Background detect fraction as a thing
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cb916bb4a3
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1 changed files with 27 additions and 14 deletions
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@ -182,6 +182,15 @@ class BackgroundDetectThing(Thing):
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def tolerance(self, value: float) -> None:
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self.thing_settings["tolerance"] = value
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@thing_property
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def fraction(self) -> float:
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"""How much of the image needs to be not background to label as sample"""
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return self.thing_settings.get("fraction", 7)
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@fraction.setter
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def fraction(self, value: float) -> None:
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self.thing_settings["fraction"] = value
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def background_mask(self, image: np.ndarray) -> np.ndarray:
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"""Calculate a binary image, showing whether each pixel is background
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@ -204,17 +213,24 @@ class BackgroundDetectThing(Thing):
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This action will acquire a new image from the preview stream, then
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evaluate whether it is foreground or background, by comparing it
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too the saved statistics. This is done on a per-pixel basis, and
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the returned value (between 0 and 1) is the fraction of the image
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the returned value (between 0 and 100) is the fraction of the image
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that is background.
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"""
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background = cam.grab_jpeg()
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background = np.array(Image.open(background.open()))
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current_image = cam.grab_jpeg()
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current_image = np.array(Image.open(current_image.open()))
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# we're working in the LUV colourspace as it collect colours together in a human-intuitive way
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background_LUV = cv2.cvtColor(background, cv2.COLOR_RGB2LUV)
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mask = self.background_mask(background_LUV)
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return np.count_nonzero(mask) / np.prod(mask.shape)
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current_image_LUV = cv2.cvtColor(current_image, cv2.COLOR_RGB2LUV)
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mask = self.background_mask(current_image_LUV)
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return np.count_nonzero(mask) / np.prod(mask.shape) * 100
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@thing_action
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def image_is_sample(self, cam: CamDep) -> bool:
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"""Label the current image as either background or sample"""
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b_fraction = self.background_fraction(cam)
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fraction_threshold = self.fraction
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return (100 - b_fraction) > fraction_threshold
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@thing_action
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def set_background(self, cam: CamDep):
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@ -253,6 +269,7 @@ class BackgroundDetectThing(Thing):
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return {
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"background_distributions": bd.model_dump() if bd else None,
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"tolerance": self.tolerance,
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"fraction": self.fraction,
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}
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@ -352,7 +369,6 @@ class SmartScanThing(Thing):
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logger.info(f"Based on an overlap of {overlap}, we will make steps of {dx}, {dy}")
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dz = 3000 # This is used for autofocus - make configurable?
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sample_coverage = 7 # TODO: make this configurable
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# construct a 2D scan path
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path = [[stage.position["x"], stage.position["y"]]]
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@ -391,15 +407,12 @@ class SmartScanThing(Thing):
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)
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# Check if the image is background
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background_fraction = background_detect.background_fraction()
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background_coverage = round(
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100 * background_fraction, 1
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)
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image_is_sample = background_detect.image_is_sample()
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# if more than 92% of the image is background, treat it as background and continue
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if 100 - background_coverage < sample_coverage:
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if not image_is_sample:
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category = "background"
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logger.info(f"Skipping {stage.position} as it is {background_coverage}% background.")
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logger.info(f"Skipping {stage.position} as it is {round(background_detect.background_fraction(),0)}% background.")
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else:
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# if not, it's sample. run an autofocus and use the updated height
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new_pos = [
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@ -484,7 +497,7 @@ class SmartScanThing(Thing):
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if len(true_path) > 750:
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break
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except InvocationCancelledError:
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logger.error("Stopping scan because it was cancelled.", exc_info=1)
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logger.error("Stopping scan because it was cancelled.", exc_info=0)
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except IOError as e:
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logger.error(
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f"Stopping scan because of an IOError (most likely a full disk): {e}",
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