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This commit is contained in:
Joel Collins 2020-04-28 13:42:26 +01:00
parent 9ff24e8e37
commit 1a6088a816
1 changed files with 31 additions and 22 deletions
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53
openflexure_microscope/api/default_extensions/camera_stage_mapping/camera_stage_calibration_2d.py
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@ -14,19 +14,22 @@ from camera_stage_tracker import Tracker, move_until_motion_detected
from functools import partial
def backlash_corrected_move(get_position, move, backlash_amount, pos):
"""Make two moves, arriving at `pos` from a consistent direction"""
displacement = pos - get_position()
backlash_vector = (displacement < 0).astype(np.int)*backlash_amount
backlash_vector = (displacement < 0).astype(np.int) * backlash_amount
if np.any(backlash_vector > 0):
move(pos - backlash_vector)
move(pos)
def bake_backlash_corrected_move(get_position, move, backlash_amount):
"""Return a function that performs backlash-corrected moves"""
return partial(backlash_corrected_move, get_position, move, backlash_amount)
def calibrate_xy_grid(tracker, move, step = 100, n_steps=4, backlash_compensation=0):
def calibrate_xy_grid(tracker, move, step=100, n_steps=4, backlash_compensation=0):
"""Make a series of moves in X and Y to determine the XY components of the pixel-to-sample matrix.
Arguments:
@ -38,44 +41,50 @@ def calibrate_xy_grid(tracker, move, step = 100, n_steps=4, backlash_compensatio
step : float, optional (default 100)
The amount to move the stage by. This should move the sample by approximately 1/10th of the field of view.
"""
try: # Ensure that the tracker has a template set
try: # Ensure that the tracker has a template set
_ = tracker.template
except:
tracker.acquire_template()
tracker.reset_history() # make sure we get rid of the initial (0,0) point
tracker.reset_history() # make sure we get rid of the initial (0,0) point
starting_position = tracker.get_position()
# Move the stage in a square, recording the displacement from both the stage and the camera
try:
for x in (np.arange(n_steps) - n_steps/2.0)*step:
for y in (np.arange(n_steps) - n_steps/2.0)*step:
for x in (np.arange(n_steps) - n_steps / 2.0) * step:
for y in (np.arange(n_steps) - n_steps / 2.0) * step:
move(starting_position + np.array([x, y, 0]))
tracker.append_point()
finally:
move(starting_position)
# We then use least-squares to fit the XY part of the matrix relating
# We then use least-squares to fit the XY part of the matrix relating
# pixels to distance
# stage_positions should be the stage positions, with a zero mean.
# image_positions should be the same, but calculated from the images
stage_positions, image_positions = tracker.history
stage_positions = stage_positions.astype(np.float)
stage_positions -= np.mean(stage_positions, axis=0)
stage_positions = stage_positions[:,:2] # ensure it's 2d
stage_positions = stage_positions[:, :2] # ensure it's 2d
image_positions -= np.mean(image_positions, axis=0)
#image_positions *= -1 # To get the matrix right, we want the position of each
# image relative to the template, rather than the other way around
A, res, rank, s = np.linalg.lstsq(image_positions, stage_positions) # we solve pixel_shifts*A = location_shifts
# image_positions *= -1 # To get the matrix right, we want the position of each
# image relative to the template, rather than the other way around
A, res, rank, s = np.linalg.lstsq(
image_positions, stage_positions
) # we solve pixel_shifts*A = location_shifts
transformed_image_positions = np.dot(image_positions, A)
residuals = transformed_image_positions - stage_positions
fractional_error = norm(residuals) / stage_positions.shape[0] step
fractional_error = norm(residuals) / stage_positions.shape[0]
print(f"Ratio of residuals to displacement is {fractional_error})")
if fractional_error > 0.05: # Check it was a reasonably good fit
print("Warning: the error fitting measured displacements was %.1f%%" % (fractional_error*100))
print(f"Calibrated the pixel-location matrix.\nResiduals were {fractional_error*100:.1f}% of the shift.")
return {
"image_to_stage_displacement": A,
"moves": (stage_positions, image_positions),
"fractional_error": fractional_error
}
if fractional_error > 0.05: # Check it was a reasonably good fit
print(
"Warning: the error fitting measured displacements was %.1f%%"
% (fractional_error * 100)
)
print(
f"Calibrated the pixel-location matrix.\nResiduals were {fractional_error*100:.1f}% of the shift."
)
return {
"image_to_stage_displacement": A,
"moves": (stage_positions, image_positions),
"fractional_error": fractional_error,
}