def __init__(self):
"""
Instance vars:
| *name* : name of the algorithm
| *parent* : name of the appropriate category
| *kernelsize* : blurring kernel size that will be used as slider
for the UI. Consider that a value n is treated as 2*n+1 to
guarantee an odd box filter. For example the value 1 gives
a neighbourhood of size 3x3.
| *sigmaX* : gaussian kernel standard deviation in X direction
| *channel1* : checkbox if computing the first color channel
| *channel2* : checkbox if computing the second color channel
| *channel3* : checkbox if computing the third color channel
"""
Algorithm.__init__(self)
self.name = "Gauss Blur"
self.parent = "Preprocessing"
self.kernelsize = IntegerSlider("kernelsize", 1, 20, 1, 1)
self.sigmaX = FloatSlider("sigmaX", 1.0, 100.0, 0.1, 1.0)
self.channel1 = CheckBox("channel1", True)
self.channel2 = CheckBox("channel2", True)
self.channel3 = CheckBox("channel3", True)
self.integer_sliders.append(self.kernelsize)
self.float_sliders.append(self.sigmaX)
def __init__(self):
"""
Instance vars:
| *name* : name of the algorithm
| *parent* : name of the appropriate category
| *kernelsize* : blurring kernel size that will be used as slider
for the UI. Consider that a value n is treated as 2*n+1 to
guarantee an odd box filter. For example the value 1 gives
a neighbourhood of size 3x3.
| *sigmaX* : gaussian kernel standard deviation in X direction
| *channel1* : checkbox if computing the first color channel
| *channel2* : checkbox if computing the second color channel
| *channel3* : checkbox if computing the third color channel
"""
Algorithm.__init__(self)
self.name = "Gauss Blur"
self.parent = "Preprocessing"
self.kernelsize = IntegerSlider("kernelsize", 1, 20, 1, 1)
self.sigmaX = FloatSlider("sigmaX", 1.0, 100.0, 0.1, 1.0)
self.channel1 = CheckBox("channel1", True)
self.channel2 = CheckBox("channel2", True)
self.channel3 = CheckBox("channel3", True)
self.integer_sliders.append(self.kernelsize)
self.float_sliders.append(self.sigmaX)
def __init__(self):
"""
Instance vars:
| *name* : name of the algorithm
| *parent* : name of the appropriate category
| *f_strength* : Parameter regulating filter strength.
A larger value of the parameter means that more noise and also
more image details will be removed
| *f_col* : The same as h but for color components. For most images
value equals 10 will be enough to remove colored noise and do
not distort colors
| *template_size* : size in pixels of the template patch that
is used to compute weights. Consider that a value n is treated
as 2*n+1 to guarantee an odd box filter. For example the value 1
gives a neighbourhood of size 3x3.
| *search_size* : size in pixels of the window that is used
to compute weighted average for given pixel.
A larger value of the parameter means a larger denoising time.
Consider that a value n is treated as 2*n+1 to
guarantee an odd box filter. For example the value 1 gives
a neighbourhood of size 3x3.
"""
Algorithm.__init__(self)
self.name = "FM Denoise Color"
self.parent = "Preprocessing"
self.f_strength = FloatSlider("filter strength", 1.0, 100.0, 0.1, 1.0)
self.f_col = FloatSlider("filter strength color", 1.0, 100.0, 0.1, 1.0)
self.template_size = IntegerSlider("template window size", 1, 20, 1, 3)
self.search_size = IntegerSlider("search window size", 1, 20, 1, 10)
self.integer_sliders.append(self.template_size)
self.integer_sliders.append(self.search_size)
self.float_sliders.append(self.f_strength)
self.float_sliders.append(self.f_col)
def __init__(self):
"""
Instance vars:
| *name* : name of the algorithm
| *parent* : name of the appropriated category
| *attribute* : A valid edge attribute present in the graph.
| *attribute_threshold_value* : A threshold value for
the given attribute
| *operator* : A logical python operator.
