Visualizers

Visualizers are used to render the output of a node. They are used in the visualizers field of the Node configuration.

Table Of Contents

• BBoxVisualizer
• ClassificationVisualizer
• KeypointVisualizer
• SegmentationVisualizer
• EmbeddingsVisualizer
• OCRVisualizer
• InstanceSegmentationVisualizer
• InstanceSegKeypointVisualizer
• FOMOVisualizer

BBoxVisualizer

Visualizer for bounding box detection task.

Parameters:

Key	Type	Default value	Description
labels	dict[int, str] | list[str] | None	None	Either a dictionary mapping class indices to names, or a list of names. If list is provided, the label mapping is done by index. By default, no labels are drawn
draw_labels	bool	True	Whether to draw class labels for predictions and targets
draw_scores	bool	False	Whether to append prediction confidence scores to the rendered labels. If draw_labels=False, only the score is drawn
colors	dict[str, tuple[int, int, int] | str] | list[tuple[int, int, int] | str] | None	None	Colors to use for the bounding boxes. Either a dictionary mapping class names to colors, or a list of colors. Color can be either a tuple of RGB values or a hex string
fill	bool	False	Whether to fill the bounding boxes
width	int	1	The width of the bounding box lines
font	str | None	None	A filename containing a TrueType font
font_size	int | None	None	Font size used for the labels
scale	float	1.0	Scales the canvas and the annotations by a given factor

Example:

KeypointVisualizer

Visualizer for instance keypoint detection task.

Parameters:

Key	Type	Default value	Description
visibility_threshold	float	0.5	Threshold for visibility of keypoints. If the visibility of a keypoint is below this threshold, it is considered as not visible
connectivity	list[tuple[int, int]] | None	None	List of tuples of keypoint indices that define the connections in the skeleton
visible_color	str | tuple[int, int, int]	"red"	Color of visible keypoints
nonvisible_color	str | tuple[int, int, int ] | None	None	Color of non-visible keypoints. If None, non-visible keypoints are not drawn
radius	int | None	None	Radius of drawn keypoint dots. If None, dynamically determine this based on image dimensions
scale	float	1.0	Scales the canvas and the annotations by a given factor
draw_indices	bool	False	Draw keypoint indices next to the keypoints

This visualizer also supports inherited BBoxVisualizer options for the bounding box overlay, including draw_labels and draw_scores.

Example:

SegmentationVisualizer

Visualizer for segmentation tasks.

Parameters:

Key	Type	Default value	Description
color	str | tuple[int, int, int]	"#5050FF"	Color of the segmentation masks
alpha	float	0.6	Alpha value of the segmentation masks
scale	float	1.0	Scales the canvas and the annotations by a given factor

Example:

ClassificationVisualizer

Visualizer for classification tasks.

Parameters:

Key	Type	Default value	Description
include_plot	bool	True	Whether to include a plot of the class probabilities in the visualization
color	tuple[int, int, int]	(255, 0, 0)	Color of the text
font_scale	float	1.0	Scale of the font
thickness	int	1	Line thickness of the font
multi_label	bool	False	Set to True for multi-label classification, otherwise False for single-label
scale	float	1.0	Scales the canvas and the annotations by a given factor

Example:

EmbeddingsVisualizer

Visualizer for embedding tasks.

Parameters:

Key	Type	Default value	Description
z_score_threshold	float	3.0	Threshold for z-score filtering. Embeddings with z-score higher than this value are considered as outliers and are not drawn.

Example:

OCRVisualizer

Visualizer for OCR tasks.

Parameters:

Key	Type	Default value	Description
font_scale	float	0.5	Font scale of the text. Defaults to 0.5.
color	tuple[int, int, int]	(0, 0, 0)	Color of the text. Defaults to (0, 0, 0).
thickness	int	1	Thickness of the text. Defaults to 1.
scale	float	1.0	Scales the canvas and the annotations by a given factor

Example:

InstanceSegmentationVisualizer

Visualizer for instance segmentation tasks.

Parameters:

Key	Type	Default value	Description
labels	dict[int, str] | list[str] | None	None	Either a dictionary mapping class indices to names, or a list of names. If list is provided, the label mapping is done by index. By default, no labels are drawn
draw_labels	bool	True	Whether to draw class labels for predictions and targets
draw_scores	bool	False	Whether to append prediction confidence scores to the rendered labels. If draw_labels=False, only the score is drawn
colors	dict[str, tuple[int, int, int] | str] | list[tuple[int, int, int] | str] | None	None	Colors to use for the bounding boxes. Either a dictionary mapping class names to colors, or a list of colors. Color can be either a tuple of RGB values or a hex string
fill	bool	False	Whether to fill the bounding boxes
width	int	1	The width of the bounding box lines
font	str | None	None	A filename containing a TrueType font
font_size	int | None	None	Font size used for the labels
scale	float	1.0	Scales the canvas and the annotations by a given factor

Example:

InstanceSegKeypointVisualizer

Visualizer for joint instance segmentation and keypoint tasks.

This visualizer overlays:

• bounding boxes
• instance segmentation masks
• keypoints

Parameters:

Key	Type	Default value	Description
labels	dict[int, str] | list[str] | None	None	Either a dictionary mapping class indices to names, or a list of names. If list is provided, the label mapping is done by index
draw_labels	bool	True	Whether to draw class labels. Defaults to True
draw_scores	bool	False	Whether to append prediction confidence scores to the rendered labels. If draw_labels=False, only the score is drawn
colors	dict[str, tuple[int, int, int] | str] | list[tuple[int, int, int] | str] | None	None	Colors to use for the bounding boxes and masks. Either a dictionary mapping class names to colors, or a list of colors. Color can be either a tuple of RGB values or a hex string
fill	bool	False	Whether to fill the bounding boxes
width	int | None	None	The width of the bounding box lines
font	str | None	None	A filename containing a TrueType font
font_size	int | None	None	Font size used for the labels
alpha	float	0.6	Alpha value of the segmentation masks
visibility_threshold	float	0.5	Threshold for visibility of keypoints. If the visibility of a keypoint is below this threshold, it is considered as not visible
connectivity	list[tuple[int, int]] | None	None	List of tuples of keypoint indices that define the connections in the skeleton
visible_color	str | tuple[int, int, int]	"red"	Color of visible keypoints
nonvisible_color	str | tuple[int, int, int] | None	None	Color of non-visible keypoints. If None, non-visible keypoints are not drawn
radius	int | None	None	Radius of drawn keypoint dots. If None, dynamically determine this based on image dimensions
draw_indices	bool	False	Draw keypoint indices next to the keypoints
scale	float	1.0	Scales the canvas and the annotations by a given factor

Example

FOMOVisualizer

Visualizer for FOMO object detection.

Parameters:

Key	Type	Default value	Description
colors	dict[str, tuple[int, int, int] | str] | list[tuple[int, int, int] | str] | None	None	Colors to use for the different point classes. Either a dictionary mapping class names to colors, or a list of colors. Color can be either a tuple of RGB values or a hex string
visibility_threshold	float	0.5	Threshold for visibility of detected points. If the visibility of a point is below this threshold, it will not be shown
radius	int	5	Radius to draw the points
scale	float	1.0	Scales the canvas and the annotations by a given factor

Example: