Label Visualization

Class: LabelVisualizationBlockV1

Source: inference.core.workflows.core_steps.visualizations.label.v1.LabelVisualizationBlockV1

The LabelVisualization block draws labels on an image at specific coordinates based on provided detections using Supervision's sv.LabelAnnotator.

Type identifier

Use the following identifier in step "type" field: roboflow_core/label_visualization@v1to add the block as as step in your workflow.

Properties

Name	Type	Description	Refs
name	str	Enter a unique identifier for this step..	❌
copy_image	bool	Enable this option to create a copy of the input image for visualization, preserving the original. Use this when stacking multiple visualizations..	✅
color_palette	str	Select a color palette for the visualised elements..	✅
palette_size	int	Specify the number of colors in the palette. This applies when using custom or Matplotlib palettes..	✅
custom_colors	List[str]	Define a list of custom colors for bounding boxes in HEX format..	✅
color_axis	str	Choose how bounding box colors are assigned..	✅
text	str	The data to display in the text labels..	✅
text_position	str	The anchor position for placing the label..	✅
text_color	str	Color of the text..	✅
text_scale	float	Scale of the text..	✅
text_thickness	int	Thickness of the text characters..	✅
text_padding	int	Padding around the text in pixels..	✅
border_radius	int	Radius of the label in pixels..	✅

The Refs column marks possibility to parametrise the property with dynamic values available in workflow runtime. See Bindings for more info.

Available Connections

Compatible Blocks

Check what blocks you can connect to Label Visualization in version v1.

• inputs: Florence-2 Model, Model Monitoring Inference Aggregator, Label Visualization, Depth Estimation, Florence-2 Model, Triangle Visualization, CogVLM, Image Blur, OCR Model, Model Comparison Visualization, Detections Transformation, Line Counter Visualization, Circle Visualization, Relative Static Crop, Detections Stitch, Trace Visualization, Byte Tracker, Multi-Label Classification Model, Object Detection Model, Path Deviation, Clip Comparison, Dimension Collapse, Detections Consensus, Detection Offset, Velocity, Stitch Images, Gaze Detection, Reference Path Visualization, Llama 3.2 Vision, Bounding Rectangle, Detections Filter, Cosine Similarity, Polygon Visualization, Roboflow Dataset Upload, Identify Outliers, Time in Zone, Detections Merge, Segment Anything 2 Model, Roboflow Custom Metadata, SIFT, Single-Label Classification Model, Image Threshold, Path Deviation, Local File Sink, Keypoint Visualization, Ellipse Visualization, Crop Visualization, Color Visualization, Image Slicer, VLM as Classifier, Dynamic Crop, JSON Parser, Google Gemini, OpenAI, Instance Segmentation Model, Dot Visualization, Instance Segmentation Model, Roboflow Dataset Upload, Keypoint Detection Model, Keypoint Detection Model, Stability AI Inpainting, Identify Changes, Line Counter, Google Vision OCR, Template Matching, Corner Visualization, Overlap Filter, Background Color Visualization, Polygon Zone Visualization, Byte Tracker, Camera Focus, Grid Visualization, Perspective Correction, Stability AI Image Generation, VLM as Detector, Line Counter, Image Slicer, CSV Formatter, OpenAI, Clip Comparison, Blur Visualization, Dynamic Zone, Classification Label Visualization, Image Convert Grayscale, Time in Zone, Image Preprocessing, Slack Notification, SIFT Comparison, Byte Tracker, Detections Stabilizer, OpenAI, Pixel Color Count, YOLO-World Model, Stability AI Outpainting, Anthropic Claude, Size Measurement, Webhook Sink, Moondream2, Camera Calibration, Buffer, Mask Visualization, Bounding Box Visualization, Distance Measurement, Pixelate Visualization, Twilio SMS Notification, Email Notification, PTZ Tracking (ONVIF).md), Image Contours, Detections Classes Replacement, Object Detection Model, Absolute Static Crop, Stitch OCR Detections, Halo Visualization, SIFT Comparison, LMM For Classification, VLM as Detector, LMM, VLM as Classifier
• outputs: Florence-2 Model, Label Visualization, Florence-2 Model, Depth Estimation, Triangle Visualization, CogVLM, Image Blur, OCR Model, Model Comparison Visualization, Line Counter Visualization, Circle Visualization, Relative Static Crop, Barcode Detection, Detections Stitch, Trace Visualization, Multi-Label Classification Model, Object Detection Model, Clip Comparison, Gaze Detection, Stitch Images, Dominant Color, Reference Path Visualization, Llama 3.2 Vision, Polygon Visualization, Time in Zone, Segment Anything 2 Model, Roboflow Dataset Upload, Single-Label Classification Model, SIFT, Image Threshold, CLIP Embedding Model, VLM as Classifier, Keypoint Visualization, Crop Visualization, Image Slicer, Color Visualization, Ellipse Visualization, Google Gemini, Dynamic Crop, OpenAI, Instance Segmentation Model, Multi-Label Classification Model, Dot Visualization, Instance Segmentation Model, Roboflow Dataset Upload, Keypoint Detection Model, SmolVLM2, Keypoint Detection Model, Stability AI Inpainting, Google Vision OCR, Single-Label Classification Model, Template Matching, Corner Visualization, Background Color Visualization, Polygon Zone Visualization, Camera Focus, Stability AI Image Generation, Perspective Correction, VLM as Detector, Image Slicer, OpenAI, Qwen2.5-VL, Clip Comparison, Blur Visualization, Classification Label Visualization, Image Convert Grayscale, Image Preprocessing, SIFT Comparison, Byte Tracker, Pixel Color Count, Detections Stabilizer, OpenAI, YOLO-World Model, Stability AI Outpainting, Perception Encoder Embedding Model, QR Code Detection, Anthropic Claude, Moondream2, Camera Calibration, Buffer, Mask Visualization, Bounding Box Visualization, Pixelate Visualization, Object Detection Model, Image Contours, Absolute Static Crop, Halo Visualization, LMM For Classification, VLM as Detector, LMM, VLM as Classifier