See python module operator
"""
Algorithm.__init__(self)
self.name = "Edge Attribute"
self.parent = "Graph Filtering"
self.attribute = DropDown("Attribute", {"width", "length"})
self.drop_downs.append(self.attribute)
self.attribute_threshold_value = FloatSlider("Attribute treshold", 0.0,
20.0, 0.1, 10.0)
self.float_sliders.append(self.attribute_threshold_value)
self.operator = DropDown(
"Operator", {
"strictly smaller", "smaller or equal", "equal",
"greater or equal", "strictly greater"
})
self.drop_downs.append(self.operator)
def __init__(self):
"""
Instance vars:
| *name* : name of the algorithm
| *parent* : name of the appropriated category
"""
Algorithm.__init__(self)
self.name = "Smooth 2 Nodes"
self.parent = "Graph Filtering"
def __init__(self):
"""
Instance vars:
| *name* : name of the algorithm
| *parent* : name of the appropriated category
"""
Algorithm.__init__(self)
self.name = "Smooth 2 Nodes"
self.parent = "Graph Filtering"
def __init__(self):
"""
Instance vars:
| *name* : name of the algorithm
| *parent* : name of the appropriate category
"""
Algorithm.__init__(self)
self.name = "Simple Cycle"
self.parent = "Graph Filtering"
def __init__(self):
"""
Instance vars:
| *name* : name of the algorithm
| *parent* : name of the appropriate category
"""
Algorithm.__init__(self)
self.name = "Guo Hall"
self.parent = "Graph Detection"
def __init__(self):
"""
Instance vars:
| *name* : name of the algorithm
| *parent* : name of the appropriate category
"""
Algorithm.__init__(self)
self.name = "Otsus"
self.parent = "Segmentation"
def __init__(self):
"""
Instance vars:
| *name* : name of the algorithm
| *parent* : name of the appropriate category
"""
Algorithm.__init__(self)
self.name = "Otsus"
self.parent = "Segmentation"
def __init__(self):
"""
Instance vars:
| *name* : name of the algorithm
| *parent* : name of the appropriate category
"""
Algorithm.__init__(self)
self.name = "Keep only LCC"
self.parent = "Graph Filtering"
def __init__(self):
"""
Instance vars:
| *name* : name of the algorithm
| *parent* : name of the appropriate category
"""
Algorithm.__init__(self)
self.name = "Guo Hall Thinning"
self.parent = "Thinning"
def __init__(self):
"""
Instance vars:
| *name* : name of the algorithm
| *parent* : name of the appropriate category
"""
Algorithm.__init__(self)
self.name = "Guo Hall Graph Detection"
self.parent = "Graph Detection"
def __init__(self):
Algorithm.__init__(self)
self.name = "Color Enhance"
self.parent = "Preprocessing"
self.left_pct = FloatSlider("left percentage", 0.0, 10.0, 0.1, 2.5)
self.right_pct = FloatSlider("right percentage", 0.0, 10.0, 0.1, 2.5)
self.channel1 = CheckBox("channel1", True)
self.channel2 = CheckBox("channel2", True)
self.channel3 = CheckBox("channel3", True)
self.float_sliders.append(self.left_pct)
self.float_sliders.append(self.right_pct)
def __init__(self):
Algorithm.__init__(self)
self.name = "Color Enhance"
self.parent = "Preprocessing"
self.left_pct = FloatSlider("left percentage", 0.0, 10.0, 0.1, 2.5)
self.right_pct = FloatSlider("right percentage", 0.0, 10.0, 0.1, 2.5)
self.channel1 = CheckBox("channel1", True)
self.channel2 = CheckBox("channel2", True)
self.channel3 = CheckBox("channel3", True)
self.float_sliders.append(self.left_pct)
self.float_sliders.append(self.right_pct)
def __init__(self):
"""
Instance vars:
| *name* : name of the algorithm
| *parent* : name of the appropriate category
| *threshold* : threshold [1-254]
"""
Algorithm.__init__(self)
self.name = "Constant"
self.parent = "Segmentation"
self.threshold = IntegerSlider("Threshold", 1, 254, 1, 127)
self.integer_sliders.append(self.threshold)
def __init__(self):
"""
Instance vars:
| *name* : name of the algorithm
| *parent* : name of the appropriate category
| *threshold* : threshold [1-254]
"""
Algorithm.__init__(self)
self.name = "Constant"
self.parent = "Segmentation"
self.threshold = IntegerSlider("Threshold", 1, 254, 1, 127)
self.integer_sliders.append(self.threshold)
def __init__(self):
"""
Instance vars:
| *name* : name of the algorithm
| *parent* : name of the appropriate category
| *constant* : threshold constant [-10-10]
| *blocksize* : threshold blocksize [3-23]
"""
Algorithm.__init__(self)
self.name = "Adaptive"
self.parent = "Segmentation"
self.blocksize = IntegerSlider("Threshold Blocksize", 1, 20, 1, 5)
self.constant = IntegerSlider("Threshold Constant", -10, 10, 1, 2)
self.integer_sliders.append(self.blocksize)
self.integer_sliders.append(self.constant)
def __init__(self):
"""
Instance vars:
| *name* : name of the algorithm
| *parent* : name of the appropriate category
| *constant* : threshold constant [-10-10]
| *blocksize* : threshold blocksize [3-23]
"""
Algorithm.__init__(self)
self.name = "Adaptive"
self.parent = "Segmentation"
self.blocksize = IntegerSlider("Threshold Blocksize", 1, 20, 1, 5)
self.constant = IntegerSlider("Threshold Constant", -10, 10, 1, 2)
self.integer_sliders.append(self.blocksize)
self.integer_sliders.append(self.constant)
def __init__(self):
"""
Invert Color object constructor
Instance vars:
| *name* : name of the algorithm
| *parent* : name of the appropriate category
| *channel1* : checkbox if computing the first color channel
| *channel2* : checkbox if computing the second color channel
| *channel3* : checkbox if computing the third color channel
"""
Algorithm.__init__(self)
self.name = "Invert Color"
self.parent = "Preprocessing"
self.channel1 = CheckBox("channel1", True)
self.channel2 = CheckBox("channel2", True)
self.channel3 = CheckBox("channel3", True)
def __init__(self):
"""
Invert Color object constructor
Instance vars:
| *name* : name of the algorithm
| *parent* : name of the appropriate category
| *channel1* : checkbox if computing the first color channel
| *channel2* : checkbox if computing the second color channel
| *channel3* : checkbox if computing the third color channel
"""
Algorithm.__init__(self)
self.name = "Invert Color"
self.parent = "Preprocessing"
self.channel1 = CheckBox("channel1", True)
self.channel2 = CheckBox("channel2", True)
self.channel3 = CheckBox("channel3", True)
def __init__(self):
"""
Instance vars:
| *name* : name of the algorithm
| *parent* : name of the appropriated category
| *compnt_size* : A threshold value for the size of
the connected components
| *operator* : A logical python operator.
