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Co-authored-by: Joe Knapper <joe.knapper@glasgow.ac.uk>
This commit is contained in:
Julian Stirling 2025-11-12 19:11:14 +00:00
parent 23f87869ac
commit 38b8c5baa5
2 changed files with 9 additions and 8 deletions
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12
src/openflexure_microscope_server/background_detect.py
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@ -267,8 +267,8 @@ class ColourChannelDetectLUV(BackgroundDetectAlgorithm):
class ChannelDeviationLUV(BackgroundDetectAlgorithm):
"""Compare the standard deviations of the LUV channels in a grid to background data.
This uses an LUV colour space, each image is divided into an 8x8 grid of images
each the standard deviation of each channel of each image is calculates and compared
Using an LUV colour space, each image is divided into an 8x8 grid of images.
The standard deviation of each channel of each image is calculated and compared
to the median standard deviation for a grid of background images.
"""
@ -295,11 +295,11 @@ class ChannelDeviationLUV(BackgroundDetectAlgorithm):
u_cut = bg_stds[1] * self.settings.channel_tolerance
v_cut = bg_stds[2] * self.settings.channel_tolerance
decisions = (
populated_regions = (
(stds[:, :, 0] > l_cut) | (stds[:, :, 1] > u_cut) | (stds[:, :, 2] > v_cut)
)
return float(100 * np.sum(decisions) / 64)
return float(100 * np.sum(populated_regions) / 64)
def image_is_sample(self, image: np.ndarray) -> tuple[bool, str]:
"""Label the current image as either background or sample.
@ -330,12 +330,12 @@ class ChannelDeviationLUV(BackgroundDetectAlgorithm):
def _chunked_stds(img: np.ndarray, n_rows: int = 8, n_cols: int = 8) -> np.ndarray:
"""Split image into a grid and calculated std of each channel in each chunk.
"""Split image into a grid and calculate std of each channel in each chunk.
:param img: The image to analyse
:param n_rows: The number of rows in the grid
:param n_cols: The number of cols in the grid
:return: A nummpy array of the grid of standard deviations.
:return: A numpy array of the grid of standard deviations.
"""
h, w = img.shape[:2]
row_height = h // n_rows

5
src/openflexure_microscope_server/things/autofocus.py
View file

@ -812,7 +812,7 @@ class AutofocusThing(lt.Thing):
return "restart", capture_id
return "continue", capture_id
# Fint the peak
# Fit the peak
x = range(n_imgs)
coeffs, cov = np.polyfit(x, sharpnesses, deg=2, cov=True)
a = float(coeffs[0])
@ -833,7 +833,8 @@ class AutofocusThing(lt.Thing):
# 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
# Check both the peak from fitting and the sharpest image are not at the
# edge of the stack.
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: