CSemanticSegmentationIO¶

class ikomia.dataprocess.pydataprocess.CSemanticSegmentationIO¶

Define input or output managing common information extracted by semantic segmentation task. Such task are able to automatically set a class for each pixel of an image. Thus, this input/output stores labelled image, class names and class colors. Among others, algorithms like DeepLabV3+, UNet or TransUNet are semantic segmentation tasks.

Import

from ikomia.dataprocess import CSemanticSegmentationIO

Methods

`__init__`(arg1)	__init__( (object)self) -> None :
`get_mask`(self)	Get labelled image (ie graylevel) where each pixel has a specific value corresponding to its class.
`get_class_names`(self)	Get class names list associated with the semantic mask.
`get_colors`(self)	Get colors associated with class names.
`get_polygons`(self)	Get polygons of connected components extracted from segmentation mask.
`set_class_colors`(self, colors)	Set class colors associated with segmentation mask.
`set_class_names`(self, names)	Set class names associated with segmentation mask.
`set_mask`(self, mask)	Set the mask of the semantic segmentation output.

Overridden methods

`clear_data`(self)	See `clear_data()`.
`from_json`(self, jsonStr)	Set input/output data from JSON formatted string.
`is_data_available`(self)	See `is_data_available()`.
`load`(self, path)	Load instance segmentation input/output for JSON file.
`save`(self, path)	Save instance segmentation input/output to JSON file.
`to_json`(self)	Return input/output data in JSON formatted string (compact mode and image encoded as JPEG).

Inherited methods

`copy_static_data`(self, io)	Copy the static data from the given input or ouput.
`get_unit_element_count`(self)	Get the number of unit elements in terms of processing scheme.

Attributes

`auto_save`	Auto-save status
`data_type`	I/O data type
`dim_count`	Number of dimensions
`description`	Custom description to explain input/output type and use
`displayable`	Displayable status (Ikomia Studio)
`name`	I/O name
`source_file_path`	Path to the source file used as workflow input (if any)

Details

__init__((object)arg1) → None¶

__init__( (object)self) -> None :: Default constructor
__init__( (object)arg1, (CSemanticSegmentationIO)arg2) -> None :: Copy constructor

from_json((CSemanticSegmentationIO)self, (str)jsonStr) → None :¶

Set input/output data from JSON formatted string.

Args:

str: data as JSON formatted string

from_json( (CSemanticSegmentationIO)self, (str)jsonStr) -> None

get_mask((CSemanticSegmentationIO)self) → object :¶

Get labelled image (ie graylevel) where each pixel has a specific value corresponding to its class. You can find the correspondence between pixel value and class name with ikomia.dataprocess.pydataprocess.CSemanticIO.get_class_names().

Returns:: grayscale mask
Return type:: Numpy array

get_class_names((CSemanticSegmentationIO)self) → object :¶

Get class names list associated with the semantic mask. Class index in the list corresponds to the pixel value in the mask.

Returns:: class names
Return type:: list of str

get_colors((CSemanticSegmentationIO)self) → object :¶

Get colors associated with class names.

Returns:: colors as [r, g, b] list
Return type:: list of int list

get_polygons((CSemanticSegmentationIO)self) → object :¶

Get polygons of connected components extracted from segmentation mask.

Returns:: list of CGraphicsItem based objects

is_data_available((CSemanticSegmentationIO)self) → bool :¶

See is_data_available().

is_data_available( (CSemanticSegmentationIO)self) -> bool

load((CSemanticSegmentationIO)self, (str)path) → None :¶

Load instance segmentation input/output for JSON file.

Args:

path (str): path to JSON file

load( (CSemanticSegmentationIO)self, (str)path) -> None

save((CSemanticSegmentationIO)self, (str)path) → None :¶

Save instance segmentation input/output to JSON file.

Args:

path (str): path to JSON file

save( (CSemanticSegmentationIO)self, (str)path) -> None

set_class_names((CSemanticSegmentationIO)self, (object)names) → None :¶

Set class names associated with segmentation mask. If colors are not defined, random colors are automatically generated.

Parameters:: names (list of str) – class names, index in the list must match the pixel value in the mask

set_class_colors((CSemanticSegmentationIO)self, (object)colors) → None :¶

Set class colors associated with segmentation mask. Sizes of names and colors must be equal.

Parameters:: colors (list of list of int) – colors as [r, g, b] list

set_mask((CSemanticSegmentationIO)self, (object)mask) → None :¶

Set the mask of the semantic segmentation output.

Parameters:: mask (numpy array) – segmentation mask

to_json((CSemanticSegmentationIO)self) → str :¶

Return input/output data in JSON formatted string (compact mode and image encoded as JPEG).: Returns:

string: JSON formatted string

to_json( (CSemanticSegmentationIO)self) -> str

to_json( (CSemanticSegmentationIO)self, (object)options) -> str :

Return input/output data in JSON formatted string. JSON format options can be set, possible values are:

[‘json_format’, ‘compact’] (default)
[‘json_format’, ‘indented’]
[‘image_format’, ‘jpg’](for the mask - default)
[‘image_format’, ‘png’]

Args:

list of str: format-specific options encoded as pairs [option_name, option_value]

Returns:

string: JSON formatted string

to_json( (CSemanticSegmentationIO)self, (object)options) -> str