CSemanticSegmentationTask

class ikomia.dataprocess.pydataprocess.CSemanticSegmentationTask

Base class for semantic segmentatio task in Computer Vision. It consists in labelling each pixel of input image with a class. Common outputs for such task are graylevel mask and color labelled image for visualization. It defines a task with the following properties:

Inputs:

Outputs:

Derived from C2dImageTask.

Import

from ikomia.dataprocess import CSemanticSegmentationTask

Methods

__init__(arg1)

__init__( (object)self) -> None :

get_names(self)

Get class names.

get_results(self)

Get semantic segmentation results as a CSemanticSegmentationIO instance.

get_image_with_mask(self)

Get a visualization image composed by original input image and colored segmentation mask.

get_image_with_graphics(self)

Get visualization image where all extracted information are embedded (graphics items).

get_image_with_mask_and_graphics(self)

Get a visualization image composed by original input image, colored segmentation mask and embedded graphics (outlines of connected components).

read_class_names(self, path)

Populate class names from the given text file (one line per class).

set_colors(self, colors)

Set colors associated with class names.

set_names(self, names)

Set class names.

set_mask(self, mask)

Set the segmentation mask computed from the input image.

emit_add_sub_progress_steps(self, count)

See emit_add_sub_progress_steps().

emit_graphics_context_changed(self)

See emit_graphics_context_changed().

emit_output_changed(self)

See emit_output_changed().

emit_step_progress(self)

See emit_step_progress().

Overridden methods

end_task_run(self)

See end_task_run().

Inherited methods

add_input(self, input)

Add new input to the task.

add_output(self, output)

Add new output to the task.

apply_graphics_mask(self, origin, processed, ...)

Apply the mask generated from graphics to the result image so that only masked areas seems to be processed.

apply_graphics_mask_to_binary(self, origin, ...)

Apply the mask generated from graphics to the binary source image.

begin_task_run(self)

See begin_task_run().

create_input_graphics_mask(self, index, ...)

Generate a binary mask image from the task graphics input at the specified index.

create_graphics_mask(self, width, height, ...)

Generate a binary mask image from the given graphics input object.

execute_actions(self, action)

Method called when a specific action is requested from the associated widget (see emit_send_process_action()).

forward_input_image(self, input_index, ...)

Forward input image at position input_index to output at position output_index.

get_elapsed_time(self)

Get the time of the last execution in milliseconds.

get_graphics_mask(self, index)

Get the binary mask generated from graphics input at position index.

get_input(self, index)

Get input at position index.

get_input_count(self)

Get the number of inputs.

get_input_data_type(self, index)

Get input data type at position index.

get_inputs(self)

Get the whole list of inputs.

get_output(self, index)

Get output at position index.

get_output_count(self)

Get the number of outputs.

get_output_data_type(self, index)

Get output data type at position index.

get_outputs(self)

Get the whole list of outputs.

get_parameters(self)

Get values of task parameters.

get_progress_steps(self)

See get_progress_steps().

global_input_changed(self, is_new_sequence)

See global_input_changed().

graphics_changed(self)

See graphics_changed().

is_graphics_changed_listening(self)

Check whether the task is listening to graphics changed event.

is_mask_available(self, index)

Check whether a binary mask from graphics input is available at position index.

parameters_modified(self)

Notify that the task parameters have changed.

remove_input(self, index)

Remove input at the given position.

run(self)

See run().

set_action_flag(self, action, is_enable)

Enable or disable the given action.

set_active(self, is_active)

See set_active().

set_input(self, input, index)

Set input at position index with the given one.

set_input_data_type(self, data_type, index)

Set the data type for the input at position index.

set_inputs(self, inputs)

Set the whole list of inputs with the given one.

set_output(self, output, index)

Set output at position index with the given one.

set_output_color_map(self, index, ...)

Bind a display color map to an image output.

set_output_data_type(self, data_type, index)

Set the data type for the output at position index.

set_outputs(self, outputs)

Set the whole list of outputs with the given one.

set_parameters(self, values)

Set values of task parameters.

stop(self)

See stop().

update_static_outputs(self)

Determine output data type automatically from input data types.

Attributes

name

Task name (must be unique)

type

Main purpose or data type on which the task is dedicated to.

Details

__init__((object)arg1) → None
__init__( (object)self) -> None :

Default constructor

__init__( (object)self, (str)name) -> None :

Construct CSemanticSegmentationTask object with the given name.

Args:

name (str): task name, must be unique

get_names((CSemanticSegmentationTask)self) → object :

Get class names. Call read_class_names() to populate names from text file.

Returns:

class names

Return type:

str list

get_results((CSemanticSegmentationTask)self) → CSemanticSegmentationIO :

Get semantic segmentation results as a CSemanticSegmentationIO instance.

Returns:

semantic segmentation data

Return type:

CSemanticSegmentationIO

get_image_with_mask((CSemanticSegmentationTask)self) → object :

Get a visualization image composed by original input image and colored segmentation mask. A transparency factor is applied to see both information.

Returns:

color mask image

Return type:

2D numpy array (3 channels)

get_image_with_graphics((CSemanticSegmentationTask)self) → object :

Get visualization image where all extracted information are embedded (graphics items).

Returns:

visualization image

Return type:

2D Numpy array

get_image_with_mask_and_graphics((CSemanticSegmentationTask)self) → object :

Get a visualization image composed by original input image, colored segmentation mask and embedded graphics (outlines of connected components). A transparency factor is applied between original image and mask.

Returns:

visualization image

Return type:

2D numpy array (3 channels)

read_class_names((CSemanticSegmentationTask)self, (str)path) → None :

Populate class names from the given text file (one line per class).

Parameters:

path (str) – path to class names definition file

set_colors((CSemanticSegmentationTask)self, (object)colors) → None :

Set colors associated with class names. The given list must have the same size as names list. If not provided, random colors are generated while populating the name list (read_class_names()).

Parameters:

list (colors (list of) – r, g, b integer values in range [0, 255])

set_names((CSemanticSegmentationTask)self, (object)names) → None :

Set class names. The function generate associated random colors if none is defined.

Parameters:

names (list of str)

set_mask((CSemanticSegmentationTask)self, (object)mask) → None :

Set the segmentation mask computed from the input image.

Parameters:

mask (2D - 1 channel numpy array) – segmentation mask

emit_add_sub_progress_steps((CSemanticSegmentationTask)self, (int)count) → None :

See emit_add_sub_progress_steps().

emit_graphics_context_changed((CSemanticSegmentationTask)self) → None :

See emit_graphics_context_changed().

emit_output_changed((CSemanticSegmentationTask)self) → None :

See emit_output_changed().

emit_step_progress((CSemanticSegmentationTask)self) → None :

See emit_step_progress().

end_task_run((CSemanticSegmentationTask)self) → None :

See end_task_run().

end_task_run( (CSemanticSegmentationTask)self) -> None