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: Label Visualization, Blur Visualization, Background Color Visualization, Contrast Equalization, Reference Path Visualization, Detections Filter, Stability AI Outpainting, Image Slicer, Pixelate Visualization, Single-Label Classification Model, Clip Comparison, CSV Formatter, Seg Preview, Overlap Filter, Image Preprocessing, Color Visualization, SIFT Comparison, Email Notification, Circle Visualization, Object Detection Model, Moondream2, VLM as Classifier, Model Monitoring Inference Aggregator, OCR Model, Absolute Static Crop, Path Deviation, LMM, Time in Zone, Morphological Transformation, Gaze Detection, Detections Consensus, Crop Visualization, OpenAI, Florence-2 Model, Classification Label Visualization, Byte Tracker, Segment Anything 2 Model, Cosine Similarity, SIFT Comparison, Time in Zone, YOLO-World Model, PTZ Tracking (ONVIF).md), Detection Offset, Icon Visualization, Detections Transformation, Distance Measurement, VLM as Detector, Line Counter Visualization, Grid Visualization, Halo Visualization, Size Measurement, Dynamic Zone, Twilio SMS Notification, Time in Zone, Detections Stitch, Llama 3.2 Vision, Image Blur, Slack Notification, Velocity, Byte Tracker, OpenAI, Multi-Label Classification Model, Image Slicer, OpenAI, Dynamic Crop, Pixel Color Count, Mask Visualization, Detections Merge, Stitch Images, SIFT, Google Vision OCR, LMM For Classification, Keypoint Visualization, Bounding Box Visualization, SAM 3, Byte Tracker, SAM 3, Object Detection Model, Path Deviation, Detections Combine, Anthropic Claude, Image Contours, Polygon Zone Visualization, Ellipse Visualization, Line Counter, Email Notification, Clip Comparison, Depth Estimation, Roboflow Dataset Upload, Image Convert Grayscale, VLM as Detector, Roboflow Custom Metadata, SAM 3, CogVLM, Buffer, Keypoint Detection Model, Stitch OCR Detections, Bounding Rectangle, Keypoint Detection Model, Line Counter, JSON Parser, Camera Calibration, Polygon Visualization, Detections Classes Replacement, Identify Changes, Triangle Visualization, Template Matching, Roboflow Dataset Upload, Anthropic Claude, Model Comparison Visualization, Corner Visualization, Florence-2 Model, Google Gemini, Google Gemini, EasyOCR, Stability AI Image Generation, Identify Outliers, QR Code Generator, Relative Static Crop, Dot Visualization, Dimension Collapse, Local File Sink, Instance Segmentation Model, Stability AI Inpainting, Camera Focus, Detections Stabilizer, Webhook Sink, Image Threshold, VLM as Classifier, Perspective Correction, Instance Segmentation Model, OpenAI, Trace Visualization
• outputs: Google Vision OCR, Label Visualization, LMM For Classification, Blur Visualization, Background Color Visualization, Contrast Equalization, Reference Path Visualization, Keypoint Visualization, Stability AI Outpainting, Bounding Box Visualization, Image Slicer, Pixelate Visualization, Single-Label Classification Model, Clip Comparison, SAM 3, Perception Encoder Embedding Model, Seg Preview, Byte Tracker, Image Preprocessing, SAM 3, Color Visualization, SIFT Comparison, Qwen2.5-VL, Object Detection Model, Dominant Color, Anthropic Claude, Circle Visualization, Image Contours, Object Detection Model, QR Code Detection, Polygon Zone Visualization, Ellipse Visualization, Email Notification, Clip Comparison, Moondream2, VLM as Classifier, OCR Model, Absolute Static Crop, Depth Estimation, LMM, Time in Zone, Morphological Transformation, Roboflow Dataset Upload, Gaze Detection, Crop Visualization, OpenAI, Florence-2 Model, Barcode Detection, Image Convert Grayscale, SAM 3, CogVLM, VLM as Detector, Multi-Label Classification Model, Classification Label Visualization, Buffer, Keypoint Detection Model, Segment Anything 2 Model, Keypoint Detection Model, YOLO-World Model, Polygon Visualization, CLIP Embedding Model, Camera Calibration, Icon Visualization, Triangle Visualization, Template Matching, Roboflow Dataset Upload, Anthropic Claude, Model Comparison Visualization, Corner Visualization, Florence-2 Model, Google Gemini, Google Gemini, EasyOCR, VLM as Detector, Line Counter Visualization, SmolVLM2, Halo Visualization, Stability AI Image Generation, Relative Static Crop, Dot Visualization, Detections Stitch, Llama 3.2 Vision, Image Blur, OpenAI, Instance Segmentation Model, Multi-Label Classification Model, Image Slicer, OpenAI, Stability AI Inpainting, Dynamic Crop, Single-Label Classification Model, Camera Focus, Pixel Color Count, Detections Stabilizer, Instance Segmentation Model, VLM as Classifier, Mask Visualization, Perspective Correction, Image Threshold, OpenAI, Trace Visualization, Stitch Images, SIFT

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[rle_instance_segmentation_prediction, instance_segmentation_prediction, object_detection_prediction, keypoint_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
}