See python module operator
"""
Algorithm.__init__(self)
self.name = "Connected Component"
self.parent = "Graph Filtering"
self.compnt_size = IntegerSlider("Component Size", 0.0, 20.0, 1.0, 10)
self.integer_sliders.append(self.compnt_size)
self.operator = DropDown("Operator", {"strictly smaller",
"smaller or equal",
"equal",
"greater or equal",
"strictly greater"})
self.drop_downs.append(self.operator)
def __init__(self):
"""
Fast nl Means Denoising object constructor.
Instance vars:
| *name* : name of the algorithm
| *parent* : name of the appropriate category
| *f_strength* : Parameter regulating filter strength.
A larger value of the parameter means that more noise and also
more image details will be removed
| *template_size* : size in pixels of the template patch that
is used to compute weights. Consider that a value n is treated
as 2*n+1 to guarantee an odd box filter. For example the value 1
gives a neighbourhood of size 3x3.
| *search_size* : size in pixels of the window that is used
to compute weighted average for given pixel.
A larger value of the parameter means a larger denoising time.
Consider that a value n is treated as 2*n+1 to
guarantee an odd box filter. For example the value 1 gives
a neighbourhood of size 3x3.
| *channel1* : checkbox if computing the first color channel
| *channel2* : checkbox if computing the second color channel
| *channel3* : checkbox if computing the third color channel
"""
Algorithm.__init__(self)
self.name = "FM Denoise"
self.parent = "Preprocessing"
self.f_strength = FloatSlider("filter strength", 1.0, 100.0, 0.1, 1.0)
self.template_size = IntegerSlider("template window size", 1, 20, 1, 3)
self.search_size = IntegerSlider("search window size", 1, 20, 1, 10)
self.channel1 = CheckBox("channel1", True)
self.channel2 = CheckBox("channel2", True)
self.channel3 = CheckBox("channel3", True)
self.integer_sliders.append(self.template_size)
self.integer_sliders.append(self.search_size)
self.float_sliders.append(self.f_strength)
def __init__(self):
"""
Fast nl Means Denoising object constructor.
Instance vars:
| *name* : name of the algorithm
| *parent* : name of the appropriate category
| *f_strength* : Parameter regulating filter strength.
A larger value of the parameter means that more noise and also
more image details will be removed
| *template_size* : size in pixels of the template patch that
is used to compute weights. Consider that a value n is treated
as 2*n+1 to guarantee an odd box filter. For example the value 1
gives a neighbourhood of size 3x3.
| *search_size* : size in pixels of the window that is used
to compute weighted average for given pixel.
A larger value of the parameter means a larger denoising time.
Consider that a value n is treated as 2*n+1 to
guarantee an odd box filter. For example the value 1 gives
a neighbourhood of size 3x3.
| *channel1* : checkbox if computing the first color channel
| *channel2* : checkbox if computing the second color channel
| *channel3* : checkbox if computing the third color channel
"""
Algorithm.__init__(self)
self.name = "FM Denoise"
self.parent = "Preprocessing"
self.f_strength = FloatSlider("filter strength", 1.0, 100.0, 0.1, 1.0)
self.template_size = IntegerSlider("template window size", 1, 20, 1, 3)
self.search_size = IntegerSlider("search window size", 1, 20, 1, 10)
self.channel1 = CheckBox("channel1", True)
self.channel2 = CheckBox("channel2", True)
self.channel3 = CheckBox("channel3", True)
self.integer_sliders.append(self.template_size)
self.integer_sliders.append(self.search_size)
self.float_sliders.append(self.f_strength)
def __init__(self):
Algorithm.__init__(self)
self.name = "Image Reduce"
self.parent = "Preprocessing"
self.ratio = IntegerSlider("Reduction %", 1, 100, 1, 50)
self.integer_sliders.append(self.ratio)