Input and Output Bindings

The available connections depend on its binding kinds. Check what binding kinds Label Visualization in version v1 has.

Bindings

• input

  • image (image): The image to visualize on..
  • copy_image (boolean): Enable this option to create a copy of the input image for visualization, preserving the original. Use this when stacking multiple visualizations..
  • predictions (Union[instance_segmentation_prediction, keypoint_detection_prediction, object_detection_prediction]): Model predictions to visualize..
  • color_palette (string): Select a color palette for the visualised elements..
  • palette_size (integer): Specify the number of colors in the palette. This applies when using custom or Matplotlib palettes..
  • custom_colors (list_of_values): Define a list of custom colors for bounding boxes in HEX format..
  • color_axis (string): Choose how bounding box colors are assigned..
  • text (string): The data to display in the text labels..
  • text_position (string): The anchor position for placing the label..
  • text_color (string): Color of the text..
  • text_scale (float): Scale of the text..
  • text_thickness (integer): Thickness of the text characters..
  • text_padding (integer): Padding around the text in pixels..
  • border_radius (integer): Radius of the label in pixels..

• output

  • image (image): Image in workflows.

Example JSON definition of step Label Visualization in version v1

{
    "name": "<your_step_name_here>",
    "type": "roboflow_core/label_visualization@v1",
    "image": "$inputs.image",
    "copy_image": true,
    "predictions": "$steps.object_detection_model.predictions",
    "color_palette": "DEFAULT",
    "palette_size": 10,
    "custom_colors": [
        "#FF0000",
        "#00FF00",
        "#0000FF"
    ],
    "color_axis": "CLASS",
    "text": "LABEL",
    "text_position": "CENTER",
    "text_color": "WHITE",
    "text_scale": 1.0,
    "text_thickness": 1,
    "text_padding": 10,
    "border_radius": 0
}