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cam=cam,
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stage=stage,
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autofocus=autofocus,
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logger=logger,
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)
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# 1 big step followed by 3 small steps
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minimum_offset_small = dict_generate(
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small_step, stream_resolution, direction, factor=0.65
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)
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while np.abs(delta[axis]) > np.abs(minimum_offset_small[axis]) and not parasitic_motion:
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z_diff = predict_z(positions = stage_coords, axis = axis, relative_move = step_sizes_big[axis], stage = stage, csm = csm)
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@ -246,48 +387,22 @@ class RangeofMotionThing(Thing):
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stage_coords.append(stage.position)
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failure_count = 0
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# Turn into function
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for loop in range(3):
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image1 = cv2.resize(np.array(Image.open(cam.grab_jpeg().open())), dsize=(0,0), fx= 1, fy= 1)
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delta, offset, focus_data, wrong_axis = move_and_measure(
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step_size=dict_generate(
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small_step, stream_resolution, direction
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),
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axis=axis,
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delta=delta,
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image1=image1,
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autofocus_proc=False,
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focus_data=focus_data,
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csm=csm,
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autofocus=autofocus,
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cam=cam,
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)
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logger.info(f"Offset measured as {delta[axis]}")
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while np.abs(delta[axis]) < np.abs(minimum_offset_small[axis]) and failure_count < 3:
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logger.info(f"Correlation failed. Refocusing to check. Attempt {failure_count + 1}/3")
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focus_data = autofocus.looping_autofocus(dz = 1000)
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image2 = cv2.resize(np.array(Image.open(cam.grab_jpeg().open())), dsize=(0,0), fx= 1, fy= 1)
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failure_count += 1
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offset = [x * 1 for x in fft_image_tracking.displacement_between_images(
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image_0 = image1, image_1 = image2, sigma=10, fractional_threshold=0.1, pad=True)] # Units is pixels
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delta['x'] = int(offset[1])
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delta['y'] = int(offset[0])
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logger.info(f"Displacement found was {delta[axis]}. Minimum offset is {minimum_offset_small[axis]}")
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stage_coords.append(stage.position)
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cor_lat_steps.append(offset)
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if np.abs(delta[wrong_axis]) > np.abs(wrong_axis_max_small[wrong_axis]):
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logger.info(f"Parasitic motion detected in {wrong_axis}-axis whilst measuring {axis}-axis.")
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parasitic_motion = True
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break
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if np.abs(delta[axis]) < np.abs(minimum_offset_small[axis]): # this means the edge has been found
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logger.info("Edge has been found.")
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break
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stage_coords, cor_lat_steps, delta, parasitic_motion = small_moves(
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small_step=small_step,
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stream_resolution=stream_resolution,
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direction=direction,
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axis=axis,
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delta=delta,
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focus_data=focus_data,
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stage_coords=stage_coords,
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cor_lat_steps=cor_lat_steps,
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minimum_offset_small=minimum_offset_small,
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csm=csm,
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cam=cam,
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stage=stage,
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autofocus=autofocus,
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logger=logger,
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)
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# Motion detection
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logger.info("Running motion detection")
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