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Move and measure and z predict are now separate functions

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Chish36 2025-07-22 16:29:17 +01:00 • committed by Julian Stirling
parent 71e21d1e72
commit 0267251a18
1 changed files with 39 additions and 34 deletions
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73
src/openflexure_microscope_server/things/stage_measure.py
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@ -56,6 +56,37 @@ def steps_generate(small_step, z_perc, big_step, dir, stream_resolution):
return step_sizes_small, minimum_offset_small, z_steps, minimum_offset_z, step_sizes_big, wrong_axis_max_small, wrong_axis_max_z
def predict_z(pixel_per_step, positions, axis, direction, stage: StageDep, cam: CamDep):
res_dic = {
'x': cam.stream_resolution[0],
'y': cam.stream_resolution[1]
}
lateral_positions = [i[axis] for i in positions]
z_positions = [i['z'] for i in positions]
parameters, covariance = curve_fit(quadratic, lateral_positions, z_positions)
relative_move = direction * 2 * res_dic[axis]/(2*pixel_per_step) # This is the number of steps to cover 200% of the FOV.
z_dest = quadratic(stage.position[axis] + relative_move, *parameters)
z_diff = z_dest - stage.position['z']
return z_diff
def move_and_measure(step_size, axis, delta, image1, csm: CSMDep, autofocus: AutofocusDep, cam:CamDep):
if axis == 'x':
csm.move_in_image_coordinates(x = step_size['x'], y = 0)
wrong_axis = 'y'
else:
csm.move_in_image_coordinates(x = 0, y = step_size['y'])
wrong_axis = 'x'
focus_data = autofocus.looping_autofocus(dz = 800)
image2 = cv2.resize(np.array(Image.open(cam.grab_jpeg().open())), dsize=(0,0), fx= 1, fy= 1)
offset = [x * 1 for x in fft_image_tracking.displacement_between_images(image_0 = image1, image_1 = image2, sigma=10, fractional_threshold=0.1, pad=True)] # Units is pixels
delta['x'] = int(offset[1])
delta['y'] = int(offset[0])
return delta, offset, focus_data, wrong_axis
class RangeofMotionThing(Thing):
def rom_axis(
self,
@ -85,14 +116,12 @@ class RangeofMotionThing(Thing):
focused_positions = []
axis_results = {}
pixel_per_step = ((1/abs(csm.image_to_stage_displacement_matrix[0][1]))
+ (1/abs(csm.image_to_stage_displacement_matrix[1][0])))/4
# Generate required dictionaries for step sizes and minimum offsets
stream_resolution = cam.stream_resolution
res_dic = {
'x': stream_resolution[0],
'y': stream_resolution[1]
}
step_sizes_small, minimum_offset_small, z_steps, minimum_offset_z, step_sizes_big, wrong_axis_max_small, wrong_axis_max_z = steps_generate(20, 50, 200, direction, stream_resolution=stream_resolution)
delta = {
@ -115,17 +144,7 @@ class RangeofMotionThing(Thing):
# Medium sized steps
for loop in range(4):
image1 = cv2.resize(np.array(Image.open(cam.grab_jpeg().open())), dsize=(0,0), fx= 1, fy= 1)
if axis == 'x':
csm.move_in_image_coordinates(x = z_steps['x'], y = 0)
wrong_axis = 'y'
else:
csm.move_in_image_coordinates(x = 0, y = z_steps['y'])
wrong_axis = 'x'
focus_data = autofocus.looping_autofocus(dz = 800)
image2 = cv2.resize(np.array(Image.open(cam.grab_jpeg().open())), dsize=(0,0), fx= 1, fy= 1)
offset = [x * 1 for x in fft_image_tracking.displacement_between_images(image_0 = image1, image_1 = image2, sigma=10, fractional_threshold=0.1, pad=True)] # Units is pixels
delta['x'] = int(offset[1])
delta['y'] = int(offset[0])
delta, offset, focus_data, wrong_axis = move_and_measure(step_size = z_steps, axis = axis, delta = delta, image1 = image1, csm = csm, autofocus = autofocus, cam = cam)
logger.info(f"Offset measured as {delta[axis]}")
stage_coords.append(stage.position)
cor_lat_steps.append(offset)
@ -140,15 +159,7 @@ class RangeofMotionThing(Thing):
# 1 big step followed by 3 small steps
while np.abs(delta[axis]) > np.abs(minimum_offset_small[axis]) and parasitic_motion == False:
pixel_per_step = ((1/abs(csm.image_to_stage_displacement_matrix[0][1]))
+ (1/abs(csm.image_to_stage_displacement_matrix[1][0])))/4
lateral_positions = [i[axis] for i in focused_positions]
z_positions = [i['z'] for i in focused_positions]
parameters, covariance = curve_fit(quadratic, lateral_positions, z_positions)
relative_move = direction * 2 * res_dic[axis]/(2*pixel_per_step) # This is the number of steps to cover 200% of the FOV.
z_dest = quadratic(stage.position[axis] + relative_move, *parameters)
z_diff = z_dest - stage.position['z']
z_diff = predict_z(pixel_per_step = pixel_per_step, positions = focused_positions, axis = axis, direction = direction, stage = stage, cam = cam)
logger.info(f"Z calibration complete.")
@ -163,19 +174,13 @@ class RangeofMotionThing(Thing):
stage_coords.append(stage.position)
focus_data = autofocus.looping_autofocus(dz = 800)
failure_count = 0
for loop in range(3):
image1 = cv2.resize(np.array(Image.open(cam.grab_jpeg().open())), dsize=(0,0), fx= 1, fy= 1)
if axis == 'x':
csm.move_in_image_coordinates(x = step_sizes_small['x'], y = 0)
else:
csm.move_in_image_coordinates(x = 0, y = step_sizes_small['y'])
focus_data = autofocus.looping_autofocus(dz = 800)
image2 = cv2.resize(np.array(Image.open(cam.grab_jpeg().open())), dsize=(0,0), fx= 1, fy= 1)
offset = [x * 1 for x in fft_image_tracking.displacement_between_images(image_0 = image1, image_1 = image2, sigma=10, fractional_threshold=0.1, pad=True)] # Units is pixels
delta['x'] = int(offset[1])
delta['y'] = int(offset[0])
delta, offset, focus_data, wrong_axis = move_and_measure(step_size = step_sizes_small, axis = axis, delta = delta, image1 = image1, csm = csm, autofocus = autofocus, cam = cam)
logger.info(f"Offset measured as {delta[axis]}")
while np.abs(delta[axis]) < minimum_offset_small[axis] and failure_count < 3: