spym.process.filters¶
Module Contents¶
Functions¶
gauss(image, size=3) |
Apply Gaussian smoothing filter. |
median(image, size=3) |
Apply median smoothing filter. |
mean(image, size=3) |
Apply mean smoothing filter. |
sharpen(image, size=3, alpha=30) |
Apply a sharpening filter. |
destripe(image, min_length=20, hard_threshold=0.4, soft_threshold=0.2, sign=’positive’, rel_threshold=None) |
Find and remove scan stripes by averaging neighbourhood lines. |
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class
spym.process.filters.Filters(spym_instance)¶ Filters.
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gauss(self, **kwargs)¶ Apply Gaussian smoothing filter.
- Args:
- size: size of the filter in pixels.
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median(self, **kwargs)¶ Apply median smoothing filter.
- Args:
- size: size of the filter in pixels.
-
mean(self, **kwargs)¶ Apply mean smoothing filter.
- Args:
- size: size of the filter in pixels.
-
sharpen(self, **kwargs)¶ Apply a sharpening filter.
- Args:
- size: size of the filter in pixels. alpha: weight.
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destripe(self, **kwargs)¶ Find and remove scan stripes by averaging neighbourhood lines.
- Args:
- min_length: only scars that are as long or longer than this value (in pixels) will be marked. hard_threshold: the minimum difference of the value from the neighbouring upper and lower lines to be considered a defect. soft_threshold: values differing at least this much do not form defects themselves, but they are attached to defects obtained from the hard threshold if they touch one. sign: whether mark stripes with positive values, negative values or both. rel_threshold: the minimum difference of the value from the neighbouring upper and lower lines to be considered a defect (in physical values). Overwrite hard_threshold.
- Returns:
- destriped 2d array.
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spym.process.filters.gauss(image, size=3)¶ Apply Gaussian smoothing filter.
- Args:
- image: numpy array. size: size of the filter in pixels.
- Returns:
- filtered numpy array.
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spym.process.filters.median(image, size=3)¶ Apply median smoothing filter.
- Args:
- image: numpy array. size: size of the filter in pixels.
- Returns:
- filtered numpy array.
-
spym.process.filters.mean(image, size=3)¶ Apply mean smoothing filter.
- Args:
- image: numpy array. size: size of the filter in pixels.
- Returns:
- filtered numpy array.
-
spym.process.filters.sharpen(image, size=3, alpha=30)¶ Apply a sharpening filter.
- Args:
- image: numpy array. size: size of the filter in pixels. alpha: weight.
- Returns:
- filtered numpy array.
-
spym.process.filters.destripe(image, min_length=20, hard_threshold=0.4, soft_threshold=0.2, sign='positive', rel_threshold=None)¶ Find and remove scan stripes by averaging neighbourhood lines.
- Args:
- image: 2d numpy array. min_length: only scars that are as long or longer than this value (in pixels) will be marked. hard_threshold: the minimum difference of the value from the neighbouring upper and lower lines to be considered a defect. soft_threshold: values differing at least this much do not form defects themselves, but they are attached to defects obtained from the hard threshold if they touch one. sign: whether mark stripes with positive values, negative values or both. rel_threshold: the minimum difference of the value from the neighbouring upper and lower lines to be considered a defect (in physical values). Overwrite hard_threshold.
- Returns:
- destriped 2d array.