Background detect fraction as a thing

This commit is contained in:
jaknapper 2024-01-07 15:00:46 +00:00
parent cb916bb4a3
commit a53062af83

View file

@ -182,6 +182,15 @@ class BackgroundDetectThing(Thing):
def tolerance(self, value: float) -> None:
self.thing_settings["tolerance"] = value
@thing_property
def fraction(self) -> float:
"""How much of the image needs to be not background to label as sample"""
return self.thing_settings.get("fraction", 7)
@fraction.setter
def fraction(self, value: float) -> None:
self.thing_settings["fraction"] = value
def background_mask(self, image: np.ndarray) -> np.ndarray:
"""Calculate a binary image, showing whether each pixel is background
@ -204,17 +213,24 @@ class BackgroundDetectThing(Thing):
This action will acquire a new image from the preview stream, then
evaluate whether it is foreground or background, by comparing it
too the saved statistics. This is done on a per-pixel basis, and
the returned value (between 0 and 1) is the fraction of the image
the returned value (between 0 and 100) is the fraction of the image
that is background.
"""
background = cam.grab_jpeg()
background = np.array(Image.open(background.open()))
current_image = cam.grab_jpeg()
current_image = np.array(Image.open(current_image.open()))
# we're working in the LUV colourspace as it collect colours together in a human-intuitive way
background_LUV = cv2.cvtColor(background, cv2.COLOR_RGB2LUV)
mask = self.background_mask(background_LUV)
return np.count_nonzero(mask) / np.prod(mask.shape)
current_image_LUV = cv2.cvtColor(current_image, cv2.COLOR_RGB2LUV)
mask = self.background_mask(current_image_LUV)
return np.count_nonzero(mask) / np.prod(mask.shape) * 100
@thing_action
def image_is_sample(self, cam: CamDep) -> bool:
"""Label the current image as either background or sample"""
b_fraction = self.background_fraction(cam)
fraction_threshold = self.fraction
return (100 - b_fraction) > fraction_threshold
@thing_action
def set_background(self, cam: CamDep):
@ -253,6 +269,7 @@ class BackgroundDetectThing(Thing):
return {
"background_distributions": bd.model_dump() if bd else None,
"tolerance": self.tolerance,
"fraction": self.fraction,
}
@ -352,7 +369,6 @@ class SmartScanThing(Thing):
logger.info(f"Based on an overlap of {overlap}, we will make steps of {dx}, {dy}")
dz = 3000 # This is used for autofocus - make configurable?
sample_coverage = 7 # TODO: make this configurable
# construct a 2D scan path
path = [[stage.position["x"], stage.position["y"]]]
@ -391,15 +407,12 @@ class SmartScanThing(Thing):
)
# Check if the image is background
background_fraction = background_detect.background_fraction()
background_coverage = round(
100 * background_fraction, 1
)
image_is_sample = background_detect.image_is_sample()
# if more than 92% of the image is background, treat it as background and continue
if 100 - background_coverage < sample_coverage:
if not image_is_sample:
category = "background"
logger.info(f"Skipping {stage.position} as it is {background_coverage}% background.")
logger.info(f"Skipping {stage.position} as it is {round(background_detect.background_fraction(),0)}% background.")
else:
# if not, it's sample. run an autofocus and use the updated height
new_pos = [
@ -484,7 +497,7 @@ class SmartScanThing(Thing):
if len(true_path) > 750:
break
except InvocationCancelledError:
logger.error("Stopping scan because it was cancelled.", exc_info=1)
logger.error("Stopping scan because it was cancelled.", exc_info=0)
except IOError as e:
logger.error(
f"Stopping scan because of an IOError (most likely a full disk): {e}",