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Make autofocus considerably stricter, forcing a parabolic fit, and only one turning point

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Background can still pass this much stricter check
This commit is contained in:
Julian Stirling 2025-11-06 19:30:30 +00:00
parent dabb195b08
commit cf92d4f3ac
1 changed files with 41 additions and 10 deletions
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51
src/openflexure_microscope_server/things/autofocus.py
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@ -795,28 +795,59 @@ class AutofocusThing(lt.Thing):
* capture_id - the buffer id of the sharpest image
"""
sharpest_index = np.argmax([capture.sharpness for capture in captures])
sharpnesses = np.array([capture.sharpness for capture in captures])
sharpest_index = np.argmax(sharpnesses)
# The buffer id of the sharpest image
capture_id = captures[sharpest_index].buffer_id
sharpness_length = len(captures)
n_imgs = len(captures)
# If only testing one image, then by definition the sharpest is central
if sharpness_length == 1:
if n_imgs == 1:
return "success", capture_id
# If testing three images, test if the centre is the sharpest
if sharpness_length == 3:
if n_imgs == 3:
if sharpest_index == 1:
return "success", capture_id
if sharpest_index == 0:
return "restart", capture_id
return "continue", capture_id
# For larger stacks, test if the best image is not within two of the edge of the stack
# ie for a stack of 7 images, best image must be between 3rd and and 5th
exclusion_range = 2
# Fint the peak
x = range(n_imgs)
coeffs, cov = np.polyfit(x, sharpnesses, deg=2, cov=True)
a = float(coeffs[0])
fit_func = np.poly1d(coeffs)
if sharpest_index < exclusion_range:
return "restart", capture_id
if sharpest_index >= sharpness_length - exclusion_range:
# find the peaks's x-position
turning = float(fit_func.deriv().roots[0])
sigma_a = float(np.sqrt(cov[0, 0]))
# Estimate the upper 95% confidence bound for the x^2 term
ci_high = a + 2 * sigma_a
# If the high 95% confindence interval of the peak is not negative then the
# fit isn't sure if this is a peak or a U shape, so we continue.
if ci_high >= 0:
return "continue", capture_id
# For larger stacks, test if the best image is not within two of the edge of
# the stack ie for a stack of 7 images, best image must be between 3rd and 5th
edge_size = 2
# Check both the peak from fitting and the sharpest image are not the
if turning < edge_size - 0.5 or sharpest_index < edge_size:
return "restart", capture_id
if turning > n_imgs - (edge_size - 0.5) or sharpest_index >= n_imgs - edge_size:
return "continue", capture_id
# Also take the difference of neghboring points to check for turning points
d_sharpnesses = sharpnesses[1:] - sharpnesses[:-1]
# Filter out any points that are not prominent
prominent = abs(d_sharpnesses) / np.mean(abs(d_sharpnesses)) > 0.5
d_sharpnesses = d_sharpnesses[prominent]
# count the sign changes
turning_points = int(np.sum(d_sharpnesses[1:] * d_sharpnesses[:-1] < 0))
if turning_points > 1:
return "continue", capture_id
return "success", capture_id